esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Approaches for the Estimation of Operation Durations  
This section aims to evaluate the differences between the two approaches for the discretization of forecast uncertainties described in Section 2.3. Therefore, this experiment applies both approaches to the months from April 2000 until October 2000. This procedure was repeated for different estimation horizons of one to four weeks. For the sliding-window approach, the parameter \(\omega\) was optimized manually in steps of 0.05 for each month, so that the estimation error was as small as possible. It is to mention that this is only possible when applied to historical information. For real-world applications, a best-guess must be applied.  
Table 4 summarizes the results of the comparison. Therefore, it first states the minimal, mean, and maximal actual duration of the operation _Install OWT_ for each month as a reference. The actual values result from applying the Markov approach to the historical weather records without using forecast uncertainties, as described earlier in Section 2.6. Afterward, the table lists the corresponding approaches' estimation errors for prediction horizons of one to four weeks. The table provides the estimation error as the standard deviation between the estimation and the actual value over the corresponding period for each month. The final column _Mean_ always depicts the mean value of the corresponding row. Therefore, all these horizons start at the first of the current month until the end of the noted week.  
As expected, the error increases with an increasing horizon. For one week, the average estimation error is approximately one hour for both approaches, increasing to 84.92 h for the sliding-window approach and 15.19 h for the Markov approach at four weeks. In general, for one or two weeks, both approaches perform similarly well, but for longer horizons, the Markov approach outperforms the sliding-window approach by far. Figure 6 shows an example of the estimations for weeks 2 and 3 of the April dataset. Comparing both approaches shows that the Markov-Chain approach results in smoother estimations but tends to underestimate the real duration slightly. Nevertheless, its estimation follows the general shape of the actual duration quite closely. In contrast, the sliding-window approach tends to overestimate. In conclusion, the Markov approach generally performs better and, additionally, is not dependent on an optimal parametrization. Therefore, the remaining experiments provided in this article use the Markov approach for the discretization of weather uncertainties.
## 3 Materials and Methods
### Material Characterization  
The microscopic morphology and lattice spacing of the materials were observed using a scanning electron microscope (SEM, G300, Zeiss, Jena, Germany) and a transmission electron microscope (TEM, Talos F200X, Thermo Scientific, Waltham, MA, USA). With the aid of a Fourier transform infrared spectroscopy apparatus (INVENIO R, Bruker, Borken, Germany), the CF precursor's chemical bonding structure was confirmed. Powder X-ray diffraction (XRD, ADVANCE D8, Bruker, Karlsruhe, Germany) was used to collect the crystal structure of the materials. The surface chemical composition and bonding structure of the CF materials were analyzed by X-ray photoelectron spectroscopy (XPS, AXIS-SUPRA+, Kyoto, Japan). Thermogravimetric analysis (TG, 209 F1 Libra, Netzsch, Selb, Germany) was employed to characterize the chemical bonds in the precursors. Information regarding the carbon content in the CF ceramic samples was acquired through the use of a carbon-sulfur analyzer (CS844, St. Joseph, MI, USA). A laser confocal Raman spectrometer (LabRAM HR Evolution, Horiba, Kyoto, Japan), was deployed to determine the extent of carbon graphitization in the CF materials. Additionally, the specific surface area and pore size distribution were measured with a physical adsorption analyzer (ASAP2460, Micromeritics, Norcross, GA, USA).
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 9 Performance Analysis  
The target of a rail diagnostics system is to detect most of the defects affecting rails, while minimizing the number of false alarms [47]. Its performance can be assessed through the so-called _receiver operating characteristic curves_ (ROC), which represent the trade-off between the _probability of detection_ (PD) and the _probability of false alarm_ (PFA) achievable by varying the threshold level adopted in defect detection. A related performance indicator is the _area under the receiver operating characteristic curve_ (AUC) that provides an overall indication of the \"goodness\" of detection. In fact, AUC = 1 means that perfect detection is achieved for the given threshold (i.e., PD = 1 and PFA = 0); conversely, if AUC = 0, detection is jeopardized by false alarms, so that PD = 0 and PFA = 1.  
In this section, various numerical results referring to the on-board and land-based systems described in the previous sections are illustrated.
