esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Differences in Object Positioning Between USCP and Non-USCP Groups  
The second hypothesis of this study aimed to determine whether object positioning parameters differ between the USCP and non-USCP groups during bimanual lifting tasks. This analysis is particularly relevant for understanding how motor impairments associated with USCP affect performance across the Max and SubMax levels of force control. The results showed no statistically significant differences in positional performance parameters between the USCP and non-USCP groups in either the Max or SubMax zones, suggesting that both groups perform similarly under controlled conditions. Nine parameters demonstrated better positioning performance in the SubMax zone than the Max zone across both groups. Analysis of variability, as measured by the interquartile range (IQR) of tilt angles, revealed important distinctions. The USCP group exhibited higher variability than the non-USCP group across both zones, as shown by higher IQR values. This indicates greater variation in their ability to control tilt during these tasks. These findings align with those of [PERSON] et al. (2015), who noted that children with USCP often adopt unique motor control strategies in coordinated tasks [29]. Such variability may reflect adaptive strategies or compensatory mechanisms employed by the USCP group to overcome their motor impairments. The non-USCP group, lacking these impairments, exhibited a more consistent performance across most tasks. Increased variability likely stems from neuromuscular and motor control impairments, as supported by prior studies [29; 30], which showed that children with CP exhibit decreased precision and efficiency in upper limb tasks. In addition to positional performance, spillage prevention rates also differed slightly between the groups. Spillage prevention was consistently better in the SubMax zone for both groups, with the non-USCP group showing a slight but consistent advantage at all water levels.
## 3 Seismic Control Effectiveness of the TID for Base-Isolated Structures
### TID Parameters and Time History Analysis  
In this part, the mass ratio of the TID is defined as 0.05, 0.1, and 0.2 respectively, and other related parameters use the optimal values obtained in Section 2. The specific parameters are presented in Table 5. In the dynamic time history analysis, the 20 far-fault ground motions, 12 near-fault ground motions without pulse subset, and 10 near-fault ground motions with pulse subset in Section 2.1 are also selected as input ground motions. Ground motion intensity is typically quantified using peak ground acceleration (PGA). In this study, the PGA values of the 42 earthquake records are adjusted to 0.4 g.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline
**5-Storey** & **IS** & **1** & **2** & **3** & **4** & **5** & & & & & & & & \\ \hline Mass & & & & & & & & & & & & & & \\ (\(10^{3}\) kg) & 6.8 & 6.0 & 6.0 & 6.0 & 6.0 & 6.0 & & & & & & & & \\ Stiffness & & & & & & & & & & & & & & \\ (\(10^{3}\) kN/m) & & & & & & & & & & & & & & \\ Damping & & & & & & & & & & & & & & \\ (\(\rm kN/m/s\)) & 30 & 67 & 58 & 57 & 50 & 38 & & & & & & & & \\
10-Storey & IS & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & & & \\ Mass & & & & & & & & & & & & & & \\ (\(10^{3}\) kg) & 380 & 380 & 380 & 280 & 280 & 275 & 275 & 275 & 275 & & & & & \\ Stiffness & & & & & & & & & & & & & & \\ (\(10^{3}\) kN/m) & & & & & & & & & & & & & & \\ Damping & & & & & & & & & & & & & & \\ (\(10^{3}\) kN/m/s) & 3 & 5 & 5 & 4.8 & 4.7 & 4.7 & 4 & 3.7 & 3.4 & 2.8 & 2.8 & & & & \\ (\(10^{3}\) kN/m/s) & 15-Storey & IS & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15 \\ Mass & & & & & & & & & & & & & & & \\ (\(10^{3}\) kg) & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 & 981 \\ Stiffness & & & & & & & & & & & & & & & \\ (\(10^{3}\) kN/m) & 45 & 1913 & 1864 & 1815 & 1717 & 1668 & 1570 & 1520 & 1422 & 1275 & 1177 & 1030 & 883 & 687 & 490 & 400 \\ Damping & & & & & & & & & & & & & & \\ (\(10^{3}\) kN/m/s) & 8 & 18.5 & 18.0 & 17.5 & 16.6 & 16.1 & 15.2 & 14.7 & 13.7 & 12.3 & 11.4 & 10.0 & 8.5 & 6.6 & 4.7 & 3.9 \\ \hline \end{tabular}
\end{table}
Table 4: Properties of the three base-isolated structures.  
