esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method
### Definition of the Modelled Geophysical Gravity Signals-South America
#### 2.2.1 Glaciers  
The mass change due to glaciers melting in South America is derived from integrating the information on areal extent of glaciers from the RGI (Randolph Glaciers Inventory) catalogue [20] and ice thickness variations from glaciological observations available for a selection of glaciers in the WGMS (World Glacier Monitoring Service) database [21]. RGI outlines are shown in Figure 1a, while the locations of the glaciological observations are reported in Figure 1b (yellow dots). The glaciological observations have been elaborated so that a yearly thickness variation for each glacier was obtained.  
The WGMS observations cover only a subset of the glaciers of South America, so we extrapolated average deglaciation rates to the other unmonitored glaciers in order to obtain  
Figure 1: **(a)** Glaciers’ outlines according to the RGI database (red) and lakes (blue) in South America; **(b)** location of the glaciers with observed change rates in WGMS; **(c)** gravity signal at 250 km-altitude calculated from our ice thickness model; **(d)** yearly mass change for each RGI glacier according to our model (blue) and cumulative effect as function of latitude (in dark orange).  
a more realistic mass change estimation. The average rates were estimated by analyzing the database WGMS and RGI and looking at relations between deglaciation rate and area, height, and geographic position. We found no significant statistical relation between all the parameters. We only noted that the large glaciers (area > 10 km\({}^{2}\)) are systematically melting with yearly average rates of \(-\)1.0 m/yr +/\(-\)0.91 m/yr. Smaller glaciers show larger dispersion of the rates, with an average variation of \(-\)0.40 m/yr +/\(-\)0.50 m/yr. These two rates were used for extrapolating the rates on the unmonitored glaciers, dividing them into the above-mentioned two area-classes.  
The final model of the yearly mass change variation for each glacier in SA is shown in Figure 1d: the plot illustrates the various contributions as function of latitude. The brown curve plots a cumulative variation, where we can see that Patagonian glaciers (at latitudes \(-\)50\({}^{\circ}\)) lose over 20 Gt/yr of mass. Plot of the gravity effect at 250 km for the glaciers thickness variation is shown in Figure 1c.
## 3 Observational Data Sources and Processing
### Ionospheric Data Sources and Processing Methods  
Electron density data from the Langmuir Probe (LAP) of the electromagnetic satellite Zhangheng-1(known as CSES) are used as ionospheric data. The satellite was launched on 12 February 2018. It is a sun-synchronous satellite with an orbital inclination of 97.4\({}^{\circ}\), a flight altitude of 507 km, and a revisit period of five days. The indication of local time is about 2 AM for Zhangheng-1. The LAP makes measurements every 3 s in survey mode. When passing over the seismic zone and Chinese region, it switches to detailed inspection mode and makes measurements every 1.5 s. Because the ionospheric changes at night are less affected by interference, this study analyzed only the ascending orbit data. These data correspond to local night, and data with \(Kp\geq 3\) (\(Kp\) is an index that describes global geomagnetic activity) are excluded to eliminate the effects of magnetic disturbances. According to previous studies, most ionospheric disturbance anomalies occurred within 15 days before an earthquake [50,51]. The ascending orbit data for 30 days before the earthquake were selected for analysis, and the Kriging method (a method for unbiased optimal estimation of variables in a finite region based on variogram theory and structural analysis) was used to interpolate between adjacent orbits.  
Due to the ionospheric electron density exhibiting relatively significant annual and seasonal changes, the ascending orbit data in the quarter before the earthquake were used as background data, where data with \(Kp\geq 3\) were removed. A \(5^{\circ}\times 5^{\circ}\) grid was used, and the average value of each grid in the quarter was calculated as the background value to analyze changes during the study period in this area. According to the orbital position of the electromagnetic satellite, the orbit closest to the earthquake was found, and the part that was closer to the epicenter and had higher abnormal values was selected for the study.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### How the Optimizations Were Achieved
#### 5.1.1 Route Request and Route Reply Optimization  
We increased the hello message interval to reduce the control packet overheads on the channel since nodes are less likely to be transmitting when a route request is initiated. The route waiting list for route replies was expanded to accommodate a greater number of entries, as for the hello message list. Furthermore, the route request and route reply SDU sizes were slightly increased, which also had a good impact on the PDR, as well as the throughput, as observed in preliminary experiments that are not reported in this study. As a result, we used the same value in all of the tests reported.
