esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Detailed Experimentation and Results Analysis with Pix2 PixHD
#### 5.4.1 Pix2 PixHD with Different Types of Edge Inputs
This suggests that Edge02 is effective in highlighting variations within objects that are not apparent with just the external contour information provided by Edge01. However, the technique also has limitations, as seen in the incomplete generation of the axe's handle's entirety. This incomplete part could result from the inherent challenges in the edge detection process, where certain features may not exhibit enough contrast after the greyscale conversion and Gaussian blur application in the input edge, making it difficult for the Canny algorithm to identify all relevant boundaries. From this experiment, we conclude that Edge02 can enhance the model's ability to generate internal object details,  
Figure 7: Analysis of different edge inputs and their synthesis results. Left to right: ground-truth images, edge inputs (Edge01, Edge02, Hybrid Edge) on the top line, and corresponding synthesis outcomes on the bottom line. leading to more detailed and textured representations, but it may also omit crucial parts of an object if the edge extraction is not complete in some cases. This partial representation can change the overall accuracy and realism of the images that are created, because important parts of objects might be misrepresented or completely missed. This can make the model less useful in situations where precise and detailed object reconstructions are needed. Addressing the limitations found in generating X-ray images with Edge01 and Edge02, we used Hybrid Edge as the input, which is designed to provide a comprehensive solution that not only accurately delineates the object's shape but also captures its internal nuances and material differences with greater precision, as shown in Figure 8. The development of Hybrid Edge significantly improved object representation, as demonstrated by the method achieving the best scores on different edge inputs, confirming its superior effectiveness in generating high-quality and detailed X-ray images.
# GPS Results from Long Time Monitoring of Geodynamic Processes in South-Western Bulgaria
###### Abstract  
Monitoring of geodynamic processes by modern GNSS techniques in the area of Sofia and South-Western Bulgaria has continued for 25 years. To study the modern crustal movements in the area, Global Positioning System (GPS) data acquired between 1996 and 2021 are analyzed to obtain the velocity field for South-Western Bulgaria. For a time period of almost 25 years, the monitoring has covered 28 stations. They have been measured in different years and in a number of campaigns. Despite the difference in the measurements, the obtained results are quite homogeneous in the different localities of the studied area and show clear uniform tendencies. All velocities are in the southern direction. They are in the range of 1.5 mm/year to slightly over 3 mm/year, almost reaching 4 mm/year. The velocities of the stations tend to increase from north (stations around Sofia), passing through an intermediate locality (between Sofia and Kyustendil-Pazardhik), clearly increasing in the southernmost part of the country (around Gotse Delchev). This velocity field motivates N-S expressed extension with increasing rates from North to South. The difference in the velocity rates tends to change along geologically suggested active fault zones. The obtained results generally confirm previously data, but with much better accuracy and details at the local level. This way, both the repeated measurements and extension of the geodynamic network prove to be a powerful tool for a better understanding of present-day geodynamics.  
G 2022 12682022022022022022022022022022022022022202220222222222  
Monitoring of geodynamic processes by modern GNSS technics in the area of Sofia and South-Western Bulgaria has continued for 25 years. To study the modern movements of the Earth's crust during 1996-1997, a geodynamic network was built in the area around Sofia, covering South-Western Bulgaria. The network is designed for high-precision GNSS measurements, determination of coordinates and velocities of points, calculation of active strain in the area, and long-term monitoring crustal movements. The points have been stabilized so that the network covers the main tectonic structures in the area. The first GPS measurements of the Sofia Geodynamic Network were made in 1996. GPS measurement of all points of the network with processing and analysis of the results has, so far, been performed only in two epochs, in 1997 and 2000 [5; 6; 7; 8]. During 2001-2020, the network was condensed and expanded with the stabilization of new points [9; 10]. GPS measurements were periodically conducted for particular points of the network.  
