esa-sceva/satcom-chunk-collection huggingface dataset:
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 4 Discussion  
This study outlines the design and justification, development, implementation, and subsequent evaluation of an online TMS to be used by amateur rugby union teams. The S&C coaches highly valued the measures of sRPE, muscle soreness, and sleep duration while the players found muscle soreness, fatigue, sRPE, and RTT most useful. Conversely, the main barriers to effective training monitoring from all six coaches' perspective are a lack of player compliance, data inconsistency, gathering data around matches, and external confounders. The main barriers from the players' perspective are a lack of feedback and evidence of data use from their coaches. However, 73% of S&C coaches working with teams playing at the highest level of amateur rugby union in Ireland monitor training [7], and this current study offers both a method that can be effective, and pitfalls to avoid for both practitioners and researchers alike.  
Figure 4: Players’ responses to the 1–5 Likert scale questions (1 = the most negative response, 5 = the most positive response) evaluating the training monitoring system.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### Design and Implementation of the RC-NB-LDPC Code
#### 2.3.2 Encoder Architecture  
The encoder-coding block diagram is shown in Figure 5. It is divided into three parts. The input buffer part is used to buffer the information transmitted by the upper level and output the data when the encoding part is needed. The second part is the encoding part, which is used to calculate the encoding result and output the calculation result to the output buffer module. The output buffer part is used to store the encoding result and the communication with the downstream module.  
The hardware structure of the NB-QC-LDPC code encoder is shown in Figure 6, which includes an input module, an encoding module, and an output module.  
Figure 4: Generation matrix G structure of NB-LDPC codes.  
Figure 5: The coding block diagram.  
In addition to buffering the source symbol function passed by the previous level, the input module also completes the cross-clock domain operation of the data. Furthermore, according to the currently input code rate information, the source symbol is truncated into a sequence of corresponding lengths, so that the coding module performs coding. Therefore, the input module mainly includes a fifo module and a corresponding control module. When the \"encoding_over\" signal of the encoding module is high, it indicates that the encoding module has been encoded, and can receive a new source frame.  
The coding module is a core module of the NB-LDPC encoder, and its function is to perform a matrix multiplication operation on the source information and the generation matrix in the Galois field to obtain a coding result. The coding module mainly includes an operation module, a RAM storage module and a corresponding control module.  
The output module mainly performs the function of storing the encoded result and communicating with the downstream module. The encoding module inputs the encoding result to the output module. When the \"out_ready\" signal is high, it indicates that the next level has been prepared for reception, and the encoding result may be output; otherwise, the output module temporarily stores the encoding result. When the output module is full, the output \"in_ready\" signal is turned low, indicating that new data cannot be accepted. Therefore, the output module mainly contains the fifo module and the corresponding control module.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 4 Simulations  
In this section, numerical simulations of the BER performance under non-ideal synchronization conditions are proposed, both for constant time offset and jitter.
## 3 Results and Discussion
### The pH, Water Holding Capacity (WHC), and Cooking Loss  
The effects of SC-CO\({}_{2}\) on the pH values of raw chicken meat stored at 4 \(\pm\) 1 \({}^{\circ}\)C for seven days are presented in Table 3. The result revealed that low- and medium-pressure (7.4 and 11.4 MPa, respectively) SC-CO\({}_{2}\) affected (\(p<0.05\)) the pH of the meat samples on the zeroth day of storage compared to the control and high-pressure (15.4 MPa) SC-CO\({}_{2}\). However, no changes (\(p>0.05\)) were recorded between the control and the application of SC-CO\({}_{2}\) on the third and seventh day of storage. A similar study confirmed the current results, that is, no changes in pH were observed after the application of SC-CO\({}_{2}\) on the meat of the porcine _longissimus dorsi_ muscle [7; 10]. The increase in the pH values from the zeroth day to the third and seventh days may be due to the exposure of the acidic amino acid groups as proteins unfolded during the pressure application. Treatment of high pressure and temperature can affect molecular interactions and protein conformation, resulting in protein denaturation and meat aggregation [11]. The effects of water holding capacity (WHC) of the raw chicken meat in chilled storage after the application of SC-CO\({}_{2}\) are shown in Table 3. The results showed that the treated samples were not affected similarly as the control sample. The denaturation of the muscle protein occurs as a result of the combination of temperature and low pH, which then becomes responsible for a reduction in WHC. For example, the pale breast fillets displayed slightly lower absolute pH and WHC than the control meat, demonstrating a close relationship between both the color of meat and the ultimate pH and WHC [27]. Based on the lower temperature used during the treatments and less affected pH values of the chicken meat, the result of the WHC subsequently was not affected by the SC-CO\({}_{2}\) treatment. Table 3 shows the cooking loss values after the application of SC-CO\({}_{2}\) on the raw chicken meat stored at 4 \(\pm\) 1 \({}^{\circ}\)C for seven days. No difference (\(p>0.05\)) was recorded in all the samples. This result is confirmed by a similar research study conducted on porcine _longissimus dorsi_ muscles [10]. Meat lost both volume and weight by fluid removal during the cooking process. This increase in fluid content contributes to changes in meat's textural characteristics which are in addition to the changes
