esa-sceva/satcom-chunk-collection huggingface dataset:
# Applying Reinforcement Learning to Protect Deep Neural Networks from Soft Errors
## 2 Related Work
### Protecting DNNs from Soft Errors
Although this approach mitigates the faulty behaviors by solely suppressing the output values of neurons, it still cannot provide a guarantee to correct the constrained value. (3) Bit-level protection is a fundamental solution for protecting DNNs from soft errors as it directly and explicitly safeguards the basic units within a DNN. In particular, the bit-level protection supposes to cope with Silent Data Corruption (SDC) [5], which usually refers to the corruptions in data without any explicit indication caused by flipping bits [4; 17]. These bits are identified as vulnerable bits, which require protection. Several approaches [48; 49] based on the Error Correction Code (ECC) are used to protect the DNN from soft errors. For example, RL-based approaches in [49] exhibit adaptability to select different modes by analyzing the power consumption and error-correcting schemes. However, these schemes are more efficient to protect bit-level data in conventional applications, such as bit streams in the communication systems. Some research studies [50; 51] propose RL-based methods to address vulnerable bits in the context of DNN hardware. For example, an RL-based selective scheme [50] is proposed to protect DNNs by identifying critical bits. The agent protects the DNNs by masking the vulnerable bits across the network. Nevertheless, the extensive amount of DNN parameters could lead to the agent spending an unaffordable amount of time performing satisfactorily. To solve this issue, a compositional mechanism is used in [51] by designing global and local agents to boost the training of the RL. For instance, to protect a ResNet-18 [52] with more than 15 layers, the methods need to design local agents for each layer. Each of these local agents obtains independent local reward functions [50; 51] to analyze the DNN performance. Meanwhile, a global agent with an additional reward function is used to evaluate the protection efficiency of these layer-wise agents. Such a design increases the complexity to design the reward functions and the computational costs to converge the training of the agents.
## 3 Results and Discussion
### Regeneration of CPP200  
The recyclability of an absorbent is very important for its practical application, especially in terms of cost minimization. Therefore, the reusability of CPP200 in the adsorption/desorption of MB was studied (Figure 10A). A quantity of 50.0 mg of CPP200 was shaken in 20.0 mL of MB (10 mg/L) at 25 \({}^{\circ}\)C for 90 min under oscillatory conditions, and MB was desorbed from the CPP200-MB using 40.0 mL of HCl (0.1 mol/L). CPP200 could be recovered by suction filtration, after which another adsorption was conducted using the recycled CPP200. The above operations were repeated 10 times (Figure 1B). In the first cycle, the removal rate reached 100%. Furthermore, we could clearly see that even after nine cycles of adsorption/desorption, the removal rate of CPP200 for MB still exceeded 95%. It is worth noting that the removal rates exceeded 99% during the first five cycles, and the removal rate remained high (93.27%) after the tenth cycle of adsorption/desorption. The hydrogen bonding and electrostatic interactions between CPP200 and MB were damaged under acidic conditions, hence the adsorption capacity of CPP200 could be restored [57]. The reutilization results confirmed the excellent regenerative characteristics of CPP200 as well as demonstrated its potential for applications in wastewater treatment. CPP200 may be used as a green, sustainable, highly efficient and recyclable adsorbent for cationic organic dyes.  
Figure 10: (**A**) Recycle rate of CPP200 for MB in successive 10 adsorption\(-\)desorption cycles (\(C_{\text{MB}}=10\) mg/L, adsorbent dosage of 50.0 mg and contact time of 90 min, T = 25 \({}^{\circ}\)C). (**B**) The adsorption of MB from actual water samples by CPP200 (MB concentration of 50 mg/L, adsorbent dosage of 5.0 mg and contact time of 90 min, T = 25 \({}^{\circ}\)C).  
Figure 9: FT\(-\)IR spectra of CPP200 pre-and post-adsorption of MB, AYR and NR.
