esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Performance of MILP and CP Models
#### 5.1.1 Test Instances  
To validate the proposed approaches in this work, we present in this section a description of the test instances that were used. Most of the datasets were initialized on the real database of the studied printing company over a period of 2 weeks. We collected from the production database all the data related to products: operations, processing times, setup times, waiting times, and resource calendar.  
To test the performance behavior of the proposed solution approaches and to investigate their efficiency, it is necessary to build several sets of instances in various production environments and different conditions. To this end, some test problems have been applied in a variety of conditions with inspiration from the illustrated case study. Each test set is generated by varying the problem size. It can be characterized by \(N\), the number of jobs, \(O\), the total number of operations, and \(M\), the number of machines. The result tables will not mention the number of machines as it remains constant at 19. The different instances are named WOS for Workshop Scheduling followed by the number of the instance.  
An extensive set of numerical experiments have been conducted by considering different problem sizes. The aim is to investigate which jobs and operations the model is not able to find solutions for in a reasonable resolution time.  
Based on the combination of the two abovementioned factors, two categories of instances are arranged as the small- and large-sized instances. These categories correspond to different workload situations, respectively: low-workload situation and normal- to high-workload situation.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 2 Notation and Preliminaries
### Distance Functions
#### 2.2.1 Euclidean Distance  
The _Euclidean distance_(K
# Swing Analysis and Control Research of the Space Tethered Combination in the Maneuver Process
## 3 Swing Control and Simulation Study
### Feedback Linearization Control
For the short period swing in the case with the short tether, the swing elimination control Equation (12) can quickly stabilize the internal and external swing angle. This indicates that the feedback linearization swing control is effective, and can suppress the swing of the space towing tethered combination. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Condition** & **Value** & **Parameter** & **Value** \\ \hline \(\theta\)/rad & 0.52 & \(l\)/m & 1 \\ \(\dot{\theta}\)/(rad/s) & 0 & \(m\)/kg & 1 \\ \(\varphi\)/rad & 0 & \(a_{g}\)/(m/s\({}^{2}\)) & 9.8 \\ \(\dot{\varphi}\)/(rad/s) & 0 & \(k_{1\theta}\) & 0.2 \\ \(\dot{\upsilon}\)/(m/s) & 0 & \(k_{2\theta}\) & 10 \\ \(s\)/m & 0 & \(k_{1\varphi}\) & 0.2 \\  & & \(k_{2\varphi}\) & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation initial parameters. Figure 4: Simulation results without control. (**a**) The trajectory of the target mapped to the \(XY\)-plane; (**b**) The response of in-plane and out-plane swing angle.
## 4 Case Study of Data Collaboration
### Visual Traffic Flow Detection Based on DFA  
Based on the hotspots in Figure 7 and the peak hours in Figure 6, this section selected seven intersections in Sanyang Square as core data sources for further rush hour data mining.  
In the case study described in this paper, UAV video data are used in conjunction with GPS data as auxiliary data for traffic flow mining. This paper designed an improved differential fusion algorithm (DFA), which extracts the moving target and reduces the sensitivity of the target to light through the method of background difference and multi-frame difference reduction, identifies and tracks the car in the UAV, calculates the traffic flow of the moving target in the detection area, and uses the similarity deduplication algorithm to cooperate with the GPS data, and the resulting data are used as a benchmark for the visualization of digital twin technology.  
Figure 6: Distribution of temporal travel characteristics on weekdays and weekends. (**a**) Hourly car flow of Monday. (**b**) Hourly car flow of Thursday. (**c**) Hourly car flow of Saturday. (**d**) Hourly car flow of Sunday.
## 2 Materials and Methods
### Internal On-Line Monitoring  
On-line monitoring becomes a vital functionality for systems expected to operate for a long time in radiation environments, where the electronics is ageing additionally by TID accumulation. After several years or months (for higher doses) of operation, some ASIC parameters, in particular biasing currents, will have to be adjusted.  
The SALT has several monitoring functions and blocks beyond the already described _NZS_ data packets and SEU counters. The most important, for the reasons just discussed, is the 6-bit monitoring ADCs (same as in front-end channels) connected to almost all internal biasing DACs. These DACs set biasing of analogue front-end blocks (preamplifier bias current, shaper bias current, input test pulse amplitude, reference current for common mode voltages, single-to-differential converter bias current) and other functional blocks such as the SLVS biasing current and reference voltage. The user can check the actual current or voltage by reading the ADC output value via the l2C interface. The exceptions are the DACs located in the DLL and the PLLs, which are not directly monitored. However, their operation is checked indirectly by observation of the internal control voltages \(V_{DLL}\) and \(V_{PLL}\) in DLL and all PLLs, respectively (see Figures 4 and 6).  
