esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning Methods for Space Situational Awareness
## 3 Case Study  
In this and the following section, we show preliminary results and comparisons by applying deep learning methods for SSA applications in a simulated environment. Although deep learning could be employed for several different tasks, as well as in different stages of a generic processing chain, in our tests, we will focus on a well-defined and paradigmatic use case, i.e., small moving object detection in LEO from radar signals. To do that, we simulate the standard processing chain of a monostatic pulse-Doppler radar that detects the radial velocity of moving targets at specific ranges. The radar output is used to feed a neural network architecture that provides the number of detected targets, as shown in Figure 4.  
Figure 4: Flowchart of the experimental evaluation presented in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Description** & **Reference Number** \\ \hline \hline \multirow{7}{*}{Deep Reinforcement Learning} & A controllable ground-based telescope observing satellites in LEO is simulated & [109] \\  & in a reinforcement learning environment. & \\ \cline{2-3}  & The Proximal Policy Optimization (PPO) method, a fuzzy neural network, and & \\  & Deep Reinforcement Learning (DRL) were used to demonstrate a proposal for & [110] \\  & active object tracking of FFSM systems (FNN). & \\ \cline{2-3}  & For the SSA sensor tasking problem, four DRL agents are trained using & \\  & population-based training and proximal policy optimization. & [79] \\ \cline{2-3}  & This paper provides the first results to solve the sensor-tasking and & \\  & sensor-management (SM) problem for Space Situational Awareness (SSA) & [111] \\  & using the asynchronous advantage Actor-Critical Method (A3C). & \\ \cline{2-3}  & A fast detection technique of space debris with grid-based learning is & \\  & proposed. The image is divided into 14 \(\times\) 14 grids, then the fast grid-based & \\  & neural network (FGBNN) is used to pinpoint the location of the spatial debris & [112] \\  & in the grids. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reinforcement Learning-based methods for SSA space debris detection.
###### Abstract  
This study investigates the presence of undeclared meat species in beef sausages and analyzes the impact of poultry meat addition on fatty acid composition. A total of 68 beef sausage samples produced and obtained from markets in Kosovo were analyzed, comprising 43 industrial and 25 traditional (homemade) sausages. Using the Liquid Chip Display (LCD) Array System, Meat 5.0, we detected that 52.94% of the samples contained meat from other species, with poultry being the most frequently added (45.55%), followed by mutton (4.41%) and turkey (2.9%). Notably, 46.42% of industrial sausages with added meat and 100% of homemade sausages with mixed meat were undeclared, highlighting significant mislabeling concerns. Fatty acid analysis with Gas Chromatography Flame Ionization Detection (GC-FID) revealed that sausages with poultry meat exhibited lower levels of saturated fatty acids (SFAs) and higher polyunsaturated fatty acids (PUFAs), particularly linoleic (C18:2) and alfa-linolenic (C18:3) acids. The inclusion of poultry meat significantly reduced the proportion of palmitic (C16:0) and stearic (C18:0) acids while increasing unsaturated fatty acids. As the percentage of poultry meat increased, SFAs decreased from 54.08% (at 10% poultry meat) to 29.55% (at 90%), while PUFAs rose from 4.09% to 26.64%. These findings indicate that poultry addition enhances the nutritional profile of sausages by improving the fatty acid balance. This study highlights the need for stricter labeling regulations to ensure consumer transparency. Future research should explore these modified products' sensory and quality attributes to assess their market acceptance.  
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Footnote †: journal: Applied sciences  
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Footnote 1: Kosovo Food and Veterinary Laboratory, Food and Veterinary Agency, Str. Lidibia e Pejes. No 241, 10000 Pristina, Kosovo [EMAIL_ADDRESS] (D.M.); [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS] (V.G.-Z.); [EMAIL_ADDRESS] (M.S.-Z.); [EMAIL_ADDRESS] (D.B)  
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Footnote 2: Faculty of Veterinary Medicine, Sa. Cyril and Methodius University, Lazar Pop-Trajkov 5-7, 1000 Scopje, North Macedonia; [EMAIL_ADDRESS] (D.J.); [EMAIL_ADDRESS] (Z.H.-M.)  
