esa-sceva/satcom-chunk-collection huggingface dataset:
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 4 Algorithm Analysis
### Verification of the AS-FT  
The simplified Gaussian function verifies the 1D, 2D, and 3D methods of the AS-FT. The spatial domain and wavenumber domain expressions of the Gaussian function are shown in Table 1, where \(a=0.001\). The sampling range of spatial domain \(x\), \(y\), \(z\), and wavenumber domain \(k_{x}\), \(k_{y}\), \(k_{z}\) are shown in Table 1. The forward and inverse transform are uniformly sampled, and the number of sampling points is 101.  
The forward transform result is shown in Figure 2. Compared with the analytical solution, the Relative root mean square (Rrms) error [25] of the 1D forward transform result is 0.012%, the Rrms error of the 2D forward transform result is 0.016%, and the Rrms error of the 3D forward transform result is 0.020%.  
The inverse transform result is shown in Figure 3. Compared with the analytical solution, the Rrms error of the 1D inverse transform result is 0.005%, the Rrms error of the 2D inverse transform result is 0.008%, and the Rrms error of the 3D inverse transform result is 0.011%. To sum up, the forward and inverse transform of the AS-FT method has high accuracy.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Spatial Domain**} & \multicolumn{1}{c}{**Wavenumber Domain**} & \multicolumn{1}{c}{**Spatial Domain**} & \multicolumn{1}{c}{**Wavenumber**} \\  & & **Sampling** & **Domain Sampling** \\ \hline
1D & \(f(x)=e^{-a\lambda^{2}}\) & \(F(k_{x})=\sqrt{\pi/a}e^{-k_{x}^{2}/4a}\) & \((-100\ \mathrm{m},100\ \mathrm{m})\) & \((-0.2,0.2)\) \\ \hline
2D & \(f(x,y)=e^{-a(x^{2}+y^{2})}\) & \(F(k_{x},k_{y})=(\pi/a)e^{-(k_{x}^{2}+k_{y}^{2})/4a}\) & \((-100\ \mathrm{m},100\ \mathrm{m})\) & \((-0.2,0.2)\) \\ \hline
3D & \(f(x,y,z)=e^{-a(x^{2}+y^{2}+z^{2})}\) & \(F(k_{x},k_{y},k_{z})=\sqrt{\pi^{3}/a^{3}}e^{-(k_{x}^{2}+k_{y}^{2}+k_{z}^{2})/4a}\) & \((-100\ \mathrm{m},100\ \mathrm{m})\) & \((-0.2,0.2)\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Gaussian function expression and sampling range in spatial domain and wavenumber domain.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Slicing  
Slicing is the method by which network providers can create multiple, independent virtual networks over shared physical infrastructure for 5G networks. While traditional mobile networks treat all incoming traffic similarly, 5G network slices can provide customized network services and scale up or down as the demand for that slice changes. Slicing is made possible by SDN and NFV; the separated control plane is composed of ready-for-change software that facilitates adaptive and intelligent network management. 5G networks providers will create customized slices based on use cases (video, IoT, Industry robotics, etc.) created to meet the unique service-level agreements (SLA) [52] (see Figure 5). 5G networks can pair DL and data collected with mobile edge computing to automatically manage slice creation. Automatically spinning up resources for network slices first requires predictions of network demand and user location to assign resources correctly at edge nodes. To achieve useful traffic
## 3 Results and Discussion
### Analysis of Phenomena through a High-Speed Camera  
The effects of the welding parameters on the behaviors of the arc and molten pool were investigated based on the images acquired from the high-speed camera in Figure 7. Figure 9 shows high-speed images. The high-speed filming was performed with a bead on plate configuration, and the welding conditions were applied at 5 m/min for both \(X_{2}\) and \(X_{3}\). As shown in Figures 9 and 10, the leading arc directly strikes the solid-state base metal, which forms a narrow molten pool. Therefore, the leading arc had a direct effect on the penetration depth. The trailing arc is transferred to the molten pool created by the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **Adj. SS** & **Adj. MS** & **F** & \(p\) \\ \hline Regression & 5 & 71.40 & 14.28 & 173.37 & 0.00 \\ Linear & 3 & 68.18 & 22.73 & 275.91 & 0.00 \\ \(X_{1}\) & 1 & 3.07 & 3.07 & 37.26 & 0.00 \\ \(X_{2}\) &  
