esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Cost Data-Driven Robot Collision Localization Using a Sparse Modular Point Matrix
## 2 Materials and Methods
### Optimal SMPM Structure
From the average accuracy results of different movement point numbers in UD, LR, and CT, the relative differences in average accuracy for SMPMs removing single points (Cell 1-1, Cell 1-3, Cell 1-5, and Cell 1-7) compared to the MPM are 1.29%, \(-\)0.2%, and \(-\)0.02%; for SMPMs removing double points (Cell 2-3 and Cell 2-4), they are \(-\)0.03%, \(-\)1.18%, and \(-\)1.56%;  
Figure 7: Collision localization accuracy with different forms of SMPMs at four corners. and for SMPMs removing four points (Cell 3-2), they are 2.16%, \(-\)0.7%, and 0.62%. This indicates that information contained in some of the farthest adjacent points is not always redundant. However, even with the removal of these points, collision localization accuracy does not significantly decrease compared to using the MPM, suggesting that removing the farthest neighboring points is effective in reducing the dataset size while maintaining high collision localization accuracy. Furthermore, it is noteworthy that the use of the Cell 3-2 sparsification form consistently demonstrates excellent collision localization performance for SMPMs positioned at different locations. This sparsification form, compared to others, minimizes the required data collection scale to the greatest extent. Hence, we consider the Cell 3-2 sparsification form of the SMPM the optimal choice.
# Improvement of Subsurface Thermal Characteristics for Green Parks
## 4 Discussion
As the temperature gradient in nature is not applied abruptly, as in the laboratory experiments, the difference between the ambient and gained temperatures is small. Examining the field section data over extended periods, the temperature difference is in the order of 1 degree for the 20% clay content and 1.5 to 2 degrees for the 15% clay content, as indicated in Figures 10 and 11. The loss of heat is also found to have three typical zones of temperature changes, in which a fast change occurs after exposure to cooling at first, a transition zone is observed, and finally a slow rate of cooling is reached, where approximately 90% of the ambient or surrounding temperature is achieved. Knowledge of these thermal characteristics will help designers select the appropriate fill required for designing green areas in parks and landscape zones. However, these characteristics need to be weighed against the hydraulic conductivity and irrigation requirements of the project. The development of numerical models and simulations to forecast the thermal behavior of clay-sand mixtures under various environmental circumstances should also be a primary goal of future research in this area. These models can offer a practical and affordable way to assess the thermal insulation, energy efficiency, and long-term sustainability of green parks. The examination of the thermal expansion and contraction behavior of clay-sand mixtures during temperature fluctuations is another area of research that should be undertaken. This is crucial in places with extreme temperature swings, because differential thermal expansion can cause cracks and other types of damage to green parks. The design of more robust and resilient green park facilities will benefit from an understanding of the con nection between the thermal characteristics of clay-sand mixtures and their susceptibility to heat-induced deformation. Pure clay has a thermal conductivity of up to 0.1 W/mK, while pure sand has a range of 0.25 to 0.35 W/mK. However, when clay and sand are combined, the mixture's heat conductivity may be altered. Due to the clay's lower thermal conductivity, combinations with a higher clay component typically have lower thermal conductivity. The clay-sand mixtures' compaction and moisture content also play roles. Thermal conductivity is found to be higher in highly compacted and highly moist mixtures. The heat capacity of soils in general is in the order of 0.800 Joules/g-K.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods
#### 5.2.1 Evaluating Throughput Performance  
The throughput per user often drops when more users join the 5G mmWave network because of resources being shared. The system may regulate user density and enhance throughput for high-speed data transmission by employing efficient hybrid beamforming  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multirow{2}{*}{**Software specifications**} & OS & Ubuntu 22.04 \\ \cline{2-3}  & Tool & Ns-3.35 with Python–3.9.6 (64-bit) version \\ \hline \multirow{2}{*}{**Hardware specifications**} & RAM & 4 GB \\ \cline{2-3}  & Hard disk & 500 GB \\ \hline
**Parameter** & Value/description \\ \hline
**Base station(BS)** & The 4-element hybrid beamforming antenna array \\ \hline
**User node(UN)** & 100-element antenna array \\ \hline
**IRS type** & Single-layer IRS (not multi-layer SIM) \\ \hline
**Channel model** & Saleh–Valenzuela channel model \\ \hline
**Mobility model** & Gauss–Markov mobility model \\ \hline
**Noise model** & AWGN with variance \(\sigma^{2}\) \\ \hline
**Modulation/PHY** & Not mentioned explicitly \\ \hline
**Beamforming strategy** & DAH-BT-LSTM (uses long short-term memory), trained with \\  & Mu-MATD3 agent-based RL \\ \hline
**Channel estimation method** & RCTS-AMP-RSSI-AS (LAMP-based two-stage network with \\  & row compression) \\ \hline
**Congestion control** & Modified mms-TCP and low-latency congestion control \\  & schemes (LLCCS) \\ \hline \hline \end{tabular}
\end{table}
Table 4: System specifications.  
