esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Evaluation
### DeepGun vs. Deep Architectures as Feature Extractor

Table 4: Comprehensive quantitative analysis of firearm detection results obtained with different deep architectures as feature extractor and four state-of-the-art OCCs implemented on VISILAB benchmark firearm dataset. framework, which leverages body landmarks and visual gun features, performs effectively across various deep architectures, including AE and VAE. These findings not only highlight the model's adaptability to unified features but also confirm its strong generalization ability, making it well-suited for different deep learning networks. Another notable finding from Table 4 is that when firearm detection is performed using state-of-the-art OCCs and deep feature extractors, the SVM model achieves remarkably high recall. This indicates that combining SVM with various feature extractor implementations results in fewer false negatives (FNs). A similar trend is observed in implementations using MCDE with other feature extractors. Precision, on the other hand, measures the proportion of correct positive predictions. A model with high precision produces fewer false positives (FPs) predictions. IFC and GMM-based implementations exhibit the opposite trend, achieving higher precision than recall. This suggests a lower number of FPs, making them suitable for scenarios where minimizing false alarms is a priority. However, for highly sensitive locations, an SVM-based implementation may be more appropriate due to its superior recall performance. The performance of various feature extraction methods was also evaluated on the UCF-Crime and YouTube datasets, as shown in Table 5 and Table 6, respectively. From Table 5, we observe that Nas-NetLarge, MobileNetV2 and DeepGun with IFC exhibit comparable performance. Notably, MobileNetV2 with IFC integration outperforms all other implementations across all five performance metrics on this challenging dataset. Conversely, Table 6 indicates that DeepGun, when combined with IFC and SVM, achieves the highest performance among all feature extraction methods on the YouTube dataset. Overall, maintaining a balance between recall and precision emerges as the most effective strategy. An optimal firearm detection model should minimize both false positives and false negatives. In this regard, the DeepGun implementation with IFC demonstrates strong performance across diverse datasets by effectively leveraging both visual firearm features and body pose information.
## 3 Case Study
### Analysis Results
#### 3.3.1 Construction of SFEMM Model
SFEMM model are closer to those of the measured values when the EM and ESD of the predicted and measured values of the models are compared. In order to more intuitively represent the distribution of prediction errors of each model, box plots are used for error comparison and a quantitative analysis of the prediction accuracy of each model. Figure 11 shows the box plots of predicted errors for each model. It can be seen from the figure that the following applies: (1) There are significant differences in the error distribution of predicted values among the four models. Compared to other models, the SFEMM model predicts a shorter error box, smaller interquartile range, and more concentrated error distribution, all within 1.5 times the interquartile range (IQR), with almost no outliers present, indicating that the SFEMM model achieves high stability in accuracy. (2) Compared to the other models, the SFEMM model predicts that the mean and median errors are closer to 0, indicating that the SFEMM model achieves higher prediction accuracy and good prediction performance. To show the applicability and stability of the SFEMM model under different measurement points more intuitively, a monitoring model is established for the horizontal displacement of the B4 and B5 measuring points of the rockfill dam, and the trend of its displacement is predicted. Figure 12 shows the model prediction accuracy of each measuring point. (1) Similar to point B3, the prediction errors of the SFEMM models at points B4 and B5 increased with the increase in the amount of predicted data, further indicating that the amount of model-predicted data should be controlled to ensure their high accuracy. (2) The overall prediction effect of the first 60 sets of displacements at points B3 and B5 is better than that of the subsequent ones, and the prediction error of the first 80 sets of displacements at point B4 is less than 0.2 cm. The above results indicate that the SFEMM model achieves  
Figure 10: Prediction error statistics for each model. Figure 9: Comparison of prediction results of various models. similar prediction accuracy for different point displacements, and the accuracy decreases as the amount of predicted data increases. Therefore, when predicting the displacement of different measuring points, the maximum amount of predicted data should be set to ensure the accuracy and credibility of the calculation results. Based on the above analysis results, when the maximum predicted data volume should be within 60-80 sets, the error can be basically guaranteed to be less than 0.2 cm, and the analysis effect is optimal. As shown above, this model offers advantages, such as small predictive errors, high predictive accuracy and stability, and a high correlation and similarity of the distribution shapes between predictions and measurements. The SFEM model is highly feasible and superior to other models in predicting the displacement of rockfill dams. Figure 11: Box plots of predicted errors for each model. Figure 12: Prediction accuracy of SFEMM model at different measurement points.
