esa-sceva/satcom-chunk-collection huggingface dataset:
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 2 Taxonomy
### Network Types  
IoT-based health-care systems are dependent on various topologies to effectively deliver services. IoT networks provide numerous services on a short-range. For example, Wireless Local Area Networks  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Technology** & **Types** & **Frequency** & **Data Rate** & **Range** & **Power Usage** \\ \cline{2-7}  & NFC & PAN & 13.56  MHz & 100–400 kbps & 10 cm & Very Low \\ \cline{2-7}  & Bluetooth 4 & PAN & 2.4 GHz & 1 Mbps & 0.1 Km & Low \\ \cline{2-7}  & Bluetooth 5 & PAN & 2.4 GHz & 2 Mbps & 0.25 Km & Very Low \\ \cline{2-7}  & ISO/IEC 15693 & PAN & 3.56  MHz & 6.6–26 Kbit/s & 1–1.5 m & Very Low \\ \cline{2-7}  & Z Wave & LAN & 968–908 MHz & 100 kbps & 100 m & Very Low \\ \cline{2-7}  & RFID & LAN & 13.56 MHz \(-\)2.45  GHz & 40–640 kbps & 1–100m & Low \\ \cline{2-7}  & Thread & LAN & 2.4  GHz & 250 Kbits/s & 10–100m & Low \\ \cline{2-7}  & Wi-Fi & LAN & 2.4  GHz & 802.11(b)11 M; & & & \\  & & & 2.4  GHz & (g) 54 M; & 50 m & Low-High \\  & & & & 5 GHz (n) 0.6, & & & \\  & & & & (Gac) 1 Gbps & & & \\ \cline{2-7}  & ZigBee & LAN & 2.4  GHz & 250 kbps & 10–100 m & Very Low \\ \cline{2-7}  & WiMAX & WAN & 10–66 GHz & 11–100 Mbps & 50 km & High \\ \cline{2-7}  & LoRa & WAN & 868/915 MHz & 50 kbps & 25 km & Low \\ \cline{2-7}  & LoRaWAN & WAN & Numerous & 0.3–50 kbps & 2–5 km (Urban) & & \\  & & & & 0.3–50 kbps & 15 km (Sub urban) & Low \\ \cline{2-7}  & ZigRox & WAN & 868/915 MHz & 300 bps & 50 km & Low \\ \cline{2-7}  & 4G & WAN & 700, 1700, & 2800 MHz & Up-to 12 Mbps & Up-to 10 Km & High \\ \cline{2-7}  & 5G & WAN & At Low Bands & Up-to 3.6 Gbps & Up-to 10 Km & High \\ \cline{2-7}  & 5G & WAN & At High Bands & 10 Gbps & \textless{1 Km} & High \\ \cline{2-7}  & (NB-IoT) & WAN & 850 MHz & 245 kbps & Up-to 35 Km & High \\ \cline{2-7}  & (EC-CSM IoT) & WAN & 890 MHz & Up-to 140 kbps & Up-to 100 Km & High \\ \cline{2-7}  & LTE-M (M1) & WAN & 700, 1450–2200, 5400 MHz & 0.144 Mbps & 35 km & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of existing proposed wireless communication technologies and their parameters for smart health-care.  
(WLANs), BANs, and Wireless Personal Area Networks (WPAN) are used in indoor health-care applications. While mobile e-health-care using Metropolitan Area Networks (MANs), Wide Area Networks (WANs), and mobile communication networks. The discussed systems present particular structures in term of size, capacity, data, coverage, and delay requirements.
# Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials
## 2 HOSS Simulations and FDEM Model for Dynamic Fracture
The mesh used in this work contains constant strain triangles with an average size of 0.01 m. This corresponds to a total of approximately 160,000 cells and 480,000 edges. The tensile strength of the material was set to 8 MPa. Mohr-Coulomb fracture model is implementedat the interface of the triangular finite elements, which describes the strength of the material in shear. The shear cohesion and the internal angle of friction were set at 24 MPa and 31 degrees respectively. 190 high-fidelity HOSS simulations were performed for different initial crack locations and orientations. Loading conditions, material parameters, and domain dimensions are unchanged for all 190 HOSS simulations. HOSS output was stored at every 2000 time steps with each time-step being \(10^{-8}\) s. Each simulation took about 4 h of computation time on 400 processors. At the point of failure, the material is unable to bear further load resulting in Mode I failure. The sample is considered as failed when a set of fractures connect the two opposite boundaries of the domain. Figure 1: Fracture model: A schematic representation of the fracture model in Hybrid Optimization Software Simulation Suite (HOSS) for Mode I loading. Note that the HOSS simulations have both normal springs and shear springs. But in Figure 1 we have only shown normal springs for convenience. Figure 2: Initial boundary value problem: A pictorial description of the HOSS simulation setup for a problem with 20 pre-existing cracks. 190 high-fidelity HOSS simulations were performed. For each simulation, the location and orientation of the initial cracks were randomly chosen. Orientations were chosen from 0-degree, 60-degree, or 120 degrees. Loading conditions, material parameters, and domain dimensions are unchanged for all 190 HOSS simulations. HOSS has been previously validated for several applications and it is shown to be reliable [49; 69; 70; 71]. Validation of HOSS that may be of particular interest is cases addressing the brittle failure of geomaterials. In this regard, HOSS has modeled split Hopkinson bar experiments on granite [69], producing excellent comparison to experimental data in addition to capturing the complex crack networks that evolve during high-rate loading conditions. Furthermore, HOSS has directly compared simulated and experimental fracture patterns in granite samples under low-rate compressive loads [71]. HOSS simulated experimental fracture patterns in shale have also been compared with promising results in [PERSON] et. al. [70]. Based on this previous validation work, we aim to develop surrogate models that can emulate HOSS. For large components, HOSS simulations can be computationally costly. This can become important when many simulations are required, for example in uncertainty quantification or sensitivity studies. Surrogate models that can emulate HOSS could quickly provide large sets of data for such investigations.
# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs  
In this section, we delineate the leading energy-efficiency-related issues unsettling numerous routing-based solutions. Along with this, we introduce the problems related to such energy-efficiency related issues. Furthermore, we also apprehend the way through which research works have been made to resolve all such issues.  
Power-efficient approaches for MANETs can be classified into eight categories, according to their basic operation:  
1. Approaches based on adaptations of the radio state operational mode.
2. Routing protocols based on adaptive load balancing.
3. Location-based routing protocols.
4. Multicast-based routing protocols.
5. Energy-efficient proactive (link state-based) routing protocols.
6. Energy-efficient reactive (source-initiated-based) routing protocols.
7. Transmission power control-based routing protocols.
8. Cross-layer based routing protocols.  
In the following subsections, we describe these categories of power-aware optimization solutions for MANETs.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Cache Management
#### 3.4.1 Reuse Prediction  
Cache block reuse prediction is a technique to predict whether the block currently residing in the cache is likely to be accessed again before being replaced. Previous techniques have not significantly combined various input [12]. The proposed model is possible to increase cache efficiency by weighting various inputs and analyzing correlations with each other. This paper does not use perceptron algorithm but uses a similar perceptron learning algorithm [12]. In this paper [12], when a block is accessed a predictor works. If the block was in the cache (i.e., cache hit), the predictor predicts the possibility of reusing in the near future. If the block is predicted as it will not be reused, the block will not be stored in the cache again and vice versa.  
Figure 9 shows the structure of the predictor presented in the paper [12]. The predictor uses six features: the memory access trace and the PC of the memory instruction that caused the eviction, and the memory address bits. Each feature has its own table and indexes into the corresponding entry to the hash by applying the XOR gate to each feature with the PC of the instruction. This predictor is similar to perceptron predictor known as hashed perceptron [26]. Next, when the weights of the feature table are extracted through the calculated index and the weights extracted from each table are added to exceed the threshold, it is determined that the accessed block is not reused. If the sum of corresponding weights exceeds the threshold, the predictor is predicted that the prediction is incorrect.  
