esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 2 Related Works
### Radio Frequency Identification (RFID)-Based Location  
In general, existing RFID-based location schema can be classified into three categories: range-based, fingerprinting, and proximity [8; 12]. In the range-based RFID location scheme, many distance measurement technologies are proposed for calculating location. Those classical technologies include the angle of arrival (AOA) [13], the time of arrival (TOA) [14], the time difference of arrival (TDOA) [15], the received signal strength indicator (RSSI) [16], and the received signal phase (RSP) [17]. Although it can usually achieve sufficient location accuracy, there are a couple of drawbacks. First, the time must be precisely aligned between the RFID tags and the readers, which is challengingdepending on the methods and the hardware. More sophisticated hardware results in greater cost. Second, for RSSI measurement, it suffers from multipath fading and shadowing such as with indoor environments. For high-level accuracy, sophisticated hardware is used, which means some extra cost. In the fingerprinting scheme, the location is calculated by comparing the similarity between the real-time RSSI fingerprinting and the pre-measured prior RSSI fingerprinting. The probability of the match determines their location. However, the fingerprinting should be collected as a prior, making it more complicated and impractical in some emergency applications [18]. In the proximity scheme, the tag location is determined by the position of the RFID reader that detects the target tag. The reader can only detect the tags' proximity but not their exact locations. Therefore, denser RFID readers achieve a higher location accuracy. To reduce the cost, reference tags deployed at a fixed position in an area of interest are used to replace RFID reader. For both RFID readers and reference tags, denser RFID readers are necessary for higher location accuracy, which creates higher cost and more work to position site the reference tag. Unfortunately, some scenarios such as a museum do not allow the installation of reference tags. Therefore, this scheme also faces environmental and hardware challenges in deployment [18; 19].  
In smart cities initiatives, the exact coordinates of the infrastructure are necessary for inspection and management. However, proximity within a radio range information is not sufficient for LBS-based services. Another method--a fingerprinting method with pre-measured variables--may be impractical owing to the dynamic urban environment. Therefore, devising a scheme with rapid deployment and immune to the environment is a crucial challenge, especially, in large-scale and emergency scenarios.
## 2 Materials and Methods
### Transmission Electron Microscopy (TEM)  
TEM was performed by standard technique. Detached MSCs and S-MSCs after 12, 24, and 48 h starvation were centrifuged at 1000 rpm for 10 min, and pellets were fixed in glutaraldehyde 2.5% and cacodylate sodium buffer, pH 7.4, for 90 min at room temperature, and then rinsed in cacodylate sodium buffer overnight and post-fixed in 1% aqueous osmium (OsO\({}_{4}\)) for 90 min at room temperature. Dehydration was performed at increasing ethanol concentrations (50% to 100%), and then, the samples were embedded in epoxy resin (Epon 812, Electron Microscopy Science, Hatfield, PA, USA). Semi-thin sections were stained with toluidine blue. Observations and micrographs of counterstained thin sections were performed on a Jeol JEM (Tokio, Japan) operating at 100 kV. The observations were carried out at several positions across each grid to avoid biased selections; ten grids were observed for each sample.
###### Abstract  
An islanding operation of distributed generations (DGs) in emergencies due to a fault in distribution systems can be a means of power supply for important loads in outage areas by facilitating the self-sufficient capability of DGs forming microgrids. This paper presents an optimization-based intentional islanding scheme to derive a near-optimal service restoration (SR) plan. The anti-parallel operation of DGs is considered a new constraint that avoids more than two DGs in an island thereby, enabling simpler control and operation of the distribution system in an emergency. Each island is created by an island partitioning scheme based on the tree representation of the network and fast searching scheme for the tree structure considering load importance, and a genetic algorithm (GA) is utilized to explore possible SR solutions. Case studies on IEEE 69-bus distribution system according to various fault locations are conducted, and the simulation results show that the proposed scheme can restore more loads with higher priority in outage areas by the intentional islanding of DGs. Furthermore, the time for deriving the optimal solution can be reduced since the evaluations for infeasible solutions are not performed.  
