esa-sceva/satcom-chunk-collection huggingface dataset:
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 4 Experiments
### Effect of Hyper-Parameter Choices  
We conducted experiments to evaluate the performance of our model SentiBERT-BiLSTM-CNN under different hyper-parameter settings. Specifically, the model was trained with a combination of three values of epochs (6, 8, 10, 12, and 14) and two values of batch sizes (16, 32), creating ten experiments, as shown in Table 2. It can be seen that when the model was trained with 10 epochs and with a batch size of 32, the model achieved the best performance, with an F1 of 0.8956. We also observe a consistent performance improvement as the number of epochs increases from 6 to 10, and beyond 10 epochs, the gain is not apparent. The training was efficient because SentiBERT has been pre-trained and was only fine-tuned on our dataset. It is noted that for this set of experiments, we applied a basic cross-entropy loss function. The effect of the fused loss function is reported in the next subsection.
The reliable, secured, and shortest path from the source to the destination is found by flooding the packets through the cluster-head nodes. This continues until the first route-request packet reaches the destination. The destination directs the route-reply packet to follow the same path as the first route-request packet. In this study, the energy-efficient and optimal path was identified through the application of SOPR, BEER, and OTP-based encryption and decryption algorithms. These proposed strategies were tested using the NS2 simulator and compared to the existing energy-efficient and secure routing protocols. Simulations revealed that the proposed WBAN protocol improved the performance of the network, including throughput, packet-delivery ratio, and security when compared to traditional protocols. Furthermore, the proposed protocol decreased the energy consumption, delay, and time required to detect malicious nodes. Figure 1: WSN without and with clustering.
## 3 Evaluation Process of the BS 7910 Method  
The BS 7910 evaluation method is proposed on the basis of engineering critical assessment (ECA), which provides guidelines and suggestions for assessing the acceptability of flaws in metallic structures. The methods described in this standard can be applied to all stages of design, manufacturing, and operation over the lifecycle of a structure. The BS 7910-2019 standard describes three different options, which are summarized as follows:  
1. Option 1 is a conservative evaluation process, which is relatively simple to apply. The evaluation level does not require detailed stress-strain data of the analyzed materials.
2. Option 2 is an evaluation method based on the application of the stress-strain curve of materials.  
Figure 1: Schematic of the FAD.  
3. Option 3 uses the numerical simulation method to generate a FAD. The evaluation method of this level is not limited to materials with ductile tearing. In this presented study, the true stress-strain data were derived by the Ramberg-Osgood model and corresponding parameter values. At the same time, considering the convenience of calculation and appropriate conservativeness, the assessment of the girth weld safety of the X65 steel pipeline will be guided by Options 1 and 2 described in BS 7910. Based on the specific design conditions and through stress level analysis of the girth weld, the reliability of the girth weld defects under specific operation conditions will be determined through nondestructive testing. In the evaluation process, the load and stress conditions of the pipeline, fracture toughness, and crack characteristics will be determined, the material curves and failure assessment curves will be constructed, the load and fracture ratios will be calculated, and the FAD will be drawn and evaluated (Figure 2).
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 6 Conclusions  
This study evaluated the performance of eight MANET routing protocols under dynamic network conditions using OMNeT++ 6.0.1 with INET-4.5.0. Simulations were conducted with varying node densities ranging from 20 to 500 nodes, and the Random Waypoint Mobility model was used to replicate realistic node movement patterns. Key performance metrics, including throughput, delay, jitter, energy consumption, and packet delivery ratio, were analyzed to assess each protocol's strengths and weaknesses.  
The results showed that proactive protocols like OLSR and DSDV are well-suited for stable environments with moderate mobility due to their consistent performance in small networks. Reactive protocols, including AODV, AOMDV, and TORA, performed better in dynamic and high-mobility scenarios, where adaptability is critical. Hybrid protocols, particularly GRP, demonstrated a balanced approach, combining the benefits of both proactive and reactive strategies. GRP achieved superior overall performance, including higher delivery ratios and up to 30% lower energy consumption compared to reactive protocols, making it ideal for diverse and large-scale networks.  
