esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results  
The training process of the proposed model begins with a predefined architecture that integrates multiple 3D convolutional layers specifically designed to capture both spatial and temporal features from input video sequences. To validate the robustness and efficiency of this architecture, a series of experimental configurations were applied to two publicly available datasets commonly used in the literature: RWF2000 and RLVS.  
The initial purpose of these experiments was to evaluate the model's performance on these public datasets, establishing a comparative benchmark prior to applying the model to the newly developed dataset introduced in this study. The model's performance was subsequently contrasted with the results obtained when utilizing the proposed dataset, which was specifically designed to address limitations found in existing datasets, such as representativeness, class balance, and precise labeling. This experimental approach not only allows for the evaluation of the model's effectiveness but also serves to validate the relevance and quality of the proposed dataset.  
The experimental configurations included variations in video sequence length, frame sampling rate, and video resolution. These parameters were carefully adjusted to analyze their impact on the model's overall performance. Particular attention was paid to metrics such as accuracy, the AUC-ROC, and processing time, with the objective of identifying improvements in the model's ability to discriminate between criminal and non-criminal incidents.  
The experiments were designed using a data partitioning strategy consisting of three sets: training, validation, and evaluation. To ensure robust training and mitigate the risk of model overfitting, 80% of the data were allocated to the training set, which the model used to adjust its weight and learn the inherent features of the data. Ten percent (10%) of the data were reserved for the validation set, which was employed during training to continuously assess model performance and adjust hyperparameters as necessary without directly influencing the learning process. Finally, the remaining 10% were assigned to the evaluation set, providing an objective measurement of the model's final performance and ensuring a fair and unbiased assessment.  
A total of 30 epochs were conducted, during which the model was trained from scratch. In each epoch, the model adjusted to the training data, and its performance was evaluated on the validation set. This methodology ensured a comprehensive evaluation of the model's ability to generalize to unseen data.
## 4 Results
### Evaluation of Correctness of Automatic Matching Pairs of Scans  
In order to determine the effectiveness of connection of scans, the correctness and completeness of orientation of all pairs of scans were analysed for investigated test sites. The following assumptions were made for the needs of evaluation: Full-registration, which did not require final registration by means of another method (deviation on points \(\leq\)0.01 m and correct distribution of tie points), semi-registration, which is the approximate registration (approximate exterior parameters considered as initial parameters for ICP) and no-registration, which does not allow it to perform the process of orientation. In order to present the extent of performed investigations and the number of combinations of connection of scans in pairs for all test sites and projections, results of connection of scans are presented in Tables 5-8. The green colour marks correct matching (full-registration), the initial orientation that requires the final registration  
Basing on results listed in Table 5 it may be stated that most of pairs were correctly matched.  
All pairs of scans were correctly matched and oriented using the FAST and SURF detectors.  
In the case of Test site III (and office space with a narrow lobby) higher diversification of correct matching of particular pairs of scans may be observed. All pairs of scans were correctly matched with an orthoimage using the FAST detector. When the connection of pairs of scans in the spherical projection is analysed, it is recommended to commonly apply two detectors (FAST and SURF). In the case of the Mercator projection, it is not recommended to apply the FAST detector.  
Performed tests proved that the worst results were obtained for Test site IV (an empty shop in the shopping mall) with smooth walls and dark surfaces of the air-conditioning system. In the case of such rooms, an individual approach to scanning and the use of raster images is required. Performed tests proved incorrect operation of applied algorithms although the scans were acquired with the high overlap. The registered intensity and the point cloud density did not allow detection of the sufficient number of  
and completeness of the data orientation process with the use of the SURF and FAST algorithms. The authors plan to perform the analysis of a selection of other detectors for such objects.
