esa-sceva/satcom-chunk-collection huggingface dataset:
# Lossless Compression of Large Aperture Static Imaging Spectrometer Data +
## 3 Compression Scheme
### Data Rearrange  
In the pushbroom scanning approach of the LASIS sensor, each row of the CCD detector successively observes the same points in object space. The regrouping of the same row of each frame of the LASIS data cube allows us to obtain a complete scene of the full field of view under the same optical path difference (OPD) as illustrated in Figure 4. By scanning through all the OPD positions, we can obtain a data cube that represents the response of the same field of view under all OPD bands. Since this data cube is similar to the form of data acquired by temporally modulated Fourier transform imaging spectrometers, i.e., dynamic interference imaging spectrometers, we denote this cube as a LADIS (Large-Aperture Dynamic Imaging Spectrometer). The extraction of the LADIS is a generalized coordinate transformation from the photo-acquisition space, \(\phi_{(X,\,Y,\,F)}\), to the OPD-image space, \(\phi_{(x,\,y,\,z)}\), as illustrated in Figure 5. The matrix formula for this generalized coordinate transformation can be written as  
\[\begin{pmatrix}x\\ y\\ z-z_{0}\end{pmatrix}=\begin{pmatrix}1&0&0\\ 0&1&\frac{f}{W}\frac{\
u}{\
u_{F}}\\ 0&\frac{f}{L}&0\end{pmatrix}\begin{pmatrix}X\\ Y\\ F\end{pmatrix}, \tag{1}\]  
where \(f\) is the focal length of the Fourier lens; \(H\) is the height of the sensor; \(\
u\) is the relative velocity between the sensor and the ground; \(\
u_{F}\) is the frame frequency; \(L\) is the shear from the lateral shearing interferometer; \(F\) is the frame number in the photo-acquisition space, \(\phi_{(X,Y,F)}\); \(z_{0}\) is the zero OPD position in the OPD-image space, \(\phi_{(x,y,z)}\); and \(z_{0}\) can be assumed to correspond to \(Y=0\) in the photo-acquisition coordinate.  
Figure 4: LADIS extraction procedure.  
Figure 3: Compression scheme of the proposed method.  
Unlike the LASIS and LAMIS, a frame of LADIS data contains two-dimensional spatial information representing the response of all the pixels in the field of view under the same OPD. Therefore, the overlapped interference and spatial information in each frame can be separated. We can obtain the interferogram of a spatial location by extracting the same pixel of each frame.
## 3 Results
### Gray Correlation Analysis Results
#### 3.3.1 Correlation between Carbon Emissions and Urban Land Area
One can observe that the energy consumption per unit of GDP for urban activities carried out in residential areas is lower than that of industrial areas. Therefore, its effect on the intensity of urban carbon emissions is less significant. The correlation between carbon emissions per unit of land and the area of each type of land (\(H\)) is generally positive and is above 0.5. It has a negative correlation with the area of green space. The correlation between carbon emission per unit land use and residential land use is the highest, with a value of 0.678, followed by road and public-square land use and commercial-service land use, with 0.634 and 0.554, respectively, and the lowest correlation was with industrial land use, with a value of 0.538.
# Swin-YOLO for Concealed Object Detection in Millimeter Wave Images
## 4 Experiments
### Implementation Details  
Our proposed Swin-YOLO is implemented in Python 3.8 and PyTorch 1.8.1 compilation environment. The training, validation, and testing were performed on an NVIDIA Quadro RTX4000 GPU, and the operating system used was Ubuntu 18.04 LTS. For training part, we used the pre-trained model of the YOLOv5 baseline network on the COCO dataset [50]. Due to changes in the original structure, our proposed network used some of the pre-trained models of YOLOv5 and shared weights in the backbone network to improve the model's generalization ability. The pre-trained model also helped to save training time. In terms of model size, we chose YOLOv5l as the baseline. We trained the model on the laboratory's MMW concealed object dataset for 50 iterations, with a warm-up training of 3 iterations to stabilize the gradient change. We set the initial learning rate to 0.001, weight decay coefficient to 0.0005, and used the One Cycle strategy to gradually reduce the learning rate. The batch size was 4. The detector updated parameters and reduced losses using the SGD optimizer, with a momentum of 0.937. In terms of Anchor setting, we selected the anchor boxes from the initial settings of YOLOv5 and calculated through the Autoanchor, obtaining an anchor above threshold (AAT) of 6.88 and a best recall rate (BRP) of 1.000, which is well adapted to the dataset.