Figure 10: First mode shape of the three base-isolated structures.
## 5 Materials and Method
### Calculation of Removal Rates and CLIs  
The removal rates of cyanobacteria and the associated metabolites in the DWTP were calculated using average concentration of targeted pollutants in each season as:  
\[\eta_{i}=(c_{i0}-c_{i})/c_{i0}\times 100\% \tag{1}\]  
where \(\eta\) is the removal rate of all corresponding compounds designated by its subscript \(i\), which represents chlorophyll-\(a\), cyanobacteria and each metabolite. \(c_{i0}\) and \(c_{i}\) are the average concentration of chlorophyll-\(a\), cyanobacteria and each metabolite measured in each season in the effluent from the preceding step \(c_{i0}\) and from this treatment step \(c_{i}\). Total removal rates were obtained when \(c_{0}\) and \(c\) are the concentrations in the raw water and the finished water, respectively. The values with no detections or below limit of quantitation were set equal to zero for the calculations.  
To ensure the safety of drinking water, the CLIs were calculated using Equations (2)-(5):  
\[CLI=Min\ (MT_{1},\ MT_{2},\ MT_{3},\ \ldots\ldots) \tag{2}\]  
\[MT=G_{Chl-a}/(1-\eta_{met}) \tag{3}\]  
\[G_{Chl-a}=G_{met}/c^{\prime}{}_{met} \tag{4}\]  
\[c^{\prime}{}_{met}=c_{met}/c_{Chl-a} \tag{5}\]  
where \(CLI\) represents the chlorophyll-\(a\) limits at intake; \(MT\) is the maximum tolerable value for chlorophyll-\(a\) in the drinking water source, which was modified according to [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [20]; and \(\eta_{met}\) is the total removal rates of the targeted metabolites in the DWTP. \(G_{Chl-a}\) is the chlorophyll-\(a\) concentration equivalent to the guideline standard of the studied metabolites [19]. \(G_{met}\) is the guideline standards or OTCs of the studied metabolites' concentration in the drinking water. \(c^{\prime}{}_{met}\) is the cell quota of metabolites, e.g., in pg per cell; \(c_{met}\) is the metabolite concentration in water; and \(c_{chl-a}\) is the chlorophyll-\(a\) concentration in water.  
This work was supported by grants from National Natural Science Foundation of China (41471075 and 41171366), Major Science and Technology Program for Water Pollution Control and Treatment of China (2017 ZX07603-005), and Science Foundation of Nanjing Institute of Geography and Limnology, CAS (NIGLAS2012135013). The authors thank [PERSON] for the improvement of the written English.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed reagents/materials/analysis tools; and [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# Impact of Differing Instability Devices on Postural
[PERSON] and [PERSON] [14] found that a 4-week intervention using a wobble board exercise program improved the perception of functional stability in a sample of individuals with chronic ankle instability (CAI). Similarly, [PERSON] et al. [17] reported that a four-week wobble board training program provided positive outcomes in both clinical and patient-oriented settings. [PERSON] et al. [18] found that the use of a multi-station training program using several instability devices (exercise mats, Dynair, BOSU, mini trampoline, and foam roller) over six weeks improved patient perception and dynamic stability in a sample of athletes with CAI. Lastly, studies have considered the use of foam pads during unilateral balance training programs. [PERSON] et al. [19] found there to be reductions in AP sway parameters (range and mean velocity) after completing a 4-week training program. This points to the effectiveness of using instability devices to improve patient outcomes. However, there are conflicting recommendations within the literature as to the implementation of instability devices in rehabilitation settings or during research [6; 7; 9]. This is largely dependent upon the particular devices being used within the investigation itself and points to the continued expansion of the available data as new products become commercially available. Thus, as these new products become available to practitioners, it is essential to identify where they fall within this continuum of devices that are currently available and how they may be implemented in training sessions. This can be accomplished through cross-sectional evaluations of postural stability using established instability devices, such as a foam pad. Previous investigations comparing instability devices have also only used the instability device themselves and not compared the changes in postural sway to those on a firm surface [6; 8; 9]. Therefore, this investigation aims to examine the parameters of postural sway between the traditional foam pad (Airex) used in practice, a novel instability device (SlackBlock), and a firm surface (control).