## 2 Materials and Methods
### The Classification Algorithm
The data for the parameter tuning were divided into 10 sets of training and validation datasets, allowing cross-validation of the models evaluated and then fed to the neural network algorithm. The algorithm returns the performance of the models on the validation sets, which is the basis for selecting the tuned parameters that allowed best performance for the model. The model is trained on a joint dataset (train + validation) to obtain the best performing model. The performance of the best model is assessed using a test dataset, unseen during the previous phases of the workflow.
## 4 Comparison and Validation  
Figure 7a depicts a comparison of the load-displacement relationships obtained from FEM with different mesh sizes and experimental results. The mesh element sizes are 0.2 mm, 0.3 mm, 0.4 mm, 0.5 mm, and 0.6 mm for FEM-1 to FEM-5, respectively. As can be seen from Figure 7a, the mesh element size has a slight effect on the FEM results, with smaller element sizes closer to the EXP results, i.e., FEM-5 matches up best with EXP. By comparing FEM-1 to FEM-5, it can be observed that the results of FEM-4 and FEM-5 overlap almost exactly, but the time consumed by the latter is about three times that of the former, and therefore, the FEM with a mesh element size of 0.4 mm, i.e., FEA-4, was used in the following investigations in terms of calculation time and computational accuracy.  
Figure 5: Possible contact pairs in the 2D FEA models.  
Figure 6: Comparison of load–displacement relationships of the gasket-in-groove with different friction coefficients.  
Additionally, Figure 7a shows that the FEA-4 simulations are in good agreement overall with the experimental results, but deviations in magnitude are also observed within 10%. From the point of view of engineering applications, this level of deviation is acceptable. Hence, it is considered that the FEM proposed in this study is reasonable and capable of accurately simulating the nonlinear behavior of the gaskets. Deviations between the FEA and EXP results probably resulted from the plane strain condition assumed in the FEA, which is incompatible with the real conditions that no displacement constraints are imposed on the ends of the gaskets in experiments.  
Figure 7b shows a comparison of the failure pressure (i.e., air-leakage pressure) \(p_{f}\) from the experimental test and FEA results with different initial average contact pressure \(p_{i}\). It was found that the variation trend on \(p_{i}\)-\(p_{f}\) is consistent between the two methods; i.e., \(p_{f}\) shows a decreasing trend with increasing \(p_{i}\), which further justifies the reliability of the FEA model of this paper. Moreover, it can also be observed that FEA results are slightly higher than the experimental test results, which implies that the FEA may overestimate the sealing capacity of the gasket-in-groove, which is possibly related to the microscopic rough morphology of the gasket surface. Microscopically, the gasket surface in the FEA is assumed to be completely flat (i.e., 0 roughness), whereas in experiments, the gasket surfaces are not smooth due to the manufacturing workmanship even \"undulating\". The \"undulating\" surfaces of the gasket have the potential to result in a different distribution of the contact pressure from the smooth surfaces and thus affect the failure pressure.  
To clarify, the above comparison between the numerical simulations and the experimental results was carried out in static conditions. A comparison of the results obtained from the two methods under cyclic loads or the effect of fatigue is unknown and needs further investigation, which is beyond the scope of this paper.
## 2 Related Work
In the CoRaSat project [24], Ka-band spectrum sharing between either single GSO or single NGSO (with static or mobile earth stations) and terrestrial users is mainly based on database techniques together with supplementary cognitive techniques as spectrum sensing. A centralized database and interference modeling is used to obtain the cognitive zones. Inside cognitive zones, opportunistic operations are only allowed with the use of cognitive techniques to reduce the interference, while outside these zones, entities in the network can transmit without restrictions. Cognitive zones are also applied in [25] for dual satellite FSS systems in 17.3-17.7 GHz band. The cognitive zone around incumbent broadcasting satellite service feeder links is determined by employing the characteristics of the links, which are stored and obtained from databases. Similar to the CoRaSat project, the cognitive FSS terminals can freely utilize the same frequency band outside cognitive zones. In the FREESTONE project [26], the possibilities to use frequency sharing techniques in SATCOM are investigated, and several application scenarios and use cases are defined. Oneof the use cases is the coexistence of terrestrial FS and satellite FSS systems in 17.7-19.7 GHz Ka-band. A database design is proposed, requiring information from the national FS registry, and information about the secondary FSS terminals. Based on the database, it is calculated where and how the FSS users are allowed to operate in the band. Authors in [27] extend on scenarios evaluated in the FREESTONE project and analyze sharing between GSO and NGSO satellite in Ka-band. They describe how a spectrum database could be utilized in controlling and assisting coexistence in such a scenario. It is required that systems' main operational characteristics are stored in a spectrum database, such as frequency allocations, orbital positions, antenna patterns, etc. It is worth noting that the introduction of dynamic entities like NGSO satellites or mobile ESs makes the use of a database approach very challenging [28]. Therefore, while solutions based on centralized databases may be appropriate for scenarios with a limited number of NGSO satellites (as assumed in all previously reported work), they might not be scalable to support upcoming large NGSO constellations. The downsides of approaches based on the centralized database are further investigated in Section 4. Similar to other studies on satellite-terrestrial and dual satellite coexistence, studies on coexistence techniques relying on databases have not yet addressed the problem of GSO, NGSO, and terrestrial networks simultaneously operating in the same frequency band.
