esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Study
### Loading Protocol and End Conditions  
Each earthquake has unique characteristics, and loading histories cannot represent real earthquake movements. Different testing protocols have been developed globally and adopted to deduce the seismic performance of entire structures, sub-structures, and structural members under cyclic loading. The loading protocol recognized by the European Convention for Constructional Steelwork (ECCS) [18] is considered to be appropriate for the assessment of cyclic behavior of steel tubular sections and has been employed in an ongoing study to enable comparison of results with [1,15]. The loading sequence comprised one cycle at a displacement amplitude of 0.25\(\Delta\)y, 0.5\(\Delta\)y, 0.75\(\Delta\)y, and 1.0 \(\Delta\)y, followed by three cycles of 2\(\Delta\)y, 4\(\Delta\)y, 6\(\Delta\)y, 8\(\Delta\)y, etc., as shown in Figure 3. The standardized axial cyclic displacement \(\Delta\)a/\(\Delta\)y is the governing parameter of the loading protocol, where \(\Delta\)a is the applied displacement at the loaded end and \(\Delta\)y is the tensile yield displacement obtained by the product of yield strain and member length [1,15]. The loading path was symmetric in tension and compression, and specimens were first loaded in compression and then in tension. A fixed-fixed boundary condition was applied and, in finite element modeling, the end sections were coupled to a reference point at each side and all degrees of freedom were restrained, with the exception that longitudinal translation was allowed at the loaded end for the application of axial displacement.  
Figure 3: ECCS [18] loading protocol (\(\Delta\)a/\(\Delta\)y = ratio of applied displacement to the yield displacement).  
Figure 2: Mesh pattern of braces.
## 3 Results and Discussion
### Thermogravimetric Analysis  
Figure 6 shows the TGA results for capsules fabricated with varying A/R ratios. When the testing temperature is below 100 \({}^{\circ}\)C (before 25 min), all of the capsules show less than 2% weight loss. When the testing temperature exceeds 100 \({}^{\circ}\)C, linear increasing weight loss trends can be observed for capsules with different A/R ratios. Weight loss of capsules in this region is most likely due to the dehydration of calcium alginate gels. Among all the analyzed capsules, capsules with an A/R ratio of 10/90 show the lowest weight loss during the tests, and the weight loss trend increases with the alginate contents. As the temperature reaches 160 \({}^{\circ}\)C (referred as the asphalt mixing temperature) and is held for 15 min, the weight loss of the capsules retains a linear trend, and the final weight loss percentages of capsules with A/R ratios of 60/40, 50/50, 40/60, 30/70, 20/80 and 10/90 are 10.5%, 7.9%, 5.6%, 4.9%, 3.8% and 2.2%, respectively. This indicates that the capsules, except surface moisture evaporation, do not experience any significant degradation at 160 \({}^{\circ}\)C. Meanwhile, when the A/R ratio is lower than 40/60, the final weight loss is lower than 5%, which means capsules with these ratios are relatively more stable and are capable of surviving the asphalt mixing temperature.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### Design and Implementation of the RC-NB-LDPC Code
#### 2.3.4 SSRAA Module  
The encoder of the QC-LDPC code can be completed by the SSRAA circuit, and its implementation structure is shown in Figure 9.  
The multiplication and addition operations in the encoding of the NB-LDPC code are operations of the Galois field. For an 8-ary LDPC code, each symbol consists of three bits. The addition of the Galois field is essentially a bitwise XOR operation, as shown in the right part of Figure 10. The multiplication of the Galois field is designed by the looking-up table method, as shown in the left part of Figure 10. First, the input data din1 and din2 are respectively looked up in the ROMA, the result is added in the eight-element domain, and the addition result is checked in the ROMB. The result is the multiplication of din1 and din2 in \(GF(8)\).  
Figure 8: The state machine of the output module.
## 2 Materials and Methods
### Ratio Correction Method  
In digital signal processing, the sampling signal is a series of finite-length discrete data. Amplitude spectrum of a signal can be gained by the discrete Fourier transform (DFT) or FFT. In other words, the amplitude spectrum of a signal is the result of sampling in the equal frequency domain according to \(\Delta\omega=2\pi/N\) after the convolution of the signal spectrum and a window function. If the frequency of the periodic signal is on a certain spectral line exactly, the frequency, amplitude and phase are accurate. If the frequency is between the two adjacent spectral lines, instead of the main lobe center of the window spectrum, the frequency, amplitude, and phase reflected by the peak spectral line are inaccurate [15].  
