esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
The characteristics of mesospheric inversion layers in the high stratosphere and mesosphere over OHP are the focus of this research. Seasonal trends and variability were studied using the OHP dataset over a lengthy period of time, from 1978 to 2019. In the high stratosphere and lower mesosphere, we confirmed an annual cycle, with a maximum in winter and a minimum in summer. The lidar observations showed night-to-night fluctuations, as well as inter-annual variability in the mesospheric inversion layers. Based on 42 years of Rayleigh lidar data in the middle atmosphere (30-85 km), we also generated mesospheric inversion layer climatology. In addition, the reasons for different classifications of MILs, as well as diverse dynamics that result in the formation of mesosphere inversion layers, were attempted to be clarified (MILs). Finally, we analyzed the seasonal variations in the mesospheric inversion layer activity that observed the dissipation of gravity waves in all seasons. The long-term changes appear to be more difficult to be interpreted either the total MIL occurrence or derived according to the classes, showing at least above OHP that there is no climate evidence associated with MIL occurrence suggesting also no significative climate effect on gravity wave interaction with the mean flow. Attribution of MIL categories to formation processes is not obvious. Analyses with global data with frequent observations will be required using a satellite constellation, for example, [55]. Another alternative is to use much more measured parameters to clarify the processes involved, and the combined analyses of temperature and wind could be also a good indicator of the spatial MIL extensions and then the related processes of either tides, gravity waves, or planetary waves.  
**Author Contributions:** Conceptualization, P.K. and A.H.; methodology, P.K., A.H., M.M., and R.W.; software, M.A. and R.W.; validation, P.K., A.H., and R.W.; formal analysis, M.A.; investigation, M.A., R.W., and G.F.; resources, P.K. and A.H.; data curation, A.H., M.A., and R.W.; writing--original draft preparation, M.A.; writing--review and editing, R.W., M.M., and P.K.; visualization, M.A.; supervision, P.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research associated with MARTIC was funded by CNES, and by the Programme National de Teledetection Spatiale.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are freely available can be obtained at the NOAA (National Oceanic and Atmospheric Administration)NDACC portal (www.ndaccdemo.org ) and at the French atmospheric data portal AERIS: cds-espri.ipsl.fr/NDACC/.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
### Review of Techniques for Remote Sensing Cropland in Savanna Landscapes
The technique has been recommended for semi-arid study sites where there is a high frequency of mixed pixels and reflectance is dominated by the soil background [35]. Research has shown that a basic three-endmember spectral mixture model can represent over 95% of all observed Landsat image spectra, producing fraction estimates that closely agree with _in situ_ measurements and quantitative output that directly relates to the physical characteristics of land cover [41]. Spectral unmixing has been effectively applied to both mono and multi-temporal data in semi-arid environments in monitoring desertification and desertification risk [42, 43], rangeland degradation [44], and mapping invasive grasses [45]. Logistic regression is a flexible alternative to the traditional supervised classification approach. [PERSON] _et al._ (2013) [46] discuss the \"desirable qualities\" of logistic regression underscoring the rather unrestrictive model assumptions, the model's incorporation of both categorical and continuous variables into the classification scheme, ease of model comparisons, and a focus on direct modeling of class probabilities. Environmental variables have been paired with logistic regression to identify distributions of land cover [47] but coupled with multispectral data and derivatives, logistic regression has been utilized to classify land cover [48], identify areas of advancing land degradation [49], and mapped burned lands [50]. We adopt logistic regression in the classification protocol presented here, pairing the logit model with multispectral derivatives generated from temporal intra-band differencing and spectral unmixing.