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Footnote 3: Food Science and Technology Program, University for Business and Technology (UBT), Kalabria, 10000 Pristina, Kosovo  
* Correspondence: [EMAIL_ADDRESS]
# Predictive Business Process Monitoring Approach Based on Hierarchical Transformer
## 3 Preliminaries and Methods
### Concept Drift Detection
#### 3.4.3 Third Layer: Case-Level Transformer  
The third layer Transformer model takes the encoding matrix of each event subsequence obtained in the second layer as input, which can be denoted formally as \(\mathbf{X}^{(all)}=(\mathbf{S}_{1},\mathbf{S}_{2},\cdots,\mathbf{S}_{k})\), where \(\mathbf{S}_{i}\in\mathbb{R}^{l_{i}\times d}\) denotes the encoding matrix of the ith subsequence; \(l_{i}\) is the length of the subsequence; and \(d\) is the dimension of each event encoding vector. Since the length of each subsequence is different, the encoding matrix of each subsequence is processed using maximum pooling to convert it into a vector format. Suppose \(\mathbf{S}_{i}=(\mathbf{s}_{i,1},\mathbf{s}_{i,2},\cdots,\mathbf{s}_{i,k})\), the maximum pooling vector can be denoted as follows.  
\[\mathbf{s}_{i}^{\prime}=max\_pool(\mathbf{s}_{i,1},\mathbf{s}_{i,2},\cdots, \mathbf{s}_{i,k}) \tag{31}\]  
Then, position encoding is introduced for each event subsequence to model the positions of different event subsequences throughout the business process cases. In contrast to the traditional Sinusoidal absolute position encoding used in the second layer of the Transformer model, this layer uses learnable position encoding, i.e., learning a position vector \(\mathbf{p}_{i}\) for each position \(i\in[1,k]\). Compared to the Sinusoidal absolute position encoding with fixed values, learnable position encoding is more flexible in the third layer. The main reasons are that the number of event subsequences contained in the business process cases is relatively small. Because the value of k is small, so learning the location vector does not bring a high additional computational cost; Sinusoidal absolute location coding has a relatively small location difference in short sequences, and learnable position encoding can better distinguish different location information according to the actual characteristics of the data.  
The pooled event subsequence vector and the position vector can be combined to obtain \(\boldsymbol{\Gamma}^{\prime}=\big{(}\boldsymbol{s}_{1}^{\prime}+\boldsymbol{p}_{1},\boldsymbol{s}_{2}^{\prime}+\boldsymbol{p}_{2},\cdots,\boldsymbol{s}_{k}^{ \prime}+\boldsymbol{p}_{k}\big{)}^{T}\), which is used as the input of the Transformer model, and \(\boldsymbol{\Gamma}\) is calculated according to Equation (5) to Equation (11) as the final encoded representation of the business process case.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
###### Abstract  
Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to \(\sim\)16% from 2015-2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the \(k\)-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model's performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method.  
PV forecasting; missing data imputation; support vector regression; linear interpolation; mode imputation; \(k\)-nearest neighbors; multivariate imputation by chained equations +
Footnote †: journal: Applied Sciences
# Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement
## 1 Introduction
### Microwave Links  
Recently, the use of microwave (MW) links has been introduced in several studies for the operational estimation of precipitation from tropospheric radio links [17]. Received signal levels of MW links provide complementary information with respect to rain gauges, disrometers, radars and satellites. An important advantage of using such links is the cost reduction achieved by exploiting already existing tools developed for other purposes. Concerning satellite links, studies of attenuation due to clouds and precipitation along the propagation path have been intensively carried out, mainly in order to optimally design satellite communication systems. The idea of using such a \"signal of opportunity\" for measuring precipitation is more recent. We mention hereafter some promising results provided by experiments which compare rain rate measurements by rain gauge networks or weather radars with rain estimates obtained from the evaluation of the attenuation in commercial satellite links at Ka [18] or Ku band [19], and also by experiments aiming at retrieving rainfall fields from geostationary broadcast television satellite links [20; 21]. In this paper, we show an innovative application based on acquiring measurements of satellite downlink signal attenuation measurements for estimating rainfall fields. The algorithm used for retrieving rainfall is described, together with the architecture of a first prototype of the measurement system. A TBR sensor has been installed for comparison and validation purposes. Some preliminary results of rainfall retrieval are illustrated and discussed for some selected case studies.
