esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Process Capability
Arithmetic mean \(\overline{x}\) and standard deviation \(s\) of measurements are given by the formula:  
\[\overline{x}=\frac{1}{n}\sum x_{i}, \tag{2}\]  
\[s=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_{i}-\overline{x})^{2}}\quad\text{for n }>30, \tag{3}\]  
Figure 1: Taguchi and Taylor quality loss functions. Source: own study. \[s=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}}\quad\text{ for n }\leq 30, \tag{4}\]  
where:  
* \(x_{i}\), measurement value;
* \(n\), sample size. As follows from the above dependence, in order not to generate excessive losses associated with maintaining defective products, the process capability index should be [36]:  
\[C_{P}\geq 1, \tag{5}\]  
In the assessment of process capability in industrial practice, the distribution limited by six standard deviations is taken as the measure of the scatter of measurement results [37]. For the normal distribution of the process variable, the \(C_{P}\) process capability indicator takes the form:  
\[C_{p}=\frac{USL-LSL}{6\sigma}, \tag{6}\]  
where:  
* _USL_, upper tolerance limit;
* _LSL_, lower tolerance limit;
* \(n\), sample size;
* \(\sigma\), standard deviation of the general population. Due to the growing requirements of customers, especially global concerns, the criteria for the minimum limit value of the \(C_{P}\) coefficient have been adopted for some industries [38]. According to [PERSON] et al., [39] the minimum values of \(C_{P}\) coefficient for selected industries are for the machinery industry \(C_{P}=1\), for the automotive industry \(C_{P}=1.33\), and in the aviation industry \(C_{P}=2\). Analyzing the capability of the production process, three ranges of the \(C_{P}\) value can be presented. When \(C_{P}>1\) the process dispersion is smaller than the width of the tolerance range, and this is the recommended process capability. For \(C_{P}=1\), the tolerance range is equal to the process dispersion, and this is a satisfactory process capability [40]. When \(C_{P}<1\), the tolerance range is narrower than the process dispersion--defective products are produced in excessive quantity. Then, the process capability is insufficient [41]. Another interpretation of the process capability coefficient value can be found in the literature, e.g., [PERSON] states that low process capability is \(C_{P}<1\), average \(1<C_{P}<1.3\) and high \(C_{P}>1.33\)[41]. The interpretation may be different for different types of industry and processes, in this paper the capability at \(C_{P}=1\) level will be accepted as satisfactory. Whether or not a production process to be executed is capable of achieving the assumed performance parameters depends, among others, on the reliability of the machines and technological devices that make up the system under design [42]. Layouts and temporal structure optimization of manufacturing requires application of a multi-criteria approach in designing production systems [43].
## 2 Materials and Methods
### AoR Estimation
#### 2.2.3 Assessment of Functional Axes' Dispersion and Accuracy  
Compared to the SARA approach, given the different mathematical formulation, the IHA methodology yields the estimation of generally quite scattered set of axes that require some averaging, as already discussed regarding the MHA definition. However, it is interesting to assess how much the axes are dispersed around an average position and orientation. For this reason, linear (\(d_{\rm eff}\)) and angular (\(\chi_{\rm eff}\)) dispersion parameters were calculated as a measurement of the rms distance and orientation between the MHA and the IHAs of the corresponding dataset [38].  
Although it does not retain the same physical meaning, due to the different formulation, the same methodology was applied to the SARA dataset so as to evaluate an average AoR estimated by SARA algorithm. Indeed, due to the matrices (\(R_{\rm i}\), \(S_{\rm i}\)) and vectors (\(T_{\rm i}\)\(D_{\rm i}\)) being time-dependent, variability in the positioning and direction of the AoR must be expected for the SARA method too.  
