esa-sceva/satcom-chunk-collection huggingface dataset:
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 4 Result
### The T3S Effectiveness Testing
#### 4.3.2 Test for User Satisfaction Subjected to the Designed Application
Furthermore, the T3S application provides users with up-to-date information regarding their real-time location, prevailing weather conditions, and other relevant factors, thereby augmenting overall safety and comfort levels. The application provides a comprehensive health monitoring system for travelers, as showcased in Figure 8a. The system captures basic health information during the  
Figure 7: The average score of each question. registration process, including weight, height, BMI, gender, age, and blood type, which enables the application to predict potential health risks. Additionally, GPS coordinates on the traveler's smart device are used to retrieve weather information from their location. The application calculates the heat index and displays this information on the screen to alert travelers to potentially harmful weather conditions. The application incorporates sensor data from smartwatch devices, such as heart rate, steps taken, and calories used, to predict and diagnose the traveler's health status. The application identifies six different health statuses and alerts users to harmful heat index zones to ensure their safety while traveling. Figure 8b showcases the application's emergency medical assistance feature, which displays a pop-up alert to inquire about a traveler's abnormal symptoms. If there is no response, the system automatically sends a request for medical assistance to the nearest facility, ensuring prompt attention during emergencies. The application's search capabilities for tourist attractions, restaurants, hospitals, hotels, and stores are displayed in Figure 8c. The search function allows users to search by location name or type, making it easy to navigate and find the desired location. Search results display a preview of the place, including the place name, address, and distance from the current location coordinates. Figure 8d provides detailed information about a specific location, including a gallery of up to eight preview photos, coordinates for system navigation, location type, telephone number, address, and nearby grocery stores. Additionally, the application provides a daily weather forecast for the next six hours, including temperature, humidity, and visual signals or icons to quickly inform users of the weather conditions. The application calculates and displays heat index zones to inform users of potential health risks due to weather conditions. Figure 8e showcases the map screen displaying the location of tourist attractions, which works in conjunction with the Tourist Location and Health Information screens to provide location coordinate data. The application generates a marker on Google Maps for each location, enabling users to navigate to their desired destination by clicking on the marker.
## 2 Materials and Methods
### Fine Adjustment Device (FAD)  
Figure 3 shows the three-dimensional (3D) model of the FAD and its fabricated prototype. It is composed of a passive linear stage (XLSL120-2, 2 mm/rotation, Misumi Co., Japan), a passive rotary stage (RTRM40-R, 5\({}^{\circ}\)/rotation, Misumi Co., Japan), a laser pointer supporter, a long arm, a guiding hole block, and a guiding pattern block. Two passive stages are used to move the long arm in a plane. Using these, the operator can accurately adjust the position of the hole. The laser pointer supporter includes the laser pointer inside and was used as a part of the PGSLP that is described in Section 2.3. The guiding hole block has holes to guide the drilling device, and the guiding pattern block includes the grid and circular patterns for easier adjustment. In our work, the grid pattern and the circular pattern were used together to combine the advantages of each [6,10]. Either of two blocks can be fixed to the long arm of the FAD by replacement. As shown in Figure 4a, the dimension of the pattern block was 80 mm by 40 mm and included the thin metal wires arranged in a grid pattern at 4 mm intervals, as shown in Figure 4a,b. For the grid pattern, nitinol wires that were 1.33 mm in diameter were used for the principal axes and wires that were 0.72 mm in diameter were used for other lines, as shown in Figure 4b. The pattern block also included one circular metal part (outer diameter: 20 mm, inner diameter: 16 mm) as in Figure 4, which helps the operator make the circles co-axial. The intersection point of two thick nitinol wires was set to the center of the ring pattern. The X-ray image of the guiding pattern block is shown in Figure 4c.  
Figure 3: Proposed fine adjustment device. (**a**) 3D model (**b**) Fabricated prototype.  
Figure 2: Procedure of drilling device guidance.