## 4 Extraction Methods
[32] (102-108%) with MeOH extraction and subsequent SPE, and [PERSON] et al. [82] (103-108%) with dansyl chloride derivatization. In the case of the employment of gas chromatography (GC) for the analysis of 4(5)MEI, the most common extraction technique is the use of ion-pair extraction with bis-2-ethylInspheMBate (BEHPA) and subsequent derivatization with isobutyl chloroformate (Figure 3). In the ion-pair extraction procedure with BEHPA, the sample is extracted using a solvent, such as MeOH, which is evaporated to dryness and dissolved in phosphate buffer pH 6.0. A portion of the solution is mixed with 0.1 M BEHPA in chloroform. After centrifugation, a portion of the chloroform phase is mixed with HCl 0.1 M. ACN-isobutanol-pyridine (50:30:20, \(v/v/v\)) is then mixed with a portion of the aqueous hydrochloric phase and subsequently, isobutyl chloroformate, 1.0 M NaHCO\({}_{3}\)\({}^{-}\)/CO\({}_{3}\)\({}^{-2}\), and chloroform are added. After vortexing, the 4(5)MEI is extracted in the organic layer [43]. Good to excellent recoveries have been reported with the previously described extraction and derivatization methods from [PERSON] et al. [86] (101%) and [PERSON] et al. [52] (103.86-111.08%). Solid phase microextraction (SPME) in combination with the GC-MS analysis have also been used by [PERSON] and [PERSON] [40] for the extraction of 4(5)MEI from beverages. The researchers tested four fibers, polydimethylsiloxane (PDMS), polydimethylsiloxane-divinylbenzene (PDMS-DVB), polyamide (PA), carboxon-polydimethylsiloxane (CAR-PDMS). As a result, PDMS-DVB was evaluated as the most suitable.
# Deep Learning-Based Symptomizing Cyber Threats Using Adaptive 5G Shared Slice Security Approaches
## 4 Results  
In contrast to the previous two-level anomaly detection approach at the 5G RAN (Figure 7), we extended our method to a multi-tier security approach across the 5G network (Figure 5). The vCTS module can symptomize the suspicious NetFlow data from communication channels at the Core, Edge, and RAN levels. The vCTS can be virtually initiated to optimize and secure the network slices from external attacks. We evaluated three DL models to deal with both temporal and spatial data models in discriminative manners on an opted dataset. Moreover, the chosen dataset was used to evaluate Bi-LSTM, CNN-LSTM, and CNN models rather than a single model in order to consider all data aspects of input for the future meta-data nature 5G slices. For the optimum model selection in anomaly detection, the performance of all opted models has gone through exhaustive evaluation and discussion. The models have been trained appropriately after some feature engineering and pre-processing for suitable input feature vectors. The dataset has split into training sets (80%), presenting \"X_train\" and \"X_test\", in order to validate models, and test sets (20%), which are used to evaluate two classes, namely \"Normal\" and \"Botnet\", using bi-label classification for cyber threat symptomizing. Models were trained to a maximum of 20 iterations for stable output. \"Train-Test loss\" and \"Train-Test accuracy\" of each model were analyzed. The behavior of each DL model was observed and compared to all model outcomes. The analysis of each model is described, considering the metrics recall, precision, F1, and confusion matrix.
# The Firmware Design and Implementation Scheme for C Form-Factor Pluggable Optical Transceiver +
## 2 Related Work  
This section describes the firmware design requirements for CFP transceiver operation through analysis of the CFP-MSA standard [11]. Version 1.0 of this standard was published in June 2009, and the current version is 2.6. The MSA standard consists of a CFP management interface, a CFP register, and CFP control and monitoring.
## 3 Results and Discussion
### Adsorption Experiments
#### 3.2.2 Effect of Adsorbent Dose on the Adsorption Process  
To study the influence of the amount of sorbent on the adsorption process, different amounts of PM4 were used (0.5, 1.0 and 2.0 g/L), keeping the other variables constant (50 mg/L, 25 \({}^{\circ}\)C, natural pH of solution: 6.8 for AY 42 and 6.9 for AR 213). The results are presented in Figure 9. As the amount of adsorbent increased, an increase in dye removal was noticed. These results can be explained by the higher number of active sites available as the adsorbent dose increased, facilitating the adsorption process [14; 58].  
The removal efficiency of the investigated dyes increased rapidly as the amount of PM4 increased from 0.5 to 1.0 g/L. An increase in the amount of adsorbent above 1.0 g/L did not significantly improve the adsorption of dyes, so it has not been justified for use. In order to obtain a good removal efficiency, using less adsorbent, further studies were performed using 1.0 g/L of PM4.
