esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Examples
### Extensive Numerical Examples  
In this subsection, two extensive numerical examples are described to verify the effectiveness of the proposed multiscale design method in complicated heat transfer problems. The models are shown in Figure 6. The optimized results obtained by the SIMP method were used for comparison.  
As shown in Figure 9, in the gradient-distributed heating example, the relative density distribution of the single-scale structure was asymmetric and in dendritic form. The dendritic distribution was clustered toward the position with \(T=0\). Due to gradient-distributed heating, the area of relative density distribution in the high temperature was larger than that in the regions with lower temperatures. The relative density distribution of the cellular structure was also asymmetric and in dendritic form. The dendritic distribution was more concentrated towards the position with \(T=0\) and high-temperature regions. As can be seen in the obtained Voronoi-GSCS, in the area of low relative density, the distribution of holes was sparse, and in the area of high relative density, the distribution of holes was dense. The distribution of holes showed a gradient trend.  
Figure 8: Size effect analysis: (**a**) the Voronoi-GSCS obtained with a strut width of \(t=0.08\); (**b**) the Voronoi-GSCS obtained with a strut width of \(t=0.06\); (**c**) the Voronoi-GSCS obtained with a strut width of \(t=0.04\).  
As shown in Figure 10, in the uniform distributed heating (II), the relative density distribution was symmetric along the diagonal line and around the four positions where the predefined temperature was \(T=0\). The obtained relative density distributions of the single-scale structure and cellular structure were nearly the same. The reconstructed Voronoi-GSCS was dense around the four positions and sparse in other regions. The microstructure was well-connected without connection mismatch.  
Figure 10: Results of uniform distributed heating (II): (**a**) the relative density distribution obtained by the SIMP method; (**b**) the relative density distribution obtained by the proposed multiscale method; (**c**) the reconstructed Voronoi-GSCS.  
Figure 9: Results of gradient distributed heating: (**a**) the relative density distribution obtained by the SIMP method; (**b**) the relative density distribution obtained by the proposed multiscale method; (**c**) the reconstructed Voronoi-GSCS.  
The heat compliances of the extensive numerical examples are given in Figure 11. In the gradient-distributed heating example, the heat compliances for the single-scale structure and cellular structure were 1232.82 and 1028.18, respectively. In the uniform distributed heating (II), the heat compliances for the single-scale structure and cellular structure were 2704.11 and 2573.38, respectively. The heat transfer performance improved by 16.60% and 4.83%, respectively. The advantages of the cellular structure were verified again by the extensive numerical example.
# Exploring the Role of Emotions in Arabic Rumor Detection in Social Media
###### Abstract
(**a**) A rumor post that contains the emotion of fear and evokes emotions such as doubt, anger, and fear in related comments, as illustrated in red words. (**b**) A non-rumor post that contains no emotion and evokes positive emotions. Second, emotional features and sentiments are extracted by various lexicons and publicly available pre-trained models trained in the Arabic language and then compared in terms of their ability to detect rumors. Third, a deep learning framework is proposed in order to detect rumors that utilize emotions. Furthermore, this study investigates the effect of combining user and content features into emotion-based rumor detection algorithms. Fourth, extensive analyses have been undertaken on the role of emotions in rumor detection from three angles: top contributing emotions, emotion distribution in news and comments, and emotion distribution across different topic areas. To the best of our knowledge, this work presents the first investigation into the role of emotions in rumor detection in the Arabic language. The results are likely to enrich Arabic NLP and may encourage other researchers to conduct more research in this area. The research seeks to answer the questions listed below:  
* RQ1: Are emotional cues and sentiments in news and replies capable of detecting rumors independently, without additional information? * RQ2: Can emotional cues and sentiments in news and replies improve rumor detection when used as supplementary features for textual content? * RQ3: How do emotion, sentiments, user, and content features contribute to distinguishing between rumors and true news in Arabic social media? The rest of this paper is structured as follows. Section 2 presents background on rumors and the key topics related to our research, followed by a review of the relevant literature on rumor detection in Section 3. Section 4 describes the dataset and presents the proposed approach for addressing this issue. Experimental results are presented in Section 5. Finally, Section 6 concludes the study and outlines possible future directions for research in detecting rumors.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### WSN Challenges and Utility in Hydrology  
There are several challenges faced in outdoor environmental monitoring WSN deployments, including power management, node maintenance, network scaling, heterogeneous deployment, and overall network cost [93].