Keywords:anti-parallel operation; distributed generation; distribution system; intentional islanding; islanding partitioning; optimization +
Footnote †: journal: Applied sciences
## 3 Results
### Discussion
#### 3.4.3 Extension of Rilem TC 212-ACD Protocol  
The Rilem TC 212-ACD protocol has established a methodology to identify the AE source damage mode based on the RA value (RT/amplitude) and the average frequency (AF) trending. Based on this, as the damage mode shifts from mode-I crack opening to shear-dominated and more complex modes, the RA value rises and the AF drops [37]. The protocol is validated for steel rebars-reinforced concrete samples and structural elements and, lately, the research community has been investigating whether these trends are valid for other cement-based materials, fibre/textile reinforced materials, and materials of different dimensions [38; 39]. From this perspective, in Figure 6 the RA-AF plots are given, and each point is the average value of the hits detected at the respective load cycle and pause periods. The results are not identical for the two cases under study. In detail, for the REF panel at which damage evolves without the intervention of debonding, there is a clear transition from mode-I crack formation to more complex and shear-oriented damage.  
This is an expected result, since at early loading stages the cracks form at the TRC skin. At a moment, specifically at 3.5 kN/m\({}^{2}\), a jump in the RA value is obtained and the AF drops. This is the moment of interfacial debonding, but also of cement extensive cracking and textile activation. The latter is verified by the RA-AF trends that follow, since beyond that point the values of RA-AF are shifted towards shear damage. As proven by the DIC and AE energy/RT analysis above, the interfacial debonding and TRC fracture occur simultaneously until global failure.  
On the other hand, no clear trends in the RA-AF averages are obtained for the \"debonded series\". As illustrated in Figure 6b, there is a clear moment of interfacial debonding at the pause of 1.5 kN/m\({}^{2}\) (15% of ultimate load); once again, this result is validated with the above analyses. The data before and after this moment stand at the same area and cannot be distinguished. This is because of the bad interfacial bonding of the sandwich components, therefore there is shear-dominant AE activity before and after the damage release. Comparing the pre-debonding stage at the REF and IID stage, it is evident that the AF drops and RA value increases in the IID sample; this shift proves the presence of early shear-dominated activity. The Rilem TC 212-ACD protocol highlights the fact that important micro-damage due to shear friction is present as early as at the pause of 0.5 kN/m\({}^{2}\) (5% of ultimate load). This is an essential information for the structural integrity analysis of the panel, and should be correlated to the micro-phenomena that lead to durability loss.  
Figure 6: RA value (μs/V)-AF (kHz) average values at each loading step for **(a)** REF and **(b)** IID.
###### Abstract  
Net primary productivity (NPP) of vegetation is considered an important indicator for ecological stability and is the main object for analyzing the factors influencing the terrestrial carbon cycle. Recent studies have made clear the changes in the NPP of vegetation and its influencing factors at various scales. However, the variations in NPP based on different land cover types under various natural conditions, along with their driving factors, remain not well understood. In this study, spatial overlay analysis was used to investigate the link among climatic, soil moisture (SM), and topographic parameters and NPP of various land cover types after analyzing the spatial and temporal trends of NPP in the Songnen Plain from 2001 to 2020. Additionally, the contribution of each influence factor to the NPP of different land cover types was calculated using the elastic net regression model. The elastic net regression model eliminates the multicollinearity among the influencing factors while maintaining the model stability, and the R\({}^{2}\) of all lands is greater than 0.62, which can effectively quantify the contribution of each influencing factor to NPP. The results show a continuously increasing trend of the overall NPP in the research area over the selected 20 years, and NPP increased most significantly in forest land (FOR). Precipitation (PRE) and NPP showed high correlations in all the different land cover types, while the correlations between NPP and other influencing factors were significantly different. In addition, we found that perennals led to a more significant degree of NPP enhancement, and the effect of topographic conditions on NPP was mainly reflected in differences in moisture conditions due to surface runoff. From the results of the modeling calculations, the cumulative contribution of PRE to NPP ranks first in all land types and is the most vital influencing factor of NPP in the Songnen Plain. SM was an important influence, but the contribution of NPP was greater in land classes with shallow root systems. The results of the study revealed the positive transformation relationship of NPP among land cover types in ecologically fragile areas, which provides a reference for ecological restoration and rationalization of land use structure in zones such as intertwined agricultural and pastoral zones.  
