esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrete-Time Asymptotic Tracking Control System for a Satellite with a Solar Panel
## 1 Introduction
### Organization of This Paper  
This paper is organized into six sections. Section 1 provides an introduction to the subject and briefly reviews the existing literature on satellite attitude control. Section 2 introduces the satellite model under consideration and presents a preliminary formulation of the asymptotic tracking control problem. In Section 3, we develop continuous-time and discrete-time state space models and examine their properties, which are essential for our further considerations. Section 4 is the most substantial part of the paper, where we formally define the discrete-time control problem to be solved and systematically develop the discrete-time controller for satellite attitude tracking with zero asymptotic error. The tool used to solve the problem is the discrete-time version of the general regulator theory. Section 5 presents the results of numerical computations, demonstrating the effectiveness of the obtained theoretical discrete-time results in the digital control system with zero-order hold. MATLAB R2024b and its Control Systems Toolbox are used as the computational environment. Some concluding remarks are given in Section 6, which completes this paper.
## 4 Results and Discussion
### Limitations and Future Work  
Due to the simple structure of the case in this paper, the number of pipe diameter grades is small, and there are fewer nominal diameters and pipe segments to choose from, so the results before and after optimization are not obvious. In addition, the number of iterative steps in this case is restricted, and the iterative objective function would increase after the calculation of the scheme in Figure 13. Therefore, the convergence of the algorithm introduced in this paper needs to be further verified using more complex  
Figure 15: Optimized result when DN100 is increased by 33.1% or DN200 is decreased by 37.8%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Values** & **Units** & \begin{tabular}{c} **Result** \\ **(RMB)** \\ \end{tabular} & \
\begin{tabular}{c} **Rate of Change** \\ **of Parameter** \\ \end{tabular} &
\begin{tabular}{c} **Rate of Change** \\ **of Result** \\ \end{tabular} \\ \hline \(L\) & 15 & year & 649,075.22 & \(-\)25\% & 12.19\% \\ \(L\) & 25 & year & 539,796.42 & +25\% & \(-\)6.70\% \\ \(C(d_{j})\) & - & \(\text{RMB}/\text{m}\) & 455,193.48 & \(-\)25\% & \(-\)21.32\% \\ \(C(d_{j})\) & - & \(\text{RMB}/\text{m}\) & 701,952.06 & +25\% & 21.33\% \\ \(C_{e}\) & 0.45 & \(\text{RMB}/\text{(kWh)}\) & 561,882.10 & \(-\)25\% & \(-\)2.88\% \\ \(C_{e}\) & 0.75 & \(\text{RMB}/\text{(kWh)}\) & 595,263.44 & +25\% & 2.89\% \\ \(C(d_{DN100})\) & 761.76 & \(\text{RMB}/\text{m}\) & 650,205.82 & +25\% & 12.38\% \\ \(C(d_{DN150})\) & 1080.58 & \(\text{RMB}/\text{m}\) & 619,871.07 & +25\% & 7.14\% \\ \(C(d_{DN200})\) & 1507.83 & \(\text{RMB}/\text{m}\) & 589,042.24 & +25\% & 1.81\% \\ \(C(d_{DN100})\) & 810 & \(\text{RMB}/\text{m}\) & 672,862.29 & +33.1\% & 16.38\% \\ \(C(d_{DN200})\) & 750 & \(\text{RMB}/\text{m}\) & 561,895.12 & \(-\)37.8\% & \(-\)2.81\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: The effect of changing parameters on the results.  
cases. Additionally, part loads have an impact on optimization study. In reality, loads fluctuate and change continuously over time. Although we found that the part load had an impact no greater than 10% on the results, it is valuable to take part loads into account to verify the effectiveness of the proposed design method after we obtain detailed load data in the future.
# Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor
## 2 Background
### Arithmetic to Boolean (A2B) Masking  
Conversion algorithms from arithmetic to Boolean masking can be broadly classified into two types based on the concept of hardware adders: carry look-ahead-based algorithms and ripple-carry-based algorithms. The first category operates with two values, i.e., _Generation_ and _Propagation_, whether carry values are either propagated (at least one input is 1) or generated (both inputs are 1). Carry look-ahead-based algorithms can be implemented with Boolean operators such as SHIFT, AND, and XOR. A conversion algorithm of the first category, presented in FSE 2015 by [PERSON] et al., reduces the complexity to a logarithmic scale [21]. However, it is hard to speed up this algorithm using memory storage, such as with pre-computation tables. On the other hand, algorithms in the second category iterate the task of adding the carry value generated in the lower stages to the upper stages. Ripple-carry-based algorithms are easy to optimize in terms of execution time by using memory. There have been various comparisons between the two categories. However, the performance of each algorithm and type of algorithm depends on the implementation environment, such as memory size, CPU architecture, and cryptographic algorithms.  
In this paper, we propose an A2B algorithm to optimize memory storage based on the second type of algorithm.
## 4 Data Collection  
The National Time Service Center of the Chinese Academy of Sciences possesses the GNSS signal quality assessment system of the Haoping 40-meter radio telescope (HRT). The system utilizes a 40 m dish antenna and the receiving/transmission equipment as the core, which can realize the multiple angles, multi-levels, and all-around detection and evaluation of the navigation signal quality in the GNSS by collecting data related to the GNSS satellite space signal quality. The MP combination value is not only related to the satellite signal itself but also to the configuration and environment of the antenna and receiver [16]. The 40-m dish antenna and monitoring receiver II produced by the National University of Defense Technology were used to collect observations to analyze the code bias of the BDS-3 satellites. The 40-m dish antenna, with a high gain for the navigation signal, radiates the signal input power (energy) intensively and ensures a pure electromagnetic  
Figure 1: Footprints of BDS-2 and BDS-3 operational satellites.  
environment surrounded by mountains. The observations with low noise are better able to reflect the satellite-induced code bias. In addition, the beam of the 40-m dish antenna is very narrow and highly directional, so only one satellite can be observed at a time. The 40 m dish antenna and its surrounding environment are shown in Figure 2.  
In this experiment, the observation data of fifteen BDS-3 MEO satellites are collected with the B1I/B2b/B3I/B1C/B2a frequency bands on the day of year (DOY)199-206 in 2019, the PRN numbers of which are C19/C20/C21/C22/C23/C24/C25/C26/C27/C28/C30/C32/C33/C35/C37 using the NUDT receiver, and the sampling interval is 1 s. Since cycle slips of C35 and C37 B2b observations occur more frequently, so they cannot be used in the experiment. The elevations of most satellites observed are in the range of 10\({}^{\circ}\)-85\({}^{\circ}\), contributing to the analysis of the relationship between the MP combination value and elevation. The methods of creating MP combinations with the available frequency bands are shown in Table 2, in which the other frequency band is an arbitrary choice.
# Reliability Analysis of Small-Sample Failure Data for Random Truncation High-Voltage Relay
## 2 Principle of Random Truncation Small-Sample Reliability Analysis
### Model Selection and Reliability Evaluation  
#### 2.5.1 Model Selection  
If two or more distribution models pass the linear correlation coefficient test and the d-test simultaneously, the root mean square error (RMSE) and relative root mean square error (MRMSE) are applied to determine the optimal reliability model, as shown in Formulas (11) and (12) [24].  
\[RMSE=\sqrt{\frac{1}{p}\sum\limits_{i=1}^{p}\left[\hat{F}(t_{i})-M(t_{i}) \right]^{2}} \tag{11}\]  
\[MRMSE=\sqrt{\frac{\sum\limits_{i=1}^{p}\left[\hat{F}(t_{i})-M(t_{i})\right]^{ 2}}{\sum\limits_{i=1}^{p}\left[M(t_{i})\right]^{2}}} \tag{12}\]  
where \(\hat{F}(t_{i})\) is the distribution function value of the distribution model obtained from the parameter estimation, and \(M(t_{i})\) is the empirical distribution function.