While dispersion parameters allow one to assess the variability in joint kinematics, they do not provide a measurement of the AoR estimation accuracy by themselves. Indeed, the axes might result to be quite low scattered but with a significant bias error in the orientation or position relative to the real AoR. For this reason, a measurement of the distance between the exact AoR, imposed by joint kinematics setup in the multi-body model, and the estimated AoR (both with helical axis and SARA algorithms) was performed, similarly to previous works in the literature [26]. To do so, the intersections between the estimated AoR and two quasi-sagittal planes perpendicular to the \(v\) axis of the LCS (see Figure 3a) and located at a distance of \(\pm 50\) mm from the origin were evaluated. Similarly, the intersections of the exact AoR with the two planes were also calculated. Then, the distance between each couple of intersection points was computed in both quasi-sagittal planes (\(d_{\rm Lat}\) for the lateral and \(d_{\rm Med}\) for the medial side), yielding the final value of:  
\[\Delta=\sqrt{\frac{d_{\rm Lat}{}^{2}+d_{\rm Med}{}^{2}}{2}}, \tag{4}\]  
where \(\Delta\) is the rms distance between exact and estimated AoRs, representing an overall descriptor of the AoR estimation accuracy. The large distance (100 mm) selected between the two quasi-sagittal planes was chosen to stress any misalignment between the exact AoR and the estimated one. Moreover, the two planes have also an anatomical interpretation since they could approximately represent two lateral and medial sagittal planes enclosing the ankle complex and, in particular, the malleoli, which are relevant anatomical references in the determining of the ankle joint rotation axis.
## 3 Positioning Model Establishment and Solution  
Taking the anchor point as the center of the sphere, the actual measured distance is the radius to form 4 spheres. Under the ideal situation in which the signal is not disturbed and there is no error, it can theoretically intersect at a point that is the target point to be positioned. When there is a measurement error and it is disturbed, the four spheres intersect in a small area, and the target point is in this area. Since it is impossible to determine which anchor point is disturbed, therefore, an approximate point near the target point can be obtained by calculating the mean value of the coordinates of the four vertices of the intersection area.  
Two-dimensional plane positioning requires at least three anchor points and one label; three-dimensional spatial positioning requires at least four anchor points and one label. The four anchor points need to be fixed in a certain corner of the indoor place, and the site selection of the anchor points should be placed symmetrically in the four corners; there is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Before Cleaning** & \multicolumn{2}{c}{**After Cleaning**} \\ \hline
**Normal Data Sample** & **Abnormal Data Sample** & **Normal Data Sample** & **Abnormal Data Sample** \\ \hline
**77,122** & 79,176 & 76,145 & 78,269 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of the number of samples before and after cleaning.  
Figure 3: Four sets of data to remove “useless” points.  
no obstacle in the middle. The distances from the target point to the four anchor points are measured under normal signal and interference conditions, respectively, and the target points need to be positioned in these two cases.  
In this paper, the triangular centroid positioning algorithm is used to accurately position the data. When solving the three-dimensional positioning coordinates, due to the positioning accuracy problem caused by the geometric layout of the anchor points, an optimization algorithm is adopted, which replaces the inverse of the distance sum in the traditional algorithm with the square root of the difference between the actual value and the theoretical value.
###### Abstract  
Advances in image-based remote sensing using unmanned aerial vehicles (UAV) and structure-from-motion (SfM) photogrammetry continue to improve our ability to monitor complex landforms over representative spatial and temporal scales. As with other water-worked environments, coastal sediments respond to shaping processes through the formation of multi-scale topographic roughness. Although this topographic complexity can be an important marker of hydrodynamic forces and sediment transport, it is seldom characterized in typical beach surveys due to environmental and technical constraints. In this study, we explore the feasibility of using SfM photogrammetry augmented with an RTK quadcopter for monitoring the coastal topographic complexity at the beach-scale in a macrotidal environment. The method had to respond to resolution and time constraints for a realistic representation of the topo-morphological features from submeter dimensions and survey completion in two hours around low tide to fully cover the intertidal zone. Different tests were performed at two coastal field sites with varied dimensions and morphologies to assess the photogrammetric performance and eventual means for optimization. Our results show that, with precise image positioning, the addition of a single ground control point (GCP) enabled a global precision (RMSE) equivalent to that of traditional GCP-based photogrammetry using numerous and well-distributed GCPs. The optimal model quality that minimized vertical bias and random errors was achieved from 5 GCPs, with a two-fold reduction in RMSE. The image resolution for tie point detection was found to be an important control on the measurement quality, with the best results obtained using images at their original scale. Using these findings enabled designing an efficient and effective workflow for monitoring coastal topographic complexity at a large scale.  