## 4 Results and Discussion
### Feature Scoring
#### 4.5.2 Receiver Operating Characteristic (ROC)  
The ROC curves (Figure 3) depict the performance of various ML models in distinguishing between malware and goodware samples generated by Python matplotlib Table A1 libraries. It is important to note that the NB follows a slightly different approach compared to the other models, as it does not have a predict_proba method available in the Python Scikit-Learn library as illustrated in Table A1. To adapt to this distinction, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ML Models** & **A** & **F1** & **R** & **P** & **AUC** & **S** \\ \hline LR & 0.88 & 0.92 & 0.92 & 0.92 & 0.93 & 0.80 \\ SVM & 0.72 & 0.67 & 0.51 & 0.98 & 0.88 & 0.98 \\ NB & 0.84 & 0.83 & 0.73 & 0.96 & 0.84 & 0.96 \\ RF & 0.96 & 0.96 & 0.93 & 0.99 & 0.98 & 0.96 \\ XGB & 0.93 & 0.96 & 0.96 & 0.96 & 0.97 & 0.95 \\ KNN & 0.92 & 0.91 & 0.87 & 0.97 & 0.93 & 0.89 \\ \hline \hline \end{tabular}  
* A = accuracy, F1 = F1-score, R = recall, P = precision, AUC = area under the curve, and S = specificity.  
\end{table}
Table 6: ML Models and performance evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Chi2 \% Score** & **Gini \% Score** & **API Category** \\ \hline NtAllocateVirtualMemory & 10.18 & 6.81 & Kernel management \\ NtProtectVirtualMemory & 9.70 & 7.86 & Kernel management \\ LdrGetProcedureAddress & 7.41 & 4.80 & System management \\ NtFreeVirtualMemory & 6.78 & 2.93 & Kernel management \\ FindResourceExA & 5.74 & 1.55 & Resource management \\ NtClose & 5.40 & 3.84 & Kernel management \\ GetSystemTimeAsFileTime & 4.70 & 3.25 & System information \\ NtDelayExecution & 3.64 & 3.07 & System management \\ NtDeviceControlFile & 2.85 & 1.32 & File system \\ \hline \hline \end{tabular}
\end{table}
Table 5: Top 10 selected features, Chi2 and Gini importance percentage scores, and API categories.  
prediction method for the Naive Bayes classifier was employed. This resulted in two ROC curves, as shown in Figure 3, one dedicated to the Naive Bayes classifier and the other representing the remaining ML models. Examining the ROC curves reveals that the RF and XGB models performed exceptionally well. This indicates their effectiveness in achieving high true-positive rates while maintaining low false-positive rates. The KNN model also demonstrated a favourable ROC curve, indicating strong discriminatory power. On the other hand, the LR, SVM, and NB models showed relatively lower ROC curves compared to RF, XGB, and KNN. This implies a relatively lower ability to accurately classify instances from both classes.
# Enhancing Weather Forecasting Integrating LSTM and GA
## 3 Data and Methods
### Forecasting with Regression Models  
Given the data from the two locations, it was necessary to find a relation between the variables measured at Pinhao and Quinta de Santa Barbara, in order to predict the missing values from the measurements at Quinta de Santa Barbara. The regression methods selected for this study included MLR [19], LASSO, and ridge regressions [34; 35].  
The MLR is given by  
\[Y=\beta_{0}+\beta_{1}\cdot x_{1}+\beta_{2}\cdot x_{2}+\cdots+\beta_{n}\cdot x_{n} \tag{1}\]  
where \(Y\) is the dependent variable, which corresponds to the missing values between 2010 and 2022 at Quinta de Santa Barbara; \(x_{i},i=1,2,\ldots,n\) are the independent variables, corresponding to the measurements in Pinhao; \(\beta_{0}\) is the constant term, \(\beta_{i}\) with \(i=1,2,\ldots\); and \(n\) are the regression coefficients. For the LASSO and ridge models, the L1 and L2 regularization terms are added to Equation (1). Therefore, in order to take into account the advantages of these three methods, a combined model was considered to predict the missing data.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Durability Properties
#### 3.3.2 Water Absorption  
Figure 10 presents the results of the water absorption test conducted on mortar specimens following ASTM C642-06 [31], evaluated at curing durations of 3, 7, 28, 56, and 91 days. The water absorption of mortar cubes initially decreases up to 15% and increases as the replacement level of cement reaches 30% across all curing ages. Notably, the lowest water absorption values were observed at a 10% MGWP replacement of cement. Specifically, at 28 days, water absorption reduced by 22.3% and 12.8% for the 10% and 15% MGWP replacements, respectively, when compared to the control mix. At 56 days, the reductions were 27.8% and 8.8%, and at 91 days, the reductions were 35.1% and 9.1% for the 10% and 15% replacements, respectively. Mortar with 10% MGWP replacement reached a water absorption value of just 5.0%, the lowest observed at 91 days of curing, outperforming all other mixes. This suggests enhanced matrix compaction, strength, and durability in the presence of MGWP, which is known to reduce the water absorption of the mortar.  
