esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Quality of Service
### Space Diversity  
Space diversity allows bypassing obstacles and interference by modifying the routing within networks organized in mesh topology. Such ability is however impossible with star topologies, as each device communicates exclusively and directly with a central coordinator. In case of obstacles that block the wireless communication, no alternative path is available and the communication cannot be established. Within the selected standards, the WISA specification and the 802.15.4e FA MAC standard are foreseen to be deployed in star topology only. Their protection against wireless channel obstructions and fluctuations is therefore reduced in comparison with the other standards. However, the maximal distance between the central coordinator and the sensors is shorter than between devices deployed in mesh topology. The probability of an obstacle breaking the communication is therefore low, if careful network planning is performed.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 3 MKID Applications in Observational Astronomy  
As MKIDs have originally been developed for astronomical instrumentation [1; 7] it is not surprising that their most established application so far is in observational astronomy, detecting and characterizing photons. We have divided this chapter in two parts for low energy photons (mid-IR and longer wavelengths) and higher ones (near-IR, optical and shorter wavelengths) for better clarity. But the working principle of MKIDs in these ranges is also slightly different. For higher energies, single photons break enough Cooper pairs to cause detectable single photon signal spikes, allowing single photon counting and energy resolution. For lower energies, starting around 1.5 \(\upmu\)m it becomes increasingly difficult to identify single photon events. Instead, MKIDs are operated in a way (see below) that their signal represents the energy flux impacting the detector. This way, photon energy and photon flux become indistinguishable--less photons of higher energy will result in the same signal then more photons with less energy--on a single pixel basis, but the detector's wavelengths limit is increased significantly.
## 3 Experimental Results
### Performance Evaluation
#### 3.3.4 Dataset of 1600 Videos  
The models were trained, validated, and tested using a 1600-video dataset, as indicated in Table 4. The table illustrates that SynthSecureNet outperformed both ResNet50V2 and MobileNetV2. The results in Table 4 were obtained during the testing phase, and the training duration that was observed during the training phase is also included in Table 4.  
Figure 4: Confusion matrices for SynthSecureNet, ResNetV2, and MobileNetV2 (1200-video dataset).  
Figure 3: Confusion matrices for SynthSecureNet, ResNetV2, and MobileNetV2 (800-video dataset).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **F1 Score** & **Recall** & **Precision** & **Accuracy** & **Training Duration** \\ \hline SynthSecureNet & 98.31\% & 98.25\% & 98.37\% & 98.15\% & 46 min \\ ResNetV2 & 91\% & 89\% & 90\% & 90\% & 26 min \\ MobileNetV2 & 91\% & 92\% & 90\% & 90\% & 19 min \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison using the 1200-video dataset.  
The SynthSecureNet architecture demonstrated a high level of performance in the detection of violence, as demonstrated by its F1 score of 98.88%, recall of 98.89%, precision of 98.87%, and accuracy of 98.77%. However, ResNet50V2 and MobileNetV2 both achieved an F1 score of 91%, indicating that their reliability levels are equivalent.  
The SynthSecureNet model required an extended training duration (1 h and 40 min) compared with ResNet50V2 (30 min) and MobileNetV2 (1 h and 11 min).  
Figure 5 illustrates the confusion matrices acquired during the testing process. The effectiveness of SynthSecureNet in accurately classifying violent and non-violent activities was demonstrated by its 9896 correct predictions and 123 incorrect predictions.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
## 4 Applications of Deep Learning in Agriculture
### Weather Forecasting  
Weather information is becoming increasingly crucial in the emerging agriculture sector, emphasizing accuracy and control while cultivating crops. The use of information technology, which includes weather forecasts and other features such as satellite and aerial imaging, GPS guidance, sensors, drones, variable fertilizer application, and plant health indicators, is a crucial component of this technique.  
Authors [52; 53] developed LSTM deep learning models to predict frost in plants by measuring low temperatures. Despite the high computational cost of LSTM models, they provided excellent time-series prediction results to find/expect ice in the plants.