Figure 1: Horizontal GPS velocity with respect to Eurasian plate with 95% confidence. Slim lines show suggested active faults in the area. The topography is shown from green to brown—lower to higher elevation, respectively. The elevation of the highest peaks in the area is given in meters.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Data Requirement and Generation  
The preprocessing of the raw signals is performed as a first step. The signal matrix \(X_{t}^{s}\) of samples received from the \(s\)-th SU during the \(t\)-th sensing period can be denoted as  
\[X_{t}^{s}=\begin{bmatrix}x_{t}^{1}(1)&x_{t}^{1}(2)&\dots&x_{t}^{1}(N)\\ x_{t}^{2}(1)&x_{t}^{2}(2)&\dots&x_{t}^{2}(N)\\ \vdots&\vdots&\ddots&\vdots\\ x_{t}^{M}(1)&x_{t}^{M}(2)&\dots&x_{t}^{M}(N)\\ \end{bmatrix} \tag{5}\]  
where \(N\) is the number of samples in a period and \(M\) is the number of antennas in each SU. As discussed in the previous section, the use of ML models, such as standard CNNs, to process 2D data has certain drawbacks and limitations. One major limitation is the high computational complexity, requiring specialized hardware for training, which can lead to slower execution, longer online detection times and limited suitability for real-time applications on mobile and low-power devices. As a result, the choice of participating SU devices in cooperative detection processes is limited. To address this, we propose vectorizing multi-antenna data into a one-dimensional format, which can facilitate easier processing and analysis using machine learning models. This approach can also improve the memory efficiency by significantly reducing the memory overhead and decreasing the memory footprint compared to artificially expanding the data to higher dimensions. This memory efficiency arises from data preprocessing techniques and the model architecture. The presence of fewer parameters means that less memory is required to store the model's weights and intermediate activations during training, resulting in lower memory usage  
Figure 3: BiLSTM architecture.  
during forward and backward passes in the training process. This advantage is particularly significant when working with limited computational resources, making it a practical choice, especially in resource-constrained environments. Additionally, the application of algorithms to one-dimensional vectors can be faster. The following expression of \(R_{t}^{s}\) is obtained by vectorizing the received matrix signal \(X_{t}^{s}\):  
\[R_{t}^{s}=\text{vec}(X_{t}^{s}) \tag{6}\]  
We proceed to organize the input samples as labeled data by rearranging them in the following format: \((R_{1}^{s},y_{1}),(R_{2}^{s},y_{1}),\ldots,(R_{T}^{s},y_{T})\), where the label of the \(t\)-th data point is denoted by \(y_{t}\in\{0,1\}\) to represent the hypotheses \(H_{1}\) and \(H_{0}\), and \(T\) represents the total number of data points (i.e., sensing periods taken into consideration).
## 5 Results and Analysis
### Practicality Assessment  
In line with Equation (3), a regression analysis is performed on the chosen 32 IMs and seven DMs. Figure 8 illustrates the regression coefficient (\(b\)) corresponding to each IM and DM. The data indicate that \(b\) remains relatively stable when varying the IM parameters for a given DM. Among the DMs, \(\theta_{\text{max}}\), \(\theta_{\text{c}}\), and \(D_{\text{e}}\) exhibit higher practicality levels. Conversely, \(R_{\text{E}}\), \(F_{\text{NC}}\), \(F_{\text{SC}}\), and \(M_{\text{C}}\) score lower in terms of practicality, a finding that opposes their efficiency standings. Therefore, a comprehensive assessment of DMs via other metrics is essential to yield sensible DM parameters. To extend the discussion on IM practicality, Figure 9 presents the \(b\) values for each IM when \(D_{\text{e}}\) is employed as the DM. The analysis reveals that acceleration-type and spectral-type IMs have higher \(b\) values with less variability, thus indicating superior practicality. In contrast, displacement-type IMs are less practical. An overarching comparison of \(b\) values across various IMs under each DM is provided in Figure 10. This comparison leads to the conclusion that the most practical IMs are \(I_{\text{v}}\), \(I_{\text{a}}\), PGA, SMA, ASI, and A95.  
Figure 6: The value of \(\beta_{\text{D}}\) under each IM with \(F_{\text{NC}}\) as the DM.  