leading arc, which expands the molten pool. Moreover, the solid surface induced by the leading arc force is filled with the molten metal generated by the trailing arc, the expansion of the molten pool remains stably maintained, and \(Y_{leg}\) increases [22]. A schematic of the behavior of the molten pool and arc is shown in Figure 10. The configuration of the lap fillet joint with the 2 mm thickness top plate and the 4 mm thickness bottom plate was used for high-speed filming. Table 8 shows the welding conditions, high-speed images, and cross-sections of the welding bead. WFR was fixed at 9.5 and 5.0 to observe the bead shape according to \(X_{2}\) and \(X_{3}\) when they have the same thermal energy. When the value of \(X_{2}\) was higher than that of \(X_{3}\), the \(Y_{leg}\) was 7.6 mm and the \(Y_{pen}\) was 3.0 mm. On the other hand, when \(X_{3}\) was higher, the \(Y_{leg}\) was 8.0 mm and the \(Y_{pen}\) was 2.4 mm, that is, in the lap fillet joint as well, it was confirmed that the leading arc had an effect on the penetration depth formation, and the trailing arc had an effect on the leg length formation. When the value of \(X_{2}\) was higher than that of \(X_{3}\), a large number of porosities were observed in the weld metal. According to previous studies, porosities escape in the vertical direction [23]. When the penetration is deep, porosities become trapped because there is insufficient time to escape before solidification. In terms of material defects as well as weld bead geometry affected by process variables, the deformation of the material under the same heat input condition is less than that of single welding because the leading and trailing arcs are separated in tandem welding. Additionally, in the case of porosities, as mentioned above, the flow of the tandem molten pool is better than that of single arc welding, so trapped porosities are quickly released to the outside.  
Figure 9: High-speed camera images of the molten pool when \(X_{2}\) and \(X_{3}\) are 5.0 m/min: behaviors of the leading arc and the molten pool at 80.72 ms; behaviors of the trailing arc and the molten pool at 81.28 ms.
## 2 Materials and Methods
### Determination of Analysis Objects and Data Preprocessing  
The data acquisition and processing processes in this paper focus on the final examination results of 2019 and 2020 undergraduates from a university's college of information [20]. Data preprocessing plays a crucial role in ensuring the integrity and standardization of the data. Several steps were performed. Firstly, any data samples with missing values, outliers,or repeated values were removed. Additionally, students who had dropped out, suspended their studies, or transferred to other programs were excluded from the analysis as their final exam score data may be incomplete. The dataset also does not involve students from other disciplines or majors, so its applicability is mainly limited to academic performance in computer science. This careful selection resulted in a final dataset of 292 students' final exam score data, including student number, semester, course number, course name, and test scores. Furthermore, it is recognized that different courses may have variations in difficulty and scoring criteria, which could introduce bias in direct comparative analysis using the original course scores. To address this concern, the student overall course performance was standardized. Specific steps were taken to transform the scores to a common scale, ensuring fair comparisons across courses. The two major steps are shown as follows:  
Step 1: Calculate the standard deviation, using the following formula:  
\[\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(X_{i}-\mu\right)} \tag{1}\]  
where \(X_{i}\) represents the score of the \(i\)-th student, \(\mu\) represents the mean of all students, and \(N\) is the total number of all students.  
Step 2: Standardize student performance, using the following formula:  
\[X_{std}=\frac{X_{i}-\mu}{\sigma} \tag{2}\]  
By completing these steps, we obtained a standardized dataset of 292 computer students' final scores.  
In addition, triple classification is one of the prediction models. We use 70% of the samples and use different methods to predict, to analyze the precision, recall, and F1-score. In the subsequent experiment, students were divided into three categories: fail, pass, and excellent. The number of samples of each category in the training dataset was not counted, and no obvious potential bias was found in the dataset.