## 2 Calculation Procedure and Input Parameters  
The hydration and reactive transport calculations were performed as presented schematically in Figure 1. Initially, pore solution concentration, composition of hydrate assemblage, solid solution composition of calcium aluminosilicate hydrate (CASH), and total porosity were calculated from the hydration model for the input of chemical composition and physical properties of PC, and mixing conditions such as water to cement ratio, the amount of sodium sulphate in the mixing water, temperature, and relative humidity The details of PC and mixing conditions used in the simulations are tabulated in Table 1. The simulation results from the hydration model were given to the reactive transport model, together with boundary conditions for thermodynamic equilibrium and multispeciestransport calculations. The reactive transport simulations were performed using the incorporated multicomponent diffusion module in PHREEQC. The basic theory behind thermodynamic equilibrium reactions, cement hydration, and multi-ionic transport is described in the authors' previous works [21; 25; 26], and given in Appendix A. The thermodynamic properties for various phases and minerals found in the cement system were collected from CEMDATA07 [27] and others [28; 29], and the data were converted into a format suitable for PHREEQC. The converted data (reported in Reference [21]), together with the PHREEQC default thermodynamic database [23], were used for every calculation; the thermodynamic database for minerals and phases is given in Table A1. The dissolution rate of clinker minerals and their activation energy are described in Appendix A.2. The ideal solid solution model was assumed for CASH, and its composition consisted of CASH_SCA, CASH_INFCA, CSH_T2C, CSH_T5C, CSH_Jen, and CSH_TobH, as given in Table A1. Thermodynamic properties of aqueous species used in the simulations are from PITZER database, provided with PHREEQC and the Pitzer interaction parameters from references [30; 31; 32].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Oxide Composition (wt \%)** & **Mineralogical Composition (wt \%)** \\ \hline SiO\({}_{2}\) & 21.60 & C\({}_{3}\)S & 50.61 \\ Al\({}_{2}\)O\({}_{3}\) & 5.40 & C\({}_{2}\)S & 23.83 \\ Fe\({}_{2}\)O\({}_{3}\) & 2.60 & C\({}_{3}\)A & 9.92 \\ CaO & 64.00 & C\({}_{4}\)AF & 7.90 \\ SO\({}_{3}\) & 2.00 & Gypsum & 2.09 \\ MgO & 1.80 & Calcite & 1.00 \\ Na\({}_{2}\)O & 0.38 & CaO\_free & 1.00 \\ K\({}_{2}\)O & 0.55 & & \\ \hline \multicolumn{3}{c}{Physical properties} & Mixing conditions \\ Density (g/cm\({}^{3}\)) & 3.18 & W/B & 0.55 \\ Blaine (m\({}^{2}\)/kg) & 334 & Temp. (\({}^{\circ}\)C) & 25 \\ \multicolumn{3}{c}{RH} & Fully saturated \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition and physical properties of PC and mixing conditions.  
Figure 1: Schematic diagram for the calculation procedure.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 6 Security and Performance Analysis
### Security Analysis
At the same time, the introduction of a third-party authentication entity is omitted to ensure that users can obtain accurate access results. That is, when the user initiates an access request, the smart contract will be triggered to operate. Based on the characteristics of the blockchain, accurate results can be guaranteed. 3. Privacy. The privacy issues in the blockchain system can be divided into account privacy and transaction privacy, corresponding to the user's identity privacy and data privacy, respectively. In this data management architecture, the user's identity information, physical address, and IP address are not associated with the public information (such as the user's public key and account address) in the system, and no node can infer the user's real identity information from the public information in the system. The system runs an automatic trigger mechanism based on Smart contract, which reduces the risk of privacy leakage caused by human intervention. Therefore, user identity privacy has been well protected. 4. Anti-conspiracy attack. In order to ensure correct access control, the scheme must be able to resist conspiracy attacks. Sometimes, these devices may collude with each other in an interest-driven attempt to verify valuable data with other devices, and none of which independently have a satisfactory set of attributes. However, in our scheme, subjects are accessing the IoT equipment resources through proxy devices, and the agent devices act as the implementation point of the strategy and interact with Smart contracts, which can resist the collusive attack among the devices of the IoT.
# Green's Function, Earthquakes, and a Fast Ambient Noise Tomography Methodology
## 3 Results and Discussion  
### Experimental Green's Functions (EGFs)  
Multiplication in the frequency domain, along with the use of thread-based parallelism, allows for the rapid calculation of EGFs between pairs of stations. Two quality control measures were taken to accept or reject a potential EGF between pairs of stations. Firstly, a signal-to-noise ratio (SNR) threshold of 8 was used, where SNR is defined as the ratio of the maximum absolute value of the signal (from \(-80\) s to 80 s time lag) to the standard deviation of a noisy signal window (from 100 s to 200 s time lag). Secondly, each EGF group should be in the range of 1.8-5.5 km/s. The envelope was calculated as the absolute value of the analytic signal, i.e., its Hilbert Transform. The EGFs obtained are presented in Figure 2. It is worth noting that no stacking was used, since both the pre-processing and the cross-correlation was applied to the full 10 days of recording of each station. From a total of 0.5 \(\times\) n(n \(-\) 1) = 105 (for n = 15) cross-correlations, 40 were rejected.
