esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Elmo  
One of the key tools for analyzing SCAs is the Emulator for power Leakages for the M0 (ELMO) [29], an open-source framework designed for power consumption modeling in embedded systems. ELMO enables researchers to simulate the effects of different cryptographic implementations, allowing for precise measurement and evaluation of potential vulnerabilities [15]. By leveraging such tools, researchers can develop and assess novel countermeasures against SCAs in a controlled environment.  
Additionally, ELMO uses instruction flow analysis to predict power consumption cycles, providing insights into the power signatures of cryptographic operations. It categorizes assembly instructions into different groups, such as arithmetic logic unit (ALU) operations, load/store operations, and shift operations, which helps refine the accuracy of power analysis predictions.  
Recent updates to ELMO have incorporated ML-based analysis, enhancing its ability to simulate real-world attacks [30]. The integration of AI-driven predictive models into  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Technique** & **Description** \\ \hline \multirow{4}{*}{Randomize Power Consumption} & \multirow{2}{*}{Dummy Operations} & Insert operations that do not affect computation but randomize power patterns. \\ \cline{2-3}  & & Randomize the order of independent operations to vary power timing. \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \cline{2-3}  & & \\ \hline Constant Power Consumption & \multirow{2}{*}{Signal Balancing} & Equalize power usage across operations through uniform switching activity. \\ \hline \end{tabular}
\end{table}
Table 2: Classification of hiding techniques.  
SCA research has allowed for more effective assessment of countermeasures and attack techniques. Researchers are now utilizing these advanced simulation tools to refine cryptographic implementations and develop adaptive defenses against evolving threats [31].  
Table 3 presents the classification of assembly instructions used in ELMO for power analysis. By categorizing instructions into arithmetic, shift, load/store, and multiplication operations, ELMO enables researchers to analyze the energy consumption patterns of cryptographic implementations more effectively.  
As DL techniques continue to evolve, they present both challenges and opportunities in the field of cryptographic security. On the one hand, ML models enhance the ability of attackers to conduct sophisticated SCAs; on the other hand, they also provide avenues for developing more resilient countermeasures. Understanding these dynamics is crucial for advancing the security of cryptographic systems and ensuring robust protection against emerging threats.
# Planar Array Failed Element(s) Radiation Pattern Correction: A Comparison
###### Abstract  
Phased arrays are widely used in different fields, such as broadcasting, radar, optics, and space communications. The principle of phased arrays is to generate a directed signal from a large number of antennas to be steered at any desired angle. This, however, increases the probability of defective elements in an array. Faulty elements in an array cause asymmetry and result in increased sidelobe levels which rigorously distort the radiation pattern. Increased sidelobe radiation wastes energy and can cause interference by radiating and receiving signals in unintended directions. Therefore, it is necessary to find a method that can provide accuracy in the radiation pattern transmitted or received in the presence of failed element(s) in an array. This paper compares the few available optimization methods, namely, simulated annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Pattern Search (PS) methods. For each method, various types of failures were examined, and the most suitable techniques to recover the far-field radiation are recommended. The optimization is then carried out by selecting the optimal weights of the remaining working elements in the planar array. The optimized radiation pattern's efficiency was evaluated by comparing the Signal to Noise Ratio (SNR) value of the optimized radiation with reference and failed radiation patterns. The PSO method showed a better performance compared to all the other methods in reducing the failed radiation pattern's SNR value. In various types of failure tests, this method reduced the failed radiation pattern's SNR from 1 to 10 dB. This method also successfully produced a radiation pattern that closely matches the reference pattern before any failed element(s) are presented in the array. The life cycle of a planar array system with faulty elements can be increased by optimizing the remaining active elements in the array with the PSO method. It also reduces the cost of restoring and replacing the failed elements in an array regularly. This approach also prevents near-field measurement that requires complicated processes using costly equipment.  