## 3 Results and Discussion
### Effect of the Inlet Gas Flow Rate Hydrodynamic Characteristics of Sieve Tray
#### 3.2.3 Effect of the Inlet Gas Flow Rate on Tray Liquid Holdup  
One can calculate liquid holdup as  
\[h_{L}=A_{tray}\times h_{cl} \tag{11}\]  
Figure 7 shows the liquid holdup on the tray when the inlet gas flow rate is increased. Figure 7 shows that increasing the gas volume flow rate has a significant effect on liquid holdup on the tray. The liquid holdup on the tray is drastically increased when the gas volume flow rate is increased between 12 and 20 Nm\({}^{3}\)/h. It is noted that the increase in flow rate between 16 and 20 Nm\({}^{3}\)/h is more significant than the increase in flow rate between 12 and 16 Nm\({}^{3}\)/h, while the increase in flow rate between 20 and 24 Nm\({}^{3}\)/h has a small effect on liquid holdup. The trend of this effect is similar for all pressure values studied. The possible reason for this behavior is the increase in the superficial velocity of the gas in the absorber due to the increase in inlet gas flow rate, the increase in gas velocity causes the liquid to be trapped on a tray, resulting in the accumulation of the liquid on the tray, and as a result, the liquid holdup will increase. It appears also that when the gas inlet flow rate is increased after 20 Nm\({}^{3}\)/h, the liquid holdup is almost constant. This trend may be due to the high gas velocity accelerates the liquid to flow into the downcomer, resulting in a steady liquid holdup.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
### Modal Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
Modal analysis is a method used to determine the natural frequencies and mode shapes of a structure, assuming that the structural stiffness matrix and mass matrix remain unchanged; there are no time-varying loads within the structure [19]; and spring damping are not considered. In reality, the assembly process of each device varies, leading to differences in the components produced. The damping data for springs is not easily obtainable. These factors result in variations in the actual damping ratios of three-degree-of-freedom low-frequency resonant mixers. Measurements must be taken after assembly to determine the damping, and adjustments such as modifying the excitation force can be made to accommodate different damping levels. Therefore, this study chooses to ignore spring damping during simulation. In the case of no damping, the curve of acceleration and amplitude with frequency variation can be approximated by a discrete point to form an approximate curve of change under small damping. In the case of uncertain damping, this curve can well reflect the performance of the equipment. Modal analysis can be used to identify the vibration characteristics of mechanical components, namely the inherent frequencies and mode shapes of the structure. It is a necessary preliminary analysis process for harmonic response analysis [20]. For the three-degree-of-freedom low-frequency resonant mixer involved in this study, utilizing finite element modal analysis is an efficient method for calculating the mode of a vibration system.  
.1.1 Preparatory Work for Modal Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
Due to the complexity of the three-degree-of-freedom low-frequency resonant mixer, it is necessary to simplify the model appropriately without affecting the analysis results. Bolted connections and welded parts between the components are combined using Boolean operations to reduce model complexity [21]. The resulting x-t file is then imported into ANSYS Workbench, and the model is meshed using tetrahedral elements.  
The manner in which springs behave during computer simulation directly affects the simulation results. In ANSYS Workbench, there are two methods for applying springs: geometry-to-ground and geometry-to-geometry. Since this study primarily focuses on the vibration characteristics of the equipment's core part, the framework is omitted in the simulation, and the geometry-to-ground method is chosen for applying springs. Because the components of the mixer are structurally limited to produce motion only in the vertical direction, the spring is set using the longitudinal stiffness method, and then the parameters of the spring are input into ANSYS Workbench. Boundary conditions for each assembly are then set, constraining the degrees of freedom in the X and Y directions and releasing only the Z-axis freedom to simulate the actual working condition of the equipment.
## 4 Results and Discussion
### DA-OA-ICOS Combined with White Noise Perturbation
#### 4.1.1 Effect of White Noise Perturbation on Sawtooth Wave Signals  
The DA-OA-ICOS were generated by the signal generator with white noise perturbation of different modulation depths (0%/5%/10%/15%/20%/25%/30%/35%). The experimental temperature and pressure were maintained at 298 K and 101 kPa, respectively. Under the white noise perturbation with different modulation depths, the measured sawtooth wave signals showed different fluctuating waveforms as shown in Figure 3. The results indicated that the original sawtooth wave signal (black) without white noise perturbation showed obvious noise fluctuations. The fluctuations showed a regular noise distribution, which mainly originated from the cavity mode noise [29]. As the modulation depth of the white noise perturbation increased, the noise fluctuations in the sawtooth wave signal reduced significantly. Simultaneously, the amplitude of the sawtooth wave signal also decreased, and the suppression effect of cavity mode noise was gradually enhanced. It was demonstrated that the white noise perturbation played a key role in reducing the cavity mode noise.