# The Integration of AI and Metaverse in Education: A Systematic Literature Review
## 4 Results and Discussion
### Rq1: What Disciplines Are Covered by the AI-Driven Metaverse in Education?
In addition, the metaverse can offer medical students digital lessons, with the ability to dissect and to examine anatomical devices in detail, as well as monitor the evolution of pathological processes and simulate surgical procedures on human avatars [78]. Students' motivation and engagement in the metaverse environment was measured quantitatively with a MANCOVA model (i.e., a statistical technique to analyze the relationship between multiple dependent and independent variables), which showed that the differences between the experimental group (metaverse education environment) and the control group (conventional education environment) were considered statistically significant. This strengthened the study's conclusion that the integration of AI and the metaverse for the purposes of education can significantly improve learning outcomes, with 88% of students positively influenced by the metaverse learning technologies [26]. In a further study, the Attention, Relevance, Confidence, and Satisfaction (ARCS) model was used to evaluate students' interactions with AR. The questionnaire demonstrated AR led to a 77% improvement in the students' comprehension, with the use of AR applications in education proving to increase students' motivation and participations through gamified learning experiences [79].
## 5 Computational Experiments
### Exact Method  
For the solution of the mathematical model presented in Section 5.1, we used the solver IBM ILOG CPLEX. CPLEX found the optimal solution to the proposed problem, respecting the capacity and processing time constraints. All factories start with the stock available according to Table 1.  
Figure 23: Description of the production and transport steps of \(p_{2}\) at \(f_{3}\)—GA.  
Figure 22: Description of the production and transport steps of \(p_{1}\) at \(f_{3}\)—GA.  
Figure 24: \(p_{1}\) stock behavior at the distribution center—GA. *: corresponds to the moment of meeting the demand.  
As in the GA, the product with the longest quarantine time, \(p_{1}\), establishes the value of \(Tmax\) also on \(d_{26}\). However, unlike the solution obtained by APS, the demand for \(p_{2}\) in the DC is satisfied before \(p_{1}\). Another consequence of the long quarantine period of \(p_{1}\) is that the logistic operations of this product are concentrated in the final periods.  
In the first 5 days, the initial stock of \(p_{1}\), both in \(f_{1}\) and \(f_{3}\), is fully used as input for \(p_{2}\) production or transported to the \(DC\) (see Figures 26-29). As \(p_{1}\) requires 20 days of quarantine, excluding initial stocks, there are no finished inputs for the production of \(p_{2}\) until \(d_{21}\).  
The following comments are anchored in the figures of Appendix A.2. At \(f_{1}\), the stock of \(p_{3}\) shows a sharp reduction in the first 3 days, with dedicated shipments for \(DC\), resuming production again at the end of the time horizon.  
It is observed that all \(p_{3}\) originated from \(f_{1}\) is transported directly to the \(DC\). Therefore, the production of \(p_{4}\), which depends on \(p_{3}\), is all carried out in \(f_{4}\).  
The initial stocks of \(p_{3}\) and \(p_{4}\) remain at \(f_{4}\) until \(d_{5}\) and \(d_{14}\), respectively. The \(p_{3}\) in \(f_{4}\) productions are available from \(d_{12}\) and are used for the production of \(p_{4}\) or transported to the \(DC\). The demand for \(p_{3}\) and \(p_{4}\) is met in \(d_{25}\).  
Products \(p_{5}\) and \(p_{6}\) are produced and transported according to demand as quickly as possible, which happens on \(d_{20}\) and \(d_{22}\), respectively.  
All initial stock of \(p_{5}\) in \(f_{2}\) goes to the \(DC\) on the first day. From this, \(p5\) is being released, and its transport \(DC\) seeks to optimize the capacity of the vehicles (which are type 1 or 2, used simultaneously).  