The LHCb DAQ system specification [36] imposes additional monitoring requirements on the SALT. Each TFC command has a counter (32 or 48-bit long) and a snapshot register bounded to it. Counters keep running values; to read them, an additional TFC command called Snapshot was introduced. This command rewrites the current value of all TFC counters to snapshot registers from which the values may be read via the l2C protocol. The same mechanism was used for some other running parameters like free space in the memory buffer or number of sent Idle packets.
# PBR Clutter Suppression Algorithm Based on Dilation Morphology of Non-Uniform Grid
## 4 Experiment result and Analysis  
In this section, the proposed algorithm is tested on a simulated scenario in Section 4.1 and a real scenario in Section 4.2. The computer configuration for experiment: Inter(R) Core(TM) i7-4790 CPU @ 3.60 GHz. RAM: 16.0 GB. All operations in this section run on Matlab R2018a.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 4 Analysis of Influencing Factors of Cable Damage Identification  
### Influence of Noise on Damage Identification  
#### 4.1.1 Influence of Noise on Location Identification of Damage Cable  
The girder deflection data before and after cable damage in this paper are obtained based on finite element numerical simulation, while the girder deflection data before and after cable damage in actual engineering should be field-measured data. When the beam deflection of cable-stayed bridge is detected, the test signal will be interfered with by various factors. In order to test the anti-noise performance of the above method, white Gaussian noise is added to the numerical simulation of beam deflection difference data, and then the damage location and damage degree are identified.  
Taking single-cable damage and double-cable damage conditions as examples, white noise (signal-to-noise ratio of 20 dB) is added to the deflection difference data of the girder corresponding to each working condition to obtain the first derivative of the deflection difference of the girder. Then, the wavelet analysis is carried out on it. Figure 14 shows the  
\begin{table}
\begin{tabular}{c c c} \hline
**Damage Index** & \(\tau_{\mathbf{R7}}\) & \(\tau_{\mathbf{K16}}\) \\ \hline \(z_{1}\) & 0 & 0.33 \\ \(z_{2}\) & 0.17 & 0.83 \\ \(z_{3}\) & 0.33 & 0.17 \\ \(z_{4}\) & 0.5 & 0.67 \\ \(z_{5}\) & 0.67 & 0 \\ \(z_{6}\) & 0.83 & 0.5 \\ \(z_{7}\) & 1 & 1 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental design of Kriging proxy model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & \multicolumn{3}{c}{**Recognized Value**} & \multicolumn{3}{c}{**Error**} & \multicolumn{3}{c}{**Recognized Value**} & \multicolumn{3}{c}{**Error**} \\
**Single or** & **Actual Value** & \multicolumn{3}{c}{**(After the Reduction**} & \multicolumn{3}{c}{**(After the Reduction**} & \multicolumn{3}{c}{**(Before the Reduction**} & \multicolumn{3}{c}{**(Before the Reduction**} \\
**Double-Cable** & & & **in the Measurement** & & **in the Measurement** & & **in the Measurement** & & **in the Measurement** \\
**Damage** & & & **Points)** & & & & **Points)** & \\ \cline{2-10}  & **M7** & **M16** & **M7** & **M16** & **M7** & **M16** & **M7** & **M16** \\ \hline Case 1 & 0.9 & 0.85 & 0.9010 &  
distribution curve of the first derivative of the deflection difference of the girder before and after cable damage under different damage conditions with the addition of white noise.  
According to Figure 15, when a single cable is damaged, the damage cable is M16. In the case of double-cable damage, the damage cables are M16 and M7. It can be seen that noise does not affect the accuracy of damage cable location identification.
# Information-Aware Secure Routing in Wireless Sensor Networks
## 3 Model and Methods
### Information-Aware Secure Routing (IASR)  
Each node in the WSN is associated with information such as the trust value (attack probability) and status (residual energy and the distance to the sink). We use the attack probability to represent the trust value of a node, and we use the energy consumption required for sending packets from a node to the sink to reflect the distance from the node to the sink.  