## 2 Numerical Modeling and Analysis
### Laser Scanning Procedure  
Laser scanning of the human torso involves the use of three-dimensional (3D) imaging technology to create a highly detailed digital representation of the upper body, including the chest and spine. The procedure follows these general steps: (i) Patient Preparation: During the scanning process, the patient must wear appropriate clothing, avoiding loose or reflective clothing, to ensure accurate data capture. (ii) Scanner Calibration: The laser scanning equipment is calibrated to ensure precision and accuracy according to instructions by the manufacturer. (iii) Patient Placement: The patient is placed in a designated area, and the laser scanning device is positioned to capture the upper body. (iv) Laser Scanning Process: The scanner emits laser beams onto the surface of the upper body. These lasers create a point cloud by measuring the distance between the scanner and the surface at various points. As the scanner moves or rotates, it captures data from different angles to build a comprehensive 3D model. Multiple scanning passes may be used to ensure complete coverage and accuracy. (v) Data Registration: The collected point cloud data is then aligned to ensure that all scans are properly combined to form a cohesive and accurate 3D representation. (vi) Mesh Generation: The point cloud data is converted into a mesh, a continuous surface composed of interconnected triangles. This mesh represents the detailed geometry of the scanned torso.
## 2 Materials and Methods
### Fuzzy Estimator  
Fuzzy logic (FL) is one of the disciplines of computational intelligence that studies the adaptive mechanisms that enable or facilitate intelligent behavior in a complex and changing environment. It may be argued that it employs the value space of falsehood (0), truth (1), and multi-valued between 0 and 1 [46]. Consequently, fuzzy inference systems (FIS) have been integrated to assist in reducing uncertainty in the determination of physicochemical variables of fruits [20]. As a part of this, the following FIS architecture was proposed, which is shown in Figure 10. It uses as descriptors the orange sub-regions used to determine the different CCI and as outputs the class, sugar, and firmness. The proposed system architecture comprises three fuzzy systems that estimate the variables maturity, degrees Brix, and firmness.  
Table 2 presents five models proposed for training with 10 epochs. The models were developed using Matlab's Fuzzy Logic and Neuro-Fuzzy Designer Apps, which enabled us to classify the samples according to their degree of maturity. The primary distinction between the proposed models is the number of sub-regions used for input and the amount of membership function (MF) of the triangular type. Model 1 employed a 3 \(\times\) 3 sub-region, three membership functions, and an error rate of 0.766. Model 2 employed a 3 \(\times\) 3 and 5 \(\times\) 5 sub-region, six membership functions, and an error rate of 0.705. Concerning Model 3, it is observed that it employed three different sub-region sizes, including 3 \(\times\) 3, 5 \(\times\) 5, and 11 \(\times\) 11, in addition to nine membership functions, with an error of 0.623. Model 4 employed the aforementioned sub-regions in conjunction with 21 \(\times\) 21 and 12 membership functions, resulting in an error of 0.613. Model 5 employed the 3 \(\times\) 3, 5 \(\times\) 5, 11 \(\times\) 11, 21 \(\times\) 21, and 31 \(\times\) 31 sub-regions, along with 15 membership functions, resulting in an error of 0.172. The triangle membership function was selected for its simplicity of implementation and low computational cost.  
Figure 10: Proposed fuzzy inference system.  
Table 3 presents the architecture of Models 6 to 10, which predict the degree Brix of oranges. These models share similar characteristics with maturity models, including inputs and membership functions. Table 3 shows that the mean square error varies in the prediction of the physicochemical variable content using the set of oranges, Valencia.  
Table 4 shows the architecture of Models 11 to 15, which predict the firmness of oranges. These models have similar characteristics to the models for maturity and Brix, including inputs and membership functions.
# A Study on the Optimisation of Tennis Players' Match Strategies from the Perspective of Momentum
## 5 Conclusions  
1. A system was constructed for the quantification of momentum in tennis players, with the objective of calculating the momentum scores of players in order to reflect critical moments and turning points in matches. The verification results were found to be consistent with actual match scenarios;
2. The present study sought to explore the influence mechanism of player momentum on match trends, by means of a comprehensive analysis of momentum scores de  
Figure 4: Line chart of predicted values and actual values.  