Figure 9: Effect of MGWP on sulfate resistance of mortar.  
Water absorption, which is generally associated with the structural pores--especially in the aggregate-interface zone--decreases with the MGWP, indicating refinement in this region and a corresponding improvement in overall durability. This finding aligns with the trends observed in the compressive strength and porosity results. Additionally, at a 30% MGWP replacement, water absorption increases by 33.0%, 35%, and 33.8% after 28, 56, and 91 days of curing, respectively, when compared to the control mix, as indicated in Figure 10. These results highlight that the water absorption of mortar decreases as the content of MGWP increases up to 15%, which is consistent with previous studies [41; 61; 62].
## 5 Discussion  
This systematic review aimed to identify critical parameters essential for the accurate forecasting of TCO for CWPs; it also highlighted the vital role of AI and ML technologies in TCO forecasting. Our approach involved conducting a literature review to list different methods for predicting these parameters. Our analysis explored the potential of AI-based algorithms for enhancing the predictive modeling of TCO in CWPs. Despite the advancements in AI-based solutions [114, 119, 124], there is a noticeable gap in research explicitly targeting comprehensive TCO forecasting for CWPs. One of the weaknesses identified is the limited exploration of the complex correlations and causations among parameters that influence TCO.  
The parameters identified were categorized as primary and secondary based on their direct and indirect influence on TCO. Analyzing the time series nature of these parameters is vital for predicting TCO as they offer insights into patterns, trends, and potential future changes [115]. The findings indicate that the cooling load [3, 50, 57, 110, 111, 112, 127], energy consumption and electricity price/tariff [6, 30, 98, 113], cooling capacity/chiller size [6, 30, 57, 110, 111, 112], and COP [6, 127] are the primary parameters that must be considered for TCO prediction. Secondary parameters, including meteorological data, such as temperature and weather data [3, 30, 52], occupancy variance [30], PLR [111], power consumption [6, 26], and chiller type and configuration [6], indirectly influence TCO by affecting the primary parameters. For example, cooling load is influenced by secondary factors such as weather, external temperature, and internal heat sources (e.g., equipment, lighting, and occupants) in the room or building facility [110, 123, 125]. Buildings with poor insulation may face higher cooling demands due to heat leakage from the outside [86]. Additionally, internal heat gains from occupants or equipment can fluctuate throughout the day, dynamically affecting the cooling load [115, 123]. Moreover, when cooling loads are high, the energy consumption increases, consequently raising the total electricity costs and thus leading to higher operational expenses [121, 123]. Therefore, the data of these parameters are dynamic and vary according to cooling requirements [119]. Accurately cap turing these data's fluctuations, patterns, and seasonal variations is essential for effectively training predictive models [48].
## 4 Hardware-in-the-Loop Real-Time Simulation
### Robustness Verification  
Figure 12 shows the maximum lateral deviation and width of the turning passageway of the SRT passing through curves with different radii (R20-60 m) at different speeds (1-8 m/s) and different payloads (30-60 t). Comparing Figure 12a and 12b, it can be observed that under the condition of a constant payload, the tracking error and lane occupation increase with increasing speed. By comparing Figure 12c and 12d, it can be  
Figure 11: Simulation result of the continuous curve passing: (**a**) lateral deviation; (**b**) heading error; (**c**) steering angle; (**d**) hinge force.  
Figure 10: Test line for continuous curve simulation.  
found that under the constant passing speed, the tracking error and lane occupation width increase with the payload increase. However, the overall value is within a small range, with the tracking error ranging from 0.016 to 0.267 m and the lane occupancy width ranging from 2.79 to 3.38 m, thus verifying the robustness of the control strategy. The influence of the curve radius is more significant than the payload and vehicle speed. This is because the control strategy proposed in this paper aims to reduce the hinge force by taking the CG of the first and the fourth module and each hinge point as the tracking control points. When the curve radius is smaller, the deviation between the CG of the middle module and the target path will be more significant, resulting in greater tracking error and lane occupancy width.
###### Abstract  
Sensors in the Internet of Things (IoT) generate large amounts of data, which requires high-speed data transmission. In order to achieve the parallel transmissions of the wireless sensor network on the transmission layer, the performance of stream control transmission protocol (SCTP) and transmission control protocol (TCP) in the wireless sensor network under different packet error rates was simulated and compared. A dynamic multipath handover method for SCTP (MS-SCTP) was proposed to improve the transmission performance, which selects the transmission path according to the packet error rate and the retransmission ratio in the sender's buffer. The TCP and SCTP protocol switching method (TCP-SCTP) was proposed to detect the current network traffic and adjust the MS-SCTP or TCP method. Analysis and simulation results show that MS-SCTP and TCP-SCTP could improve network throughput and reduce packet loss rate. MS-SCTP and TCP-SCTP can be combined with other technologies and channel allocation algorithms to improve network traffic.  