t  
topographic remote sensing; coastal monitoring; high-energy beaches; gravel spit; structure-from-motion photogrammetry; multirotor drone; RTK-GNSS; permanent GCPs; DEM error; workflow optimization
# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 4 Performance and Experimental Results
### Analysis of the Trained YOLO Model Based on Real-Time Experiments
#### 4.2.2 Experiment 2: Occlusion  
This experiment established the ability of our model to detect a safety belt buckling status when the pillar loop was partially occluded. The camera was adjusted as explained in experiment 1 The experiment was conducted in a stationary car. The experiment tested the ability of the model to detect the safety belt status in occlusion conditions. Figure 15 depicts various scenes (A, B, and C) where the model showed superiority in detecting the safety belt status in different occlusion conditions.
## 3 Results and Discussion
### The Reducing Sugars Changes  
No significant changes were observed in the total content of reducing sugars in green peppers as a result of microwaving (4.1 GE mg/g d.b.) relative to the control (4.2 GE mg/g d.b.). In contrast, a significant increase in the content of reducing sugars in green peppers was observed as a result of cooking (4.7 GE mg/g d.b.). No significant differences were observed in the total content of reducing sugars in yellow peppers (5.6 GE mg/g d.b.) that were subjected to cooking (5.5 GE mg/g d.b.).  
However, a significant reduction in the total content of reducing sugars in yellow peppers was observed as a result of microwaving (3.5 GE mg/g d.b.) relative to the control (5.6 GE mg/g d.b.). In contrast, a significant reduction in total reducing sugars was observed in red peppers due to cooking (1.1 GE mg/g d.b.) relative to the control (4.4 GE mg/g d.b.). According to a study by [PERSON] et al., thermal treatment of red peppers involving a process of blanching in hot water resulted in a reduction in the content of reducing sugars [21].  
The total content of reducing sugars in microwaved red peppers (3.2 GE mg/g d.b.) was also significantly reduced relative to the control (4.4 GE mg/g d.b.). According to a study by [PERSON] et al., the content of reducing sugars, which is about 63.1 g/100 d.b. in fresh red peppers, is significantly reduced by the microwave drying process to 61.1 g/100 g d.b. [21].  
Cooking plant raw materials in water causes dynamic heat penetration into the tissues. This results in the degradation of tissue and cellular structures, which involves the process of releasing certain types of substances, such as sugars, which move into hot water due to their high solubility [41].  
Perhaps the increase in total reducing sugars in green peppers due to cooking that we observed was due to less tissue damage to the peppers (and thus lower mass transfer), which was not conducive to the free passage of sugars into the boiling water.  
The textural characteristics and dry weight of bell peppers after heat treatment are presented in Table 3.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup
#### 4.1.4 Attack Detection Rate  
Figure 7 depicts the attack detection rate as a percentage of the total number of attacks. The simulation was run four times for 30, 60, 90, or 120 nodes. Our proposed mechanism was again compared with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The numbers of nodes are shown on the \(x\)-axis and the attack detection rate is shown on the \(y\)-axis. The results are presented for rank, Sybil, and sinkhole attacks. Observe that the proposed mechanism has a higher attack detection rate for the different numbers of nodes. For a rank attack, the average detection rates were 0.28, 0.16, and 0.21 for MMTM-RPL, DCTM-IoT, and RBAM-IoT, respectively. A high detection rate means that attacks are detected earlier. MMTM-RPL has a higher detection rate since, because of the clustering, the parameters are collected faster. Moreover, the MAs played an important role here by sending parameters to the fog layer and by responding to attackers immediately. The attack detection rate was higher for sinkhole and Sybil attacks.  
Figure 6: Comparison of control message overhead for different attacks: (**a**) Rank attack, (**b**) Sybil attack, and (**c**) Sinkhole attack.  
Figure 7: Comparison of attack detection rate for different attacks: (**a**) Rank attack, (**b**) Sybil attack, and (**c**) Sinkhole attack.
