esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Further Work
### The Security Adaptation Ontology  
The security adaptation ontology is pre-configured and populated with the necessary knowledge before the system start-up. At this stage of development, we consider pre-determined ontology data based on the expert knowledge. Therefore, ontology modification at this stage refers to updating pointers in the existing knowledge contained as RDF data markups to reflect the current security posture of the monitored environment. Automated knowledge management, e.g., adding new threat members to the _Threat_ entity in the ontology, can be done either by utilizing a machine learning technique, which can learn from the situations being dealt with, by employing update service resources or by other related methods. Such management methods are not considered in the study scope.  
Furthermore, we have used only abstract level contextual requirements, such as patient's preferred privacy, availability, usability levels and a few service and device physical capabilities for each _Property_ used in the ontology. However, these requirements are vital metrics for adaptation and must be carefully understood and designed. Further research is required to analyze and formulate such metrics and measurements that can reflect user preferences, security and services requirements and thing capabilities, as well as how they influence each other during the adaptation process. Potential work to approach in this regards can be security metric and quality of protection (QoP) modeling techniques, such as [48; 49; 50].
## 6 Comparison with State-of-the-Art Methodologies  
To conduct a fair comparison of the proposed model, we selected related studies that applied machine learning, deep learning, and metaheuristic techniques to the DMR-IR thermogram dataset. The results are tabulated in Table 4, demonstrating our model's effectiveness, when compared to studies that produced textural features from thermal images [19; 20] and those that utilized CNN-based approaches [23; 29]. It is important to highlight that the splitting approach employed by the studies [19; 29] prevents data leakage, an issue some other studies have not addressed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Work Ref.** & **Methodology** & **No. of Features** & **Instances/Split Method** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline
[29] & Optimizing CNN by Bayesian optimization & - & 1120 Data augmentation & 92 & 92 \\ \hline
[22] & Deep Feature and a Hybrid GA and GWO & 29 & 1126 (70\% train, 10\% validation, 20\% test) & 100 & - \\ \hline
[20] & Textural features and GA & 42 &
\begin{tabular}{c} 300 (70\% train, 30\% test \\ –test splitting with fourfold \\ cross-validation) \\ \end{tabular} & 96.15 & 95.89 \\ \hline
[23] & Deep learning & - & 461 (fourfold cross-validation) & 93 & - \\ \hline
[19] & HOG features and LTR & 44 & \begin{tabular}{c} 1120 (70\% train, 15\% validation, \\ 15\% test) \\ \end{tabular} & 95.8 & 95.4 \\ \hline \hline \
\begin{tabular}{c} Proposed \\ methodology \\ \end{tabular} &
\begin{tabular}{c} HSMO based HOG \\ features and \\ hyperparameter \\ optimization \\ \end{tabular} & 33 & 1522 (70\% train, 30\% test) & 98.27 & 98.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of the proposed method with related work.  
While our method slightly underperforms compared to models leveraging deep learning and hybrid feature selection techniques [22], it offers several distinct advantages. CNN models are often more complex and computationally expensive, whereas our approach, which relies on handcrafted feature extraction, reduces feature dimensionality by 74.22% while still achieving a 98.27% accuracy. Additionally, our method provides interpretable features, which can be highly beneficial in clinical or real-world applications where transparency is critical for decision making.  
Furthermore, our model automates the parameter-tuning process, eliminating the need for manual tuning and facilitating broader adoption in practical scenarios where ease of use and computational efficiency are essential.
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 4 Empirical Study
### Experimental Results
#### 4.3.1 Composite Dispatching Rules (CDR)  
In addition to the simulation results of CDR, we also recorded the outcomes of the other three widely used dispatching rules: FIFO (first in first out), EDD (earliest due date), and CR (critical ratio); these outcomes are shown in Table 6 to demonstrate the effectiveness of CDR. We perform normalization based on CDR, and the normalization results are shown in Figure 7, to clearly illustrate the differences between these dispatching rules.  