## 3 Proposed Method
### FasterNet Lightweight Network
#### 3.2.2 Partial Convolution (PConv)  
Partial Convolution (PConv) significantly enhances the computational efficiency by incorporating a mask into the convolution operation, as shown in Equation (4). Instead of applying convolution comprehensively, as in traditional methods, PConv performs the operation only on a subset of the input feature map. This approach allows PConv to reduce unnecessary computations and memory accesses, effectively ignoring redundant portions of the input. A comparison between partial convolution (PConv) and ordinary convolution is illustrated in Figure 4.  
Modeled as follows:  
\[x=\left\{\begin{aligned} & W^{T}(X\odot M\frac{sum(1)}{sum(M)}+b),\; sum(M)>0\\ & 0,\;otherwise\end{aligned}\right. \tag{4}\]  
\(x\) is the input feature vector, \(M\) is the input Mask, \(W\) is the convolution kernel, \(b\) is the bias. Where \(\odot\) denotes element-wise multiplication, and \(1\) has the same shape as \(M\) but with all the elements being \(1\). As can be seen, the output values depend only on the unmasked inputs. The scaling factor \(\text{sum}(1)/\text{sum}(M)\) applies appropriate scaling to adjust for varying amounts of valid (unmasked) inputs. After each partial convolution operation, we then update our mask as follows: if the convolution was able to condition its output on at least one valid input value, then we mark that location as valid.  
PConv achieves rapid and efficient feature extraction by applying filters to only a small subset of the input channels while keeping the remaining channels unchanged. This approach is particularly suitable for running deep learning models on resource-limited devices because it significantly reduces computational demands without sacrificing performance.
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
## 2 Engineering Background  
The Chahasu Coal Mine is located in the southwestern region of Dongsheng District, Ordos City, Inner Mongolia. It primarily mines the 3-1 coal seam, which has an average burial depth of 436.5 m, an average dip angle of 2\({}^{\circ}\), and an average thickness of 4.3 m. The geological structure of the mine is relatively simple, with the stratigraphic column shown in Figure 2.  
The roof and floor of the coal seam consist predominantly of soft rocks with poor stability. Mining the 3-1 coal seam is likely to damage the overlying strata, leading to caving or collapse zones. To protect surface structures, a full backfill method is planned for managing the roof using gob. The CT301 panel has a length of 265 m. The mining and filling operations follow a \"three-mining, one-filling\" cycle, with a filling step distance of 2.6 m. One mining and filling cycle is completed daily. GER is implemented in the CT301 headgate. The layout of the working face is shown in Figure 3.  
Figure 2: Stratigraphic column.  
To promote the resource utilization of solid waste, Chahasu Coal Mine employs cemented paste backfill (CPB) materials, which are prepared by mixing and stirring a proportion of coal gangue, fly ash, cementing materials, and water. As a key research topic in green mining technologies, CPB has been extensively studied by numerous scholars. [PERSON] et al. [37] conducted a numerical simulation to investigate the impact of filling interval time (FIT) on the stress distribution in the mined-out area. The results indicated that as the FIT increased, the stress in the filled mining area decreased. Using an orthogonal experimental design, [PERSON] et al. [38] determined the optimal mixture ratio of CPB materials and employed the probabilistic integration method to assess the control effect of filling mining on surface subsidence. Their findings demonstrated that filling mining significantly reduced surface subsidence and the extent of the subsidence-affected area. [PERSON] et al. [39], through experimental and theoretical analysis, proposed using the final backfill rate as an indicator to evaluate the effectiveness of CPB, and developed a corresponding filling effect characterization model. [PERSON] et al. [40] established a strength prediction model for CPB under different curing temperatures using MATLAB R2018a and a BP neural network.  