Keywords:planar array; optimization; failed elements; radiation pattern; correction; simulated annealing; genetic algorithm; particle swarm optimization; pattern search method +
Footnote †: journal: Applied Sciences
## 4 The Proposed Scheme: Architecture and Components
### The Governance Layer  
This layer consists of various entities, such as the police station (PS), the municipal corporation (MC), and healthcare centers (HC), which are responsible for monitoring and controlling the COVID-19 pandemic effect in the country. We represent them as \(\{E_{PS},E_{MC},E_{HC},E_{UAV}\}\). At the governance layer, we consider that a given region \(R\) is demarcated into functional zones controlled by \(E_{MC}\). There are \(l\) hospital setups in any zone that provide critical COVID-19 resources (beds, ventilators, and medicines) [39]. Any zone is guarded for inspection via a UAV swarm network. Each entity (\(\{E_{PS},E_{MC},E_{HC},E_{UAV}\}\)) has a UAV controller (acting as a ground control station) that controls the UAVs in the COVID-19 layer via a 6G-based UAV communication.  
Figure 2: Monitoring, inspecting, sanitizing, and measuring social distancing in COVID-19 and future pandemics.  
The data acquired by the COVID-19 layer from UAVs are forwarded to the 6G-based communication layer, where different intelligent functions (operational, environmental, service) are applied to the data to offer predictive services in the governance layer. We want to mention that the entire architecture uses salient features of the 6G communication to route their data from one layer to other. Once the governance layer acquires the intelligent data, different governance bodies can take specific actions, such as providing physical assistance to COVID patients, emergent assistance to the red zones, and sending more sanitization and medical supplies. Furthermore, there is no central node that manages the entire architecture; only in the governance layer, each governance body has a ground control station that is used for controlling different UAVs working in the COVID-19 layer. However, they are decentralized because all the governance bodies depend on each other and continually exchange data. For instance, the healthcare center UAV controller controls the UAV measuring body temperature; when it acquires the data from the UAV (measure temperature), it will store the data in the repository, which is shared with other governance bodies, such as police stations and municipal corporations, to take necessary action in the COVID-19 layer.
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 4 Simulation and Analyses
The middle mini-batch size of 10,240 achieved moderate performance. After 6000 epochs, the loss value approached 0.55. Hence, the choice of mini-batch size is a trade-off between convergence speed and error precision. The most suitable mini-batch size can be identified to meet the specific requirements. In this article, we recommend that a smaller mini-batch size be used first. When the loss tends toward stability, the mini-batch size can be increased until the loss value reaches the required precision. As mentioned above, the computational complexity of SIC is related to the number of UEs. Suppose the number of UEs in a cluster is \(L\) and the computational complexity of SIC is \(O(L)\). For a trained DNN model, the signal detection is the process of forward propagation, and its computational complexity is \(O(1)\). This means that the trained DNN model can realize efficient and real-time signal detection. Figure 14: SER performance with channel estimation error.
## 4 Results and Discussion
### Limitations and Future Work  
Despite the proposed high-frequency signal generation framework demonstrating superior performance across multiple evaluation metrics and exhibiting strong stability and adaptability under various market conditions, several limitations remain that warrant further investigation. The current macro factor modeling mechanism incorporates policy sentiment vectors extracted via language models; however, it does not fully exploit high-frequency unstructured textual sources such as social media feeds, analyst reports, and real-time news streams. Future work could integrate reinforcement learning or online learning approaches, where strategy backtesting feedback could be introduced as a reward signal to construct a dynamic update mechanism, thereby enhancing the robustness of returns and the effectiveness of risk control. On the other hand, although the current model integrates structured graph modeling, multi-scale temporal encoding, and semantic macro factor modeling, the interactions among these modules are primarily based on simple feature concatenation and attention mechanisms. A tighter coupling of components through joint optimization has yet to be established. Subsequent research could explore joint module training and collaborative attention mechanisms to further enhance the coherence and interpretability of the overall framework.
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 5 Conclusions  
In this paper, we verified the principle that the autocorrelation of the PN sequence was not affected by low-resolution quantization. With the help of this property, we introduced a novel synchronization framework which consists of two parts: one is the timing estimator with one-bit ADCs, and the other is the CFO estimator with multi-bit ADCs. The proposed scheme has lower complexity and can significantly reduce power consumption due to the use of low-resolution ADCs. Moreover, the proposed method was shown to be comparable to the performance of the full-resolution scheme. In our future work, we will explore the applicability of the proposed framework to other modulation schemes and improve the dynamic range of the signal. Given the low complexity of the framework, the future goal is to implement the proposed scheme on the DSP.  