## 5 Results and Discussion
### Comparative Discussion
Using the Yelp 2014 database, the highest precision of 0.914, highest recall of 0.933, highest F-measure of 0.923 and smallest RMSE of 0.114 are measured by the proposed spider Taylor-ChoA-based HAN. Table 4 describes the evaluation outcomes of the existing method with accuracy and RMSE. The methods taken for the analysis include Trigram+UPF, TextFeature UPF, JMARS, UPNN (CNN), UPNN (NSC) and NSC+UPA [8; 30]. \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Datasets** & **Metrics** & **DNN** & **CNN+LSTM** & **Bi-GRU** & **CNN** & **Proposed Method** \\ \hline \multirow{4}{*}{IMDB} & Precision & 0.695 & 0.765 & 0.805 & 0.901 & **0.931** \\  & Recall & 0.724 & 0.775 & 0.825 & 0.928 & **0.954** \\  & F-measure & 0.709 & 0.770 & 0.815 & 0.914 & **0.943** \\  & RMSE & 0.314 & 0.214 & 0.102 & 0.085 & **0.075** \\ \hline \multirow{4}{*}{Yelp 2013} & Precision & 0.741 & 0.765 & 0.799 & 0.895 & **0.925** \\  & Recall & 0.769 & 0.804 & 0.841 & 0.901 & **0.935** \\  & F-measure & 0.755 & 0.784 & 0.819 & 0.898 & **0.930** \\  & RMSE & 0.315 & 0.275 & 0.235 & 0.175 & **0.112** \\ \hline \multirow{4}{*}{Yelp 2014} & Precision & 0.725 & 0.799 & 0.841 & 0.895 & **0.914** \\  & Recall & 0.733 & 0.814 & 0.854 & 0.912 & **0.933** \\ \cline{1-1}  & F-measure & 0.729 & 0.806 & 0.848 & 0.904 & **0.923** \\ \cline{1-1}  & RMSE & 0.335 & 0.241 & 0.201 & 0.133 & **0.114** \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of proposed spider Taylor-ChoA-based HAN with existing methods using based on review rating prediction, in terms of precision, recall, F-measure and RMSE. The best results are displayed in boldface.
# Effect of Adding Waste Polyethylene and GGBFS on the Engineering Properties of Cement Mortar
## 3 Results and Discussions
### Resistance to Sulfate Attack  
As shown in Figure 12, when the W/B was 0.5 after 28 days, the weight loss rate of the sample of cement mortar with different proportions of waste PE added was \(-\)7.5% to \(-\)8.9% after five recycles. This result indicated that when the added volume of waste PE increased, the weight loss was higher, and the ability to resist sulfate was worse. The reason was mainly the increased waste PE, which led to increased internal holes that allowed the sulfate solution to erode the sample.  
Figure 11: The resistivity of cement mortars with the addition of different ratios of waste PE and GGBFS replacement.  
After 28 days, the weight loss rate of all proportions of waste PE with the addition of 10% GGBFS increased by \(-\)7.4% to \(-\)8.4%, while added 20% GGBFS increased by \(-\)6.6% to \(-\)7.1%, respectively. This result indicated that the increase in the replaced volume of GGBFS could reduce the improvement in the erosion ability of sulfate.  
In summary, if the added volume of waste PE increased, it generated more holes. If the replaced volume of GGBFS increased, hydration in the early phase was slow, and the weight loss rate of the sample could increase. However, the hydrates produced by the pozzolanic reaction of GGBFS filled the pores of the samples and made the mortar structure dense. In the late period, the decrease in the weight loss rate was apparent, and when 20% GGBFS was replaced, the durability was higher.
