esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Methodology
### Mesh Strategy and Boundary Conditions  
The computational mesh utilized for the numerical simulation is presented in Figure 3. All the domains are meshed by hexahedral structured grid generated by ICEM CFD. The boundary layers are adopted near the wall to ensure calculation accuracy. The first cell size is selected as \(2\times 10^{-4}\) mm and the y+ value is less than 1.  
The flow in blade cascade and LS is complex turbulence. In this paper, the commercial software ANSYS CFX18.2 is selected and the Shear Stress Transfer (SST) model is utilized for calculation [20; 24; 25]. The working fluid is \(\mathrm{SCO}_{2}\) and the accurate physical properties are from NIST [26]. The property data is imported in software by the RGP format file to achieve the accurate simulation.  
The boundary conditions are introduced as follows. The periodic symmetric model is adopted for lower calculation cost. The detailed condition parameters are shown in Table 3. The interface is set between the contacted surfaces of two adjacent domains and the other surfaces are set as adiabatic. The frozen rotor method is adopted to deal with rotating and stationary interfaces [24; 27; 28].  
The mass flow rate and output power are two important performance parameters of the axial-inflow turbine. Thus, the grid independence study is carried out for these two parameters. The output power is defined as:  
\[W=\frac{2\pi n}{60}\times T_{z}, \tag{3}\]  
where \(n\) represents rotation speed and \(T_{x}\) denotes torque of rotor along axial direction.  
The seal clearance is selected as 0.2 mm. The grid independence study is conducted base on the mass flow rate and power. It concludes from the results shown in Table 4 that the variations of these two parameters are almost negligible after the grid number exceeds \(2.5\times 10^{6}\). Therefore, the adopted grid number in this paper is \(2.5\times 10^{6}\).  
Figure 3: Computational Mesh.
# Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks
## 3 Detection of SQL Injection Using Probabilistic Neural Network
### Data Collection  
SQL injection datasets are not publicly accessible, and some organizations are not willing to share the dataset. In this approach, we collected data from a python library known as Lib-injection, which contains all forms of SQL injections [27]. We collected 6000 SQL injection queries and 3500 normal SQL queries.
# A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map
###### Abstract  
To resolve the contradiction between existing watermarking methods--which are not compatible with the watermark's ability to resist geometric attacks--and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was preprocessed using the henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.  
henon map; multi -watermarking; DTCWT-DCT; robustness Article
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 3 Method
### LoRa Emulator  
In this paper, we utilize LoRa as an example to demonstrate the feasibility of directly adopting the terrestrial LPWAN technologies into IoT-over-Satellite systems. The structure of the LoRa emulator consists of two main components: (i) LoRa transmit signal emulator, which is responsible for creating LoRa waveform based on the payload data, and the (ii) LoRa receiver responsible for the demodulation and the decoding of the LoRa packet pack into the payload data.
# Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
## 2 Harmony Search and Kernel Fuzzy Clustering
### Kernel Fuzzy Clustering  
In the past decade, several studies on the kernel fuzzy clustering method (KFC) have been conducted [18; 19]. A kernel method means using kernel functions to map the original sample data to a higher-dimensional space without ever computing the coordinates of the data in that space. The kernel method takes advantage of the fact that dot products in the kernel space can be expressed by a Mercer kernel \(K\), given by \(K(x,y)\equiv\Phi(x)^{T}\Phi(y)\), where \(x,y\in R^{d}\)[20].  