Figure 7: Efficiency ranking under different DMs.
# Lightweight Transformer Network for Ship HRRP Target Recognition
## 3 Experimental Results and Analysis
### Feature Visualization  
To verify the separability of the features extracted by different models, the original HRRP data and the features extracted by the six different models were input to the two-dimensional ISOMAP for visualization, and the results of feature visualization are shown in Figure 7. The data points in ten colours in Figure 7 represent ten types of targets. In Figure 7a, the ten types of ship targets in the original HRRP data are basically overlapped together, and the separability is poor. In Figure 7b,c, the feature separability extracted by RNN and LSTM models is better than Figure 7a, and the spacing between target samples of different categories increases, but still, most of the features overlap and are still inseparable. Figure 7d,e shows significantly better feature extraction after Transformer and Transformer + LR models than Figure 7b,c, with better aggregation of the same category targets and increased spacing between samples of different category targets, making the features of each target separable. However, the distances of samples within the same category are greater than the distances of samples between different categories, resulting in fuzzy boundaries of different target features. In Figure 7f,g, the features extracted by Transformer + LR + GLTs and Transformer + LR + GLTs + AC models are separable, with clearer boundaries between classes and almost no overlap.
## 1 Introduction
This could result in enormous costs for the network and disruptions to data continuity [3]. Another sort of attack that should also be taken into account is the bogus attack. Bogus information attack is a type of attack on the VANET network consisting in sending the false information through the network (hoax road event favoring traffic diversion) to gain personal benefits [4]. In this attack, the attacker can be outsider/intruder or insider/legitimate user. The attacker broadcasts false information in the vehicular network to affect the decisions of other vehicles by spreading the false information in the network. For example, a vehicle can imitate a heavy traffic on one road to prevent another vehicle from choosing that road. This attack is an example of application attack [5]. The main contribution of the paper is the introduction of new algorithm: Bogus & Sybil Enhanced Behavior Processing & Footprint (B&SEBP&F), which is the continuation of the previous one, Bogus & Sybil Trust Level & Timestamp (B&STL&T) mentioned in [6]. The new algorithm was also tested using new road events and compared with the previous one. The results of the tests were presented and analyzed in the paper. The paper is organized as follows. In Section 2, we describe the selected aspects of bogus attack in the VANET network and the created solution to monitor the node behavior and interaction with other nodes. In Section 3, we present the selected aspects of a Sybil attack with the algorithm implemented inside the authors' VANET network. Section 4 deals with the prototype algorithm against bogus and Sybil attacks (B&STL&T). In Section 5, we present the results obtained from new algorithm and compares it with its previous version and the solutions presented by other authors. Finally, Section 6 draws the discussion and conclusions.
## 3 Discrete-Time Adaptive Delay Compensator Based on Kalman Filter  
To address the variant time delay in RTHS, numerous control strategies have been proposed and validated over the past 20 years. The feedforward-based approaches have received widespread attention for their fast compensation characteristics in terms of phase and amplitude. By taking the inverse of the control plant model as the feedforward controller, the time delay and amplitude discrepancy can be eliminated thoroughly. However, the mathematic model of the control plant is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Description** & **Definition** & **Units** \\ \hline \(J_{1}\) & Tracking time delay & \(J_{1}=\underset{k}{\operatorname{argmax}}\left(\sum\limits_{i}d_{i}d_{i-k}^{ \rm{m}}\right)\) & ms \\ \(J_{2}\) & Normalized root mean square of the tracking error & \(J_{2}=\sqrt{\frac{\sum_{i=1}^{N}\left(d_{i}-d_{i}\right)^{2}}{\sum_  
straightly proper, and the feedforward controller thus often consists of an additional low-pass filter and the inverse model of the control plant [35; 36], which may cause design difficulties. Concerning adaptive delay compensation methods, their effectiveness largely depends in the performance of the parameter estimation method. This study proposes a KF-based discrete-time adaptive delay compensation (KF-ADC) method, which is an improvement of the general adaptive compensation method proposed by [PERSON] et al. [13]. As shown in Figure 3, the KF-ADC method incorporates two modules, namely the discrete-time model online estimation module and a feedforward controller. Details of the proposed KF-ADC method are presented in this section.
