esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 3 Materials and Methods
### Composite Statistical Analysis of PPD
#### 3.2.3 Statistical Process Control Analysis  
SPCA is a well-known statistical technique to measure and control the process quality in a manufacturing production line [13; 14]. It is extensively used in the manufacturing industry to maintain processes while machines are making products. A control chart is one of the essential tools in SPCA, which is used to control and monitor the quality of production processes [15]. The control chart provides quality characteristics by plotting sample statistics, such as sample process mean or process variance. It can detect early on when a process is out of control, so the manufacturer can correct the system by checking the process or machine conditions. With this, the final products will have fewer defects, and quality will increase.  
In this paper, X and R control charts are used to monitor processes. Both charts are commonly used in SPCA. They are used separately to measure production process quality. To control the process quality, three straight lines need to be calculated: upper control limit (UCL), centerline (CL), and lower control limit (LCL). The X chart represents the mean value of a process or range value if the process is centered on the target. The R chart represents the standard deviation of the production process if the process spread remains within the right range. The X chart is calculated by the following formulas [13; 14; 15]:  
\[\text{UCL}=\overline{\textbf{x}}+\textbf{A}_{2}\overline{R} \tag{6}\]  
\[\textbf{CL}=\overline{\textbf{x}}=\frac{\textbf{\Sigma}\overline{\textbf{x}}} {m} \tag{7}\]  
\[\text{LCL}=\overline{\textbf{x}}-\textbf{A}_{2}\overline{R} \tag{8}\]  
where \(A_{2}\) is a coefficient whose value depends on the sample size, \(\overline{R}\) is an average value of the ranges of all observed samples and \(m\) is the number of the production process unit per one day. If a process falls outside the control limits there is a statistical certainty that an abnormal process has happened, which is a sign of a defective product.  
The R chart uses the sample production process ranges to monitor changes in the spread of a production process. Values are calculated by the following formulas [13; 14; 15]:  
\[\text{UCL}=\textbf{D}_{4}\overline{R} \tag{9}\]  
\[\textbf{R}_{i}=\textbf{Max}(\textbf{x}_{i})-\textbf{Min}(\textbf{x}_{i}) \tag{10}\]\[\mathbf{CL}=\overline{\mathbf{R}}=\frac{\Sigma\overline{\mathbf{R}_{i}}}{m} \tag{11}\]  
\[\mathrm{LCL}=\mathbf{D_{3}}\overline{\mathbf{R}} \tag{12}\]  
where \(\mathbf{D_{3}}\) and \(\mathbf{D_{4}}\) are coefficients whose values depend on samples of production process size; \(\overline{\mathbf{R}}\) is an average value of the ranges of all observed samples and \(\boldsymbol{m}\) is the number of the production process unit per one day. If the LCL value of the R and X charts is 0, it is not visible; it is flush with the horizontal axis.
## 3 System Model and Preliminaries
### Detecting Surface Reflection  
Since underwater environments are bounded by the sea surface and bottom, it is inevitable for most of the acoustic transmissions to bounce from either of these two boundaries. The only exceptions, where signals never bounce, are signals forming the Sound Fixing and Ranging (SOFAR) channel where multiple LEPs prohibit the signal from reaching the boundaries. Reflected signals in underwater environments exhibit a phase change that is dependent on the boundary they bounced from. Since the specific acoustic impedance of air-water-interface is roughly 3500 times higher than that of air, a sea-surface reflected signal will exhibit a phase reversal of 180\({}^{\circ}\), i.e., a phase shift of \(\pi\). Meanwhile, the phase change of signals reflected from the seabed (\(\rho_{bcd}\)) are given by [24]:  
\[\rho_{bcd}=-2\tan^{-1}\frac{\sqrt{\left(\sin\theta_{i}\right)^{2}-\mu^{2}}}{v \ \cos\theta_{i}} \tag{1}\]  
where \(\theta_{i}\) is the incident angle on the sea bottom, \(\mu\) is the ratio of the water SS to the bottom material SS, and \(v\) is the ratio of the density of the sea bottom material to the water density of the layer just above the seabed. Moreover, we assume that nodes are synchronized to a level that enables a pair to identify the phase shift among exchanged beacons. Finally, we assume that signals reflected more than once from any boundary experience high phase distortion and lose most of their power, and consequently cannot be decoded.
