esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Understanding and Mapping of Regions in Cities Using Google Street View Images
## 4 Methodology
### GPS Point and Image Acquisition Procedure  
To obtain the images to create the dataset for experimentation, we use the three APIs sequentially explained below. The first step is to select a city and obtain its list of streets, using GeoAPI Espana. Once the streets are obtained, we use this list and the Google Maps Directions API. These allow us to gather the instructions to travel between all the streets in the list, obtained in the former step. The instructions contain GPS coordinates that indicate the path to reach the target location. These GPS coordinates usually concern corners and intersections where the user should turn during the travel. Using these coordinates, we can obtain an initial graph with the points of the streets in the city.  
The next step consists of using pairs of coordinates obtained with the Directions API. These coordinates are used to collect the GPS coordinates between them; in other words, to obtain the coordinates of places that are not corners or intersections and were not provided by the API. This procedure allows us to complete a GPS graph of the city with the information of several points inside a street. The following step consists of using these coordinates and the Google Street View Static API. This API lets us obtain an image from a given GPS position of the map, from the view of a car, with several angle options. Therefore, we request four images for every position in our city graph. We select pictures at  
Figure 3: Google Street View image point of view.  
angles \(\theta=0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), \(270^{\circ}\). These angles allow us to code the information surrounding a point or location in the city, as the use of a single image will omit these data. Figure 4 illustrates the procedure explained. After this step, we proceed to compute a semantic descriptor for every acquired image and to generate the semantic map, as will be explained in the following sections.
# Similar Supergraph Search Based on Graph Edit Distance
## 4 Straightforward Method for Constrained Problem  
In Section 2, we defined the problem tackled in this paper. Here, for the sake of simplicity, we first provide methods for outputting the following:  
\[\{g_{i}\in G\mid\exists q^{\prime}\subseteq_{i}q\ s.t.\ ed(g_{i},q^{\prime}) \leq\theta,q^{\prime}\text{is connected},|V(g_{i})|\leq|V(q^{\prime})|\}, \tag{4}\]  
by constraining the original problem. In the subsequent sections, we explain how the method can be extended to solve the original problem. **Definition 1** (AcGM code [33]). : _Given a graph \(g=(V,E,\Sigma,\ell)\), we assign vertex identifiers \(u_{1},u_{2},\ldots,u_{|V|}\) to vertices in \(g\) and assume that a subgraph of \(g\) induced by vertices \(u_{1},u_{2},\ldots,u_{i}\ (1\leq i\leq|V|)\) is connected. \(g\) is represented by an adjacency matrix of dimension \(|V|\times|V|\) for which its elements \(x_{i,j}\) are \(\ell((u_{i},u_{j}))\) if \((u_{i},u_{j})\in E\) or 0 otherwise. The AcGM code of graph \(g\) is defined by concatenating the elements in the upper-right part of the adjacency matrix, as follows:_  
\[code(g,\langle u_{1},u_{2},\ldots,u_{|V|}\rangle)=s_{1}s_{2}\cdots s_{|V|}, \tag{5}\]  
_where each code fragment \(s_{i}\) is defined as follows:_  
\[s_{i}=\ell(u_{i})x_{1,i}x_{2,i}\cdots x_{i-2,i}x_{i-1,i}.\]  
\(s_{1}s_{2}\cdots s_{i}\) is called a prefix of Equation (5). In addition, \(c_{2}\subseteq c_{1}\) means that a prefix of an AcGM code \(c_{1}\) is equal to another AcGM code \(c_{2}\) and a graph expressed by AcGM code \(c\) is denoted as \(g(c)\). **Definition 2** (Linear ordering between AcGM codes). : _When two AcGM codes \(\alpha=a_{1}a_{2}\cdots a_{k}\) and \(\beta=b_{1}b_{2}\cdots b_{h}\) fulfill either of the following conditions, we say that \(\beta\preceq\alpha\):_  
* \(\exists t\ s.t.\ 1\leq t\leq min(k,h)\)_,_ \(a_{q}=b_{q}\) _for_ \(q<t\) _and_ \(b_{t}\prec_{e}a_{t}\)_;_
* \(\beta\subseteq\alpha\)_;_  
_where \(\prec_{e}\) is a linear ordering between code fragments [34]._  
**Example 1**. : _Given the graph with four vertices shown in Figure 3, we have \(4!=24\) possible permutations of the four vertices. Among the permutations, according to Definition 1, code\((g,\langle v_{2},v_{3},v_{4},v_{1}\rangle)\) does not generate an AcGM code because a subgraph induced by \(\langle v_{2},v_{3}\rangle\) is not connected. We can generate 12 AcGM codes from the graph and we show three AcGM codes as follows. Symbols on the left side of the following adjacent matrices are vertex IDs and the letters on the upper side of the matrices are vertex labels.