## 3 Advantages, Characteristics and Types of Digital Twin
### Classification of Digital Twin
#### 3.3.3 Application  
DT can also be categorized depending on their applications. The two broad applications of a DT are prediction and interrogation [47]. A _Predictive DT_, as the name suggests, predicts future behaviour and performance of its physical counterpart whereas an _Interrogative DT_ is used to interrogate the current or past state of its physical counterpart irrespective of its location. DTs can also be divided depending on if the focus of application is on product, process, or performance [46,78]:  
* _Product DT:_ It is used for prototyping as it analyses the product under different conditions and makes sure that the next physical product is behaving as planned. By virtually validating the product, the prototyping can be rapid as the total development time is reduced and there is no longer a need to develop multiple of them.
* _Production DT:_ It is used for validating the processes by simulating and then analyzing them even before the actual production. This helps in developing an efficient  
Figure 5: Types of DT based on level of integration (adapted from [2]). (i) Digital Model; (ii) Digital shadow/Static Digital model; (iii) Digital Twin/Dynamic Digital Model.  
production methodology under different conditions. The data from Product and Production DT can be used together for monitoring and maintenance of the machinery.
* _Performance DT:_ It is used for decision-making processes by capturing, Aggregating, and analyzing data from smart products and plants. Since Performance DT includes performances of both product and production, it optimizes the operations depending on the availability of plant resources, which creates an opportunity to improve on the Production and Product DTs via a feedback loop.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 1 Introduction
* In times of emergencies or natural disasters, UAVs can be deployed for search and rescue missions, damage assessment, and communication support. Control theory plays a pivotal role in optimizing UAV behavior in dynamic and challenging environments [14]. * Integrating UAVs into smart cities requires adherence to aviation regulations and safety standards. Control theory contributes to developing UAV control systems that comply with these regulations, ensuring responsible and lawful operations [15]. * Smart cities are dynamic environments with constantly changing conditions. Control theory enables UAVs to adapt and respond in real-time to changing environmental factors, enhancing their reliability and effectiveness [16]. Its application ensures the safe, efficient, and reliable operation of UAVs, enabling them to fulfill their potential in transforming urban landscapes and addressing various challenges for a smarter, more sustainable future.
# Virtual Reality in Discrete Event Simulation for Production-Assembly Processes
## 2 Materials and Methods
### Analysis of the Production-Assembly Process for the Creation of a Simulation Model  
Before creating the simulation model itself, it is important to develop an analysis of the production and assembly process. First, drawing a diagram of the production-assembly process from the available information is necessary.  
Description of the production process chart (Figure 2): The input for the production-assembly process will be materials and semi-finished products imported by subcontractors at regular intervals. Production begins with the pressing of materials. After pressing the materials, the production of the car body follows. This body is moved to the paint shop and then to the intermediate warehouse where the drying process takes place. In parallel with these processes, the process of machining shafts and gears takes place. Subsequently, these shafts and gears are heat treated. Only after the heat treatment process are the shafts and gears assembled with the mechatronics into the gearbox housing. After the transmission is assembled and the car body is dried, these parts of the car are moved to the assembly process, where the transmission is inserted into the body, as well as the installation of brakes, wheels and other parts that were imported by subcontractors. After this process, the final car moves to the final production process, which is visual inspection and testing. After testing, the car is transported by truck to the store, where the customer or customers buy the car.  
Another part of the analysis of the production-assembly process is the creation of a graph of the production process. Information is obtained from the graph of the production process which is used for the analysis of the continuity of the individual activities of the production process. With this graph, data such as, e.g., times of production operations, number of robots performing activities, number of pieces of materials, semi-finished products and products as well as transport distances can be illustrated. The last part of the analysis will be the process of creating a 3D simulation model and its display in VR (Figure 3). This method analyzes the detailed procedure of how to create such a 3D simulation model and display it in VR through the information obtained.  
