esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of GIS and IoT Integration: Applications and Architecture
## 3 GIS and IoT Integration Use Cases
### Transportation  
Transportation plays a critical role in enabling communication between people. It also provides other types of exchange such as trading and facilitates the movement of people and goods [50]. This category covers a wide range of applications including smart parking [42], public transportation [51], intelligent transportation systems [52]. With the rise in the number of vehicles, modern societies are paying more attention to transportation management [43; 53; 54] which faces the challenges of efficiency, safety, and sustainability [50]. The optimal managing of traffic not only reduces traffic congestion on peak hours but also can be beneficial for other services such as emergency ones. Therefore, some studies [55; 56] are performed to enhance the quality of current related services. Transportation pertains inherently to location, flows, and interactions which GIS is good at dealing with them. The combination of GIS and Transportation called GIS-T [50; 57] holds some benefits in facing the problems like transportation planning [58; 59], design [60], maintenance [61], and decision-making [62; 63]. In Reference [57], GIS-T is discussed in three stages namely the map view, the navigational view, and the behavioral view. The map view stage relates to inventory and description of transportation systems. The second stage, the navigational view, focuses on connectivity and planarity in addition to storing time-dependent attributes. Finally, dealing with transportation-related events as dynamic ones are proposed in the behavioral stage. In order to tackle the future challenges of transportation systems, the combination of WebGIS/cloud computing/big data is suggested in [50]. In addition, IoT by providing affordable sensors together with the proliferation of internet infrastructure can be helpful in GIS-T. Reference [64] proposes an IoT-based ITS constructed by three components namely the sensor system, monitoring system, and the display system. Reference [65] proposes a technique by correlating Global Positioning System (GPS) data and local GIS information to face the challenges of latency and limitations of bandwidth when transmitting the location of vehicles in Intelligent Transportation Systems. As GIS and IoT integration examples, in [66], using GIS, Radio-Frequency Identification (RFID), and cloud computing technologies, a parking navigation system is presented which facilitates finding parking lots for users near their destinations. After processing the gathered data which is converted into GIS-supported formats, users can be informed through their smart mobile devices whether there is an empty parking lot at the parking and if so, the system will display GIS images of empty parking lots and the navigation to them. An emergency management system is proposed in [67] in order to manage public road transport networks that useIoT as a means of observing traffics and road infrastructures. In this research, GIS is also exploited to enhance situational awareness and perform some emergency operations.
## 3 Materials and Methods
### Preparation of Ethyl 2-[(2-(4-antipyrinylthiazol-2-yl)amino)-2-oxoethyl)-thio]acetate (**2**)
\({}^{1}\)H-NMR (DMSO-\(d_{6}\)): \(\delta\) 2.43 (s, 6H, 2 CH\({}_{3}\)), 2.68 (s, 3H, CH\({}_{3}\)), 3.34 (s, 3H, CH\({}_{3}\)), 6.92 (s, 2H, NH\({}_{2}\)), 7.04 (s, 1H, pyridine-H\({}_{5}\)), 7.26 (s, 1H, thiazole-H\({}_{5}\)), 7.34-7.58 (m, 5H, Ar-H), 12.38 ppm (s, 1H, CONH). \({}^{13}\)C-NMR (DMSO-\(d_{6}\)): \(\delta\) 12.58, 20.33, 23.68, 34.16, 98.74, 107.86, 109.47, 118.63, 122.03, 126.13 (2C), 128.81, 129.49 (2C), 133.14, 143.56, 145.42, 154.50, 159.27, 160.49, 162.41, 163.83, 164.43, 166.32. Analysis for C\({}_{24}\)H\({}_{22}\)N\({}_{6}\)O\({}_{2}\)S\({}_{2}\) (490): Calcd. : C, 58.76; H, 4.52; N, 17.13%. Found: C, 58.62; H, 4.58; N, 17.24%.
## 4 Analysis of Examples
### Estimation of Probability Density Function  
For the estimation of the probability density function of travel time, the results are shown in Figure 12.  
From Figure 12, it can be found that the overall probability of wave peak weakening after the earthquake shifts to the direction of long travel time. the pre-earthquake curve reaches its peak at about 1155 s of observation time, namely the probability of 47.6 \(\times\)\(10^{-3}\), when the earthquake damage index is 0.1, the pre-earthquake curve reaches its peak at about 4652 s of observation time, namely the probability of 44.3 \(\times\)\(10^{-3}\), the pre-earthquake curve reaches its peak at about 4436 s of observation time. The peak value of the wave is 30.1 \(\times\)\(10^{-3}\). The peak of the pre-earthquake curve was reached at about 5368 s, and the peak value was 23.2 \(\times\)\(10^{-3}\) This phenomenon becomes increasingly obvious with the increase in earthquake damage index, which is consistent with the results of qualitative analysis.