vegetation NPP; land cover types; trend analysis; influencing factors; Songnen Plain 2023
## 4 Analysis of Numerical Results
### Influence of Tunnel Excavation Surface Spacing
#### 4.1.2 Excavation Process Displacement Analysis  
Figure 8 shows the effect of different progress differences on the variation in displacement at monitoring point 1. Monitoring point 1 is located at the location of the right tunnel under the embankment. The two tunnels cross the dike at the same time when the spacing between the tunnel excavation faces is 0. The displacement curve at the monitoring point is steeper, indicating that the displacement rate of the overlying rock layer is large when the double tunnels are dug simultaneously. The displacement tends to stabilize when the tunnel palm surface crosses the embankment. When the tunnel excavation surface spacing is 25 m, the displacement curve of monitoring point 1 during excavation is still continuously rising. However, it can be found that the slope of the rising section decreases more than before at this time, indicating that the displacement rate of the overlying rock layer decreases as the excavation progress difference increases during the expansion of the double tunneling progress difference from 0 to 25 m. When the tunnel excavation surface spacing is 50 m, the displacement increases with the increase in excavation distance, and the curve appears to slow down at the stage when the tunnel excavation distance reaches near 100 m and then continues to grow again. This indicates that the influence area of the palm faces of the two tunnels has started to separate when the distance between the tunnel excavation faces is 50 m. The curve already shows a more obvious step surface at 75 m distance from the tunnel excavation, which means that the influence area of the palm surface of the two tunnels has been roughly separated at 75 m difference in the progress of excavation. At a distance of 100 m, the curve is similar to that at 75 m, but the step surface is more obvious. At this point, the palm face of the later tunnel is already located outside the influence area of the palm face of the first tunnel, and the influence on ground settlement is similar to that of the two tunnels excavated separately. It can be seen that the further the spacing between the two tunnels before and after excavation, the smaller the amount of ground settlement and the impact on the surrounding soil. Therefore, synchronous excavation with a spacing of 100 m between the two tunnels can be chosen for this project in order to reasonably shorten the construction period.  
Figure 7: Variation curve of displacement at different monitoring points with the spacing of tunnel excavation surface.
## 4 Results and Discussion
### Anemia Severity Detection  
Hematologists face challenges when identifying and predicting abnormal RBC cell structures at the pixel level using a simple microscope due to their dense and overlapped patterns. The dense and overlapped pattern of RBC causes the unsuccessful prediction of severity and changes in anemic cells with respect to the normal cell using a compound microscope. This study's main objective was to classify anemic RBC elements and detect anemia severity levels to further classify the anemic images into mild or chronic. In 3-TierDCFNet, module-II performs severity detection in anemic RBC elements and classifies input image as mild or chronic. The results obtained during the severity detection module are shown in Table 5.  
Results indicate that our model outperforms the detection of chronic anemia stages because severity detection parameters like shape, size and central white pallor size have more prominent changes in chronic stages compared to healthy RBC. The confusion matrix of severity detection measures is shown in Figure 11.  
Figure 10: Comparison among individual and global training, validation, and test accuracies with relevant losses of all tiers. The error bars show the difference between accuracy and relevant loss values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classes** & **Accuracy (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Specificity (\%)** \\ \hline Mild & 96.82 & 95.96 & 96.89 & 95.96 \\ Chronic & 98.94 & 96.83 & 97.49 & 96.61 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation of anemia severity detection module in 3-TierDCFNet during testing.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
## 3 Model
### Attacker Model
#### 3.1.3 Calculation of Feasible Attack Flows  
The attack traffic to be allocated on each attack direction path \(j\) is a non-negative variable \(x_{j}\). The objective is to minimize the sum of \(x_{j}\).  
\[min\sum_{j=1}^{\text{m}}x_{j} \tag{1}\]  
For the total links \(L\) in the network, the attacker seeks a traffic distribution on these links and satisfies the following two constraints:  
1. For all target links \(T\in L\), the traffic needs slightly higher than the capacity \(C_{T}\) to ensure congestion: \[\sum_{j\in P_{T}}x_{j}>C_{T}\] (2)
2. For all non-target links \(U\in L\), the traffic needs slightly higher than the capacity \(C_{U}\), to ensure that traffic does not become congested: \[\sum_{j\in P_{U}}x_{j}<C_{U}\] (3)  
By satisfying these two sets of constraints, the target link can be squeezed with the minimum bandwidth without causing congestion on other links.  
