esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reliability Sensitivity Analysis of Emergence Extension  
In this section, the reliability sensitivity analyses of the NLG emergence extension system comprising reliability analysis and sensitivity analysis are investigated to provide a reference for the development and validation of a feasible plan for reducing emergence extension failure and faults.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Bottom Events** & **Reliability Models** &
\begin{tabular}{c} **Safe Boundary** \\ **Equation** \\ \end{tabular} & **Reliability Index** & **Distribution** \\ \hline
1 & Excess passive torque \(X_{1}\) & Starting reliability \(M\) & \(M=M_{d}-M_{r}\) & \(\beta=\frac{\overline{M}_{r}-\overline{M}_{r}}{\sqrt{C_{r}^{2}M_{r}^{2}+C_{r}^ {2}M_{r}^{2}}}\) & Normal \\ \cline{3-6}  & & Continuous & \(M_{\omega}=\omega-\omega^{*}\) & \(\beta=\frac{\overline{n}_{r}-\overline{r}}{\sqrt{C_{r}^{2}M_{r}^{2}+C_{r}^ {2}M_{r}^{2}}}\) & Normal \\ \hline
2 & Imprecise lowing \(X_{2}\) & Movement & & & \\ \hline
3 & Unlocked upper-lock \(X_{3}\) & precision Reliability \(\theta_{0}=\theta-\theta^{*}\) & \(\beta_{0}=\frac{\mu_{\theta}-\mu_{\omega}}{\sqrt{\mu_{\omega}^{2}+V_{\phi}^{2 }}}\) & Normal \\ \hline
4 & Failed locked lower-lock \(X_{4}\) & & & \\ \hline
5 & Static strength failure \(X_{5}\) &
\begin{tabular}{c} Static \\ strength reliability \\ \end{tabular} & \(M_{s}=S-L\) & \(\beta_{M}=\frac{\mu_{s}-\mu_{\mathrm{L}}}{\sqrt{C_{r}^{2}M_{r}^{2}+C_{r}^ {2}M_{r}^{2}}}\) & Normal \\ \hline \hline \end{tabular} Note: \(\overline{M}_{d}\)—the mean of driving torque \(M_{d}\); \(\overline{M_{r}}\)—the mean of resistance torque \(M_{r}\); \(C_{d}\)—the variable coefficient of driving torque; \(C_{r}\)—the variable coefficient of resistance torque; \(\overline{\omega}\)—the mean of angular velocity \(\omega\); \(C_{\omega}\)—the variable coefficient of angular velocity; \(\overline{\omega}\)—the mean of allowed angular velocity \(\omega\); \(C_{\omega}\)—the variable coefficient of velocity or angular velocity; \(\mu_{\theta}\)—the mean of \(\theta\);
## 1 Introduction
In this work, the set of SVM classifiers relied on two learning features: the curvelet transformation and the LBP. Then, the final classifier was built by merging the SVM and artificial neural network (ANN) classifiers. Six different feature descriptors were evaluated by [5] with four classifiers, i.e., 1-nearest neighbor (1-NN), SVM, quadratic linear analysis (QDA), and random forest (RF) classifiers. Additionally, an ensemble method to determine the local characteristics of breast cancer tumors was proposed by [24]. The model was executed by aggregating a vector of locally aggregated descriptors (VLAD) on the Grassmann vector. The VLAD was represented as a collection of multidimensional and spatially changing signals on a higher-order linear dynamic analysis system. Furthermore, a classification method that relied on an unsupervised learning approach was proposed by [25] to addressed the different qualities of histology images, and by learning a domain-invariant space, it is attempted to extract the feature vectors of benign images. In these works, the BreaKHis dataset was also used to evaluate the performance of the corresponding methods. CA and digital image join the common architecture. Hence, image processing tasks to extract features are common to CA processing provided the appropriate rules. In addition,  
Figure 1: Example breast cancer histopathology images derived from the BreaKHis dataset. CA is a universal computation, equivalent to Turing machines, meaning that CA can compute any computing tasks given the appropriate rules. On this basis, we propose CA's based model for feature extraction in breast cancer images for classification as opposed to the outstanding CNN deep learning in the state-of-the-art compared methods. Feature extraction conducted in the convolution layer in CNN for image classification is a promising technique in literature. However, it faces drawbacks in computational complexity and overfitting problems. This research aims at solving these drawbacks in feature extraction by using a CA-based model, which is totally different from the approach in CNN. In breast cancer image classification, we use the magnification of histopathological images of breast cancer as usual practices in cancer diagnosis. This paper proposes a deep cellular automata (CA)-based feature extraction for breast cancer image classification by focusing on histopathological images. The main contributions of this paper are summarized as follows. (1) We propose a novel feature extraction method for image classification tasks by using the deep CA approach. (2) We present a customized case in which the proposed model can deal with breast cancer image classification. The remainder of the paper is organized as follows. In the next section, we review the related works on the breast cancer image classification task. Section 3 presents the proposed framework for the deep CA and the classification process. In Section 4, the details of the experiments and their results are comprehensively presented. Finally, Section 5 provides the conclusion of the paper and suggestions for future works.
## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.4 Predicting Sea Level Rise with DL
This approach demonstrated global applicability for analyzing sea level patterns and forecasting changes over 1-3 years. [PERSON] et al. [165] analyzed hourly tide gauge data from the Australian Bureau of Meteorology (1990-2022) to predict sea levels in Australia. They employed a hybrid CNN-Bidirectional GRU model, which processes sequences in both forward and backward directions to better capture temporal dependencies, revealing annual sea level rises of 6.1 mm in Darwin and 5.6 mm in Milner Bay over the study period. [PERSON] et al. [162] developed a model to predict sea levels in the Chesapeake Bay, U.S., with an emphasis on spatiotemporal dynamics. The model combined a CNN for spatial feature extraction with an LSTM for capturing temporal patterns. By integrating physical relationships, this data-driven method has proved its effectiveness in improving coastal management. Exploring alternative approaches, [PERSON] et al. [166] applied Transformer-based DL methods for sea level prediction using four months of data from Indonesia. Their model successfully forecasted sea levels over two weeks, outperforming widely used LSTM approaches. [PERSON] et al. [192] developed a deep belief network to create a unified high-quality sea level model by integrating altimetry and tide gauge data, focusing on the Mediterranean Sea as a case study. The model demonstrated robustness against input data distribution and produced sea level anomalies with a spatial resolution of \(0.25^{\circ}\times\,0.25^{\circ}\). In summary, advancements in DL have enhanced the ability to analyze and predict sea level variations using data from tide gauges and satellite altimetry, and these innovations are vital for addressing the impacts of rising sea levels on both human communities and natural ecosystems.
# The Knapsack Problem with Conflict Pair Constraints on Bipartite Graphs and Extensions
## 4 Integer Programming Formulations
### Experimental Analysis  
The formulation MaxStarIP\({}^{\prime}\) has been studied and used in algorithms to solve KPCC [6]. There are also effective branch-and-bound algorithms to solve KPCC for general graphs [6; 28; 44]. However, to the best of our knowledge, there is no computational study available that compares the different formulations of KPCC. Note that the complexity of KPCC can vary significantly depending on the structure of the underlying graph and the level of tightness of the budget constraint. Also, tight efficiently computable upper bound estimates on the stability number of a graph can affect the efficacy of our formulations StarIP and MaxStarIP. The following Table 1 provides some easily computable upper bounds on the stability number of the graph \(G=(V,E)\).  
In the table above, \(\Delta\) denotes the maximum degree and \(\delta\) denotes the minimum degree of vertices in the graph \(G=(V,E)\).  
Recall that the integer programming formulation MaxStarIP belongs to the class StarIP. Therefore, it can be strengthened by replacing \(|A(i)|\) with an upper bound on the stability number of the subgraph \(G(i)\) of \(G\) induced by \(A(i)\). When MaxStarIP is strengthened using the bound UB\(k\) obtained from \(G(i)\) to replace \(|A(i)|\), \(k=1,2,3,4,5,6\), we denote the resulting MaxStarIP formulation by MaxStarIP\(k\). Thus, in addition to MaxStarIP, we have the strengthened formulations MaxStarIP1, MaxStarIP2, MaxStarIP3, MaxStarIP4, MaxStarIP5, and MaxStarIP6. The major goals of the experimental analysis were to:  
1. Compare the relative performance of the standard KPCC formulation given in the introduction, MaxStarIP, and its variations MaxStarIP\(k\), \(k=1,2,3,4,5,6\) using the general purpose solver GUROBI.
