esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
* The target reliability index is set at \(\beta_{50}\) = 3.2 [7].
* The load factors are set as \(\gamma_{\text{G}}=\gamma_{\text{Q}}=1.2\).
* The characteristic values for the variable loads V\({}_{\text{Q}}\) = 0.4 are 1.32 times higher than the current ones, and for loads V\({}_{\text{Q}}\) = 0.2, they are 1.15 times higher. The current variable load table values should be multiplied by 1.15-1.32.
* The material safety factors are the same for all materials, \(\gamma_{\text{M}}=1\), and the characteristic values of the basic materials, steel, timber and concrete, are in quantiles \(\beta_{\text{q}}\) = 3.30, 2.61 and 2.72, respectively, which means that the fictitious material safety factors are \(\gamma_{\text{M}}=1.18\), 1.21 and 1.37, respectively, and \(\gamma_{\text{G}}=\gamma_{\text{Q}}=1.2\).
* As the characteristic values are higher than the current ones, the fictitious load factors compared with the current Eurocodes are \(\gamma_{\text{Q}}=1.38\) for loads V\({}_{\text{Q}}\) = 0.2 and \(\gamma_{\text{Q}}=1.58\) for loads V\({}_{\text{Q}}\) = 0.4.
* According to this study, the current Eurocodes are about 13% and 14% over safe in timber and concrete designs for \(\beta_{50}\) = 3.2, respectively, and steel designs are about 10% unsafe.
* The proposed codes have the same load tables as the corresponding design value codes and can be used as the design value codes by setting the load factors at unity \(\gamma_{\text{G}}=\gamma_{\text{Q}}=1\) and by multiplying the material factors by 1.2.
* The proposed codes result in savings in structural material, especially in timber and concrete and savings in design work. The codes are simpler and result in fewer design errors, less design output and less design checking, the reliability accuracy is better, the unsafe design cases are negligible, and the environmental impact is improved.  
The research received no external funding.  
**Institutional Review Board Statement:** Not applicable.
## 3 Interaction Enhanced ICA  
From the literature review in Section 2.2, it is obvious that previous work on ICA mostly focuses on improving the assimilation operation (_i.e._, the evolution within each empire), and pays little or no attention on the competition operation (_i.e._, the interaction among all empires). Ironically, the competition operation is what distinguishes ICA from other evolutionary algorithms, as its name implies. With ICA, each empire evolves almost separately, and the only interaction among empires is through the competition operation. However, the competition operation just removes a colony from the weakest empire and then adds the colony to another empire. Intuitively, the weakest empire does not benefit from this operation. The empire winning a colony does not benefit much from this operation either, because the quality of the colony is poor. Notably, the growth and decline of the numbers of colonies in these two empires rebalance the computational effort towards the stronger empire. Furthermore, in each generation, the assimilation operation affects all colonies, while the competition operation affects only one colony. Therefore, the competition operation has much less impact on the performance than the assimilation operation.  
To strengthen the power of the competition operation, or more generally to enhance the quality of interaction among empires, we add an interaction step to ICA such that the niche of each empire can be shared by other empires. Since the imperialist of an empire is the best country in the empire, exchanging information about the imperialist with other empires posts a greater benefit than exchanging information about a colony, as did in the original competition operation. The resulting algorithm, called Interaction Enhanced ICA, is shown in Figure 2. It differs from the original ICA (see Figure 1) in three aspects. Firstly, the assimilation steps of [PERSON] _et al._[35] are adopted to improve the diversity of the assimilation operation (lines 4 and 5 in Figure 2). Secondly, an interaction step is added (line 10 of Figure 2). Two ways to perform the interaction step are described in detail in Sections 3.1 and 3.2. Thirdly, a new parameter \(\rho\) is introduced to control the frequency of competition (lines 11 and 12 in Figure 2) where \(0\leq\rho\leq 1\). The original ICA can be regarded as having \(\rho=1\) since competition occurs in every generation. Intuitively, a smaller \(\rho\) causes the number of empires to reduce at a slower speed. Since the preceding interaction step only works with more than one empire, a small \(\rho\) can increase the impact of the interaction step.
