esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 2 Data, Methods, and Techniques
### Remote Sensing Methods for a Chlorophyll Content
Finding three ranges of wavelengths guaranteeing the least error to estimate the chlorophyll a content enabled an accuracy of 7.8 mg chl a/m\({}^{3}\) in the range of 1.2 to 236 mg chl a/m\({}^{3}\), medium relative error 18.3%, with a high determination factor R\({}^{2}\approx\) 0.96. The version of the model with two wave length ranges provided much worse results. Despite the high accuracy of in situ research for low concentrations (below 10 mg chl a/m\({}^{3}\)), there are relative errors reaching almost 100%. Remote bathymetrical tests of shallow water, conducted by [PERSON] et al. [34; 35], yield the possibility of linking the effect of bottom reflectance and chlorophyll concentration. The developed model includes many different reflectances, including bottom reflectance and the absorption of radiation for various water components, such as phytoplankton pigments. However, the model is focused on the remote determination of depth. The number of model parameters is very large, making it troublesome to simultaneously determine the data for several wavelengths. Therefore, the absorption of radiation by phytoplankton is an input parameter for this model, not an output enabling the determination of chlorophyll concentration. In the case of oligotrophic sea waters, the computing models are simpler. Computational difficulties appear when many water components with spectrally different properties affect the calculation of the content of a given water component. This usually requires the use of the reflectances of many water components for many wave lengths. Particularly large complications appear in the case of shallow and turbid water. The model of chlorophyll a content estimation in a reservoir used for drinking water intake was presented in this work. This application model is characterized by high accuracy, taking into account the bottom effect and interactions between chlorophyll and mineral or organic suspension.
## 3 Results
### Results of the Distributed Optimal Design Piezoelectric Cantilever Simulation  
Further investigation was performed by conducting a simulation on the optimal design piezoelectric cantilever beam. The analysis was conducted as previously, by comparing the optimal shape (in a) piezoelectric cantilevers response to uniform cantilevers results (in b) in Figure 9.  
From the graphs it can be seen that optimal shape cantilever has a maximum voltage generation of 1.4 V at 21.5 Hz. Compared to the uniform cantilever response which is in a broadband of 20-23 Hz, but peaks at 20 Hz and is equal to 1.07 V. A clear difference of \(\sim\)30% improvement in electrical potential value for optimal shape cantilever. Comparing mechanical power, optimal shape cantilever generates 0.32 mW at 21.5 Hz, uniform--0.25 mW. Electric Power generation results in 0.3 mW for Optimal and 0.24 mW for uniform shape cantilever. First, the power output is analyzed as a function of vibration frequency, with a fixed electrical load. The electrical load is 35 k\(\Omega\).  
From Figure 10, it is clear that the load dependency is from 10\({}^{2}\)\(\Omega\) to 10\({}^{7}\)\(\Omega\). The peak in energy harvested corresponds to an electrical load of 12.5 k\(\Omega\) for unimorph of the optimal thickness and 10.1 k\(\Omega\) for constant thickness unimorph.  
Figure 9: Voltage output (blue), mechanical power (green), and electrical power (red) as a function of frequency: (**a**) optimized design; (**b**) uniform design.  
The modeling and simulation results demonstrate the performance advantages of the optimized cantilever design in both structural and functional terms. The distributed thickness profile ensures compliance with both geometric and frequency constraints. Additionally, it substantially enhances energy harvesting performance, particularly by increasing voltage output and improving strain localization. Despite slight differences in electrical power under load variation, the optimized beam consistently exhibits higher voltage generation, better strain distribution, and more efficient resonance behavior.  
Overall, the results confirm that structural optimization--specifically through gradient-based thickness shaping--provides a reliable and effective strategy for enhancing the electromechanical performance of cantilever-type piezoelectric energy harvesters operating in low-frequency environments.