## 4 Implementation and Discussion
### Performance Analysis
#### 4.2.8 Confidentiality and Privacy  
The PoF is designed in such a way that, only the owner of a EHR can directly access the personal data. Since sensitive information and non-sensitive data are separated before data sharing, there is no chance of medical data leaking in PoF. Off-the-chain data storing is an iconic feature of this study. This architecture stores hashes of the locally stored personal information in the blockchain. Those hashes of locally stored personal information are shared with all decision-making entities. However, illegal modification of personal information attack can happen in two situations:  
* The attack on blockchain data (on-the-chain data): If a node (attacker) attempts to modify a block illicitly, the consensus from all legitimate nodes can demolish the attack. Similarly, decentralized architecture helps to retrieve lost information or corrupted information from leftover nodes. Block data can also cross check with locally stored hash information of similar data [45].
* The attack on local data store (off-the-chain data): If a node (attacker) attempts to modify locally stored information and successfully changes (corrupts) the local database information, the node will not be corrupted and unknown for long. During collaborative medical decision-making, hashes of every local databases and blockchain databases are cross-checked before a decision is finalized. While initiating the next collaboration, hashes of the corrupted node and the rest of the nodes will be mismatched. Because of this off-the-chain data storing architecture, hashes of local and blockchain databases can be checked.  
Therefore, off-the-chain blockchain successfully secures the integrity of the data to uphold the overall privacy of the proposed PoF based collaborative medical decision-making system [78].
## 4 Overview of the Privacy and Security Requirements in a BYOD Environment
### CIA Triad Criteria Security and Privacy Requirements  
The CIA triad, which encompasses the principles of confidentiality, integrity, and availability, serves as a fundamental model for the development of security systems [16]. These three categories, confidentiality, integrity, and availability, have traditionally formed a classification system for security and privacy requirements [16]. For several reasons, it is essential to include these requirements within the privacy guidelines for BYOD. Firstly, confidentiality plays a critical role in ensuring security and privacy [36]. Its primary objective is to prevent unauthorized access to sensitive company information. Cybersecurity issues may arise because BYOD devices and enterprises are connected to the internet. For instance, the man-in-the-middle (MITM) attack represents one of the threats targeting the security of BYOD users. This attack aims to intercept and steal information exchanged between two parties. Secondly, the integrity requirement ensures that data is only accessible to authorized BYOD devices and remains unmodified. Preserving data integrity becomes challenging in BYOD scenarios, as the organization loses visibility and control when a BYOD device operates outside its network, leading to potential data corruption or loss [37]. Therefore, integrity is a fundamental security requirement for organizations employing BYOD devices [38]. Lastly, availability emphasizes the continuous accessibility of devices, data, and resources, ensuring that BYOD devices always have secure access to services. In cases of an unexpected surge in data traffic volume, client-server communications may suffer from data loss [39].
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 2 Related Work
### LRX Algorithm  
The classical RX is a global hyperspectral anomaly detection algorithm. It calculates the Mahalanobis distance between the pixel to be detected and the mean value of the background to determine whether the pixel is anomalous. However, it is important to note that the covariance matrix in the RX algorithm is calculated based on all pixels in the image, treating the entire image as the background. As a result, it is susceptible to the influence of background pixels when calculating the Mahalanobis distance. On the other hand, the LRX algorithm is a local hyperspectral anomaly detection algorithm [28]. The detection window of LRX consists of a target window and a background window, with the background window being much larger than the target window. In the LRX algorithm, the covariance matrix is calculated in the region by subtracting the background window from the target window, which alleviates the problem that the RX algorithm is easily affected by background pixels.  
Assuming that the number of bands of hyperspectral images is \(D\), the background containing \(N\) pixel points can be represented as a matrix \(X_{b}=[x_{1},x_{2},\cdots,x_{N}]\) of size \(N\times D\). Here, \(x_{i}=[x_{1i},x_{2i},\cdots,x_{Di}]\), where \(x_{ni}\) denotes the spectrum of each pixel.  