# A Review on Standardizing Electric Vehicles Community Charging Service Operator Infrastructure
## 6 Open Issues and Research Challenges/ Future Challenges and Research Opportunities
### Reliable Communication Protocol  
_What is the challenge?_  
Communication protocols for EV charging play an important role in the charging safety of EVs along with its equipment, such as electric cable, communication line, etc. For example, as we already discussed, different charging standards use different communication protocols, which can cause an interruption in EV charging due to compatibility issues. For example, we can consider if EVs along with the CHADEMO (CAN communication protocol) charging standard arrives at a CS with the available CCS combo 1 (PLC communication protocol) charging standard for charging. However, the incompatible communication protocol can transmit the wrong message to the CS for charging, which can cause overcharging or even a battery explosion of the EV. Therefore, it is essential to prevent communication threats for safe EV charging.  
_Possible solution: Compatible communication protocol_  
The compatible communication protocol is a plausible solution to ensure the reliability of communication between an EV and CS or electric grid for efficient charging. In 2009, Open Charge Alliance established an open charge point protocol (OCPP), so that EVs and CS can communicate with the same open-source communication protocol. However, if is a mismatch exists between communication protocols, the charging performance and reliability of the EV can be affected.
## 2 Background
### Geological Condition  
The underground powerhouse is inside the left bank slope over 300 m high. The horizontal burial depth of the underground powerhouse is 600-1000 m and the vertical burial depth is 260-330 m. The strata surrounding the underground powerhouse are upper Permian Emeishan formation (P\({}_{2}\beta\)) basalts in monoclonal formations, with strikes of N40\({}^{\circ}\)-45\({}^{\circ}\)E, SE dip directions and dip angles of 15-20\({}^{\circ}\). From upstream to downstream, the strata are P\({}_{2}\beta_{4}\)\({}^{2}\), P\({}_{2}\beta_{4}\)\({}^{1}\), P\({}_{2}\beta_{3}\), P\({}_{2}\beta_{4}\)\({}^{2}\), P\({}_{2}\beta_{2}\)\({}^{3}\) and P\({}_{2}\beta_{2}\)\({}^{2}\) (Figure 3a). The lithology covers aphanitic basalt, oblique tholeite, amygdalaal basalt, breccia lava and tuff. The Columnar jointed basalt of three classes is in P\({}_{2}\beta_{3}\)\({}^{2}\), P\({}_{2}\beta_{3}\)\({}^{3}\) and P\({}_{2}\beta_{4}\)\({}^{1}\). The tuff is in P\({}_{2}\beta_{3}\)\({}^{6}\), P\({}_{2}\beta_{3}\)\({}^{4}\) and P\({}_{2}\beta_{2}\)\({}^{4}\), with a thickness of 20-80 cm, which is weak and easily softens after absorbing water. The surrounding rock mass is hard and brittle and is mainly subblock structures and local block or mosaic structures. Most of the surrounding rock mass belongs to the III1 category, while some are II or IV.  
The geological structures in the underground powerhouse area cover faults, interlayer shear zones, internal shear zones, large fissures and joints. Faults are generally small-scale and rigid structural planes with rock cuttings. Among these faults, the larger ones are F13  
Figure 3: Geological conditions and excavation scheme of the left bank underground powerhouse: (**a**) a geological cross-section along unit #6, (**b**) the stratified excavation scheme and (**c**) the direction of the in situ stress.  
and F\({}_{14}\), which are exposed in the tailrace tunnels and are 50-200 cm wide. The others are small faults with widths of 5-20 cm. The interlayer shear zone C\({}_{2}\) extends along the middle of the P\({}_{2}\beta_{2}\)\({}^{4}\) tuff, with a strike of N42\({}^{\circ}\)-45\({}^{\circ}\)E, a dip direction of SE and a dip angle of 14-17\({}^{\circ}\). C\({}_{2}\) is 10-60 cm thick and is prone to softening due to its filling of mud and rock cuttings. Internal shear zones cover LS\({}_{3152}\) and LS\({}_{3253}\) to LS\({}_{3256}\), which are above the top arch of the main powerhouse with a low dip angle. Large fissures are 50-100 m long and share strikes of N40\({}^{\circ}\)-60\({}^{\circ}\)W and dip angles of 65-85\({}^{\circ}\); joints have lengths of 2-5 m and widths of less than 1 cm.