Data curation, J.L.; formal analysis, Z.L.; funding acquisition, Z.L.; methodology, J.L.; software, J.L.; supervision, X.X.; validation, X.X.; writing--original draft, J.L.; writing--review & editing, Z.L. and X.X.  
The paper was supported by the National Natural Science Foundation of China (No. 61801319, No. 61871422), the Opening Project of Artificial Intelligence Key Laboratory of Sichuan Province (No. 2017 RZJ01), the Opening Project of Key Laboratory of Higher Education of Sichuan Province for Enterprise Internationalization and Internet of Things (No. 2017 WZJ01), the Sichuan University of Science and Engineering Talent Introduction Project (No. 2017 RCL10, No. 2017 RCL11), the Education Agency Project of Sichuan Province (No. 18 ZB0419), the Major Frontier Project of Science and Technology Plan of Sichuan Province (No. 2018 JV0512), and the Sichuan Institute of Technology Graduate Innovation Foundation (No. y2018036).  
The authors declare no conflict of interest.
# Economic Routing of Electric Vehicles using Dynamic Pricing in Consideration of System Voltage
## 3 Charging Strategy for Charging Stations
Using the Gauss-Seidel method, the system operator calculates the power flow of the system based on the schedule obtained from the CS [10]. By analyzing power flow with the schedule, the voltage can be predicted in advance. A voltage threshold value is set according to the system conditions. When the voltage exceeds the threshold value, a dynamic pricing signal is sent. The dynamic pricing signal is calculated using Equation (7). When the system operator sends the price signal to the CS, the CS immediately responds to this signal and sends the information to the EVs. The equation guiding the above interaction is as follows:  
\[DP_{i,t}=\left( \begin{array}{c}\alpha(V_{DL}-V_{i,t})\times C,V_{i,t}<0.95\\ \alpha(V_{UL}-V_{i,t})\times C,V_{i,t}>1.05\end{array} \right. \tag{7}\]  
where  
\(\alpha\): weight for dynamic pricing;  
\(V_{DL}\): lower limit voltage value;  
\(V_{UL}\): upper limit voltage value;  
\(V_{i,t}\): voltage of bus \(i\) at time \(t\); and  
\(C\): instantaneous voltage drops loss cost. It is difficult to obtain accurate statistical data of the cost due to instantaneous voltage drops, so they are estimated using statistical data of the cost [11; 12]. In a typical power system, the voltage range is from 0.95 to 1.05 p.u., so the maximum and minimum voltages are set to 1.05 and 0.95, respectively. Figure 3 shows a flow chart of the overall system.
## 3 Results and Discussion
### Dry and Wet Bulk Densities
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factor** & **Sum of Squares** & **Degrees of Freedom** & **Mean of Squares** & \(F\)**-Value** & \(p\)**-Value** & **\% Contribution** \\ \hline Cement Type (CT) & 542,783 & 2 & 271,392 & 344.4 & 0.0000 & 29.1 \\ Foam Amount (FA) & 526,782 & 2 & 263,391 & 334.3 & 0.0000 & 28.2 \\ Additive Type (AT) & 52,151 & 3 & 17,384 & 22.1 & 0.0000 & 2.8 \\ CT\({}^{*}\)FA & 165,006 & 4 & 41,251 & 52.4 & 0.0000 & 8.8 \\ CT\({}^{*}\)AT & 86,025 & 6 & 14,337 & 18.2 & 0.0000 & 4.6 \\ FA\({}^{*}\)AT & 271,135 & 6 & 45,189 & 57.3 & 0.0000 & 14.5 \\ CT\({}^{*}\)FA\({}^{*}\)AT & 164,559 & 12 & 13,713 & 17.4 & 0.0000 & 8.8 \\ Error & 56,735 & 72 & 788 & & & 3.0 \\ Total & 1,865,175 & 107 & & & 100.0 \\ \hline \hline \end{tabular} \({}^{*}\) Interaction between factors.  