algorithms. Stacked metasurfaces are employed in SIM-ADMT [38] for multi-user beamforming; nevertheless, the overhead is greater (see to Section 3). The proposed method utilizing IRS-assisted HB achieves enhanced throughput.  
Table 5 and Figure 3 suggest that the method provides 53 MBPS throughput with 20 users, which is much greater than 17 MBPS for SIM-ADMT, 23 MBPS for FB-TCP, and 35 MBPS for UL-SCH + PUSCH. The suggested system sustains a competitive throughput of 70 MBPS when the number of users rises to 60, whereas the other schemes fluctuate, with UL-SCH + PUSCH falling to 43 MBPS. The suggested system shows this by leading with 90 MBPS when we reach 100 users, while SIM-ADMT reduces to 32 MBP, and SFB-TCP drops to 40 MBPS. In general, the suggested plan continuously exhibits higher throughput, indicating that it can be handled with high user densities in 5G mmWave networks.
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 6 Experiment Results and Discussion
### Experiment Setup  
We trained our models using the predefined subsets of the SIXray dataset. SIXray10, SIXray100, and SIXray1000 have the ratio of negative samples over positive samples at 1:10, 1:100, and 1:1000, respectively. All the images in the training subsets were used to train the detection model, while only the pixel-level annotated images that are in the training subsets were used to train the segmentation model. We generated the patches used to train the segmentation model as described in the previous section. The spatial dimensions of all the patches were resized to 192 \(\times\) 192.  
We trained Faster-RCNN for 80,000 iterations with a batch size of 2 using stochastic gradient descent (SGD) [41] with a base learning rate of 0.001, which is linearly decayed by a factor of 0.1 after the 30,000 th and 50,000 th iterations, and a momentum of 0.9. We trained DeepLabV3+ for 250 epochs with a batch size of 32 using Adam optimizer [42] with a base learning rate of 0.001, which is linearly decayed by a factor of 0.1 after the 75 th, 150 th and 200 th epochs. For further comparison, we also trained Mask R-CNN [43], a state-of-the-art end-to-end instance segmentation framework, using all of the samples in the training set with complete annotations. Mask R-CNN was trained with the same backbone and configurations as Faster R-CNN.
## 3 Experimental Results and Analysis
### Security Analysis
#### 3.2.4 Correlation Analysis  
In plain images, adjacent pixels are often highly correlated in three directions: horizontal, vertical, and diagonal, which makes it possible to predict current pixels by adjacent pixels. An eligible encryption algorithm should eliminate such correlations. The correlation among pixels can be evaluated by the correlation coefficient in Equation (15):  
\[r_{x,y}=\frac{E(x-E(x))E(y-E(y))}{\sqrt{D(x)}\sqrt{D(y)}} \tag{15}\]  
where \(x\) and \(y\) are values of adjacent pixels. \(E(x)\) and \(D(y)\) are the expectation and variance of \(x\) and \(y\) over some pairs of samples, respectively. Finally, the correlation coefficient values \(\left(r_{x,y}\right)\) are between \(-1\) and \(+1\): the closer to zero, the less correlations among chosen pixels. In addition, if the correlation coefficient is a positive number, the variables are directly related. If, on the other hand, the coefficient is a negative number, the variables are inversely related.  