Suppose that \(H0\) represents the scenario where the target does not exist, and \(H1\) denotes the scenario where the target does exist.  
\[\left\{\begin{array}{l}H0:x=n,\text{ when the target does not exist}\\ H1:x=as+n,\text{ when the target exists}\end{array}\right., \tag{1}\]  
where, \(x\) represents the vector of the pixel to be detected, \(n\) denotes the background noise vector, and \(s\) is the target spectrum vector.  
\[\mu_{b}=\frac{1}{N}\Sigma_{i=1}^{N}x_{i}, \tag{2}\]  
\[C_{b}=\frac{1}{N}\Sigma_{i=1}^{N}(x-\mu_{b})(x-\mu_{b})^{T}, \tag{3}\]  
\[LRX(x)=(x-\mu_{b})C_{b}^{-1}(x-\mu_{b})^{T}, \tag{4}\]  
where, \(\mu_{b}\) represents the mean value of the background window, and \(C_{b}\) represents the covariance matrix of the background window. The LRX detector calculates the Mahalanobis distance between the spectrum of the pixel to be detected and the mean vector of the background window. If the Mahalanobis distance exceeds a predefined threshold, the pixel is considered anomalous.
# A Low Power IoT Sensor Node Architecture for Waste Management Within Smart Cities Context
## 1 Introduction  
Wireless sensor networks (WSN) are employed in the most diverse contexts, especially when the monitoring of random phenomena is requested. Indeed, by gathering data as much as possible, better forecasts could be performed: a WSN could be exploited with the purpose of achieving this task. In addition, they are also used whenever the monitoring of environmental parameters has to be accomplished. Nowadays, such parameters should be thought in a more broader sense: beside the standard atmospheric ones (such as temperature or moisture), also the level of smog or the level of traffic are purpose of study in urban environments. Beside those, also waste management has been for several years a hot topic whose optimization has been constantly increasing. This is the typical framework within which Smart Cities are defined: deploy sensor nodes forming a network, better if wireless, so as to monitor the occurrence and the extent of events in order to prevent car crashes, or to safeguard inhabitants health, or to manage assets and resources efficiently. Of course, these are only few instances since WSN suit well to disparate applications.  
A WSN deployed in an urban scenario has to fulfill several requirements: sensor node casing has to be designed in a suitable manner so to protect the node from atmospheric agents, real time monitoring should be ensured if needed, power consumption must be optimized in case the node has to be installed in places not covered by the electrical grid or by renewable energy sources. The latter reflects the case of study of this paper since the node has to rely only on a limited source of power provided by four AA lithium batteries, as it will be seen later on.  
This paper is composed as follows: in Section 2 we propose the state of the art of the employment of WSN within Smart Cities and of the solutions to the problem of solid waste management toowhile in Section 3 we present some related works regarding energy-saving policies for sensor nodes. In Section 4 the proposed node architecture is explained while in Section 5 the energy-saving policy chosen for the sensor node as well as an estimate of the consumption are shown. In Section 6 the tests performed are presented, while in Section 7 some concluding remarks and future works are highlighted.
## 3 Simulation Tests and Analysis
### Reconstruction Error Analysis
#### 3.3.3 Effect of Sensor Placement  
Sensor placement has a profound influence on reconstruction accuracy. Given random locations, different sensor distributions can result in different reconstruction solutions, and the errors between them are often larger than 1%. Optimal sensor placement can yield a better reconstruction result and lower measurement cost.  
The proposed sensor placement optimization algorithm was tested by calculating the reconstruction results for optimal sensor placement and comparing them with those for random placement. The first eight basis vectors and 12 sensors were used in the reconstruction. Noise was also added to the original measurement data. The averages of the reconstruction errors from 1000 runs were used to obtain the overall result. Figure 5 compares the results for different test cases.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Case Number** & **Original Data** & **SNR = 40 dB** & **SNR = 45 dB** & **SNR = 50 dB** \\ \hline
1 & 0.59 & 2.11 & 1.38 & 0.90 \\
2 & 0.70 & 2.18 & 1.40 & 1.00 \\
3 & 0.84 & 2.20 & 1.43 & 1.09 \\
4 & 1.05 & 2.35 & 1.58 & 1.26 \\
5 & 1.15 & 2.42 & 1.67 & 1.35 \\
6 & 0.84 & 2.31 & 1.46 & 1.09 \\ special test case & 0.98 & 2.53 & 1.59 & 1.23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reconstruction errors (%) in seven test cases with a 0.3% sample rate (12 sensors).  
