esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Brain Tumor Segmentation with Spectral-Spatial Graph Neural Networks
## 2 Methods and Material
### Experimental Setup  
For the development of our model in this study, we partitioned the BraTS Dataset into three segments: 70% of the cases were allocated for training, while 15% were set aside for validation and another 15% for testing. The data were split randomly, utilizing a specific random seed to ensure the reproducibility of our results. The training of the model was performed on a personal computer equipped with an 8 GB Nvidia GPU, a process that spanned 20 h. Bayesian optimization was utilized for the fine-tuning of hyperparameters, with a comprehensive discussion on this approach and its outcomes provided in the following sections and Table 3.  
In our experimental setup, we rigorously tested three distinct Graph Neural Network (GNN) models to assess their performance. The Spatial GNN model employed the GraphSAGE algorithm, known for effectively aggregating local neighborhood features. The Spectral GNN model utilized the Chebyshev Convolution (ChebConv) layer, aiming to leverage its spectral filtering capabilities for capturing global graph features. Lastly, our innovative Spectral-Spatial GNN model combined the local feature extraction of GraphSAGE with the global perspective of ChebConv, offering a comprehensive approach to graph data analysis. This strategic integration of models aimed to provide an in-depth comparative analysis, shedding light on their individual and collective strengths in addressing our research challenges.
## 2 Measurement Campaign
### Measurements  
The measurement methodology was based on the ability of the device under investigation to receive information from various devices spread across the campus.  
Two sets of equipment were used to collect the measured data, consisting of an SX1276 module coupled to an Arduino fixed at a height of 3 m and a laptop as a transmitter (Tx), which moved along the established route with an average speed of 35 km/h. The other set of equipment was configured as a receiver (RX), positioned on top of the \"Mirante do Rio\" building, which is installed 30 m above sea level, as shown in Figure 4, to capture the signal with the message containing the latitude, longitude and RSSI values, accounting for a total of 128 kbit. The measurements were performed by considering the uplink of the signal (car to building), and this situation represents the worst channel condition to the detriment of the downlink (building to car).  
The data collected were composed of the RSSI value and its geographic coordinates, considering the randomness of the signal in the scenario with mobility, and were measured five consecutive times on the same route. An average of 246 samples per measurement were obtained, totaling 1230 measurements, with messages received in short intervals of 4.5 s or medium distances of up to 43.74 m (for a car traveling at 35 km/h).
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### GNSS Error Analysis and Modeling  
According to the randomness of error characteristics, the positioning error of GNSS can be divided into two parts: Systematic error and random error. The main components of systematic error are ephemeris error, satellite clock error, receiver clock error, and refraction error of the ionosphere and troposphere. The main components of random error are multipath effect error and measurement error [28].  
After obtaining the ephemeris parameters of the satellite, the space position of the satellite can be calculated through the given satellite ephemeris. There is often a deviation between this position and the actual space position of the satellite. The range error caused by this position can be expressed as follows:  
\[\delta p_{e}^{s}=\frac{r_{\beta_{s}}^{\beta}}{\left|r_{\beta_{s}}^{\beta} \right|}.u_{as}^{\beta}\delta r_{os}^{o}+\frac{r_{\beta_{s}}^{\beta_{s}}}{ \left|r_{\beta_{s}}^{\beta}\right|}.u_{as}^{\beta}r_{os}^{o}\delta u_{os}^{o}+ \frac{r_{\beta_{s}}^{\beta_{s}}\wedge r_{\beta_{s}}^{\beta_{s}}}{\left|r_{\beta _{s}}^{\beta}\right|r_{\beta_{s}}^{\beta}}.u_{as}^{\beta}\delta z_{os}^{o}, \beta\in i,e,I, \tag{28}\]  
where \(u\) represents the unit vector and the line-of-sight unit vector, \(s\) represents the satellite body coordinate system, and \(o\) represents the orbital coordinate system. \(\delta r_{os}^{o}\), \(\delta z_{os}^{o}\), and \(r_{os}^{o}\delta u_{os}^{o}\) are the radial error, lateral error, and track direction error in the orbital coordinate system, respectively; \(r_{os}^{o}\) is the position of the real satellite; \(r_{\beta_{s}}^{\beta}\) is the projection distance of a point in the general coordinate system under the coordinate system; \(r_{\beta_{s}}^{\beta}\) is the corresponding velocity; \(\wedge\) represents the antisymmetric operation; and \(\delta\rho_{e}^{s}\) is the ephemeris error.