The Exact Method achieved the same result of the GA reaching a maximum completion time of 26 days. Thus, the total horizon of production and distribution of products, including all processes of production, transport, storage, and restrictions was completed in this period and demand was met accurately, with no remaining production.  
The significant difference between the Exact Method and the solutions obtained by APS and the GA is that their submitted production plan ensures that finished products are made available in the distribution center with sufficient time for marketing within the shelf life.
# ResGDANet: An Efficient Residual Group Attention Neural Network for Medical Image Classification
## 4 Experiments
### Ablation Research and Visualization
#### 4.4.2 Ablation Experiment of Attention Module  
To more intuitively and visually validate the classification performance of the proposed ResGDANet model in this paper, we used Grad-CAM [39] to visualize the attention generated by ResGDANet without attention modules, without DCAF, without RMT, and with both attention modules. Figure 8 demonstrates that without attention modules, the network's focus is limited to specific portions of the target region. With the addition of the DCAF module, the region of interest expands, though it shows misalignment with the target area. The combination of both attention modules outperforms the individual modules, enabling more precise localization and coverage of target objects. The visualization performance achieved by incorporating only the spatial attention module and RMT module is marginally inferior to that of using solely the DCAF module. The RMT module exhibits a weaker ability to localize boundary regions compared to the DCAF module. However, when compared to visualizations without attention modules, the RMT module shows enhanced attention to the target region.  
Figure 8: Visualization results of the attention module ablation experiments show that regions with higher weights exhibit higher heat values. The first column displays the original input images, the second column shows the visualization results without the RMT module, the third column presents the visualization results without the DCAF module, and the fourth column illustrates the visualization results with both attention modules integrated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline ResGDANet (G = 1) & 80.78 & 81.05 & 80.78 & 80.91 \\ ResGDANet (G = 2) & 82.27 & 83.14 & 82.27 & 82.71 \\ ResGDANet (G = 4) & 83.74 & 82.11 & 83.74 & 82.90 \\ ResGDANet (G = 8) & 82.37 & 82.28 & 82.37 & 82.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of ResGDANet across different groups on the COVID19-CT dataset.  
To further validate the effectiveness of each strategy and module, we also conducted ablation experiments on the ISIC2018 dataset. The experimental results, presented in Table 4, primarily validate the effectiveness of various attention modules. Table 4 demonstrates that the incorporation of both RMT and Dual-Channel Attention Fusion modules yields superior performance compared to the baseline without attention modules.
# AI, Optimization, and Human Values: Mapping the Intellectual Landscape of Industry 4.0 to 5.0
## 4 Theoretical Perspectives
### Sensor Technologies  
Advanced sensor networks enable the continuous collection of high-resolution data for dynamic control and system refinement. These sensors not only track machine performance and product quality but also monitor worker well-being and environmental conditions, contributing to both efficiency and safety [51; 59].  
Next-Generation Communication Technologies  
Technologies such as 5G and beyond offer the ultra-low latency and high bandwidth required for seamless communication among distributed systems, including robotic swarms, smart sensors, and remote operations [24]. These networks enable real-time synchronization and coordination across geographically dispersed production environments, supporting agile and resilient manufacturing ecosystems [62].
## 2 System Modeling
### Dynamics of Flying-Base Manipulators  
The dynamic model of a flying-base manipulator can be expressed in matrix form by the following equations (for more details see [18]):  
\[\begin{bmatrix}[\mathbf{H}_{b}]&[\mathbf{H}_{bm}]\\ [\mathbf{H}_{bm}]^{T}&[\mathbf{H}_{bm}]\end{bmatrix}\begin{bmatrix}\ddot{\mathbf{x}}_{b}\\ \ddot{\mathbf{q}}\end{bmatrix}+\begin{cases}\mathbf{c}_{b}\\ \mathbf{c}_{m}\end{cases}=\begin{cases}\mathbf{F}_{b}\\ \mathbf{\tau}\end{cases}+\begin{bmatrix}[\mathbf{J}_{b}]^{T}\\ [\mathbf{J}]^{T}\end{bmatrix}\mathbf{F}_{e} \tag{5}\]  
Figure 3: Schematic illustration of the aerial manipulator and its geometrical parameters.  