The secure routing model is information-aware [42]. In this model, each sensor node chooses a path to the sink so as to minimize its cost in the presence of malicious nodes. From the node information collected, the current and future trust value and status of each adjacent node are sensed by  
Figure 3: Route in wireless sensor networks (WSNs) based on the serial–parallel system.  
the proposed cost function. Therefore, the minimum cost relay node will be found. The cost function for a node accounts for three aspects: (1) The attack probability for the path from itself to the sink, (2) the residual energy of each node in the path, and (3) the distance from each node in the path to the sink.  
When a node \(n\) senses data and transmits it to the sink, it chooses a relay node \(s_{n}\) from its adjacent nodes and then forms a \(K\)-hop transmission path \(l_{n}=\langle m_{1},\cdots,m_{K+1}\rangle\) to the sink in turn. The cost function of node \(n\) consists of two parts, \(c_{n(1)}\) and \(c_{n(2)}\), which are calculated with the attack probabilities and status values of nodes in the path, respectively.  
\[\begin{split} c_{n(1)}&=A_{L}\\ &=1-\prod_{k=1}^{K}(1-Attack_{\langle m_{k},m_{k+1}\rangle})\\ &=Attack_{\langle n,s_{n}\rangle}-(Attack_{\langle n,s_{n}\rangle }-1)[1-\prod_{k=2}^{K}(1-Attack_{\langle m_{k},m_{k+1}\rangle})]\\ &=Attack_{\langle n,s_{n}\rangle}-(Attack_{\langle n,s_{n}\rangle }-1)c_{s_{n}(1)}\\ c_{n(2)}&=E_{L}\\ &=\sum_{k=1}^{K}(\frac{\eta e_{\langle m_{k+1},sink\rangle}}{ \eta e_{\langle m_{k+1},sink\rangle}+e_{m_{k+1}}}\times\frac{1}{K})\\ &=(\frac{\eta e_{\langle s_{n},sink\rangle}}{\eta e_{\langle s _{n},sink\rangle}+e_{rs_{n}}}\times\frac{1}{K})+\sum_{k=2}^{K}(\frac{\eta e_{ \langle m_{k+1},sink\rangle}}{\eta e_{\langle m_{k+1},sink\rangle}+e_{m_{k+1} }}\times\frac{1}{K})\\ &=(\frac{\eta e_{\langle s_{n},sink\rangle}}{\eta e_{\langle s _{n},sink\rangle}+e_{rs_{n}}}\times\frac{1}{K})+c_{s_{n}(2)}\end{split} \tag{8}\]  
The cost function of \(n\) is  
\[c_{n}=c_{n(1)}+c_{n(2)}. \tag{9}\]  
Equations (7)-(9) show that the cost of a node \(n\) is determined by the qualities of the current hop in the path and the cost of the adjacent node \(s_{n}\) chosen by \(n\); thus, global optimization can be ensured.
# Iterative Analog-Digital Multi-User Equalizer for Wideband Millimeter Wave Massive MIMO Systems
## 5 Performance Results
Moreover, the performance of Algorithm 1 and the fully digital bound is very closed, with a penalty lower than the penalty of ULA case. This occurs because the proposed equalizer explores the correlative feature of channel, and thus, with the UPA configuration where the correlative level of channel is higher than ULA case, the proposed equalizer is more efficient. Finally, in Figures 11 and 12, we evaluate the impact of the path loss and the shadowing effects on the performance. As it can be seen the proposed algorithm outperforms the two-step one, with just four iterations. Also, the proposed algorithm almost achieves the fully digital bound. In general, we can draw the same conclusions as those drawn for scenarios without path loss and showing effects.
## 5 Numerical Results  
In this section, we report our numerical results for the IAB model specified in Section 3 and parameterized in Section 4. The main metrics of interest are the uplink and downlink packet delivery latencies as functions of the system parameters.  
To calculate the packet arrival rates, 1000 UEs were distributed over an area of size \(1260\times 1260\) m. The coverage radius was set to 105 m for the IAB-nodes and to 210 m for the IAB-donor. We set \(\text{TTI}=1\) ms, \(T=6\) TTIs, \(\lambda=200\)\(s^{-1}\), and \(\alpha=4\).  
Finally, the ratios between micro-phases for each network tier were set as follows:  
* Topology #1: Tier 1: 2/3 T, Tier 2: 1/3 T.
* Topology #2: Tier 1: 5/6 T, Tier 2: 1/2 T, Tier 3: 1/3 T.
* Topology #3: Tier 1: 2/3 T, Tier 2: 1/2 T, Tier 3: 1/3 T.