After the solution \(x_{j}\) is obtained, let \(D\) be the maximum absolute bandwidth increase in the uplink, MaxUp, as follows:  
\[D=\max_{L\in E_{\text{uplink}}}\sum_{j\in P_{T}}x_{j} \tag{4}\]
# Study on Low Temperature Drying Characteristics of Chicken Manure in a Humid Environment
## 2 Materials and Methods
### Calculation of Drying Parameters  
Wet basis moisture content and dry basis moisture content are the main parameters for evaluating the moisture content of chicken manure. The wet basis moisture content was calculated using Equation (1), and the dry basis moisture content was calculated using Equation (2):  
\[\mathbf{w}=\frac{W_{\textit{timely}}-W_{\mathbf{0}}(1-M_{\mathbf{0}})}{W_{\textit{timely}}} \times\mathbf{100\%} \tag{1}\]  
\[\mathbf{X}=\frac{W_{\textit{timely}}-W_{\mathbf{0}}(1-M_{\mathbf{0}})}{W_{\mathbf{0}}(1-M_{ \mathbf{0}})} \tag{2}\]  
where \(W_{timely}\) is the timely weight (g) of chicken manure samples; \(W_{\mathbf{0}}\) is the initial weight (g); \(M_{\mathbf{0}}\) is the initial moisture ratio of chicken manure samples; \(w\) is the wet basis moisture content; \(X\) is the dry basis moisture content, and it can also be calculated using Equation (3):  
\[\mathbf{X}=\mathbf{w}/(1-\mathbf{w}) \tag{3}\]  
When comparing the moisture content changes of different groups, it is more objective to use the moisture ratio (\(MR\)). The values of moisture content obtained under different conditions could be converted into the moisture ratio. The moisture ratio can be calculated using Equation (4) [18]:  
\[\mathbf{MR}=\left(\mathbf{X_{t}}-\mathbf{X_{e}}\right)/\left(\mathbf{X_{0}}-\mathbf{X_{e}}\right) \tag{4}\]  
where \(X_{t},\ X_{0}\), and \(X_{e}\) are the local, initial, and equilibrium chicken manure moisture ratios (dry basis), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Air Velocity (m/s)** & **Thickness (mm)** & **Temperature (\({}^{\circ}\)C)** \\ \hline
1 & 1.2 & 40 & 15 \\
2 & 1.2 & 40 & 25 \\
3 & 1.2 & 40 & 35 \\
4 & 1.2 & 20 & 25 \\
5 & 1.2 & 60 & 25 \\
6 & 0.6 & 40 & 25 \\
7 & 1.8 & 40 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Drying conditions of different experimental groups.  
The value of the equilibrium moisture ratio \(X_{e}\) is much smaller than \(X_{t}\) and \(X_{0}\)[11]. Therefore, \(X_{e}\) is ignorable and Equation (4) can be simplified as:  
\[\mathbf{MR}=\mathbf{X_{t}}/\mathbf{X_{0}} \tag{5}\]  
The drying rate (\(DR\)) of the chicken manure within a given time interval was calculated as follows:  
\[\mathbf{DR}=\frac{\mathbf{X_{t}}-\mathbf{X_{t+\Delta t}}}{\Delta t} \tag{6}\]  
where: \(X_{t}\) is the weight of chicken manure at the beginning of a 2 h drying period, \(\Delta t\) is the drying time interval (2 h), and \(X_{t+\Delta t}\) is the weight of the chicken manure at the end of a 2 h drying period.
## 5 Conclusions  
According to the experimental and theoretical study on heat transfer coefficients of building external surfaces in the tropical island region, some main findings can be summarized as below:  
1. Based on full-scale measurements in the tropical island region, the function expression between CHTC and wind speed was investigated to be CHTC = 5.56 + 4.48\(u\) (R\({}^{2}\) = 0.94), and the CHTC predictive formula was validated to be reliable, with a mean percentage error of 2.50% for estimating CHTCs in the tropical island region.  
2. The characteristics of daily average heat transfer coefficients in the tropical island region were as follows: The variation trend of CHTC was coincident with wind speed, and the daily average CHTCs were all concentrated in the range of 10-60 W/(m\({}^{2}\cdot\)K); RHTC was almost impervious to wind speed and rainfall variations, keeping unchanged at 5.8 W/(m\({}^{2}\cdot\)K); and EHTC showed strong randomness and indeterminacy owing to rainfall effects, and it had a wider changing range from 0 to 160 W/(m\({}^{2}\cdot\)K).
