esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Search Algorithms
### Overview of Whale Optimization Algorithm  
The whale optimization algorithm (WOA) was recently implemented to solve various non-linear optimization problems [26]. The principle of how the WOA works is based on the way in which humpback whales trap their food. This special form of hunting is called the bubble net feeding method. The process consists of several whales coming together and forming a spiral around a school of fish making bubbles below them so that they do not escape and push them towards the surface. Humpback whales can recognize the location of the prey and surround it. This behavior is represented by the following equations:  
\[\mathbf{X}(t+1)=\mathbf{X}^{*}(t)-\mathbf{A}\cdot\mathbf{D} \tag{21}\] \[\mathbf{D}=\left|\mathbf{C}\cdot\mathbf{X}^{*}(t)-\mathbf{X}(t)\right| \tag{22}\]  
where \(t\) indicates the current iteration, \(\mathbf{A}\) and \(\mathbf{C}\) are coefficient vectors, \(\mathbf{X}^{*}\) is the position vector of the best solution obtained so far, \(\mathbf{X}\) is the position vector, and \(\cdot\) is an element-by-element multiplication. It is worth mentioning here that \(\mathbf{X}^{*}\) should be updated in each iteration if there is a better solution. The vectors \(\mathbf{A}\) and \(\mathbf{C}\) are calculated in the following way:  
\[\mathbf{A}=2\mathbf{a}\cdot\mathbf{a} \tag{23}\] \[\mathbf{C}=2\mathbf{r} \tag{24}\]  
where \(\mathbf{a}\) decreases linearly from 2 to 0 over the course of iterations and \(\mathbf{r}\) is a random vector between \([0,1]\).  
It is worth mentioning that humpback whales swim around their prey by closing a circle and along a spiral path simultaneously. To model this behavior simultaneously, it is assumed that there is a 50% probability of selecting if the circle mechanism is closed or the position of the whales. The mathematical model that describes the previous process is the following:\[\mathbf{X}(t+1)=\begin{cases}\mathbf{X}^{*}(t)-\mathbf{A}\cdot\mathbf{D}&\text{if }p \geq 0.5\\ \mathbf{D}\cdot e^{bl}\cdot\cos(2l\pi)+\mathbf{X}^{*}(t)&\text{if }x\geq 0.5 \end{cases}\]  
where \(b\) is a constant for defining the shape of the logarithmic spiral, \(l\) is a random number in \([-1,1]\) and \(p\) is a random number in \([0,1]\). In each iteration, search agents update their positions with respect to a randomly chosen search agent or the best solution obtained so far. The parameter \(\mathbf{a}\) is reduced from 2 to 0 to provide exploration and exploitation. The basic steps of the WOA are summarized in the pseudocode as shown in Algorithm 4:  
```
Begin  Calculate the fitness of each search agent \(X^{*}\) = the best search agent while\(t\leq Max\)Iterdo for each search agent do if\(|\mathbf{A}|\leq 1\)then  Update the position of the current search elseif\(|\mathbf{A}|\geq 1\)then  Select a random search agent \(\mathbf{X}_{r}\) and  Update the position of the current agent endif endfor  Update \(\mathbf{a}\), \(\mathbf{A}\), and \(\mathbf{C}\)  Update \(\mathbf{X}^{*}\) if there is a better solution \(t=t+1\) endwhile return\(\mathbf{X}^{*}\) endBegin
```  
**Algorithm 4**Wale Optimization Algorithm
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 3 Preliminaries
### Problem Statement  
The main research problems considered in this study can be summarized as follows:  
* Many routing algorithms based on ML or RL are inherently complex. The intricate urban road structures and constantly changing vehicle conditions further exacerbate the challenges in program execution. These algorithms confront high levels of uncertainty, thereby making it difficult to achieve satisfactory results in the end.
* In VANET routing protocols based on Q-learning, many protocols rely on RSUs to forward data or monitor road traffic at intersections, which is difficult to meet in many practical scenarios.
* Many routing protocols cannot detect network congestion. The routing protocol should be able to select the forwarding according to the busy status of the next-hop node to improve the network performance.  
* The protocol needs to adjust parameters in real-time based on the vehicle environment to prevent performance degradation due to the environment's dynamic changes.