* Topology #4: Tier 1: 5/6 T, Tier 2: 2/3 T, Tier 3: 1/2 T, tier 4: 1/6 T.  
Note that, to cover the area under consideration, we need:* 9 networks of Topology # 0 (only IAB-donor),
* 7 networks of Topology # 1 (IAB-donor + 3 IAB-nodes),
* 6 networks of Topology # 2 (IAB-donor + 5 IAB-nodes),
* 4 networks of Topology # 3 (IAB-donor + 7 IAB-nodes) or
* 2 networks of Topology # 4 (IAB-donor + 14 IAB-nodes).  
Figure 9 shows the end-to-end delay as a function of the number of nodes in the network. Adjusting the micro-phases on the tiers can reduce packet latency by approximately 5 ms in the upstream. As a baseline, we utilized the latency observed in a network with one IAB-donor and no IAB-nodes (Topology #0, \(N=1\) in Figure 9). In this case, the deployment is significantly more expensive, although the delay is minimal.  
Figure 10 depicts the end-to-end delay vs. the number of hops. It can be observed that the trend is close to linear, and the delay increases by about 4-6 ms with each network hop (node tier).  
Figure 9: Delay vs. number of nodes.
# A Comprehensive Survey of Isocontouring Methods: Applications, Limitations and Perspectives
## 6 Proposed Future Research Activities
### Convergence  
Convergence is similar to the consistency of the algorithm and examines the extent to which the problem is solved with perturbed input data. In the context of convergence, the speed with which the solution is approximated is of central importance.  
Although the above categories allow a standardized examination of the developed methods, they require knowledge of the analytical solution. For this reason, we suggest introducing suitable test functions that have an appropriate degree of complexity and whose analytical solution is known. A possible function for two-dimensional solution sets would be, for example, the simple parabolic function of the form \(f(x_{1},x_{2})=x_{1}^{2}+x_{2}^{2}\). In this case, a value \([x_{1},x_{2}]\) calculated by the algorithm, which should result in a previously defined function value \(v\), can then be inserted into the analytical form in order to review the quality with which the nominal value was approximated, i.e., \(f(\tilde{x}_{1},x_{2})-v=\varepsilon\) is calculated, with \(\varepsilon\) being the deviation according to the nominal value. Note that the use of test functions is common practice in the field of nonlinear optimization--for instance, in order to verify the solution quality of developed algorithms. Examples include Himmelblau's function [82] or the Rosenbrock function [83]. To obtain a statistically reliable statement, a certain number of experiments must also be carried out. Here, it is important to limit the number of parameters to be varied in the experiments to a manageable level. In this context, the authors [PERSON] et al. propose, in [84], that the number of computer experiments \(n_{\text{t}}\) to be carried out should be determined by the relation \(n_{\text{t}}\approx 10\cdot n_{\text{D}}\), where \(n_{\text{D}}\) describes the number of parameters to be varied. Subsequent statistical evaluation can then be used to draw conclusions about the quality of the procedure by using the arithmetic mean and the corresponding standard deviation. However, the method that we propose requires that the results obtained by the algorithm are available in a form that is suitable for evaluation in the categories mentioned, such as the generation of point clouds that can be used as raw data.  
Key aspects of the proposed method have already been successfully demonstrated in [39] through the systematic comparison of two functions in MATLAB(r) for isocontouring. The categories considered include the quality of the results achieved, taking into account perturbations in the input data and the computing time required. Test functions were defined in analytical form in advance in order to be able to compare the calculated results with the exact solution in each case. With the help of the procedure, it was thus possible to make quantitative statements with regard to the investigated aspects, in addition to the qualitative presentation of the results.