## 3 Results and Discussion
### Optimization Results and Analysis of Mulberry Drying Technology
#### 3.2.5 Determining the Weights of the Comprehensive Indexes  
The common factor variance in the evaluation indexes was determined by principal component analysis (PCA), and the proportions of the common factor variance of the indexes were used as weights for the indexes [29]. The results are shown in Table 9. The soluble solid content, chewiness, and VC content of the mulberry all accounted for more than 20% of the weights. According to the test results, these were the three indexes for the microwave vacuum drying of mulberry.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Vacuum Degree** & **Value of** & **Soluble** & **Chewiness** & **Hardness** & **Vc** \\
**/MPa** & **Chromatis** & **Solid \%** & & **N** & **mg (100 g)\({}^{-1}\)** \\ \hline
0.01 & 3.23 & 37.74 & 6.35 & 6.32 & 0.27 \\
0.02 & 3.02 & 38.58 & 7.53 & 5.96 & 0.48 \\
0.03 & 2.97 & 39.02 & 8.56 & 5.67 & 0.56 \\
0.04 & 2.98 & 39.64 & 8.97 & 5.36 & 0.68 \\
0.05 & 2.75 & 39.99 & 9.65 & 5.33 & 0.83 \\
0.06 & 2.54 & 40.65 & 9.89 & 4.99 & 1.15 \\
0.07 & 2.56 & 42.85 & 10.34 & 4.74 & 1.28 \\
0.08 & 2.33 & 42.30 & 8.94 & 4.23 & 1.33 \\
0.09 & 2.22 & 42.41 & 7.65 & 3.20 & 1.45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Effects of the vacuum degree on the drying quality of mulberry.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Microwave** & **Value of** & **Soluble** & **Chewiness** & **Hardness** & **Vc** \\
**Power** & **Chromatis** & **Solid** \%** & & **N** & **mg (100 g)\({}^{-1}\)** \\ \hline
200 & 2.93 & 37.7 & 6.43 & 3.80 & 0.28 \\
250 & 2.85 & 38.2 & 7.79 & 4.16 & 0.57 \\
300 & 2.79 & 39.7 & 8.04 & 4.32 & 0.79 \\
350 & 2.96 & 40.85 & 9.34 & 4.44 & 1.38 \\
400 & 2.88 & 41.9 & 9.65 & 4.50 & 0.88 \\
450 & 2.46 & 42.5 & 9.91 & 4.49 & 0.56 \\
500 & 2.77 & 43.6 & 10.23 & 4.54 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effects of the microwave power on the drying quality of mulberry.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Value of Chromatis** & **Soluble Solid** & **Chewiness** & **Hardness** & **Vc** \\ \hline Material surface temperature/\({}^{\circ}\)C & 0.02 \({}^{\ast}\) & 0.012 \({}^{\ast}\) & 0.016 \({}^{\ast}\) & 0.03 \({}^{\ast}\) & 0.035 \({}^{\ast}\) \\ Vacuum degree/MPa & 0.008 \({}^{\ast\ast}\) & 0.004 \({}^{\ast\ast}\) & 0.002 \({}^{\ast}\) & 0.008 \({}^{\ast\ast}\) & 0.002 \({}^{\ast\ast}\) \\ Microwave power/W & 0.04 \({}^{\ast}\) & 0.009 \({}^{\ast\ast}\) & 0.024 \({}^{\ast}\) & 0.011 \({}^{\ast}\) & 0.014 \({}^{\ast}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Variance analysis of the mulberry quality under different drying technological parameters.
## 3 Signal Control Method
### Coordination of Main and Pre-Signal Control
#### 3.2.2 Model of Coordination Control
In contrast, if the length of the integrated waiting area is smaller than the minimum queue length of vehicles in the waiting area, vehicles will possibly queue up after the pre-signal, thus the resources in the waiting area will not be fully utilized [30]. As such, it is idealistic that the length of the integrated waiting area is between the maximum and minimum of queue length. \[g_{T}^{s}=0 \tag{4}\]5  
(3) Control model  
A multi-objective linear programming problem is considered. The objectives can be divided into two levels. Level 1: minimizing the total phase times of the through movement and the left-turn. Level 2: minimizing the starting time difference between the main signal and pre-signal of the two movements with the same weight \(\min({t_{T}}^{\prime}+{t_{L}}^{\prime})\). Then, the optimization model turns to be a \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Unit** & **Description** \\ \hline R & s & Red time of the main signal \\ G & s & Duration of green at the main signal \\ G\({}^{\text{s}}\) & - & Start of green at the main signal \\ \(g\) & s & Duration of green at the pre-signal \\ \(g^{\text{s}}\) & - & Start of green at the pre-signal \\ T & s & Cycle \\ f\({}_{f}\) & s & Clearance time of the integrated waiting area \\ f\({}^{\text{i}}\) & s & Difference of the start of green between main \\  & & and pre-signal phase \\ \(l\) & m & Length of the integrated waiting area \\ \(v_{s}\) & m/s & Average speed of the vehicle passing through \\  & & - the integrated waiting area \\ \(S\) & pcu/h & Saturation flow rate for each lane in the \\  & & \multicolumn{2}{c}{integrated waiting area} \\ \(Q\) & pcu/h & Traffic volume \\ \(N\) & — & Number of available lanes in the Integrated \\  & & \multicolumn{2}{c}{waiting area} \\ \(s\) & pcu/h & Saturation flow rate of the lane before the \\  & & \multicolumn{2}{c}{pre-signal} \\ \(n\) & — & The number of lanes before the pre-signal \\ \(X_{max}\) & — & Maximum lane saturation in the integrated \\  & & \multicolumn{2}{c}{waiting area} \\ \(x_{max}\) & — & Lane maximum saturation before pre-signal \\ \(\theta\) & m & Traffic queue length in the waiting area \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of the relevant variables. linear programming problem with the objective of Equation (19), and constraints (3)-(18), which can be solved by the commercial optimizer Lingo. \[\text{min}M(G_{T}+G_{L})+({t_{T}}^{\prime}+{t_{L}}^{\prime}) \tag{19}\]  
\(M\) is an arbitrarily large positive constant number.