2. Study the impact of the tightness of the budget constraints on the formulations that we compare.
3. Study the impact of sparsity of \(G\) on the formulations that we compare.  
Explore the heuristic value of the formulations.  
It may be noted that UB1 will be at least as tight as UB2 and that UB4 applies to a connected graph. For these reasons, we will not use UB2 and UB4 in our experiments.  
All of the computational experiments were carried out on a PC with the Windows 10 Enterprise 64-bit operating system, Intel(R) Core(TM) i7-3770 CPU @ 3.40 GHz, and 16.0 GB of memory. The calling program was written in Python (Python 3.6.1) and called GUROBI 9.0 using the interface gurobipy. The time limit for GUROBI was set to 3600 s. Most of the test problems were solved to optimality within this time limit, except some large or structured instances.
###### Abstract  
In this paper, we consider a low-latency Mobile Edge Computing (MEC) network where multiple User Equipment (UE) wireless reports to a decision-making edge server. At the same time, the transmissions are operated with Finite Blocklength (FBL) codes to achieve low-latency transmission. We introduce the task of Age upon Decision (AuD) aimed at the timeliness of tasks used for decision-making, which highlights the timeliness of the information at decision-making moments. For the case in which dynamic task generation and random fading channels are considered, we provide a task AuD minimization design by jointly selecting UE and allocating blocklength. In particular, to solve the task AuD minimization problem, we transform the optimization problem to a Markov Decision Process problem and propose an Error Probability-Controlled Action-Masked Proximal Policy Optimization (EMPPO) algorithm. Via simulation, we show that the proposed design achieves a lower AuD than baseline methods across various network conditions, especially in scenarios with significant channel Signal-to-Noise Ratio (SNR) differences and low average SNR, which shows the robustness of EMPPO and its potential for real-time applications.  
low-latency mobile edge computing; age upon decision; finite blocklength regime; deep reinforcement learning; maskable proximal policy optimization
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 5 Description of the Scenarios for FANET Test
### Scenario 2--Baud Rates of 38,800 bps and 115,200 bps  
According to Figure 14, this scenario simulates the situation where a HUB node fails and exits the network during the transmission of image data, forcing the nodes to be reorganized, searching for an alternate route (i.e., another hub) to continue the packet transmission between the ED and the BS. The XBee Modules were configured in such a way that the ED did not have a direct link to the BS. Thus, the transmitted packets always hop through a HUB. In this configuration, when the HUB is deactivated, the ED must find an alternative route through another HUB to send packets, since it is not able to transmit packets directly to the BS, that is, without the hop. In order to simulate this scenario, the baud rates of the ED and BS were set to 38,800 bps, while the baud rate of the HUBs was 115,200 bps.  
In this new arrangement, the coordinator was on the ground again. However, in this case, two UAVs work as routers (HUBs) maintaining their formation close to each other. The first and second UAVs flew at altitudes of 70 m and 80 m, respectively, with a distance varying from 400 to 600 m from the BS. A third UAV (ED) was added to the network as the end device, flying 80 m high and distant 900 to 1110 m from the BS. This condition is described in Table 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **BS** & **HUB** & **ED** \\ \hline ZigBee Function & Coordinator & Router & End Device. \\ Distance from BS & - & 400–600 m & 900–1100 m \\ Baud Rate & 115,200 bps & 115,200 bps & 115,200 bps \\ Sent Bytes & 128,000 & - & 128,000 \\ Sent packets & 500 & - & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Scenario 1: Configuration and position nodes.  
Figure 14: ZigBee devices in configuration scenario 2. (**a**) ZigBee devices in P2P configuration scenario; (**b**) ZigBee devices in a scenario without failure; (**c**) ZigBee devices in a scenario with failure.
