esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Phase-Locked Loops
### Continuous Model  
Using the assumptions that the phase error is small enough and that the input noise is uncorrelated with the incoming waves, the linearized model for a PLL in the continuous-time domain is presented as shown in Figure 1. The device is composed of a phase detector (discriminator), a loop filter \(F(s)\), and a voltage-controlled oscillator (VCO) \(V(s)\) with a discriminator gain \(A\) and VCO gain \(K\), where \(s=\sigma+j\omega\) denotes the Laplace operator. In the figure, \(\phi(t)\) symbolizes the measured input phase at time \(t\), \(\hat{\phi}(t)\) and \(\hat{\phi}^{-}(t)\) represent the estimates of the phase rate and phase, respectively, and \(n(t)\) denotes the noise.  
The purpose of the loop filter is to reduce the influence of the noise to produce an accurate estimate of the original signal. First-/second-/third-order loop filters (selected depending on user requirements) are widely used in GNSS receivers. The loop filter should be designed to satisfy the filter design criteria, namely minimizing VCO phase noise jitter due to noise effects (i.e., minimization of the root-mean-square noise error), and maintaining the transient error in the VCO phase caused by specified changes in the signal phase (i.e., the minimization of the transient error). These criteria are effectively met using the variational method for optimization via the Lagrangian multiplier method for continuous-time PLLs [1] and discrete-time PLLs [3].  
The VCO can be uniquely specified using a simple analog integrator represented by \(1/s\) (the Laplace transform of an integrator in the continuous-time domain):  
\[V(s)=1/s \tag{1}\]  
The closed-loop transfer function of the continuous-time PLL \(H\)(s) is given by:  
\[H(s)=\frac{AKV(s)F(s)}{1+AKV(s)F(s)} \tag{2}\]  
Typically, the feedback loop gain \(AK\) is assumed to be 1 (i.e., unity gain). Then, the transfer function in (2) can be rewritten as:  
\[H(s)=\frac{V(s)F(s)}{1+V(s)F(s)}=\frac{F(s)}{s+F(s)} \tag{3}\]  
Table 1 presents a summary of first-/second-/third-order tracking loops and their characteristics. The filter order and the loop filter natural radian frequency \(\omega_{0}\) (which is obtained from \(B\)) are determined in the design stage depending on the user requirements. \(a_{i}\) and \(b_{i}\) represent the filter coefficients of the \(i\)-th-order loop filters [2].
## 5 Results Analysis and Discussion  
### Results Analysis  
#### 5.1.1 Security Analysis  
The clustering results of the feature data have been subjected to PCA (Principal Component Analysis) [33]. According to the conclusions of the literature, such as [14; 20; 29], the design parameters selected through our semi-supervised clustering meet the needs of safety and comfort in tunnel design. In our work, as illustrated in Figure 9, the feature data underwent dimensionality reduction through PCA. The driving simulation generated various features that were then subjected to clustering. To determine the safety of a particular working condition, we adopted a threshold of 80% for clustering the driver features. If more than 80% of the driver features fell within a certain cluster, the corresponding working conditions were considered safe. Consequently, we identified 12 working conditions that met the safety criteria based on the clustering results. To evaluate the safety of the working conditions, we utilized the Euclidean distance from the clustering center. Working conditions that exhibited shorter distances to the clustering center as a whole were deemed safer. Consequently, our analysis allows us to affirm that the four safest working conditions, namely I-14, I-30, I-53, and I-55, were identified based on the clustering results. Therefore, it is evident that the utilization of semi-supervised K-means clustering combined with clustered clusters effectively distinguishes between safe and unsafe working conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Level of Factors** & **Sidewall** & **Roof** & **Pavement** & **SFS** \\ \hline
1 & no view & no view & no view & no view \\
2 & WSP o BBG & WLP o BBG & G & Y \\
3 & BLP o WBG & G w/p P & Y & Specification style \\ \hline \end{tabular}
\end{table}
Table 6: Table of experimental factor levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Index** & **Sidewall** & **Roof** & **Pavement** & **SFS** \\ \hline II-1 & no view & no view & no view & no view \\ II-2 & W & PCP o Gray-BG & R & B \\ II-3 & W & WLP o BBG & no view & B \\ II-4 & WSP o BBG & no view & Y & Specification style \\ II-5 & G & no view & Y & B \\ II-6 & WSP o BBG & G w/o P & no view & Y \\ II-7 & BLP o WBG & no view & G & Y \\ II-8 & BLP o WBG & WLP o BBG & no view & Specification style \\ II-9 & W & WLP o BBG & G & B \\ \hline \end{tabular}
\end{table}
Table 7: Interior test program.  