## 3 Materials and Methods
### Methodology
#### 3.2.5 Feature Engineering  
To extract features from tweets, the two most widely used feature extraction methods are used including BoW and TF-IDF.  
**Bag of Words**: BoW is a simple technique to extract features from simplified text or data and is commonly used in natural language processing and information retrieval [38]. For text classification, BoW is used to count the occurrence of a word in a text and forms a feature vector containing the number of occurrences of each unique word. The BoW is mostly used to build the vocabulary of all matchless words and train the learning  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Tweets before Pre-Processing** & **Tweets after Pre-Processing** \\ \hline People have to take more precaution. time to educate everyone effectively to undo Covid19 second wave. & people precaution time educate everyone effect undo covid19 second wave \\ \hline In the meantime, \#COVID-19 cases in schools have not flared up as much as some feared amid the \#pandemic restrict\(\in\)1[[https://t.co/tcVBMglogOB](https://t.co/tcVBMglogOB)]([https://t.co/tcVBMglogOB](https://t.co/tcVBMglogOB)) (accessed on 1 September 2021) & meantime covid19 case school flare fear amid pandemic restrict \\ \hline England: High school face mask may be lifted & england high school face mask lift \\ \hline If education around learning what a credible source is wasn’t so poor, this wouldn’t be necessary. It’s amazingly \(\text{d}\hat{a}\hat{a}\in\)1[[https://t.co/8](https://t.co/8) sb711 ALo6]([https://t.co/8](https://t.co/8) sb711 ALo6) (accessed on 1 September 2021) & educ around learn credible source poor necessary its amaze \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample tweets from the dataset before and after preprocessing.  
Figure 2: Ratio of sentiment with and without SMOTE.  
models through their frequencies. BoW feature vectors from the following sample text data statements are shown in Table 6. Sample statements are  
S1: _england high school face mask lift_  
S2: _wear mask right way_  
**Term Frequency-Inverse Document Frequency**: TF-IDF is a feature extraction technique used to extract weighted features from text data. It provides the weight of each term in the corpus to improve the performance of learning models [39]. TF-IDF is a product of TF and IDF. TF can be calculated as:  
\[\text{TF}(t,d)=\frac{n_{t}}{N_{(T,d)}} \tag{1}\]  
where \(n_{t}\) represents the number of occurrences of term \(t\) in a document \(d\), while \(N_{(T,d)}\) indicates total terms \(T\) in that document. IDF of a term indicates how important it is in the whole corpus [40], and it can be calculated as:  
\[\text{IDF}=log\frac{D}{n_{d}} \tag{2}\]  
where \(D\) is total number of documents in the corpus, whereas \(n_{d}\) is the number of documents where the term \(t\) appears. Using TF and IDF, TF-IDF can be calculated as  
\[\text{TF-IDF}=\text{TF}*\text{IDF} \tag{3}\]  
For a better understanding of TF-IDF, Table 7 shows the results of TF-IDF on two pre-processed data samples.
# Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
## 2 The Proposed Method
### Pole Mask and Depth Map Fusion  
The method of mask and depth map fusion has the following advantages: (1) Using the two-dimensional recommendation area provided by the mask, most of the interfering background can be quickly excluded. The mask is fused with the depth map to form a frustum [32], which reduces the 3D search space, the number of point clouds, and the difficulty of point cloud processing. (2) The image has rich color and texture information, so the image detector can detect instance objects more accurately and efficiently, which can be used as a front-end strategy for point cloud processing. At the same time, the point cloud has three-dimensional spatial structure information, which can make up for the image lack of depth information.  
By using the depth information of the depth map and the known projection matrix, the 2D pixel points of the segmented pole mask can be raised to the 3D frustum containing the pole object, which is used as the input of point cloud segmentation model. Further, after eliminating the interference points through point cloud segmentation processing, a more accurate pole point cloud can be obtained. The frustum generated by the predicted mask is used as the point cloud segmentation object during testing. When training the point cloud segmentation model, the frustum generated by the ground-truth mask is used as the training dataset to ensure the correctness of the training dataset and labels. The randomly transformed ground-truth mask is closer to the predicted mask of the image segmentation model, which helps to improve the robustness of the point cloud segmentation model.