Suppose the sample dataset is given as \(\mathbf{X}=\{\mathbf{X}_{1},\mathbf{X}_{2},...,\mathbf{X}_{n}\}\), and each sample has \(K\) attributes \(\mathbf{X}_{i}=\{X_{i1},X_{i2},...,X_{iK}\}\). Firstly, the original sample data is normalized following Equation (2):  
\[x_{jk}=\frac{X_{jk}-X_{k}^{\min}}{X_{k}^{\max}-X_{k}^{\min}} \tag{2}\]  
The purpose of kernel fuzzy clustering is to minimize the following objective function:  
\[J=\sum\limits_{i=1}^{C}\sum\limits_{j=1}^{N}u_{ij}^{m}\left|\left|\Phi\left( \mathbf{x}_{j}^{\prime}\right)-\Phi\left(\mathbf{v}_{i}^{\prime}\right)\right| \right|^{2} \tag{3}\]  
\[\mathbf{x}_{j}^{\prime}=\text{diag}(\mathbf{w})\cdot\mathbf{x}_{j} \tag{4}\]  
\[\mathbf{v}_{i}^{\prime}=\text{diag}(\mathbf{w})\cdot\mathbf{v}_{i} \tag{5}\]  
Constraints are:  
\[\sum\limits_{i=0}^{C}u_{ij}=1,1\leq i\leq N \tag{6}\]  
\[\sum\limits_{k=1}^{K}w_{k}=1 \tag{7}\]  
where \(C\) is the number of clusters, \(N\) is the number of samples, \(K\) is the number of features, \(\mathbf{v}_{i}=\left[v_{i1},v_{i2},\ldots,v_{iL}\right]\) is the \(i\)th cluster center, \(u_{ij}\) is the membership of \(\mathbf{x}_{i}\) in class \(i\), \(\mathbf{w}=\left[w_{1},w_{2},\cdots,w_{K}\right]^{T}\) is the feature-weight vector, and \(m\) is coefficient, \(m>1\).  
According to the relevant literature [17; 20; 21], Equation (3) is simplified as:  
\[J=2\sum\limits_{i=1}^{C}\sum\limits_{j=1}^{N}u_{ij}^{m}\left(1-K\left(\mathbf{ x}_{j}^{\prime},\mathbf{v}_{i}^{\prime}\right)\right) \tag{8}\]  
where \(K(\mathbf{x},\mathbf{x}^{\prime})\) is a Mercer kernel function. In this paper, the (Gaussian) radial basis function (RBF) kernel is adopted:  
\[K\left(\mathbf{x},\mathbf{x}^{\prime}\right)=\exp\left(-\frac{\left\|\mathbf{ x}-\mathbf{x}^{\prime}\right\|_{2}^{2}}{2\sigma^{2}}\right) \tag{9}\]  
The clustering center and membership matrix is:  
\[u_{ij}=\left(\sum\limits_{r=1}^{C}\frac{1-K\left(\mathbf{x}_{j}^{\prime}, \mathbf{v}_{i}^{\prime}\right)}{1-K\left(\mathbf{x}_{j}^{\prime},\mathbf{v}_{ i}^{\prime}\right)}\right)^{-\frac{1}{m-1}} \tag{10}\]  
\[\mathbf{v}_{i}=\frac{\sum\limits_{j=1}^{N}u_{ij}\cdot K\left(\mathbf{x}_{j}^{ \prime},\mathbf{v}_{i}^{\prime}\right)\cdot\mathbf{x}_{j}}{\sum\limits_{j=1}^ {N}u_{ij}\cdot K\left(\mathbf{x}_{j}^{\prime},\mathbf{v}_{i}^{\prime}\right)} \tag{11}\]
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 4 Our Proposed Routing Schema
### Intercluster Communication  
In this section, we use a greedy algorithm to construct a chain for intercluster communication to avoid long-distance communication. The process of the formation of the chain can be divided into the following steps:  
_Step1_: The sink broadcasts a Chain_Formation message in the whole network to require all the CHs to report their ID and position.  
_Step2_: Once the sink receives the information regarding CHs, it will choose the closest CH as the leader. The leader takes the responsibility to transmit the data to the sink directly.  
Figure 4: CHs selection.  
_Step3_: The greedy algorithm is executed in other CHs and each CH only transmits its data to a relay CH that is closer to the sink compared with itself.  
The result of chain generation is shown in Figure 5.
## 4 Influencing Factors Analysis
### Dimension Parameters of the Key
#### (3) Key aspect ratio (B/H)  
The relationship between the ultimate shear capacity \(V_{u}\) and key aspect ratio (B/H) is approximately proportional, see Figure 14. With the increase of B/H from 1.0 to 4.0, the average shear stress \(\tau\) on the key root section reduced from 10.53 MPa to 9.67 MPa, the reduction amplitude is 8.5%. Shown as in Figure 15.  