# How Secure Are Two-Way Ping-Pong and LM05 QKD Protocols under a Man-in-the-Middle Attack?
## 4 Conclusions
Note that \(I_{AE}(D_{\text{CM}})\) approaches \(I_{AE}=1\) asymptotically and that a maximal \(D_{CM}\) after which [PERSON] and [PERSON] have to abort the protocol is not defined. This is related to our analysis (in Section 3) of the security proof given in [20] which the authors call unconditional. In the analysis in Section 3 we show that their proof does not cover the man-in-the-middle attack (MITM) and that therefore cannot be called \"unconditional.\" To better understand the problem of absence of a maximal tolerable \(D_{\text{CM}}\) (after which [PERSON] and [PERSON] have to abort the protocol) in two-way protocols, in Section 3 we compare protocol with a newly proposed one-way asymmetric BB84-like protocol [22] (mcasBB84) in which \(\mathbb{X}\) basis serves as MM and \(\mathbb{Z}\) basis as CM under a MITM attack (mcasBB84-MITM). We show that mcasBB84-MITM without defined maximal \(D_{\text{CM}}\) would be completely equivalent to two-way protocols under MITM. However, the mcasBB84 protocol resolves the problem of a maximal \(D_{\text{CM}}\) by means of a predetermined threshold value \(D_{\text{pdCM}}\). When \(D_{\text{CM}}>D_{\text{pdCM}}\) [PERSON] and [PERSON] abort the protocol ([22], p. 7). The security calculated for such \(D_{\text{pdCM}}\) ([22], pp. 7-10), i.e., an elaborated PA procedure, can be called a \"conditional security.\" An additional advantage (with respect to exponential attenuation of photons in optical fibres) of mcasBB84 is that photons do not travel from [PERSON] to [PERSON] and back to [PERSON], but only from [PERSON] to [PERSON] (see Table 1). A similar solution for two-way protocols would be to redesign the protocol so as to either calculate a critical \(D_{\text{crit-CM}}\) at which [PERSON] and [PERSON] would be able to erase all bits [PERSON] might have possessed via privacy amplification (PA) or to predetermine threshold value of the disturbance in the CM, \(D_{\text{pd-CM}}\), for which PA calculations might be carried out. The former calculation, if possible, would provide us with an unconditionally security and the latter one would provide us with a conditional security of the protocols. How to do either of the calculations is an open question, but we conjecture that the former calculation is not feasible. **Funding:** Supported by the Ministry of Science and Education of Croatia through the Center of Excellence for Advanced Materials and Sensing Devices (CEMS) funding, and by MSE grants Nos. KK.01.1.1.01.0001 and 533-19-15-0022. Computational support was provided by the cluster Isabella of the Zagreb University Computing Centre and by the Croatian National Grid Infrastructure (CRO-NGI). Financial supports by the Alexander von Humboldt Foundation as well as by the German Research Foundation (DFG) and the Open Access Publication Fund of Humboldt-Universitat zu Berlin are acknowledged. **Conflicts of Interest:** The author declares no conflict of interest.
## 3 System Model  
### Network Model  
The network is formed by randomly deploying n nodes across the network area. Each node has a unique ID and an array of properties, including initial energy, processing capacity, and communication energy. All network node positions are fixed and cannot be moved from one area to another, thereby reducing the intervention between nodes. The sink node serves as the central point of the network. The CH node establishes communication between all sensor nodes and collects data from each sensor node for transmission to a higher level. The sink node selects the CH node by executing the CH selection algorithm. The sink node adopts the COA algorithm to select the optimal CH node. Eventually, the CH creates the cluster using Euclidean distance. The CH obtains information from the cluster nodes and transmits it to the sink node. The network model of COA is illustrated in Figure 1.
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
## 2 Methods  
This study focuses on the task of forecasting ship traffic flow in multiple ports, which is a typical time series prediction problem. This problem can be formulated as follows:  
\[(X_{t-\tau+1},X_{t-\tau+2},\dots,X_{t})\xrightarrow{f(*)}(X_{t+1},X_{t+2},\dots, X_{t+n}) \tag{1}\]  
As mentioned above, in this task, the model is given historical data of \(\tau\) consecutive time steps as input. After being transformed through the function \(f(*)\), the model predicts the future states of port traffic data for n consecutive time steps. Here, \(N\) represents the number of ports, \(C\) represents the feature dimension of the data, and \(X_{t}\in\mathbb{R}^{N\times C}\) represents the traffic state of each port at time \(t\). Since the purpose of this study is to forecast the inbound and outbound traffic of ports, the initial value of \(C\) is 2 (for outbound traffic and inbound traffic).