# LaRoAwan CubeSat with an Adaptive Data Rate: An Experimental Analysis of Path Loss Link Margin
###### Abstract
This study aims to investigate the signal propagation integrity of the 433 MHz frequency band, and the capability of a low-earth orbiting satellite carrying a LoRaWAN payload as the gateway to collect data from the nodes on the ground area without internet connectivity. The signal level and the capability of service delivery at the LEO, which is around 550 km altitude, are investigated. ADR is used to analyze the signal adaptation of dynamic signal selection at the worst-case scenario and the reception of nodes at the gateway level. The data from the gateway are then dumped into the ground station as the satellite passes the ground station. Additionally, the link budget is considered in this study while analyzing the signal integrity and path loss output at the end nodes. Multiple  
Figure 1: Direct-to-Satellite (DTS) vs ground data relay architecture. attenuators were employed to simulate the signal at various distances to determine the practical link performance. This paper is divided into six sections: Section 2 provides the literature review on the related works from previous studies. Section 3 gives an overview of different IoT technologies and their modulation comparison, along with the pros and cons. Section 4 is the methodology that covers the theoretical aspects of this study and link budget calculation. There is also an explanation of the hardware setup and how the path loss is simulated to achieve maximum distance. Section 5 is the results obtained by performing the experiment. Finally, Section 6 is the conclusion of this study.
## 4 Capability on Obstacle Avoidance
### Comparison Front Wheel Steering (FWS) and Rear Wheel Steering (RWS)
#### 4.1.3 Closed Loop Simulation  
A pylon-controlled single lane change course was set for closed loop simulation, as shown in Figure 20. Maximum steering angle, maximum steering speed, and maximum steering acceleration are all sufficiently large for avoidance operation.  
Figure 19: Steering wheel angle and lateral displacement on open loop simulation.  
Figure 18: Tracks image of front wheel steering (FWS) and rear wheel steering (RWS) to avoid obstacle.  
In a closed loop, the driver recognizes the given lane change course in advance. Therefore, it makes it easier to pass the obstacle by steering a little to the opposite side before starting avoidance, as shown on the left in Figure 21. However, it is impossible to avoid oncoming obstacles in this way in the real world.  
As shown in Figure 21, in the case of FWS, the rise of the steering angle after starting avoidance is quite gentle, and the steering angle is considerably smaller and longer in the latter half of avoidance than in open loop. In the case of RWS, a large steering angle is required to recover from the delay of open loop avoidance. It is about a 1.6 times larger angle in the first half of avoidance and about twice as much in the second half. Overshoot at end of avoidance is also noticeable, indicating the difficulty of the avoidance operation in RWS.
# Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring
###### Abstract
[41] used a Kalman filter algorithm to achieve spatiotemporal fusion of existing Landsat TM and 250-m NDVI MODIS (MOD13Q1) images for predictions of synthetic Landsat NDVI values. [PERSON] et al. [42; 43; 44; 45] used a Kalman filter algorithm to produce a 30-m-resolution LAI time-series, with a 4-day time-step using the Landsat TM LAI data and the S-G filtered MODIS LAI time-series. They then applied the 30-m-resolution LAI time series to model crop growth. Using the methods described in the references above, the Kalman filter can be applied using basic parameters, and it has the flexibility of being independent of the number of medium-resolution images obtained. The Kalman filtering algorithm uses a linear relationship to represent the time update of images, and considers the relationship between the image of the target and its forward and backward time by recursive algorithm. Nonetheless, the lack of observation data in the target period will lead to large errors in the measurement update. Fortunately, the ESTARFM model is good at producing the details of observation data [15; 31]. Therefore, we improved the Kalman filtering algorithm using the observation data producing operator of ESTARFM model, where the input of the images was generated by the ESTARFM model as an observation into the Kalman filtering algorithm. The harmonized image datasets with high temporal resolution same to MODIS image and medium spatial resolution (30 m) were produced using the improved Kalman filtering model by harmonizing GF-1 WFV images and Landsat 8 OLI images. The growth condition of summer corn from 5 June to 6 October 2014 was then analyzed in combination with meteorological data and evapotranspiration (ET) and latent heat flux (LE) data. The target of this research is to evaluate the potential and practicality of harmonizing multi-source remote sensing images using the improved Kalman filtering model and applying it to the crop growth monitoring.