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Literature Survey: Plant Disease/Nutrient Deficiency Monitoring Systems  
Many authors in the literature have proposed plant disease/pest/weed detection systems that employ the above-described general format. Literature shows that this technology of plant disease detection models has been developing at a faster rate in the last two decades and achieving high success in terms of classification accuracy and efficiency.
## 3 Problem Formulation  
Here, we mainly try to propose an novel precoding method, enabling communicating between BS and UEs while also being applicable to radar sensing. To achieve a balance between communication and radar sensing functions in the distributed feedback DFRC system, we introduce a weighting parameter \(\delta\) where \(0\leq\delta\leq 1\) (The weighting factor \(\delta\) serves as a design parameter that allows system operators to balance radar and communication functionalities. When \(\delta=0\), the system focuses on minimizing the communication MSE without considering radar performance. Conversely, \(\delta=1\) emphasizes radar beampattern design. In practice, \(\delta\) can be chosen based on empirical tests, system design guidelines, or performance specifications). The optimization problem is subsequently defined as follows:\[\begin{split}\min_{\mathbf{X},\mathbf{X}_{0},\rho}&(1-\delta) \Big{(}\|\mathbf{S}-\rho\mathbf{H}\mathbf{X}\|_{F}^{2}+\rho^{2}ULN_{0}\Big{)}+ \delta\|\mathbf{X}-\mathbf{X}_{0}\|_{F}^{2}\\ \text{s.t.}& x_{b,l}\in\mathcal{X},\ \forall b,l\\ &\frac{1}{L}\mathbf{X}_{0}\mathbf{X}_{0}^{H}=\mathbf{R}\end{split} \tag{11}\]  
Due to the constant modulus requirements on both the real and imaginary components, the problem in Equation (11) is considered NP-hard [36]. This problem can be solved through an exhaustive search of complexity \(4^{BL}\), but this inevitably results in a high computational burden.  
To address the aforementioned challenges, we establish an iterative framework using the separate adaptive tuning parameters \(\mathbf{X}\), \(\mathbf{X}_{0}\), and \(\rho\). More concretely, this framework involves a three-step iterative process that partitions variables of the optimization problem into several blocks (or groups). In each iteration, only one block of variables is updated, while variables in the other blocks are treated as constants.
## 7 Experimental Results
When transitioning from TLSv1.2 to TLSv1.3 cipher suites, changes in MQTT protocol versions do not significantly affect performance, contrasting with observations from TLSv1.2 suites. Figure 6:3 Performance Analysis of Data Throughput: MQTT V3.11 over IEEE 802.3 ab Connections with TLSv1.3 Encryption Protocols across Quality of Service Tiers. Figure 6:4 Performance Analysis of Data Throughput: MQTT V5.0 over IEEE 802.3 ab Connections with TLSv1.3 Encryption Protocols across Quality of Service Tiers. Figure 6:5 Performance Analysis of Data Throughput: MQTT V3.11 over IEEE 802.11n/ac Connections with TLSv1.3 Encryption Protocols across Quality of Service Tiers. Figures 20 and 21 present the delay distribution for TLS v1.3 cipher suites at QoS0 level on 3 GPP-4G links and MQTT V5. The analysis of the boxplot in Figure 22 reveals that certain cipher suites, such as ECDHE-RSA-AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305, show the highest variability in latency, suggesting significant fluctuations under certain network conditions, potentially due to encryption complexity or network congestion. In contrast, suites like AES128-SHA256 and ECDHE-ECDSA-AES128-SHA demonstrate more stable and consistent performance with fewer deviations in latency. The presence of outliers,  
Figure 6:6 Performance Analysis of Data Throughput: MQTT V5.0 over 3 GPP-4G Connections with TLSv1.3 Encryption Protocols across Quality of Service Tiers. Figure 6:7 Performance Analysis of Data Throughput: MQTT V5.0 over IEEE 802.11n/ac Connections with TLSv1.3 Encryption Protocols across Quality of Service Tiers. Figure 6:8 Performance Analysis of Data Throughput: MQTT V3.11 over 3 GPP-4G Connections with TLSv1.3 Encryption Protocols across Quality of Service Tiers. particularly for more variable suites, indicates occasional instances of high latency, likely caused by external factors such as network interference or congestion. Overall, the choice of cipher suite has a noticeable impact on latency performance, with some suites exhibiting less predictable behavior, which may affect their suitability for certain applications.