## 2 Materials and Methods
### Data Acquisition  
Data acquisition was conducted using the GS2 IP broadband IP system developed by Central South University. This equipment features simultaneous transmission/reception of pseudo-random spread-spectrum signals, multi-channel array synchronous observation (expandable up to 1200 channels), and real-time calculation/display of amplitude and relative phase parameters at multiple frequencies. The survey deployed two profiles with measurement arrays shorter than the total profile length (as shown in Figure 1). Each profile measured 5200 m (with a 1200 m measurement array length) at 40 m station intervals. When selecting the transmission frequency band, a narrow frequency range reduces both depth and mineral resolution, while a wider range improves resolution but increases noise interference and inversion complexity. To balance deep detection capability with the polarization response of target minerals and minimize electromagnetic coupling, transmission frequencies were set at 0.0625 Hz, 0.125 Hz, 0.25 Hz, 0.5 Hz, and 1 Hz. Each measurement array covered 440 m (12 channels), requiring three array movements per profile. The current injection and measurement point distributions are detailed in Table 2, where data entries indicate positions along the profile--e.g., current electrodes  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen Type** & **Average Resistivity** & **Average Chargeability** \\  & **(\(\Omega\cdot\)m)** & **(\%)** \\ \hline Medium-fine- to medium-grained & 430 & 0.5 \\ porphyritic biotite granite & 380 & 0.6 \\ Quartz sandstone & 520 & 0.3 \\ Skarn & 360 & 0.35 \\ Carbonaceous shale & 290 & 3.7 \\ Silstone & 620 & 0.05 \\ Mica-bearing quartz sandstone & 170 & 0.8 \\ Pyrite & 200 & 11 \\ Scheelite-bearing marble & 90 & 0.25 \\ Disseminated tungsten–tin ore & 206 & 2.1 \\ Disseminated sulfide-bearing & 126 & 13 \\ tin–tungsten (molybdenum) ore & 55 & 41 \\ \hline \end{tabular}
\end{table}
Table 1: Statistical table of electrical parameters of rock ore in the mining area.  
\"(A,B)\" at \"(180,260)\" denote electrode A positioned at 180 m and electrode B at 260 m from the profile origin. For fixed current electrode positions (e.g., \"(A,B)\" at \"(180,260)\"), measurement electrodes \"(P,P)\" sequentially occupied positions \"(0,40), (40,80), (80,120), (120,160), (160,200), (200,240), (240,280), (280,320), (320,360), (360,400), and (400,440)\". The current electrodes then advanced to the next position \"(140,300)\" while repeating the measurement electrode sequence. This process (illustrated in Figure 2) continued until the entire profile was completed through successive array deployments. The electrode spacing can be appropriately selected based on the size and burial depth of the target body.  
The survey area is located in the mountainous region of southern Hunan, where environmental interference during data acquisition was minimal. Standardized operational procedures were implemented throughout the data collection process, complemented by the designed large-spacing multi-channel observation array. Consequently, the acquired data exhibit excellent quality and high reliability.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
###### Abstract  
A significant increase has been observed in the use of Underwater Wireless Sensor Networks (UWSNs) over the last few decades. However, there exist several associated challenges with UWSNs, mainly due to the nodes' mobility, increased propagation delay, limited bandwidth, packet duplication, void holes, and Doppler/multi-path effects. To address these challenges, we propose a protocol named \"An Efficient Routing Protocol based on Master-Slave Architecture for Underwater Wireless Sensor Network (ERPMSA-UWSN)\" that significantly contributes to optimizing energy consumption and data packet's long-term survival. We adopt an innovative approach based on the master-slave architecture, which results in limiting the forwarders of the data packet by restricting the transmission through master nodes only. In this protocol, we suppress nodes from data packet reception except the master nodes. We perform extensive simulation and demonstrate that our proposed protocol is delay-tolerant and energy-efficient. We achieve an improvement of 13% on energy tax and 4.8% on Packet Delivery Ratio (PDR), over the state-of-the-art protocol.  