## 1 Introduction
[10] concluded that the morphometric analysis methodology is advantageous and cost-effective, especially for inaccessible areas (ungauged basins with limited data) in order to prioritize them regarding their susceptibility to soil erosion. With the development of satellite observation technology, satellite remote sensing data have been extensively used to detect the changes caused by erosion and to estimate them quantitatively [11]. The main aim of this paper is to evaluate the relationship between soil erosion rates estimated from the RUSLE model and the landscape patterns and to draw conclusions regarding the most erosion-prone areas or sub-basins based on morphometric parameter analysis by combining remote sensing and geographic information system technologies with in situ data. As such, the specific objectives of this study are as follows:  
1. Estimate soil erosion rates using the Revised Universal Soil Loss Equation (RUSLE);
2. Correlate soil erosion and landscape pattern using landscape metrics;
3. Prioritize erosion-prone areas or sub-basins according to the morphometric parameter results. From this point on, the paper is structured as follows. A brief description of the data and methods is given in Section 2. Section 3 represents the results of the RUSLE model for estimating soil erosion rates, the landscape metrics, and the morphometric parameters. These results are then discussed and used to identify the most susceptible areas to soil erosion that require further consideration.
## 3 Control Methods
### Rule-Based Controller  
The rule-based motor torque setpoint generator is an empiric solution to the GLOSA/RBS problem. Initially, the SPaT data providing the traffic light status are retrieved from the surrounding infrastructure in the intersection. Then, the rule set is divided into three scenarios that depend on the status of the traffic light \((L_{s})\): green, yellow, and red. Intersection-crossing or free-driving are the states that the CEV can be in. The selected motor torque \((\tau)\) ensures that the vehicle navigates in the environment without crossing the intersection during the red light. The navigation strategy can achieve power restoration by switching between motoring \((\tau>0)\), regenerative \((\tau<0)\), or neutral \((\tau=0)\) behavior of the motor while keeping the longitudinal speed \((\dot{x})\) under the road's speed limit \((\dot{x}_{lim})\) and maintaining comfort. In summary, the driving scenario that the CEV faces depends on \(L_{s}\)  
Figure 3: Electric machine loss map. Dashed lines represent the torque–speed characteristic limit. The solid line separates the motoring quadrant (positive torque) from the regenerative quadrant (negative torque).  
\(\hat{x}\) and the pose \((X)\) to produce a desired \(\tau\). The algorithm for the rule-based controller is Algorithm 1.  
```
0:\(X,\hat{x},L_{s}\) Output:\(\tau\) if\(L_{s}\) is green then if\(X\geq d_{a}\)then if\(\hat{x}\geq\hat{x}_{lim}\)then\(\tau\leftarrow-\tau_{min}\) elseif\(\hat{x}<0\)then\(\tau\leftarrow\tau_{min}\) else\(\tau\leftarrow\frac{2}{3}\tau_{max}\) endif elseif\(d_{a}<X<d_{d}\)then if\(\hat{x}\geq\hat{x}_{lim}\)then\(\tau\gets 0\) elseif\(0<\hat{x}<\hat{x}_{lim}\)then\(\tau\leftarrow\tau_{max}\) else\(\leftarrow\frac{1}{2}\tau_{max}\) endif else\(\tau\leftarrow\frac{2}{3}\tau_{max}\) endif elseif\(L_{s}\) is yellow then if\(X\geq d_{a}\)then if\(\hat{x}\geq\hat{x}_{slow}\)then\(\tau\leftarrow-\frac{2}{3}\tau_{max}\) elseif\(0<\hat{x}<\hat{x}_{slow}\)then\(\tau\leftarrow-\tau_{min}\) else\(\tau\leftarrow\tau_{min}\) endif elseif\(d_{a}<X<d_{d}\)then if\(\hat{x}\geq\hat{x}_{slow}\)then\(\tau\leftarrow-\frac{5}{6}\tau_{max}\) elseif\(0<\hat{x}<\hat{x}_{slow}\)then\(\tau\leftarrow-\tau_{min}\) else\(\leftarrow\frac{1}{6}\tau_{max}\) endif else\(\tau\leftarrow\frac{2}{3}\tau_{max}\) endif elseif\(L_{s}\) is red then if\(X\geq d_{a}\)then if\(\hat{x}\geq\hat{x}_{slow}\)then\(\tau\leftarrow-\frac{1}{4}\tau_{max}\) elseif\(\hat{x}\leq 0\)then\(\tau\leftarrow\tau_{min}\) else\(\tau\leftarrow\frac{1}{10}\tau_{max}\) endif elseif\(d_{a}<X<d_{d}\)then if\(\hat{x}\geq\hat{x}_{slow}\)then\(\tau\leftarrow-\tau_{max}\) elseif\(0<\hat{x}<\hat{x}_{slow}\)then\(\tau\leftarrow-\tau_{min}\) else\(\leftarrow\frac{1}{6}\tau_{max}\) endif else\(\leftarrow\frac{2}{3}\tau_{max}\) endif