\end{table} 
Table 7: Analysis of variance for wet density (28 days of curing). Figure 4: The dry and wet density related to (**a**) cement type, (**b**) foam amount, and (**c**) additive type for different curing times, where the middle point is the mean, the box is the confidence interval (95%), and the whisker shows the standard deviation. One possibility to reduce the density is to increase the amount of foam, which makes a greater contribution to the density variability.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 3 System Model and Framework Overview
### Framework Overview  
We design a personalized privacy protection framework for mobile crowdsensing, as shown in Figure 1. The framework is mainly divided into three parts:  
1. The platform provides differential privacy protection according to the location of workers, completes task allocation and ensures that the data are not excessively distorted.
2. According to the private space decomposition (PSD) broadcast area selection algorithm, the appropriate task broadcast range is calculated for task release and the task is accepted under the purpose probability.  
Figure 2: Dynamic spatial meshing strategy and meshing process.  
3. Design cost reward mechanism and select appropriate workers to complete the task. Figure 1 describes the workflow of the whole platform when a task needs to be published.  
In the process of crowdsensing, the task requester first submits the task requirements, and then the platform broadcasts the task to workers. Workers choose to accept the task, and the platform selects among them based on cost or optimal performance. The selected worker with a handheld mobile device performs sensing and returns the sensed data to the platform. During this process, workers need to upload their location and other privacy information to the platform, which may pose a risk of privacy leakage. Our model includes three parts: personalized private space decomposition framework, task broadcast strategy and task allocation algorithm based on worker compensation. These parts correspond to step 4 (workers accept the assignment), step 3 (area broadcast to workers) and step 5 (choose the lowest-paid worker based on cost) in Figure 1, respectively. Our model protects user privacy during the crowdsensing process to avoid privacy leakage. Details of these parts are introduced in Section 4.
# XOR Multiplexing Technique for Nanocomputers
## 2 Error Distributions in the XOR Multiplexing System  
### An XOR Multiplexing Unit  
As shown in Figure 1, the XOR multiplexing unit has the same structure as the NAND multiplexing unit: duplicate an XOR gate \(N\) times, and replace two inputs and output of the XOR gate by a bundle of \(N\) lines [14; 22], where the XOR gate has an error probability \(\varepsilon\).  
The randomizing unit U is supposed to perform a random permutation of input signals; it has randomized interconnections, namely, each signal from the first input bundle and the second input bundle are randomly paired to connect to the next stage. Through this operation, inputs from the first bundle will be randomly paired with inputs from the second bundle to form the input pairs of the duplicated XOR gates [22]. Generally, there are three types of output error, von Neumann fault, Stuck-at-1 error and Stuck-at-0 error. However, based on the analysis results, we found that the (cumulative) probability distributions of these three types of error are quite similar; they only have a slightly different appearance. Hence, in this article, we only consider the von Neumann fault (i.e., it inverts its output).  
Figure 1: The XOR and the NAND multiplexing unit: **(a)** The XOR multiplexing unit; **(b)** The NAND multiplexing unit.
# Key Issues on Integrating 5G into Industrial Systems
## 4 Modbus Protocol
### Introduction to Modbus  
#### 4.1.1 Modbus Communication Method  
Modbus defines the application-layer message transmission protocol on the seventh layer of the OSI model. Other OSI layers are different based on different communication media but the message format is the same at the application layer. It provides client/server communication between devices connected to different types of buses or networks. Modbus defines three different communication media:  
1. Ethernet: The corresponding communication mode is Modbus TCP/IP, applied to Ethernet links. Modbus/TCP is widely used in industrial automation and control systems because of its ease of implementation and its open and license-free features. Modbus/TCP clients, such as SCADAs, PLCs, and human-machine interfaces (HMIs), initiate communication with PLCs (Modbus/TCP servers), which then respond to an appropriate request (e.g., read or write process data) based on the function code value [43];
2. Asynchronous serial transmission: The corresponding communication mode is Modbus RTU or Modbus ASCII, applied to serial port links, such as RS232, RS485, RS422, etc. It is especially suitable for short-distance communication between field devices [44];
3. High-speed token passing network: The corresponding communication mode is Modbus PLUS, which is suitable for scenarios that require high-speed and reliable communication. Especially, in large-scale industrial automation systems with a large number of devices, the stability and real-time performance of communication can be guaranteed [45].