The results illustrated in Figure 10 highlight the exemplary safety compliance across four key working conditions--namely, I-14, I-30, I-53, and I-55--where participant numbers reached 30, 29, 30, and 29, respectively. Impressively, these conditions boast safety compliance rates of 100%, 96.7%, 100%, and 96.7%, underscoring a robust adherence to safety protocols within the study cohort.  
Figure 10: Proportion of people who are safe.  
Figure 9: The safety clustering results after PCA dimensionality reduction.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 4 Advantages and Disadvantages of Pooling Approaches  
The upsides and downsides of pooling operations in the numerous CNN-based architectures is discussed in Table 1, which would help researchers to understand and make their choices by keeping in mind the required pros and cons. Max pooling has indeed been applied by several researchers owing to its simplicity of use and effectiveness. Detail analysis was performed for further clarification of the topic. \begin{table}
\begin{tabular}{c c c c} \hline
**Type of Pooling** & **Advantages** & **Drawbacks** & **References** \\ \hline \multirow{4}{*}{Max Pooling} & - & Performs more effectively when integrated & - & Deterministic in spirit. \\  & & with simple classifiers and sparse code. & - & The distinguishing \\  & - & It complements sparse representations due to & characteristics vanish & \\  & statistical features. & & when the majority of the & \\  & - & Eradicating no maximal elements might & & elements in the pooling \\  & & expedite calculation for upper layers. & & region are available in \\  & & & significant magnitudes. & \\ \hline \multirow{4}{*}{Average Pooling} & \multirow{4}{*}{-} & \multirow{4}{*}{Easily understandable.} & - & Forthcoming in spirit. \\  & & & If minor magnitudes are & \\  & - & Execution is uncomplicated. & & considered, the contrast & \\ \hline \multirow{4}{*}{Gated Max} & - & Responsive in style. & \multirow{4}{*}{-} & \multirow{4}{*}{[41]} \\  & - & It is adaptive in whether the volume fraction & & \\ \cline{1-1}  & & can fluctuate based on the properties of the & & training parameters. \\ \cline{1-1}  & & pooling region. & & \\ \hline \multirow{4}{*}{Mixed Max} & - & Stochastic pooling. & - & Once it has been learned, \\ \cline{1-1}  & - & Facilitates in the problem of & & the mix proportion does \\ \cline{1-1}  & & overfitting avoidance. & & not really respond and \\ \cline{1-1}  & & & adapt to the attributes of & \\ \cline{1-1}  & & the region & & \\ \cline{1-1}  & & & being integrated. & \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of different pooling approach in CNN. _Performance Evaluation of Popular Pooling Methods_  
The performance among the most latest pooling methods has been investigated systematically for the purpose of image classification in this section. We would like to emphasize that the primary aim of this study is to fairly assess the influence of the pooling strategies in the CNNs, not to establish the optimum classification architecture. Table 2 evaluates
## 5 Conclusions  
This study presents a multimodal framework designed to detect pedestrian panic behaviors within crowded micro-road networks. The methodology integrates spatial analysis using convolutional neural networks (CDNet) to identify panic-prone areas based on abnormal crowd density contours, temporal trajectory anomaly detection through LSTM networks to recognize irregular pedestrian movements, and semantic analysis via Transformer models interpreting verbal distress signals converted from real-time speech-to-text using Baidu AI. Additionally, a multi-layer perceptron (MLP)-based multimodal fusion approach effectively integrates these diverse data streams for enhanced panic detection accuracy.  
Comprehensive experiments conducted on datasets from the Itaewon-Stampede accident and benchmark datasets validate the efficacy of the proposed approach. The multimodal model achieved 91.7% accuracy, 89.2% precision, 87.3% recall, and an F1-score of 88.2% on the Itaewon dataset, significantly surpassing the single- and dual-modality baselines. A similar performance was maintained in the SUMO/Unity simulations (F1-score 86.9%), demonstrating the framework's robustness and cross-domain generalizability.  