We compute the scene-referred color values using Equation (1), which are all floating-point values in the R-, G-, B-channels. Therefore, the analysis is represented in three R-, G-, B-channels. Table 7 shows the correlation coefficients calculated from 5000 pairs of adjacent pixels in three directions: horizontal (\(H\)), vertical (\(V\)), and diagonal (\(D\)) for three channels in the plaintext and encrypted images. The statistics show that the correlation coefficients have been dramatically reduced in the ciphered image, and the values are very close to zero.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**VOH**} & \multicolumn{2}{c}{\(\chi^{2}\) **Value**} \\ \cline{2-5}  & **Plain** & **Encrypted** & **Plain** & **Encrypted** \\ \hline R & 2,707,096.750 & 1653.078 & 451,182.792 & 275.513 \\ G & 3,589,377.078 & 1633.258 & 598,229.513 & 272.210 \\ B & 15,759,698.078 & 1692.586 & 2,626,616.346 & 282.098 \\ E & 104,124,933.656 & 1552.016 & 17,354,155.610 & 258.669 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Variance of Histogram (VOH) and \(\chi^{2}\) values for the test image, “memorial”.  
Figure 8: All 20 stego encrypted images in four channels pass the \(\chi^{2}\) test.
## 2 Sequential Algorithm and Its Software Implementation
### Classical Implementation of the Conjugate Gradient Method  
The vector \(x\) with \(N\) components, which is a solution (pseudo-solution) of System (1), can be found using the following iterative algorithm that constructs the sequence \(x^{(s)}\). We set \(p^{(0)}=0\), \(s=1\), and an arbitrary initial approximation \(x^{(1)}\). Then we execute the following sequence of actions \(N\) times:  
\[r^{(s)} =\begin{cases}A^{T}\big{(}A\,x^{  
**Remark 1**.: _If we do not use the recurrent notation and calculate the residual \(r^{(s)}\) at each iteration in the same way as at the first iteration (\(s=1\)), then the number of arithmetic operations required to complete the iteration process will double. When solving \"large\" problems, this is the motivation for using the recursive form of the conjugate gradient method._  
This algorithm can be written as the following pseudocode (see Algorithm 1).  
```
Data:\(A\), \(b\), \(x\equiv x^{(1)}\) Result:\(x\)\(s\gets 1\)\(p\gets 0\)while\(s\leqslant N\)do if\(s=1\)then \(r\gets A^{T}(Ax-b)\) else \(r\gets r-\dfrac{q}{(p,q)}\) end if \(p\gets p+\dfrac{r}{(r,r)}\) \(q\gets A^{T}(Ap)\) \(x\gets x-\dfrac{p}{(p,q)}\) \(s\gets s+1\)  end while
```  
**Algorithm 1**Pseudocode for a sequential version of the \"classical\" algorithm  
The Python code for the function that implements the conjugate gradient method for solving System (1) will have a fairly compact form (see Listing 1).  
```
defconjugate_gradient_method(A,b,x): s=1 p=zeros(size(x)) whiles<=N: ifs==1: r=dot(A.T,dot(A,x)-b) else: r=r-q/dot(p,q) p=p+r/dot(r,r) q=dot(A.T,dot(A,p)) x=x-p/dot(p,q) s=s+1 returnx
```  
**Algorithm 2**Pseudocode for a sequential version of the \"classical\" algorithm  
It is assumed that the standard package numpy is used for calculations. As a consequence, this seemingly sequential software implementation of the algorithm actually contains parallel computing. This is due to the peculiarity of the function dot() used for basic calculations from the package numpy. This function is implemented in the C++ programming language and automatically uses multithreading in calculations if the program is running on a multi-core processor--all processor cores are used through the use of OpenMP parallel programming technology.
# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
###### Abstract  
The presence of malevolent nodes in mobile ad hoc networks (MANETs) would lead to genuine security concerns. These nodes may disturb the routing process or deform the pattern of the data packets passing through the network. The MANET is extremely liable to attacks, owing to its characteristics of the network framework, such as the absence of infrastructure, moveable topology, and a centralized control unit. One of the most common attacks in MANETs is the blackhole attack. MANET nodes are susceptible to spectacular degradation of network performance in the presence of such attacks. In this regard, detecting or preventing deceitful nodes that will launch blackhole attacks is a challenge in MANETs. In this paper, we propose an Enhanced Blackhole Resistance (EBR) protocol to identify and resist nodes that are responsible for blackhole attacks. EBR can avoid congested traffic by passing the data packets through a safe route with the minimum RTT. The EBR protocol uses a combination of time to live (TTL) and round trip time (RTT), which is also called a TR mechanism, to detect the blackhole attacks. Our algorithm does not require any cryptographic or authentication mechanisms. Simulation results prove that EBR behaves better than other protocols in terms of throughput, end-to-end delay, packet delivery ratio, energy, and routing overhead.  