# Distributed Online Multi-Label Learning with Privacy Protection in Internet of Things
## 4 Distributed OMLC Algorithm
### Abstracted Problem Formulation  
The task of multi-label classification is to find a function \(h:\mathcal{X}\rightarrow\mathcal{Y}\) mapping from the instance space to the label space in the most efficient way, where \(\mathcal{X}\in\mathbb{R}^{d}\) stands for the d-dimensional instance space and \(\mathcal{Y}\in\{1,0\}^{q}\) stands for the label space with \(q\) labels. Considering that the data are stored in a centralized way, then the initial problem can be reduced to a global least-squares problem. The global problem for the whole network is described as projecting each instance to the label space as close as possible through a projection matrix \(P\in\mathbb{R}^{d\times q}\),  
\[P=\arg\min_{P\in\mathbb{R}^{d\times q}}\frac{1}{2}\|P^{\prime}X^{\prime}-Y^{ \prime}\|_{F}^{2} \tag{1}\]  
In a distributed scenario, the whole network is split by \(m\) nodes and each node only has access to its local data. As a result, all instances and labels are reorganized into \(m\) local instance sets, \(X_{1},X_{2},\ldots,X_{m}\) and m local label sets, \(Y_{1},Y_{2},\ldots,Y_{m}\). Under this scenario, the global problem is reshaped as  
\[P=\arg\min_{P\in\mathbb{R}^{d\times q}}\sum_{i=1}^{m}\frac{1}{2}\|X_{i}P-Y_{i} \|_{F}^{2} \tag{2}\]  
We provide each node with its own projection matrix \(P_{i}\). When all nodes have the same \(P_{i}\), this problem is equivalent to Equation (2):  
\[\begin{split}& P^{*}=\arg\min_{P_{i}\in\mathbb{R}^{d\times q}}\sum_{i= 1}^{m}\frac{1}{2}\|X_{i}P_{i}-Y_{i}\|_{F}^{2}\\ & s.t.\ P_{1}=P_{2}=\ldots=P_{m}\end{split} \tag{3}\]  
At this time, an optimal projection matrix \(P^{*}\), which fits the overall dataset, is obtained. The problem of interest in this article is to find an iterative update of \(P_{i}\) for each node and provide an algorithm that fits the distributed scenario.
## 3 Proposed Framework  
The proposed strategy aims to predict the level of congestion in the network, compare them with the QoS and QoE requirements of the teleoperation system, and finally provide the optimal QoS and QoE solution to ensure the stringent requirements. To achieve this goal, the proposed RL-based ITE framework deals with the main two challenges: (1) estimate the network congestion and comparison with domain-specific requirements in real-time, and (2) select the optimal haptic codec to decrease the data rate without degradation in QoE performance and improve the system stability and transparency. This section presents  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Proposed Technique** & \begin{tabular}{c} **Improved QoS/** \\ **QoE Factors(s)** \\ \end{tabular} & \begin{tabular}{c} **Effectiveness** \\ \end{tabular} & \begin{tabular}{c} **Key Contribution** \\ \end{tabular} \\ \hline OAHS [21] & SNR & Mixed & \
\begin{tabular}{c} Propose an adaptive sampling method to tune PDb thresholding in real time with a focus on minimizing network impairments for the telehaptic system. \\ \end{tabular} \\ \hline Data-drive [22] & SSIM & Sig. & To meet the stringent requirements of Tactile Internet ultralow delayed, the data-driven technique was proposed. \\ \hline PLC [23] & SNR, HSSIM & Sig. & To minimize the coding delay, and coding loss and to improve the bit error rate the E2E perceptual-lossless codec was presented to ensure the quality of tactile services. \\ \hline PVC-SLP [24] & SNR, PSNR & Sig. & A sparse linear prediction coding-based vibrotactile technique was presented, where a cutaneous sensitivity function was employed to improve the quality of vibrotactile signals. \\ \hline VC-PWQ [25] & SNR, PSNR & Sig. & Introduce a hybrid scheme by coupling wavelet transformation and vibrotactile perceptual model for haptic-driven applications. \\ \hline Peak-Suppressing [26] & MSE & Mixed &
\begin{tabular}{c} Present a peak-suppressing scheme to adjust the PDb and reduce packet rate with a focus to minimize the network load that leads to delay and packet loss. \\ \end{tabular} \\ \hline Adaptive Sampler [27] & MOS & Not Sig. & An adaptive sampling scheme for selected haptic sample transmission was presented to guarantee the QoS of the teleoperation systems. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the haptic codec techniques for emerging Tactile-driven services. The Effectiveness column indicates the level of significance like Significant (Sig.), Not Significant (Not Sig.), and Mixed. The Sig. means schemes use standard database and baseline comparison, Not Sig. means use a standard database but lack baseline comparison and the Mixed means presented scheme shows baseline comparison in the absence standard database.  
a detailed discussion of the proposed ITE to deal with the aforementioned challenges. At first, we will discuss the ECN mechanism and how ECN-marked packets are used to predict the levels of congestion in communication networks. Secondly, we briefly discuss the Q-learning algorithm to select the optimal codecs based on the level of congestion.