###### Abstract  
Remaining useful life (RUL) prediction of rolling bearings is of significance for improving the reliability and durability of rotating machinery. Aiming at the problem of suboptimal RUL prediction precision under cross-working conditions due to distribution discrepancies between training and testing data, enhanced cross-working condition RUL prediction for rolling bearings via an initial degradation detection-enabled joint transfer metric network is proposed. Specifically, the health indicator, called reconstruction along projection pathway (RAPP), is calculated for initial degradation detection (IDD), in which RAPP is obtained from a novel deep adversarial convolution autoencoder network (DACAEN) and compares discrepancies between the input and the reconstruction by DACAEN, not only in the input space, but also in the hidden spaces, and then RUL prediction is triggered after IDD via RAPP. After that, a joint transfer metric network is proposed for cross-working condition RUL prediction. Joint domain adaptation loss, which combines representation subspace distance and variance discrepancy representation, is designed to act on the final layer of the mapping regression network to decrease data distribution discrepancies and ultimately obtain cross-domain invariant features. The experimental results from the PHM2012 dataset show that the proposed method has higher prediction accuracy and better generalization ability than typical and advanced transfer RUL prediction methods under cross-working conditions, with improvements of 0.047, 0.053, and 0.058 in the MSE, RMSE, and Score.  
remaining useful life prediction; reconstruction along projection pathway; deep adversarial convolution autoencoder network; joint domain adaptation loss
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
###### Abstract  
The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases--PubMed, Science Direct, and Web of Science--and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field.  
class activation mapping; deep learning; explainable ml; interpretability; medical image
# Gas-Liquid Mixability Study in a Jet-Stirred Tank for Mineral Flotation
## 2 Methodology
### Multiphase and Turbulence Modelling  
The transient three-dimensional Reynolds-averaged Navier-Stokes (RANS) equations are analyzed in the context of turbulent fluid flow, focusing primarily on the flow field averaging effects rather than transient details. Each phase is treated as a continuous medium permeable to one another, with the slurry considered a continuous, incompressible fluid and the bubbles as proposed continuous phases. By treating bubbles as continuous phases, the RANS model can compute the average behavior of the flow field by integrating the overall impact of bubbles on the flow field. The jet-stirred tank operates under isothermal conditions, thus excluding considerations for heat transfer processes. [PERSON] et al. [21] investigated gas-liquid mass transfer in an aeration tank using the Euler-Euler model and mixing model, respectively, and compared these with experimental results. Their findings indicated that the Euler model is more suitable for simulating gas-liquid two-phase flow. Therefore, the interaction between air and liquid phases in the flow field inside the mixing tank is simulated using the Euler-Euler multiphase flow model in this study. With the mixing tank as the control body, the continuity equation is:  
\[\frac{\partial}{\partial t}(\rho)+\
abla\cdot(\rho\
u)=0 \tag{1}\] \begin{table}
\begin{tabular}{c c} \hline \hline
**Structures** & **Value** \\ \hline Inlet diameter/mm & 10 \\ Outlet diameter/mm & 80 \\ Cylinder diameter/mm & 500 \\ Cylinder height/mm & 680 \\ Distance of inlet from the bottom surface of the cylinder/mm & 80 \\ Nozzles/pcs (pieces) & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Structural dimensions of jet-stirred tank. Figure 1: Geometric model of jet-stirred tank; (**a**) schematic view in XY plane; (**b**) schematic view in XZ direction; (**c**) sectional view in YZ plane. where \(\
u\) is the mass-average velocity of the mixture; \(\
u=\frac{\sum_{i=1}^{n}\alpha_{i}\rho_{i}\mathrm{u_{i}}}{\varrho}\); \(\alpha_{i}\), \(\rho_{i}\), \(u_{i}\) are the volume fraction, density, and average flow rate of phase i, respectively; \(\rho\) is the average density of the multiphase flow, \(\rho=\sum_{i=1}^{n}\alpha_{i}\rho_{i}\), and n is the number of phases. The momentum equation can be expressed as follows:  
\[\left\{
## 3 Methodology
### Model Construction: Two-Stage Multi-Objective Model for TTDP-CD
#### 3.4.2 Model Constraints  
There are two categories of model constraints: permanent technical constraints and customized constraints. As demonstrated by Equations (15)-(18), permanent technical constraints guarantee that designed routes are rational and valid [4]. Equation (15) ensures time connectivity. Equations (16) and (17) ensure that attractions are visited during the opening hours, where \(TF_{i}\) denotes time feasibility. In MOOPTW, the times of entering and exiting an attraction must be identical, and each attraction may only be visited once; therefore, Equation (18) is used to ensure the route connectivity.  