## 3 Modular Gait Generation Principle
### Driven Module Waves with Overlapping
#### 3.2.2 Illustrations for MG-II  
With the proposed gait generation algorithm in Figure 7, Figure 8 demonstrates the locomotion gait transmissions of the 12-segment robot with MG-II under two cases: total or partial actuated segments. Figure 8a shows a gait generation demonstration for a 12-segment robot under U-DM, while Figure 8b shows a gait generation demonstration for a 12-segment robot under B-DM. Assuming that each driven module contains four segments, the number of transmission segments is less than the number of segments in one driven module (for the driven mode with MG-II). Then, overlapped segments can occur in the transmission of the driven module. The overlapped segments keep the state of the previous movement, which are called \"K\" segments, as shown in Figure 8.  
Figure 7: Routine of the generation algorithm for MG-II.  
Regarding the gait generation in Figure 8a for a 12-segment robot under U-DM with four actuated segments, there is a \"K\" segment when transmitting the three segments every time. The number of transmission segments is equal to three-quarters of the module phase difference. The number of body segments in the U-DM equals the sum of the number of transmission segments and the number of \"K\" segments. The earthworm-like locomotion robot advances 3 \(\Delta l\) with each transmission of the U-DM. In this gait module mode, locomotion speed depends only on the number of transmission segments.  
Figure 8: The gait generation demonstrations for a 12-segment robot with two different driven modules with MG-II. The “K” indicates keeping the previous state segments. A gait generation demonstration for a 12-segment robot under U-DM is shown in (**a**). The frequency of the receding peristaltic wave is 1/4. The amplitude of the receding peristaltic wave is 3. A gait generation demonstration for a 12-segment robot under B-DM (“\(0111\)”) is shown in (**b**). The frequency of the receding peristaltic wave is 1/6. The amplitude of the receding peristaltic wave is 2.  
For the B-DM with gait generation, as per Figure 8b, the body state of the B-DM indicates \"0111\". The earthworm-like robot advances 2 \(\Delta l\) with each transmission of the B-DM. The locomotion speed is related to the number of actuated segments and the number of transmission segments. There is only one \"K\" segment when transmitting two segments in every transmission, as shown in Figure 8b, since there is a nonactuated segment at the tail end of the driven module (looking from right to left). The number of \"K\" segments is related to the arrangement of the actuated segments and the nonactuated segments in the driven module.
## 4 Problem Formulation and Brief System Architecture
### Application Layer
#### 4.3.1 Prediction Model Pipeline  
Prediction analysis provides the early detection of a disease, which can minimize the cost of hospitalization and reduce mortality rate. A proper estimation of early disease prediction can provide an accurate diagnosis and fast treatment facility to a patient. Thus, we build a prediction model pipeline to predict a disease more accurately.  
Prediction target is the intersection of the interest set of disease and the possible set of disease. Suppose \(I=\{I_{1},I_{2},I_{3},...,...,I_{n}\}\) is the interest set of disease which is calculated by etiology analysis. \(\chi=\{\chi_{1},\chi_{2},...,...,\chi_{n}\}\) be the set of possible disease measured by clinical vital signs and observation tools, which we collect with biosensor devices. Thus, the prediction target is represented in Equation (6).  
\[PT=I\cup\chi \tag{6}\]  
In the top unit of prediction modeling, we use the MapReduce algorithm to analyze a patient's etiology function. A patient's genetic sequence, such as DNA information, is a constant value collected from the patient's cloud server. The severity of diseases in specific geography at a given time interval will be gathered from the cloud database, and the epigenetic features or the genetic diseases to which a patient is exposed will be collected from his medical history. All those are put into the map class for tokenizing with a key and value, and then sent to reduce the class to get interest sets of disease. We take some vital signs which we are integrated with a patient's emotional state to get the possible set of disease.  
Figure 2: The data warehouse architecture.  