###### Abstract  
Orthogonal time frequency space (OTFS) modulation has recently found its place in the literature as a much more effective waveform in time-varying channels. It is anticipated that OTFS will be widely used in the communications of smart vehicles, especially those considered within the scope of Internet of Things (IoT). There are efforts to obtain customized traditional point-to-point single-input single-output (SISO)-OTFS studies in the literature, but their BER performance seems a bit low. It is possible to use cooperative communications in order improve BER performance, but it is noticeable that there are very few OTFS studies in the area of cooperative communications. In this study, to the best of the authors' knowledge, it is addressed for the first time in the literature that better performance is achieved for the OTFS waveform transmission in a selective decode-and-forward (SDF) cooperative communication scenario. In this context, by establishing a cooperative communication model consisting of a base station/source, a traffic sign/relay and a smart vehicle/destination moving at a constant speed, an end-to-end BER expression is derived. SNR-BER analysis is performed with this SDF-OTFS scheme and it is shown that a superior BER performance is achieved compared to the traditional point-to-point single-input single-output (SISO)-OTFS structure.  
O tFS; SDF; BER analysis; cooperative communications; smart vehicles; IoT; IoV; V2X 20242453243390/s24165324  
1
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 4 Emulation Results and Discussion
### Feasibility of IoT-over-Satellite Using LoRa  
The results captured in Figures 10 and 12 are based on the worst-case scenario, in which we assumed that the ground devices are using a highly congested radio band with numerous actively transmitting devices, the lowest spreading factor, and a narrow band is used for the IoT-over-Satellite communication. The result might sound discouraging as the bit error rate can be over 30% when the transmit power is about 27 dBm. However, if the IoT-over-Satellite communication uses the highest spreading factor and a broadened bandwidth in a less congested radio channel, as shown in Figure 16, the PER can be reduced to less than 5% with the same transmit power in cases II to IV. This is due to the increased ratio of energy per bit to the spectral noise density (\(E_{b}/N_{o}\)), reduced interference level, and the higher Doppler shift's tolerance in the larger bandwidth. Note that the PER in case I is still over 37% due to the PER in case I being dominated by the satellite availability meaning that the PER remains unchanged even if we increase the transmit power. As a result, by using this emulation framework, the IoT-over-Satellite system planner can adjust the parameters of their communication protocol to investigate the performance IoT-over-Satellite system, find the maximum number of allowed users, and optimize their parameter for the communication protocol.  
Figure 14: Illustration of the energy wasted as a function of the transmit power for the different access methods. The maximum number of transmissions is set to two messages per day with a size 12 bytes payload.  
Figure 15: The energy wasted per day for case I as a function of the number of satellites in the constellation. The IoT devices transmit power is \(P_{t}=27\) dBm.
# Mandibular Second Molar Impaction-Part II: Etiology and Role of the Third Molar
###### Abstract  
(1) Introduction: Mandibular second molar (MM2) impaction is a relatively rare dental anomaly, but its early detection is imperative for orthodontic diagnosis and treatment. This study evaluates early radiographic characteristics of MM2 in the process of its impaction and the role of the third molar (MM3) in this process. (2) Materials and Methods: Panoramic radiographs of orthodontic patients of Chinese-American origin (n = 5575), age 9-15 years (mean 13.13) were examined for MM2 impactions and the presence of MM3. (3) Results: A total of 151 patients with 212 impacted MM2s were detected in the Chinese-American study group. Unilateral impactions were found more than bilateral and were distributed equally between the left and right sides. Mesial angulation of the impacted teeth was present in 92.7% of the cases, and MM3s were found adjacent to the impacted MM2s in 97.4% of the cases. (4) Conclusions: MM2 differential root developments with deficient mesial root lengths appeared to be the primary cause for MM2 impaction. Posterior crowding and arch length deficiency were contributing factors causing MM2 impaction. This impaction occurred early, before the third molar bud was developed. It sometimes occurred even when the third molar was congentially missing. Nevertheless, the possible involvement of the third molar in MM2 impaction needs further investigation.  
impaction; etiology; mandibular second molar; mandibular third molar
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Delayed Self-Heterodyne Method  
The delayed self-heterodyne method involves the addition of an acousto-optic frequency shifter (AOM) based on a self-homodyne, which moves the center frequency to the high-frequency region to avoid interference from the environmental noise near the zero frequency in the system and improves the testing accuracy [29]. The basic structure of the delayed self-heterodyne measurement method includes a MZI, a Michelson interferometer (MI), and a cyclic gain compensation structure.