## 3 Results and Discussion
### Validation of the WAX Mixed-Mode SPE/HPLC-UV Method  
The signal intensity (peak area) for each target analyte is proportional to its concentration within the range of 0.01-2.0 \(\upmu\)g L\({}^{-1}\) for NAP and 0.05-10.0 \(\upmu\)g L\({}^{-1}\) for KEP, DIF, MEF, DIC, and NIF with the correlation coefficients (r\({}^{2}\)) > 0.994. Using signal to noise method, the estimated LOD and LOQ were in the range of 0.002-0.025 and 0.003-0.088 \(\upmu\)g L\({}^{-1}\), respectively, as shown in Table 3. Additionally, the optimized SPE/HPLC-UV method displayed excellent recoveries of 88.3-101.8% for all pharmaceuticals in spiked samples. The intra-day and inter-day precision were also studied in terms of the relative standard deviation (RSD, \(n\) = 3) of the recoveries obtained by analyzing 500 mL of spiked samples within one day and during a period of three consecutive days, respectively. Moreover, the outstanding repeatability of good intra- and inter-day shown in Table 3 illustrate the feasibility of the proposed WAX mixed-mode SPE/HPLC-UV method for trace analysis of various acidic drugs, which offers an alternative for simultaneous detection of strongly and weakly acidic NSAIDs in real environmental water.
## 4 Findings
Many approaches evaluated their approach by testing it with more specific parameters such as AES encryption and decryption rate [95; 112], validation of ACL rules [118], effectiveness and feasibility [102], computation cost for PDSS [111], functionality [94], probability of the illegality [113], latency [114; 118], bandwidth consumption [115], IoT elements activity [100], bit error rate [125], cost [97; 102; 105; 107; 108; 127; 128], K-fold cross-validation [123], DTM in the overlay [122], sustainability [110], different latency metrics [130], and calculations based on credentials [132]. As mentioned earlier, we focused on the target application when implementing the taxonomy of this study. The included studies proposed different solutions to address IoT applications' privacy or security aspects. In addition to the detailed information in the above tables and figures, in the following subsections, we will discuss each study from an architectural and operational perspective to ensure that this review provides a comprehensive overview.
# Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs
## 5 Discussion
### Pair Selection Strategies  
Additional image pairs available during a time period can improve data fusion results, especially when some key pair dates (e.g., the green-up and senescence stages for crops) are available. This implies that with a few key pairs of input we may be able to produce high quality data fusion results for a long time-series. However, in many areas and applications, we do not have control over the numbers and dates of clear image pairs. For the study sites analyzed here, after three pairs of Landsat and MODIS images are used, the improvement of the <MAD> is not very obvious (Figure 12). This is likely not the case for fusion of images depicting quantities that change more rapidly than surface reflectance, such as evapotranspiration. This observation is also specific to the cropping system investigated here. Double cropping systems or crops with multiple harvests per season (e.g., alfalfa) will likely require more input image pairs to successfully predict the full growing season with adequate accuracy.  
The year-long predictability of an image pair depends on the pair date. Pairs from periods of rapid change have lower long-term predictive skill even though they are good for nearby dates. These pairs should be included, but limited to nearby prediction dates. Additionally, the optimal image pair may be related to the spatial texture in Landsat images. For example, a base image pair from spring may have similar spatial texture to that from autumn. The prediction for autumn images using a spring image pair may work better than using a summer image. Since we do not know the texture change at the Landsat scale on the prediction date, additional information beyond the Landsat and MODIS images will be needed to decide the optimal pair to use. This information could be related to the crop growth calendar, historical vegetation phenology from past years, or observations from the ground. A key is to select the pair that can best describe the spatial variation on the prediction date. If land covers were stable in the past years, the image pair from previous years within the same season may be used [15]. However, this could be a problem in areas like site 1, where corn and soybean crops are generally rotated on an annual basis.