# An Improved YOLO Model for Traffic Signs Small Target Image Detection
## 2 YOLOv5 Algorithm
### The Loss Function in YOLOv5  
The YOLOv5 model's loss function constellates from three significant portions, which are the Bounding Box Loss, Objectness Loss, and Class Loss.  
The Bounding Box Loss measures the difference between the predicted bounding box and its actual counterparts for the center coordinates and dimensions. This part of the loss function is crucial for predicting the location and size in object detection tasks:  
\[\text{Box\_Loss}=\sum{(\text{bx}-\text{tx})^{2}+\sum{(\text{by}-\text{ty})^{2 }+\sum{(\text{bw}-\text{tw})^{2}+\sum{(\text{bh}-\text{th})^{2}}}}} \tag{1}\]  
Here, bx, by, bw, and bh represent the bounding box's actual parameters, while tx, ty, tw, and th represent the predicted parameters.  
The Objectness Loss is instrumental in discerning whether a detected object exists within the bounding box. Incorporating this loss allows the model to encode the presence or absence of an object, thereby enhancing detection effectiveness:  
\[\text{Obj\_Loss}=-\sum{\text{object\_label}\cdot\log(\text{Pr(object)})+(1- \text{object\_label})\cdot\log(1-\text{Pr(object)})} \tag{2}\]  
where, object_label represents the actual label denoting the presence of an object, while Pr(object) represents the predicted probability of the object's existence.  
The Class Loss measures the precision of the model's prediction regarding an object's class. This is defined as:  
\[\text{Class\_Loss}=-\sum{\text{class\_label}\cdot\log(\text{Pr(class\_i)})} \tag{3}\]  
In this context, class_label is the actual class label, while Pr(class_i) is the model's predicted probability for that class. Collectively, these three components assemble to create the total loss function L for the YOLOv5 model:  
\[\text{L}=\lambda\_box\cdot\text{Box\_Loss}+\lambda\_obj\cdot\text{Obj\_Loss }+\lambda\_class\cdot\text{Class\_Loss} \tag{4}\]  
Here, \(\lambda\_box\), \(\lambda\_obj\), and \(\lambda\_class are adjustable weight coefficients, determining each loss part's significance during the optimization process. Adjusting these coefficients will allow us to optimize the model's performance for various tasks.
## 4 Discussion
### Finding 1: Calibration Leading to an Increase in Dimensional Stability
Their research indicated that higher cure temperatures led to reduced shrinkage, likely due to the expansion of materials at elevated temperatures [37]. In plastic injection molding, glass beads can be added to enhance mechanical properties or reduce shrinkage. [PERSON] et al. [38] delt with this phenomenon as they study three types of deformation during the cooling of PA6 specimens. In his experiments, increasing the glass bead content led to a decrease in shrinkage. Perhaps a similar approach could be used to improve the manufacturing accuracy of precision bonded joints. Another additive under consideration is carbon nanotubes (CNTs), which have gained popularity for various applications. In bonded joints, they are used in research to improve mechanical properties [70; 71] or to benefit from the changes in piezo resistance that CNTs bring to monitor stresses in the bonded joint [72].
## 8 Conclusions  
Real-time highway traffic flow prediction is considered one of the highest priority applications for intelligent transportation systems since abnormal traffic flow can have severe impacts on people's time, money, and lives. Providing traffic flow information in real-time will assist drivers to reach their destination faster and safer. However, traffic data is very challenging to process and evaluate in real-time due to its huge size. In this study, we designed and evaluated a scalable distributed machine learning-based scheme to predict highway traffic flow in (near-) real-time. The system maintains security and privacy since the data will not be shared at a centralized node. Stacking ensemble learning is utilized to implement the DMLRF model that takes the RFR models with different hyper-parameters' values as inputs. KA and Apache Spark are utilized to process the data and provide the prediction results in real-time. A dataset obtained from FDOT-D4 is used to build all the models in this research to predict highway traffic flows. The proposed model was evaluated and compared to other base-line models, LR, LogR, and KNN in terms of RMSE, MSE, MAE, \(R^{2}\), and adjusted \(R^{2}\). The proposed scheme demonstrated high accuracy in predicting abnormal traffic flows and outperformed the base-line models while maintaining scalability and data privacy. For future work, we will design and evaluate a real-time accident prediction scheme using distributed machine learning.  