## 4 Results
### Ablation Study  
The ablation study presented in Figure 10 clearly demonstrates the significant impact of integrating a Temporal Convolutional Network (TCN) layer into the CNN architecture, particularly in noisy environments. Across varying SNR levels, the CV-CNN-TCN model consistently outperforms its CNN-only counterpart, with the most notable improvements observed in low-SNR conditions (\(-20\) dB to \(0\) dB). For instance, at \(-10\) dB, the TCN-enhanced model achieves approximately \(0.65\) accuracy compared to the baseline CNN's \(0.45\), a \(44\%\) relative improvement. This underscores the TCN's ability to capture long-range temporal dependencies and maintain robustness against noise. While both models converge at higher SNR levels, the TCN variant retains a slight edge, suggesting its broader utility even in cleaner data scenarios.  
Figure 8: Comparison of (**a**) CLDN, (**b**) SCRNN, (**c**) Resnet, and (**d**) CNN confusion matrix.  
Figure 9: Comparison between (**a**) CV-CNN-TCN and (**b**) CV-CNN-TCN-DCC confusion matrix.
# Using Graph Embedding Techniques in Process-Oriented Case-Based Reasoning
## 4 Neural-Network-Based Semantic Graph Similarity Measure
### Adapted Neural Network Structure
#### 4.2.4 Training and Optimization  
Whereas [PERSON] et al. [23] assume their training examples to be pairs of graphs that are labeled as similar or dissimilar for training, our training examples are graph pairs with ground-truth similarity values. Hence, each of our training graph pairs is labeled with the ground-truth similarity value, in the range of \([0,1]\). This yields a regression-like problem where the neural network aims to predict similarities that are close to the ground-truth similarities. For this regression problem, the use of a Mean Squared Error (MSE) loss is suitable. When given a batch \(B\) of graph pairs \((G_{1},G_{2})\in B\), the ground-truth similarity of each graph pair \(sim(G_{1},G_{2})\in[0,1]\), and the predicted similarity from one of our models for each graph pair \(fsim(h_{G_{1}},h_{G_{2}})\), the MSE loss \(L_{\text{mse}}\) is computed as follows:  
\[L_{\text{mse}}=\frac{\sum_{(G_{1},G_{2})\in B}(sim(G_{1},G_{2})-fsim(h_{G_{1} },h_{G_{2}}))^{2}}{|B|} \tag{15}\]  
The MSE sums up all squared differences in predicted similarity \(fsim(h_{G_{1}},h_{G_{2}})\) and labeled similarity \(sim(G_{1},G_{2})\), and then divides this value by the number of all batched training examples to obtain the average deviation. As originally proposed by [PERSON] et al. [23], sGEM and sGMN also use the Adam optimizer [52] to optimize the networks' weights according to the MSE loss value.  
Application of Semantic Graph Embedding Model and Semantic Graph Matching Network in Similarity-Based Retrieval  
The Semantic Graph Embedding Model (sGEM) and the Semantic Graph Matching Network (sGMN) are designed to be used as trainable similarity measures--that is, learning how to predict the similarities of pairs of NEST graphs. The possible application scenarios of sGEM and sGMN in Process-Oriented Case-Based Reasoning (POCBR) are widespread since similarity measures are used in several different tasks, e.g., for retrieving, adapting, or retaining cases [1,2]. We restrict ourselves to the application of the embedding models for retrieving the \(k\)-most similar cases according to a query (case retrieval) in this work.  
Thereby, the integration of the neural networks into a standard retrieval scenario and a MAC/FAC retrieval scenario is examined (see Section 2.3 for an introduction to retrieval and [19; 21] for previous work on MAC/FAC in POCBR).  
Figure 5 shows the retrieval process, which makes use of two frameworks, i.e., a POCBR framework (e.g., ProCAKE [24]), and a Deep Learning (DL) framework (e.g., TensorFlow [53]). These two frameworks interact while training the neural network in an offline phase (see Section 5.1) and predicting pairwise graph similarities in a retrieval with the neural network. A MAC/FAC retrieval, additionally, performs a subsequent similarity computation with a knowledge-intensive similarity measure (see Section 5.2).