# Model Retraining upon Concept Drift Detection in Network Traffic Big Data
## 6 Conclusions  
This paper introduces a framework that not only employs Isolation Forest for efficient anomaly detection but also integrates drift detection and model retraining mechanisms. In addition to statistical performance metrics like accuracy, precision, recall, and F1-score, drift detection is determined using the anomaly score, new anomaly rate, and drift factor. Both incremental as well as full retraining were performed and analyzed.  
In summary, the results show that retraining has a positive impact on the model's robustness. There is a steady increase in the model's performance with incremental retraining and a significant positive impact on the model's performance with full model retraining. Moreover, incremental retraining is successfully performed using small batch sizes.
## 3 Results
### Fruit Growth and Productivity
#### 3.4.1 Season 2015
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DAFB Range** & **Treatment** & \multicolumn{2}{c}{**V**} & \multicolumn{2}{c}{**AGR**} \\  & & **(cm\({}^{3}\))** & **(cm\({}^{3}\) day\({}^{-1}\))** \\ \hline \multirow{6}{*}{38–77} & T & 23.18 & 0.58 & b \\  & M & 20.93 & 0.49 & c \\  & F & 21.46 & 0.65 & a \\  & S & 21.00 & 0.51 & c \\ \cline{2-5}  & _F-value_ & _3.14_ & _5.25_ & \\  & _p-value_ & _0.108_ & _0.041_ & \\ \hline \multirow{6}{*}{77–105} & T & 41.35 & 0.61 & a \\  & M & 37.95 & 0.60 & a \\  & F & 39.42 & 0.58 & a \\  & S & 36.56 & 0.43 & b \\ \cline{2-5}  & _F-value_ & _3.56_ & _5.83_ & \\  & _p-value_ & _0.088_ & _0.033_ & \\ \hline \multirow{6}{*}{105–125} & T & 61.77 & a & 1.09 & a \\  & M & 59.42 & a & 1.16 & a \\  & F & 58.13 & a & 0.98 & a \\  & S & 50.13 & b & 0.73 & b \\ \cline{2-5}  & _F-value_ & _5.08_ & & 9.14 & \\  & _p-value_ & _0.044_ & & _0.012_ & \\ \hline \multirow{6}{*}{125–148} & T & 104.46 & a & 2.26 & a \\  & M & 107.10 & a & 2.42 & a \\ \cline{1-1}  & F & 91.26 & b & 1.86 & b \\ \cline{1-1}  & S & 85.28 & b & 1.87 & b \\ \cline{1-1} \cline{2-5}  & _F-value_ & _5.30_ & & _5.06_ & \\ \cline{1-1} \cline{2-5}  & _p-value_ & _0.040_ & & _0.044_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average fruit volume (V) and absolute growth rate (AGR) recorded for T, M, F and S treatments in different fruit growth stages (Days After Full Bloom range, DAFB Range), in 2015. Within each stage, a by-time repeated ANOVA was performed. For each variable, different letters indicate a statistical difference at \(p\leq\) 0.05.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 4 Research Methodology
### Data Samples  
In this study, a variety of datasets were collected, and these datasets represent real-world activity that was captured by various reputable organizations. These datasets are represented as pcap files. Six datasets were used for this research, and these were collected from Zeustracker [71], Stratosphere [72], Abuse.ch [73], and Dalhousie University [74]. Abuse.ch correlates samples from commercial and open-source platforms such as VirusTotal, ClamAV, Karspersky, and Avast [73]. Dalhousie University's botnet samples are part of the NIMS botnet research project and have been widely utilized by many researchers [74]. Table 10 defines all the data sets that were used during this research and provides some information around the banking malware variants collected and used during this research. Table 10 also specifies the year that the samples were detected and categorized by the antivirus vendor and depicts the number of flows extracted from these samples.  
Each pcap file is made up of network flows, and for this research, the network characteristics for each flow were extracted from these pcap files. A flow is a sequence of packets flowing between a source and a destination (IP and port combination) during a certain period of time. Figure 9 shows the process used to extract the characteristics from each flow, and the first step was to set up and configure Netmate-flowcal on a virtual machine, which allowed the pcap files to be input into the Netmate-flowcal tool. Netmate-flowcal then calculated the key statistics of each flow within each pcap file and output these into an.out file, which had to be converted to a text file and cleaned. The text file was then converted into an excel file, and the data were prepared, cleaned, and then converted into a CSV file, which prepared the file for the machine learning algorithm. As there were thousands of pcap files, a script was developed to automate this process.  
