esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### In-Field Activities  
Field campaigns were performed at the SN and TRP channels for calibration and intercalibration of the instrumental setup and validation of satellite-derived products. At each station, water turbidity profiles were acquired with an Ocean Seven 316-Plus probe (Idronaut, Brugherio, MB, Italy), equipped with an optical backscatter sensor (Seapoint Sensors, Inc., Exeter, NH, USA). Turbidity data from profiles were used for sensor intercalibration.  
An extensive series of transects were acquired with a boat-mounted ADCP in different meteorological and tidal conditions, providing detailed information on the evolution of currents in the TRP and SN sections (yellow lines in Figure 1). The instrument used was a 600 kHz RDI Workhorse Rio Grande ADCP (Teledyne RD Instruments, San Diego, CA, USA) set to operate at a 0.5 m vertical cell size with the exclusion of the surface and bottom blanking ranges, approximately 1 and 2 m, respectively. Instantaneous discharge data from the transects were used for the calibration of the vertically averaged velocities measured by fixed bottom-mounted ADCPs according to the index-velocity method to obtain discharge time series [41]. Moreover, to calculate both time series of water turbidity from the fixed ADCPs and the instantaneous 2-D distribution of water turbidity in the inlet cross-sections recorded during ADCP transects, the acoustic backscatter was converted according to the following procedure. After the acquisition of each transect with the boat-mounted ADCP, profiles of turbidity, temperature and salinity were acquired with the CTD probe on the vertical of the fixed ADCPs, while the boat-mounted ADCP was set to acquisition. The calibration of the acoustic backscatter of both instruments was performed, using the CTD probe data as input for the post-processing with the software Sediview (HR Wallingford, Wallingford, Oxfordshire, UK). The software enables the calculation of water turbidity, starting from the acquired backscatter and water properties (Temperature, Salinity, Turbidity).  
Several field surveys were undertaken to gather data for the validation of satellite-derived turbidity. Synchronously to the overpasses of Landsat8 (L8) and Sentinel-2 (S2) satellites, in situ turbidity profiles were carried out with the CTD probe at 8 stations along a transect from the lagoon to the open sea, through the Lido inlet (pink dots in Figure 1). From January 2020, time series of turbidity from fixed monitoring stations were also used for assessing the accuracy of satellite products.
## 4 Controller Design
### Control Strategy  
As in Section 3.4 the desired active angle \(\alpha_{ds}\) is controlled to be \(\theta\)/2 in the FSSC mode, and is controlled to be zero in the WSC mode, which is done by a displacement control. In AQSC mode the KARE-1 is basically driven by a PD control, Equation (12), in which the desired value of \(\alpha\) as well as the driving torque \(\tau_{a}\) is calculated by the body assistance ratio \(\mathbf{\Re}\). Based on the two values and state variables of the KARE-1, the desired actuator torque \(\tau_{m}\) is calculated by Equation (13).  
\[\tau_{a}+N\tau_{m}=J\ddot{\alpha}+b\dot{\alpha}+k\alpha \tag{12}\]  
\[\tau_{m}=N^{-1}\Big{(}IQ+b\dot{\alpha}+k\alpha-\tau_{a}\Big{)} \tag{13}\]  
\[Q=\ddot{\alpha}_{ds}+K_{\mathrm{v}}\Big{(}\dot{\alpha}_{des}-\dot{\alpha} \Big{)}+K_{\mathrm{p}}(\alpha_{\mathrm{des}}-\alpha) \tag{14}\]  
where \(J\) is the rotational inertia of a linkage \(\overline{HG}\); b and k are respectively the viscous friction and stiffness coefficient of joint H. Control gains for K\({}_{\mathrm{p}}\) and K\({}_{\mathrm{v}}\) are tuned through the critical damping condition using error dynamics; \(\dot{\alpha}_{ds},\ddot{\alpha}_{des}\) are determined from a given time function of \(\alpha_{ds}\) using the polynomial spline technique.