Figure 5: Comparison of reconstruction errors between the optimal placement and random placements in different test cases with different levels of noise. (**a**) Original measurement data; (**b**) SNR = 40 dB; (**c**) SNR = 45 dB; (**d**) SNR = 50 dB.  
As can be observed from the figure above, in all six test cases, optimal sensor placement could yield a better reconstruction resolution than placing sensors randomly. Although optimal sensor placement may not obtain the best solution, this approach is more adaptable in different conditions and is less sensitive to noise.  
Overall, the results in this section suggest that the proposed method is feasible and can yield an acceptable reconstruction solution. The next section, therefore, shows the performance of the proposed method in a gas combustion experiment.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 4 Validation of the Predicted Clocks  
Generally, predicted clock accuracy can be evaluated by the deviation of prediction from its true value. However, the true value of the in-orbit clock is unknown. Therefore, a reference benchmark should be given. On the other hand, the predicted clock can be checked together with its related orbit  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Satellite** & **Smooth Factor** \\ \hline  & C01 & 0.10 \\  & C02 & 0.26 \\ GEO & C03 & 0.10 \\  & C04 & 0.10 \\  & C05 & 0.10 \\ \hline  & C06 & 0.10 \\  & C07 & 0.50 \\ IGSO & C08 & 0.38 \\  & C09 & 0.10 \\  & C10 & 0.10 \\  & C13 & 0.10 \\ \hline  & C11 & 0.26 \\ MEO & C12 & 0.50 \\  & C14 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Recommended smooth factor for each BDS-2 satellite.  
Figure 7: Clock differences between true (GFZ precise clock products) and prediction, using different smooth factors.  
for precise point positioning. In this paper, both evaluations are carried out, and the later one is more important, as it will give a preliminary assessment of the positioning accuracy which is the main goal of this research.
## 4 Discussion  
The average accuracy indicators obtained by integrating phase congruence image processing with the SVM classification models and the multilayer perceptron ANN are achieved because the processing method generates characterization vectors with greater variation for each corrosion pattern present in steel; in this way, it is easier to draw thresholds that separate the sets of information between the classes. On the other hand, it is important to highlight the average accuracy of 98% achieved by the Fuzzy ARTMAP ANN processing the input information generated by the GLCM method; one answer to this result is the correct coupling of both procedures. The ANN only processes information in the range (0,1) or is normalized, and the vast majority of the indicators of the GLCM method from its origin are in this range, so the transformation is not necessary; this does not mean that the process of normalization is incorrect; however, it is still a transformation of the original nature of the data that represents a state of the steel in the metallographic image. By achieving an average accuracy of 98% with both digital image processing methods, the multilayer perceptron ANN was demonstrated to be a robust classification model for recognizing intergranular corrosion patterns in AISI 304 stainless steel.  
Using the multilayer perceptron ANN as a classification model, according to the sensitivity and specificity indicators, the GLCM characterization method shows a minimal improvement compared to the phase congruency method. However, computationally speaking, an important indicator is the convergence time; in this sense, the phase congruency technique presents a shorter duration since an artificial neural network architecture with fewer neurons in the hidden layer is required. The indicator of interest to maximize is the accuracy; both methods provide, on average, a 98% accuracy in the task of the recognition of metallographic patterns present in stainless steel affected by temperature.  
The methodology presented recommends the phase congruence method, since, despite characterizing the images with larger vectors compared to the GLCM method, an adequate accuracy for the recognition task is achieved in less time; this is because it provides a greater degree of detail in the description of the microstructure of stainless steel, facilitating the work of the ANN and giving way to the recognition of the intergranular corrosion present in AISI 304 stainless steel by integrating the multilayer perceptron ANN and digital processing of the metallographic images.  