Although the proposed framework achieves strong performance in experimental settings, practical deployment may face constraints such as limited computational resources, adverse environmental conditions, and privacy considerations. Future work will explore model compression, domain adaptation, and multi-camera coordination to enhance the real-world applicability of public safety monitoring. Furthermore, the initial cross-site evaluation of datasets, such as unseen urban festivals, indicates that the proposed model maintains a strong panic recognition ability under different population compositions and environmental conditions without the need for additional fine-tuning. While current trajectory modeling is based on sequence learning via LSTM networks, it may be constrained by the assumptions of trajectory regularity. Future work will explore model-free representations, such as graph-based encoders or adaptive motion embeddings, to enable the system to generalize more effectively across unseen, highly dynamic crowd behaviors.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, L.H., R.Z. and C.L.; writing--review and editing, B.W., R.Z., A.R. and Y.C., and visualization, L.H., C.L. and Y.M.; supervision and project administration, R.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 72374154).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ethical and privacy restrictions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Analysis of the Results
### Multipath Component
It was also noticed that the value of the correlation coefficient in a few cases is greater than the selected acceptability threshold and typically oscillates close to this critical value. Based on the presented results, it can be assumed that the best results for the UWB \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{MN} & \multirow{2}{*}{**SCN**} & \multirow{2}{*}{**LOS/NLOS**} & \multirow{2}{*}{**PDF**} & \multicolumn{6}{c}{**868 MHz NB**} & \multicolumn{6}{c}{**6489 MHz UWB**} \\ \cline{5-10}  & & & **P\({}_{1}\)** & **P\({}_{2}\)** & \(\chi^{2}\)** & \(r\)** & **P\({}_{1}\)** & **P\({}_{2}\)** & \(\chi^{2}\)** & \(r\)** \\ \hline \multirow{10}{*}{TOF} & \multirow{5}{*}{APR} & Rice & 0.934 & 0.369 & 8.18 & 0.91 & 0.945 & 0.167 & 10.30 & 0.87 \\  & & Nakagami-m & 2.304 & 1.144 & 7.39 & 0.92 & 8.802 & 0.948 & 9.92 & 0.87 \\  & & Weibull & 1.131 & 3.034 & 7.92 & 0.91 & 1.030 & 5.322 & 7.78 & 0.83 \\  & & Rayleigh & 0.756 & - & 4.05 & 0.87 & 0.689 & - & 0.68 & 0.53 \\  & & **Lognormal** & \(\mathbf{-0.049}\) & **0.347** & **4.44** & **0.92** & \(\mathbf{-0.055}\) & **0.169** & **8.90** & **0.85** \\ \cline{2-10}  & & \multirow{5}{*}{NLOS} & Rice & 0.686 & 0.617 & 9.38 & 0.87 & 0.948 & 0.325 & 5.27 & 0.96 \\  & & Nakagami-m & 1.507 & 1.231 & 11.04 & 0.87 & 2.698 & 1.114 & 4.51 & 0.98 \\  & & Weibull & 1.144 & 2.320 & 10.45 & 0.86 & 1.119 & 3.478 & 5.42 & 0.95 \\  & & Rayleigh & 0.785 & - & 10.98 & 0.88 & 0.745 & - & 2.09 & 0.69 \\  & & **Lognormal** & \(\mathbf{-0.080}\) & **0.412** & **11.48** & **0.97** & \(\mathbf{-0.046}\) & **0.332** & **4.30** & **0.97** \\ \hline \multirow{10}{*}{\(\text{TOF}\)} & \multirow{5}{*}{LOS} & Rice & 0.908 & 0.384 & 8.28 & 0.94 & 0.941 & 0.313 & 4.05 & 0.96 \\  & & Nakagami-m & 2.140 & 1.119 & 7.55 & 0.97 & 2.899 & 1.082 & 3.47 & 0.98 \\ \cline{1-1}  & & Weibull & 1.115 & 2.874 & 7.85 & 0.94 & 1.105 & 3.481 & 4.20 & 0.95 \\ \cline{1-1}  & & Rayleigh & 0.748 & - & 4.25 & 0.83 & 0.736 & - & 3.14 & 0.73 \\ \cline{1-1}  & & **Lognormal** & \(\mathbf{-0.070}\) & **0.360** & **6.41** & **0.98** & \(\mathbf{-0.052}\) & **0.312** & **5.49** & **0.99** \\ \cline{1-1} \cline{2-10}  & & Rice & 0.851 & 8.133 & 14.02 & 0.89 & 0.913 & 0.409 & 9.91 & 0.95 \\ \cline{1-1}  & & Nakagami-m & 1.204 & 1.233 & 12.87 & 0.88 & 1.940 & 1.169 & 9.00 & 0.98 \\ \cline{1-1}  & & Weibull & 1.161 & 2.082 & 12.11 & 0.88 & 1.137 & 2.849 & 9.70 & 0.96 \\ \cline{1-1}  & & Rayleigh & 0.813 & - & 14.01 & 0.89 & 0.765 & - & 5.11 & 0.88 \\ \cline{1-1}  & & & **Lognormal** & \(\mathbf{-0.095}\) & **0.477** & **11.51** & **0.98** & \(\mathbf{-0.062}\) & **0.392** & **9.27** & **0.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameters of PDFs fitted to empirical distributions of small-scale fading amplitudes for \(\text{TOF}\).