## 2 The Building, Temporary Instrumentation and Methodology
### Parametric Analysis Procedure
#### 2.3.3 Singular Value Matrix of the Orthogonal Projection Matrix (\(\mathbf{S}\))  
The orthogonal projection matrix is decomposed into its singular values considering the pre- and post-multiplication of the so-called weighting matrices. The diagonal matrix \(\mathbf{S}\) is of order \(m\), which indicates the order \(n_{s}\) of the system with \(m=n_{s}\). The criterion for defining the value of m depends on the assignment of the weighting matrices.  
3.4 Weighting Matrices of the Orthogonal Projection Matrix (\(\mathbf{W}_{1}\) and \(\mathbf{W}_{2}\))  
There are three algorithms with which the weighting matrices can be defined:  
1. The principal component (PC) algorithm.
2. The unweighted principal component (UPC) algorithm.
3. The canonical variant algorithm (CVA).  
The first two algorithms calculate the order \(m\) of the singular value matrix by detecting the position at which the diagonal of the matrix \(\mathbf{S}\) starts to become zero. On the other hand, the CVA calculates the order \(\mathbf{m}\) by detecting the position at which the values of the diagonal of the matrix \(\mathbf{S}\) stop converging to one, where the tolerance is defined by the engineer.
## Appendix A Open-Source Data Explanation  
There are total of 15 measurements for the open-source dataset for the continuous factory process. Not all were used because of missing values or non-measured values. The details are provided below in Table 11. For the experimental setup in process parameter optimization, input parameters to the surrogate predictive model were categorized as either controllable or uncontrollable static based on intuitive reasoning. Two experimental scenarios, limited and extended controllability, were evaluated, as outlined in Section 5. A comprehensive classification of these parameters is provided in Table 12.  
All typically controllable motor parameters were used as control inputs. To test the proposed algorithm's flexibility and robustness, an extended set of parameters was added, including raw material feeder and temperature-related variables. Feeder parameters are generally adjustable in industrial settings. Temperature parameters, based on their naming convention (...Actual), may represent either sensor readings or setpoints, depending on the system. Due to the lack of clear documentation, the authors used engineering judgment to classify these variables. This extended configuration enabled broader experimental evaluation under more flexible control scenarios.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Output Measurement** & **\% Missing Values** \\ \hline Measurement5 & 95.12\% \\ Measurement11 & 74.26\% \\ Measurement7 & 62.19\% \\ Measurement1 & 41.88\% \\ Measurement14 & 35.53\% \\ Measurement6 & 33.38\% \\ Measurement12 & 22.63\% \\ Measurement8 & 5.52\% \\ Measurement9 & 5.12\% \\ Measurement13 & 2.42\% \\ Measurement10 & 1.90\% \\ Measurement4 & 1.24\% \\ Measurement3 & 0.96\% \\ Measurement2 & 0.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Percentage of zero values for each output measurement.