# Towards Mobile Federated Learning with Unreliable Participants and Selective Aggregation
## 2 Background and Related Work
### Systems Heterogeneity  
The storage, computational, and communication capacities of individual devices in federated networks may differ due to differences in hardware (CPU and memory), network connectivity (3G, 4G, 5G, and Wi-Fi), and power (battery level) [19]. In addition, the network size and systems-related constraints on each device typically result in only a small fraction of the devices being active at once. Moreover, it is not uncommon for an active device to lose connection with the server (dropout) in a given iteration due to connectivity or power constraints [8].  
These system-level characteristics dramatically increase the number of challenges, such as straggler mitigation and fault tolerance [5]. Proposed FL approaches must therefore anticipate a low amount of participation, tolerate heterogeneous hardware, and be robust enough to dropped devices in the communication network [5; 8].
## 6 Experiments and Results
### Experiment Environments and Parameter Setting  
#### 6.1.1 The Structure of the Simulated and Real Networks  
The simulated network is a regular \(21\times 21\) grid network, and the center node is set as the sink node, shown in Figure 2a. In the simulated network, the physical quantities can be accurately sensed and compared with the recovered data, so the proposed method can be evaluated confidently. The real data collection scenario is the wireless network of Intel Berkeley Research Lab [37], which has 54 nodes and the 5 th node is the sink node, shown in Figure 3. The sensing data of the real network include several physical quantities, such as temperature, humidity and illumination, _etc._ The data were collected over several months with a sensing time slot of half minute.
# Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection
## 2 Methodology
### Saliency Tuned YOLONet
The CBL component is composed of convolution, batchnorm, and leaky_rule activation functions. The Res Unit component draws on the residual structure in the Resnet network, allowing the network to be built deeper. The CSPX component draws on the CSPNet network structure and consists of convolutional layers and X Res unit modules. The spatial pyramid pooling (SPP) component contains a multi-scale pooling structure, which can then complete multi-scale feature fusion. The overall framework is the same as other versions of the YOLO network, including three parts: backbone, neck, and detection heads. Of these, the backbone network combines many new methods, such as CSPX, Mish activation function, and dropblock. In the neck part, it mainly includes SPP module, FPN, and a PAN structure. For the prediction part, it includes three multi-scale detection heads and uses an improved CIOU_loss loss function. In addition, we introduced the DNL module in the last two Res units of the CSPX structure. For example, in the CPS8 module, the first six residual units only contain two CBM blocks,  
Figure 4: The specific details module of the YOLOv4 network with the DNL module added. The YOLOv4 network model consists of three parts: backbone, neck, and detection head. This article mainly modified the backbone module, adding the DNL module to the some CSPX modules. Therefore, it can extract more salient feature information. and the last two residual units are added to the DNL. The DNL module can reduce the difficulty of joint learning in the original non-local module. At the same time, it can model two different visual cues, regional relations and salient boundaries, to improve the accuracy of salient feature extraction. Thus, the improved YOLONet has a greater advantage for object detection.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 3 Results and Discussion
### Removal of As(III) from Aqueous Solutions  
#### 3.2.1 Effect of Different Materials  
In order to allow for a comparison of the adsorption capacities of different materials, several monomeric and binary materials were chosen to evaluate the As(III) adsorption  
Figure 3: (**a**) FTIR spectra of Ti\({}_{3}\)C\({}_{2}\) MXene, functionalized CNTs and nZVI/MXene@CNTs; (**b**) XPS survey spectra of nZVI/MXene@CNTs; (**c**) C 1s; (**d**) Fe 2p; (**e**) O 1s; (**f**) VSM spectra of nZVI/MXene@CNTs.  
performance in this part, and the results are shown in Figure 4a. In comparison to unmodified CNTs, the adsorption of functionalized CNTs were increased by nearly 60%, and the adsorption capacities of nZVI/MXene and MXene/CNTs was increased by about 20.12 mg/g and 141.07 mg/g compared to MXene, respectively. It was indicated that the performance of each component in removing As(III) further improved with the modification of CNTs and the binary and ternary treatment of MXene. It can be inferred that the enhanced performance of nZVI/MXene@CNTs might be related to the introduction of oxygen-containing groups and the increase in a specific surface area, favorably demonstrating its better adsorption performance as a treatment material for the efficient removal of As(III) from water. More interestingly, as shown in Table S1 (Supplementary Materials), the nZVI/MXene@CNTs was much more capable of removing As(III) than the other materials.