# Assessment of Parent-Child Interaction Quality from Dyadic Dialogue
## 2 Related Work
### Dyadic Parent-Child Interaction Coding System and Parent-Child Interaction Therapy  
The Dyadic Parent-Child Interaction Coding System, fourth edition (DPICS-IV), is a structured behavioral observation tool that assesses essential parent and child behaviors in standardized situations. DPICS-IV has proven to be a valuable adjunct to PCIT and has been used extensively to evaluate other parenting interventions and research objectives as well [6]. Over the years, DPICS has been utilized in various studies addressing a wide range of clinical and research questions. [PERSON] et al. highlight the development of DPICS and discuss its current usage as a treatment process or outcome variable. The authors also provide a summary of the ways in which DPICS has been adapted and describe the process by which it is designed to undergo adaptation [45].  
The DPICS-IV scoring system is based on the frequency counts of ten main categories, Neutral Talk, Labeled Praise, Unlabeled Praise, Behavior Description, Reflection, Information Question, Descriptive Question, Direct Commands, Indirect Commands, and Negative Talk. However, in previous work, eight categories were commonly used, where Information Question and Descriptive Question were combined into a single category called Question, and Indirect Commands and Direct Commands were combined as Commands [9, 46, 47]. Both [PERSON] et al. and [PERSON] et al. have suggested that not all DPICS codes are equally important for therapy outcomes and have placed more emphasis on Negative Talk. In addition, [PERSON] et al. found that the DPICS Negative Talk factor demonstrated a high discriminant capacity (AUC = 0.90) between samples, and a cut-off score of 8 allowed the classification of mother-child dyads with 82% sensitivity and 89% specificity [46].  
The process of labeling DPICS codes manually for each sentence in a conversation is a time-consuming and labor-intensive task that requires trained experts. Confirmatory factor analysis is then used to verify the factor structure of the observed variables [48]. However, [PERSON] et al. have developed SpecialTime, an automated system that can classify transcript segments into one of eight DPICS classes. The system uses a linear support vector machine trained on text feature representations obtained using TF-IDF and part-of-speech tags. The system achieves an overall accuracy of 78%, as evaluated by the authors using an expert-labeled corpus [9].  
PCIT helps parents improve interaction quality with children with behavior problems. The therapy instructs parents to employ effective dialogue during interactions with their children [49].
# Compact Design and Impact Ionization: Utilizing Small-Sized Thyristors in a 4-Stage Marx Generator
## 3 Results
### Delay Measurements  
Our system is designed to deliver fast-falling edges; however, it is essential to analyze the time elapsed between the input signal and the resulting output. To assess this delay time, we set up a probe at the input signal of the system, which also served as the trigger for the oscilloscope. A second probe was placed at various test points around the PCB to capture the delay at each stage of the system.  
Figure 14 presents the input signal (\(U_{input}\)) alongside the output signal at test point 2 (\(U_{TP2}\)), as defined in Figure 4. The vertical blue lines indicate the time differences between the two signals.  
We define the time delay as the interval between the point where the input signal reaches 10% of its maximum value and the point where the output signal drops to 10% of its minimum value. Table 3 summarizes the measured delay times for each step of the system. We observe a consistent delay of 1 ns between Marx stages, which is attributed to the fixed length of the path that the current must travel to trigger each successive stage. This measured delay contrasts with the predicted 400 ps (Table 1) for a pure stripline on the outer layer of the PCB. This discrepancy can be explained by the fact that the signal path is not a continuous, uninterrupted stripline; rather, it is segmented into islands with components placed between each island. These components introduce additional parasitic capacitance and inductance, which affect the signal propagation speed.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Stage Test Point** & **Delay Time (ns)** \\ \hline G-B SiC MOSFET out & 5.4 \\ \hline TP1 & 6.2 \\ \hline TP2 & 7.3 \\ \hline TP3 & 8.3 \\ \hline TP4 & 9.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Delay times for each stage of the system (as referred to in Figure 4).  
Figure 14: Measured delay between the input signal of the system and the second Marx stage output signal (TP2 in Figure 4).
