esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm
## 3 Measuring Chain Error Model  
Considering the properties of the measurement chain that depend on the transmittance of fragments of this chain, it is worth analyzing these properties in the frequency domain. This approach enables a deterministic description of the influence of the transmittance on the input and processing of errors, while this description takes into account the spectrum of the processed measurement signal [18]. Let us assume that the analyzed measurement chain processes a physical quantity that changes over time, marked as \(s(t)\). This quantity is converted by the analog part of this circuit into a voltage signal \(y(t)\), which in the quantization process is converted into its discrete representation. Subsequent samples of this signal are marked with the symbol \(x(i)\). The \(y(t)\) signal is sampled with the frequency \(f_{s}\), and \(N\) samples of this signal are taken in the measurement window, and then applied to the input of the stage computing the discrete wavelet transform algorithm. The algorithm discussed provides the output of the measuring chain which is a vector of \(M\) output quantities, marked as \(X(j)\). A block diagram of the measuring chain is shown in Figure 1.  
Figure 1: Scheme of an exemplary measuring chain.  
Further, the signal \(s(t)\) processed by the measurement chain is described as:  
\[\dot{s}(t)=\sum_{i=0}^{\infty}\dot{s}(t,\omega_{s,i}), \tag{19}\] \[\dot{s}(t,\omega)=E_{s,\omega}(\omega)\sin(\omega t+\varphi_{s, \omega}(\omega)), \tag{20}\]  
where \(\omega_{s,i}\) is the pulsation of the \(i\)-th harmonic of this signal, \(E_{s,\omega}(\omega)\) is the amplitude and \(\varphi_{s,\omega}(\omega)\) is the phase shift of the selected harmonic of this signal with pulsation \(\omega\). In the real case, the signal \(s(t)\) that is disturbed by the resultant error signal \(e_{s,\Sigma}(t)\) can be represented as:  
\[\dot{s}(t)=\dot{s}(t)+e_{s,\Sigma}(t)=e_{s,r}(t)+\sum_{i=0}^{ \infty}\dot{s}(t,\omega_{s,i}), \tag{21}\] \[\ddot{s}(t,\omega)=\dot{s}(t,\omega)+E_{s,\varepsilon}(\omega) \sin(\omega t+\varphi_{s,\varepsilon}(\omega)), \tag{22}\]  
where \(E_{s,\varepsilon}(\omega)\) is the amplitude and \(\varphi_{s,\varepsilon}(\omega)\) is the phase shift in the selected harmonic of the error signal, and \(e_{s,r}(t)\) is the signal associated with a random error. It is assumed that, in Equations (19) and (21), the constant component of the described signals constitutes harmonics with index \(i=0\), where \(\omega_{s,0}=0\) rad/s and \(\varphi_{s,0}=\pi\) rad. The properties of the signal in question must be determined at the stage of designing the measurement chain or identified in the case of an existing chain, which will be presented in an example later in this paper.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 2 Development and Implementation of the 3D Model
### Parallel Monte Carlo Implementation  
Working with probabilistic models often presents a significant challenge due to the necessity of conducting numerous analyses for the same problem, resulting in high computational costs. This challenge becomes even more pronounced when conducting inverse analyses for three-dimensional models, significantly escalating computational expenses. Consequently, optimizing computational time becomes imperative to ensure the feasibility of the proposed methodology. In addressing this issue, the Monte Carlo (MC) procedure has been implemented with a parallelization strategy. This strategy serves as a crucial resource, enabling the execution of three-dimensional simulations within a viable computational execution time.  
The implementation of parallelization in this work employs OpenMP (Open Multi-Processing), an application programming interface (API) supporting multi-platform shared-memory parallel programming in C/C++ and Fortran [29; 30; 31]. The parallel algorithm for the Monte Carlo method operates based on the principles of the sequential algorithm but adopts a master-slave parallel model. This model facilitates the management of the program, enabling the simultaneous execution of several independent finite element method (FEM) analyses.  
A pseudo-code outlining the Parallel Monte Carlo (PMC) procedure is presented in Algorithm 3. The selection of this parallelization strategy stems from its ease of implementation and compatibility with the proposed methodology, which necessitates conducting numerous independent analyses. However, an in-depth discussion regarding the intricacies of parallelization extends beyond the scope of this work and is available in Rita [16] for further insight.  