Spectrum correction is to find the abscissa of the center of the main lobe, as shown in Figure 1a. Assume the spectrum of the window spectrum function is \(f(x)\) and its function expression is known. Besides, it is symmetrical about \(Y\)-axis. \(x\) and \(x+1\) are adjacent spectral lines and they are closest to the peak of frequency. Their corresponding window spectrum function are \(f(x)\) and \(f(x+1)\). Their corresponding discrete spectrum are \(y_{x}\) and \(y_{x+1}\). \(\Delta x=-x\) is the spectral line correction and it can be calculated by the following Equation.  
\[\left\{\begin{array}{l}y_{x}=f(x)\\ y_{x+1}=f(x+1)\end{array}\right. \tag{2}\]  
So, constructing a function  
\[V=F(x)=\frac{f(x)}{f(x+1)}=\frac{y_{x}}{y_{x+1}} \tag{3}\]  
where \(V\) is the ratio of the two spectral lines and it is a function of \(x\). The interval between these two spectral lines is 1. The inverse function \(V\) is  
\[x=g(V)=g(\frac{y_{x}}{y_{x+1}}) \tag{4}\]  
Figure 1: (**a**) Schematic diagram spectrum correction; (**b**) spectrum of window function.  
where the condition for the existence of the inverse function \(V\) is that \(x\) is in one-to-one correspondence with \(V\) in Equation (3). Then, the frequency correction value can be calculated according to \(\Delta x=-x\). Therefore, it is called ratio correction method.  
In the actual calculation, the main lobe center \(x_{0}\) is at the true frequency of signal, as shown in Figure 1b. \(y_{k}\) and \(y_{k+1}\) are two adjacent spectral lines in the main lobe. \(k\) and \(k+1\) are the serial number of spectral lines. \(\Delta x=-x\) is the spectral line correction and it can be calculated by equation  
\[\left\{\begin{array}{l}V=\frac{y_{k}}{y_{k+1}}\\ x=g(V)\end{array}\right. \tag{5}\]  
Then, the correction frequency formula is as following  
\[f_{k}=\frac{(k+\Delta x)F_{S}}{N} \tag{6}\]  
where \(F_{s}\) is the sampling frequency and \(N\) is the sampling number.
## 3 Methodology
### Data analysis  
#### 3.2.1 Data Normalization Using Ordinal Encoder and Log10 Transform  
The normalization technique organizes a database to minimize duplicate and redundancy data. We encode categorical variables as an integer array. The input of this transformer is identical to the integer or a string array and represents a value obtained according to the category (discrete) characteristics. This section converts features into ordinal integers. As a result, one integer column (0 to n \(-\) 1) appears in one element, and n is the number of categories. We implemented OE normalization for all selected components [13]. Figure 2 shows plots of component 6 with and without OE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{5}{c}{**Component**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline DasTemp & 0.868 & & & & & \\ OutletValve & 0.848 & & & & & \\ GPS\_ABS\_LAT & & & & & & \\ WS\_WIND\_LAT & & & & & & \\ EtalonTemp & 0.943 & & & & & \\ WarmBoxTemp & 0.931 & & & & & \\ WIND\_N & & & \(-\)0.783 & & & \\ WS\_WIND\_LON & & 0.688 & & & & \\ WS\_SIN\_HEADING & & 0.507 & & & & \\ WIND\_E & & & 0.682 & & & \\ WIND\_DIR\_SDEV & & & 0.661 & & & \\ WS\_COS\_HEADING & & & & & & \\ CavityTemp & & & & 0.684 & & \\ CAR\_SPEED & & & & 0.590 & & \\ CH4 & & & & & & 0.908 & \\ CavityPressure & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rotated component matrix.  
Additionally, we transformed the log10 scale transform with all seven components after OE. The results are shown in Figure 3. The distribution of the initial values of the data mentioned in earlier work [9] is similar.