From Table 6 and Figure 7, we can draw the following conclusions:  
1. Compared to FIFO, CDR improved CT, MOV, ODR, EU, and PCSR by 0.74%, 2.53%, 1.49%, 2.57%, and 1.50%, respectively;
2. Compared to EDD, CDR improved CT, MOV, and EU by 1.89%, 1.82%, and 5.00%, respectively, while reducing ODR and PCSR by 0.53% and 0.44%, respectively;
3. Compared to CR, CDR improved CT, MOV, ODR, EU, and PCSR by 0.14%, 0.94%, 1.14%, 5.38%, and 1.63%, respectively;  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dispatching** & **Running** & **MOV** & **PCSR** & **CT** & **ODR** & **EU** \\
**Rules** & **Time (s)** & **(Steps)** & **(\%)** & **(Days)** & **(\%)** & **(\%)** \\ \hline CDR & 4.372 & 90,238 & 91.08 & 42.98 & 84.23 & 69.56 \\ FIFO & 2.047 & 88,012 & 89.73 & 43.30 & 82.99 & 67.82 \\ EDD & 4.284 & 88,627 & 91.48 & 43.81 & 84.68 & 66.25 \\ CR & 3.293 & 89,401 & 89.62 & 43.04 & 83.28 & 66.01 \\ \hline \hline \end{tabular}
\end{table}
Table 6: PSO-SVM Simulation results of CDR, FIFO, EDD, and CR.  
Figure 7: Comparisons among CDR, FIFO, EDD, and CR.  
* In terms of CT, MOV, and EU, CDR performed better than FIFO, EDD, and CR. In terms of ODR and PCSR, EDD performed better than CDR. EDD selects wafers to be processed according to the urgency degree of the due date; therefore, the ODR and PCSR of EDD ought to be higher;
* Simulation results showed that, compared to the other three common dispatching rules, CDR could satisfactorily improve performance. No dispatching rule is perfect, but, with overall consideration, CDR is outstanding.
## 2 Materials and Methods
### Automation Engineering for Sensor/Actuator Integration and Control  
EtherCAT from Beckhoff served as bus system based on the international IEC standard IEC 61158-1 protocol [30] ensuring communication between different components via real-time Ethernet. The EtherCAT bus system linked the sensors, the motor controllers of the linear actuators and the load cells with a real time PC as control unit. A schematic figure is given in Figure 5. TwinCAT from Beckhoff was running on the real time PC to create a virtual programmable logic controller (PLC) working as EtherCAT master while all other components in the EtherCAT network were considered slaves. The additional, real-time capable measurement and control system ADwin from Jager Messtechnik was integrated to the bus system via EtherCAT Pro II EtherCAT-SL plug-in card.  
This procedure enabled the integration of the analog sensor signals from inductive probes IP1-3 and load cells LC1-2 and a digital signal from the robot control regarding start/stop of the closed-loop control. The conversion of analog values into digital values was done with a 16-bit multiplexer and an operating range of \(\pm 10\) V was addressed for sensor signals. The signals were transferred to the bus system at a frequency of 1000 Hz. It should be noted that the load cell signals were preprocessed in advance to reduce noise by applying a moving average over 50 data points within the ADwin program.  
The PLC program queried all values to be recorded from the sensors via the EtherCAT bus system. The signals were evaluated in the PLC that included the software-based implementation of the control-loop and then forwarded the change in the controlled variable to the motor controllers via the EtherCAT bus system. It should be noted that the closed-loop control strategies were implemented on the virtual PLC since the latencies are thereby shortest (determined latency: 10 ms).  