```
1: Variables initialization
2: Read maxcm and stop criteria ; //maxmc is the maximum number of samples
3: Read threads ; //ntthreads is the number of threads
4: j = 0 ; //counter of performed samples
5:while stopping criterion is not reached do
6: j = j + ntthreads
7: Initialize the parallel environment
8: For each block with ntthreads samples do
9: Perform the FEM analysis
10: Save \((P,\delta)\) curve and relevant results of the sample
11: endfor
12: Finalize the parallel environment
13:endwhile
14: Perform the statistical analysis
```  
**Algorithm 3** Parallel Monte Carlo Procedure
## 2 Preliminaries
For a network \((D,s,t,c)\) and flow \(f\), we say that arc \(a\) is _\(f\)-unsaturated_ if \(f(a)<c(a)\), that is, if it is possible to send some extra flow through \(a\). A _blocking walk of flow_\(f\) is a directed walk \(P=(v_{1},a_{1},v_{2},a_{2},\ldots,a_{k-1},v_{k})\), such that \(a_{i}\in A\) and \(v_{i}\in V\) (\(\forall i\)) and the following properties hold. \[\text{arc }a_{i}\text{ points from }v_{i}\text{ to }v_{i+1}\text{ for }i=1,2,\ldots,k-1\text{ (\emph{i.e.}, $P$ is a walk) and} \tag{1}\] \[v_{2},v_{3},\ldots,v_{k-1}\text{ are nonterminal vertices and}\] (2) \[\text{each arc, }a_{i}\text{, is }f\text{-unsaturated and}\] (3) \[v_{1}\text{ is terminal or there is an arc }a^{\prime}=v_{1}u\text{, such that}f(a^{\prime})>0\text{ and }a_{1}<_{v_{1}}a^{\prime}\text{ and}\] (4) \[v_{k}\text{ is terminal or there is an arc }a^{\prime\prime}=wv_{k}\text{, such that}f(a^{\prime\prime})>0\text{ and }a_{k-1}<_{v_{k}}a^{\prime\prime} \tag{5}\]  
Therefore, directed walk \(P\) is blocking if each player that corresponds to an inner vertex of \(P\) is happy and capable of increasing the flow along \(P\). Moreover, \(v_{1}\) can send extra flow, either because \(v_{1}\) is a terminal node or because \(v_{1}\) may decrease the flow toward some vertex, \(u\), that \(v_{1}\) prefers less than \(v_{2}\), and at last, \(v_{k}\) can receive some extra flow, either because either \(v_{k}\) is a terminal node or \(v_{k}\) can refuse flow from \(w\), which \(v_{k}\) ranks below \(v_{k-1}\). We say that a walk, \(P\), is _\(f\)-unsaturated_ if property Equation (3) holds for each arc, \(a_{i}\), of \(P\). An \(f\)-unsaturated walk \(P=(v_{1},v_{2},\ldots,v_{k})\) is _\(f\)-dominated at \(v_{1}\)_ if Equation (4) does not hold, and \(P\) is _\(f\)-dominated at \(v_{k}\)_ if Equation (5) does not hold. A flow, \(f\), of a network with preferences is _stable_ if no blocking walk of \(f\) exists. In the _stable flow problem_, we have given a network with preferences, and our task is to find a stable flow if it exists. A special case of the stable flow problem is the stable allocation problem of [PERSON] and [PERSON] [3]. The _stable allocation problem_ is defined by finite disjoint sets \(W\) and \(F\) of workers and firms, a map, \(q:W\cup F\rightarrow\mathbb{R}\), a set, \(E\), of edges between \(W\) and \(F\), along with a map, \(p:E\rightarrow\mathbb{R}\), and for each worker or firm, \(v\in W\cup F\), a linear order, \(<_{v}\), on those pairs of \(E\) that contain \(v\). We shall refer to pairs of \(E\) as \"edges\", and hopefully, it will not cause ambiguity. Quota \(q(v)\) denotes the maximum of total assignment that worker or firm \(v\) can accept, and capacity \(p(wf)\) of edge \(e=wf\) means the maximum allocation that worker \(w\) can be assigned to firm \(f\) along \(e\). An _allocation_ is a nonnegative map, \(g:E\rightarrow\mathbb{R}\), such that \(g(e)\leq p(e)\) holds for each \(e\in E\), and for any \(v\in W\cup F\), we have:  
\[g(v):=\sum_{x:vx\in E}g(vx)\leq q(v) \tag{6}\]  
that is, the total assignment, \(g(v)\), of player \(v\) cannot exceed quota \(q(v)\) of \(v\).