# IIoT System for Intelligent Detection of Bottleneck in Manufacturing Lines
## 5 Discussion  
### Predictive Maintenance as a Reference  
Various authors have conducted studies on industrial maintenance, with a focus on predictive manufacturing in smart environments, Industry 4.0 references, and challenges with the Internet of Things [64]. They have examined current paradigms, such as Big Data [10; 11; 51], Machine Learning [26; 27; 28; 29; 65], and the broader use of artificial intelligence to develop mechanisms for predicting problems and, ultimately, enhancing and optimizing industrial maintenance. This exploration has enabled the application of the different techniques, tools, and architectures discussed in this manuscript, particularly referencing Architecture 4.0 [13; 33; 35], and supervisory algorithms utilized in predictive maintenance [6; 8; 27], adapted for real-time monitoring and ensuring industrial data quality.  
In addition to the help that can be provided by the analysis of historical data related to field elements, the experience of the maintenance technician must be considered. The role they have and their knowledge about the machinery is fundamental to be able to perform follow-ups based on experience and serve as a basis for the knowledge of the predictions. In addition, in this context, the technician can determine key variables with the help of information that can impact the bottleneck analysis.
## 3 A Case Study
### Discussion
As a result, the pick tip failure probabilities over the shearing-down stage in both scenarios are nearly the same. The influences of the drum advance direction on the pick tip failure probability can be further explored by comparing Figure 7 with Figure 6 and comparing Figure 9 with Figure 8. From Figures 6 and 7, it can be found that the tip failure probability in the shearing-down stage is much higher than that in the sump-in stage although the rock thickness distribution in both stages are the same. This finding is also evidenced by the comparison between Figures 8 and 9. In Scenario 3, the rock removed in the sump-in stage is slightly more than that in the shearing stage, but the tip failure probability over the sump-in stage is \(1.6\times 10^{-4}\%\), much lower than the tip failure probability over the shearing-down stage which is 0.07433%. A major cause for this failure probability difference is the different maximum DOC due to the different drum advance directions in these two stages. In the shearing-down stage, the drum advance direction is perpendicular to the floor. If the drum involves rock cutting in a revolution, it will often cut the rock with the maximum DOC. By contrast, in the sump-in stage, the drum advances along the roof. When the rock thickness is less than the drum's cutting radius, the DOC of the rock cut by the drum will be less than the maximum DOC. As abovementioned, a small change in DOC can result in a large variation in pick tip failure probability. From the above analysis, it is noted that the prediction accuracy of the bending force on pick tip directly affects the accuracy of pick tip failure probability estimation. Currently, the bending force was analyzed based on the formula given by [PERSON] [14] and laboratory rock cutting tests [11]. However, laboratory rock cutting tests are often costly and time-consuming. Additionally, measuring forces on individual picks in situ is difficult. Therefore, new methods such as inverse analysis [27; 28] may be needed to help force prediction.
## 3 Numerical Analysis
### STPA Performances in JCAS Scenarios and Benchmarking
#### 3.2.1 Communication Performance in Scenario 1: Non-Overlapping Communication and Sensing Beams  
We consider the situation in Figure 4a and assume a UE with a single-isotropic antenna in the range of \(r_{UE}=50\) m and an angle of \((\theta_{UE},\phi_{UE})=(20^{\circ},50^{\circ})\). The channel in (5) from the JCAS aperture to the user is modeled by a dominant LoS path, besides NLoS paths, which include \(N_{cl}=8\) clusters each with \(N_{ray}=10\) rays and having 10 dB power less than the LoS path. The overall power of the channel, including LoS and NLoS paths, is normalized for the channel, as in [14]. The scatters are distributed uniformly within all elevations \(0^{\circ}\leq\theta\leq 90^{\circ}\) and azimuth \(-180^{\circ}\leq\phi<180^{\circ}\) angles. The channel from BS to the user via the target \(\mathbf{H}_{target,1}\) is also considered in the NLoS path. Based on the estimated channel and desired scanning angles, \(\mathbf{w}_{JCAS}\) and based on that, the spectral efficiency \(R\) can be calculated. The average of \(R\) from 200 channel realizations is obtained, where the scatters at random angles are generated at each realization. As shown in Figure 11, all apertures demonstrate the same spectral efficiency for the LoS communication, since the gains of the three apertures at the direction of the UE are equal. However, the advantages of STPA are its modular design compared to SPA and the superiority of its sensing capabilities over CUPA, which is studied in the following section.