Conceptualization, H.A., I.M. and H.A.-N.; methodology, H.A., I.M. and H.A.-N.; software, H.A.; validation, H.A., I.M. and H.A.-N.; formal analysis, H.A., I.M. and H.A.-N.; investigation, H.A., I.M. and H.A.-N.; resources, H.A. and I.M.; writing--original draft preparation, H.A.; writing--review and editing, H.A., I.M., H.A.-N. and E.A.; visualization, H.A. and E.A.; supervision, I.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data available upon request.  
This work is part of the Smart Drive initiative at Tecore Networks Lab at Florida Atlantic University.  
The authors declare that they have no conflict of interest.
# Approximate Networking for Universal Internet Access
## 1 Introduction
### Why Adopt Approximate Networking?
#### 1.3.2. Diversity of User & Application Profiles  
The Internet's \"digital divide\" is not a binary divide. There is a spectrum of connectivity options and digital capabilities accessible to people around the world (see Figure 2). In some places, ultra high-speed broadband connections are available, while there are hosts of places where there is no connectivity at all, however, most places lie somewhere in between. User and application profiles and requirement vary greatly: at one extreme, we have applications that require extremely high throughput (e.g., video on demand) and low latency (e.g., tactile Internet); on the other extreme, we have applications that have minimal throughput and latency requirements (e.g., smart meters, which report back low-volume data relatively infrequently). Users can also have vastly different service requirements and financial strength. In the face of such great diversity, the approximate networking framework can avoid the difficulties of single-size-fits-all networking solutions, furthermore, these diversities can be exploited and applications and users provided services and resources commensurate to their requirements.
## 3 Results
### Simulation Example  
In order to validate the effectiveness of these conclusions of Theorem 1, \(N=80\) UAVs are taken as an example to execute the cohesive flocking task. Each UAV governed by (1) adjusts its behavior under the control protocol (11). The communication radius \(D\) is 4 m.  
Figure 2 gives the readers a detailed intuition into the results of the proposed simulation. As shown in Figure 2a, 80 UAVs start flying from these green star points, and their final positions due to the simulation time \(T=100\) s is expressed by these colorful balls. Each curve denotes the trajectory of one UAV. The curves with different colors denote the trajectories of different UAVs. Figure 2a has given a clear exhibition that 80 UAVs asymptotically converge to flying in a flock. Eighty UAVs are considered in this case, thus there are \(\frac{N(N-1)}{2}=\frac{80\times 79}{2}=3160\) curves in Figure 2b. Each curve denotes the distance between two UAVs. According to the simulation result shown in Figure 2b, the distances between any two UAVs finally become stable. During the evolutionary process, the distances between any two UAVs are all larger than 0, that is to say, there is no collision between any two UAVs. More importantly, the interference among UAVs can be reduced in terms of adjusting the repulsive term, such as increasing the value of repulsive coefficient \(b\). Figure 2c-e, respectively, gives the velocities, the heading angles, and the flight-path angles of 80 UAVs versus time.  
The above simulation results have definitely proved that these UAVs will form a cohesive and consistent configuration without collisions between any two UAVs.  