A proportional controller was utilized in all cases due to its fast response times even if small deviations cannot be corrected completely. Ziegler-Nichols method [31] was utilized for determining the proportional gains for load cells (proportional gain: 1) and inductive probes (proportional gain: 1000) which were kept constant in all cases considered. The PLC program was utilized for data recording of all sensors in addition. The realized control cabinet in testing mode is shown in Figure 6. The circuit diagram including the dimensioning of the automatic fuses and the cable cross-sections was designed for an operating voltage of 24 V for the control cabinet. Furthermore, the assignment of the slots on the ADwin measurement and control system was determined, and the arrangement of the bus nodes was specified. Three sockets (230 V) were provided for running the real time PC and additional devices.  
Figure 5: Schematic figure of the bus system for sensor and actuator integration.
## 3 Validation of MANTiS-32 Sensing Performance
### Wireless Communication Performance Assessment of the MANTiS-32 System
However, beyond this range, a notable decline was observed: the signal strength dropped to 60% at 60 m and further to 26% at 80 m. Despite this degradation, the data acquisition rate was largely unaffected, as shown in Table 3, indicating that the system maintained stable data transmission at up to 80 m. At 100 m, however, the RSSI sharply declined to 8%, resulting in a complete failure to acquire data (0% acquisition rate). This limitation is likely attributable to the performance constraints of the 3 dBi antenna used in the test. Therefore, to extend the effective communication range, the use of higher-gain antennas, such as 6 dBi or 9 dBi models, is recommended. These alternatives are expected to enhance signal propagation and improve long-distance communication performance in field deployments. Moreover, since the data acquisition rate is strongly influenced by both communication distance and sampling frequency, the results summarized in Table 4 demonstrate that even when the transmission distance was extended from 20 m to 80 m and the sampling  
Figure 10: Distance-based received signal strength (RSSI). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distance** & 20 m & 40 m & 60 m & 80 m & 100 m \\ \hline
**Strength** & 88\% & 85\% & 60\% & 26\% & 8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: RSSI measurements across transmission distances. frequency increased from 20 Hz to up to 200 Hz, the system maintained 100% data acquisition with no observed packet loss. This indicates that the MANTiS-32 system is capable of stable and lossless data transmission under varying sampling conditions within this range. However, at a transmission distance of 100 m, where the Received Signal Strength Indicator (RSSI) declined sharply to approximately 8%, the system failed to acquire valid data, resulting in a 0% acquisition rate. This outcome highlights the critical threshold below which wireless signal degradation compromises reliable data collection, emphasizing the importance of maintaining sufficient signal strength for long-range monitoring applications. The results of this experiment demonstrate that reliable wireless communication and consistent data acquisition were maintained at up to 80 m, even within an indoor environment designed to simulate obstructed conditions. These findings suggest that the validated communication range is more than adequate to support structural monitoring operations on small- to medium-span bridges under comparable field conditions.
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 5 Experiments  
### Datasets  
To validate the DSTSAN model, four real traffic datasets are selected and widely utilised in the PeMS system [66], namely PEMS03, PEMS04, PEMS07, and PEMS08. These datasets were collected by the Caltrans Performance Measurement System (PeMS) using sensors that span across major urban areas in the state of California, USA. The sensors collected data at a frequency of once every 30 s, and the collected data were aggregated every 5 min to form a data point. Each sensor accumulated 288 data points per day. The dataset includes information such as traffic speed, traffic flow, and the adjacency matrix of the traffic network. Table 1 presents a summary of the detailed parameters for the four datasets:
# EduChat: An AI-Based Chatbot for University-Related Information Using a Hybrid Approach
## 5 Intent Classifier Based on Improved Random Forest Algorithm (Machine Learning Module)
### The Improved Random Forest (IRF) Algorithm
(c) Compute the overall error using Equations (2) and (3) as: \[err\_ov(T)=\sum\limits_{m=1}^{N}err\_ov(x_{i})*cost(T)\] \[N=\text{total number of samples in the tree }T.\] (d) Compute the positive training error, using Equations (2) and (4) as \[err\_pos(T)=\sum\limits_{m=1}^{N}err\_pos(x_{m})*cost(T)\] \[p\text{ is total number of minority instances in the tree }T.\] (e) Assign the tree \(T_{i}\) with \(weight(T_{i})=1/err\_ov(T_{i})\)  Tree with lower error will obtain higher weight. (f) In case that the overall training errors of the two or more trees are the same, the weights are assigned using the formula: \[weight(T_{i})=1/err\_pos(T_{i})\]