```  
**Algorithm 1** Rule-based controller
# A Novel Fractional Sine Chaotic Map and Its Application to Image Encryption and Watermarking
## 4 Image Encryption and Decryption
### Significance of the Proposed Algorithm  
Our proposed algorithm presents a two-layer defence mechanism against non-recipient decryption. The first layer of defence is the genesis block, a unique set of SHA-256 code accessible only to the receiver and sender. The diffusion of the encrypted image with a different genesis block than the original one results in a corrupted image, which, on further processing, will not yield any information about the image. The second layer of defence is the chaotic key of the DFSCM which is sensitive to high precision of \(10^{-16}\), thus making it difficult to break into the algorithm. Thus, our algorithm developed with DFSCM and the concept of blockchain for encryption of images provides a high level of security for the transmission of information. The experimental evaluation of the results also supports our claim in comparison with some recent literature based on chaotic systems.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Voltage Standing Wave Ratio  
For a microstrip antenna, the voltage standing wave ratio (VSWR) should not be greater than \(2\) across the entire frequency bandwidth. Ideally, this value should be \(1\). Figure 8 shows the voltage standing wave ratio as a function of frequency for the proposed antenna. The continuous line shows the results of the simulations obtained in CST Microwave Studio, while the dashed line shows the results of the measurements made for the physical model of the antenna. The VSWR value obtained for the simulation results at \(3.75\) GHz resonance frequency was \(1.52\), and at \(5.17\) GHz it was \(1.20\). The VSWR value  
Figure 6: Appearance of the laboratory stand during the measurements of (**a**) electrical parameters of the antenna and (**b**) radiation patterns of the antenna.  
Figure 7: The reflection coefficient as a function of frequency for the proposed antenna working in 5G and LTE-U systems.  
obtained for the measurement results at a resonance frequency of 3.52 GHz was 1.07, and at the frequency of 5.42 GHz it was 1.18. The values presented in Figure 8 show that the proposed antenna works in the entire assumed frequency band (for the 5G and LTE-U system), i.e., from 3.40 GHz to 5.85 GHz.
# Exploratory Research on Satisfaction Degree in Distance Education
## 5 Results and Discussion
### The Relationship between Students' Distribution and Satisfaction
#### 5.2.2 Distribution of the Grades  
Among the students surveyed, there were 1672 freshmen, 373 sophomores, 459 juniors, and 8 seniors, accounting for 66.4%, 14.4%, 18.3%, and 0.3% of the sample, respectively. Freshmen students were the main subjects of the investigation. As shown in Table 4, the final statistics show that the satisfaction ratios of the four grades from freshman to senior were 0.8908, 0.8762, 0.8784, and 0.7083, which shows that the freshmen have the highest satisfaction with distance education. The younger the students surveyed, the higher their satisfaction, indicating that age is a key factor affecting satisfaction with distance education.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Grade** & **Freshmen** & **Sophomore** & **Junior** & **Senior** \\ \hline ASP & 0.8908 & 0.8762 & 0.8784 & 0.7083 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Satisfaction ratio for different grades.  
Figure 2: Students’ satisfaction with the learning platform. (**a**) Satisfaction with the platform. (**b**) Offline and online preferences.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 4 Proposed Connectivity-Based Clustering Schemes  
This section explains how efficiently vehicles are divided into clusters using link connectivity metric. Firstly, connectivity of vehicles is calculated and then converted to the adjacency matrix for graphical representation of VANETs. Lastly, the adjacency matrix is mapped to spectral clustering for dynamic formation of clusters and CH selection.