where the variables are as follows:  
* \([\mathbf{H}_{b}]\ \in\mathbb{R}^{6x6}\) represents the base inertia matrix;
* \([\mathbf{H}_{bm}]\ \in\mathbb{R}^{6 xn}\) represents the base-manipulator coupling inertia matrix;
* \([\mathbf{H}_{m}]\ \in\mathbb{R}^{nxn}\) represents the manipulator inertia matrix;
* \(\mathbf{c}_{b},\ \mathbf{c}_{m}\ \in\mathbb{R}^{6 xn}\) are velocity-dependent non-linear terms (Coriolis and centrifugal terms);
* \(\mathbf{F}_{b}\ \in\mathbb{R}^{6}\) is the total force/torque applied to the base;
* \(\mathbf{\tau}\in\mathbb{R}^{n}\) is the vector of the joint torques;
* \(\mathbf{F}_{e}\ \in\mathbb{R}^{6}\) is the external force/torque on the end-effector.  
The inertia matrices above can also be used to express the relationship between the base and joint velocities (\(\dot{\mathbf{x}}_{b}\) and \(\dot{\mathbf{q}}\)) and the total momentum of the system (\(\dot{\mathbf{h}}\)) [19]:  
\[[\mathbf{H}_{b}]\dot{\mathbf{x}}_{b}+[\mathbf{H}_{bm}]\ \dot{\mathbf{q}}=\dot{\mathbf{h}}=\begin{cases} \mathbf{p}\\ \mathbf{K}\end{cases} \tag{6}\]  
where \(\mathbf{p}\in\mathbb{R}^{3}\)and \(\mathbf{K}\in\mathbb{R}^{3}\) represent the linear and angular momentum of the system, respectively. The angular momentum is calculated with respect to the UAV CoG (\(G_{\text{U}AV}\)).  
The differentiation of the momentum formula (6) yields the following acceleration constraint:  
\[[\mathbf{H}_{b}]\ddot{\mathbf{x}}_{b}+[\mathbf{H}_{bm}]\ddot{\mathbf{q}}+\Big{[}\dot{\mathbf{H}}_{ b}\Big{]}\dot{\mathbf{x}}_{b}+\Big{[}\dot{\mathbf{H}}_{bm{bm}}\Big{]}\dot{\mathbf{q}}=\dot{ \mathbf{h}}=\frac{d}{dt}\begin{cases}\mathbf{p}\\ \mathbf{K}\end{cases} \tag{7}\]  
which introduces a relationship between the system generalized velocities/accelerations and the rate of change in the total momentum, \(\dot{\mathbf{h}}\) (which is directly related to the external forces/torques, \(\mathbf{F}_{ext}\in\mathbb{R}^{6}\), acting on the system; see Equation (12) in the following section).
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 3 Materials and Methods
### Degraded Image Generation  
Given one HR image \(\mathbf{x}^{(i)}\), the generation of a noisy degraded LR image \(\mathbf{y}_{\mathbf{n}}^{(i)}\) during inference is indicated by the dotted blue lines in Figure 1. IDFlow first samples two latent variables \(\mathbf{z}_{b}^{(i)}\) and \(\mathbf{z}_{\mathbf{n}}^{(i)}\) from the Gaussian distribution. It then takes \(\mathbf{z}_{b}^{i}\) and \(\mathbf{x}^{(i)}\) as input and uses the reverse flow of BDM to generate synthetic clean LR image \(\mathbf{y}^{(i)}\). The global average pooling is performed on \(\mathbf{y}^{(i)}\) as the RNM condition feature \(\mathbf{u}_{\mathbf{n}}^{(i)}\). \(\mathbf{z}_{\mathbf{n}}^{(i)}\) and \(\mathbf{u}_{\mathbf{n}}^{(i)}\) are taken as input via the reverse flow of RNM to generate synthetic noise output \(\mathbf{n}^{(i)}\). At last, the noisy LR image \(\mathbf{y}_{\mathbf{n}}^{(i)}\) is obtained by adding \(\mathbf{n}^{(i)}\) to \(\mathbf{y}^{(i)}\).  
Thus, IDFlow can explore the learnt conditional distribution \(p_{\mathbf{y}_{\mathbf{n}}|\mathbf{x}}(\mathbf{y}_{\mathbf{n}}|\mathbf{x})\) by sampling various degraded LR images via \(\mathbf{y}_{\mathbf{n}}^{(i)}=f_{IDFlow}^{-1}(\mathbf{z}_{b}^{(i)},\mathbf{z }_{\mathbf{n}}^{(i)};\mathbf{x}),\mathbf{z}_{b}^{(i)},\mathbf{z}_{\mathbf{n}}^ {(i)}\sim p_{\mathbf{z}}\) given one HR image \(\mathbf{x}\). Here, \(f_{IDFlow}^{-1}(\cdot)\) is the reverse function of IDFlow. We simply utilize a Gaussian distribution \(\mathcal{N}(0,\tau)\) with variance \(\tau\), which is also called sampling temperature. As discussed in [21], the better results are achieved when sampling with a slightly lower variance.