## 4 Results and Discussion  
### Comparison between Numerical and Experimental Results  
Experimental studies were modeled with the FE program, and the numerical and experimental results were compared. The uplift capacity/displacement curves were obtained and the compatibility between the results was examined. The uplift capacity/displacement curves obtained for H/B = 2 embedment ratio in typical unreinforced loose sand soil conditions are presented in Figure 13. For unreinforced sand, the uplift capacity value measured as a result of the centrifuge test is 381 kN, while the estimated uplift capacity value as a result of the finite element analysis is 374 kN. It was found that the values obtained as a result of numerical and experimental studies are in good agreement for all load/displacement curves. The results were also compared for the breakout factor.  
Figure 11: Very fine mesh and boundary conditions for a strip anchor (H/B = 5).  
Figure 12: Effect of mesh size (H/B = 5, dense sand).
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Experimental Scenario Settings
#### 4.3.3 Impact of Client Quantity on Total Execution Time  
Finally, we assess the impact of the number of clients on the maximum task completion time. Similar to the previous section, we kept the number of task types constant at 10, and all other parameters, except for the number of clients, were set as shown in Table 2. The number of clients varied across different scenarios. In the small-scale scenario, the total number of clients was set to 2, 3, 4, 5, and 6. In the medium-scale scenario, the total number of clients was set to 4, 6, 8, 10, and 12. In the large-scale scenario, the total number of clients was adjusted to 6, 9, 12, 15, and 18. Figure 10(a)-c illustrate the impact of the number of clients on the maximum task completion time under three different scale scenarios.  
As shown in the figures, during the initial stage, as the number of clients increases, the task completion time decreases. This is because increasing the number of clients reduces the average number of tasks that each client needs to handle. Additionally, tasks are more evenly distributed among the detection points, allowing for more efficient utilisation of server resources, thus reducing the maximum task completion time. However, in subsequent stages, as the number of clients continues to increase, the reduction in the maximum task completion time becomes smaller and exhibits fluctuations. This is due to the fact that the number of servers remains constant; hence, despite the increase in the number of clients, the impact on the maximum task completion time diminishes. Furthermore, regardless of the scenario or the number of clients, the maximum task completion time for the Hybrid Genetic Algorithm (HGA) is consistently lower than that of similar algorithms, demonstrating its superior performance.  
Figure 11: Impact of client quantity on different scenarios: (**a**) small-scale, (**b**) medium-scale, and (**c**) large-scale.  
Figure 10: Impact of server quantity on different scenarios: (**a**) small-scale, (**b**) medium-scale, and (**c**) large-scale.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 4 Case Studies, Results and Discussion
### Impact of Wind Farm Location on System Transient Stability  
In this scenario, an experiment is performed by changing the location of the wind generator from the bus 1 to bus 4 of IEEE 14-bus system. The results show that instability is changed by changing the location of WFs. As can be seen in the omega angle response in Figure 9, the first peak during the disturbance almost approaches 1.01 p.u. and it settles down in 3 s to 4 s. Now, if we look at the theta angle response in Figure 10, the positive and negative peaks end at different values for different generators at 0.8 s, but the system could regain the transient stability within 4 s.  
Figure 8: Generator angles for WFs at bus 1 and bus 4.  
Figure 10: Generator angles for WFs at bus 4.  
Figure 9: Generator speeds for WFs at bus 4.  
Comparing the Figures 5 and 6 with the Figures 9 and 10, these figures clearly indicated that by changing the location of the turbine from bus 4 to bus 1, the transient instability of this IEEE 14-bus system increases. Therefore, the location of wind turbine integration into the power system is one of the key factors affecting the transient stability margin of a system with wind farms, and the wind farm should be carefully determined in the planning stage considering transient stability.