## 3 Proposed Solutions
### The Selection of a Listening Window to Establish a Connection with the Device
#### 3.1.2 Shifted Time Windows  
The second proposed algorithm uses also three windows. The first one is centered around the expected reception time, the second one shifted up, and the third one is shifted down, as shown in Figure 3. The length of the windows, as previously, is scaled by the factor \(\alpha\). The duration of the first window is set to \(2\cdot\alpha\cdot\sigma\), the second one to the interval \(<-3\cdot\alpha\cdot\sigma,\alpha\cdot\sigma>\), and the third one to \(<-\alpha\cdot\sigma,3\cdot\alpha\cdot\sigma>\). The shift scenario assumes the use of two asymmetric windows focused on the possibility of receiving packets from the two tails of the normal distribution, positive and negative in relation to the expected time of packet arrival.  
Figure 2: Linear increasing of time window.
## 1 Introduction
* _An efficient scaling policy for CEAS framework_: Taking into account dynamic workload, a scaling policy is created for the proposed CEAS framework that can either add or remove the clusters so as to optimise the virtual instances. New data storage systems are introduced to store the data while performing the scale-down operations on the dynamic virtual clusters. The resources can be easily either scaled up during heavy workload or scaled down for saving power consumption subject to limited available resources. The novelty of the proposed auto-scaling method is that an absolute amount of resources can be determined and allocated for the AWS to process and analyse the big data. * _Sentiment Analysis of Twitter data with MapReduce over CEAS framework_: MapReduce is exploited for distributed computation of large data sets over Hadoop clusters of computing resources. Specifically, the MapReduce technique is applied to the auto-scaling cluster via the proposed CEAS framework that allows us to perform sentiment analysis on Twitter data of various universities. The experimental results show that the proposed scaling method in the CEAS framework handles the workload efficiently by dynamically adjusting the virtual computing resources. The rest of this paper is organised as follows: Section 2 summarises related works on distributed scaling of Hadoop clusters for cloud computing and big data analysis with MapReduce and Amazon EMR as motivation for our work. Section 3 presents the proposed CEAS framework with various functionalities for auto-scaling and sentiment analysis using MapReduce technique. Sentiment analysis of Twitter data and the performance of CEAS framework is presented in Section 4 to validate the effectiveness of the proposed approach. Finally, Section 5 concludes this paper with recommendation for further research.
## 3 Database of Water Content and Hyperspectral Information
### Analysis of Hyperspectral Information
#### 3.3.3 Extraction of Hyperspectral Information  
To check whether geometric and atmospheric correction was completed, data comparison was performed according to a round-trip flight within the orthophoto. The data extraction point is the same: the top of number 60.  
Figure 10(a) is an orthophoto when moving from the departure coordinates to the turning coordinates, and Figure 10(b) is an orthophoto when moving from the turning coordinates to the arrival coordinates. At this time, the input coordinates were the same, but there was a difference in the actual photographed range. This is a coordinate error that occurs during the drone flight process and caused by the push-broom method. This phenomenon can also occur in reverse and is difficult to predict. Therefore, when flying a drone, the measured area must be checked at least twice, and an orthophoto that is clearer or has a larger shooting area should be selected. Figure 10(c) is the hyperspectral information extracted from the 60 th location of each orthophoto before correction, and Figure 10(d) is the hyperspectral information after correction. The reflectance before correction differed by 400-800 nm, but the reflectance after correction had very similar values. Accordingly, it was confirmed that atmospheric correction was appropriately reflected.  
Figure 10: Atmospheric correction: (**a**) Raw data; (**b**) Black references; (**c**) White references; (**d**) Final data after correction.