In comparison, the impact of \(tan\theta\) on \(V_{u}\) is slightly larger than that of \(d\) and **B/H**. But in general, dimension parameters of the key have little effect on the ultimate shear bearing capacity of the key  
Figure 14: The curve of \(V_{u}\) versus B/H.  
Figure 13: The curve of \(V_{u}\) versus tan\(\theta\).  
Figure 15: The curve of \(\tau\) versus B/H.
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 2 Theory of BDS-3 TGD/DCB Correction Models
### Correction Model with Broadcast Satellite Clocks  
In order to further study the relationship between TGD and DCB, Figure 3 gives comparison results of the TGD and DCB from April 2020 where the broadcast ephemeris are provided by iGMAS and the DCB parameters by CAS. We need to pay attention to the B3I signal, which is the reference signal of the satellite clock offset for BDS-3 broadcast ephemeris. The difference between TGD and DCB for each satellite is also plotted in red or blue bars for 27 satellites. \(DCB_{B1C-B1I}\) and \(DCB_{B2a-B3I}\) can be expressed as:  
\[\left\{\begin{array}{c}DCB_{B1C-B3I}=\left\{\begin{array}{c}DCB_{C1X\_C6I} =DCB_{C1X\_C6I}\\ DCB_{C1P\_C6I}=DCB_{C1P\_C6I}\\ DCB_{C5X\_C6I}=DCB_{C1X\_C6I}-DCB_{C1X\_C5X}\\ DCB_{C5P\_C6I}=DCB_{C1P\_C6I}-DCB_{C1P\_C5P}\end{array}\right. \tag{3}\]  
As showed in Figure 3, the broadcast TGD parameter was consistent with the DCB parameter from CAS. Moreover, the root mean square (RMS) of the TGD and DCB difference is better than 2 ns. Thus, \(TGD_{B1 Cp}\) and \(TGD_{B2 ap}\) can be interpreted as B1C-B3I and B2a-B3I differential code bias, respectively.  
For single-frequency users, \(B_{j}\) can then be written as:  
\[\left\{\begin{array}{c}B_{B1I}=-TGD_{1}=-DCB_{B1I-B3I}\\ B_{B3I}=0\\ B_{B1C}=-TGD_{B1 Cp}=-DCB_{B1C-B3I}\\ B_{B2a}=-TGD_{B2 ap}=-DCB_{B2a-B3I}\end{array}\right. \tag{4}\]For dual-frequency users, \(B_{i/j}\) can be then written as:  
\[\left\{\begin{array}{c}B_{B1I/B3I}=-\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}} \cdot DCB_{1}=-\frac{f_{B1I}^{2}}{f_{B1I}^{2}-f_{B3I}^{2}}\cdot DCB_{B1I-B3I}\\ B_{B1C/B2a}=-\left(\frac{f_{B1C}^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}\cdot TGD_{B1 Cp}- \frac{f_{B2a}^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}\cdot TGD_{B2 ap}\right)\\ =-\left(\frac{f_{B1C}^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}\cdot DCB_{B1C-B3I}-\frac{f_ {B2a}^{2}}{f_{B1C}^{2}-f_{B2a}^{2}}\cdot DCB_{B2a-B3I}\right)\end{array}\right. \tag{5}\]
## 5 Conclusions  
This study presents a descriptive statistical analysis of electric field measurements in four different regions in Greece, focusing on both the existing cellular networks and the emerging 5G technology. The findings reveal that electric field levels vary considerably across different environments, with urban areas exhibiting higher values due to greater population density and a higher concentration of BS. Cellular networks, especially 4G, contribute significantly to the overall electric field levels, whereas the 5G network shows only a minor contribution. Recording the electric field at the 3.5 GHz band without generating data traffic and examining its correlation with distance from BS, we observed that, in general, as distance increased, the recorded electric field decreased. Our results confirm that distance from BS antennas should be included as a predictive feature in AI models for EMF levels, not only for 2G, 3G, and 4G networks, but also for 5G. This finding highlights the enduring significance of distance as a key factor for accurately predicting EMF levels in the 5G band, where its influence on field strength is clearly observed.  