\[t_{i}^{a}+t_{i}^{s}+t_{ij}=t_{j}^{a} \tag{15}\]  
\[\sum_{i=1}^{N_{A}}TF_{i}\cdot x_{i}=0 \tag{16}\]  
\[TF_{i}=\left\{0,t_{i}^{a}\geq t_{i}^{a}\text{ {and }}t_{i}^{d}\leq t_{i}^{c}\right. \tag{17}\]  
\[\sum_{i=1}^{N_{A}}x_{ij}=\sum_{j=1}^{N_{A}}x_{ij}\leq 1 \tag{18}\]  
Customized constraints ensure that the itinerary meets the tourist's personalized needs. Equation (19) limits the total time spent within the time budget (\(T_{max}\)). Equation (20) ensures that the attractions in the itinerary satisfy the tourist's preference for three attraction categories, where \(\alpha_{C}\) denotes the weight of tourist's preference for category \(C\) and \(\epsilon\) indicates the deviation threshold between the itinerary and the preference.\(l_{C}^{i}\) is used to judge the category of \(A_{i}\). If \(A_{i}\) is in category \(C\), \(l_{C}^{i}=1\); otherwise, \(l_{C}^{i}=0\).  
\[\sum_{i=1}^{N_{A}}\sum_{j=1}^{N_{A}}x_{ij}t_{ij}+\sum_{i=1}^{N_{A}}x_{i}t_{i}^ {s}+t_{0}^{s}+t_{0}^{\epsilon}\leq T_{max} \tag{19}\]  
\[\sum_{C\in\{n,c,\epsilon\}}\left(\frac{\sum_{i=1}^{N_{A}}l_{C}^{i}x_{i}}{\sum _{i=1}^{N_{A}}x_{i}}-\alpha_{C}\right)^{2}\leq\epsilon \tag{20}\]
## 5 Simulation
### Increasing Number of Connections
#### 5.1.1 Average End to End Delay  
As shown in Figure 10, WEAMR with our hotline-assisted approach outperforms both AODV and AOMDV as the number of connections increase. The average end to end delay increases with the increase in the number of simultaneous connections, but the average delay for AODV and AOMDV is much higher because these protocols work in hop by hop fashion for packet delivery and the hotline infrastructure is not used, whereas WEAMR shows lower end to end delay due to the fact that hotline-assisted routing can handle more simultaneous connections and reduce the number of intermediate hops.  
\begin{table}
\begin{tabular}{|l|l|} \hline Simulation Environment & NS3 \\ Routing Protocol & WEAMR \\ Inter-Gateway Routing & OSPF \\ Intra-Cluster Communication & Wireless (802.15.4) \\ Number of Nodes & 1,000 \\ Node mobility & 0 m/sec \\ Total Terrain Area & 1,000 m \(\times\) 1000 m \\ Simulation Time & 1,000 s \\ Packet Loss nodes to node & 2\% \\ Packet Loss gateway to gateway & 0\% \\ Total Runs & 15 \\ \hline \end{tabular}
\end{table}
Table 1: Simulation setup.
# Application of the Clark-Wright Method to Improve the Sustainability of the Logistic Chain
## 6 Discussion
These technologies are designed to improve the efficiency of waste collection, minimize environmental impact, and increase customer satisfaction through automated route adjustments [50; 51]. Research [52; 53] discusses IoT-assisted waste collection and real-time route optimization. The advantage is that this approach increases collection efficiency, reduces fuel costs, and contributes to reducing the city's environmental footprint. Challenges include high initial technology costs, the need for employee training, and reliance on reliable internet connectivity for data transmission.