## 2 Literature Review
### Research Model and Hypothesis Development
#### 2.2.2 Social Value  
Social value is the degree to which someone believes that they make a good impression on others (peers, family, friends). Studies have shown that the opinions of peers, family members, and friends significantly impact users' behavior in ICT acceptance. The encouragement to use technology received by individuals from close contacts can significantly influence their positive attitude towards accepting it. Consequently, higher social support for adopting a technology results in individuals' intention to utilize ICT being greater [51; 52]. Therefore, social value refers to the perceived usefulness of a digital item in enhancing a person's social well-being, including its potential symbolic meanings and indications of affiliation to a social group. The use of AR technology may rely on how customers consider themselves or desire to be regarded by others. Using information technology (IT) can enrich one's self-image and reveal group membership, with social values in technology use encompassing symbolic meanings, social relationships, and consumer identity. AR apps, which often allow users to share their experiences on social media, can also create a sense of personal fulfilment and help maintain social relationships [4].  
Social values play a vital role in determining customer behavior. Social value is the advantage of a product or service to fulfil one's wish to acquire honor or social satisfaction [53]. Some studies showed that social value affects customer satisfaction [54; 55; 56; 57; 58].  
During exploratory shopping, consumer inspiration significantly predicted adoption intention. The ability to mix and match outfits, unique to VTO apps, led to positive responses. The study also emphasized significant potential in utilizing social elements, like sharing avatars and collaborative customization, to enhance VTO experiences [5]. Social value is a dimension related to the disposition of an individual to buy (or not buy) at both the product and brand levels [52].  
Extending the UTAUT, ref. [9] proposed a model in which social influence and innovativeness indirectly affect behavioral intention.
## 2 System Architecture and Design Principles  
The overall system architecture is illustrated in Figure 1 and mainly consists of signal processing modules (data preprocessing, channel encoding, and modulation/demodulation) implemented on the FPGA, as well as peripheral devices including a digital-to-analog converter (DAC), analog-to-digital converter (ADC), optical antennas, and laser driver circuit. During the communication process, the information intended for transmission is initially written into the DDR by the processing system (PS). Subsequently, the programmable logic (PL) reads and packages the data via the advanced extensible interface (AXI) protocol, completing the data preprocessing. The packetized data are then encoded using RS coding to improve robustness in the complex underwater environment. To generate the baseband signal for modulation, the encoded data are first converted from parallel to serial format and then scrambled to enable the detection of hopping edges, ensuring accurate bit synchronization at the receiver. The scrambled data are modulated and converted to an analog signal using the DAC; this then drives the laser through the laser driver circuit. The laser beam is expanded and collimated before being transmitted through the underwater channel. At the receiver, the APD detector captures the optical signal. The output signal is sampled by the ADC. The sampled signal is demodulated and decoded, and then written back to the DDR via the AXI protocol. The processed data are read by the PS and displayed on a host computer, completing the entire underwater communication process. The specific design principles and analysis of the data preprocessing, channel coding, and modulation/demodulation schemes are as follows.