# Space-Constrained Scheduling Optimization Method for Minimizing the Effects of Stacking of Trades
## 1 Introduction
That is, SCS should be solved using a scientific approach, and the development of an integrated system that combines various techniques is required. This study presents an area-oriented method for establishing space-constrained schedules that minimize stacking of trades. This method was developed into a system named \"Space-constrained Scheduling Optimization for Minimizing Stacking of Trades\" or \"SSO.\" In order to achieve the goals of this study, the following specific research procedures were performed. First, prior studies on SCS were analyzed to examine the necessity and uniqueness of this study, and a method development direction was established. Second, an objective function was defined considering the effect of work area interference as well  
Figure 1: Impact of site density on labor productivity (adopted from [PERSON] [8]). as assumptions regarding SCS that were identified through a literature review. Third, the space-constrained scheduling optimization (i.e., SSO) method was developed by combining Primavera 6 (P6), building information model (BIM), and GA. Fourth, case studies were conducted to validate the detailed application methods and performance of SSO. Finally, the research contributions and limitations were discussed.
###### Abstract  
Coping with the skyrocketing needs for massive amounts of data for the future Fifth Generation (5G) network, Device-to-Device (D2D) communications technology will provide seamless connectivity, high data rates, extended network coverage, and spectral efficiency. The D2D communications are a prevalent emerging technology to achieve the vision of symmetry in the Internet of Things (IoT) services. However, energy resource constraints, network stability, traffic congestion, and link failure of the devices are the crucial impediments to establish an optimal route in the D2D communications based IoT 5G network. These obstacles induced packet drop, rapid energy depletion, higher end-to-end delay, and unfairness across the network, leading to significant route and network performance degradation. Therefore, in this paper, an energy, mobility, queue length, and link quality-aware routing (EMBLR) approach is proposed to overcome the challenges and boost network performance. Moreover, a multicriteria decision making (MCDM) technique is utilized for the selection of the intermediate device in an optimal route. Extensive simulation has been conducted and proven that the proposed routing approach significantly enhances network performance. Overall, results have been carried out in Quality of Service (QoS) performance metrics and compared with other well-known routing approaches.  
D2D communications; IoT; 5G network; routing; backpressure; EMBLR
## 3 Dynamics of the Knee Exoskeleton and Problem Formulation  
### Rehabilitation-Oriented Wearable Exoskeletons  
Wearable rehabilitation exoskeletons typically offer one to three degrees of freedom per leg, primarily for sagittal plane motion, with configurations tailored to specific rehabilitation goals. This study focuses on a 1-DoF knee-joint rehabilitation exoskeleton, prioritizing simplicity and effectiveness for addressing knee rehabilitation needs. As illustrated in Figure 1, the design features two segments representing the thigh and shin, connected by a rotational axis at the knee joint for smooth sagittal plane motion. Adjustable straps secure the exoskeleton to the leg, ensuring proper alignment and ease of use for individuals with knee-joint restrictions. The range of motion (ROM) of the knee is intentionally constrained to \(0^{\circ}\leq\theta\leq 90^{\circ}\), which corresponds to typical rehabilitation movements such as walking, sitting, and stair climbing. It is important to note that this ROM is specifically relevant to the early stages of recovery. In contrast, the ideal knee flexion ROM for a fully rehabilitated individual extends from \(0^{\circ}\) of extension to \(130^{\circ}\) of flexion. This ROM is maintained throughout the present study, as defined by the following condition:  
\[0^{\circ}\leq\theta\leq 90^{\circ} \tag{1}\]The 1-DoF knee-joint exoskeleton is designed to replicate sagittal plane motion, simplifying mechanical design and control strategies for rehabilitation [51]. Its dynamic model incorporates key biomechanical parameters, such as joint rotation and mass distribution, to represent the flexion-extension cycle critical for rehabilitation. While prior studies [52; 53] focused on physical prototypes, this work emphasizes theoretical modeling and control strategies, building on studies like [54; 55]. The schematic design (Figure 1) highlights the exoskeleton's kinematic simplicity, making it a suitable platform for control strategy evaluation in simulation and future experimental validation.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### Selection of FSSs Based on Its Classification and Performance  