# SACT: A New Model of Covert Communication Based on SDN
## 5 Experimental Results  
In this section, we evaluate the performance of our system SACT. We deploy SACT system on a server (Ubuntu 16.04 OS, 2.2 GHz, 16 GB RAM, Qingdao, China), using eclipse development tools and Java programming language. We use Floodlight 1.9 (Qingdao, China), Openflow 1.3 (Qingdao, China), Mininet 2.3 (Qingdao, China) and OpenvSwitch 2.8 (Qingdao, China) to build simulation environment.Sniffer is used to capture packets at the receiving end.The corresponding performance tests are carried out in two different test environments: LAN and WAN. Three aspects are tested and analyzed: detection rate analysis, false alarm rate analysis, and antiattack analysis.
## 3 Results and Discussion
### Applicability and Reusability of WA  
The fluoride adsorption amount and removal efficiency of WA in groundwater and wastewater are presented in Figure S3. Compared to the fluoride adsorption amount of WA obtained from the kinetic experiments (112.09 mg/g), the adsorption amounts in groundwater and wastewater were lower, at 80.62 mg/g and 86.89 mg/g, respectively. However, WA still achieved a fluoride removal efficiency of more than 65%, indicating its potential for treating actual wastewater highly polluted by fluoride, such as that originating from the semiconductor industry [11].  
The results of WA reuse are presented in Figure S4. In the first adsorption cycle, WA exhibited a fluoride adsorption amount of 123.29 mg/g. However, in the second cycle, this capacity dropped to 62.22 mg/g, representing a 50% reduction in removal efficiency compared to that in the initial cycle. Given that WA is a waste material with minimal production costs, reuse is not recommended due to the significant decline in performance and the potential costs associated with managing secondary pollution from the regeneration process [55].
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 2 Research Significance  
Permafrost is widely distributed along the QTEC, which is sensitive to external thermal disturbance [1]. With the engineering structures constructed, the pre-existing hydrothermal balance of natural foundation has been seriously disturbed. Coupled with frequent freeze-thaw cycles, the thermal imbalance of engineering structures will lead to uneven degradation of the underlying permafrost, then resulting in different degrees of engineering diseases [2; 3; 4; 5]. Thermal conductivity is a prime influence factor of permafrost response to the external thermal disturbance and also a determining parameter of the freeze-thaw circle, which governs the unsteady heat-transfer process of soil [7; 8]. Therefore, the comprehensive acknowledgement on the soil thermal conductivity variation law along the QTEC is a prerequisite of design and construction of the engineering structures in this area.  
As far as the authors know, this paper proposes for the first time to use massive specimens sourced from QTEC to reveal a variation law of soil thermal conductivity and their parameter-influencing mechanisms, and also develops reasonable prediction models for different temperature conditions. The research results of this paper can contribute to the construction and maintenance of engineering applications in cold regions.
# A Comprehensive Evaluation of Possible RNSS Signals in the S-Band for the KPS
## 4 FoMs Analysis
### AMSR Analysis Results  
Table 10 enumerates the AMSR of each candidate signal according to the receiver front-end bandwidth. The receiver filter is assumed to be an ideal brick-wall filter with a unity gain over the passband. Note that values less than 0.01 were considered as 0.  