Each sample in the CSV files was labeled, and this identified whether the sample was benign or malware. A label of '0' was applied to the benign traffic samples and a label of '1' was applied to the Zeus malware traffic samples. The Pandas library was used to create and manipulate the data frame and prepare the data for the machine learning tasks.  
Figure 9: Feature rankings calculated by SelectKBest.
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 4 Results and Discussion  
The proposed framework was tested on datasets featuring both sensor-based inputs and traditional glove sensor data. Initial results indicate improved recognition accuracy and reduced inference time compared to baseline methods. Further analysis highlights the system's ability to adapt to varied signing speeds and environmental conditions, making it a viable solution for real-world applications.  
Performance Comparison Study:  
HGE-ACN outperforms the Dynamic Dense Spatiotemporal Graph Convolutional Neural Network (DDSTGCNN) [18], Graph-Based Multimodal Sequential Embedding Network (MSeqGraph) [17], and Boundary-Adaptive Encoder (BAE) [23] in sign language sentence recognition. Compared to previous approaches, HGE-ACN has higher recognition accuracy and reduced mistake rates. Real-time sign language recognition systems can rely on its adaptive convolutional technique and sensor data fusion to handle signing speed and style changes. HGE-ACN can create inclusive deaf communication systems, as shown by this comparison.  
Figure 5: Classification of three-level output.
# Design and Experiment of High-Resolution Multispectral Polarization Imaging System
## 6 Development and Experiment of a High-Resolution Polarization Imaging Instrument  
### System Development  
The visible light micro-polarization detector and infrared micro-polarization detector were purchased, and the pod mounted on the UAV platform was designed and processed. The whole system was installed and adjusted and electronically controlled, and the pitch rotation function was initially realized. The physical diagram of the system is shown in Figure 6.
# Recent Trends on UV filters
###### Abstract  
UV filters are the shield ingredients of sunscreens against the negative effects of solar radiation. Since the discovery of the first UV filter, nearly 30 filters have become commercially available. Over the years, innovation and regulatory updates have driven their use by the cosmetic industry. This work aimed to characterize commercial sunscreens and unveil the main trends by analyzing the labels of 444 sunscreens formulations that are currently being marketed. Avobenzene, octycylene, and bis-ethylhexyloxyphenol methoxyphenyl triazine were the three UV filters with the highest usage frequencies (>40%). Emulstified preparations and sprays were the preferred forms, while the most frequent SPF was 50+. Differences were noted between adult and children's sunscreens, namely the higher usage of inorganic filters for the latter. Over the past few years, the five most used UV filters remained the same, but octocyclene, ethylhexyl salicylate, and nano methylene bis-benzothizolyl tetramethylbutylphenol had decreased usage. An increasing tendency towards the use of the inorganic UV filter titanium oxide was also observed. Overall, this study characterized the true market impact of approved UV filters and how the market has evolved over recent years. This insight can help pave the way for the design of new UV filters and is helpful for the assessment of environmental risks.  
UV filters; sunscreens; cosmetics regulation; market trends; formulations; SPF +
Footnote †: journal: Applied Physics Letters
# Optimal Energy Efficiency Fairness of Nodes in Wireless Powered Communication Networks
## 6 Conclusions  
In this paper, we studied the energy efficiency proportional fairness for downlink multi-tier heterogeneous WPCNs. The coverage probability, average number of active nodes, and the amount of energy harvesting from WPT in networks is obtained as a function of the user association probability and receive threshold with the aid of stochastic geometry. The original energy efficiency proportional fairness utility function is designed with the user association probability and receive threshold as a non-convex optimization problem. We relax the objective problem as a convex optimization problem according to the properties of receive threshold and association probability. By exploiting an alternating optimization, a novel suboptimal low-complexity iterative algorithm was proposed to obtain a suboptimal solution. Simulation results showed that the proposed optimal scheme achieves a significant improvement in system energy efficiency proportional fairness compared to the baseline scheme.  