3. Whether evaporation was considered or not, THTC differences were all less than 2.5 W/(m\({}^{2}\cdot\)K) from February to May, since these months possessed relatively low or medium wind speeds and very few total rain hours. Except for the several months above, THTC differences were all more than 4.6 W/(m\({}^{2}\cdot\)K), and for July particularly, the THTC difference reached up to 12.5 W/(m\({}^{2}\cdot\)K) under the conjoint action of high wind speed and strong rainfall. Additionally, in terms of the whole year, the THTC difference was 5.2 W/(m\({}^{2}\cdot\)K). Consequently, the effect of evaporation heat transfer on THTC is significant and cannot be ignored directly.
4. When evaporation was taken into account, the daily average THTC ranged from approximately 16 to 220 W/(m\({}^{2}\cdot\)K) throughout the year. Furthermore, the number of days that THTC was more than 25W/(m\({}^{2}\cdot\)K) was 219, occupying a 60% proportion of year-round time.
5. THTCs with consideration of evaporation in winter and summer were respectively 33.4 W/(m\({}^{2}\cdot\)K) and 38.9 W/(m\({}^{2}\cdot\)K) in the tropical island region, which are much larger than the recommended values in the current national standard.  
[PERSON] and [PERSON] designed and performed the experiments; [PERSON] analyzed the data; [PERSON] conceived and wrote the paper.  
This research was funded by National Natural Science Foundation of China, grant number 51590912 and 51578013, and Natural Science Foundation of Beijing, Youth Program, grant number 8184061.  
The authors would like to express their gratitude to all the people and organizations involved in this study.  
The authors declare no conflict of interest.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### Asset Management
#### 4.1.1 Real-Time Health Monitoring and Performance Analysis  
Digital Twins enable real-time health monitoring and performance analysis of grid components [90]. By leveraging sensor data and advanced analytics, they can provide instant feedback on the condition of electrical assets, detect anomalies, and assess performance degradation over time [91]. This approach allows for the immediate identification of potential issues before they escalate, ensuring the reliability and efficiency of the smart grid.
## 4 Measurement Setup and Results
### Experimental Configuration  
To emulate realistic conditions, measurements were conducted in an indoor laboratory environment. The radar transmitted an FMCW signal with a bandwidth of 100 MHz and a chirp duration of 200 us. The effective radiated power was set to 10 dBm EIRP, compliant with SRD regulations. The radar received the backscattered second harmonic signal through a three-element monopole array with an estimated gain of 8 dBi. The received signal was digitized using a 16 bit ADC at a sampling rate of 744 ksps, allowing sufficient resolution for beat frequency extraction. A Direct Memory Access (DMA) interface enabled real-time data acquisition and processing. Range ground truth was obtained using a Bosch GLM 500 laser distance meter (Robert Bosch GmbH, Gerlingen, Germany) with millimeter-level precision.  
Figures 9 and 10 illustrate the radar hardware configuration used during the measurement campaign. Figure 9 provides a schematic representation of the harmonic FMCW radar setup, including the transmitter (2.4-2.5 GHz), receiver (4.8-5.0 GHz), and passive harmonic tag augmented with two reflectors at fixed offsets (5.5 cm and 12.5 cm). These reflectors moved together with the tag and simulated near-field multipath conditions. Figure 10 presents photographic evidence of the experimental scene, showing the radar system under test in an indoor environment. The left photo offers a top-down view of the radar-target-reflector setup, while the middle and right images show front views highlighting the TX/RX antennas and the placement of the passive tag and reflectors. These visualizations support real-world measurement claims and demonstrate the physical setup used to evaluate peak detection performance. Note that the FPGA implementation mentioned in the abstract was conducted offline to estimate hardware feasibility and was not deployed in this measurement loop.  
Figure 9: Schematic of the harmonic radar system with a passive harmonic tag and two reflectors. Reflector 1 and Reflector 2 were positioned at 5.5 cm and 12.5 cm from the tag, respectively, and moved together with the tag as a group. The distance \(d\) represents the range between the radar and the tag [34].