If the receiver bandwidth is infinite, the AMSR of the BPSK signal is theoretically zero. The BOC signal has a larger AMSR as the modulation order is higher. The autocorrelation function distorted by the filtering increases AMSR. The cosine-BOC signal was more affected by the filtering due to the shape of the PSD, and the difference in the AMSR value according to the front-end bandwidth was relatively significant. Figure 7 shows the autocorrelation function according to the front-end bandwidth of BPSK(1) with the smallest AMSR and \(\text{BOC}_{\text{c}}\)(3,1) with the largest AMSR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Candidates**} & \multicolumn{4}{c}{**AMSR**} \\ \cline{2-4}  & \(B_{r}\)=\(\infty\) & \(B_{r}\) = 16.5 MHz & \(B_{r}\)=2\(f_{c}\) or 2\(f_{s}\)+\(f_{c}\) \\ \hline BPSK (1) & 0 & 0 & 0 \\ BPSK(4) & 0 & 0 & 0 \\ BPSK(5) & 0 & 0 & 0 \\ BPSK(8) & 0 & 0 & 0 \\ \(\text{BOC}_{\text{s}}\)(1,1) & 0.25 & 0.25 & 0.25 \\ \(\text{BOC}_{\text{s}}\)(2,1) & 0.56 & 0.55 & 0.54 \\ \(\text{BOC}_{\text{s}}\)(2,2) & 0.25 & 0.25 & 0.25 \\ \(\text{BOC}_{\text{s}}\)(3,1) & 0.68 & 0.67 & 0.68 \\ \(\text{BOC}_{\text{s}}\)(3,2) & 0.44 & 0.42 & 0.42 \\ \(\text{BOC}_{\text{s}}\)(3,3) & 0.25 & 0.25 & 0.25 \\ \(\text{BOC}_{\text{s}}\)(4,4) & 0.25 & 0.25 & 0.25 \\ \(\text{BOC}_{\text{s}}\)(5,2) & 0.62 & 0.62 & 0.62 \\ \(\text{BOC}_{\text{c}}\)(1,1) & 0.25 & 0.26 & 0.49 \\ \(\text{BOC}_{\text{c}}\)(2,1) & 0.56 & 0.58 & 0.77 \\ \(\text{BOC}_{\text{c}}\)(2,2) & 0.25 & 0.28 & 0.49 \\ \(\text{BOC}_{\text{c}}\)(3,1) & 0.68 & 0.70 & 0.87 \\ \(\text{BOC}_{\text{c}}\)(3,2) & 0.44 & 0.47 & 0.67 \\ \(\text{BOC}_{\text{c}}\)(3,3) & 0.25 & 0.29 & 0.49 \\ \(\text{BOC}_{\text{c}}\)(4,4) & 0.25 & 0.49 & 0.49 \\ MBOC & 0.18 & 0.20 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Autocorrelation main peak-to-secondary peak ratio (AMSR) of candidates in the S-band.  
Figure 7: Autocorrelation function of BPSK(1) and \(\text{BOC}_{\text{c}}\)(3,1).
## 3 The Extended/Generalized Finite Element Methods (XFEM/GFEM)
### Crack Propagation  
The methods presented so far in this section mainly deal with discretising cracked domains using fixed meshes. For propagating cracks, principles of classic linear elastic fracture mechanics, as presented in Section 2, can be applied. Within this framework, stress intensity factors (SIFs) are the main tool used to both indicate the occurrence and determine the direction of crack propagation under certain loading conditions.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 2 Study Area and Data
### Meteorological Data  
Meteorological data are also essential inputs to the CSIRO GFS model. In this study, the ground-based meteorological observation data (wind speed and direction, temperature and relative humidity) from the three stations nearest (Figure 1) to the Esperance wildfire (ESPERANCE AERO (No.956380), SALMON GUMS RES. STN (No. 956390), MUNGLINUP WEST (No.956440)) were downloaded from the Australian Bureau of Meteorology [51]. The data has a temporal resolution of 10-min, which is in accordance with the Himawari-8 data.
## 4 Results and Discussion
### Recommendation  
Based on the test set analysis, the **TCN in the four-sensor non-overlapping configuration** emerges as the most suitable model for ITS applications, achieving the highest test set performance metrics. The advantages of this configuration include:  
* **Enhanced Temporal Modeling**: TCN's architecture effectively captures both short- and long-term dependencies in the data, even on unseen data.
* **Improved Contextual Understanding**: The use of multiple sensors provides richer context, enabling better interpretation of dynamic environments in real-world scenarios.
* **Optimal Data Utilization**: Non-overlapping data with multiple sensors avoids redundancy, enhancing generalization and preventing potential overfitting.
* **Robustness and Scalability**: High test set performance indicates that the model is robust and can generalize well, increasing adaptability to different ITS challenges.
## 4 Discussion
### Performance Comparison with Previous Studies  
Two previous studies reported deep learning models for diagnosing ONFH using pelvic or hip radiographs as input data (Table 3) [22,23]. In 2019, [PERSON] et al. [22] developed a deep learning algorithm using anteroposterior hip radiographs for 1892 anteroposterior hip radiographs as the input data. Its sensitivity and specificity were 84.8% and 91.3%, respectively. The AUC was 0.930. In 2020, [PERSON] et al. [23] used 3,136 anteroposterior hip radiographs as input data to develop a deep learning algorithm. The AUC of their algorithm was 0.974. The sensitivity and specificity were 90% and 94.6%, respectively.  