These findings provide practical insights for selecting routing protocols tailored to specific applications, such as IoT systems, disaster recovery, and military operations. Future work can focus on integrating machine-learning techniques with hybrid protocols to further enhance their efficiency and adaptability in complex network environments.  
Conceptualization, I.M.S., N.S.A., W.M.A., H.M.E. and R.A.S.; methodology, I.M.S., N.S.A. and R.A.S.; software, I.M.S. and N.S.A.; validation, W.M.A., H.M.E. and R.A.S.; writing--original draft preparation, I.M.S. and N.S.A.; writing--review and editing, W.M.A., H.M.E. and R.A.S.; project administration, H.M.E. and R.A.S.; funding acquisition, W.M.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Princess Nourah Bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R500), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the data are part of an ongoing study and time limitations.  
The authors would like to acknowledge Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2025R500), Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia, for supporting this project.  
The authors declare no conflicts of interest.
## 2 Background and Related Work
### Renewable Energy Generation Integrated in the Grid
However, both refrigerators and electric water heaters are low-power household appliances and the power control required a large number of such appliances. Besides, on account of the intermittent and unstable power supply, the controllable load should be adjusted frequently, which would affect the normal life of users and the service life of the electrical appliances. Although the strategies mentioned in the above papers could achieve stability after renewable energy is integrated into the grid, they all required some additional devices or even the devices of the user terminal, leading to additional costs. In view of solving the instability and intermittency of the grid integrating with renewable energy, this paper proposed to add datacenters as electric loads into the grid so as to consume surplus power generated from renewable energy stations. This strategy could help achieve a win-win effect since on the one hand it could help maintain the normal operation of the grid integrating renewable energy through the adjustment of the datacenters and, on the other hand, the datacenter itself, as a big consumer of electricity, could leverage the renewable energy more effectively to further reduce the operational costs.
## 5 Materials and Methods
### LSTM Model Training and Performance Evaluation
#### 5.4.4 Integration with Route Optimization  
Once trained, the LSTM model was integrated into the genetic algorithm framework to dynamically estimate travel times. The predicted trip durations were used as an additional optimization parameter, allowing real-time adaptation to traffic variations. This approach enhanced route efficiency, reducing total travel time and improving the decision-making process in sales route optimization.  
By incorporating LSTM-based predictions, the system dynamically adapts to varying conditions, ensuring that the optimization algorithm selects the most time-efficient routes under real-world traffic scenarios.
## 4 Experimental Methodology  
**Design**. We designed four sets of experiments to evaluate (1) log parsing performance, (2) anomaly detection performance, (3) lightweight performance, and (4) the impact of parameters.  
**Evaluation.** For log parsing, we first assessed the number of log events parsed, which is a direct indicator of model performance. Then, accuracy and F1-measure are selected as evaluation criteria, which are the most commonly used metrics. To calculate accuracy and F1-measure, true positive (TP), true negative (TN), false positive (FP), and false negative (FN) are obtained. TP means that log messages belonging to the same template are clustered in the same group; TN means that log messages belonging to different templates are clustered in different groups; FP means that log messages belonging to different templates are clustered in the same group; FN means that log messages belonging to the same template are clustered in different groups. For anomaly detection, we used precision, recall, and F1-measure as evaluation criteria, which were also calculated using TP, TN, FP, and TN. For the lightweight evaluation, central processing unit (CPU) usage, running time, and memory consumption were measured. The CPU is the core resource of a server system, and the running time and memory consumption represent the time and space complexity, respectively. To evaluate the impact of parameters, we mainly considered two parameters: separation and LCS thresholds. All experiments were conducted 20 times, and the average results were used for analysis.  