## Materials and Methods  
This section is dedicated to the formulation of the proposed mathematical model to solve the presented case study and the description of the instances to determine its performance. As mentioned in the previous section, the picker routing in the selection area is not being carried out in the time established by the company, since the route provided by the WMS does not contemplate the weight of the product; therefore, the employees need to generate alternate routes considering this characteristic. They still had not failed to process any order, but they required more staff to achieve that, and therefore, high costs for this concept arose. Also in Section 2, the authors set out the problem of routing picking (in a warehouse it is often solved employing models based on the TSP) with a variety of formulations in the literature, probably the most prominent being carried out by citing [46]. However, in this formulation, there is no demand associated with the clients, which means that for our research topic we should use a model that contemplates this criterion, as is the case of the capacitated vehicle routing problem (CVRP), which is a generalization of the TSP. According to the characteristics provided by the company, the following was also considered for the formulation of the mathematical model: The demand for the product is stable and known (changes arise when a new product is introduced); there is always product available; the boxes of the different products have the same dimensions; a product must occupy a single space; the spaces for the assignment of products in the area are well determined; the maximum capacity of each of the spaces is known, and this depends on the type of product that will be assigned; the weight of each box is known; each tour begins in the aisle of area A; and considering the study of [47], all employees take the same time to go from one place to another. The notation for the SLAUPR model is:  
**Sets**  
\(I\): denotes the set of spaces, indexed by \(i,j\). \(K\): denotes the set of different products types available, indexed by \(k\). \(P\): denotes the set the orders, indexed by \(p\). \(O\subseteq K\): denotes the set of different products to assign, indexed by \(o\). **Parameters**  
\(b_{kp}=\) Demand of product \(k\) in order. \(q_{jk}=\) Capacity of spaces \(i\) for product \(k\). \(x_{k}=\) Weights of products \(k\). \(t_{ij}\) = Matrix of the travel time go from space i to space j of picking area. The distances were created based on the location of spaces and the standard size of a pallet, where each unit of measurement is equivalent to one second. **Variables**  
\[u_{pij}= \begin{cases}1,&\text{If the order $p$ is contained in the tour from $i$ to $j$}\\ 0,&\text{Otherwise}\end{cases} \]  
\[e_{jk}= \begin{cases}1,&\text{If space $j$ include product $k$}\\ 0,&\text{Otherwise}\end{cases} \]  
\(s_{jp}\) auxiliary variable for subtours. \(v_{pjk}\) Quantity of boxes to pick in the order \(p\) in the space \(j\) of product \(k\).
## 3 Transmittance through a Subwavelength Slit on a Real Metallic Plate
As the real part of the guide wavelength increases, the interval of the transmission peaks decreases, as shown in Figure 3, illustrating the same principle as Table 1. Consequently, as the imaginary part of the guide wavelength increases, the attenuation of the transmission peaks also increases. When the relative permittivity has only a real part, constant maximum transmission peaks in Figure 3a,b are found. The maximum transmission peaks are obtained as 29.23 and 14.55, when the gap widths are 4 and 8 nm, respectively. It is important to understand the meaning of these values. First, 29.23 times the incident power density for the gap width of 4 nm [Figure S1] is transmitted into the opposite (right-hand) region in Figure 1. The same applies to 14.55 times the incident power density for the gap width of 8 nm [Figure S2] in Figure 3b. Whenever the resonant conditions for both cases are satisfied, the constant width (m) times the incident power density (W/m) is transmitted into the opposite region, regardless of the width of the subwavelength slit. In other words, the incident power density having a constant width, which is much wider than that of the subwavelength slit, is transmitted into the right side of Figure 1 regardless of the gap width \(2g\), when the resonant condition (thickness of the plate) is satisfied. It is necessary to mention the blue asterisks in Figure 3a, which are remarked from [29]. The transmittances versus the thickness of the Ag plate are obtained when Ag is \(-14.88-j0.39\) (at \(\lambda_{0}=582.08\) nm), which is the same relationship as indicated by the blue solid line using the MMT. The overall transmittance levels are similar to each other, and the intervals of the maximum transmittances are also analogous. The maximum transmittances are observed at different resonant thicknesses of the plate, and the difference between [29] and our result is roughly \(0.02\lambda_{0}\). Furthermore, the maximum transmittance markers increase, even though the thickness of the plate increases. This is unnatural, because the electromagnetic wave passing through the slit should be dissipated more as the plate thickness increases. On the contrary, the maximum transmittances by the solid lines of our work gradually decrease and are physically agreeable. Therefore, the transmittances of our work are more convincing than those of [29].
## 3 Results  
### Descriptive Statistics for Primary and Secondary Variables  
The descriptive statistics of the selected sample pastoralist household in the study area explained by age, sex, total family size, education status, access to credit, livestock holding, asset value, access to mass media, access to veterinary extension services, distance to market, and access to rangeland are presented in Table 3.  