## 4 Evaluation and Results
### Evaluation Metrics  
To evaluate the performance of YoloMal-XAI, we employ several metrics designed to assess both binary and multi-class classification tasks. These metrics help in understanding the model's ability to distinguish between benign and malicious APK samples, as well as to classify the samples accurately across the five malware categories. The following evaluation metrics are employed:  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Optimizer** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Training Time (h)** \\ \hline Adam & 99.24 & 99.66 & 99.32 & 99.49 & 1.18 \\
**AdamW** & **99.68** & **99.87** & **99.70** & **99.79** & **0.81** \\ SGD & 99.75 & 99.75 & 99.92 & 99.83 & 1.51 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of different optimizers.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Epochs & 100 \\ Input Image Size & 256 \(\times\) 256 \\ Batch Size & 32 \\ Optimizer & AdamW \\ Initial Learning Rate (lr0) & 0.001 \\ Weight Decay & 0.0005 \\ Cosine Learning Rate Schedule (cos_lr) & Enabled \\ Warmup Epochs & 3 \\ Warmup Momentum & 0.8 \\ Mixup Augmentation & 0.2 \\ Auto Augmentation & RandAugment \\ Pretrained Weights & Yes \\ Model Variants & YOLO11n, YOLO11s, YOLO11m, \\ YOLO11l, YOLO11x \\ \hline \hline \end{tabular}
\end{table}
Table 7: YOLO11 final training configuration.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Name** & **Type** \\ \hline GPU & NVIDIA RTX 3050 Ti \\ CPU & 11 th Gen Intel® CoreTM i5-11400H \\ Deep Learning Framework & PyTorch 2.4.1 \\ CUDA Version & 11.8 \\ RAM & 16 GB \\ Operating System & Windows 11 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Hardware and software setup for model evaluation.  
* Accuracy: Measures the proportion of correctly classified samples (both benign and malware) out of all samples. \[\text{Accuracy}=\frac{\text{True Positives}+\text{True Negatives}}{\text{Total Samples}}\] (7)
* Precision: Evaluates the proportion of true-positive samples among all predicted positives. \[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}}\] (8)
* Recall (Sensitivity): Assesses the ability to identify all true instances of a class. \[\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Negatives}}\] (9)
* F1-Score: The harmonic mean of precision and recall, offering a balanced evaluation for imbalanced datasets. \[\text{F1-Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}\] (10)
* Area Under the ROC Curve (AUC-ROC): For binary classification, we compute the AUC-ROC to evaluate the trade-off between true-positive rate (TPR) and false-positive rate (FPR) across thresholds. A higher AUC indicates better discriminative ability.
* Macro- vs. Micro-Averaged F1 (Multi-Class):
* Macro-F1 treats all classes equally by computing the unweighted mean F1 across classes.
* Micro-F1 aggregates true positives, false negatives, and false positives globally before computing the F1-score. This is useful when class imbalance exists.