## 3 Experimental Setup
### Data-to-Image Conversion
#### 3.4.4 Id-Linear Image Construction  
Since the Id current is provided as a 1D time-series signal, 1024 samples were reshaped into a 32 \(\times\) 32 2D array using the same sequential reshape method as in the previous experiments. The resulting image was grayscale. As this reshaping method is consistentwith the space-filling curve (SFC) linear approach, this method is denoted as Id-Linear in this study.
# Earthquake-Induced Pounding of Medium-to-High-Rise Base-Isolated Buildings
## 2 Materials and Methods
### Numerical Models  
The detailed 2D structural component numerical models of both buildings have been created and applied in the analysis. The discretization was conducted by dividing each structural member into 20 beam-column elements. Therefore, the total number of 900 and 1440 finite elements has been used in the numerical model of five-story and eight-story steel frame buildings, respectively. All connections between the structural members have been defined as fixed. The linear elastic behavior has been adopted for both frame structures. The Rayleigh damping has been applied in the numerical analysis, considering the damping ratio of 3% for both steel buildings.  
The utilized isolation system is composed of elastomeric isolators with lead core inside. Their behavior has been simulated in the analysis by multi-linear non-elastic model (see [12; 23]), which is defined by the following parameters: Effective stiffness (\(K_{\text{eff}}\)), effective damping (\(C_{\text{eff}}\)), initial stiffness (\(K_{1}\)), and yield force (\(f_{\text{y}}\)). The values of these parameters for the structural models of five-story and eight-story buildings are presented in Table 1. These values have been computed based on the natural period of each seismically isolated structure and the hysteresis loop of the isolation system considered.  
It should be underlined that appropriate values of mechanical parameters of seismic isolators are very important for simulations and design of base-isolated buildings. Usually, nominal parameters available in technical data sheets suggested by the manufacturers of seismic isolators are taken into account in such cases. However, actual mechanical properties (especially stiffness) of these isolators are strain-rate sensitive and can be considerably different from nominal values available in data sheets. The results of the in-situ tests show that the real, experimentally obtained stiffness value of the particular isolator can be even 2.2 times larger than the nominal design value (see [36]).  
Figure 2: Cross sections of structural members of five-story and eight-story buildings.