Among the conventional--and, in practice, the most widely used--methods for mapping and creating a graphic representation of the production process are the following:  
1. VSM--value stream mapping;
2. Scheme of production processes;
3. Production sequence diagram;
4. Workflow chart;
5. Graph of the production process;
6. Diagram of two-handed work;
7. Group work diagram;
8. Diagram of machine-hand work.
# Intrinsic and Extrinsic Factors Affecting the Color of Fresh Beef Meat--Comprehensive Review
## 3 Results and Discussion
### Extrinsic Factors
#### 3.2.4 Chilling Rates  
The temperature at which carcasses are processed might influence the uniformity of meat color parameters. Heavier and fatter animals processed under intensive systems were more prone to experience slower chilling rates and faster pH decline post-mortem, which might develop in high-rigor temperatures, also referred to as PSE (pale, exudative, and soft) meat [107, 108]. The high-rigor condition occurs when pH decreases at values lower than 6 when the muscle temperature is higher than 35 \({}^{\circ}\)C [108]. This defect might also promote shrinkage in muscle fibers, longer sarcomeres, and improved light scattering, which resulted in lighter (higher L*) muscle color [109]. Intramuscular color variations were reported in m. semimembranosus (SM), due to a difference in chilling rates that caused a faster pH decline in the inner part of the muscle when the temperature was higher than 35 \({}^{\circ}\)C compared to the outer part [52, 53]. Another important quality defect arises when carcasses attain 0 \({}^{\circ}\)C in less than 5 h, which might result in the so-called cold shortening [110]. It was reported that cold-shortening defects are more frequent than high-rigor under industrial operations [111]. Carcasses with low fat/meat conformation are more likely to confront this problem and may generate high inconsistency in the meat color under industrial operations [111, 112]. In ovine muscle, fast chilling (0 \({}^{\circ}\)C in 5 h) resulted in higher pH and superficial metmyoglobin accumulation and lower L* values than in the conventional chilling treatment (2.75 \({}^{\circ}\)C in 14.5 h). It was reported that cold shortening reduced the visual appearance of ovine carcasses below the consumer's acceptability threshold (metmyoglobin: oxymyoglobin ratio lower than 3.5) [110]. In beef, cold-shortening was related to small carcasses, which showed slower pH decline rates, leading to meat toughness [111]. Cold-shortening might also decelerate carcasses' glycolysis processes which can eventually result in DFD meat [96]. In conclusion, chilling rates post-mortem might play a key role in preventing meat quality problems and in achieving uniform carcass colors. As mentioned before in this paper, the meat industry needs to standardize the animal weight at slaughter, in a way that the carcasses can be evenly cooled under commercial conditions.
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 2 Cognitive Threat Analytics IDS
The anomaly processing pipeline starts with the anomaly detector outputs and converts them into a single value with high classification strength. This process is illustrated in Figure 1, in which the output of the anomaly detector is processed in three layers. The first layer aggregates the input results of anomaly detectors by using two functions: the unsupervised (Ebtables) function and the supervised (Iptables) function [21]. These functions are designed to increase the precision and preserve recall. The second layer uses Linux-Availability Protection System (LAPS) models [21], to smooth the anomaly values and decrease the false-positive rate. Finally, the outputs from different LAPS models are once again aggregated by GRM and link aggregation bonding functions. The results are averaged together to produce the final anomaly score. The Linux-Availability Protection System (LAPS) [21] is a method designed to lower false positive rates by smoothing the output of a detector. This way, accidental spikes in anomaly scores are ignored while locally repetitive patterns of malicious behavior are preserved. It works by replacing an output of a detector by a weighted average of itself over similar samples. Similarity is defined as a Gaussian function of Euclidean distance in a predefined feature space [22]. Multiple different feature spaces are used in the pipeline in parallel to achieve better classification strength. Post-processing of the final anomaly score is the process of converting the numerical output of the anomaly processing pipeline into actionable intelligence that can be presented to a network operator. It involves clustering the flows, attaching labels and severities, sorting by anomaly scores, and displaying the output. As discussed above, network traffic using both HTTP and HTTP(S) protocols is analyzed, but the amount of information that can be extracted from HTTP(S) traffic is heavily reduced in comparison to HTTP traffic. With many powerful networking companies and organizations advocating encryption and initiatives, such as encryption, decreasing adoption difficulties (for both malicious and legitimate entities), and recent years have witnessed a dramatic increase in encryption pervasiveness. Thus, it is a suitable time to develop threat detection algorithms with excellent performance on encrypted traffic and which improve the performance of existing algorithms. Figure 1: Anomaly processing pipeline architecture. (LAPS: Linux-Availability Protection System)