# Application-Aware SDN-Based Iterative Reconfigurable Routing Protocol for Internet of Things (IoT)
## 5 Proposal of SADFIR
### Network Settling Phase (NSP) of SADFIR
As our proposed SADFIR has an additional distributed layer at the forwarding infrastructure layer, so distributed SDN controllers broadcast the potential \(CC_{EFDCHs}\) nodes to continue the final distributed selection procedure. The potential \(CC_{EFDCHs}\) utilize the following formula to select the most suitable CH:  
\[SCC_{EFDCHs}=\left\{ \begin{array}{ll}\frac{P_{j}}{1-P_{s}(rmd \frac{1}{P_{j}})\times d}&\quad\text{if }n\in CC_{EFDCHs}\\ 0&\quad\text{otherwise}\end{array} \right. \tag{5}\]  
where \(SCC_{EFDCHs}\) represents most suitable \(CC_{EFDCHs}\), \(P_{j}\) is desired percentage of final CHs and the value of \(P_{j}=0.3\). While \(n\in CC_{EFDCHs}\) represents the group of IoT devices from which \(SCC_{EFDCHs}\) are being selected. Finally, \(SCC_{EFDCHs}\) advertise the leading status and all \(non_{EFDCHs}\) select the best CHs based on the criteria of RSSI and minimum distance. The SADFIR repeats these reconfigurations formulations throughout the network operations in every round. Distributed SDN controllers optimize the clustering formulation by computing the network analytical cost over 10 th consecutive rounds and selects the most economical setting for network reconfiguration. In-network settling phase, one major feature of the proposed method is to evenly distribute the communication load of IoT devices with limited energy and communication resources. The optimization of communication load of networks \(N\) clustered IoT devices include two major intra-cluster communications and inter-cluster communications responsibilities. Member devices of cluster initialize \(f\) report with \(fe\left[1,F\right]\) sensed reports, while the basic assumption defines application type supported by the specific application. Member node \(i\)th with \(f\) type application reports denoted as \(\Gamma_{if}\) to selected \(j\) leading node of \(SCC_{EFDHs}\), while whole collected data from cluster members represented as \(\Psi_{if}\). Meanwhile specific cluster collection is denoted as \(\sum_{j=1}^{N}\Psi_{if}=1\). While intra-cluster communication cost is represented by \(Z_{ij}\), and inter-cluster communication is denoted as \(Z_{js}\). As distributed SDN controllers successfully fetch the network topology statistics by link layer protocols, so proposed model utilizes the computational ability to calculate analytical consumption of residual energy resources for different possible reconfiguration settings for the consecutive epoch of rounds. Let \(\varrho_{f}\) to produce binary variable taking values 0 and 1 to report sensing value generation at the source node.
## 3 Results
### Forest Productivity during and after the Drought and/or Warming Scenarios
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{2}{c}{**NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)]**} & **Absolute Change** & **Relative Rate of** \\ \cline{2-5}  & **Baseline** & **Disturbance** & **[kgC m\({}^{-2}\) yr\({}^{-1}\)]** & **Change [\%]** \\ \hline \multirow{6}{*}{July} & \multirow{6}{*}{1.014} & 0.986 (D1) & \(-\)0.028 & \(-\)2.76 \\  & & 0.966 (D2) & \(-\)0.048 & \(-\)4.73 \\  & & 0.935 (D3) & \(-\)0.079 & \(-\)7.79 \\  & & 0.941 (W1) & \(-\)0.073 & \(-\)7.20 \\  & & 0.905 (W2) & \(-\)0.109 & \(-\)10.75 \\  & & 0.869 (W3) & \(-\)0.145 & \(-\)14.30 \\  & & 0.910 (D1 + W1) & \(-\)0.104 & \(-\)10.26 \\  & & 0.847 (D2 + W2) & \(-\)0.167 & \(-\)16.47 \\  & & 0.768 (D3 + W3) & \(-\)0.246 & \(-\)24.26 \\ \hline \multirow{6}{*}{August} & \multirow{6}{*}{1.048} & 1.008 (D1) & \(-\)0.040 & \(-\)3.82 \\  & & 0.970 (D2) & \(-\)0.078 & \(-\)7.44 \\  & & 0.905 (D3) & \(-\)0.143 & \(-\)13.65 \\  & & 0.988 (W1) & \(-\)0.060 & \(-\)5.73 \\  & & 0.958 (W2) & \(-\)0.090 & \(-\)8.59 \\  & & 0.928 (W3) & \(-\)0.120 & \(-\)11.45 \\  & & 0.941 (D1 + W1) & \(-\)0.107 & \(-\)10.21 \\  & & 0.857 (D2 + W2) & \(-\)0.191 & \(-\)18.23 \\  & & 0.736 (D3 + W3) & \(-\)0.312 & \(-\)29.77 \\ \hline \multirow{6}{*}{September} & \multirow{6}{*}{1.078} & 1.055 (D1) & \(-\)0.023 & \(-\)2.13 \\  & & 1.007 (D2) & \(-\)0.071 & \(-\)6.59 \\ \cline{1-1}  & & 0.841 (D3) & \(-\)0.237 & \(-\)21.99 \\ \cline{1-1}  & & 1.023 (W1) & \(-\)0.055 & \(-\)5.10 \\ \cline{1-1}  & & 0.994 (W2) & \(-\)0.084 & \(-\)7.79 \\ \cline{1-1}  & & 0.965 (W3) & \(-\)0.113 & \(-\)10.48 \\ \cline{1-1}  & & 0.992 (D1 + W1) & \(-\)0.086 & \(-\)7.98 \\ \cline{1-1}  & & 0.887 (D2 + W2) & \(-\)0.191 & \(-\)17.72 \\ \cline{1-1}  & & 0.526 (D3 + W3) & \(-\)0.552 & \(-\)51.