# Random Routing Algorithm for Enhancing the Cybersecurity of LEO Satellite Networks
## 3 Random Routing Algorithm
The node disjoint paths only share the source and destination points [54]. * Satellite-to-satellite disjoint paths (k-DS): This algorithm finds the k shortest paths with the node disjointness forced only on the inter-satellite links while allowing overlaps in the paths of the uplinks and the downlinks. * Limited-overlap shortest paths (k-LO): The algorithm finds the k shortest path between the source and destination ensuring that these paths are sufficiently dissimilar to each other. It is an implementation of the ESX algorithm, as shown in [55], in that it finds the k shortest paths with a similarity score of less than 50%. The proposed random algorithm k-RAND is summarized in Algorithm 1. We generate a random variable A between 0 and 1. For the values of A between 0 and \(a_{1}\), we select the shortest path (k-SP) routing algorithm; for values between \(a_{1}\) and \(a_{2}\), k-DG is selected and so on. The values of \(a_{1}\), \(a_{2}\), and \(a_{3}\) will be determined based on the maximization of the statistics of the cost of attack. There will be a trade-off between these two competing measures (cost and MaxUp) for the DDoS attacks against LSNs. ```
Generate A randomly between [0, 1] If \(0\leq A<a_{1}\) Use shortest paths (k-SP) Else If \(a_{1}\leq A<a_{2}\) Use round-to-ground disjoint paths (k-DG) Else If \(a_{2}\leq A<a_{3}\) Use satellite-to-satellite disjoint paths (k-DS) Else Use limited-overlap shortest paths (k-LO)
```  
**Algorithm 1** k-RAND algorithm  
In order to find the parameters of the proposed k-RAND algorithm, i.e., \(a_{1}\), \(a_{2}\), and \(a_{3}\), we formulate a Bayesian optimization problem. The Bayesian method is a derivative free approach to find the minima of expensive-to-evaluated functions. The cost function of the optimization is the average cost of the DDoS attacks on the LSN. The solution is then obtained using the Bayesian approach [56; 57]. \[\min_{a_{1},a_{2},a_{3}}\ \ C_{avg} \tag{1}\]  
where \(C_{avg}\) denotes the average cost of attacks.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology  
Recently, several classic RVS approaches (FEMA 154 [35], EMS-98 [42], the RISK-UE Project [43], OASP [44], and NRCC [45]) have been established. Buildings constructed before the current standards may be susceptible to strong seismic effects. Thus, these fast evaluation methods are critical for current structures. Data from the 1989 Loma Prieta and 1994 Northridge earthquakes in California were used to calculate basic structural hazard (BSH) ratings in the first iteration of the FEMA 154 RVS method [35], which evaluated seismic hazard using damage probability matrices based on the modified Mercalli intensity (MMI). The Medvedev-Sponheuer-Karnik (MSK) scale-based developed EMS-98 scale is employed to classify buildings both qualitatively (building type and vulnerability) and quantitatively (indicating degrees of damage). Building type, susceptibility, and damage are classified subjectively and quantitatively using the MSK scale-based EMS-98 scale [46; 47]. The 2003 damage estimate approach for the RISK-UE [43] had three steps: LM1, LM2, and LM3 methodologies. The LM1 method identifies vulnerability indices and classes. Basedon the capacity and fragility model, the microseismic model (LM2) of the RISK-UE has been utilized to analyze the seismic risk in urban areas. The LM3 method calculates the structure seismic response utilizing thorough acceleration time histories and nonlinear dynamic analysis. The Earthquake Planning and Protection Organization (OASP) created the Greek RVS approach called (OASP-0). In the OASP, the structural system, including structural components and material qualities, is considered when using an RVS technique. The current NRCC [14] guideline evaluates structural and nonstructural aspects to assess threats to beams, columns, foundations, slabs, partition walls, ceilings, and building significance.  
A consensus-based Likert-ANN approach has been adopted in this study to predict earthquake damage in residential buildings with RC frames. For this purpose, this research focused on selecting structures made of reinforced concrete with a maximum height of 7 stories. Furthermore, this assessment specifically focused on structures that were affected by the catastrophic Kahramanmaras earthquake that occurred on 6 February 2023. In the analysis, 194 buildings up to seven stories with different structural characteristics were evaluated based on linguistic variables, followed by a feed-forward backpropagation (FFBP) neural network. The proposed model was developed based on structured analyses of the facade photographs of the selected buildings, and its seismic performance was evaluated. A Likert scale was used to assess the structural integrity of the buildings, and four damage levels were differentiated. Following that, ANN inputs from the building properties and output data from the structural analysis results were combined to create the proposed model. Lastly, the data were divided into two subsets to train and test the proposed model.