## 5 Damage Characterization, Objective Functions, and Optimization Algorithm
### Objective Features and Functions
The modal domain provides a large set of damage sensitive features, such as the natural frequencies, MAC or COMAC of modal shapes and derivatives [144], modal strain energy [161; 147], modal strain [162], damping [163], dynamic flexibility matrix, residual force vector, and a combination of some features, for instance, a combination of the measured modal damping and the computed strain energy distribution [164]. It is found that the modal curvatures are useful parameters for damage detection when using the response-based approach [165] but not as useful when using the model-based approach [166]. Among the three types of features, the time domain features have advantages of avoiding the data reduction that may cause the loss of important information and not being limited by the assumption of linearity as the modal analysis approach is. Therefore, when the structures show high nonlinearity, the time domain features are more suitable than the frequency and modal domain features. The main disadvantage of time domain features is the considerable computational effort for the calculation of time responses. On the other hand, the frequency feature can provide more information on damage in a desired frequency range when compared to the modal feature that is extracted from a very limited range around the resonances. Nevertheless, the modal features can be identified from output-only data measured under operational conditions and does not require the structure to be excited by artificial forces as the frequency domain feature requires. It is worth noting that the feature used for damage occurrence identification may be a global one, while it is better if the one used to determine damage locations and extents is a local one, and must be sensitive to structural local damage.
## 3 Results and Discussion
### Focal Contamination in Composts  
The study of fecal contamination in the present study tracked four representative groups: coliforms (total, fecal and _Escherichia coli_), fecal enterococci, sulfite-reducing clostridia and _Salmonella_ spp. The first three indicator groups were quantified in their levels in composts (Figure 3a-d), while for _Salmonella_ spp., only the presence was searched. In addition, the recorded levels of enterococci and _E. coli_ were compared with those established by current European legislation [39]. This legislation indicates that composts to be used as fertilizers cannot contain levels higher than 1000 cfu \(\mathrm{g}^{-1}\) in _E. coli_ or enterococci (but not necessary in both groups) and must have an absence of _Salmonella_ spp. in 25 g of compost. According to the results obtained, only one facility exceeded these microbial limits and contained _Salmonella_ spp. in the compost, MSW2. The rest of the facilities fulfilled the legislation. Even so, four facilities exceeded the enterococci content (VR2, MSW2, MSW3 and SS3). However, they did not exceed the _E. coli_ and _Salmonella_ spp. limits, so they complied with the requirements. These results showed that the fecal enterococci group was more resistant to the conditions prevailing during composting than the _E. coli_ and  
Figure 2: Percentage of degradation in organic fractions during industrial composting processes. Abbreviations: vegetal residue (VR); municipal solid waste (MSW); sewage sludge (SS); holocellulose (cellulose + hemicellulose); soluble fraction (include reducing sugars, total proteins, N-NO\({}_{3}\)\({}^{-}\) and N-NH\({}_{4}\)\({}^{+}\)); Soluble Organic Carbon (SOC).  
_Salmonella_ spp. groups. This behavior was also reported in composting by [40]. The justification for this resistance lies in the very nature of the group of enterococci. This microbial group, unlike the other two, is Gram-positive and has a slightly higher thermotolerance. Sulfate-reducing clostridia are not delimited in the European legislation. In spite of this, the quantification of this group is of great interest because it is a good indicator of fecal contamination, and complementary to those commonly used [41]. The counts obtained in this strict anaerobic group were found to be closely linked to the nature of the composted waste. Thus, those bio-materials where anaerobic situations are created, contained a higher amount of sulfite-reducing clostridia (SS > MSW > VR). In short, according to our results, the composting processes carried out in industrial facilities were able to generate adequate sanitization conditions to produce fertilizers that satisfy the current legislation; with the exception of the MSW2 treatment facility, where minimum thermal sanitization conditions were not maintained. The compost generated in the latter facility can only be disposed of in a controlled landfill.