## 2 Deep Echo State Network
### Deep Echo State Network  
DeepESN, as shown in Figure 2, was first introduced by [PERSON] [11; 14], as a stacked reservoir computing (RC) architecture, where multiple reservoir layers are stacked on top of each other. The state transition functions of hidden layers in DeepESN are expressed as:  
\[\begin{cases}\mathbf{x}^{1}(t)=\alpha^{1}\mathbf{x}^{1}(t)+(1-\alpha^{1}) \tanh(\mathbf{W}_{i}\mathbf{u}(t-1)+\mathbf{W}_{r}^{1}\mathbf{x}^{1}(t-1)),&l= 1\\ \mathbf{x}^{l}(t)=\alpha^{l}\mathbf{x}^{l}(t)+(1-\alpha^{l})\tanh(\mathbf{W}_ {p}^{l-1}\mathbf{x}^{l-1}(t-1)+\mathbf{W}_{r}^{l}\mathbf{x}^{l}(t-1)),&l\in[2, L]\end{cases} \tag{5}\]  
where the superscript(1 and \(l\)) is the layer notation, with a total of \(L\) hidden layers in the network. \(\mathbf{x}^{l}(t)\in\mathbb{R}^{N_{x}^{l}\times 1}\) represents the l-th hidden layer (i.e., reservoir(\(l\))) with \(N_{x}^{l}\) neurons inside, \(\mathbf{W}_{i}\in\mathbb{R}^{N_{x}^{l}\times N_{u}}\) is the input weight matrix of the first hidden layer, \(\mathbf{W}_{r}^{l}\in\mathbb{R}^{N_{x}^{l}\times N_{x}^{l}}\) is the reservoir weight matrix of the \(l\)-th hidden layer, \(\mathbf{W}_{p}^{l-1}\in\mathbb{R}^{N_{x}^{l}\times N_{x}^{l}}\) is the propagate weight matrix, which connects reservoir(\(l-1\)) to reservoir(\(l\)).  
As in the standard LI-ESN approach, the reservoir weights of a DeepESN are initialized subject to similar stability constraints. In the case of DeepESN, such constraints are expressed by the necessary conditions for the ESP of deep RC networks [13] described by the following equation:  
\[\max_{l\in[1,L]}\rho\Big{(}(1-\alpha^{l})\mathbf{I}+\alpha^{l}\mathbf{W}_{r} \Big{)}<1, \tag{6}\]  
where the same leaky parameter (\(\alpha^{l}\equiv\alpha\)) in each layer is considered in this paper.  
The values in each reservoir matrix\(\{\mathbf{W}_{r}^{l}|l\in
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 4 Results and Discussion
### Performance Verification of the Proposed Model
In Figure 6e, the boom is raised for the initial 3.5 s. Subsequently, for about 7 s, the activity concludes with the bucket being adjusted by the arm to ensure it is moved to the correct position. Repositioning involves adjusting the main body's position for optimal excavation. In Figure 6f, the color change frequency of the boom pattern was primarily observed. The main change in arm pattern occurred in two vectors, beginning from 2 to 3 s.  
A total of 2070 samples were used as training data for the model. Consequently, the 2070 samples utilized for verification correspond to 5.75 h when converted into video time, or 414,000 frames when converted into image count. In the training process, 1758 samples (75% of the 2070 samples) were used as the training set. In Figure 7, the rate of change in precision and loss rate decreased significantly at 40 iterations, and learning ended at 135 iterations. The verification accuracy was measured at 96.15%. Figure 6: Sample data matrices of each of the six activities: (**a**) Waiting; (**b**) Scraping; (**c**) Excavating (under); (**d**) Excavating (front); (**e**) Dumping; (**f**) Repositioning. In the validation process, 312 samples (15% of the 2070 samples) were used as the validation set. Figure 8 shows the performance of the classifier using a confusion matrix. In this study, there were six classes, and the confusion matrix was expressed as a 6 \(\times\) 6 matrix. The diagonal values of the confusion matrix represent correctly classified predictions (True Positives, TP). Off-diagonal rows of the target class represent predictions that were misclassified as other classes (False Positives, FP). Off-diagonal columns of the target class represent predictions that were incorrectly classified into that class (False Negatives, FN). In the model, precision refers to the cases where the observed class is indeed the corresponding class, while recall is an indicator of whether the actual target has been predicted as the respective class. The precision for each observation can be expressed as TP/(TP + FP), and recall can be expressed as TP/(TP + FN). For instance, when considering the class 'Excavating (Under),' precision is calculated as \(43/(43+7+1+1)\), resulting in a value of 0.8269, while recall is calculated as \(43/(1+43+2+1+1)\), resulting in a value of 0.8958. The precision for each class, from waiting to repositioning, is sequentially listed as 100%, 100%, 82.69%, 96.15%, 96.15%, and 98.08%, with an average precision of 95.51%. The recall for each class is sequentially listed as 100%, 100%, 89.58%, 86.21%, 98.04%, and 98.08%, with an average recall of 95.31%. Accuracy is an indicator of predictive correctness across all cases relative to all classes, and it can be represented as (the sum of True Positives (TP) + the sum of True Negatives (TN))/total number of cases. In the proposed model, the accuracy of the model is calculated as \((52+51+43+50+50+51)/312=0.9519\), i.e., 95.19%. Figure 7: Accuracy and loss across training iterations.