Conceptualization, A.M. and M.C.; methodology, A.M., M.C., E.K., and T.S.; software, A.M. and S.D.; validation, S.D., N.P., and E.K.; formal analysis, A.M.; investigation, A.M., M.C., and E.K.; resources, S.D., N.P., and T.S.; data curation, S.D. and N.P.; writing--original draft preparation, A.M.; writing--review and editing, A.M., M.C., E.K., and T.S.; visualization, A.M., M.C., and N.P.; supervision, E.K. and T.S.; project administration, T.S.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript. Funding Funding for this research was provided by the European Union's Horizon Europe Framework Programme under grant agreement number 101057622 (SEAWave Project). Data Availability Statement: The data presented in this study are available upon request from the corresponding author due to the data being part of the SEAWave project with restrictions, and will be published in a public repository after the completion of the project. Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in any aspect of the study. Abbreviations The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial intelligence \\ BS & Base station \\ BSA & Base station antenna \\ EMF & Electromagnetic field \\ ICNIRP & International Commission on Non-Ionizing Radiation Protection \\ LOS & Line-of-sight \\ MIMO & Multiple-input and multiple-output \\ NLOS & Non-line-of-sight \\ NOEF & National Observatory of Electromagnetic Fields \\ POIs & Points of interest \\ SCA & Small cell antenna \\ SSB & Synchronization signal block \\ \end{tabular}
## 1 Introduction
### Research Gaps  
Despite advancements in DPT estimation models, several gaps persist in the existing literature, limiting the effectiveness and applicability of these models in various scenarios:  
* Dependence on Multiple Variables: Many models require different meteorological variables (e.g., temperature, humidity ratios, vapor pressure), complicating their use and implementation. Simplifying the input requirements can make models more accessible and easier to implement, especially in resource-constrained environments [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27].
* Limited Applicability under Varied Conditions: These conditions include the spectrum of average conditions of temperature and humidity, but also very high temperatures (above 40 \({}^{\circ}\)C) or temperatures near 0 \({}^{\circ}\)C, where condensation becomes challenging. Additionally, conditions of very high relative humidity (above 80%) and very low relative humidity (below 15%) further complicate the accuracy. Combining extreme temperatures with extreme humidity levels presents the most difficult scenarios for accurately modeling the DPT. Current empirical models and some machine learning models fail to provide accurate estimates under extreme environmental conditions. Accurate DPT estimation under a wide range of conditions is critical for applications like water harvesting and energy efficiency [8; 9; 15; 27; 28].
* Lack of Specialized Tools: Currently, there is no tool specifically designed to address the combined results of temperature and humidity conditions beyond the simplistic rule of thumb, which is often inadequate. Developing dedicated tools that can handle these combined scenarios with precision and reliability is essential for advancing practical applications and enhancing model accuracy in diverse environmental contexts.
# GNSS Interference Monitoring and Detection (GIMAD) System +
## 3 GIMAD Prototype Description  
Following the GIMAD design introduced in [1], a GIMAD prototype has been implemented and tested.  
The main components of the architecture are represented in Figure 2 and consist of the following.  
1. **Antenna set**, integrating three different antenna types for different purposes: 1. Two GNSS antennas receiving in-band GNSS signals. One antenna for RFI detection purposes and another one for GNSS clock steering with a narrow-band pass filter. 2. Wide-band Fixed Reception Pattern Antenna (FRPA), whose main mission is detecting out-of-band interferences.  
Figure 1: Operation use case (ENAIRE).  
The GIMAD antenna set has been deployed at INDRA's Barcelona 22@ roof headquarters, as shown in Figure 3.  
2. **Signal Processing Module** (SPM), with specific techniques for identification, characterization, and localization of RFI. It is composed of: (a) Measurement Analyzer Module (MAM). Post-correlator block that analyzes the observables of the GNSS receiver. It provides extra information about constellations/bands/satellite status.  