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 4 Experiments  
In this section, we present a qualitative comparison of MSEANet against state-of-the-art methods using widely recognized polyp segmentation datasets. The evaluation highlights the strengths and performance of MSEANet in comparison with existing techniques. This paper employed five widely used metrics, including mean Intersection over Union (mIoU), mean Dice coefficient (mDice), Recall, Precision, and F2-score. IoU measures the similarity between predicted segmentation results and actual segmentation results. In the following formula, \(\mathit{Prediction}\) is the prediction area of the model, \(\mathit{Ground}\)\(\mathit{Truth}\) is the true label area, \(\cap\) represents the intersection, and \(\cup\) represents the union.  
\[\mathit{IoU}=\frac{|\mathit{Prediction}\cap\mathit{Ground}\ \mathit{Truth}|}{| \mathit{Prediction}\cup\mathit{Ground}\ \mathit{Truth}|} \tag{7}\]Dice is used to measure the similarity between two sets, emphasizing the overlapping parts and being sensitive to small object segmentation. The formula is as follows:  
\[Dice=\frac{2\cdot|Prediction\cap Ground\ Truth|}{|Prediction|+|Ground\ Truth|} \tag{8}\]  
Recall quantifies the proportion of positive examples recognized by the model and represents the proportion of correctly predicted pixel points to the true positive example pixel points, with the formula:  
\[Recall=\frac{|Prediction\cap Ground\ Truth|}{|Ground\ Truth|} \tag{9}\]  
Precision evaluates the proportion of pixels predicted to be positive instances that are actually positive instances, indicating the accuracy of the prediction, and is calculated by the formula:  
\[Precision=\frac{|Prediction\cap Ground\ Truth|}{|Prediction|} \tag{10}\]  
F2-score is a weighted form of Dice and Recall, with more emphasis on Recall for tasks requiring higher sensitivity, and is calculated as:  
\[F2=\frac{\left(1+\beta^{2}\right)\cdot Precision\cdot Recall}{\beta^{2}\cdot Precision + Recall} \tag{11}\]  
where \(\beta\) is a weighting factor indicating the relatively higher importance of Recall. In this paper, \(\beta\) is equal to 2.
## 4 Experiments and Results
### Experimental Setup  
#### 4.1.1 Datasets  
Our study conducted sentiment analysis and text classification tasks utilizing long and short datasets. The details of the datasets are as follows:  
APP: This dataset for Android applications [44] comprises 752,937 metadata and product reviews from Amazon.  
Kindle: This dataset for Kindle Store [44] comprises 982,619 metadata and product reviews from Amazon.  
Electronics: This dataset for Electronics [44] comprises 1,689,188 metadata and product reviews from Amazon.  
CDs: This dataset for CDs and Vinyl [44] comprises 1,097,592 product metadata and product reviews from Amazon.  
Airline Twitter: Airline Twitter sentiment dataset [44]. This dataset comprises 14,641 tweets about major U.S. airline problems from February 2015.  
Sentiment140: This dataset was generated at Stanford University [44] by computer science graduate students, comprising 1,600,000 tweets classified into positive and negative categories. Table 1 demonstrates the statistics of the datasets used in the proposed model and describes more details.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Dataset** & **Total** & **Positive** & **Negative** \\ \hline \multirow{4}{*}{Review} & APP & 752,937 & 123,098 & 123,098 \\  & Kindle & 982,619 & 57,148 & 57,148 \\ \cline{1-1}  & Electronic & 1,689,188 & 190,864 & 190,864 \\ \cline{1-1}  & CDs & 1,097,592 & 92,766 & 92,766 \\ \cline{1-1}  & Airline Twitter & 14,641 & 2363 & 2363 \\ \cline{1-1}  & Sentiment140 & 1,600,000 & 800,000 & 800,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the datasets utilized in our work.
## 5 Results of Field Observations  
All the observed behaviors of drivers who waited in the queue after stopping for the commencement of transmitting a green signal, expected in the tests, are illustrated in Figure 5. It turns out that on average, more than 73% of drivers used mobile devices in several ways for up to 10 s after stopping. Worryingly, 55% of them were using the phone in their hand (while looking at the screen), and 9% were talking with the phone close to their ear. In addition, 3% of people were seen using a hands-free kit, the same percentage was for people using headphones in their ears, and almost 4% of people used the phone on the mount. This means that 64% of drivers committed an offense. At individual intersection entrances, the highest percentages of abnormal behavior concerned the driver using the phone in their hand and they were:  
1. the intersection of Plac Daszynskiego (Szosa Lubicka Street, Trasa Wschodnia Street, Zolkiewskiego Street), Torun, eastern approach--48%,
2. the intersection of Ford\(\acute{\text{o}}\)nska Street, Leczycka Street and Kazimierza Wielkiego Street, Bydgoszcz, west approach--48%,
3. the intersection of Szosa Okrezna Street and Bronikowskiego Street, Torun, eastern approach--56%,
4. the intersection of Rondo Ford\(\acute{\text{o}}\)nska Street, Jagiell\(\acute{\text{o}}\)nska Street and Wyszynskiego Street), Bydgoszcz, eastern approach--58%,
5. the intersection of Rondo Torun\(\acute{\text{s}}\)nska Street and Wyszyn\(\acute{\text{s}}\)skiego Street), Bydgoszcz, eastern approach--62%.  