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Absolute change and relative change rate of NPP (kgC m\({}^{-2}\) yr\({}^{-1}\)) during the drought and/or warming scenarios (July–September). Figure 4: Relationships between soil available water [kg m\({}^{-2}\)] and NPP [kgC m\({}^{-2}\) yr\({}^{-1}\)]. The grey points represent the NPP under the baseline scenario (B0) and before (January\(\sim\)May) and after (November–December, except for October) the drought scenarios. The light, moderate, and dark yellow points represent the NPP during (July–September) and after (only October) a mild (D1), moderate (D2), and severe (D3) drought scenario, respectively. The boxplots on the top and right indicate the distributions of soil available water and NPP under three drought scenarios (D1, D2, and D3), respectively.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 2 Communication Channel Modeling under Resonant EMI Coupling
### Resonant Coupling Modeling  
Even if realistic EMI coupling transfer functions can be rather complex depending on the specific physical mechanism involved in conducted/radiated EMI propagation, focusing the attention to the relatively narrow region of the spectrum included in the bandwidth of the victim channel, practical resonant coupling mechanisms can be conveniently described by a second-order resonant transfer function:  
\[G(f)=G_{0}\frac{\frac{ff}{G_{\text{R}}}}{1+\frac{ff}{Qf_{\text{R}}}-\frac{f^{2 }}{f_{\text{R}}^{2}}} \tag{3}\]  
where \(G_{0}\) is a frequency-independent coupling factor, \(f_{\text{R}}\) is the resonance frequency and \(Q\) is the quality factor.  
In view of that, for the sake of simplicity and without loss of generality, the effects of resonant EMI coupling on the EMI-induced capacity loss will be discussed in the following considering the EMI transfer function \(G(f)\) in (3).
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 3 Selection of Microbes for the Phytoremediation of Metal Contaminated Saline Soils
### Frequently Investigated PGP Properties
#### 3.1.3 Nutrition Improvement  
Phosphate-solubilizing halophilic/halotolerant microbes are not rare. In coastal environments, phosphorus can be in the mineral (HPO4\({}^{2-}\)) or organic form (monophosphate esters, nucleotides, and their derivatives, vitamins, phosphonates, and humic acids). Thebalance between these forms of phosphorus indirectly depends on oxygen distribution changes that periodically appear in tidal environments [122]. Moreover, saline environments such as tidal flats have a higher binding capacity for inorganic P because of their richness in Fe oxides [122]. Thus, solubilizing inorganic phosphate capacities of rhizosphere microbes can help salt marsh plants to extract available phosphorus. [PERSON] et al. [112] screened the PGP characteristics of _Bacillus licheniformis_ strain previously mentioned, and also found that this strain could solubilize phosphate. It was also the case for 23 of the 85 rhizbacterial strains isolated from the rhizosphere of _S. frtictoosa_, and 7 combined this PGP effect with the production of IAA. When exposed to 15% NaCl, 1 mM Cd, 0.7 mM Ni, 0.04 mM Hg, and 0.03 mM Ag, three strains of _Halobacillus_ and _Halomonas_ isolated from plant mangrove species were also capable of phosphate solubilization [47]. These strains could improve the root growth of _Sesuvium portulacastrum_ watered with a 2% NaCl solution and cultivated in a metal-polluted sandy soil originating from coastal habitats.  