From the detailed discussion on FSSs in earlier Section 4, we deduce that it is not possible to identify a particular element shape, which can work well in a certain band of frequencies or provide a specific response. From Table 2, it can be seen that single/double/multiband response may be  
Figure 22: (**a**) Two knitted, conducting textile FSSs with conducting grid (Left), conducting patch (Right), white area knitted from polyester yarn & dark areas knitted from conducting yarn [192] (**b**) Microscopic image of screen printed fabric FSS [191]. (**a,b**) Adapted from [191; 192] and reproduced by permission of the Institution of Engineering & Technology. achieved with the different number of FSS layers, such as dual band response is achieved by single layer as well as double layer FSS. Similarly, different element shapes have been used in the literature for this purpose, like a square patch with L shape realizes dual band, whereas some can be reached by circular loops of nested geometry. However, the frequency band and BW would depend on different geometric parameters, including patch/slot element dimensions, dielectric profile, and periodicity. Traditionally, two types of resonating elements are popular, including loop and the cross shape. The resonance of the former type reaches when the element length becomes equal to a wavelength, while the later mentioned resonates when the element length approaches half a wavelength. However, when the array elements are printed or etched on a dielectric substrate layer in the form of a patch or slot, the resonant frequencies are reduced by approximately a factor of \(\surd((\varepsilon_{t}+1)2)\), as defined by (12) [2]. Furthermore, combinations of different basic element types (Figure 5) have been employed in the literature to accomplish customized FSS structures and responses depending on the applications. Notably, higher FBW might be achieved by single layer, multilayer, and other complex FSS structures. However, again, this depends on the structure profile. Here, we summarize some of the key factors generally affecting the frequency response in Table 10 and may link the structure geometries, and performance characteristics keeping in view the theoretical behavior.
# A Quad-Band RF Circuit for Enhancement of Energy Harvesting
## 4 Design and Simulation  
The voltage multiplier topology is one of the most common topologies used for RF energy harvesting. Single-stage, dual-stage, even different stages were used for this topology [49]. No power biasing is required; enhanced output voltage and simplicity of design are the most important voltage multiplier topology features. In this paper, HSMS-2852 Schottky diodes have been utilized as the main rectifier component. This diode's selection was due to its low voltage drop, rapid switching at high frequencies, acceptable performance under low input power values, low saturation current, and low junction capacitance. However, the input impedance of the voltage multiplier is dynamic and it depends upon the input power level or the received signal's frequency due to the nonlinear behavior of Schottky diodes [49; 62; 65; 68; 69; 70; 73; 74]. This dynamic impedance is not matched to the receiving antenna impedance (often 50 Ohms); therefore, high power loss and low RF-DC conversion efficiency are expected.  
Therefore, it is required to know the impedance at each frequency. Advanced Design System (ADS) platform was set and used to evaluate the proposed system. The frequency was adjusted to be varied between 0 and 5 GHz since this range covers the interested quad-bands. Based on the above procedures, the model and simulation results of the rectifier's input impedance are depicted in Figure 3.  
Figure 3: Input impedance of rectifier vs. input frequency under 0 dBm input power. (**a**) Model results. (**b**) Simulation results.  
From Figure 3, it can be noticed that the impedance at the GSM-900, GSM-1800, UMTS-2100, and Wi-Fi 2.45 frequency bands under the input power of 0 dBm are 32.94-j546.916, 12.961-j265.774, 11.214-j224.381, and 9.951-j188.295, respectively. The simulation results converge with the model results as shown in Figure 3 under the 0 dBm power level. In the range of frequencies including GSM-1800, UMTS-2100, and Wi-Fi 2.45, the rectifier input impedance variation impedance versus \(P_{in}\) is slight, as can be seen from Figure 2. As a result, there will be a small mismatching. At the frequency of GSM-900, the variation of the rectifier input impedance versus \(P_{in}\) is significant. Accordingly, a large mismatching will be expected. Figures 2 and 3 show that the model formula and simulation have given close results for the rectifier's input impedance, which contribute to specifying the impedance of the circuit theoretically and numerically at the same time.