In a single-thread workload, the average Sampling Dead Block Prediction (SDBP) [27] (3.5%), Signature-based Hit Predictor (SHiP) [28] (3.8%), and Perceptron (6.1%) improved compared to the LRU when the predictor's introduction per cycle (IPC) by the LRU's IPC.  
Figure 8: MER-sort (Model-Enhanced Radix Sort) [11].
# Quantitative Assessment and Grading of Hardware Trojan Threat Based on Rough Set Theory
## 1 Introduction
Owing to its excessive comprehensiveness, using TOPSIS as continuous data is not conducive to defenders being able to make fast and effective defense decisions. To this end, we exploited Equal Frequency Division (EFD) and k-means in order to discretize the data of the HT threat. This can effectively simplify the data to improve the decision-making efficiency of defenders. The primary contributions of our work are summarized as follows. 1. We leverage RST and TOPSIS for the quantitative assessment of HT threat. Based on the attributes of the relevant HTs in an existing HT library, Trust-hub, we measure HT threat, adopting rough set theory for weight calculation and TOPSIS for quantitative assessment. The closeness coefficient is used to characterize the threat. 2. In order to address the lack of decision attributes in the corresponding information system, we introduce information content to calculate the weight of each attribute. Based on the information content and the significance of the attribute, the weights of both core and non-core attributes are obtained. 3. K-means is used to discretize threat data. Aiming at the unstable clustering results in the preliminary work, we propose the EFD method to preprocess the data and obtain the initial cluster center. Compared with other initial center optimization methods, this is more efficient and accurate. 4. We use BWP to characterize the effectiveness of clustering in order to solve the problem of the number of clusters being unavailable in advance. The number of clusters with the largest BWP is taken as the optimal choice for HT threat grading. The rest of this paper is arranged as follows: Section 2 describes the system model and assessment formulation; the proposed discretization based on EFD and the determination method of the number of clusters are shown in Section 3; Section 4 represents and discusses the statistical testing results based on HT benchmarks from Trust-hub; finally, Section 5 concludes the paper.
# Path Planning Optimization of the Load Transport Process Using Heuristic Algorithms
## 1 Introduction
Such distinctions are necessary for understanding the strengths and weaknesses of each algorithm in practical scenarios. Furthermore, specific choices regarding algorithm parameters, such as mutation probability and population size, were justified based on preliminary test results [25; 26], ensuring their applicability to various scenarios. In addition, various parameters of these algorithms are analyzed. The inverse kinematics solution presented in this paper allows us to determine the characteristic variables of the crane and to determine which of the proposed algorithms will prove to be more advantageous in terms of work safety. Moreover, although this study focuses on a single-objective approach, it is important to recognize that this choice limits the scope of the optimization process. For the purposes of this work, the following research hypothesis was defined: The choice of the PSO (Particle Swarm Optimization) algorithm in the optimization process is more beneficial due to the higher computational speed compared to the genetic algorithm (GA), while maintaining a comparable quality of solutions, which results in small differences in the final outcomes between PSO and GA. The present study is composed of six main sections. Section 1 concerns the introduction, where an overview of the available solutions to the analyzed subject and the motivation for undertaking the research are presented. Section 2 presents a short description of the problem, in which the working environment, optimization criterion and selected simulation parameters are presented. Section 3 shows a solution to the optimization problem divided into the two most commonly used heuristic algorithms: genetic algorithm and particle swarm optimization. Section 4 concerns the solution of the inverse kinematics of the machine in order to map the movement of the load along the determined optimal trajectory. Section 5 presents a discussion of the results, and Section 6 concludes the paper.