To construct the cohort, we take a study sample from the total population (all test cases from the database), which we get from our database. Then, we identify the patient who exposes the risk factor of the target prediction disease. For example, let us imagine that a target prediction of a patient is heart failure. Therefore, in cohort construction, we take the study population of all heart failure patients. The key to the cohort study is to identify the clues of the correct inclusion and exclusion criteria. From cohort construction, the system will set its training dataset and test dataset and can to find the hidden evidence of disease.
# Energy Efficiency in Short and Wide-Area IoT Technologies--A Survey
## 2 IoT Technologies
### Low-Power Wide-Area Networks (LPWANs)  
LPWANs allow long range communications at a low bit rate among connected things, such as battery-operated sensors. Therefore, they require far fewer Access Points (APs) than LPSANs. Most of the LPWAN technologies reported in this paper made their first appearance around 2015, with LoRa/LoRaWAN gaining more and more support, followed by LTE-M and NB-IoT cellular technologies.
# Optical Flow Estimation with Occlusion Detection
## 3 Occlusion Detection Strategy
### Warped Image-Based Strategy  
Using the initial matching mentioned above and EpicFlow [36], the initial optical flow estimation can be estimated, which contains abundant occlusion information. We get the occlusion reasoning  
Figure 2: Flow diagram of our matching-based strategy.  
from two aspects, one is the motion analysis, and the other is warped image. The overall flow of our matching-based strategy is shown in Figure 3.  
Based on the statistics [9] of Flying Chairs and the MPI-Sintel datasets, it is verified that the displacements of the foreground are larger than that in the background. Meanwhile, the occluded areas belong to the background in most cases. According to the transformation matrix from optical flow, if more than one pixels move to the same position, the positions with small optical flow values are marked as occlusion. Based on the above motion analysis, we can obtain occlusion detection results to some extent. Meanwhile, the warped image also contains abundant occlusion information. Under the guidance of optical flow, the warped image can be obtained by backward mapping of frame 2. Occlusion might result in undesirable ghosting artifacts in the warped image which is helpful for reasoning occlusion.  
The above two strategies can get occlusion detection separately. Since the statistical results are not applicable to each frame, the former strategy method may lead to many errors. In addition, the latter is sensitive to the threshold of the difference. Therefore, we propose a robust and reliable combined detection strategy, which is shown in Figure 4 and the initial occlusion detection is named as \(M_{w}\). According to the motion analysis, the positions in the first frame are divided into three categories. The first category is that there is no corresponding point in frame 2. A unique corresponding point in frame 2 is the second category. In addition, the last category is sharing the corresponding point in frame 2 with other positions. For \"many to one\" relationship in the third category, we only retain the smallest difference correspondence. No corresponding relationship can be directly reasoned as occlusion. In the \"one to one\" relationship, occlusion threshold is used to determine whether there is occlusion. Since the pixels in the warped image are obtained by neighborhood interpolation, the occlusion threshold can be designed to correlate with the maximum difference of the pixels in the four neighborhoods.  
Figure 4: The flowchart of our warped image-based strategy.  
Figure 3: Flow diagram of our warped image-based strategy.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 3 Materials and Methods
### Explainability with LIME  
LIME (Local Interpretable Model-Agnostic Explanations) is an interpretability technique that uses a locally approximated interpretable model to explain the predictions of complex models [66]. It can highlight the words or characters that significantly influence the categorization decision in the classification model. LIME approximates the model's decision in a relatively small area surrounding a specific instance by perturbing the input data and tracking how the model's prediction changes.  
In the context of Arabic meter classification, LIME facilitates the identification of specific words or characters that strongly influence the model's prediction. This helps to interpret and validate the patterns recognized by the transformer models. The final training parameters for the model are described in Table 3. The tuning parameters are mentioned in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameter** & **Values** & **Tuning** \\ \hline \multirow{4}{*}{Bidirectional model} & Batch size & 128 & No \\  & Layers & [1, 2, 3, 4, 5, 6, 7] & Yes \\  & Epoch & 50 & No \\  & Learning rate & \(1\times 10^{-3}\) & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Values** \\ \hline Optimizer & Adam \\ Loss function & Sparse categorical cross entropy \\ Metrics & Accuracy, F1-score, Recall, Precision \\ EarlyStopping & Patience: 8 epocs, Monitor: Validation loss \\ ReduceLRonPlateau & Patience: 2 epocs, Monitor: Validation loss \\ Model explainability & LIME \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters used in the proposed study for each model.