wireless; MANET; blackhole; attack; routing; round trip time; time to live; congestion Article
# A Method for Energy Balance and Data Transmission Optimal Routing in Wireless Sensor Networks
## 5 Simulation  
In order to verify whether the power line connection scheme can effectively improve the system lifetime, simulation was performed with MATLAB. For clustering protocols such as LEACH and EEUC, the simulation is divided into three phases, the establishment phase, the cluster head election phase, and the data transmission phase. For our proposed power line connection scheme, no clustering is required. According to the method proposed above, the routing table is calculated at the beginning, and then the data is transmitted according to the routing information until the node energy is exhausted. Referring to Reference [8], the process by which all nodes collect and transmit a set of data packets is  
Figure 7: Minimum energy consumption and number of hops (**a**) and single hop energy consumption (**b**) for different distances.  
called a \"round\". After completing a round of data acquisition and transmission, calculate the energy consumption of each node and the loss of energy transmission, and then continue to the next round. We assume that the cluster head is deployed in the center of the square area and does not compress the data. If the node is exhausted, it is removed from the system. The round represents the lifetime of the node. Some parameters used in the simulation are shown in Table 1. These parameters are set in the same way as Reference [11; 13; 15; 16; 17; 18; 29; 28], and these parameters are also used in many WSN research articles. It is also convenient to compare different protocols with the same parameters.  
The LEACH protocol is the most widely used clustering protocol, and many protocols are referenced to the LEACH protocol. CH-leach is an energy-efficient clustering protocol that uses the k-means method to select cluster heads [29]. The Energy-Efficient Uneven Clustering (EEUC) routing protocol uses uneven clustering, which makes the cluster size closer to the base station smaller and reduces the energy consumption in the cluster to reserve more energy to forward the data of the far cluster [28]. Simulation is carried out to compare the effect of our proposed scheme and these three protocols.
## 6 GA-Based Slot Assignment Algorithm  
Our polling-based data traffic control scheme decides the order of all queries to all nodes according to a schedule that ensures sufficient responsibility of unpredictable requests and stability of periodic data collection. In other words, the slots of queries for periodical data collection and slots for unpredictable on-demand should be uniformly assigned. As we described in Section 3, Genetic Algorithm (GA) is classified one of heuristic functions which can find an optimum solution for above our problem, so that we propose a GA-based slot assignment algorithm.  
The process of our GA-based slot assignment algorithm includes generating initial population, selection, mutation, crossover, repair, and evaluation as shown in Figure 6. We describe the specific process of the flow as follows.
# Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines
## 3 Results
### Experiments for the Generator
#### 3.1.2 Experiments for the Ensemble of the Wind Plant Turbines  
In this section, instead of modelling the WGEN system turbine by turbine, a model is built for all the turbines in the wind plant. Thus, the same model will be used to test the WGEN system of each turbine. Following the example above, the SCADA data are added at intervals of 1 h and records are removed in case of missing data. The first half of the available data from each turbine is used to build the model and the other half is used to test it. To determine the size of the SLFN, an exploration of the different models obtained for a wide range of \(H\) (up to \(H=90\)) is performed. Experimentally, the optimal value is H = 43 (see the minimum achieved in the test performance of Figure 5), which is slightly greater than the value obtained in Section 3.1.1 for the single turbine model WT#84 (H = 35). The regression plots of the plant model for all the turbines are shown in Figure 6).  
As will be shown, the results obtained with the full park models are significant because they give us an idea of the predictive and generalising capacity that the normality models could have. The advantage of a single model for the whole wind farm is evident because they usually have many turbines (from tens to hundreds or more). In addition, the turbines have many subsystems, so the testing of all the turbines could be done very efficiently using a model of the whole park for each system/subsystem.  
Figure 4: (**Top**) temporal sequence of the target (blue line) and the prediction made by the model (red line) are presented. (**Bottom**) the prediction error is shown.  
Figure 5: Performance of the model, in terms of the RMSE, for all the turbines of the plant versus the number of hidden nodes, \(H\). On the left, the training results; On the right, the test results.