Figure 2: Simulation results for three-dimensional collective cooperation of 80 UAVs. **(a)** trajectories of 80 UAVs, and details of the final configuration; **(b)** distances between any two UAVs versus time; **(c)** speeds of 80 UAVs versus time; **(d)** heading angle of 80 UAVs versus time; **(e)** flight-path angle of 80 UAVs versus time.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## Conflicts of Interest: The authors declare no conflict of interest. Abbreviations
\begin{tabular}{c c} MBES & MultiBeam EchoSounders \\ MC & Multi Channel \\ MERIS & Medium Resolution Imaging Spectrometer \\ MIR & Mid Infrared \\ ML & Machine Learning \\ MLD & Mixed Layer Depth \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ NASA & National Aeronautics and Space Administration \\ NCC & Normalized Cross-Correlation \\ NIC & National Ice Center \\ NIR & Near Infrared \\ NRA & NASA Radar Altimeter \\ NRCS & Normalized Radar Cross Section \\ NRT & Near Real Time \\ NSCAT & NASA scatterometer \\ NSF & National Science Foundation \\ OC & Ocean Color \\ OCh & Ocean Chlorophyll \\ OOS & Ocean Oil Spill \\ OSC & Ocean Surface Current \\ OSCAT & OceanSat SCATreometer \\ OSS & Ocean Surface Salinity \\ OSW & Ocean Surface Wind \\ OWH & Ocean Wave Height \\ OT & Ocean Tide \\ PSU & Practical Salinity Units \\ QSSA & Quasi-Single Scattering Approximations \\ RF & Random Forest \\ RFI & Radio Frequency Interferences \\ RMSE & Root Mean Square Error \\ RS & Remote Sensing \\ RSS & Remote Sensing Systems \\ RT & Radiative Transfer \\ SAR & Synthetic Aperture Radar \\ SARAL & Satellite with ARgos and ALtiKa \\ SC & Single Channel \\ SD & Ship Detection \\ SI & Sea Ice \\ SL & Sea Level \\ SMAP & Soil Moisture Active/Passive \\ SMMR & Scanning Multichannel Microwave Radiometer \\ SMOS & Soil Moisture and Ocean Salinity \\ SONAR & Sound Navigation And Ranging \\ SPOT & Satellite pour l'Observation de la Terre \\ SRAL & Synthetic Aperture Radar Altimeter \\ SSE & Sea Surface Emissivity \\ SSM/I & Special Sensor Microwave/Imager \\ SSMIS & Special Sensor Microwave Imager Sounder \\ SST & Sea Surface Temperature \\ SVM & Support Vector Machines \\ SWIR & Shortwave Infrared \\ TIR & Thermal Infrared \\ TMI & TRMM Microwave Imager \\ TOA & Top Of Atmosphere \\ TSM & Total Suspended Matter \\ UAV & Unmanned Aerial Vehicle \\ VIIRS & Visible/Infrared Imager Radiometer Suite \\ VTIR & Visible and Thermal Infrared Radiometers \\ VV & Vertical transmit and Vertical receive \\ XCTD & Expendable Conductivity/Temperature/Depth \\ \end{tabular}
## 4 Enabling Technologies for FSO Communication
### Adaptive Optics
#### 4.9.2 Implementation in FSO Systems  
FSO technology has been explored for applications involving both stationary and mobile nodes. In 6G mobile networks, it is anticipated that many antennas utilizing high-frequency bands (such as THz) will be installed on a variety of stationary nodes like traffic lights and signboards, as well as mobile nodes like trains and drones [400]. FSO presents a promising solution to deliver high-capacity mobile fronthaul connections for these antennas [9; 12].  
Figure 13: Depiction of an adaptive optics system.  
In scenarios where FSO is used with moving nodes, an FSO node must adjust its output beam to ensure the FSO signal power surpasses the minimum Rx sensitivity at the receiving node's aperture [9]. In these FSO communication applications, a significant technical challenge is the PAT. Optical beam steering is a crucial function in PAT [401]. This involves tracking the position of the paired FSO node and adjusting the beam direction accordingly as the paired node moves. The position is monitored using a position detection method, such as a position-sensitive detector. The beam direction is then altered using a rotating mechanism [9]. Traditionally, this has been achieved through mechanical mechanisms such as fast steering mirrors (FSMs), two-axis gimbals, and MEMS mirrors. While effective, these methods are often heavy, bulky, slow to adjust, and prone to mechanical failure [402; 403; 404; 405]. Alternative non-mechanical approaches, such as those using optical phased arrays or liquid crystals, have also been explored. However, optical phased arrays are complicated in terms of control and calibration, and liquid crystals offer a limited steering range. Furthermore, these non-mechanical methods are not yet sufficiently advanced for use in FSO communication systems [401].  
Furthermore, to maintain allowable PE, one approach is to use a beam Rx with a larger radius. However, this is challenging to mount on a moving FSO node due to severe constraints on size, weight, and power consumption. Another approach is to utilize a beam with a larger divergence angle at the Tx. Nevertheless, fixing the divergence angle at a larger value reduces the maximum FSO link distance [9]. Recent proposals and demonstrations of adaptive beam divergence control have shown its effectiveness in improving PAT despite PE. By adjusting the beam divergence angle according to channel conditions, this method counteracts the negative impacts of PE. Studies have demonstrated that adaptive beam divergence control can enhance the range of an FSO link and reduce OP in comparison to systems utilizing fixed beam control [9; 401].