## 3 LPWAN Technologies and Design Goals
### Radio Spectrum and Modulation  
LPWAN technologies, as subsets of IoT, provide wide coverage for outdoor communication. Previously mentioned systems work in the sub-GHz band to utilise excellent signal propagation properties of said band. LPWAN technologies are also designed to operate with a low power budget and at up to 20% higher sensitivity to connect with the base station while mitigating the effects of the multi-path fading and attenuation experienced in higher bands. Receiver sensitivity for LPWAN devices is typically planned around \(-130\) dB with narrow-band and spread spectrum options. The narrow-band technique maximizes the range by assigning narrow-band channels to each carrier, thus reducing noise effects and also reducing the processing required for decoding the signal and thus promoting inexpensive receivers. NB-IoT [34] and WEIGHTLESS-P [35] are perfect examples of narrowband techniques, while Sigfox, WEIGHTLESS-N, and TESENA [27] are examples of ultra narrow band (UNB) communication with 100 Hz bandwidth enabling the possibility of more end devices and reduced interference. Spread spectrum techniques on the other side will require higher processing to decode, since the signal is spread over a wider frequency and is below the noise floor while being more resilient to interference and jamming.
## 2 Materials and Methods
### Experimental Design and Data Analysis
#### 2.3.2 Data Analysis  
The GPU hardware used in this study is Nvidia GeForce GTX TITAN X (Nvidia Corp., Santa Clara, CA, USA). The GPU card comes along with 80 Stream Multiprocessors, along with a total of 5120 CUDA computing cores along with 12 GB of Memory. The GPU card is installed on a desktop workstation with a Ubuntu operating system (version 16.04), Intel(R) Core(TM) i7-8700 CPU @ 3.20 GHz, and 16 GB of host memory. ANSI C and CUDA 9.0 were adopted for implementing all CPU and GPU algorithms. All testing was done under the MATLAB platform (Version 2016b, Mathworks Inc., Natick, MA, USA) and all implemented algorithms were invoked in the MATLAB environment through the MEX interface.  
In total, six different implementations were done in this study: standard NCC in CPU, standard NCC in GPU, Lewis' method in CPU and Lewis' method in GPU, Luo-Konofagou method in CPU and Luo-konofagou method in GPU. Hereafter, those six implementations are referred to as **NCC-CPU**, **Lou-Konofagou-CPU**, **Lewis-CPU**, **NCC-GPU**, **Luo-Konofagou-GPU**, and **Lewis-GPU**, respectively. In this paper, the performance is mainly compared from the following two aspects: (1) Evaluating whether or not a different implementation yields substantial errors and (2) comparing computational efficiency of those three methods, given motion tracking parameters and the computing environment (i.e., CPU or GPU).
# Application of External Torque Observer and Virtual Force Sensor for a 6-DOF Robot
## 4 Experiment and Results
### Verification of the Robot Dynamic Model Based on Raspberry Pi  
This section will validate the dynamic model established using Raspberry Pi. Due to the lower computational power of Raspberry Pi compared to personal computers, there may be insufficient computational power when running the AI model. Therefore, the dynamic model of the Raspberry Pi system was conducted using a pure mathematical model,  
Figure 7: Comparison of the feedback torques and estimation (prediction) torques: (**a**) Axis 1; (**b**) Axis 2; (**c**) Axis 3; (**d**) Axis 4; (**e**) Axis 5; (**f**) Axis 6.  
and the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and correlation coefficient between actual torque and estimated torque were evaluated. Figure 8 compares the estimated torque and actual torque during the actual test. By organizing the results obtained from Figure 8, the evaluation table in Table 4 was created. It can be observed that, with the exception of the correlation coefficient of the third axis, which was lower than 0.98, the correlation coefficients of the remaining axes were all higher than 0.988. Although the accuracy was lower than using the XGBoost model on a personal computer, the results were still within an acceptable range.