# Reliability Analysis of Military Vehicles Based on Censored Failures Data
## 2 Methodology
### Reliability Estimation  
During the operation of technical facilities, the failure records form the basis for the development of reliability models. From this perspective, indices defining intervals to failure or between failure are useful [11; 37]. These intervals can be expressed in operational units referring to the operating time of the object (h, mth) or to the mileage [km] in the case of vehicles. The following reliability indicators are identified [38; 39; 40; 41]:  
* Time to failure (TTF),
* Time between failures (TBF),
* Mileage to failure (MTF),
* Mileage between failures (MBF).  
Figure 1: Flowchart of reliability modelling.  
Based on a statistically significant sample of technical objects, an empirical reliability function is determined. The following equation can be used to determine it [35]:  
\[\widehat{R}(t_{i})=1-\frac{i}{n}, \tag{5}\]  
where \(t_{i}\) is the time of occurrence of the \(i\)-th failure, \(i\) is the number of failed technical objects up to time \(T_{i}\), and \(n\) is the number of all technical objects belonging to the study sample.  
However, this form of the empirical reliability function may cause distortion for values of \(i\) close to 0 and \(n\). The solution to this problem is the Benard approximation commonly used in the literature [17; 18; 19; 42], the form of which is shown in the equation:  
\[\widehat{R}(t_{i})=1-\frac{i-0.3}{n+0.4}. \tag{6}\]  
The above estimators are applicable to scientific studies of a specified sample of technical objects, assuming equal intensity of use of all objects and complete knowledge of the exploitation process. In real technical systems, including transport systems involving military vehicles, there are significant differences in the mileage achieved, despite the aim of even usage. It is therefore proposed to adopt the Kaplan-Meier estimator used in survival analysis to estimate the value of the reliability function. It allows the analysis of censored data, useful for studying certain periods of the exploitation process, in which, after having performed a certain amount of work expressed in exploitation units, did not suffer from failure until now or ceased to operate in a given system [14]. Figure 2 shows a graphical interpretation of the study period of the exploitation process, taking into account the censored data. The Kaplan-Meier estimator for the reliability function [14; 43] is presented in the following equation:  
\[\widehat{R}(t_{i})=\prod_{j:t_{j}\leq t_{i}}^{i}\left(1-\frac{d_{j}}{n_{j}} \right), \tag{7}\]  
where \(d_{j}\) is the number of technical objects that have sustained failure at time \(t_{j}\), and \(n_{j}\) is the number of all technical objects that have lasted until time \(t_{j}\).  
Figure 2: Schematic of exploitation process with censored data.  
The standard error [44; 45] has been obtained by the Greenwood Formula (8):  
\[SE\Big{\{}\widehat{R}(t_{i})\Big{\}}=\widehat{R}(t_{i})\sqrt{\sum_{j=1}^{i}\frac{d _{j}}{n_{j}(n_{j}-d_{j})}}. \tag{8}\]
# A Study of the Bond Strength and Mechanism between Basalt Fibers and Asphalt Binders
## 3 Results and Analysis  
### Influence of Fiber Embedment Depths and Asphalt Binders on POT Results  
Figures 6 and 7 show the maximum pull-out forces and bond strengths when BFs were added to the three different asphalt binders. The pull-out test also investigated the effects of the embedment depth of the basalt fibers on the bond strength between materials. Four embedment depths of basalt fibers (6 mm, 12 mm, 20 mm, and 30 mm) were selected in this test.  
The pull-out forces increased rapidly at the beginning, indicating an increase in interfacial adhesion between the BFs and asphalt binders, so the bond strength of the  
Figure 6: The maximum pull-out forces.  
Figure 7: The bond strengths.  
interface was gradually destroyed. When the bond failure of the interface extended to the ends of the embedded fibers, the tensile force acting on the fibers reached the maximum value. Then, the basalt fibers slid into the asphalt binders, and several fibers were pulled out from the asphalt binders. Therefore, there was a clear downtrend of pulling load in the bonding interface. When the pull-out test was carried out to a certain extent, the pull-out force of the fibers declined dramatically and tended to be relatively stable until the BFs were completely pulled out from the binders. It is obvious that the pull-out force of rubber asphalt with BFs was greater than that of neat asphalt and SBS-modified asphalt.  