```  
**Algorithm 2** IRF_1  
**Testing phase. ** During the testing phase, the result for each sample in the test set is determined by assessing it against each of the trained decision trees and then aggregatingthe outcomes obtained from all the trees. In a standard random forest, the final result or label for a test sample is established by considering the majority vote from all the outcomes generated by the individual trees. However, in the case of IRF, the final outcome is decided based on the weight assigned to a specific selected tree. For a given test pattern, \(\chi^{\prime}\) the final outcome, F(x') is given as:  
\[F(x^{\prime})=f(x^{\prime})\text{ from }T_{i}\ \ \mid\text{ }weight(T_{i})=\text{highest} \tag{5}\]  
In other words, the result obtained from the tree with the highest weight for a given test sample is taken as the ultimate outcome. The entire testing process is depicted in Algorithm 3. ```
Input:\(test\) is the set of test samples, \(W=[weight(T)]\) is the set of weights of all trees \(T\). Output: Classified test set
1. Classify test with all the trees
2. For each sample \(x^{\prime}\) in test (a) for each tree \(T_{i}\) in the forest obtains the outcome label as \(y(x^{\prime})=f(x^{\prime})\) from \(T_{i}\) (b) determine the final outcome, \(F(x^{\prime})\) by using Equation (5). ```  
**Algorithm 3** IRF_2  
One distinguishing property of IRF is that the training process of each tree in this random forest model can be performed independently.
# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 2 Important Applications of UAVs in Smart Cities
### Agriculture  
Drones are used for precision agriculture. They can monitor crop health, track livestock, and even assist in planting and spraying crops. They can provide detailed aerial imagery that helps farmers make informed decisions about their crops and livestock [21].
# Helping CNAs Generate CVSS Scores Faster and More Confidently Using XAI
## 4 Methodology
### XAI Algorithm Selection
It then averages these differences, weighted by the number of ways these subsets can be arranged. This calculation provides a Shapley value that quantifies the contribution of feature \(i\) to the overall prediction, considering all possible cooperative interactions with other features. The Shapley values help in understanding the importance of each feature in the model's decision-making process. Once the SHAP values are generated, they can be used to visualize both global and local explanations. While local explanations in SHAP are only binary, one of our contributions is that we propose a method to generate multi-class local explanation using the raw Shapley values. Finally, it is essential to compare SHAP with other explainability techniques to understand its relative advantages. Two commonly used alternatives are LIME [34] (Local Interpretable Model-agnostic Explanations) and ICE [44] (Individual Conditional Expectation):  
* SHAP: SHAP offers a consistent, theoretically grounded method for explainability based on Shapley values. It provides both local and global feature importance scores and ensures consistency across instances. SHAP is particularly strong in tasks where model interpretability and fairness are crucial, as it offers unbiased, clear attributions of importance. However, it can be computationally intensive, especially for large datasets and models, and, as discussed in Section 7.2, it has limitations when applied to textual data. * LIME: LIME approximates black-box models by fitting a simpler, interpretable model around each individual prediction. LIME is model-agnostic and flexible, supporting various data types, including text, images, and tabular data. Its advantage is speed and ease of use, especially for generating quick, local explanations. However, LIME's explanations can vary depending on the random samples used, and its feature importance may lack the consistency provided by SHAP. * ICE: ICE plots visualize how the prediction for an individual instance changes as a particular feature varies. This is useful for understanding the impact of specific features on the model's output, especially in non-linear models. However, ICE is primarily a visual tool and does not provide the same comprehensive feature attribution as SHAP or LIME. It is best suited for analyzing specific feature relationships rather than offering holistic model insights. While LIME and ICE are valuable in certain contexts, SHAP's game-theoretic foundation provides stronger theoretical guarantees, particularly for generating reliable and consistent global and local explanations. Given the need for robust interpretability in cybersecurity applications, SHAP remains the most suitable choice for our work, especially when enhanced with preprocessing techniques to address its text limitations, as discussed in Section 7.2.