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 6 Analysis of Security Functionalities of ECSSP-SC
### Analysis of Security Attributes of ECSSP-SC
#### 6.1.4 Forward Security  
Even if an _Attacker_\(A\) accidentally obtains the password \(PWD\) of the user, it cannot deduce the past messages \(\{c,T,w,\ T_{U}\}\) and \(\{A_{S}.T_{S}\}\), since these messages are based on the keys \(K\) and \(K^{\prime}\), respectively, which in turn are a function of the private random number \(v\) selected by the server. In this way, the proposed ECSSP-SC is forward secure, since the past messages cannot be obtained by an _Attacker_\(A\).
## 5 Results and Discussion  
The classification results were analysed in multiple dimensions. Firstly, a comparison between the full feature set and the LOFO subsets was made to investigate the contribution of each specific feature to the overall and the individual-class classification accuracies. The accuracy of different classifiers was assessed. Secondly, a comparison between the spectral feature set (\(H_{1-4}\) + SE) and the stability feature set (\(J_{r}+S_{r}\)) was made to show their contributions to the overall classification accuracies.
## 3 Experiments and Discussion  
### Experimental System  
The experimental system consists of linear scan cameras, linear lights, industrial computer, conveyor, and other necessary mechanical parts (as seen in Figure 9). The linear scan camera is DALSA-LA-GM-02K08A, which has the resolution of 2048 \(\times\) 1, a pixel size of 7.08 \(\upmu\)m, and a max line rate of up to 52 K. The proposed algorithm was implemented in C++ using OpenCV library and ran on a computer with a i7-6700 CPU, 8G DDR memory.  
In the experiment, the conveyor belt moved at a constant speed of 2 m/s, the flat aluminum tubes to be tested were placed on the conveyor belt one by one, and the photoelectric sensor was used to detect whether the flat tubes reached the camera imaging position. When defective products are detected, the follow-up mechanism will remove the flat tubes.  
In this experiment, two different types of flat tube (namely type A and type B) were used to test the performance of the proposed method. These two kinds of tube have different length, width, height, and interval time, as seen in Table 1. The flat tube samples were put one by one on the conveyor, so the \"interval time\" in Table 1 means the time  
Figure 8: The result of cubic Bezier curve fitting. The green circle in the vertical projection histogram image represents the local peak detected by the proposed method.  
Figure 9: The detection and experimental system.  
between two flat tubes. In order to achieve real-time detection, the time duration of the algorithm should be less than the interval time.  
For every type, 200 samples were used, in which there were 100 samples with no defects and another 100 samples with scratch defects. All the defects' samples were labeled manually by quality inspectors of the manufacture factory.
# Deep Learning-Based Multimodal Trajectory Prediction with Traffic Light
## 3 Materials and Methods
### Model Details
#### 3.4.2 LSTM-Based Encoder  
There are three encoders, and each encoder receives trajectory, state, and traffic light information. An encoder was constructed using an LSTM model to model the time-series characteristics. In this study, the structure and internal dimensions of the encoders used were SGAN (Social GAN), a state-of-the-art model in this field, which is described in detail in [30].  
Figure 1: The proposed model architecture.  
Trajectory information refers to the agent's two-dimensional (x, y) position information at each time and is represented by the UTM coordinate value in the zone of the dataset. The state of an agent includes four values: velocity, tangent acceleration (m/s\({}^{2}\)), lateral acceleration (m/s\({}^{2}\)), and the vehicle angle. The traffic light information converted the listing of each traffic light value (RGB) at 10 points shown in the frame into a code, and the code was assigned to all agents at every time step. The values at that point in time for each agent were embedded through a single multilayer perceptron (MLP) and converted to a fixed-length vector, which was passed as input to the LSTM. This can be expressed as an equation as follows. In addition, e\({}_{1i}^{\mathrm{t}}\), e\({}_{2i}^{\mathrm{t}}\), and e\({}_{3i}^{\mathrm{t}}\) represent a fixed-length vector, \(\varphi(\cdot)\) is embedding function, W\({}_{\mathrm{X}}\), W\({}_{\mathrm{S}}\), and W\({}_{\mathrm{T}}\) are embedding matrices with ReLU non-linearity and W\({}_{\mathrm{en1}}\), W\({}_{\mathrm{en2}}\), and W\({}_{\mathrm{en3}}\) represent the LSTM weight of each LSTM encoder model.  