It was found that the pull-out force increased gradually with the increase in the embedment depth of the basalt fibers in the asphalt binders, but there was a decrease in the pull-out force when the embedment depth of the fibers exceeded a certain value. The optimal length of basalt fibers (20 mm) can be obtained by referring to Figure 6. These results suggest that increasing the embedment depth of basalt fibers to a certain level can improve the mechanical properties of fiber-asphalt binders. The performance of asphalt binders is adversely affected if the fiber length is too long or too short. When basalt fibers are used in HMA, the length of basalt fibers is restricted by the effects of asphalt binders.  
Figure 7 shows that the bond strength between basalt fibers and asphalt binders differs based on the asphalt binder: rubber asphalt > SBS-modified asphalt > neat asphalt. This suggests that the bond strength between basalt fibers and asphalt binders is directly affected by the compatibility between them and the properties of the asphalt binders.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 5 The Taxonomy of Middleware Architectures for WSNs
### Service-Oriented Architecture Approach
#### 5.6.2 The Tracking Applications  
The OASiS is a tracking application for example fire detection and vehicle tracking [34; 35]. The WSN-SOA has been tested for surveillance applications with the ability to detect seismic vibrations [36; 37].
# Buffer with N Policy and Active Management
## 5 Numeric Examples  
In all numerical calculations, we will assume the arrival rate \(\lambda=2\) and hyper-exponentially distributed transmission time, with parameters \((\mu_{1},\mu_{2})=(0.9,5.9)\) and \((p_{1},p_{2})=(0.0855,0.9145)\). This gives an average packet transmission time of \(\overline{F}=0.25\). Therefore, the service rate is twice the arrival rate during the transmission phase. The standard deviation of transmission time is 0.448, resulting in a coefficient of variation of 1.79. This indicates significant variability in the transmission time (i.e., packet size). Moreover, in all calculations, the buffer capacity is \(N=100\). Unless specified otherwise, the critical threshold is \(K=95\). The value of \(K\) will only be altered when this section ends, where we research the influence of \(K\) on the system's performance.
# Influence of Magnetic Field on Amplification without Inversion Induced by a Femtosecond Pulse Train
## 3 Results and Discussion
The magnetic field's influence on the variation of the amplification and population inversion of the system for different pulse repetition periods is shown in Figure 6. For a fixed relaxation time \(T_{1}\), the smaller the repetition period, the greater population that can be excited within the relaxation time after a high enough number of pulses [23], which means a larger gain. Therefore, it can be concluded that the pulse repetition period is inversely proportional to the gain. Furthermore, the enhancement of AWI induced by the magnetic field remains obvious under different repetition periods. Figure 4: (**a**) Variation of the gain Im \(\rho_{21}\) with detuning and (**b**) the projection of the maximum gain Im\(\rho_{21}\) for different magnetic fields. (**c**) Variation of population inversion (\(\rho_{22}-\rho_{11}\) ) with detuning, and (**d**) the corresponding variation of population inversion at the maximum gain Im\(\rho_{21}\) for different magnetic fields. Other parameters are the same as those in Figure 2.  
Not only does the AWI change with different pulse repetition periods, the gain of the system can be also controlled by the pulse area of the fs pulse train. Without loss of generality, three different pulse areas are studied in detail, as shown in Figure 7. When the magnetic field is added, a conclusion similar to the previous discussion can be obtained, namely that the enhancement of AWI is still observed in different pulse areas. Figure 4.0 Evolution of (**a**) the maximum gain \(\text{Im}\rho_{21}\) and (**b**) the corresponding population inversion for levels \(\left|1\right>\) and \(\left|2\right>\) with the number of pulses for B = 0 G and B = 100 G. The other parameters are \(\theta=\pi/15\) and \(T_{R}=2.8\) ns. Figure 4.1 Evolution of the maximum gain \(\text{Im}\rho_{21}\) and the corresponding population inversion with the number of pulses for levels \(\left|1\right>\) and \(\left|2\right>\) at different pulse repetition periods (\(T_{R}=2.8\) ns, 5.6 ns, 8.4 ns) when (**a**,**b**) B = 100 G and (**c**,**d**) B = 0 G. In above figures, \(\theta=\pi/15\).