This work was supported in part by the Ministry of Science and Technology of China under Grant 2016 YFE0119000, and by the Korea-China Joint Research Center Program (NRF-2016K1A3A1A20006024) through National Research Foundation, the Korean government, and Australian Research Council's Discovery Early Career Researcher Award Funding Scheme under Grant DE170100137. [PERSON] conceived the main idea and the sub-optimal iterative algorithm; all authors contributed to performance analysis and wrote the paper. The authors declare no conflict of interest.
## 3 System Model
### Task Model  
Tasks can arrive at mobile devices at any time. In the simulation presented in this work, task arrivals follow a Poisson process, where the exact task arrivals are random but the average rate of task arrival is constant at \(\lambda\). In our simulation, time is continuous and is not discretized into time slots. This allows us to accurately simulate a Poisson process, which, in turn, more accurately simulates the real-world conditions. In our model, task offloading decisions are binary, which means each task must be fully executed by a mobile device or an edge device. A task cannot be partially executed by both mobile and edge devices.  
Let T be the set of all arrived tasks in our system \(T=\{t_{1},t_{2},...\}\). At the beginning, the set \(T\) is empty, and as time passes, more tasks are added into the set. Let \(m(t_{i})=m_{j}\) indicate the mobile node \(m_{j}\) at which task \(t_{i}\) has arrived. Additionally, let \(l(t_{i})=l\) and \(d(t_{i})=d\) indicate the size \(l\) and computational workload \(d\) of the task \(t_{i}\), respectively. The computational workload of a task indicates the number of floating point operations required to execute the corresponding task. In this work, we use floating point operations instead of CPU cycles to indicate the workload of tasks. Flops is a unit of measurement that stands for floating-point operations per second. This measurement is more accurate than CPU cycles for tasks that require many floating point operations. For a task to be executed in one second, a processor with a processing capacity of \(d(t_{i})\) flops is required. If a processor has higher processing capacity, the task will execute in less than one second, and if a processor has lower processing capacity, the task will run in more than one second.
# Transmission Angle Analysis of a Type of Parallel Manipulators
## 3 Transmission Angle Analysis
 \tag{14}\]  
The determinant of matrix \(J_{x}{}^{T}J_{x}\) in Equations (13) and (14) can be rewritten as:  
\[\det(J_{x}{}^{T}J_{x})=\sum\limits_{i=2}^{n}[\mathbf{w}_{i-1}(\mathbf{w}_{i}\times\bm {w}_{j})]^{2}\ \ \ i<j\leq n\ \ n\geq 3 \tag{15}\]  
\[\det(J_{x}J_{x}{}^{T})=(\mathbf{w}_{1}\times\mathbf{w}_{2})^{T}(\mathbf{w}_{1}\times\mathbf{ w}_{2})\ n=2 \tag{16}\]  
Since \(\mathbf{w}_{i}\) represents the unit vector along the passive link, we can obtain:  
\[\cos\beta_{12}=|\mathbf{w}_{1}\times\mathbf{w}_{2}| \tag{17}\]  
For \(n\geq 3\), we can define the angle as:  
\[\cos\beta_{ij}=\frac{\mathbf{w}_{i-1}{}^{T}(\mathbf{w}_{i}\times\mathbf{w}_{j})}{\left|\bm {w}_{i}\times\mathbf{w}_{j}\right|},\cos\beta_{j(i-1)}=\frac{\mathbf{w}_{i}{}^{T}(\bm {w}_{j}\times\mathbf{w}_{i-1})}{|\mathbf{w}_{j}\times\mathbf{w}_{i-1}|},\cos\beta_{(i-1)i} =\frac{\mathbf{w}_{i}{}^{T}(\mathbf{w}_{i-1}\times\mathbf{w}_{i})}{|\mathbf{w}_{i-1}\times\bm {w}_{i}|},\ \ 2\leq i<n,i<j\leq n \tag{18}\]  
It can be clearly observed that when \(\beta_{ij}\to 90^{\circ}\) (\(1\leq i<n\),\(i<j\leq n\)), the result is \(\det(J_{x}{}^{T}J_{x})\to 0\) (\(\det(J_{x}J_{x}{}^{T})\to 0\)). As a result, angle \(\beta_{ij}\) related to the inverse kinematic singularity can be defined as a cross transmission angle (CTA) for the kinematic chain. The redundancy feature can improve the performance by avoiding the kinematic singularities. This phenomenon can be illustrated using Equation (17), only if all angles defined in Equation (18) are close to \(90^{\circ}\).