## 4 Key Enabling Techniques
### Multiple Access Techniques
#### 4.2.1 Non-Orthogonal Multiple Access (NOMA)  
NOMA is an effective and favorable technique to enhance user capacity, user fairness, and SE for 5G and beyond wireless networks [80]. In NOMA, several users are allocated similar and single resource sections (time and frequency) in a specified time. The subscriber separation is attained by various interference cancellation schemes [81, 82]. [PERSON] et al. [83] categorized into single-cell NOMA and multi-cell NOMA while [PERSON] et al. [84] classified NOMA into two general types: code-domain and power-domain multiplexing. The Performance analysis of cooperative NOMA, practical implementation aspects, and research challenges such as hardware complexity, error propagation, carrier frequency offset, and timing offset estimation are discussed in detail. Even though the implementation of NOMA in wireless communication is comparatively new but key ideas such as successive interference cancellation (SIC), superposition coding, and message passing algorithm (MPA) have previously been developed over and above two decades ago [85, 86, 87, 88]. The key features of NOMA are a balanced trade-off between system throughput, user served fairly, and greater throughput yield than Orthogonal-Multiple access (OMA). The article [89] provides a systematic combination of dynamic spectrum access and MIMO, features, challenges, and futuristics in the standardization activities regarding the execution of NOMA in 5G communications. In [54], an extensive research has been done on NOMA-enabled massive MIMO, and the spatial domain in designing subscriber pairings, pilot allocations, and relevant signal processing methods have solely been exploited.
## 4 Conclusions  
This study evaluated the effects of a composite dust suppressant (formulated with hydroxyethyl cellulose, glycerol, isotridecanol ethoxylate, and purified water) on soil strength and wind erosion resistance. The key findings are as follows:  
1. A 10-times dilution significantly enhances compressive strength, soil stability, and smoothness while reducing friction and application costs, making it an economical and effective dust suppression solution.
2. The binding effect strengthens over time, with ultimate compressive strength increasing by 570 kPa and 844.6 kPa after 14 and 28 days, respectively. At 28 days, soil strength reaches its peak as the binder-soil interaction is optimized.
3. Shear strength increases with vertical load, rising from 35 kPa to 203 kPa as load increases from 100 kPa to 400 kPa. Compared to water-treated soil, suppressant-treated samples exhibit greater cohesion and internal friction, improving stability.
4. The binder significantly strengthens the soil, increasing hardness from 25.0 HA (without binder) to 57.3 HA, optimizing soil structure, and reducing deformation.
5. The suppressant outperforms water and dustproof nets in wind erosion tests. Under level 11 wind, suppressant-treated soil showed the lowest mass loss (9.36%) within 20 min, while the dustproof net failed entirely within 6 min.
6. Future research will focus on optimizing the formulation of the composite dust suppressant to enhance its performance across different soil types. Additionally, long-term environmental impact assessments will be conducted to evaluate its biodegradability and ecological safety. Further large-scale field trials will also be carried out to validate laboratory findings and assess real-world applicability under various climatic and environmental conditions.  
Y.X.: conceptualization, funding acquisition, project administration, resources, writing--original draft. M.L.: data curation, formal analysis, investigation, software, validation. B.M.: data curation, validation, software. Y.Z.: project administration, methodology, supervision, writing--review and editing. Z.L. ([PERSON]): data curation, investigation, validation. Z.L. ([PERSON]): methodology, validation, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded supported by the Xihua University under projects RH2300001131 and Z231001.  
Not applicable.  
Data Availability Statement:Data shall be provided upon receiving reasonable request.  
Author [PERSON] was employed by the company China MCC5 Group Corp. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods  
### Substrates Preparation, Inoculation, and Determination of Physicochemical Parameters  
The _P. ostreatus_ strain was utilized to achieve a successful performance of SSF on the investigated substrates composed of CSC and LCP mixtures 80-20% \(w/w\) ratio. In addition, the growth of _P. ostreatus_ requires a high moisture content on the examined substrate of approximately 60-70%, which was achieved with a daily addition of tap water. All substrates were weighed to a final weight of 300 g, placed into glass test vessels, and sterilized at 121 \({}^{\circ}\)C for 15 min. The commercial name of the employed _P. ostreatus_ strain is \"White 2000 P67 LOTO 1551 MN 01827\" (Fungi SEM, La Rioja, Spain), and was obtained in a solid form.  
Subsequently, the substrates underwent an inoculation process in a vertical laminar flow chamber with the addition of 5% \(w/w\)_P. ostreatus_ strain to their surfaces. The glass test vessels were capped by the respective caps that were not sealed, in order to permit access to the atmospheric air as well as to preserve the moisture content intact in the examined substrates. Then, the inoculated substrates were placed in an incubator at 25 \({}^{\circ}\)C in the dark for 10 days.  