# Smart Intersections and Connected Autonomous Vehicles for Sustainable Smart Cities: A Brief Review
## 7 Integration of Smart Roads and Smart Intersections into Smart City Ecosystems
Considering the behavioral perspective, such as the impact of user acceptance and trust, can also play an effective role in the success of smart intersections. Drivers are more likely to resist system opacity or unexpected delays in response to AI-controlled signals [201]. Behavioral studies, such as pedestrian and cyclist behaviors, can improve user-centered design and signal timing strategies by improving detection and prioritization algorithms [202; 203]. From an economic perspective, the construction of smart intersections should be examined from several perspectives. First, a lifecycle cost analysis is necessary, taking into account the initial investment, maintenance costs, and long-term benefits such as congestion reduction, emission savings, and crash reduction [204]. Then, financial models such as public-private partnerships (PPPs), congestion pricing, and government subsidies should be explored and implemented [205]. Finally, equity considerations should ensure that vulnerable communities benefit equally from the development of this smart infrastructure while considering the rights and entitlements of all stakeholders [206; 207].
## 3 Materials and Methods
### Sentiment Lexicon Creation
#### 3.3.1 Selecting Candidate Sentiment Words  
After text preprocessing, the words were lemmatized as nouns, verbs, adjectives, adverbs, pronouns, etc. Previous studies selected the lemmatized nouns, adjectives and verbs as candidate sentiment words, discarding adverbs for they merely alter the degree of the polarity of the words they modify, but do not carry an inherent sentiment polarity [96; 98]. Actually, many adverbs carry sentiment polarity, such as the adverb \"luckily\" in sentence \"Luckily, there was one room available\" expresses positive emotion.  
To accurately analyze customers' feelings, we used all the lemmatized nouns (n), verbs (v), adjectives (a) and adverbs (ad) as candidate sentiment words. The nodes of the graph corresponding to candidate sentiment words from hotel reviews are connected by the bidirectional edges. Following the study of [PERSON] et al. [96], the co-occurrence frequency of node \(g_{x}\) and \(g_{y}\) in the whole dataset is assigned to the weight \(w_{xy}\) of edge \(E\) joining node \(g_{x}\) and \(g_{y}\).
## 5 Discussion
### Comparisons of Hydrocarbon Generation
#### 5.1.4 Hydrocarbon Generation of Different Lithologic Source Rocks  
In order to accurately compare the cumulative hydrocarbon generation of different lithologic source rocks, the hydrocarbon generation of mudstone, carbonaceous mudstone, and coal was measured (Figure 12). The mudstone represents the sum of mudstones in the Triassic, the Jurassic J1y formation, and the Jurassic J2 kz formation. The results show that the cumulative hydrocarbon generation of mudstone has been significantly more than that of carbonaceous mudstone and coal before the deposition of the N2k formation, which plays a dominant role in hydrocarbon generation. Since the Neogene, the hydrocarbon generation rate of coal and carbonaceous mudstone has accelerated, which is affected by source rock maturity. At present, the cumulative hydrocarbon generation of coal has reached the maximum, followed by mudstone and carbonaceous mudstone. In the section parameters, the average thickness of mudstone is 650 m, while the average thicknesses of coal and carbonaceous mudstone are only 50 m and 60 m, respectively. Therefore, among the three types of lithologic source rocks, the hydrocarbon generation capacity of mudstone, carbonaceous mudstone, and coal has gradually increased.  
Figure 11: Hydrocarbon generation of three sets of source rocks at different geological times in the eastern Kuqa Depression. The histogram is formed by the ratio of the hydrocarbon generation of different source rocks in a certain period to the total hydrocarbon generation amount in the same period; 100% represents the total hydrocarbon generation in a certain geological period.