# Pareto-Optimized AVQI Assessment of Dysphonia: A Clinical Trial Using Various Smartphones
###### Abstract  
Multiparametric indices offer a more comprehensive approach to voice quality assessment by taking into account multiple acoustic parameters. Artificial intelligence technology can be utilized in healthcare to evaluate data and optimize decision-making processes. Mobile devices provide new opportunities for remote speech monitoring, allowing the use of basic mobile devices as screening tools for the early identification and treatment of voice disorders. However, it is necessary to demonstrate equivalence between mobile device signals and gold standard microphone preamplifiers. Despite the increased use and availability of technology, there is still a lack of understanding of the impact of physiological, speech/language, and cultural factors on voice assessment. Challenges to research include accounting for organic speech-related covariables, such as differences in conversing voice sound pressure level (SPL) and fundamental frequency ((60), recognizing the link between sensory and experimental acoustic outcomes, and obtaining a large dataset to understand regular variation between and within voice-disordered individuals. Our study investigated the use of cellphones to estimate the Acoustic Voice Quality Index (AVQI) in a typical clinical setting using a Pareto-optimized approach in the signal processing path. We found that there was a strong correlation between AVQI results obtained from different smartphones and a studio microphone, with no significant differences in mean AVQI scores between different smartphones. The diagnostic accuracy of different smartphones was comparable to that of a professional microphone, with optimal AVQI cut-off values that can effectively distinguish between normal and pathological voice for each smartphone used in the study. All devices met the proposed 0.8 AUC threshold and demonstrated an acceptable Youden index value.  
AVQI; voice screening; Pareto optimization; voice disorders; dysphonia; voice quality Article
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Decision Support System (DSS)  
The DSS serves as a central hub, capable of integrating and exchanging data with the aforementioned services (Figure 2). It includes a GIS (geographic information system)-based system for geospatial analysis and can determine danger zones and hazard positions, calculate uncertainty for all its results, optimize sensor positions, conduct scenario planning, and evaluate performance and progress. Moreover, it can validate social media sensed data and predictions, determine optimal resource allocation, and provide insights for reconstruction, communication, and pest control efforts.
# The Effect of Adsorbed Volatile Organic Compounds on an Ultrathin Water Film Measurement
###### Abstract  
Using surface plasmon resonance imaging (SPRi), we have recently shown for the first time the existence of a monolayer water film between droplets during dropwise condensation. This study examines the effect of adsorbed volatile organic compounds (VOCs) on the ultrathin film measurement using SPRi. Further, the work presents the proper surface-treatment process that enables measurements of the ultrathin water layer during high-speed imaging of dropwise condensation at 3000 frame per second. In this study, two methods were applied for cleaning the surface (gold-coated glass)--(1) standard cleaning procedure (SCP) using acetone, isopropyl alcohol, and deionized water and (2) SCP followed by air plasma cleaning. This work discusses the effect of the cleaning procedures on surface roughness, contact angle, and surface chemistry using atomic force microscopy, optical microscopy, and an X-ray photoelectron spectroscopic meter. The results showed that SCP before the SPRi is a proper surface-treatment method. The effect of adsorbed VOCs during dropwise condensation on a surface treated with SCP was measured to be 0.0025 (reflectivity unit), which was 70% smaller than the reflectance associated with a monolayer water film. The results of this work confirm a monolayer water film observation during the dropwise condensation, which has been reported before.  
Keywords:volatile organic compounds; surface treatment; surface plasmon resonance; thin film measurement; XPS; dropwise condensation; monolayer; surface homogeneity; nucleation +
Footnote †: journal: Applied Physics Letters
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 4 Experimental Evaluation
### Timing Evaluation Method
#### 4.2.1 Timing Record Fusion  
Since the network, which was used to connect the virtual component, was considered as part of the automation system, the real-time instant of an FMI event was approximated by two locally recorded time stamps. The first one dated the beginning of the event distribution and was taken as soon as the event was scheduled. The second time stamp was recorded as soon as all event outputs were calculated and the event was successfully distributed. Additionally, the real-time instant at which an event was added to the queue (i.e., when it was received or predicted) and the time an event became outdated were tracked to gain further insights.  
For each real-time record, a single independent entry was logged by FMITerminalBlock. In the post-processing steps, the independent records were linked such that each time stamp was associatedwith the corresponding event entity. To simplify the prototypical program flow and to increase the level of parallelism, FMITerminalBlock did not assign unique event identifiers, and the nominal event time was used to match timing records. An algorithm that emulated the state of the event queue in post-processing was used to associate the timing records with the corresponding event entity. An error was issued in case of ambiguities, but none were observed during the experiments.