## 2 Methods and Data
### Methods
#### 2.1.2 Experiment Simulation  
To quantify the sensitivity of vegetation growth to CO\({}_{2}\), SW, and VPD and their contributions, this work set up two sets of simulation experiments based on the Partial Least Squares (PLS) regression method. The simulation experiment can be described by the following equation:  
\[Y=f(\beta_{0}+\beta_{1}\cdot CO_{2}+\beta_{2}\cdot SW+\beta_{3}\cdot VPD+\beta_{4 }\cdot P+\beta_{5}\cdot T+\beta_{6}\cdot SRAD+\varepsilon) \tag{2}\]  
where \(Y\) represents the satellite-observed and predicted LAI. CO\({}_{2}\), SW, VPD, P, T, and SRAD show the original value of annual CO\({}_{2}\) concentration, SW, VPD, total precipitation, mean temperature, and solar radiation at the annual scale, separately; \(\beta_{0}\) is the intercept of the regression equation; \(\varepsilon\) is the residual of the regression equation; and \(\beta_{1}\)-\(\beta_{6}\) represent the regression coefficients of CO\({}_{2}\), SW, VPD, P, T, and SRAD, respectively.  
In this study, the first simulation experiment (E\({}_{\text{all}}\)) was driven by the real value of all variables (i.e., CO\({}_{2}\), SW, VPD, P, T, and SRAD), that is, all variables were inputted into the E\({}_{\text{all}}\) with changed time series from 1982 to 2018. The second simulation experiments (E\({}_{\text{CO20}}\), E\({}_{\text{SW0}}\), and E\({}_{\text{VPD0}}\)) were operated by controlling CO\({}_{2}\), SW, and VPD to be unchanged, that is, the analysis separately held the three variables (CO\({}_{2}\), SW, and VPD) to be constant at an original baseline value in this experiment, while the other variables (i.e., P, T, and SRAD) were allowed to change with time from 1982 to 2018. Specifically, we held CO\({}_{2}\) to not change at its 1982 level in the E\({}_{\text{CO20}}\) experiment, while SW, VPD, P, T, and SRAD were conducted to vary from 1982 to 2018. Correspondingly, SW was held to not change at its 1982 baseline level, while the remaining variables changed from 1982 to 2018 in the E\({}_{\text{SW0}}\) experiment. VPD was held to not change at its 1982 baseline level, while the remaining variables changed from 1982 to 2018 in the E\({}_{\text{VPD0}}\) experiment.
## 2 Places, Instruments, and Data
### Data
#### 2.3.2 ASCAT Observations  
The advanced scatterometer (ASCAT) is a C-band active microwave instrument on the EUMETSAT MetOp satellites, operating since 2007. ASCAT measures the sea surface roughness, which relates  
Figure 2: Delay Doppler maps (DDMs) measured by BF-1A satellite. The four sea surface reflected DDMs were obtained in the middle of Pacific Ocean on 26 June 2019. The wind speed was approximately 6.4 m/s according to the TAO buoy array. The antenna gains to the four specular points are all approximately 12 dBi. The four simultaneously collected DDMs indicate that the instruments started working in normal conditions.  
to wind speeds 10 m above the water's surface. In this study, we used the ASCAT version 2.1 data, which are reprocessed by the remote sensing systems (RSSs) with the latest GMF, C-2015 [41]. The RSS produces daily wind field products with a spatial resolution of 25 km in the form of ascending and descending swaths. ASCAT winds are known as being reprocessed with high accuracy. Compared with mooring buoys, unbiased global ASCAT wind errors on the scatterometer measurement scale are about 0.7 m/s [42].
## 3 Materials and Methods
### Data Pre-Processing  
Data pre-processing is known as a data mining technique, where the goal is to manipulate and drop data after detecting anomalies in order to ensure or enhance the data quality. The common 4 steps for data pre-processing are: data fusion, data cleaning, data structuring, and data summarizing which we have performed on our dataset [19]. The data from the rescue station was recorded in an emergency environment where the collection of information is quite difficult and sometimes gets distorted. Hence, it was not surprising to find misspelled text information, and abnormal values of health vitals like blood glucose, respiratory rate, etc. It initiated the necessity to perform data pre-processing techniques to make the data usable for machine learning applications.  
The data pre-processing was performed in both horizontal and vertical directions. In horizontal pre-processing, we searched for rescue cases that belong to neurological complications. There were attributes with diagnostic information of the patients written by rescuers in the dataset. Hence of this information was collected directly from the patients or from their families that describe various health complaints pertaining to neurology. While gathering such information, it became critical in many cases as the diagnostic was referring to multiple complication types. For this research, we considered every case which has a hint of neurological complication and may be included with other disease types. A total of 22,782 cases were finally considered out of 25,284 where only patients who were alive during the rescue were considered. In Table 1, all the diseases that were recognized as neurological complications are listed. These diseases had the most frequent record in the rescue station's database. Moreover, we did a verification with experts to finalize this list.  