**Dataset.** Nine datasets from real-world applications and related studies were used as benchmark datasets from LogPai [32]: Hadoop distributed file system (HDFS), Windows, Thunderbird Supercomputer Server (Thunderbird), Hadoop MapReduce server (Hadoop), OpenSSH Server (OpenSSH), Apache HTTP server (Apache), Android system log (Android), high-performance cluster (HPC), and large-scale data processing platform (Spark). In addition to these datasets, different HDFS dataset sizes were selected to evaluate the execution time. We selected the 2 k version of these datasets. In addition, six size levels of the selected HDFS were used: 1 kb, 10 kb, 100 kb, 1 mb, 10 mb, and 100 mb. In the experiment to determine the number of log events, we used HDFS datasets with log message sizes of 70 K, 100 K, 1 M, 5 M, and 11 M. The actual number of log events was artificially labeled.  
**Comparison.** To demonstrate the effectiveness of the proposed method, we compared it with four state-of-the-art methods, including Drain, IPLoM, and MoLFI. In addition, to make the experimental results more comparable, we included the log parser Spell in the comparison.  
**Methodology.** For Darin, IPLoM, and MoLFI, we used the same parameter values as those reported in the original studies. For a fair comparison, the same preprocessing rules (for example, the same parameters, such as IP or numbers) to each log parser were used. The experiments were implemented using Python 2.7 on the same server running Windows 7 with 64 bits with an Intel(R) Core (TM) I7-6700 CPU and 16 GB RAM.
# Research and Applied Perspective to Blockchain Technology: A Comprehensive Survey
## 6 Development Platforms  
Now we discuss the development platforms used by the consensus algorithms. Table 5 describes some of the important features of the development environments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Comparison Parameters** & **Ethereum** & **Cosmos** & **Cardano** & **EOS** & **Bitcoin** & **Hyperledger** & **Corda** \\ \hline Token & ETH & ATOM & ADA & EOS & Bitcoin & n/a & SDK \\ \hline Public/Private & Public & Public/Private & Public & Public/Private & Public & Private \\ \hline Programming Languages & Solidity & Java, C++, & & & & & \\  & & Jython, Go & & & & & \\ \hline \multirow{3}{*}{Consensus Algorithms} & Proof of Work & Tendermint & & & & & \\  & (Currently used), & (Byzantine Fault- & & & & \\  & Proof of Sale & Totermint, Proof & & & & & \\  & (In Future) & & of stake) & & & & \\ \hline \multirow{3}{*}{Transactions Per Second} & \multirow{3}{*}{25} & \multirow{3}{*}{10,000} & \multirow{3}{*}{n/a} & \multirow{3}{*}{(theoretically)} & \multirow{3}{*}{1/3 to 1/7} & \multirow{3}{*}{More than 1000} & Between 15 \\  & & & & & & & and 16/8 TPS \\ \hline \multirow{3}{*}{Transaction Size} & \multirow{3}{*}{1 MB} & \multirow{3}{*}{250 bytes} & \multirow{3}{*}{n/a} & \multirow{3}{*}{n/a} & \multirow{3}{*}{1 MB} & Changeable \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{Open Source} & \multirow{3}{*}{True} & \multirow{3}{*}{True} & \multirow{3}{*}{True} & \multirow{3}{*}{True} & \multirow{3}{*}{True} & \multirow{3}{*}{True} & \multirow{3}{*}{True} \\ \hline \multirow{3}{*}{Pros} & Anyone can write & Works like a hub for & Scalability, Side & Parallel processing, & & & \\  & smart-contract and & blockchains, based & chain which reduce & low latency, free & Safe and secure, & & \\  & anyone can view & on Tendermint & the risk of backs. & usage (daimed not proven). & & & \\ \hline \multirow{3}{*}{Cons} & \multirow{3}{*}{Scalability issue, 25 transactions per second is very slow} & Complex technology & & & & \\  & which may have & & & & & \\ \cline{1
# Online Uniformly Inserting Points on the Sphere +
## 3 Results
### Analysis of the Upper Bound
#### Inserting Points in an Obtuse Isosceles Triangle  
For the point insertion in an obtuse isosceles triangle, the computation is similar to the previous case.  