## 2 System Model with Collision Design and Analysis of Our Proposed System
### An In-Depth Overview of Our Proposed MAC Protocol: Enhancing Network Efficiency and Performance  
We elaborate the details of our proposed MAC protocol in Figure 6, which illustrates the complete process in a step-by-step manner. Communication is initiated from the terrestrial base station to the buoy via satellite or another communication vessel. Each layer also consists of one central node located slightly below the buoy. It interacts with these vertical central nodes to collect data and feedback from the buoy. Moreover, every central node is in charge of collecting data from all relay nodes in the same layer. Then, the relay nodes gather data from other sensor nodes, which are the ordinary sensor nodes, organized into clusters. After the establishment of the UWCN, the terrestrial base station gives a standard time for synchronization. Synchronization is achieved by a scheme of layers: buoy-center relay-ordinary nodes.  
After this, the base station sends out a data acquisition order to begin the data gathering and sending step. The normal sensor nodes tabulate information and send it to th In networking, every protocol layer functions cooperatively with other layers to deliver data transmission, and the MAC layer design significantly influences the efficiency of the next layer, the routing layer. The MAC protocol we propose targets energy efficiency and collision avoidance in the routing layer in the following ways. First and foremost, the protocol maintains a reliable link state at the MAC layer through minimizing collisions as well as energy in similar layers, hence improving the reliability of the synthesized data. This stability can influence the routing layer's decision making and allow it to choose more stable and energy-efficic.  
Second, when the MAC layer makes responses based on the condition of the network forwarding process, changing channel assignments and other network parameters, it assists in selecting the appropriate routes since the communication paths are more stable and have less chance of failing due to collisions or exhaustion of power. The routing layer can then leverage this increased reliability with less frequent recalculations of a path or deaths of a packet, which reduces delays and maximizes the rate a Last but not least, the flexibility of the total system can be enhanced; for instance, the MAC layer provides information about the channel usage and energy state of nodes of the network. This allows the routing layer to receive more accurate feedback, allowing it to avoid nodes which are almost discharged or less crowded areas to enhance the routing layer results.
## 3 Constraint-Handling Techniques in Metaheuristic Algorithms
### Constraint-Handling Techniques
#### 3.2.2 Feasibility Rules  
The FR were proposed by [PERSON] [53]. Three rules define this constraint-handling technique, these are:  
1. Between two infeasible solutions, the solution with the fewest number of violated constraints is chosen.
2. Between a feasible solution and an infeasible one, the feasible solution is chosen.
3. Among two feasible solutions, the solution with the best objective function is chosen.  
In this work, the number of violated constraints is considered the sum of infeasible constraint distance \(\phi(x)\) and shown in (20).  
\[\phi(x)=\sum_{j=1}^{m}max(0,g_{j}(x)) \tag{20}\]  
Algorithm 5 describes the operation of FR. This constraint-handling technique requires three sets of solutions (three input arguments). At each iteration \(G\), those solutions are compared according to the feasibility rules. Finally, the best solutions are stored in the output arguments.  
```
1:Function \(fncCHT(\bar{a},\bar{b},\bar{c})\)  Input: \(\bar{a}\in X_{G}\) and \(\bar{b}\in U_{G}\)  Output: \(X_{G+1}\) and \(x^{best}\)
2:\(X_{G+1}=\bar{a}\) and \(x^{best}=\bar{c}\)
3:for\(i=1\) to \(NP\)do
4:if\((\phi(\bar{a}_{i})>0\) and \(\phi(\bar{b}_{i})>0\) and \(\phi(\bar{b}_{i})<\phi(\bar{a}_{i}))\) or \((\phi(\bar{a}_{i})>0\) and \(\phi(\bar{b}_{i})==0)\) or \((\phi(\bar{a}_{i})==0)\) and \(\phi(\bar{b}_{i})==0\) and \(\bar{J}(\bar{b}_{i})<\bar{J}(\bar{a}_{i}))\)then
5:\(X_{G+1}\leftarrow\bar{b}_{i}\)
6:if\((\phi(x^{best})>0\) and \(\phi(\bar{b}_{i})>0\) and \(\phi(\bar{b}_{i})<\phi(x^{best}))\) or \((\phi(x^{best})>0\) and \(\phi(\bar{b}_{i})==0)\) or \((\phi(x^{best})==0\) and \(\phi(\bar{b}_{i})==0\) and \(\bar{J}(\bar{b}_{i})<\bar{J}(x^{best}))\)then
7:\(x^{best}\leftarrow\bar{b}_{i}\)