The vertical pre-processing was done to sort out columns that contains repetitive or irrelevant information not useful for our research. As an example, the Glasgow comma scale (GCS) score is calculated by observing the response of eye-opening, verbal communication and motoric functionality of a patient. In the dataset, details of every response were recorded with individual scores. We considered working only with the total GCS score which is quite able to indicate the level of consciousness of a patient. Further, removed some columns where data availability is minimum and does not make any significant impact. After all these pre-processing, a total of 340 attributes were remaining that can be used for feature selection.
## 2 Material & Methods
### Statistical Analysis  
The normality of continuous measurements in the study was assessed using the Shapiro-Wilk (n < 50) and Skewness-Kurtosis tests. As the measurements were normally distributed, parametric tests were applied. Descriptive statistics for continuous variables in the study, including mean, standard deviation, sample size (n), and percentage (%), were expressed. \"One-Way Analysis of Variance (ANOVA)\" was utilized for comparing continuous measurements among groups. Following variance analysis, the \"Duncan test\"was employed for post hoc analysis to determine differences between groups. Pearson correlation coefficients were calculated to assess relationships between measurements. A significance level of \(p<0.05\) was adopted for calculations, and the statistical package program SPSS (IBM SPSS for Windows, ver. 26) was used for analyses.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 2 System Model
To evaluate the basic performance of the proposed scheme, this paper assumes the greedy algorithm; it allocates the antenna combination, \(s(k)\), for the \(k\)th subcarrier that maximizes the channel capacity from all antenna combinations, \(S\). The channel capacity of the \(k\)th subcarrier and the \(s(k)\)th antenna combination at STA-\(n\), \(C_{n}(k,s(k))\) is calculated by:  
\[C_{n}(k,s(k))=\log_{2}(1+SINR_{n}(k,s(k))), \tag{5}\]  
where \(SINR_{n}(k,s(k))\) is the signal to interference plus noise power ratio (SINR) given by:  
\[SINR_{n}(k,s(k))=\frac{\left|\sqrt{P}\boldsymbol{h}_{n,s(k)}(k)\boldsymbol{w}_ {n,s(k)}(k)\right|^{2}}{\sigma^{2}+\sum_{l=1,l\
eq n}^{N}\left|\sqrt{P} \boldsymbol{h}_{n,s(k)}(k)\boldsymbol{w}_{l,s(k)}(k)\right|^{2}}. \tag{6}\]  
Finally, the allocated antenna combination with a maximum channel capacity, \(s_{max}(k)\), is calculated by:  
\[S_{max}(k)=\underset{s(k)}{\operatorname{argmax}}(\sum_{n=1}^{N}C_{n}(k,s(k))). \tag{7}\]
## 4 VR-PEKS Scheme and Security Definition  
### System Model  
Our VR-PEKS scheme consists of four different entities, namely a key generation center (KGC), a data sender (DS), a data receiver (DR), and a cloud server (CS). Figure 1 shows the relationships and interactions between the entities.  
(1) Key Generation Center (KGC): This is a trusted third party. It is responsible for generating the system parameters and the keys of the sender and receiver, including the public-private key pair \((pk,sk)\) of FHE to encrypt keywords and documents, and an OPRF key \(k\).  
(2) Data Sender (DS): The DS encrypts his document using FHE encryption algorithm, \(Enc(F,pk)\). In addition, he extracts the associated keywords \(W=\{w_{1},w_{2},\cdots,w_{m}\}\) from each document and encrypts these keywords, \(Enc(W,pk,k)\), and generates an encrypted keyword index. Then, the encrypted file data and the searchable index are sent to the cloud server for storage. Finally, he generates an encrypted verified index structure to make it public for the receiver to verify the search results.  
(3) Data Receiver (DR): The DR sends the keyword trapdoor to the cloud server to search for his interested keywords. After obtaining the search result, he verifies it locally and decrypts it to acquire the plaintext file.  
(4) Cloud Server (CS): This can be an untrusted entity. It has powerful data storage and computing capabilities to provide storage and search services for users.