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 2 Sources of Magnetic Forces in PMSMs  
The functioning principle of a PMSM is based on the interaction of two magnetic fields in the air gap. These fields generate magnetic pressures and forces around the surfaces of the stator and rotor that are adjacent to the air gap. The magnetic forces generated in PMSMs can be created by three different sources: magnetic pressures originating on the stator, magnetic pressures originating on the rotor, and forces created by eccentricities. In this section, all these sources are analyzed.
# A Study on the Applicability of Waste Glass Wool and Waste Mineral Wool as Fiber Reinforcement
## 3 Methodology
### Flexural Strength Test  
The compressive strength and splitting tensile strength of concrete are considered as the most important parameters. Flexural strength on the other hand is of relatively low importance, but a critical factor that can predict the load that can lead to defects such as the start of cracks [33]. Fibers added during the mixing process of concrete are randomly dispersed while being meshed and entangled around the aggregate. Through this process, fiber-reinforced concrete has a high tensile strength compared to plain concrete [34]. This test was conducted to evaluate the flexural strength of concrete added with waste fibers, and it was conducted according to KS F 2408 \"Standard test method for flexural strength of concrete\" [35]. The prismatic specimens of 100 \(\times\) 100 \(\times\) 400 mm wet cured during the designed age were used. The specimen was placed on equipment of the same model as the compressive strength equipped with the jig for measuring flexural strength, and the load was applied at a rate of 0.06 \(\pm\) 0.04 MPa/s until the specimen was destroyed by applying the three-element loading method as shown in Figure 5.
## 3 Results and Discussion
### Physicochemical Properties of the Samples
#### 3.2.2 XRD Analysis  
Figure 4 shows the XRD patterns of these four samples under study. All of the samples exhibited typical ZSM-5 peaks (20 = 23.1\({}^{\circ}\), 23.3\({}^{\circ}\), and 24.4\({}^{\circ}\) PDF-ICDD 44-0003), suggesting the presence of MFI structure. Moreover, it can be seen that there were no evident variations in the peaks of XRD diffraction that could be detected for these three modified samples, indicating the limited impact of doping active components on the crystal structure of the support [38]. As for Co-ZSM-5 and Fe-ZSM-5 samples, the peaks at 36.8\({}^{\circ}\) indexed to Co\({}_{3}\)O\({}_{4}\) (PDF-ICDD 42-1467) and 33.1\({}^{\circ}\) attributed to Fe\({}_{2}\)O\({}_{3}\) (PDF-ICDD 33-0664) were found distinct, suggesting the loading of Co and Fe beyond the theoretical monolayer coverage on the ZSM-5 surface. With regard to Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5, the appearance of diffraction peaks was attributed to the disappearance of iron oxides, suggesting the potential existence of iron oxides in a highly dispersed state on the surface of ZSM-5 [39; 40]. Moreover, compared with Co\({}_{4}\)Fe\({}_{1}\)-ZSM-5 and Co-ZSM-5, the peaks indexed to cobalt oxides were weakened and widened, implying that the introduction of Fe\({}_{2}\)O\({}_{3}\) exerts an inhibitory effect on the growth of Co\({}_{3}\)O\({}_{4}\), thus reducing the size of cobalt oxide particles. As a consequence, there might be more active sites provided for the reactions [41].
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
## 2 Authentication Process
### Traditional Authentication
#### 2.1.4 Proof-of-Location  
Such type of authentication records when and where the user logged in. This authentication process uses the user's location to determine their identity. This type of authentication commonly uses a GPS [38], IP address, cell tower ID, and so on. The majority of Location-Based Authentication (LBA) implementations essentially require the use of a location signature (LS), which is generated with the assistance of a location signature sensor (LSS) [39]. The location signature describes the person's physical location and the timestamp of the access request.
# Periodic Surface Defect Detection in Steel Plates Based on Deep Learning
## 2 Description of Periodic Defects  
Roll marks on hot-rolled steel plates are typical periodic defects. The training and testing of deep learning networks requires a sufficient number of samples. However, due to the insufficient number of images that were actually collected, we needed manual data amplification. This section introduces features and sample amplification of the periodic roll marks.