In our study, AUC, sensitivity, and specificity metrics were calculated based on test data. The AUCs were found to be 0.912 (right hip model) and 0.902 (left hip model), which were comparable with the results of previous studies that used large amounts of data. The sensitivity and specificity were 85.7% and 90.5%, respectively, for the right hip model and 66.7% and 100%, respectively, for the left hip model. The sensitivity of the left hip model was relatively low, but the specificity was very high. The proportion of ONFH-positive  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **AUC** & **Sensitivity** & **Specificity** \\ \hline ONFH right hip & & & \\ model & 0.912 (CI 0.773–1.0) & 0.857 (CI 0.487–0.974) & 0.905 (CI 0.711–0.974) \\ ONFH left hip model & 0.902 (CI 0.747–1.0) & 0.667 (CI 0.300–0.903) & 1.000 \\ [PERSON] et al. [22] & 0.930 (CI N/ A) & 0.848 (CI 0.733–0.906) & 0.913 (CI 0.720–0.989) \\ [PERSON] et al. [23] & 0.974 (CI 0.971–0.978) & 0.900 (CI 0.877–0.923) & 0.946 (CI 0.937–0.955) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification performance comparison with prior studies.  
images for the left hip (20%) was smaller than that for the right hip (25%), which indicates that learning about the positive image might be insufficient.  
However, these previous studies included radiographs of both pre-collapsed and collapsed ONFH. Furthermore, they did not evaluate diagnostic accuracy without separating pre-collapsed and collapsed ONFH. Therefore, to the best of our knowledge, our study is the first to demonstrate the possibility of using a deep learning algorithm that is trained using pelvic radiographs to diagnose pre-collapse ONFH.
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 3 Image Space Multidirectional Occlusion Shading Model
### Shadow and Ambient Occlusion  
In Section 3.2, we present our light source model, which was defined as a disk with a radius \(d\). The light was placed on a hemisphere with a radius \(R\), as illustrated in Figure 1a. This model allowed us to simulate different shading effects, such as shadow and ambient occlusion. In Figure 1b, point and area lights are presented according to the light source radius \(d\). The point light source was used to generate hard shadows, since there was only one path from a pixel to the light sample. In this case, \(N\) was one, and \(N_{o}\) was either zero or one in Equation (2). The area light produced a soft shadow where the intensity of the soft shadow was computed by the occlusion in Equation (2) or Equation (5). Note that generally smaller \(d\) generate more hard shadows. Moreover, we could simulate ambient occlusion by choosing a large \(d\) and small \(R\) and placing the light at \(\phi=\pi/2\). This is illustrated in Figure 1c, which shows that we could produce similar conditions as in previous image-space ambient occlusion approaches, such as the image-space horizon-based ambient occlusion [5]. Figure 6 shows the different light sources applied to the engine block data. Only local illumination is used in (a), and our ISMOS model is applied in (b-d) as hard shadow, soft shadow, and ambient occlusion, respectively. (e) is a comparison image generated using the image-space horizon-based ambient occlusion [5].  
Figure 6: Different light sources applied to the engine block data. Local illumination is applied in (a). Light intensity varies pixel-by-pixel in (**b–d**) due to the intensity attenuation. The image-space horizon-based ambient occlusion [5] is used in (**e**). Note that the local illumination indicates Phong shading.
###### Abstract  
This research deals with the _dynamic_ and _continuous_ berth allocation problem (DCBAP) in which both arrived and incoming ships are considered and a quay is used as a continuous line to accommodate as many ships as possible at one time. The DCBAP is solved by a two-stage procedure. In the first stage a heuristic/metaheuristic is used to generate alternative ship placement sequences while in the second stage a specific heuristic is employed to place ships and resolve overlaps of ships for the development of a feasible solution. Different methods, including FCFS (First Come First Served), SFLA (Shuffled Frog-Leaping Algorithm), and ISFLA (Improved Shuffled Frog-Leaping Algorithm), were employed in the first stage for comparison. The experimental results show that the ISFLA outperforms the others in terms of solution quality, implying that the ISFLA has the potential to deal with the DCBAP in a container terminal.  
**Keywords: berth allocation problem (BAP); heuristic; shuffled frog-leaping algorithm (SFLA)**
# The Analysis of the Applications of Crop Seed Tape Sowing Technology and Equipment: A Review
## 3 Research on Key Technology and Equipment for Seed Tape Manufacture
### Key Technology of Seed Tape Manufacture  
The manufacture of seed tape mainly includes two key technologies; one is seed wrapping technology and the other one is seed metering technology.