## 5 Findings--Analysis of the Current Competency Level of the Owner Group and the Designer Group  
### Analysis of Current Competency Level of 'Knowledge' Domain Competencies Conclusions  
The current competency level with respect to the competency groups and the specific competencies in the 'Knowledge' domain was analyzed, as can be seen in Figure 5. As for the owner group, the respondents showed the highest level of competency in the competency group of 'Understanding FAB (3.46)', followed by 'BIM-related knowledge (3.36)', 'Construction related knowledge (2.87)', and 'Standards evaluation (2.69)'. Among the specific competencies within the 'Knowledge' domain, 'Principles of CR (3.78)' within the 'Understanding FAB' competency group was found to be the highest level of competency possessed by the respondents. Additionally, the competency level of 'Utility characteristics (3.39)' and 'Process and Instrument Diagram (3.22)' from the same competency group of 'Understanding FAB' were found to be higher compared to other competencies.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Working Experience**} \\ \cline{2-6}  & **Total** & **Construction** & \
\begin{tabular}{c} **BIM-Based** \\ **Construction** \\ \end{tabular} & **FAB** &
\begin{tabular}{c} **BIM-Based** \\ **Construction** \\ \end{tabular} \\ \hline Avg & 7.855 & 3.474 & 1.31 & 1.647 & 1.424 \\ \hline median & 7.000 & 2.000 & 0.00 & 0.000 & 1.000 \\ \hline StDev & 5.4031 & 4.4093 & 2.422 & 3.7146 & 1.5431 \\ \hline Var & 29.194 & 19.442 & 5.867 & 13.798 & 2.381 \\ \hline Min & 0 & 0 & 0 & 0 & 0 \\ \hline Max & 26.0 & 25.0 & 10.0 & 23.0 & 8.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive statistics: the designer group (N = 58).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Working Experience**} \\ \cline{2-6}  & **Total** & **Construction** & \
\begin{tabular}{c} **BIM-Based** \\ **Construction** \\ \end{tabular} & **FAB** &
\begin{tabular}{c} **BIM-Based** \\ **Construction** \\ \end{tabular} \\ \hline Avg & 8.939 & 3.028 & 2.06 & 1.972 & 1.883 \\ median & 7.750 & 1.000 & 0.00 & 0.000 & 1.500 \\ StDev & 6.1167 & 4.9303 & 3.334 & 4.0091 & 1.3053 \\ Var & 37.414 & 24.308 & 11.114 & 16.073 & 1.704 \\ Min & 0.9 & 0 & 0 & 0 & 0.5 \\ Max & 22.0 & 16.5 & 10 & 14.0 & 5.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Descriptive statistics: the owner group (N = 18).  
As shown by the respondents of the designer group, the current competency level in 'BIM-related knowledge (3.32)' within the 'Knowledge' domain marked the highest level, followed by 'Understanding FAB (2.87)', 'Construction related knowledge (2.78)', and 'Standards evaluation (2.46)'. In comparison, the level of competency was lower than that of the owner group in general. Among the specific competencies within the 'Knowledge' domain, the current level of competency in terms of 'LOD definition (3.39)' and 'Major functions of BIM software (3.37)' were high, but the competency level for most other specific competencies was found to be low (2.5 and below).
## 3 Small Scale Experimental Test
### Image Shooting  
When taking images of the target temporary experimental structure, weather conditions (e.g., rainy or snowy days) and positions (e.g., backlighting) are important factors because they affect the illuminance of the image. As the visibility of the processed image can be affected by the illuminance of the original image, the proposed method may be affected to some extent by the illuminance. As this study focused on verifying the feasibility of the proposed method, image shooting was conducted under good weather conditions and no backlight.  
The used UAV is not an expensive piece of equipment considering its field applicability, and it is easily available. The temporary structures were photographed using the DJI Mini 2 model, which can determine its own position and stabilize itself by using a GPS and downward vision system. The specifications of the used UAV are listed in Table 1. The shooting path (Figure 4) was semicircular, at altitudes of 2 m and 4 m and angles of 30 \({}^{\circ}\)and 45\({}^{\circ}\), respectively, to the ground centered on the specimen. A total of 100 images were obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Category** & **Content** & **Category** & **Content** \\ \hline \multirow{4}{*}{Camera sensor} & \multirow{4}{*}{\(1/1.2^{\prime\prime}\) CMOS, Effective pixels: 12 m} & \multicolumn{2}{c}{Fov: 83\({}^{\circ}\)} \\  & & & 35 mm format \\ \cline{1-1}  & & & equivalent: \\ \cline{1-1}  & & & 24 mm \\ \cline{1-1}  & & & Aperture: f/2.8 \\ \cline{1-1}  & & & Focus range: 1 m to \(\infty\) \\ \hline Takeoff weight & 249 g & Max ascent speed & 2 m/s \\ \hline Max speed & 6 m/s & GNSS & GPS + GLONASS + \\ \cline{1-1}  & & & ALILEO \\ \hline Max flight time & 31 min & Max descent speed & 1.5 m/s \\ \hline \end{tabular}
\end{table}
Table 1: Specifications of UAV (DJI Mini 2 model).  
Figure 3: Temporary experimental structure: (**a**) Photo of temporary structure considered; (**b**) Length of temporary structure (unit: m).