For the first point insertion, for example, inserting \(N\), the maximal gap is the diameter of circumcircle of \(\Delta PEN\), i.e., \(G_{N}=\frac{|PE|}{\sin\frac{g}{8}}=1.051|PE|\). The minimal gap is \(|PN|\), i.e., \(g_{N}=|PN|=\sqrt{2|PE|^{2}(1-\cos\frac{g}{8})}=0.618|PE|\). The gap ratio at this stage is \(r_{N}=1.34\cdot\frac{G_{N}}{g_{N}}=2.28\).  
Figure 4: Analysis of the first 2 point insertions into the pentagon. (**a**) Insert the 1 st point into the pentagon; (**b**) insert the 2 nd points into the pentagon.  
Figure 5: Insertion of the next 2 points into the pentagon. (**a**) insert \(J^{\prime}\) as the 3 rd point; (**b**) insert \(F^{\prime}\) as the 3 rd point; (**c**) insert \(E^{\prime}\) as the 3 rd point; (**d**) insert the 4 th point.  
For the second point insertion, after inserting \(M\), the maximal gap is the diameter of the circumcircle of \(\Delta PMN\), i.e., \(G_{M}=\frac{|MN|}{\sin\frac{\pi}{\delta}}=1.701|MN|\). The minimal gap is \(|MN|\), i.e., \(g_{M}=|MN|\). The gap ratio at this stage is \(r_{M}=1.34\cdot\frac{G_{M}}{SR}=2.28\).  
Thus, the gap ratio of inserting points in an obtuse isosceles triangle is at most 2.28.  
Combining all the above cases, we have the following concluding theorem.  
Theorem 1: _The maximal gap ratio of the insertion strategy is at most 3.69._
## 4 Implementation
### Prognosis Algorithm Implementation  
A prognosis becomes feasible if the features extracted from samples with increasing fault severity display a discernible trend. Faults of varying severity were induced in four machines by grinding one tooth. To identify such a trend, A clear pattern emerges by systematically analyzing the extracted features as fault severity increases, enabling the formulation of a prognosis model. This model becomes crucial for predicting and preventing potential machine failures, optimizing maintenance schedules, and ensuring uninterrupted operations. This proactive approach significantly enhances the efficiency and reliability of the machinery, resulting in improved productivity and cost savings. This prognostic endeavor necessitates an explanation of the algorithm, specifically focusing on AdaBoost, an Ensemble Learning Method for regression tasks. This method combines multiple weak regressors, particularly Decision Tree regressors, to create a robust regressor, enhancing prediction accuracy. The algorithm fits weak regressors to the data one step at a time, changing the weights of samples that were wrongly predicted to focus on the most difficult cases. The last regressor, which is a weighted sum of the weak regressors, gives us a full model for prognosis prediction that was trained and tested on different datasets. Mathematically, the Decision Tree regressor is defined in Equation (28).  
\[h(x;\theta_{i})=\begin{cases}y_{1}&\text{if }x_{j}<s\\ y_{2}&\text{otherwise}\end{cases} \tag{28}\]  
where \(x_{j}\) represents the feature value, \(s\) is the splitting threshold determined during training, and \(y_{1}\) and \(y_{2}\) are the predicted output values. The final strong regressor, denoted as \(H(x)\), is a weighted sum of the weak regressors, as explained in Equation (29):  
\[H(x)=\sum_{i=1}^{T}\alpha_{i}h(x;\theta_{i}) \tag{29}\]  
where \(T\) is the total number of weak regressors, and \(\alpha_{i}\) is the weight assigned to the \(i\)th weak regressor. The AdaBoost algorithm is trained on the training data \((X_{\text{train}},y_{\text{train}})\) and evaluated on the testing data \((X_{\text{test}},y_{\text{test}})\). Performance metrics such as mean squared error (MSE) are used to assess the effectiveness of the regressor for prognosis.