underwater wireless sensor networks; energy optimization; packet duplication; master-slave architecture; depth-based routing
## 3 Research Model and Hypotheses
### Hypotheses
#### 3.2.3 Information Quality (IQ)  
Information quality in online learning is the students' subjective assessment of the information they receive from an e-learning platform. This refers to the quality of content that students can access, which includes aspects such as clarity, understandability, style (text, audio, video), sufficiency, currency, usefulness, and reliability. The quality of information is crucial when evaluating the effectiveness of the content delivered by the e-learning system, impacting student satisfaction, engagement, and learning outcomes [12]. Regarding how the quality of e-learning information affects student satisfaction, [PERSON] and [PERSON] [32] emphasized that high information quality leads users to view the system as important and beneficial for their tasks, impacting their overall attitude and satisfaction with the system. This highlights the need to maintain high information quality in e-learning systems to improve user learning experiences and satisfaction. Hence, the third hypothesis was developed as follows:  
**H3:**: _Information quality positively influences student satisfaction_.
# Flexural Buckling Failure of Rock Slopes: A Review of Influencing Factors and Analysis Methods
## 3 Deformation Process of Flexural Buckling Failure and Its Influencing Factors
### Factors Affecting the Deformation Process of Flexural Buckling  
The deformation process of flexural buckling failure of dip rock slopes is typically affected by the coupled actions of intrinsic and triggering factors. The intrinsic factors mainly refer to the structural and geomechanical features of a dip rock slope, including the thickness of the rock layer, the dip angle of the rock layer, the shear strength parameters of the basal sliding surface (cohesion and internal friction angle), the deformation parameters of the rock mass, and others. Conversely, the triggering factors include natural factors such as earthquakes and rainfall, as well as human engineering activities such as reservoir water level fluctuations, blasting vibrations, excavation of slopes, and loading at the slope crest. The aforementioned factors from both aspects jointly influence each stage of flexural buckling of rock slopes, such as the buckling depth, and, in the case of failure, the location of the tension cracks at the rear edge of the rock slope, the location of the rupture surface near the slope toe, and the degree of fragmentation and disintegration of rock mass after the landslide.
# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 5 A Neural-Symbolic Implementation of T-Pattern
### Efficiency and Costs  
We highlight that the construction of T-patterns is performed at the cloud level, in background with respect to the ordinary activities of the IoT systems, and thus, it does not significantly impact the execution time of the other tasks involving the smart objects, while T-patterns model consumes a minimal amount of system memory, relating to the storage of a graph of relatively small dimension. This implies that no significant barriers exist for the implementation of this approach to large-scale wireless IoT systems. In order to compute T-patterns using the CILIOS inference engine, we have used, in pur simulation at the cloud level, a 64-bit workstation with a Intel(R) Core(TM) i7-10875H CPU @ 2.30 GHz and 128 GB of RAM. Table 5 shows how computation time changes with varying network sizes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & n = 2000 & n = 4000 & n = 8000 & n = 16,000 \\ \hline k = 5 & 12.5 & 40.9 & 200.7 & 744 \\ k = 10 & 13.6 & 42.13 & 212.4 & 771.12 \\ k = 15 & 16.2 & 45.20 & 221.17 & 792.28 \\ k = 20 & 18.8 & 48.41 & 233.55 & 802.24 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution time \(t\) (in minutes) of the T-pattern model construction for different values of number of smart IoT objects \(n\) and number of groups \(k\).  
Figure 5: Percentage error \(e(EXP)\) for different values of number of smart IoT objects \(n\) and number of groups \(k\).  
As a final consideration, we highlight that a different group size impacts the system performance simply based on its impact on the global size \(n\) of the multi-agent community. This is derived from several simulations that we performed by fixing \(k\) and increasing the size of a given group (that results in an increment of the parameter \(n\)). We simply re-obtained the results shown in the tables above.