Figure 3: GIMAD antenna set installed at the INDRA’s Barcelona 22@ building.  
Figure 2: GIMAD Station architecture.  
2. In-band RF Signal Analyzer Module (I-SAM). The RF output of the GNSS antenna is redirected to this module, which digitalizes the received signal and applies pre-correlation algorithms for RFI detection, characterization, and localization to the baseband IQ samples. 3. Out-band RF Signal Analyzer Module (O-SAM). It is analogous to I-SAM but focuses on out-band processing. 4. CRPA Signal Analyzer Module (C-SAM). It provides a preliminary determination of the AoA RFI source.
3. **Control, Alert, and Monitoring Module (CAMM)**, which consolidates the SPM outputs, saves data for further analysis, generates alerts, and performs monitoring and control of the stations interfacing with the control center.
4. **GIMAD Control Center (GIMAD CC)**, which aims to monitor the status of the different GIMAD stations, manage the network, and provide a more accurate localization of the RFI source. An example of the GUI interface of the GIMAD CC is shown in Figure 4.
## 3 Platform Subsystem Design
### Attitude Determination and Control Subsystem  
The attitude determination and control subsystem (ADCS) stabilizes the satellite and holds it in place, thus fulfilling the mission and payload requirements. This system consists of two parts: the determination and the control part. The former acknowledges the position and the velocity vectors of the satellite and points the orientation of the satellite faces, while the latter directs the satellite to a certain location by maintaining pointing or reducing spin rate. Sensors collect and measure different variables and transfer them to the microcontroller unit (MCU). In the MCU, libraries of environmental models, attitude determination algorithms, and control algorithms prepared by NanoAvionics will fuse the readings to attain position and velocity vectors. The control algorithms then send a command for a certain pointing requirement to the components, which will spin the satellite to the target point until it achieves the pointing accuracy. A breakdown of the Light-1 ADCS can be seen in Figure 7.  
The Light-1 ADCS determination system consists of three three-axis gyro sensors, six fine Sun sensors, three three-axis magnetometers, and a GPS. The gyro sensors are provided by NanoAvionics and embedded on the OBC board. These gyro sensors measure the angular velocity for attitude determination during an eclipse. The accuracy of the gyros is increased by averaging three gyros readings. The cross-axis sensitivity of these gyros is 1 deg/s. The fine Sun sensors are located on each side of the satellite to measure the Sun vector for attitude determination and partial Sun tracking mode. The magnetometer, embedded on the OBC board, measures the magnetic field of Earth for attitude determination. The GPS has two main components: the GPS receiver and the GPS patch antenna, which consists of the patch antenna and the low noise amplifier. The GPS receiver placed on the OBC board synchronizes payload data and time to predict when an event occurs. The timing accuracy of this receiver is 10 ns. The GPS patch antenna is located on the \(+Z\) axis face of the satellite.  
The Light-1 ADCS control system consists of four reaction wheels and three magnetorequers. The reaction wheels stabilize the CubeSat and perform fine attitude pointing, while the magnetorquers are responsible for high spinning control situations and the desaturation of the reaction wheels. The magnetorquers are the only controllers used in the detumbling mode after ejection.  
Figure 7: Light-1 ADCS breakdown.
## 2 Miniaturized X-ray/Gamma-ray Imaging Payload
### Astrophysics Observational Applications  
The intended application areas of the instrument are as follows: (i) Sun sensor/tracker, (ii) capturing bright triggers (both of space as well as of Earth origin) in hard X-rays, (iii) directional screen and verification of the radiation shielding of the detector, (iv) X-ray high resolution sky mapping, and (v) high-resolution space radiation monitor with enhanced X-ray and Gamma-ray sensitivity.  
Deployment in space for astrophysics applications requires stable and accurate location and orientation control of the spacecraft platform. Attitude control accuracy is required at the sub-degree level with stability in the range of tens of minutes for necessary long exposure of stellar X-ray and Gamma-ray objects.