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 2 Preliminaries
### Genetic Algorithm  
The genetic algorithm is an optimization algorithm based on natural selection and genetic mechanisms, suitable for solving complex global optimization problems. Its core idea is to simulate the process of biological evolution, gradually optimizing the fitness of candidate solutions through operations of \"selection\", \"crossover\", and \"mutation\", leading the population toward the optimal solution [23; 24; 25]. The following outlines the specific steps of the genetic algorithm:In the initial stage of the genetic algorithm, a random population is generated, where each individual \(\alpha\) represents a vector encoding a specific solution. Each individual is evaluated based on a fitness function \(f(\alpha)\), with the goal of maximizing the fitness function.  
In each generation, the selection operation is responsible for selecting individuals with higher fitness from the current population, increasing the probability that these individuals will be passed on to the next generation. The selection process is typically based on proportional selection according to individual fitness, meaning that the probability \(p_{i}\) of selecting an individual is positively correlated with its fitness, as given by the following formula:  
\[p_{i}=\frac{f(x_{i})}{\sum\limits_{j=1}^{N}f(x_{j})} \tag{2}\]  
where \(f(x_{i})\) represents the fitness of the \(i\)-th individual, and \(N\) is the total number of individuals in the population. Through the selection operation, individuals with higher fitness are more likely to be retained, guiding the population toward regions of higher fitness.  
The crossover operation is used to perform gene recombination within the population to generate new offspring. Typically, two individuals are selected for crossover, exchanging parts of their genes to create new individuals. Assuming a crossover point \(k\), the offspring \(x_{\text{new}}\) generated from individuals \(x_{i}\) and \(x_{j}\) can be represented as  
\[x_{\text{new}}=(x_{i1},x_{i2},\ldots,x_{ik},x_{j(k+1)},\ldots,x_{jn}) \tag{3}\]The crossover operation increases the diversity of solutions and explores a broader solution space, thereby enhancing the global search capability.  
Subsequently, the mutation operation randomly modifies certain genes of an individual with a certain probability, introducing new genetic variations to maintain population diversity and prevent premature convergence to local optima. For gene \(x_{nk}\), the mutation operation can be expressed as  
\[x^{\prime}nk=xnk+\delta,\text{ with probability }\mu \tag{4}\]  
where \(\delta\) is a small random change, and \(\mu\) is the mutation rate. The mutation operation increases the exploratory capability of the population, enabling the algorithm to escape local optima and increasing the probability of global convergence.  
Through multiple iterations of the above \"selection-crossover-mutation\" process, the genetic algorithm continuously generates new generations of individuals, gradually improving the population's fitness.
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## 4 Results
### Crack-Detection Methodology  
The crack points are identified by detecting peaks in the strain field. Provided that the signal-to-noise ratio is sufficiently high, cracks can be detected by setting a strain threshold (\(T_{e}\)) above the maximum elastic strain characteristic of the material. Additionally, the strain threshold must be higher than the strain noise level to ensure accurate crack detection [10]. However, it should be noted that the computed strain values are not equivalent to actual physical strain, which is zero in the crack area. Instead, the strain values are relative to the displacement of the crack lips, which can be used to identify cracks by applying a strain threshold.  
In the crack-detection experiments (simulated dataset and aggregate grain sample), the equivalent von Mises strain was computed from the strain field obtained as by-product of the DVC algorithm. The equivalent strain takes into account all the strain components and simplifies the strain to a single scalar value  
\[\begin{split} e_{Mises}=\frac{2}{3}[\epsilon_{xx}^{2}+\epsilon_{ yy}^{2}+\epsilon_{zz}^{2}-\epsilon_{xx}^{2}\epsilon_{yy}^{2}-\epsilon_{xx}^{2} \epsilon_{zz}^{2}-\epsilon_{zz}^{2}\epsilon_{yy}^{2}+\\ +\frac{3}{4}((\epsilon_{xy}+\epsilon_{yx})^{2}+(\epsilon_{xz}+ \epsilon_{zx})^{2}+(\epsilon_{zy}+\epsilon_{yz})^{2})]^{\frac{1}{2}}\end{split} \tag{6}\]