## 5 Performance Metrics  
To evaluate the models, we decided to use the standard metrics used in classification, e.g., accuracy, precision, recall and F1 score. Such metrics are easy and straightforward to obtain for a binary classification problems and can be computed as:  
* Accuracy = TP + TN/TP + FP + FN + FP
* Precision = TP/TP + FP
* Recall = TP/TP + FN
* F1 score = 2
* Recall) / Precision + Recall,  
where:  
* TP--True Positive examples are predicted to be positive and are positive;
* TN--True Negative examples are predicted to be negative and are negative;
* FP--False Positive examples are predicted to be positive but are negative;
* FN--False Negative examples are predicted to be negative but are positive.  
To apply such metrics in the multi-label classification, those metrics could be computed for each class (one-vs-rest approach). Usually, we need to compute the confusion matrix (see Figure 7) for eachclass \(c_{i}\in C=\{1,...,K\}\). For each class \(c_{i}\), the \(i\)-th class is considered as positive, while the rest of other classes as a negative class. Then, to summarize the performance of the classifier on all classes, metrics can be micro or macro averaged [61]. The use of micro or macro averaging is dependent on the particular use case. In the following formulas, we will use TP\({}_{i}\), FP\({}_{i}\), and FN\({}_{i}\) as the true positive, false positive, and false-negative rates associated with the class \(i\).  
Micro-averaging at first computes the confusion matrix for all classes and then calculates the overall metrics. Micro-averaging may be preferred in case of class imbalance present in the data. Micro-averaged precision and recall metrics are computed as:  
* \(\text{Precision}_{\text{micro}}=\frac{\sum_{i=1}^{|C|}\text{TP}_{i}}{\sum_{i=1}^ {|C|}\text{TP}_{i}+\text{FP}_{i}}\)
* \(\text{Recall}_{\text{micro}}=\frac{\sum_{i=1}^{|C|}\text{TP}_{i}}{\sum_{i=1}^ {|C|}\text{TP}_{i}+\text{FN}_{i}}\)
* \(\text{F1}\text{score}_{\text{micro}}=2*\frac{\text{Precision}_{\text{micro}}* \text{Recall}_{\text{micro}}}{\text{Precision}_{\text{micro}}+\text{Recall}_ {\text{micro}}}\)  
On the other hand, macro-averaging is based on the computation of precision and recall for each class and then averaging the overall metrics:  
* \(\text{Precision}_{\text{macro}}=\frac{\sum_{i=1}^{|C|}\text{Precision}_{i}}{|C|}\)
* \(\text{Recall}_{\text{macro}}=\frac{\sum_{i=1}^{|C|}\text{Recall}_{i}}{|C|}\)
* \(\text{F1}\text{score}_{\text{macro}}=2*\frac{\text{Precision}_{\text{macro}}* \text{Recall}_{\text{macro}}}{\text{Precision}_{\text{macro}}+\text{Recall}_ {\text{macro}}}\)  
To compare the models, we also used the Area Under Curve (AUC) score to evaluate the models. AUC score computes the area under the Receiver Operating Characteristic (ROC) curve. Although the AUC score is not an ideal metric to compare the models trained on highly-imbalanced data, we used it to compare the models with other models from the relevant literature. The reason behind this is the fact, that the most studies use the AUC score, as it was specified as a criterion in the Toxic Comments Classification Challenge competition.
## 4 Fuzzy Control Structure for Supervisory Ejector Cycle and Multi-Solar Collector Systems  
Solar air-conditioning systems require optimal operating conditions to secure the highly demanding indoor temperature. This section describes the implemented fuzzy control. The nonlinearities, imprecisions, variations, more than one control and the manipulated variables presented in this plant make the controller design a nontrivial task.  