## 1 Introduction
Other companies, such as Arnia [37] and Nectar Technologies Inc. [38], have developed multi-sensor solutions to monitor hive health and strength, queen bee presence, and parasite presence, to name a few, including relying on acoustic data recorded via microphones placed inside the hive. Burgeoning of such devices has allowed for acoustic hive data to be more easily measured and, oftentimes, released as open-source data to stimulate citizen science projects. Representative examples include the Nu-hive project [39] and the Open Source BeeHive (OSBH) project [40], as well as other bee detection datasets, such as [41]. These innovations have resulted in an increase in the number of publications pertaining to audio analytics of beehives. While recent reviews have focused on the technological advances in recording audio in beehives, as well as the use of multi-sensor approaches (e.g., see [13; 42; 43]), the literature lacks a review on the observed acoustic events enabled by precision apiculture. Such a review would provide researchers with insights on what characteristics future technologies should aim to monitor (or avoid). We aim to fill this gap with the present review and build on the work in [44]. In particular, focus will be placed on honeybees (_Apis mellifera_) and the bumble bee (_Bombus_ spp. ), on a wider range of applications of acoustics analyses for beehive monitoring (e.g., reactions to environmental factors), as well as more recent works proposed in the literature. The remainder of this review paper is organized as follows: Section 2 describes how the systematic review was conducted and how the studies were selected, assessed, and analyzed. Section 3 then presents and discusses the review results, with a list of recommendations for future research directions. Lastly, Section 4 presents our concluding remarks.
# A Novel Inductor-Based Non-Dissipative Equalizer
## 3 Simulation
### Comparison for PAE and NIBNDE with n Cells  
Table 8 shows that the NIBNDE has one less MOSFET than the PAE when the number of batteries is odd and the same number of MOSFETs when the number is even.  
Furthermore, the difference in the amount of inductance between PAE and NIBNDE increases with the number of batteries, which can be seen from Equations (12) and (13):  
\[\mathrm{e}_{\mathrm{odd}}=\frac{n-3}{2} \tag{12}\]  
\[\mathrm{e}_{\mathrm{even}}=\frac{n-2}{2} \tag{13}\]  
where \(\mathrm{e}_{\mathrm{odd}}\) represents the difference in the number of inductances between PAE and NIBNDE when the number of batteries is odd. \(\mathrm{e}_{\mathrm{even}}\) represents the difference in the number of inductances between PAE and NIBNDE when the number of batteries is even.  
The price of one inductor is 3 yuan and the volume of one inductor is 0.66 cm\({}^{3}\). When there are 16 cells, compared to the PAE, the NIBNDE uses seven less inductors, so it costs 21 yuan less and reduces the volume by 4.62 cm\({}^{3}\), but the equalization time of the NIBNDE is about seven times that of the PAE's equalization time. In this case, the PAE is better than the NIBNDE. However, when the number of cells is 128, the NIBNDE uses 64 inductors less than the PAE, so it costs 192 yuan less and reduces the volume by 42.24 cm\({}^{3}\). In summary, when there are a large number of cells, the cost of the inductors and the volume of the equalization circuit are as important as the equalization speed, so both the NIBNDE and the PAE have their own advantages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Expected Time (s)** & **Cell Number** & **MOSFET** & **Inductor** & **Control Complexity** \\ \hline SSIE & 2.0441 & 16 & 34 & 1 & M \\ PAE & 0.2283 & 16 & 30 & 15 & G \\ NIBNDE & 1.5862 & 16 & 30 & 8 & M \\ \hline \hline \end{tabular}
\end{table}
Table 7: The comparison between SSIE, PAE and NIBNDE in a sixteen cell battery string (G: Good; M: Marginal; P: Poor).  
Figure 8: Sixteen cell voltage trajectories in simulation (**a**) The SSIE. (**b**) The PAE. (**c**) The NIBNDE.