# Experimental Characterization of RGB LED Transceiver in Low-Complexity LED-to-LED Link
## 3 Results and Analysis
### LED Photodetector Noise Measurement  
In order to analyse the noise of the LED as a photodetector, a DC signal is transmitted. Then, the DC component of the received signal is subtracted to retrieve the receiver noise. Figure 3b depicts the resultant noise probability density function has a normal distribution, as expected. The performed FFT indicates that the noise is white. Hence, the noise of the LED photodetector is defined to be additive white Gaussian noise (AWGN).  
Figure 3: Characteristics of a light emitting diode (LED) photodetector: (**a**) received signal energy decay with increasing distance; (**b**) histogram of LED photodetector noise signal and its fitting to a Gaussian function.
## 1 Introduction
### Analysis of Demagnetization Fault Identified Signal
#### 2.3.3 Analysis of the First Auxiliary Identification Signal  
According to (4), the first electrical cycle waveform of \(SI_{\text{a1}}\) (\(SI_{\text{a1-1}}\)) relies on the healthy condition of PM1, PM2, PM(2p \(-\) 1), and PM(2p). The \(i\)th (\(i\geq 2\)) electrical cycle waveform of \(SI_{\text{a1}}\) (\(SI_{\text{a1-i}}\)) relies on the healthy conditions of PM(2\(i-3\)), PM(2\(i-2\)), PM(2\(i-1\)), and PM(2\(i\)). When PM(2\(i-3\)) (PM(2p \(-\) 1)) is in a healthy condition, Mode 6, Mode 7, and Mode 8 are defined as Mode 6H, Mode 7H, and Mode 8H, respectively. When PM(2\(k-3\)) (PM(2\(p-1\))) is in a demagnetization condition, Mode 6, Mode 7, and Mode 8 are defined as Mode 6D, Mode 7D, and Mode 8D, respectively. Therefore, the \(SI_{\text{a1}}\) under six different demagnetization fault modes listed in Table 5 is investigated.  
Take the second electrical cycle signal of \(SI_{\text{a1}}\) (\(SI_{\text{a1-2}}\)) as an example to analyze the waveform characteristics corresponding to Mode 6, Mode 7, and Mode 8. According to Table 5, \(SI_{\text{a1-2}}\) relies on the healthy condition of PM1, PM2, PM3, and PM4, which correspond to six fault mode codes given in Table 3. The \(SI_{\text{a1-2}}\) under six different fault modes is calculated through (4) and normalized through (7); the results are shown in Figure 5.  
As shown in Figure 5, the waveforms of normalized \(SI_{\text{a1-2}}\) under these modes (Mode 6H, Mode 7H, Mode 6D, Mode 8H, and Mode 8D) are different from each other. Therefore, the normalized \(SI_{\text{a1-2}}\) can discriminate these five fault modes including Mode 6H, Mode 7H, Mode 8H (Mode 7D), Mode 6D, and Mode 8D, and then, determine the healthy condition of PM1 to 4.  
As shown in Figure 5, the waveform of normalized \(SI_{\text{a1-2}}\) under Mode 7D is the same as the waveform under Mode 8H. Therefore, the \(SI_{\text{a1-2}}\) cannot discriminate be  
Figure 4: Normalized \(SI_{\text{m}}\) under six different demagnetization fault modes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Demagnetization Fault Mode**} & \multicolumn{4}{c}{**PM Number or Electrical**} & \multicolumn{4}{c}{**PM Number or Electrical**} \\  & \multicolumn{4}{c}{**Period Number (\(i=1\))**} & \multicolumn{4}{c}{**Period Number (\(1<i\leq p\))**} \\ \cline{2-7}  & \(2p-1\) & \(2p\) & \(1\) & \(2\) & \(2k-3\) & \(2k-2\) & \(2k-1\) & \(2k\) \\ \hline Mode 6H(0010) & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 0 \\ Mode 7H(0101) & 0 & 1 & 0 & 1 & 0 & 1 & 0 & 1 \\ Mode 8H(0111) & 0 & 1 & 1 & 0 & 1 & 1 & 1 \\ Mode 6D(1010) & 1 & 0 & 1 & 0 & 1 & 0 & 1 \\ Mode 7D(1101) & 1 & 1 & 0 & 1 & 1 & 0 & 1 \\ Mode 8D(111) & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Six fault modes corresponding to four consecutive PMs.  
tween Mode 7D and Mode 8H. To identify Mode 7D and Mode 8H, the second auxiliary identification signal is applied.