## 4 Experiment
### Fusion Feature Extraction Based on Sensitive IMF Selection  
In this work, EEMD was used to deal with these obtained vibration signals. In this process, the decomposition number and amplitude of the added white noise were set as 100 and 0.2 times the standard deviation of the original signal. Figure 10 displays the decomposed IMFs of these vibration signals collected by Sensor 6 under conditions S1, S2, S3, and S4, mentioned above. When analysing these waves in Figure 10, it can be observed that the high frequency information mainly occupies the first several IMFs and the last several IMFs contain less useful low frequency information. Therefore, it was necessary to develop an adaptive sensitive IMF evaluation method.  
The CCRF values of the IMFs plotted in Figure 10 were calculated and are presented in Table 2 as a digital representation. From this table, it can be observed, from the CCRF values, that the most sensitive IMFs are the first several IMFs of EEMD. That is, the last IMFs are of minimal value for fault diagnosis and can be removed as noise. The CCRF values in Table 2 indicate that the most sensitive IMFs are the first several IMFs of EEMD. In this paper, the first three IMFs with the greatest CCRF values were selected to reconstruct the vibration signals for the features. The reconstructed vibration signals of Sensors 1-6 were calculated using Equation (14). Then, the original features, identified in Ref. [46], were calculated for fusion features calculated by correlation analysis and weighted average. Table 3 displays the group weight values of the six sensors under the four considered conditions. From this table, it can be observed that the weights belonging to the six sensors were different in value; the greatest weight values belonged to Sensor 1 (once) and Sensor 3 (three times). Therefore, using a traditional averaging method to fuse the features of the six sensors would ignore the contributions of the original features. That is, the features with more useful information and the features with less useful information would contribute the same weighting when calculating the fusion features using a traditional averaging method. To fully use the pertinent information of the six sensors, fusion features were extracted using Equation (16).  
Figure 9: Waves of vibration signals in time and frequency domain under four bearing conditions.
## 5 Optimal Power Allocation Design and Energy Efficiency Analysis
### Optimal Power Allocation Design
Also, let \(Q_{1}=\frac{Q}{|{l_{1}}|^{2}}\) and \(Q_{2}=\frac{Q}{|{l_{2}}|^{2}}\), to maximize the EE through maximizing the total transmission rate with the same total transmit power and the requirement of \(Q\), and the optimal problem can be described as  
\[\max_{\delta} R_{1}^{d}+R_{2}^{d}\] (24a) s.t. \[P_{1}=\delta Q_{1} \tag{24b}\] \[P_{2}=(1-\delta)Q_{2}\] (24c) \[0\leq\{P_{1},P_{2}\}\leq P_{T}^{\max} \tag{24d}\]  
where \(\delta\) is the power distribution factor under the interference threshold. With \(\delta\), \(P_{1}\) and \(P_{2}\) also satisfy \(P_{1}|{l_{1}}|^{2}+P_{2}|{l_{2}}|^{2}=Q\). At the same time, it can be found that through finding the optimal \(\delta\) under the Q, the total transmission rate can be maximized. Let \(U^{d}=\frac{P_{1}h^{2}}{P_{P}l_{1}^{2}+\sigma_{1}^{2}}\) and \(Z^{d}=\frac{P_{1}h^{2}}{P_{P}l_{2}^{2}+\sigma_{2}^{2}}\), and the optimal problem can be re-described as  