# Mechanical and Conductive Behavior of Graphite Filled Polysulfone-Based Composites
## 1 Introduction
In our earlier research [32], we investigated polysulfone-based composite materials that were prepared using the solution method and then filled with three different forms of graphite: natural graphite (NG), artificial graphite (AG), and expanded graphite (EG), to increase the composite's mechanical characteristics and thermal conductivity. Because NG has a perfect crystalline graphite structure, whereas AG and EG both have a faulty crystalline structure that impacts their characteristics, our analysis of the fillers' morphology revealed that NG is the best filler for enhancing the thermal conductivity of the composite [32]. We found that using 70 wt.% of NG gave thermal conductivity with the maximum magnitude of 4.26 W/m\(\cdot\)K. The reason for this was because NG's sheets had a flawless crystalline structure and a high aspect ratio, which created a large conducting surface with the matrix and made heat transfer easier than with AG and EG. The poor heat conductivity of the material is a result of structural faults in the composite, such as porosity and agglomeration formation, which we identified by SEM research. In the current investigation, we used a steel mold with holes rather than a [32] steel frame in order to increase sample quality. We were able to achieve a more flawless structure, reduce material porosity, and increase material density, which allowed us to boost the thermal conductivity of composites. The goal of our work was to develop densely filled composites with adequate high density and low porosity in order to attain high conductive characteristics of composites. We want to emphasize the impact of filler particle size and aspect ratio on the conductivity and mechanical performance of the composites by using different types of graphite as a filler.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Application Type  
Based on the use application, routing protocols can represent two main categories: event-driven and time-driven protocols, depending on whether the data packets will be sent following an event or periodically.  
\begin{table}
\begin{tabular}{l
# Device-Free Crowd Size Estimation Using Wireless Sensing on Subway Platforms
###### Abstract  
Dense urban environments pose significant challenges when it comes to detecting and measuring crowd size due to their nature of being free-flow environments containing many dynamic factors. In this paper, we use a wireless sensor network (WSN) to perform device-free crowd size estimation in a subway station. Our sensing solution uses the change in attenuation of the communication links between sensor nodes to estimate the number of people standing on the platform. In order to achieve this, we use the same attenuation information coming from the WSN to detect the presence of a rail vehicle in the station and compensate for the channel fading caused by the introduced rail vehicle. We make use of two separately trained regression models depending on the presence or absence of a rail vehicle to estimate the people count. The detection of rail vehicles occurred with a near-perfect accuracy. When evaluating the resulting estimation model on our test set, we achieved a mean average error of 3.567 people, which is a significant improvement over 6.192 people when using a single regression model. This demonstrates that device-free sensing technologies can be successfully implemented in dynamic environments by implementing detection techniques and using different regression models depending on the environment's state.  
p 2024 14 9386 1
# Research on Secure Debugging Interaction of Sensor Nodes Based on Visible Light Communication
## 4 System Downlink: Transmission Efficiency Improvement  
The system downlink transmits a large number of binary codes to repair or debug the sensor node. However, the sensing latency and low resolution of ambient light sensors result in low transmission efficiency of the overall codes. To this end, we employ two compression methods within the source and channel coding.
## 3 Results
### RQ3--How Have These Proposals Contributed to Addressing Different Levels of Violence?
#### 3.3.2 Monitoring of Crime  
Crime encompasses a broad spectrum of offenses, ranging from property crimes (e.g., S5, S33), such as theft and vandalism, to crimes affecting physical integrity (e.g., S26, S28, S42, S45), including homicides and kidnappings. This category also includes drug possession and trafficking (not reported in the analyzed studies), financial fraud (e.g., S38, S39), and cybercriming, which occurs within computer systems and cyberspace (e.g., S1, S2, S3, S4, S6, S7, S8, S9, S12, S14, S15, S16, S17, S18, S19, S20, S21, S22, S23, S24, S25, S27, S29, S30, S31, S32, S33, S34, S35, S36, S37, S38, S39, S40, S41, S43, S44, S45). Cybercriming-related threats include intrusions and attacks. Although drug trafficking is not explicitly addressed in the reviewed studies, its connection to cybercriminal networks highlights the need for broader digital security strategies. In the realm of cybersecurity, research trends indicate a strong emphasis on intrusion detection and cyberattack prevention, particularly in IoT networks (e.g., S2, S3, S7, S11, S14, S19, S21, S27, S34). Several studies propose anomaly detection methods to identify threats such as DDoS attacks, botnets, and IoT-targeted crimes using hybrid or specialized deep learning models. Additionally, various proposals explore strategies for detecting and mitigating complex cyberattacks, including ransomware, SQL injections, and DoS attacks. Furthermore, other studies focus on cyberattack and cybercriming detection, particularly in malware identification (e.g., S4, S12, S17), unauthorized access (e.g., S4, S10, S15, S17), and data manipulation (e.g., S4, S12, S20). Most research prioritizes cyberattack prevention, with an emphasis on DDoS mitigation, ransomware threats, botnet detection, and data privacy concerns. Some specialized approaches, such as S22, S27, S32, and S40, specifically address cyber threats like flooding attacks and traffic manipulation in IoT environment  
RQ4--What Is the Scope, What Are the Benefits, and What Are the Limitations of These Proposals in Terms of Functionality? Table 3 demonstrates that IoT and deep learning-based solutions, primarily tested in simulations or laboratory experiments (41 studies, 91%), exhibit high scalability and accuracy in threat detection, significantly enhancing urban security. However, their implementation faces challenges such as dependence on advanced infrastructures, data quality issues, and configuration complexity. While some approaches offer moderate costs and provide accessible code on repositories such as GitHub, others require high computational resources, limiting their widespread adoption.