# Attention-Guided Network Model for Image-Based Emotion Recognition
## 2 Materials and Methods
### Guidance Strategy  
To boost the model's classification performance and its focus on regions of impact for emotion recognition, a guidance strategy was adopted. First, a binary mask representing facial areas of special interest was created. This mask was used only during the training stage, where the input was fused with the mask and specific weighting for the model to focus on these areas and thus reduce the influence of the rest. Equation (1) represents the mask-image fusion and the output \(I_{Re}\) from the model input layer. \(I_{In}\) is the image input, \(M\) is the binary mask, and \(\alpha\) is the weighted parameter, set to \(\alpha=0.125\) in this study. Figure 5 provides a graphical illustration of the mask-image fusion.  
\[I_{Re}=I_{In}*(M+\alpha) \tag{1}\]  
In neural networks, when deployed for facial emotion classification, the attention mechanism serves as a critical component to prioritize certain regions of the input image, making sure the network focuses on emotionally salient features, such as the eyebrows, eyes, and mouth. These regions often contain pivotal cues for emotion recognition. The guidance strategy, in this context, directs the attention mechanism towards these emotion  
Figure 4: Schematic of the AGFER attention block (inside the blue dashed lines).  
ally significant regions. Without this guidance, the attention mechanism might give undue importance to less relevant facial areas, potentially missing or diluting the emotionally charged features that are essential for accurate classification.  
By introducing a guidance strategy, we provide the network with prior knowledge or a hint about where the most informative regions are likely to be located. This proactive approach ensures that the attention mechanism does not waste computational resources and focus on irrelevant areas. As a result, the model is better equipped to recognize subtle emotional cues, leading to improved accuracy in emotion classification. In the realm of facial emotion classification, where subtle changes in facial expressions can differentiate one emotion from another, ensuring that the network consistently focuses on the right areas can make a significant difference in performance.  
Furthermore, a class-weighted cross-entropy loss function was used to compensate for any imbalance in class distribution. Weights were calculated as follows:  
\[w_{c}=\frac{N}{m*s_{c}} \tag{2}\]  
where \(w_{c}\) represents the weights of a certain class \(c\), \(N\) is the total number of observations, \(m\) stands for the total number of classes, and \(s_{c}\) is the number of observations for a specific class \(c\).
## 4 Discussion
### Failure of Residual Tumor Identification  
The method failed at identifying the residual tumors correctly in four out of 19 patients (Patients 2, 7, 14 and 18). A first reason for failing is the image quality. The approach was tested on 3D US volumes built based on acquired 2D images. The 3D reconstruction algorithm makes use of smoothing functions; therefore, hyperechogenic structures appear to have lower contrasts in the 3D volumes. Moreover, the time window of maximal contrast agent enhancement in the CEUS image data is short, and the 3D acquisition requires a couple of seconds. This maximal enhancement time point may be missed during the acquisition. This image quality drawback can be addressed by using directly raw data (2D images). However, with the current neuronavigation system used at the clinical University of Leipzig, we have only access to the 3D iUS reconstructed volume and not to the original 2D iUS data. A second reason is due to the algorithm itself. Artifacts located at the image borders are removed in the preprocessing step (Section 2.2). In addition, through this process, tumor areas can be lost, as well. Therefore, improvements in the pre-processing step and in the characterization of tumor residuals in the iUS images are needed.
## 2 Materials and Methods  
### Reagents and Materials  
Analytical grade trichloroethylene (TCE, \(\geq\)99.5%), toluene, 1, 2, 4-trichlorobenzene (TCB), benzene, sodium chloride (NaCl), sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)), SPME fibers coated with polydimethylsiloxane (PDMS, 100 \(\upmu\)m film thickness), carboxen/PDMS (75 \(\upmu\)m film thickness), or PDMS/divinylbenzene (PDMS/DVB, 65 \(\upmu\)m film thickness), 20-mL headspace glass vials, and crimp seals with PTFE/silicone septa were obtained from Sigma-Aldrich Co. LLC. (St. Louis, MO, USA). Deuterated TCE (TCE-_d_) was purchased from C/D/N Isotopes INC. (Pointe-Claire, Quebec, Canada).  
TCE standard solutions in acetonitrile for calibration were prepared at the following concentrations: 5, 15, 50, 150, and 500 \(\upmu\)g mL\({}^{-1}\). Water samples for calibration and validation were prepared by adding 20 \(\upmu\)L of the TCE standard solutions in acetonitrile to 10 mL D.I. water or groundwater. This procedure yields the final concentrations of 10, 30, 100, 300, and 1000 ng mL\({}^{-1}\). The concentration of TCE-_d_ solution as the internal standard was 300 ng mL\({}^{-1}\).
# Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign
## 1 Introduction  
The forthcoming Argentinean mission Satellite Argentino de Observacion con Microondas (SAOCOM) [1] is constituted by two identical Low-Earth-Orbit (LEO) satellites, SAOCOM-1A and -1B, carrying as the main payload an L-band Synthetic Aperture Radar (SAR). The SAOCOM-SAR has an active array antenna with elevation and azimuth steering capability for frequent-revisit, wide-swath, and medium resolution Earth observation.  
The mission's target applications [2] require a tight overall amplitude and phase stability performance, which is imposed to a large extent by the instrument critical elements, such as the chirp generator, the distribution network, the Transmit-Receive Modules (TRMs), the clock, and the antenna. The pre-launch assessment and verification of the phase and amplitude performance is paramount in order to limit the calibration and verification efforts to be done in-flight (e.g., during the commissioning phase).  
Typically, the instrument elements can be tested on a singular basis, and the antenna far-field radiation pattern can be reconstructed from planar-near-field-scanner (PNFS) measurements of the near field radiation patterns of the elements of the antenna by means of an accurate antenna model [3]. In the latter case, extreme care is needed in the measurement setup to avoid biases in the measurements. The far-field pattern is not available on-ground and the antenna model can be verified only in-flight, exploiting e.g., homogeneous targets for the elevation case and recording transponders for the azimuth. A typical example is the acquisition over the Amazonian rainforest. However, the in-flight data intensity profile depends on a list of other factors (e.g., pointing, time-variant instrument gains, processor gains), also to be calibrated or verified, so that it might be difficult to isolate the antenna contribution from the others, as reported for the case of Sentinel-1 in References [4; 5]. Furthermore,the in-flight verification allows a limited verification of the elevation antenna pattern, within the angular range used by the imaging swath.  
One way to check the mid- and long-term stability of the system, say from seconds to minutes, is an outdoor experiment where SAR acquisitions are carried out with the full antenna (or a part of it, e.g., one tile) pointed to targets of opportunity or man-made calibrators in the far-field. In addition, the collected power measures for different antenna pointing can be correlated to the theoretical antenna model to obtain a first far-field verification, complementary to what could be obtained with PNFS measurements.
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 3 Network Model
### RCR Update  
The network operation time is divided into epochs, and each epoch is represented as \(T\). In each epoch, the source node restarts RCR-selection by broadcasting a probe message. Then, the cooperative nodes list is updated based on the distance and residual energy of neighborhood nodes.
###### Abstract  
5G technology and IoT devices are improving efficiency and quality of life across many sectors. IoT devices are often used in open environments where they handle sensitive data. This makes them vulnerable to side-channel attacks (SCAs), where attackers can intercept and analyze the electromagnetic signals emitted by microcontroller units (MCUs) to expose encryption keys and compromise sensitive data. To address this critical vulnerability, this study proposes a novel dynamic key replacement mechanism specifically designed for lightweight IoT microcontrollers. The mechanism integrates Moving Target Defense (MTD) with a lightweight Diffie-Hellman (D-H) key exchange protocol and AES-128 encryption to provide robust protection against SCAs. Unlike traditional approaches, the proposed mechanism dynamically updates encryption keys during each cryptographic cycle, effectively mitigating the risk of key reuse--a primary vulnerability exploited in SCAs. The lightweight D-H key exchange ensures that even resource-constrained IoT devices can securely perform key exchanges without significant computational overhead. Experimental results demonstrate the practicality and security of the proposed mechanism, achieving key updates with minimal time overhead, ranging from 12 to 50 milliseconds per encryption transmission. Moreover, the approach shows strong resilience against template attacks, with only two out of sixteen AES-128 subkeys compromised after 20,000 attack attempts--a notable improvement over existing countermeasures. The key innovation of this study lies in the seamless integration of MTD with lightweight cryptographic protocols, striking a balance between security and performance. This dynamic key replacement mechanism offers an effective, scalable, and resource-efficient solution for IoT applications, particularly in scenarios that demand robust protection against SCAs and low-latency performance.  
**Keywords:** 5G; Internet of Things (IoT); side-channel attack (SCA); microcontroller unit (MCU); Diffie-Hellman (D-H); moving target defense (MTD); AES-128