The analysis reveals that, of the total number of respondents, 68.6% of household heads are male and 31.4% are female, indicating a male-dominant demography in decision-making in the pastoralist area. The level of access to critical resources varies: 60.6% of households have no access to credit, while 77.7% have access to mass media, which is vital for information dissemination, 32.2% of households have access to veterinary services, indicating decent service coverage. Access to rangeland is high at 76.6%, showing its importance in sustaining pastoralist livelihoods. The average age of household heads is 44, with family sizes averaging 8 members, indicating that large families are common in pastoralist settings. Livestock holdings average seven tropical livestock units, reflecting significant reliance on livestock assets.  
In the same way, the average asset value of respondents is USD 3155, suggesting moderate economic stability, with a wide range from USD 486 to USD 10,009. Households are, on average, 15.3 km away from the nearest market, highlighting potential challenges  
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline  & **I. Dummy Variables** & **Number of Responses** & **\% of Responses** & **Number of Responses** & **\% of Responses** \\ \hline  & & Male & & Female \\
1 & Sex (SEX) & 240 & 68.6 & 110 & 31.4 \\  & & Not accessible & & Accessible & \\
2 & Access to credit (CREDT) & 212 & 60.6 & 138 & 39.4 \\
3 & Access to mass media (MEDIA) & 70 & 22.3 & 272 & 77.7 \\
4 & Access to veterinary extension services (ACCT) & 237 & 67.7 & 113 & 32.3 \\
5 & Access to rangeland (RNGLD) & 82 & 23.4 & 268 & 76.6 \\ \hline  & II. Quantitative variables & Mean & Maximum & Minimum & Std. dev. \\
6 & Age of the household head (AGE) & 44 & 82 & 20 & 13.02 \\
7 & Total family size in the household (FAMSZ) & 8 & 17 & 0 & 2.73 \\
8 & Education status of the household head (EDUCH) & 2 & 10 & 0 & 2.33 \\
9 & Tropical livestock unit of holding (LIVEST) & 7 & 65 & 3 & 4.72 \\
10 & Asset value in USD (ASSET) & 3155 & 10,009 & 486 & 1646 \\
11 & Distance from nearest market in km (DISMA) & 15.3 & 46 & 0.02 & 16.72 \\
12 & Annual rainfall in mm & 371.4 & 652 & 172 & 102.2 \\
13 & Annual temperature in °C & 23.4 & 25.39 & 23.37 & 0.478 \\ \hline  & & & & & \\ \end{tabular}
\end{table}
Table 3: Descriptive statistics.  
in market access. Secondary data on annual rainfall show a mean of 371.4 mm with considerable variability (102.2 mm), indicating significant climatic variability. The average annual temperature is 23.4 \({}^{\circ}\)C, with minor fluctuations, pointing to consistent yet potentially stressful temperature conditions for agricultural and pastoralist activities.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Data Source  
#### 2.2.1 Forest Stand Age and Forest Cover Data  
Forest inventory data were used for comparing and validating forest stand age modeling and mapping. Forest inventory data come from China's 9 th National Forest Inventory (NFI) containing attributes of dominant tree species, average forest stand age, the proportion of dominant tree species, average forest height, forest density (number of plants per hectare), average diameter at breast height (DBH) and site index for each forest quadrant during 2014-2018, but they were updated to 2019 and rasterized into 30 m resolution maps using the nearest neighbor resampling method. The dominant tree species were determined by tree volume and tree density, and those with similar attributes were merged into one forest polygon. The average forest stand age and tree height of the dominant tree species within a forest polygon were taken as the final forest stand age and tree height [19; 20], and the forest age data of 603 quadrats were collected in this study. A Circa 2010 Thirty Meter Resolution Forest Map for China ([[http://data.starcloud.pcl.ac.cn/zh](http://data.starcloud.pcl.ac.cn/zh)]([http://data.starcloud.pcl.ac.cn/zh](http://data.starcloud.pcl.ac.cn/zh))) was selected. These data were produced based on the combination of forest census and Landsat data with a resolution of 30 m [21].