## 5 Experimenting and Results
### Application of ML Methods  
With the MATLAB ML Toolbox module, the algorithms of Linear Regression, Decision Trees, SVR, Ensembles, and GPR (described in Section 4.4) were used for predicting the consequences of oil spills on the ground with the described dataset. A cross-validation approach was used to improve the training of the applied ML algorithms (see Figure 1).  
After training, the prediction quality of the algorithms was assessed with MSE (Equation (4)), \(R^{2}\) (Equation (3)), and NRMSE (Equation (2)). The following results were obtained: Linear Regression (MSE = 0.4; \(R^{2}\) = 0.45; NRMSE = 14.2%); SVR (MSE = 0.5; \(R^{2}\) = 0.54; NRMSE = 19.1%); Decision Trees (MSE = 0.3; \(R^{2}\) = 0.32; NRMSE = 15.8%); Ensembles (MSE = 0.3; \(R^{2}\) = 0.14; NRMSE = 17.7%); and GPR (MSE = 0.1; \(R^{2}\) = 0.95; NRMSE = 4.3%). The statistical measures of GPR were also significant.
## 3 Transient Response Simulation
### Research on Incentive and Damping Parameters
#### 3.3.2 Transient Damping Calculation  
In the computation of dynamic responses within a shaft system, damping is commonly divided into categories of relative damping and absolute damping, which correspond to internal and external damping in the frequency domain. During steady-state torsional vibration analysis within the frequency domain, calculations are performed for each harmonic excitation, excluding the DC component, by directly incorporating the absolute damping values into the damping matrix. However, in the transient analysis conducted in the time domain, the impact of external factors such as ice loads, sea waves, and explosions is considered. This may result in fluctuations in the rotational speed of the propulsion shaft system, rendering the direct substitution of absolute damping values into the damping matrix inappropriate. The absolute damping torque, resulting from the discrepancy between instantaneous and average rotational speeds, is applied as an external excitation torque to the respective mass nodes. To accurately determine the absolute damping torque at a specific rotational speed, it is necessary to deduct the average rotational speed of the concentrated mass node from the calculation. The damping torque is calculated as the product of the absolute damping and the difference in torsional velocity, as depicted in Equation (13) [32].  
\[T_{t}(t)=C_{abs}\times\left[\dot{f}(t)-\dot{f}_{ane}(t)\right] \tag{13}\]  
where \(C_{abs}\) is the absolute damping, Nms/rad; \(\dot{\phi}(t)\) is the instantaneous rotational speed, rad/s; and \(\dot{\phi}_{ane}(t)\) is the average rotational speed, rad/s. Relative damping serves to compensate for the change in the twist angle between the two concentrated mass points, determined by the damping coefficient and stiffness. Relative damping does not need to calculate the average velocity and can be directly embedded in the time domain calculation damping matrix.  
In the discrete mass model, the predominant form of damping for the propeller is absolute damping, which can be categorized into two distinct forms: one form is the torque absorbed by the propeller from the main engine, namely the propeller torque in the hydrodynamic performance of the propeller; the other form occurs when the propeller, during operation, does not absorb the torque from the main engine, leading to the generation of vibrations in the propeller. In this paper, the Prodam damping model is employed, which assumes a constant damping value when the rotational speed exceeds half of the maximum continuous rating (MCR) speed. Below half the MCR speed, the damping increases linearly, as depicted in Figure 3.