rived from momentum quantification. The findings demonstrate that, within a 95% confidence interval, the cumulative momentum scores for each game exhibit a high correlation with the win-loss changes of players in that game, while the momentum scores for each point show a weaker correlation with the win-loss changes at that point. The correlation analysis results indicate that cumulative momentum can, to a certain extent, reflect match trends. The success of players in matches is not entirely random, suggesting that player match strategies can be optimised using momentum;
3. A player match strategy optimisation model based on momentum scores was constructed, with the objective of optimising real-time match strategies from two major aspects: player performance scores and match trends prediction. The performance scores calculated using the entropy weight-TOPSIS model are capable of reflecting changes in the player's state in real time, while the BP neural network model is capable of predicting changes in match trends. The integration of these two approaches enables coaches and players to dynamically adjust their strategies during the match. For instance, when the performance score indicates that the player is in an advantageous position, the player can adopt a more aggressive offensive strategy to consolidate the advantage by combining the trends prediction model to assess the likelihood of a counterattack from the opponent. Conversely, when the performance score indicates that the player is at a disadvantage, the player can adopt a more robust defensive strategy to identify opportunities to reverse the situation by combining the trends prediction model to assess the opponent's weaknesses. Furthermore, by monitoring the changes in performance scores and trends predictions in real time, players can make more precise tactical adjustments at critical moments, such as break points and match points, thereby increasing their chances of winning. This integrated analysis method not only enhances the real-time and scientific nature of match analysis, but also provides players and coaches with more intuitive and actionable strategy optimisation recommendations.
## 3 Materials and Methods
### 5G Technologies
There are several potential implementations of UAVs in the context of 5G and IoT. One such implementation is in the area of precision agriculture. UAVs can be used to gather data on crop growth, soil conditions, and other factors that affect agricultural production. These data can be transmitted in real time to a ground station for analysis and used  
Figure 8: Characteristics and technical specifications of 5G networks. to optimize planting, fertilization, and irrigation schedules [65]. 5G networks and IoT sensors can provide the necessary bandwidth and low latency for this type of application. Another potential application of UAVs in the context of 5G and IoT is in industrial inspection and maintenance. UAVs equipped with cameras and other sensors can be used to inspect and monitor equipment and infrastructure such as power lines and wind turbines. Real-time data transmission via 5G networks can enable remote monitoring and control of these systems, improving their reliability and reducing maintenance costs [66]. UAVs can also be used for emergency response and disaster management. In the event of a natural disaster, such as a hurricane or earthquake, UAVs can be deployed to assess damage and provide real-time information to first responders. The data collected can be transmitted via 5G networks to emergency management centers for analysis and decision making. UAVs can be used for surveillance and security purposes. In public safety applications, UAVs can be used to monitor crowd movements and gather intelligence on potential threats. In private security applications, UAVs can be used to patrol and monitor facilities for intruders or other security threats. The combination of UAVs with 5G networks and IoT sensors can enable a wide range of applications in various industries. With the potential to improve efficiency, reduce costs, and enhance safety, it is likely that we will see an increase in the adoption of UAVs in the coming years.
## 1 Introduction  
Position and orientation described by distances and angles between two assembled parts are determined after assembly. They can affect the product performance to a large extent, considering that assembly is generally the final step in manufacturing mechanical products [1, 2]. Furthermore, surface deviations of the manufactured surfaces result in position and orientation deviations (PODs) between the acquired position and orientation and the ideal circumstances, directly affecting the product performance [3, 4]. For instance, the machining accuracy of the machining center is affected by PODs generated after the guide rail planes of a workbench and base are assembled [5, 6]. Therefore, to ensure the performance of mechanical products, PODs should be evaluated using the predicted surface deviations in the design stage.  
Mechanical parts can be assembled according to a single mating plane. However, additional mating planes are generally required to constrain the degrees of freedom (DOFs) that are unconstrained by a single plane. For instance, the column and base of the machining center are assembled only through a single mating plane, whereas the workbench and the base are assembled through two planes: the bearing and guiding planes of the rail. These examples are shown in Figure 1.  
The position and orientation deviations of multiple planes are caused by surface deviations and are also affected by multiple possible types of positioning. An example is illustrated in Figure 2. After two parts are assembled by two mating planes, five DOFs are constrained, and the position and the orientation corresponding to the constrained DOFs are determined. Because the non-ideal morphologies of the planes at the five contact points deviate from the ideal circumstances, PODs occur, as shown in Figure 2a,b. Because of the multiple mating planes, completely constrained positioning or incompletely constrained positioning may occur. In addition, over-constrained positioning will exist if several DOFs are constrained by more than one contact point. When incompletely constrained positioning occurs, the assembly positions corresponding to the unconstrained DOFs are uncertain. Different contact points will emerge after the uncertain positions are changed, which inherently results in different PODs, as shown in Figure 2b,c. When over-constrained positioning also exists, it is uncertain whether the over-constrained DOFs will be constrained and through which contact points. Thus, the number of contact points on each constraint plane can vary, bringing about different PODs, as shown in Figure 2b,d.  
As shown, multiple mating planes are generally required when mechanical parts are assembled. Consequently, the problem of the POD computation of multiple non-ideal planes should be investigated. Furthermore, the effects of not only surface deviations but also multiple types of positioning should be considered.