# On Optimal Cell Flashing for Reducing Delay and Saving Energy in Wireless Networks
## 4 Simulation Results
### Cell Flashing vs. Non-Flashing  
Figures 5 and 6 show a cumulative distribution function (CDF) of the conditional average delay when cell radius is 500 m and 1500 m, respectively. Let \(T_{i}\) be a random variable denoting the flow-level delay of BS \(i\). In the case of an \(M/GI/1\) multi-class processor sharing system model, the conditional average delay experienced by the flow at location \(x\) when served by BS \(i\) is given by [25]:  
\[E[T_{i}|X=x,\beta] = \frac{1}{\lambda(x)}\frac{\rho_{i}(x,\beta)}{\rho_{i}(\beta)} \frac{\rho_{i}(\beta)}{1-\rho_{i}(\beta)}\] \[= \frac{1}{\mu(x)c_{i}(x,\beta)(1-\rho_{i}(\beta))}.\]  
We plot the CDF of the conditional flow delay in Figures 5 and 6 for Cases 1-4. As can be seen in Figure 5, by slightly sacrificing cell center user performances, the overall delay as well as cell edge user performances are significantly improved when cell flashing is used for cell radius 500 m. This is because the impact of inter-cell interference is lessened by cell flashing. However, when the cell radius is 1500, non-flashing is better in terms of average delay.  
To see the average delay under various flow arrival rates, we compare Cases 1-4 in Figures 7 and 8 by increasing flow arrival rates when cell radius is 500 m and 1500 m, respectively. When cell radius is 500 m, average delay is reduced by up to 75%. This means the effect of decreased delay of cell edge users is higher than increased delay of cell center users. When cell radius is 1500 m, however, the average delay increases when cell flashing is used. From Figures 5-8, we see that cell flashing is very suitable to small cell networks where lower delay and less energy consumption are achievable.  
Figure 4: Relationship between average delay and average energy consumption depending on cell radius (\(\lambda=1.7\)).  
Figure 5: Cumulative distribution function (CDF) of conditional delay (cell radius = 500 m, \(\lambda=1.7\)).  
Figure 6: Cumulative distribution function (CDF) of conditional delay (cell radius = 1500 m, \(\lambda=1.7\)).  
Figure 7: The comparison of average delay vs. arrival rate (cell radius = 500 m).
# SafeNet: SwArm for Earthquake Perturbations Identification Using Deep Learning Networks
## 2 Datasets and Observations
### Earthquake Case Study  
#### 2.2.1 2016 Sumatra Earthquake  
Figure 1 shows the Earth magnetic field and electron density residuals as obtained by applying the MASS (Magnetic Swarm anomaly detection by Spline analysis) method ([PERSON] et al. [4]) to the data measured by the Swarm Alpha satellite on 15 February 2016, i.e., 16 days before the Mw = 7.8 Sumatra 2016 earthquake localized at 4.952\({}^{\circ}\)S, 94.330\({}^{\circ}\)E, and 24 km depth. The magnetic track shows a clear anomaly highlighted by a red circle in panel E only in the Y-East component; this is compatible for signals coming from below (i.e., the internal ones, [PERSON] et al. [26]). The FFT (fast Fourier transform) spectrum shows a signature highlighted by a red circle in panel B at about 0.4 Hz (i.e., a period of 2.5 s) that could correspond to the frequency of the anomaly. Such a signature is not visible in the track without evident anomalies (the amplitude at this frequency is normally lower than the level in this figure as visible in the other examples in Text S1, Supplementary Materials). The electron density shows the clear EIA diurnal profile, but it is quite unusual that it appeared during nighttime (local time of about 1:30 a.m.). Furthermore, the electron density shows some disturbances at +5\({}^{\circ}\) geographic latitude and even they do not coincide with the magnetic anomaly; both phenomena could be produced by the preparation phase of this large earthquake. Geomagnetic indices Dst = 6 nT and ap = 4 nT depict a very quiet geomagnetic condition. Thus, all the investigated measurements show an unusual status of the ionosphere in the nighttime of 15 February 2016, without known external perturbations; furthermore, the longitude of the satellite matched with the one of the future epicenters, so finally we can consider this track a good candidate for an earthquake precursor.  
A complete investigation of nighttime Swarm Alpha from this track until earthquake occurrence is presented in Text S1 of the Supplementary Materials. Some days are affected by perturbed geomagnetic conditions and thus do not permit searching for possible seismo-induced ionospheric disturbances. Instead, on at least other 3 days (21, 25, and 26 February 2016), there are interesting signals: all of them present higher signal content in the Y-East component with respect to the vertical and North ones. These anomalies, detected inside [PERSON]'s area and a few days before earthquake occurrence, can in principle be good candidates for precursors.