# The Generalized Mohr-Coulomb Failure Criterion
## 2 Mohr-Coulomb Failure Criterion  
The M-C failure criterion states that the shear strength \(\tau_{f}\) of an isotropic geomaterial is related to the normal stress \(\sigma_{n}\) applied on the failure plane with the normal \(n\) by  
\[\tau_{f}=c+f\sigma_{n} \tag{1}\]  
where \(c\) is cohesion; the friction factor is \(f=\tan\phi\), and \(\phi\) is the frictional angle.  
At failure, \(\left(\tau_{f},\sigma_{n}\right)\) and the principal stresses \(\left(\sigma_{1},\sigma_{2},\sigma_{3}\right)\) are related by  
\[\sigma_{n}=\frac{1}{2}(\sigma_{1}+\sigma_{3})-\frac{1}{2}(\sigma_{1}-\sigma_ {3})\sin\phi \tag{2}\]  
and  
\[\tau_{f}=\frac{1}{2}(\sigma_{1}-\sigma_{3})\cos\phi \tag{3}\]  
where \(\sigma_{1}\) is the maximum principal stress and \(\sigma_{3}\) is the minimum principal stress at failure. In this study, compressive stresses are designated positive.  
The substitution of Equations (2) and (3) into Equation (1) leads to the M-C criterion in terms of \(\sigma_{1}\) and \(\sigma_{3}\)  
\[(\sigma_{1}-\sigma_{3})-(\sigma_{1}+\sigma_{3})\sin\phi-2c\cos\phi=0 \tag{4}\]  
The M-C failure criterion expressed in the Mohr stress space, namely, Equation (1), is usually applied to the strength problems, such as the slope stability analysis and the computation of lateral earth pressure on retaining walls, in the framework of the limit equilibrium method [31]. The M-C criterion expressed in the stress space, namely, Equation (4), is usually applied to the deformation analysis problems [11]. Based on the assumption of elastic perfectly plasticity, in principle, a strength problem can be solved through the deformation analysis by monotonously increasing external loads [32] or consecutively reducing the strength parameters until the limit equilibrium state [33] is reached.  
In order to conveniently determine the strength parameters of \(c\) and \(\phi\), Equation (4) is rewritten as  
\[\sigma_{1}-\sigma_{3}=k\sigma_{3}+d \tag{5}\]  
where  
\[k=\frac{2\sin\phi}{1-\sin\phi} \tag{6}\]  
and  
\[d=2c\cos\phi \tag{7}\]  
Equation (5) depicts a straight line in the plane of \(\sigma_{3}\)-\(\left(\sigma_{1}-\sigma_{3}\right)\). The parameters of \(k\) and \(d\) can be determined simply from a series of data \(\left(\sigma_{1}^{j},\sigma_{3}^{j}\right)\), \(j=1,2,\cdots\), from conventional triaxial tests, by applying the least squares method, say, as shown in Figure 1, with the superscript \(j\) denoting the \(j\)-th result.  
For conventional triaxial compression tests, \(\sigma_{1}^{j}=\sigma_{a}^{j}\), \(\sigma_{2}^{j}=\sigma_{3}^{j}=\sigma_{r}^{j}\); for conventional triaxial extension tests, \(\sigma_{1}^{j}=\sigma_{2}^{j}=\sigma_{r}^{j}\), \(\sigma_{3}^{j}=\sigma_{a}^{j}>0\). Here, \(\sigma_{a}^{j}\) and \(\sigma_{r}^{j}\) are the axial stress and the radial pressure of the \(j\)-th measurement at failure, respectively.  