Fuzzy logic has the ability to transform vague information and expert knowledge into numerical computable data, thus allowing control of plants with the aforementioned characteristics. In this section, we describe the implemented fuzzy control. The control structure is represented in two layers, as shown in Figure 2. Layer 1 is the fuzzy control of the solar collector system; the aim of Layer 1 is to control the temperature at the outputs of the heat exchanger in the solar circuit, as well as storage circuit using model-based fuzzy logic control. Layer 2 is the supervisory fuzzy control of the ejector cooling cycle, which according to different parameters such as operation modes, solar radiation disturbances and optimal conditions of operation of the ejector cooling cycle, sets the reference temperature of storage tank and the references in the mass flows of operation for the generator and evaporator. The supervisory or governor controller uses Servo-Pumps (SP) and Servo-Electronic-Valves (SEEV) as actuators for the evaporator and generator flow.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(T_{g}\) (\({}^{\circ}\)C) & \(T_{c}\) (\({}^{\circ}\)C) & \(T_{e}\) (\({}^{\circ}\)C) & kW & Working Fluid & COP & Reference \\ \hline
50–60 & 26.5–38.5 & 8–16 & 1.8–3 & R134a & 0.3–0.48 & [43] \\
65–85 & 25–40 & 6–10 & - & R134a & 0.355 & [44] \\
60–85 & 25 & \(-\)5–5 & - & R717 & 0.06–0.34 & [45] \\
40–70 & 15–40 & 10–15 & 0.5–1.1 & water & 0.1–1.2 & [46] \\
65–90 & 26–38 & 2–13 & 0.5 & R134a & 0.09–0.16 & [47] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Low operating temperatures of an ejector.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Temperature (\({}^{\circ}\)C)** & **Enthalpy (kJ/kg)** & **Pressure (MPa)** & **Phase Status** \\ \hline
1 & 65 & 279.64 & 1.891 & Saturated vapor \\ \(3,4,5\) & 30 & 93.58 & 0.77064 & Saturated liquid \\
6 & 10 & h6 = h4 = h3 & 0.41489 & Liquid-vapor \\
7 & 30.7 & 94.5235 & p7 = p1 & Subcooled liquid \\
8 & 10 & 256.16 & p8 = p6 & Saturated liquid \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cooling cycle properties.
## 5 Conclusions  
This research proposed an IDS model based on appropriate ML-based algorithms, and to develop this, the study considered three datasets (UNSW-Bot, NSL-KDD and CICIDS2017). OWSA was proposed for selecting relevant features and AWRF was considered for the classification of intrusions. Performance was validated in accordance with standard metrics, in which the results suggest a better performance. Further, accuracy was assessed for various weights. In this case, although the accuracy rate varied with weights, better outcomes were attained when the weight = 10. Further, k-fold cross-validation was undertaken on the proposed system with three datasets. For the UNSW-Bot dataset, the accuracy was better when k = 2 and 4, with 0.9973 and 0.997 accuracy. Similarly, for the NSL-KDD dataset, the accuracy value was optimal when k = 1, with 0.999 accuracy, and for CICIDS2017, the accuracy rate was optimal when k = 5 with 0.98 accuracy. Furthermore, a confusion matrix was developed for identifying the correct and misclassification rate. The confusion matrices that were attained revealed that the proposed system reached a high rate of correct classification and a low rate of misclassification. This confirmed the better performance of the proposed system. However, for confirming the improved efficacy of the proposed system compared to conventional systems, a comparative analysis was undertaken. The outcomes show improved accuracy scores of 99.92 with the NSL-KDD dataset, 0.98 with the CICIDS2017 dataset and 99.783 with the UNSW-Bot dataset. The better performance rate of the proposed system makes it applicable for real-time execution. This study could also assist network security experts in determining intrusions, which would eventually help in the development of security measures for protecting a system. As a further extension, DL-based methodologies could be utilized for ID. In addition, various other datasets could be considered.  
Conceptualization, O.A.A. and M.F.A.; methodology, O.A.A.; Formal analysis, O.A.A.; Investigation, O.A.A. and M.F.A.; Project administration, M.F.A.; Supervision, M.F.A.; Writing--original draft, O.A.A.; Writing--review and editing, O.A.A. and M.F.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: Data are contained within the article. The authors declare no conflict of interest.