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
## 4 Test Observations, Results and Analysis
### Compression Test  
#### 4.2.1 Test Results  
Ten specimens with different configurations were tested under monotonic compressive loads, and two distinctive failure modes could ultimately be generalized. Figure 6 provides the failure situation of typical specimens for each group. As shown in Figure 6a,b, in the TC and WA groups, enough deformation space between the two plates caused the T-stub flanges to clearly protrude along the compressive direction, with cracks appearing on the surface of the flange-to-web connections and the flange above the inner side of the plates, while the contact parts between the plates and flange were always tightly attached without separation. The inserted plates were not pushed outwards and the position remained unchanged. After disassembly, it can be seen there was no sign of extrusion on the inside of the bolt hole or thread failure on the surface of the bolts. In Figure 6c, the T-stub flange in group WB did not deform substantially, the two ends of flange were raised and a clear gap between the flange and inserted plates was formed. Furthermore, the compressive loads also caused cracks at the flange-to-web connection. After dismantling, the worn surface at the thread section and shank necking of the bolts could be clearly recognized (see Figure 6d), demonstrating the clear evidence of bolts yielding. All phenomena are consistent with the prediction of the mechanical model in Section 2.  
Figure 5: Relation curves between axial force and displacement measured in a monotonic tensile test, representing specimens in groups (**a**) TC, (**b**) WA (**c**) and WB.  
Figure 7 illustrates the results of the force-displacement curves of all compressed specimens. Comparing TC-P-50-40-C with TC-P-60-40-C, it is transparent that the compressive bearing capacity and stiffness incrementally improved the T-stub length, as seen in Figure 7a. The welded T-stub connections outperformed those manufactured from steel sections in rigidity and ductility performance, while they showed a similar load-bearing capacity; see TC-P-50-40-C and WA-P-50-40-C in Figure 7b,c, which are similar to the results in the tensile test. Most importantly, the compressive properties including yielding force, stiffness and ductility of all specimens increased with the growth of plate width, from 38 to 42 mm in group TC, and from 40 to 52 mm in groups WA and WB. It is noteworthy, however, that there was no significant rise in ductility after the plate width became larger than 46 mm. The reasonable width range of plates is further explored in the following sections.  
Figure 6: Failure modes of three specimens from different groups under compressive loads: (**a**) TC-P-50-40-C, (**b**) WA-P-50-40-C and (**c**) WB-P-75-40-C. (**d**) Bolts of WB-P-75-40-C after loading.  
Figure 7: _Cont._
# Data Driven Leakage Detection and Classification of a Boiler Tube
## 2 Methods and Procedures for Leakage Classification of the Boiler Tube  
In Figure 1, the proposed boiler tube leakage classification model is given. The proposed model can be divided into three main steps: (1) feature extraction through wavelet packet transform (WPT) analysis; (2) deep neural network (DNN) implementation for higher level feature extraction, which is a reduced feature set that contains highly discriminant information; and (3) provision of the reduced feature set to the SoftMax classifier to complete boiler tube leakage classification (i.e., normal state and leak state classification of the Boiler tube data).
## 4 Simulation Results and Analysis  
In this study, the image sizes \(16\times 16,32\times 32,64\times 64,128\times 128\) and \(256\times 256\) were used as the dataset for encryption. Insufficient resources, such as computers with high processing speed and good resolution, have hindered this study's ability to conduct experiments on data sets exceeding \(256\times 256\) image pixels, including those up to 12 megapixels. The images used for testing the proposed encryption are presented in Figure 7. The secret key of the Hill cipher was comprised of \(K_{h}=[87;9;6]\), and \(r=[20.1245,20.1254]\), \(x_{0}=0.71234\) which serve as the control parameter for the logistic map and the initial conditional parameter, respectively.  
Figure 6: Flowchart of the encryption process.