###### Abstract  
In this study, three of the saponins present in leaves of _Hedera helix_ L., \(\alpha\)-hederin, hederagenin, and hederacoside C were studied for their antiproliferative activity. The three saponins were analyzed in different concentrations by in vitro tests on normal fibroblasts cells and cervix epithelial tumor cells. Determination of cytotoxicity and antitumor effects was performed using the MTT method. From the tested saponins, \(\alpha\)-hederin was biocompatible in normal fibroblasts cells at concentrations between 2-10 \(\upmu\)g/mL. Its antiproliferative activity was exerted in the concentration range of 10-400 \(\upmu\)g/mL in cervix epithelial tumor cells. Similarly, hederagenin presented antiproliferative activity at concentrations between 25-400 \(\upmu\)g/mL. In turn, hederacoside C was shown to be noncytotoxic in normal fibroblasts and cervix epithelial tumor cell culture at all the tested concentrations. The obtained experimental results were analyzed by \"Mixture design\", a specialized form of the response surface method (RSM) provided by the Design Expert 11 software, and the optimal composition of obtained saponins mixture was selected and verified in vitro for antiproliferative activity. The results showed that an optimal saponins mixture has the potential to be used in pharmacological applications.  
Keywords:\(\alpha\)-hederin; hederagenin; hederacoside C; cytotoxicity; antiproliferative activity; response surface method; mixture optimization +
Footnote †: journal: Applied Chemistry and Materials Science
## 2 Designed Methodology  
The designed methodology is illustrated in Figure 2 and contains six steps divided into three main stages. The first stage does not consider a BESS in the system. At this stage, the main characteristics of the EVCS and the associated constraints are defined. In addition, optimization of the charging schedule of the buses is performed by using a GA, since this optimization technique is considered effective for this type of complex problem due to its stochastic approach [16]. For the second stage, a BESS is modelled and integrated into the system. In this second stage, different EMSs with which to control the BESS are designed and optimized, using a GA to minimize the cost of operation of the system. In the third stage, a varying grid electricity price scenario is first defined. Based on the optimized EMS, an adaptive EMS is designed for the next-day price profile. Finally, all of the designed EMSs are evaluated in a wide scenario (varying price and load power profiles) to see which one has the best economic performance.
# A Multi-Node Detection Algorithm Based on Serial and Threshold in Intelligent Sensor Networks
## 7 BER Analysis  
In the communication system, the accuracy of user information transmission is mainly measured by BER performance. In the T-MPA algorithm, because the hard decision mechanism is used to make a decision and decode the user information in advance, the correctness of information transmission is greatly reduced, the soft information is lost, and the decision of other user nodes will also be affected. This method will result in worse BER performance, especially at low threshold. In the S-MPA algorithm, because the user node information updating is integrated into the resource node information updating, the decoding and update of user information is stopped before the stability of user information is judged, which leads to incomplete transmission of user information and the loss of certain information [46].  
In this proposed algorithm, the necessary condition of the stability of sensor nodes information is judged first, and then the sensor node information updating is integrated into the resource node information updating, which improves the stability of sensor information and reduces the BER of sensor information transmission. Especially when the threshold setting is low, the decision of the necessary condition of sensor node stability is more significant to reduce BER performance of T-MPA. Finally, the iterative update of messages is carried out with the threshold decision, which reduces the loss of information during the updating process and further improves the accuracy of sensor node information transmission.