IoT; parallel transmission; packet error rate; path switching; wireless sensor network +
Footnote †: This paper is an extended version of our paper published in “[PERSON]; [PERSON]; [PERSON]; [PERSON], A Multi-path Switching Method Based on SCTP for Heterogeneous Wireless Networks in Smart IoT. In Proceedings of 2018 IEEE International Conference on Smart Internet of Things, Xi'an, China, 17-19 August 2018, doi:10.1109/SmartIoT.2018.00013”.  
* [2019]Weifeng Sun  
School of Software, Dalian University of Technology, Dalian 116620, China; [EMAIL_ADDRESS] (W.S.); [EMAIL_ADDRESS] (S.Y.); [EMAIL_ADDRESS] (Y.X.)  
* [2019]Correspondence: [EMAIL_ADDRESS]; Tel.: +86-136-0408-8221
## 5 Conclusions  
Recent advances in multimedia hardware and the widespread use of wireless sensor nodes have fostered the development of wireless multimedia sensor networks. A wireless multimedia sensor network is able to ubiquitously acquire multimedia content such as images or audio from the environment.  
Multimedia content whose characteristics include large size and a correlation between the data requires reliability in transmission. However, the main focus of existing solutions is on network efficiency, which are tolerant of data loss, are not appropriate for the transmission of multimedia data.  
Here we have proposed a reliable asynchronous image transfer protocol, RAIT. RAIT applies a double sliding window method to node-to-node image transfer to prevent packet loss caused by network congestion. One sliding window for the receiving queue is used to prevent packet loss caused by communication failure between nodes, and another sliding window for the sending queue prevents packet loss caused by network congestion. The routing node prevents packet loss and guarantees favorable coordination between the nodes by preemptively scheduling the packets for an image. RAIT implements the double sliding window method by means of a cross-layer design between the RAIT layer, routing layer, and queue layer. Experiments have demonstrated that RAIT guarantees a higher reliability of image transmission compared to the existing protocol.
## 2 Materials and Methods
### The maximum Tangential Stress and the Mean Stress Criteria under Positive Mode I Loading  
Several failure criteria have been reported in the literature, dealing with the estimation of brittle fracture conditions in U-notched specimens under various types of loading condition. The maximum tangential stress (MTS) and the mean stress (MS) criteria are two of the most prominent.  
The MTS criterion states that the crack nucleates from the U-notch tip in a brittle medium when the maximum tensile tangential stress attains the critical value, \(\sigma_{C}\), at the critical distance, \(r_{C}\), from the notch tip [46]. For brittle and quasi-brittle materials, the critical stress is often assumed to be the same as the ultimate tensile strength, \(\sigma_{u}\)[47]. The critical distance of material can be computed as:  
\[r_{c}=\frac{1}{2\pi}\bigg{(}\frac{K_{lc}}{\sigma_{u}}\bigg{)}^{2} \tag{4}\]  
where \(K_{lc}\) is the plane-strain fracture toughness of the material.  
The second failure model is the MS criterion, proposed by [PERSON] [48] to estimate brittle fracture in cracked specimens, which establishes that fracture occurs when the average value of tangential stresses over the critical distance, \(d_{C}\), from the notch tip reaches the material critical stress, \(\sigma_{C}\). [PERSON] [49] proposed the following relation for computing the critical distance, dc, which depends on the material properties:  
\[d_{c}=4r_{c}=\frac{2}{\pi}\bigg{(}\frac{K_{lc}}{\sigma_{u}}\bigg{)}^{2} \tag{5}\]  
Figure 2: Schematic of tensile stress–strain curves: (**a**) ductile material; (**b**) equivalent material.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.3 Precision  
The metric _precision_ is defined as  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}. \tag{2}\]  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & & \multicolumn{1}{c}{**Actual**} \\ \cline{3-4}  & Positive & Negative \\ \hline \multirow{2}{*}{**Predicted**} & Positive & True Positive (TP) & False Positive (FP) \\ \cline{2-4}  & Negative & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix scheme.  
This metric represents, according to Equation (2), the proportion of samples correctly classified as positive (samples with stress or TP) of the total samples classified and predicted as positive (TP + FP). Therefore, this metric quantifies the classifier's performance concerning false positives. In order to minimize FPs, precision should be close to 1.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 3 Background
### Partial Fake Speech Detection  
Until the Audio Deepfake Detection Challenge (ADD) [53; 54] started, all research was focused on distinguishing between entirely fake and real audio. The ADD challenge motivated many researchers to work on PF detection methods [29; 55; 56; 57]. We found that none of the partial fake detection tools are available to the public, so we were not able to reproduce their results or examine their detection efficiency on other PF datasets or entirely fake datasets.