The investigated substrates were subjected to freeze-drying to preserve their nutritional and enzyme content. The samples were subsequently collected in order to evaluate several physicochemical attributes, namely, proteins, total soluble sugars (TSS), reducing soluble sugars (RSS), fibers (crude fiber substances, cellulose, lignin, and ash), and \(\beta\)-glucan's presence. Moisture levels and ash content were determined by applying AOAC methods [19]. Proteins were assessed by performing the Kjeldahl method, which is AOAC's official method for the evaluation of total nitrogen [19]. The total protein content of the examined substrates was calculated by multiplying the total nitrogen content (determined by the Kjeldahl method) by a factor of 6.25. Total soluble sugars (TSS) were estimated by performing the phenol-sulfuric acid method described by [PERSON] et al. [20], whereas reducing soluble sugars (RSS) was determined by applying the 3,5-dinitrosalicylic acid (DNS) method developed by [PERSON] [21]. These sugar evaluations were conducted on aqueous extracts of the samples and the results were expressed as g/100 g of glucose. The presence of crude fiber substance (CFS) was determined according to the Weende method (AOAC Official Method 978.10) [19]. Cellulose and lignin content were assessed through the acid-detergent fiber (ADF) method (AOAC Official Method 973.18) [19].  
Finally, the content of bioactive compounds of 1,3-1,6 \(\beta\)-glucans was determined according to the MEGAZYME enzymatic assay kit (\(\beta\)-Glucan Assay Kit Yeast & Mushroom, Megazyme Product code: K-YBGL). All experiments were implemented in triplicates, and the respective results are expressed as g/100 g of their dry weight.
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 2 Lip-Reading Model Architecture
### Temporal Convolutional Network
#### 2.3.2 Dilated Convolution  
Dilated convolution, is also known as Atrous Convolution. The idea behind dilated convolution is to \"inflate\" the kernel, which in turn skips some of the points. In a network made up of multiple layers of dilated convolutions, the dilation rate is increased exponentially at each layer. While the number of parameters grows only linearly with the layers, the effective receptive field grows exponentially with the layers. As a result, the dilated convolution provides a way that not only increases the receptive field but also contributes to the reduction of the computing cost. The process of dilated convolution is illustrated in Figure 9.
# Autonomous 3D Exploration of Large Structures Using an UAV Equipped with a 2D LIDAR
## 6 Results and Discussion
### Volume Explored  
Another important metric is the volume explored. Figure 13(a) shows the progression of known space in 10 runs. The highest rate of information acquisition happened within the first half-hour highlighting the effectiveness in making the reconnaissance of the scenario.  
Figure 13(b) shows the rate of discovery of occupied space where the first half-hour also contained the highest discovery rate. This space state was the most relevant for structural inspection. Run 2 achieved both the highest map completeness (93%) and the highest surface coverage. All the components of the exploration architecture were deterministic, yet there was variability. The fluctuation came from the movement of the UAV. In each run, the UAV occupied slightly different positions. This change was enough to create different map configurations. Part of the space was unreachable due to obstacles. Within the reachable space, most of the space was free. Of the reachable space, the exploration covered an average of 92% \(\pm\) 0.4%.  
The volume explored at the end of the run for each of the analysed heuristics is shown in Figure 15. The octomap heuristic explored significantly more space, 3.071 m\({}^{3}\) (4%) more space than the nearest neighbour heuristic and 3.749 m\({}^{3}\) (5%) more space than the surface frontiers heuristic.  
The nearest neighbour and the surface frontiers heuristics achieved similar results both for the total explored space and for the occupied space. Regarding explored volume, the octomap heuristic stood out for exploring more space in total (the sum of free and occupied). When only analysing occupied space, the octomap heuristic discovered less space. However, the difference was one order of magnitude more significant for total space than it was for occupied space.  
Figure 14: The volume explored in 10 runs. (**a**) Rate of exploration: Free and occupied are combined. (**b**) Coverage of the occupied volume.