An important opportunity to extend the research related to corrosion pattern recognition in stainless steels is to deepen and extend digital image processing techniques using invariant moment characterization or histograms. As for classification tools, experimenting with Bayesian or recurrent artificial neural networks will be the next step to define the recognition phase.
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 1 Introduction: Hadronization
### The BDMPS-Z Model for Radiative Energy Loss  
The Baier-Dokshitzer-Mueller-Peigne-Schiff-Zakharov (BDMPS-Z) [4] model describes the radiative component of the total energy loss experienced by high-energy partons traversing a medium. This model was independently developed using quantum field theory techniques by [PERSON] et al. and through the path-integral formalism by [PERSON]. Despite the differing approaches, both formalisms are equivalent and yield the same nuclear modification spectra.  
As discussed extensively in [5], we focus on a heuristic interpretation of the radiative energy loss mechanisms. A key assumption of the model is that the scattering centers in the medium are static and uncorrelated. The energy loss per unit path length, \(-\frac{dE}{dz}\), is calculated under the condition that hadronization occurs within the nuclear medium, i.e., \(0\leq\omega\leq E\). Under these assumptions, it has been demonstrated [4] that the energy loss follows two characteristic regimes:  
* For short coherence lengths, \[-\frac{dE}{dz}\approx\frac{\alpha_{s}}{\pi}N_{c}\frac{\mu^{2}}{\lambda}L.\] (1)
* For large coherence lengths, \[-\frac{dE}{dz}\approx\frac{\alpha_{s}}{\pi}N_{c}\frac{\mu^{2}}{\lambda}\sqrt {E}.\] (2)  
where coherence length refers to the spatial or temporal extent over which a gluon emission is coherent, meaning the emitted gluon remains influenced by interactions with the parton and the medium. These behaviors are illustrated schematically in Figure 1, highlighting the transition between regimes as a function of the medium's properties and path length. Further details are discussed in [4].
## 3 Reactive Power Optimization Method Based on Graph Security Reinforcement Learning
### Graph Convolutional Network  
GCNs have emerged as a powerful tool for processing graph-structured data. Unlike traditional neural networks designed for grid-like data (e.g., images), GCNs can effectively handle irregular graph structures, making them suitable for applications such as social network analysis, recommendation systems, and power grid optimization.  
The input to a GCN is a graph data structure represented as \(G(C,\Lambda)\), where \(C\) is the set of nodes and \(\Lambda\) is the set of edges. Each node \(i\in C\) has associated features, which can be represented as a feature vector \(H_{i}\). The graph structure is typically encoded using an adjacency matrix \(A\), where \(A_{ij}=1\) if there is an edge between node \(i\) and node \(j\), and \(0\) otherwise. Additionally, a degree matrix \(D\) is used, which is a diagonal matrix where each diagonal entry \(D_{ij}\) represents the degree of node \(i\) (i.e., the number of edges connected to node \(i\)).  
A GCN consists of multiple layers, each performing a graph convolution operation. The core idea behind GCN layers is to aggregate information from a node's neighbors and update the node's features based on this aggregated information. The propagation rule for each layer can be described as follows:  
\[\mathbf{H}^{(l+1)}=\text{ReLU}\Big{(}\tilde{\mathbf{D}}^{-\frac{1}{2}}\tilde{ \mathbf{A}}\tilde{\mathbf{D}}^{-\frac{1}{2}}\mathbf{H}^{(l)}\mathbf{W}^{(l)}\Big{)}, \tag{8}\]where \(\mathbf{H}^{(l)}\) is the feature matrix of the \(l\)-th layer, with each row representing the feature vector of a node. \(\bar{A}=A+I\) is the adjacency matrix with added self-loops to account for the node's own features. \(\bar{D}\) is the degree matrix corresponding to \(\bar{A}\). \(\mathbf{W}^{(l)}\) is the weight matrix for the \(l\)-th layer, which is learned during training. ReLU is the rectified linear unit activation function, which introduces non-linearity into the model.