Once the parameters of \(k\) and \(d\) are derived from the above procedure, the shear strength parameters of \(c\) and \(\phi\) can be obtained by  
\[\sin\phi=\frac{k}{2+k} \tag{8}\]  
and  
\[c=\frac{d}{2\cos\phi} \tag{9}\]
###### Abstract  
Accurate monitoring of grassland biomass at high spatial and temporal resolutions is important for the effective utilization of grasslands in ecological and agricultural applications. However, current remote sensing data cannot simultaneously provide accurate monitoring of vegetation changes with fine temporal and spatial resolutions. We used a data-fusion approach, namely the spatial and temporal adaptive reflectance fusion model (STARFM), to generate synthetic normalized difference vegetation index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat data sets. This provided observations at fine temporal (8-d) and medium spatial (30 m) resolutions. Based on field-sampled aboveground biomass (AGB), synthetic NDVI and support vector machine (SVM) techniques were integrated to develop an AGB estimation model (SVM-AGB) for Xilinhot in Inner Mongolia, China. Compared with model generated from MODIS-NDVI (\(R^{2}\) = 0.73, root-mean-square error (RMSE) = 30.61 g/m\({}^{2}\)), the SVM-AGB model we developed can not only ensure the accuracy of estimation (\(R^{2}\) = 0.77, RMSE = 17.22 g/m\({}^{2}\)), but also produce higher spatial (30 m) and temporal resolution (8-d) biomass maps. We then generated the time-series biomass to detect biomass anomalies for grassland regions. We found that the synthetic NDVI-derived estimations contained more details on the distribution and severity of vegetation anomalies compared with MODIS NDVI-derived AGB estimations. This is the first time that we have generated time series of grassland biomass with 30-m and 8-d intervals data through combined use of a data-fusion method and the SVM-AGB model. Our study will be useful for near real-time and accurate (improved resolutions) monitoring of grassland conditions, and the data have implications for arid and semi-arid grasslands management.  
biomass; data fusion; STARFM; MODIS; Landsat; support vector machine (SVM) +
Footnote †: journal: Remote Sensing
###### Abstract  
Acquiring real-time ground pressure measurements from the surface of the soil in working parts of paddy fields is a challenging task. The real-time data can be used to monitor the changing state of the ground pressure of the working parts in a paddy field. To effectively reduce the accumulation of choked mud at the front end of the seeding skateboard and the contact adhesion between the skateboard and the paddy soil, a ground pressure measuring device suitable for paddy fields was designed. The device uses an Arduino controller, combined with Internet of things technology and wireless measurement technology. It can measure the pressure from 16 measuring points at the same time and transmit the measurement data to the computer remotely through the Internet of things technology, which greatly reduces the labor intensity of measuring personnel in the muddy paddy field. Analysis of the data showed that the forward tilt angle, ground pressure, and forward resistance of the seeding skateboard also increased with the increase of forward speed and vertical load. In addition, the distribution law of the ground pressure between the skateboard and the paddy soil was obtained. The conclusions show that the ground pressure measurement system can work stably in the paddy field and the measured data can be wirelessly transmitted to the computer and mobile phone.  
Keywords:ground pressure; paddy soil; seeding skateboard; internet of things; wireless measurement system +
Footnote †: journal: Applied Sciences
# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 4 The Speak Correct Interactive Experiments
### Speak Correct Feedback
#### 4.4.2 Data Set and Dictionary in Speak Correct  
In fact, the data set of the Speak Correct system presents varieties, taking into consideration the following characteristics:  
1. Gender: males or females.
2. Educational level: university undergraduate group.
3. Age: young speakers, the age is between 17-20 years.
4. Regional country: two regions in Saudi Arabia and another two regions in Egypt.  
The dataset is based on the expertise of the linguistic teachers and analysis of the data collected from 150 students (male and female). Therefore, the recordings were made in a clean environment without any vibration. The recorded files have a wave format, with mono channel sampling at 16 KHz and 32 bits.  
The total number of recording voices was 2100 samples for the entire data set (Egyptian males and females). Two regions were used for the recordings, the first region was located in the middle of Egypt (Cairo), and the second region in the northern of Egypt (Alexandria). The number of speakers used for training was 70 students (35 males and 35 females). The test corpus included 300 words and sentences to cover all the English pronunciation defects in Saudi and Egyptian accents.
## 3 The Proposed Traffic Flow-Prediction Model  
The developed encoder-decoder model structure is depicted in Figure 1. The input sequence (\(E_{in}\)) is the historical traffic flow data which enter the encoder to capture the spatiotemporal features in the representation tensor (\(E_{en}\)), then they enter into the decoder along with supplementary information \(Q_{in}=Q_{ij}|j\in[n,\cdots,n+l-1]\) to produce an out  
Figure 1: The structure diagram of the proposed MRWG BiAtten model.  
put sequence (\(Q_{out}\)) of the future traffic flow status. Here, \(Q_{ij}\) represents the predicted results for each grid during time period \(t_{j}\). Next, the encoder and decoder are formally described in detail.  