\[\begin{split}\mathrm{e}_{1i}^{\mathrm{t}}&=\varphi \left(X_{i}^{\mathrm{t}},W_{\mathrm{X}}\right),h_{\mathrm{ei}}^{\mathrm{t}}= \mathrm{LSTM}\left(h_{\mathrm{ei}}^{\mathrm{t-1}},\mathrm{e}_{1i}^{\mathrm{t }};W_{\mathrm{en1}}\right)\\ \mathrm{e}_{2i}^{\mathrm{t}}&=\varphi\left(S_{i}^{ \mathrm{t}},W_{\mathrm{S}}\right),h_{\mathrm{si}}^{\mathrm{t}}=\mathrm{LSTM} \left(h_{\mathrm{si}}^{\mathrm{t-1}},\mathrm{e}_{2i}^{\mathrm{t}};W_{\mathrm{ en2}}\right)\\ \mathrm{e}_{3i}^{\mathrm{t}}&=\varphi\left(T_{i}^{ \mathrm{t}},W_{\mathrm{T}}\right),h_{\mathrm{ti}}^{\mathrm{t}}=\mathrm{LSTM} \left(h_{\mathrm{ti}}^{\mathrm{t-1}},\mathrm{e}_{3i}^{\mathrm{t}};W_{\mathrm{ en3}}\right)\end{split} \tag{3}\]  
Here, \(h_{\mathrm{ei}}^{\mathrm{t}}\), \(h_{\mathrm{si}}^{\mathrm{t}}\), and \(h_{\mathrm{ti}}^{\mathrm{t}}\) denote the hidden state of the LSTM encoder at time \(t\) of the \(i\)-th agent. In addition, e\({}_{1i}^{\mathrm{t}}\), e\({}_{2i}^{\mathrm{t}}\), and e\({}_{3i}^{\mathrm{t}}\) represent a fixed length vector, and W\({}_{\mathrm{en1}}\), W\({}_{\mathrm{en2}}\), and W\({}_{\mathrm{en3}}\) represent an LSTM weight.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
### Applications of Blockchain in 6G Network
#### 1.1.3 Smart City  
Smart city architecture requires conventional internet support for information collection and transmission; however, the transmitted data encounter latency and security issues due to the precarious communication link between nodes of the smart city. Therefore, 6G communication technology plays an essential role in connecting devices such as the sensor in the IoT environment. However, these devices face authentication issues that affect computational costs, communication overheads, and robustness [17]. To mitigate these issues, blockchain technology offers confidentiality, integrity, and availability in IoT-enabled smart devices.  
Several researchers discussed the security challenges in a smart city context. In [18], the author aimed to enhance the data collection and processing in smart city governance. Therefore, they introduced a hybrid consortium blockchain framework that encourages public engagement in the decision-making process. The proposed approach can tackle the collusion attacks from the public verifier's group. Further, they offered a Stackelberg-game-based approach to enable persistent public participation in the transaction validation process. The work in [19] proposed an IoT-fog-cloud integrated approach with the interplanetary file system (IPFS)-based blockchain for IoT-driven smart cities. They presented a secure framework based on two-level privacy and an intrusion detection scheme. The first scheme incorporated a blockchain module to secure the IoT information and proposed a dimensionality reduction technique that transits raw IoT data in a new shape. They applied a gradient boosting anomaly detector approach for intrusion detection. Their results show that their adopted scheme outperforms in terms of accuracy, recall, F1-score, and precision compared with the conventional intrusion detection scheme. Further, [PERSON] et al. [20] incorporated blockchain with the IoT infrastructure and proposed an MEC-enabled sharing economy system for mega smart cities. They utilized smart contracts for decentralized messaging services and stored IoT data in a decentralized repository via edge networks.