8:endif
9:endif
10:endfor
```  
**Algorithm 5** Feasibility rules pseudocode.
## 5 Data Analysis and Main Findings
### Demographic Questions and Content  
The demographic questions were divided into five main sections. The first section collected basic characteristics, such as gender, age, and education level, while the second section collected job- and workplace-related information, such as whether the participant works in the government or private sector. The third section collected information on the nature of the participant's job at the organisation and their knowledge of the importance of adopting BYOD programmes in their sector. The fourth section collected information on the employment status of the participant and determined if any of the participants were not from the government or private sectors. Lastly, the fifth section collected information on the types of devices that the organisation provided the participants and the personal devices that the participants used to access the systems of the organisation (Table 3).  
In summary, the demographic section of the survey collected the gender, age, employment status, and education level of the participants as well as the types of devices that the organisation provided them and the personal devices that they used to access the systems of the organisation. It also examined the use of personal devices in the workplace and work-completion behaviours outside of the workplace. The amount of missing data in any survey should not exceed 5% of its total questions [59, 60]. Only 3.8% of the data in this present study were missing. This included participants who failed to complete the survey for technical reasons or left one or more entire sections of the survey unanswered. These responses (12) were excluded from the present study.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Factor** & **Mean** & **SD** & **Skew** & **Kurtosis** & \
\begin{tabular}{c} **Cronbach’s** \\ **Alpha** \\ \end{tabular} & **CR** & **AVE** &
\begin{tabular}{c} **Rotated Factor** \\ **Loadings** \\ \end{tabular} \\ \hline SI1 & 3.92 & 0.718 & \(-\)0.859 & 0.302 & & & & 0.807 \\ \hline SI2 & 3.71 & 0.704 & \(-\)0.64
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 3 Proposed Method
### FDNN Model
#### 3.2.1 Data Preprocessing  
The initial step involves updating the superimposed signals and channel parameters. The superimposed signal is updated before the detection process for each user. When detecting the \(i\)-th user, the superimposed signal \(\mathbf{y}_{i}\) is computed as the sum of the original signal minus the reconstructed signal from the previous user, which is written as  
\[\mathbf{y}_{i}=\mathbf{y}-\sum_{k=1}^{i}\mathrm{H}(\hat{\mathbf{x}}_{k})\,, \tag{15}\]  
\(\mathbf{y}\) represents the original signal that the BS receives. \(\mathrm{H}(\cdot)\) represents the reconstruction function, which is responsible for recovering the user's signal based on encoding estimation. With \(\mathbf{y}_{i}\), we can extract the in-phase component \(y_{i}^{\mathrm{I}}\) and orthogonal component \(y_{i}^{Q}\) as the input of the network. The channel parameter vector can be expressed as  
\[\mathbf{\Theta}_{i}=[g_{i},\varphi_{i},g_{i+1},\varphi_{i+1},\cdots,g_{N},\varphi_ {N}]\,. \tag{16}\]  
\(g_{i}\) is the symbol gain and \(g_{i}=\sqrt{P_{i}}|\mathbf{h}_{i}|\). \(\varphi_{i}\) is the symbol phase of user \(i\) at the receiving end and \(\varphi_{i}=\arctan(\frac{Im(\mathbf{h}_{i})}{Re(\mathbf{h}_{i})})\). The channel parameter vector is updated before each user's detection by subtracting the channel parameter of the previous user. Therefore, the input vector of the FDNN network is \(\mathbf{I}_{i}=[y_{i}^{I},y_{i}^{Q};\mathbf{\Theta}_{i}]\). At the output terminal, we map the modulation symbol \(s_{i}\) of user \(i\) to the one-hot code \(x_{i}\), which is written as  
\[\mathbf{x}_{i}=f_{\mathrm{OH}}(s_{i}),s_{i}\in S_{i}\,. \tag{17}\]  
The one-hot code makes it easy to calculate the cross-entropy of vectors.