These scholars have studied CPB technology from various perspectives, offering valuable references and insights for this paper. This study, using the CT301 working face at Chahasu Coal Mine as a case study, explores the stability mechanisms and control technologies of surrounding rock in GER with paste backfill, further enriching and improving the theoretical and practical understanding of CPB mining technology in GER.
## 3 Results and Discussion
### Volatile Compounds Identified by GC-IMS
#### 3.1.2 Differences of Volatile Compounds in Teas by GC-IMS  
The content of each volatile compound is shown in Table 1. A total of 75 volatile compounds were detected in the three samples. The same series of compounds were used in analyzing the changing regularities of volatile compounds in tea samples subjected to different treatments. Aldehydes, alcohols, and ketones were the major volatiles, the proportions were 36.12%, 18.04%, and 25.23%, respectively (FTL); 38.27%, 26.83%, and 19.68%, respectively (BT); and 45.26%, 22.03%, and 18.10%, respectively (GT), as shown in Figure 2. In contrast to FTLs, BT and GT have higher levels of aldehydes and alcohols, and lower levels of ketones. BT had the highest content of alcohols, while GT had the highest content of aldehydes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Volatile Compounds**} & \multirow{2}{*}{**RI**} & \multicolumn{3}{c}{**Relative Content (\%)**} & \multirow{2}{*}{**Odor Description \#**} \\  & & & & & \\  & & & & & \\
1 & Decanal & 1270.5 & 0.99 & 0.75 & 1.19 & Orange, sweet \\  
utanal & 572 & 1.18 & 0.67 & 0.56 & — \\
30 & Methyl propanal & 514.5 & 4.23 & 4.66 & 1.86 & — \\
31 & Propanal & 482.5 & 1.75 & 1.34 & 1.27 & — \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Volatile compounds identified in different teas by GC-IMS.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 3 Overloading Mitigation Strategies
### Angle Limiter
#### 3.2.2 Small-Signal Analysis  
A simple approach to evaluate the performance of AL is to consider that PLL dynamics are faster than PSL, and it is out of the loop. The small-signal model in these conditions is given in Figure 8.  
Under these conditions, the power response is mainly dominated by the PLL dynamics, \(G_{PLL}\). The synchronous reference frame-based PLL (SRF-PLL), represented in Figure 9, is the most common approach, and it can be linearized as a second-order system for rated operating conditions (19) [36]. In quasi-stationary conditions, the PCC angle \(\theta_{pcc}\) is given by (20). This angle depends on the grid angle \(\theta_{g}\), the active power and the synchronization constant between PCC and grid, \(K_{g}\)[37]. \(K_{g}\approx 1/L_{g}\) following the same assumptions as in (5).  
\[G_{pll}=\frac{\theta_{pll}}{\Delta\theta_{pcc}}=\frac{\omega_{b}k_{p}^{PLL}s+ \omega_{b}k_{i}^{PLL}}{s^{2}+\omega_{b}k_{p}^{PLL}s+\omega_{b}k_{i}^{PLL}} \tag{19}\]  
\[\Delta\theta_{pcc}=\Delta\theta_{g}+\frac{\Delta P}{K_{g}} \tag{20}\]  
The active power response of the system with AL enabled, under a grid frequency perturbation, is given by  
\[\frac{\Delta P}{\Delta\omega_{g}}=-\frac{K_{t}\omega_{b}s}{s^{2}+\omega_{b} \Big{(}1-\frac{K_{t}}{K_{g}}\Big{)}k_{p}^{PLL}s+\omega_{b}\Big{(}1-\frac{K_{ t}}{K_{g}}\Big{)}k_{i}^{PLL}} \tag{21}\]  
The PI parameters of the SRF-PLL are critical in shaping the overload mitigation behavior of the inverter. Again, the integral gain \(k_{i}^{PLL}\) determines the inertial power, whereas  
Figure 8: Angle limiter small-signal model.  