1. Encoder: Four layers are included in the encoder: a Local CNN layer, an MH-SSA layer (multi-position attention machine layer), an FC-FF layer (fully connected feed-forward network layer), and a residual connection [37] with a Norm layer [38]. Here, \(E_{in}\) is the input of the encoder and \(E_{en}\) is the encoded output.
2. Decoder: The structure of the decoder is similar to that of the encoder, consisting of a Local CNN layer, double-attention mechanism layers (dotted brown rectangle), an FC-FF layer and a Norm layer. The first layer of the attention mechanism (blue-filled rectangle in Figure 1) is the MT-SSA layer, which takes the output \(Q_{de}^{0}\) of the Local CNN layer as input to obtain the spatiotemporal feature \(Q_{de}\). The second layer is the BiAtten layer (green-filled rectangle in Figure 1), which takes \(E_{on}\) and \(Q_{de}\) as the inputs to calculate the bidirectional attention matrix \(H_{de}^{1}\) and \(U_{de}^{1}\). After the FC-FF layer and Norm layer, the decoded states can be obtained as \(H_{de}\) and \(U_{de}\).
# Theranostic Nanoplatforms of Thiolated Reduced Graphene Oxide Nanosheets and Gold Nanoparticles
## 2 Materials and Methods
### Synthesis of Thiolated Reduced Graphene Oxide  
The functionalization of GO was performed by a one-pot thiolation-reduction mechanism [34]. In summary, bwGO120S (3.25 mg) was suspended in ultrapure water to give a 1-mg/mL solution after sonication (120 min, 59 kHz, 25 \({}^{\circ}\)C) and then 220 \(\mu\)L of HBr were added under stirring (2 h at 30 \({}^{\circ}\)C). Subsequently, 220 mg of thiourea were added and the solution left to react for 24 h at 80 \({}^{\circ}\)C and then cooled at RT. After that, 2.2 mL of NaOH 4 mol dm\({}^{-3}\) were added, and the mixture stirred for 30 min, past which the mixture was filtered and washed with water over a 0.2 \(\mu\)m polytetrafluoroethylene (PTFE) membrane. The solid collected was dried at 30 \({}^{\circ}\)C for 5 h to provide rGOSH.
# Thermal Behavior of Alveoli with Phase Change Materials in Fruit Packaging
## 1 Introduction
Their findings revealed an 8.8 h thermal buffering duration when MEPCMs were integrated into food packaging systems, compared to just 6 h with bulk PCMs. [PERSON] et al. [12] have proposed new packaging configurations with ventilation designs tailored to optimize thermal and mass exchange during cooling and heating phases, showing that PCM-enhanced packaging significantly extends proper thermal conditions, compared to conventional materials. [PERSON] and [PERSON] [13] review the potential of PCMs in mitigating temperature fluctuations in food packaging, emphasizing their role in active and smart packaging solutions. They discuss the integration of multifunctional PCMs, such as microencapsulated and nanoparticle-enhanced PCMs, into packaging films, boxes, and containers to reduce post-harvest losses and improve energy management. This aligns with the current study's findings on the PCM-enhanced modular packaging systems' ability to stabilize temperature during storage and transit. The range of phase transition temperatures of the PCM chosen to be placed in the cells of fruit transport packaging must be between 4 and 8 \({}^{\circ}\)C, considering that, in general, fruitsare stored inside cold rooms between 0 and 6 \({}^{\circ}\)C [14]. With an appropriate choice of PCM, it is expected to achieve an effective reduction in energy spent on refrigeration [15]. The properties of the PCM must be such that it can maintain controlled temperature in the system, which requires, in addition to good thermal contact between the fruit and the cell, a reasonable value of latent heat. As PCM is regularly used in industrial cold technologies [16], its incorporation in an encapsulated format in macro or microstructures prevents material loss during the phase transition and facilitates heat transfer with the environment [6]. To study the effect of ambient temperature on fruits when they are removed from cold rooms, [PERSON] et al. [17] introduced a new box containing PCM with a well thermally insulated wall meant to be resistant to various temperatures. However, optimizing thermal gain requires a choice for the PCM melting point that takes account of the effect of ambient temperature [18]. According to [PERSON] et al. [19], the use of PCM in cold rooms reduces energy consumption by 5 to 6%, managing to maintain more stable temperatures. The findings of this study contribute to the broader literature by experimentally validating the comparative performance of three PCMs (RT2 HC, RT5 HC, and RT8 HC) under real-world handling conditions, offering insights into their practical applications. Unlike prior studies focusing solely on the thermal properties of PCMs, this research integrates innovative packaging designs, enabling better scalability and applicability in cold chain logistics.