## 3 Results and Discussion  
### Cd Concentration in Soil of Agricultural Land in Hainan Island  
Analysis of 4110 soil samples revealed a sample-weighted mean Cd concentration of 0.12 mg/kg (Table 2), approximately 2.14 times the natural background value of Hainan Province (0.056 mg/kg), as established in the 1970s [41]. Although the Cd concentration exceeds the natural background level, it remains below the regulatory threshold of 0.30 mg/kg set by GB15618-2018 [37], indicating a generally safe level for agricultural soils. The observed mean Cd concentration is marginally lower than the five-year average of 0.15 mg/kg in Hainan's agricultural soils, potentially reflecting variations in sampling conditions [31; 35]. Additionally, the current Cd level is notably lower than the national mean concentration of 0.16 mg/kg for Chinese farmlands [42].  
While the overall Cd levels remain within acceptable limits, certain regions exhibit significantly elevated concentrations, necessitating targeted pollution control measures. For instance, the maximum recorded Cd concentration reached 3.11 mg/kg--10.37 times the risk screening threshold--indicating severe contamination in specific areas. This highlights the urgent need for continuous monitoring of anthropogenic pollution sources and the implementation of remediation strategies to mitigate potential risks.
## 5 LPWAN Application Requirements
### Reduced Device Hardware Complexity  
In order to handle the large number of devices, low cost operation, and long range coverage, the design of small-sized low complexity devices becomes an essential requirement. The reduced hardware complexity structure enables the reduction of the power consumption in battery-powered devices, without sacrificing too much performance. The devices generally are expected to possess low processing capabilities. Simple network architecture and protocols need to be supported by the hardware. From a technology point of view, in order to achieve the required adaptability of the LPWAN devices, radio transceivers need to be flexible and software-reconfigurable.
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 5 Future Research
### Re-Centering Limitations of U-Shaped Dampers  
Although the proposed U-shaped damper offers several advantages, including high energy dissipation capacity, large deformation ability, a simple design, ease of fabrication and installation, cost-effectiveness, and adaptability to various structural applications, it is crucial to acknowledge its inherent limitation regarding re-centering capability. In contrast to self-centering systems, conventional U-shaped dampers display residual deformation following cyclic loading, which may have an adverse effect on structural performance during subsequent seismic events. Such residual deformation can accumulate over multiple cycles, which may result in increased interstory drifts and potential damage. It is therefore vital to address this limitation in order to guarantee the long-term seismic resilience of structures that employ these dampers. Strategies such as incorporating pre-tensioned tendons or shape memory alloys, as demonstrated in self-centering dissipative braces [43; 44], present potential solutions for mitigating residual deformations and enhancing the re-centering capability. These systems derive their functionality from the elastic energy stored in pre-tensioned elements, which is employed to restore the structure to its original position after an earthquake. Promising future research involves the development of hybrid systems that combine the energy dissipation capabilities of U-shaped dampers with the re-centering properties of these self-centering mechanisms. Such hybrid systems have the potential to significantly enhance the performance and broaden the applicability of U-shaped dampers in earthquake-resistant design.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 4 Minimum-Energy Scheduling Heuristic with Due Times  
The previous section considered a deterministic situation, and the solution of the minimum-energy flexible job-shop will provide the best schedule to follow during nominal conditions. The analysis of optimal solutions makes it possible to evaluate the performance of the job shop and identify its blocking points, eventually leading to its redesign or resizing. Then, different configurations of the job shop can be assessed through the optimal solution of the scheduling problem by considering a set of typical production plans. However, when considering the very common situation in which the workshop is subject to disturbances (delays, malfunctioning or breakdowns of machines, and unavailability of operators), to postponement of jobs, or to the arrival of new jobs to be processed in the same period, the optimal schedule is no more completely feasible. Until a new stabilized situation is established in the job shop and a new optimal schedule is established, short-term local decisions should be taken to keep the job shop in operation. In the case in which perturbations occur frequently, it will be necessary to establish a much more responsive system. Also, when the job shop is working full time, the segmentation of production into finite production plans should lead to sub-optimal schedules. Considering the limitations of this approach, an open-loop heuristic to cope, on a reactive basis with the minimum-energy flexible job-shop scheduling problem, is introduced in this section. In the first stage, the heuristic is developed to cope with a given production plan where jobs have release and due times; then, it is shown how to extend its application to the dynamic environment of workshops.