It is noted that this was the behavior of drivers shortly after making the stopping maneuver to wait for the commencement of transmitting a green signal. This means that drivers are fully aware that their stopping at the traffic light intersection will take a long time, allowing them, \"in their opinion\", to use this period to use a mobile device. The most frequently performed other activities while waiting for the green signal include viewing documents and correcting hairstyles or make-up.  
A statistical test of the Kolmogorov-Smirnov lambda concordance was performed to detect significant differences between the tested proving grounds for a significance level of 5%. On its basis, there was only one case where the results differed from each other in terms of statistical significance, and it was between intersection numbers 1 and 4. The difference in this case between the computational \(p\)-value (1.549) and the critical \(p\) value (1.358) is estimated at little. In other cases of comparisons, the critical \(p\) value was not exceeded.  
The comparison of undesirable behaviors of vehicle drivers while waiting for the green signal is presented in Table 1. It turns out that:  
* there was a much higher percentage of people using a mobile phone while waiting for a green signal in questionnaire surveys than in field observations; there were significantly more cases in field observations of offenses involving the use of mobile phones in the hand of drivers in a stopped vehicle.
# Observer-Based Rejection of Cogging Torque Disturbance for Permanent Magnet Motors
## 2 Problem Statement  
We consider a very common dynamic model of permanent magnet motors as follows:  
\[J_{m}\dot{\omega}(t)+B_{m}\omega(t)+d(t)=K_{m}i_{a}(t)=\tau(t) \tag{1}\]  
where \(\omega\) is the rotor angular speed, \(J_{m}\) is the inertia, \(B_{m}\) is the friction coefficient, \(d(t)\) is the cogging torque disturbance, \(K_{m}\) is the torque constant, and \(i_{a}\) is the armature current. It is assumed that the motor angular velocity \(\omega\) is measurable, while the disturbance \(d(t)\) is not. Here, the cogging torque \(d(t)\) can generally be expressed as follows:  
\[d(t)=\sum_{i=1}^{n}A_{i}\sin\left(i\omega(t)t+\phi_{i}\right) \tag{2}\]  
where \(A_{i}\) and \(\phi_{i}\) are the unknown magnitude and phase of the \(n\)-th torque harmonic, respectively, and \(n\) is any positive number. We also introduce the error signal \(e(t)\) to be regulated to zero as follows:  
\[e(t)=\omega_{r}-\omega(t)\]  
where \(\omega_{r}\) is the reference motor velocity, and it is assumed to be any constant. The configuration of the control system is shown in Figure 1. Here, we suppose that there exists a PI controller that serves as the constant reference regulation of \(\omega_{r}\) when the cogging disturbance \(d\) is zero.  
Now, the problem considered in this paper can be stated as follows. Given system (1) and (2), our objective is to design an observer of the cogging torque such that \(\lim_{t\rightarrow\infty}\left(\hat{d}(t)-d(t)\right)=0\), where \(\hat{d}\) is an estimation signal (to be seen shortly) of the actual cogging torque.  
Figure 1: System configuration.
# M-Shape PV Arrangement for Improving Solar Power Generation Efficiency
## 3 Inclined Surface Solar Radiation Calculation  
With the proposal of the isotropic model by [PERSON] and [PERSON] [21], there is an improvement of the [PERSON] [17], [PERSON] [18], and [PERSON] et al [26] models. In this paper, the isotropic model is used for simulation, and the accuracy of the model is also considered, the details of which are written below and illustrated using Figure 6 for easier understanding.  