Figure 9: SRF-PLL structure.  
the damping is determined by \(k_{p}^{PLL}\). However, active power response is also affected by the \(K_{t}/K_{g}\) factor, which reflects the ratio between the grid and total series inductance:  
\[\Delta P_{H}=\lim_{s\to 0}s\frac{\Delta P}{\Delta\omega_{g}}\frac{RoCoF}{s^{2}}= -\frac{1}{L_{\mathrm{c}}k_{i}^{PLL}}\cdot RoCoF \tag{22}\]  
\[\xi=\frac{1}{2}\sqrt{\frac{\omega_{b}}{k_{i}^{PLL}}\frac{L_{\mathrm{c}}}{L_{ \mathrm{c}}+L_{\mathrm{g}}}k_{p}^{PLL}} \tag{23}\]  
A small filter inductance \(L_{\mathrm{c}}\) leads to higher inertial power. Limiting inertial power under these conditions requires a faster PLL (high integral gain). Moreover, a system with \(L_{\mathrm{c}}<L_{g}\), e.g, a weak grid, reduces the damping capability. Increasing the proportional gain of the PLL helps to improve system damping. Hence, the performance of the AL is strongly influenced by the tuning constraints of the PLL. A high-bandwidth PLL can increase the system sensitivity to noise and make it more prone to stability issues, especially in weak grids.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 7 Results
### Reliability  
Once all STAs are associated to the network and their paths to the GW properly established, the next goal is to analyze the reliability and the cost (in terms of energy consumption) of sending data. To do that, the GW was programmed to send 20 beacons with the following sequence: beacon #1 was a _network association primary beacon_, beacons #10 and #20 were _data primary beacons_ asking for _statistics packets_; the rest of beacons were _data primary beacons_ asking for _application packets_. To send their packets, STAs had five available transmission windows (\(w=5\)).  
Results with the obtained PDR are compiled in Figure 16. After five transmission windows, PDR is above 95% in any configuration, and it even achieves values above 90% after three and four transmission windows when using X-MAC and NullRDC, respectively. In this case, NullRDC specially suffers from the effect of collisions, due to the CSMA/CA backoff implementation and the higher number of concurrently active STAs compared to X-MAC. (Main values of the CSMA/CA default backoff implementation in Contiki OS: minimum value of the backoff exponent (\(macMinBE=0\)), maximum value of the backoff exponent (\(macMaxBE=4\)), and maximum number of backoff attempts (\(macMaxCSMABackoffs=5\)).).  
Another insight from obtained results is how multi-hop topology outperforms single-hop in all possible configurations except when using X-MAC with \(E_{30/15}\) and, in an almost imperceptible extent, in transmission window #5 of extremely high error-prone cases. Again, the inherent reduction of concurrently active STAs competing for the channel during the same time period (in this case, due to the allocation of STAs to different slots according to their ring) proves beneficial for system's reliability.  
The network's ability to properly deliver data packets to its destination was also analyzed by computing the quotient between the total number of packets sent by STAs and those properly received by the GW. As shown in Figure 17, multi-hop schemes have better performance than single-hop except when significant high-error rates are applied. In highly unfavorable channels, parents usually do not receive all their expected payloads at once, so that they tend to send several packets in successive transmission windows with only partial information.  
Figure 16: _Cont._
## 1 Introduction
Some LUE models have attempted to consider the effect of soil water stress in modeling GPP. For example, the DTEC model and the EC-LUE model use evapotranspiration ratio (EF) multiplicative factors to scale LUE [25; 26]. The SMAP L4C algorithm incorporates SMAP satellite SM data to improve global GPP simulation [27; 28]. A moisture function on SM was included in the TCF model [29], which had minimum SM and maximum SM as segmentation points; the normalized function of SWC (soil moisture content) was used in the [PERSON] et al. model to characterize the effect of soil moisture on GPP [30]. Their results show that incorporating SM can achieve better simulations, especially in grassland types. However, there are still some more commonly used LUE models, e.g., the two-leaf LUE model (TL-LUE) and Vegetation Photosynthesis Model (VPM), fail to consider the impact of SM on GPP, which may lead to uncertainties especially for the case under drought conditions [31]. On the basis of the existing studies on SM application in LUE models, we attempt to develop a better functional form and set a more physically meaningful segmentation point to be applied in LUE models to improve GPP simulations under water stresses. In this study, we explore the potential of SM to improve GPP simulations under water stresses at sites over the US and Europe with typical LUE models (i.e., TL-LUE and VPM). First, we calibrate the TL-LUE and VPM parameters using the remote sensing data required for the models and evaluate model performances. Then, we analyzed the effect of different improvement schemes for incorporating SM on GPP simulations at drought-impacted flux sites. In addition, we compare the performances of the two models with these different SM modeling schemes across different ecosystem types. We expect to obtain new insights for improving GPP simulations under water stresses by LUE models with the aid of incorporated SM information, which may provide some guidelines for future model development.