Atmospheric nitrogen fixation is another PGP effect commonly screened in bioremediation techniques. In the study of [PERSON] et al. [123], beneficial effects of inoculation on growth parameters of _Salicornia europea_ were obtained under 0.2 to 0.3 M NaCl with a nitrogen-fixing rhizobacteria belonging to _Pseudomonas_ genus. The inoculated plants had higher concentrations of total N, chlorophylls, and Na\({}^{+}\) and K\({}^{+}\) in the shoots. However, no significant differences were observed on fresh and dry weight of the shoots.  
The faculty of producing siderophores helps plants in the iron nutrition and metal uptake. Siderophores exist under diverse molecular forms. According to [PERSON] and [PERSON] [124], some siderophores easily bind other metals, such as Cu, Zn, Mn, or Cr, according to the molecule composition and structure. For instance, [PERSON] et al. [92] isolated two halophilic bacteria of _Kocuria flava_ and _Bacillus vietnamensis_ that alleviate As stress in plants owing to their PGP properties. The two strains were able to produce siderophores.
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
## 3 Method
### Calculation of Ice Volume  
The ice volume is obtained by multiplying the ice area (from Sentinel-1) by the average thickness (from AMSR-2) in the ice area for each event. The cumulative ice volume is the sum of all ice production events within a year, normally between March and November.  
In this study, the ice thickness on the day prior to a next katabatic wind event is used for the calculation of ice production caused by this wind event. The reason is that the newly formed ice in a polynya would continuously grow until the next wind event blows away this ice to prepare for the next round of new ice production. Taking the data on 20 August 2017 as an example, in these two polynyas, ice thickness was inhomogeneous, and the average ice thicknesses in RISP and MSP were 0.20 m and 0.15 m (Figure 5a), respectively. The ice thicknesses in these two polynyas grew with time, and they reached at 0.26 m (at RISP) and 0.22 m (at MSP) on 22 August 2017 before the next wind event (Figure 5b). This polynya ice survived there 3 days before it was pushed away by the next wind-event for another new ice production.
## 4 Methodology
### Data Description, Collection, and Preparation
#### 4.2.1 Solution Representation  
The representation of the solution consists of two parts, identifying the optimal \(C\) and \(\gamma\) and also determining the weighting of the features. The first part contains two elements to represent (\(C\) & \(\gamma\)) and their values range from [0, 1]. Therefore, they need to be scaled into appropriate scales for the SVM. The new search space values of \(C\) range from [0, 32], while the interval range of \(\gamma\) falls within [0, 35,000]. This conversion of values is applied according to the min-max normalization equation as demonstrated in (12).  
\[B=\frac{A-min_{A}}{max_{A}-min_{A}}(max_{B}-min_{B})+min_{B} \tag{12}\]  
where \(B\) is the result of the equation and \(A\) denotes the value needed to be scaled. The upper and lower of the old values are represented by \(max_{A}\) and \(min_{A}\), while \(max_{B}\) and \(min_{B}\) are the new interval of upper and lower bounds.  
As for the second part of the solution representation, it is corresponding to features (\(F\)) of the dataset that were scaled to be utilized for the weighing mechanism. In this mechanism, each feature in the dataset indicates the 2 nd part of the solution elements generated by the WOA algorithm. Further, each element is multiplied by the value of the given feature for all instances as shown in Figure 2. In other words, suppose that we have a simple dataset consisting of five instances, where the values of the first feature for the five instances are going to be multiplied by the first element of the generated solution. Therefore, the solution structure in this work comprises two elements plus the number of elements for all features of a given dataset (\(2+F\)).
# Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
## 4 Results
### Green Tide Trend Forecast for 2022  
Data for predicting the 2022 green tide were extracted from multi-source RS images. The available data were for four scenes, which were combined with the approximate nonlinear method to fit the predicted growth curve of this green tide for 2022. According to the prediction results (Figure 13), the bloom time was expected to be from 1 June to 15 August 2022, the duration was expected to be 73 days, and the ACA was expected to be approximately 1190.90-1191.21 km\({}^{2}\). Compared with the green tide data from recent years, the predicted bloom duration and ACA in 2022 were respectively shorter and smaller, suggesting that this would be a smaller green tide bloom event in the Yellow Sea than those seen in recent years (see Figure 14).  
For the true condition of 2022, according to the Ministry of Natural Resources of the People's Republic of China, the 2022 pre-salvage operation of the Yellow Sea green tide began on May 19 and finished around mid-August in coastal cities such as Yantai. In addition, the bloom was much smaller than last year. The actual duration and bloom condition were roughly in line with our predictions.  
In fact, there have been a lot of artificial measures for the Yellow Sea green tide in recent years, such as artificial salvage, which is bound to have an impact on prediction.  
Figure 14: Results of simulation of the bloom duration and accumulative and maximum cover area of the Yellow Sea green tide from 2008 to 2022.  
Figure 13: Forecasted start and dissipation times of the Yellow Sea green tide for 2022 ((**a**) is Logistic growth curve fitting effect of the ACA of the Yellow Sea green tide (2022) and (**b**) is the forecast results of start and dissipation times of the Yellow Sea green tide for 2022).  
However, we believe that these human factors may affect the accuracy of the results over a short period but will not affect the general direction of the forecast. One was that the main function of the prediction model was to get a rough estimate of the size of the green tide blooms and to know its bloom duration and inflection point. The second was that monitoring was a dynamic process; for example, we did not know whether this RS image we selected was before or after artificial salvage. In addition, salvage and defense measures are mainly inshore, so the impact on the center of the sea is limited.  
However, the positive impact of human intervention is also one of the hot topics of attention, and we will pay further attention to this content. In addition to human factors, climate factors can also impact the green tide. Therefore, we also tried a predicted model considering climate factors, but the effect of the model was not ideal, and the stability was not good, so we did not adopt it.
# An Application of Manifold Learning in Global Shape Descriptors
## 2 Background  
In this section, we first review spectral shape analysis, more specifically global shape fingerprints, and different discretization methods of the LB operator. Then, we briefly review manifold learning, more specifically Laplacian Eigenmap, to provide the necessary foundation for developing our proposed Laplacian Eigenmap-based scale-invariant shape descriptor, which from now on we call LESI.
## 1 Introduction
A simple, cost-effective, and moderate security scheme for a bipolar OCDMA system with a phase modulator was proposed for FSO communication. The proposed system maintains the SAC-OCDMA principles, which eliminate the MAI problem. The proposed system improves the transmission rate compared to the previous scheme with an optical switch [21]. Moreover, because all signature codes were utilized to transmit data, the proposed Bi-OCDMA improved power efficiency compared to our previous scheme with dual EOM [22]. 2. The proposed system was investigated under extreme weather conditions using different SAC-OCDMA codes. The performance measurements were observed in terms of BER and quality factor (Q-factor), both for additive white Gaussian noise (AWGN) and AWGN with a fading channel. 3. The proposed Bi-OCDMA architecture was further tested under different turbulence-induced fading conditions, and a performance comparison was performed between different SAC-OCDMA. The fading model implemented the gamma-gamma fading channel model. The remainder of this paper is organized as follows. Section 2 introduces the proposed Bi-OCDMA system, including the system architecture of encoding and decoding diagrams, the derivation of the correlation function, and the multi-user structure. Section 3 presents the simulation setup and results, including the feasibility of the proposed system, performance measurement, and comparison of the Bi-OCDMA systems with extreme weather conditions in AWGN and turbulence-induced fading channels. Finally, the work is concluded in Section 4, and prospective future work is noted.