\[\max_{\delta} \log_{2}\Big{(}1+U^{d}\Big{)}+\log_{2}\Big{(}1+Z^{d}\Big{)}\] (25a) s.t. \[P_{1}=\delta Q_{1} \tag{25b}\] \[P_{2}=(1-\delta)Q_{2}\] (25c) \[0\leq\{P_{1},P_{2}\}\leq P_{T}^{\max} \tag{25d}\]  
Substituting (25b) and (25c) into (25a), the maximum of the total transmission rate can also be obtained with the following equation:  
\[\max_{\delta} \left[\log_{2}\Big{(}1+U^{d}\Big{)}+\log_{2}\Big{(}1+Z^{d}\Big{)} \right]=\frac{\partial\Big{[}\log_{2}\Big{(}1+U^{d}\Big{)}+\log_{2}\Big{(}1+Z ^{d}\Big{)}\Big{]}}{\partial\delta}=0 \tag{26}\]  
Solving (26), the optimal \(\delta=\frac{Q_{1}n-Q_{2}m+Q_{1}Q_{2}mm}{2Q_{1}Q_{2}mm}\) and \(1-\delta=\frac{Q_{2}m-Q_{1}n+Q_{1}Q_{2}mm}{2Q_{1}Q_{2}mm}\) can be obtained, where \(m=\frac{h^{2}}{P_{P}l_{1}^{2}+\sigma_{1}^{2}}\) and \(n=\frac{h^{2}}{P_{P}l_{2}^{2}+\sigma_{2}^{2}}\). Then, with (25b) and (25c), the optimal transmit powers can be finally obtained as  
\[ \begin{cases}P_{1}^{d}=\frac{Q_{1}n-Q_{2}m+Q_{1}Q_{2}mm}{2Q_{2}mm}\\ P_{2}^{d}=\frac{Q_{2}m-Q_{1}n+Q_{1}Q_{2}mm}{2Q_{1}mm}\end{cases}  \tag{27}\]
# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 3 Recognition Model
### Classification Algorithm
#### 3.4.1 Training the Network and Fitting the Weibull Model  
The entire process is shown in Algorithm 1. Firstly, parameter \(r\) is set to fit the Weibull model, which represents the number of top distances. Then, the network is optimized by the joint loss function, and the prototypical networks are trained using the Euclidean distance. The prototypes are first constructed and initialized for each class according to the feature dimension and the number of classes. After the training samples undergo feature extraction by the network, the distances between the features and each prototype are calculated, along with DCEL and PL. Finally, the network is trained and optimized by DCEL and PL, while updating the network parameters and prototypes \(a_{i}\). After the training is completed, each class of signals is intra-class tight and inter-class separable in the feature space. The features \(f(x_{ij};\theta)\) of correctly classified samples in each class are also obtained, where \(j\in\{1,2,\cdots,J\}\), with \(J\) representing the number of correctly classified samples in each class.  
Afterwards, the mean features \(\mu_{i}\) of the correctly classified samples in each class are calculated, and the distances \(d_{ij}=\left\|f(x_{ij};\theta)-\mu_{i}\right\|\) between the mean features and the features of the correctly classified samples in each class are measured. Then, the set of distances in each class is sorted, and the \(r\) largest distances are selected to fit Weibull model, which includes the Weibull shape and scale parameters \(\alpha_{i}\) and \(\beta_{i}\).  
```
step 1: Set the value of \(r\) to fit the Weibull model. step 2: Initialize prototype \(a_{i}\) and network parameters. step 3: Train the network by minimizing DCEL and PL. step 4: Update the network parameters and prototypes \(a_{i}\). step 5: Extract the features \(f(x_{ij};\theta)\) of sample \(x_{ij}\), with correct classification for each class. step 6: for\(i=1\cdots N\)do  Compute mean features \(\mu_{i}=mean_{i}\Big{(}f(x_{ij};\theta)\Big{)}\)  Fit Weibull model \(\rho_{i}(\alpha_{i};\beta_{i})=g\Big{(}\left\|f(x_{ij};\theta)-\mu_{i}\right\|,r\Big{)}\) endfor Return mean features \(\mu_{i}\) and Weibull model \(\rho_{i}\)
```  
**Algorithm 1** Training the network and fitting the Weibull model  
In Algorithm 1, \(g\big{(}\left\|f(x_{ij};\theta)-\mu_{i}\right\|,r\big{)}\) is the fitting function [