The total solar radiation (\(T_{s}\)) intercepted by a tilted plane is the sum of the direct radiation (\(D_{r}\)), the diffuse sky component (\(D_{f}\)), and the ground reflected radiation (\(G_{r}\)). They can be expressed as below:  
\[T_{s}=D_{r}+D_{f}+G_{r}, \tag{4}\]  
The direct radiation incident on an inclined surface is directly proportional to the direct radiation on the plane when the PV panel is horizontal, and it is given by:  
\[D_{r}=H_{d}\frac{\cos\theta}{\cos\theta_{z}}, \tag{5}\]  
The sky diffuse radiation (\(D_{f}\)) and the ground reflected (\(G_{r}\)) components can be written as:  
\[D_{f}=H_{f}\frac{1+\cos\beta}{2}, \tag{6}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\beta\) [\({}^{\circ}\)] & Tilt angle \\ \(\gamma\) [\({}^{\circ}\)] & Azimuth angle \\ \(\alpha\) [\({}^{\circ}\)] & Solar Elevation Angle \\ \(\theta\) [\({}^{\circ}\)] & Split angle \\ \(\theta_{z}\) [\({}^{\circ}\)] & Zenith angle \\ \hline \hline \end{tabular}
\end{table}
Table 2: Type of angles.  
Figure 6: The relationship of solar radiation and the direct, diffuse, reflect radiation.  
and:  
\[G_{r}=T_{g}(\frac{1-\cos\beta}{2})\rho. \tag{7}\]  
where \(H_{d}\) is the horizontal direct radiation, \(H_{f}\) is the horizontal diffuse radiation, and \(T_{g}\) is total ground horizontal radiation. The horizontal solar radiation data is given by NEDO [22]. \(\rho\) is ground reflectivity.  
\[\begin{split}\cos\theta&=(\sin\phi\cos\beta-\cos \phi\sin\beta\cos\gamma)\sin\delta\\ &+(\cos\phi\cos\beta+\sin\phi\sin\beta\cos\gamma)\cos\delta\cos \tau\\ &+\cos\delta\sin\beta\sin\gamma\sin\tau\\ &\cos\theta_{z}=\sin\phi\sin\delta+\cos\phi\cos\delta\cos\tau \end{split} \tag{8}\]
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 4 Evaluation
### Single Path and Concurrent Multi-Path
Figure 8: Latency comparison of SP and MMP routing. ‘MMP 4 paths’ (weighted/random) demonstrates selecting stable paths 4 times more than others and equally selecting all 4 paths by an attack (4-7 s).
## 1 Introduction  
In today's modern world, structural health monitoring is very important to assess the retrofitting and repair of structures. Structures experience man-made and natural disasters that need more precise and accurate assessment in preparation of an event or after an event. The material that is commonly used in structures is concrete. Concrete can be assessed in many ways, where the factors to be considered in the test are cost, time, and the idle period during assessment. One way of achieving a better and more economical test is through the use of a non-destructive test (NDT). The NDT in concrete is complex due to its nonhomogeneous ingredients and design mix.  
From research, there are two common tests that are available in the literature. The NDT test can be classified as insensitive or sensitive to internal damage. Insensitive to internal damage are those test methods that do not correlate well with mechanical load or any damage. In this test, an abrupt change in the measurement of a parameter indicates the sudden failure of concrete. Examples of these NDT tests are the ultrasonic pulse velocity (UPV) test, SONREB (ultrasonic pulse velocity test and rebound hammer) test, and the chain drag method. On the other hand, NDT tests that are sensitive to internal damage involve parameters that correlate well with internal load. Examples of sensitive NDT tests are the non-linear ultrasonic test [1; 2; 3] and the acoustic emission (AE) test [4].  
The focus of this study is to use the AE test that is sensitive to internal damage. It relates the mechanical behavior of concrete to the crack propagation inside it. Experiments are conducted to assess crack propagation and the localization of micro-cracks with the macro deformation of concrete under uniaxial tension [5]. This generation of elastic waves inside concrete is generated by the release of energy, as shown in Figure 1, where highly localized stresses develop ahead of the crack tip [6]. Waves are monitored by piezoelectric sensors, which convert the mechanical signal into electrical signals. Numerous studies have been conducted using AE parameters such as AE event, AE counts, AE arrival time, AE energy, and AE source location (AESL) to characterize damage and cracks in historical and new structures [7; 8; 9; 10; 11; 12]. However, few papers have been conducted to study AE in reinforced concrete (RC) beams.
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT
#### 4.1.2 Memory Management Methods  
In [7; 65; 66; 67; 68], the existing techniques for solving the storage management in UIoT are discussed, and some methods are indicated herewith. In [7], [PERSON] et al. suggested that underwater sensors need to perform some data caching due to the intermittent underwater channel characteristics. In [65], [PERSON] et al. researched Q-learning (QL), comprising of reactive and proactive strategies to reduce the network overhead related to network lifetime. In [66; 67] memory management, an essential function to store and retrieve information through smart sensing underwater devices, was studied to solve the challenges of the underwater network management system (U-NMS).