# A Small Implantable Compact Antenna for Wireless Telemetry Applied to Wireless Body Area Networks
## 4 Discussion
### Specific Absorption Rate (SAR) Measurement and Analysis  
Antennas generate electromagnetic radiation during operation, and since implantable antennas operate within human tissue, the radiation they produce is absorbed by the human body, potentially affecting health. Therefore, the SAR value of the implantable antenna must be considered during the design process. SAR is a parameter that measures the absorption of electromagnetic radiation energy by the human body, with units in W/kg. According to the IEEE C95.1-1999 standard, the average SAR value over any 1 g of human tissue should not exceed 1.6 W/kg [17]; according to the IEEE C95.1-2005 standard, the average SAR value over any 10 g of human tissue should not exceed 2 W/kg [18]. The International Commission on Non-Ionizing Radiation Protection (ICNIRP) has regulations on the radiation power of medical implant devices, limiting their maximum input power to 25 W[19].  
To determine the SAR value of the proposed antenna at an input power of 25 W, we used simulation software to set the input power of the antenna implanted in human muscle tissue to 25 W. Figure 20 shows that when the input power is 25 W, the SAR value of the antenna in 1 g of tissue is 0.0111 W/kw, and the SAR value in 10 g of tissue is 0.00127 W/kg. In the literature [20], when the input power of the implantable antenna is set to 25 W, the SAR value of the antenna in 1 g of tissue at the corresponding frequency of 2.45 GHz is 0.17 W/kg. In the literature [21], when the input power of the implantable antenna is set to 25 W, the SAR values of the antenna in 10 g of tissue at different frequencies are 0.017, 0.016, and 0.014 W/kg, respectively. By comparing the above literature, the SAR value of the antenna designed in this paper is lower when the input power meets the ICNIRP regulations.  
Figure 19: Antenna current distribution.  
Through simulation analysis and calculation, it can be determined that the maximum input power of the antenna designed in this paper, which complies with the IEEE safety standards for 1 g and 10 g of human tissue, is 6.14 mW and 17.98 mW, respectively, as shown in Table 4. The maximum input power of the antenna at this time is much greater than 25 \(\mu\)W, so this antenna can meet the SAR requirements of both IEEE and ICNIRP standards.  
In Table 4, the antenna designed in this study is compared with antennas from papers published in recent years. Compared with the implantable antennas designed in [22] and [8], the antenna designed in this paper has a wider bandwidth and lower SAR values. Although the implantable antenna proposed in [12] has lower SAR values, the antenna designed in this paper has a wider bandwidth and smaller dimensions. Through comparison, the antenna designed in this paper has superior comprehensive performance, featuring small size, wide bandwidth, and low SAR values.
# Modeling and Key Parameter Interaction Analysis for Ship Central Cooling Systems
###### Abstract  
To achieve efficient prediction and optimization of the energy consumption of ship central cooling systems, this paper first constructed and validated a high-precision multi-physical domain simulation model of the ship central cooling system based on fluid heat transfer principles and the physical network method. Then, simulation experiments were designed using the Box-Behnken design (BBD) method to study the effects of five key parameters--main engine power, seawater temperature, seawater pump speed, low-temperature fresh water three-way valve opening, and low-temperature fresh water flow rate--on system energy consumption. Based on the simulation data, an energy consumption prediction model was constructed using response surface methodology (RSM). This prediction model exhibited excellent goodness of fit and prediction ability (coefficient of determination \(R^{2}\) = 0.9688, adjusted \(R^{2}_{\text{adj}}\) = 0.9438, predicted \(R^{2}_{\text{pred}}\) = 0.8752), with a maximum relative error of only 1.2% compared to the simulation data, confirming its high accuracy. Sensitivity analysis based on this prediction model indicated that main engine power, seawater pump speed, seawater temperature, and three-way valve opening were the dominant single factors affecting energy consumption. Further analysis revealed a significant interaction between main engine power and seawater pump speed. This interaction resulted in nonlinear changes in system energy consumption, which were particularly prominent under operating conditions such as high power. This study provides an accurate prediction model and theoretical guidance on the influence patterns of key parameters for the simulation-driven design, operational optimization, and energy saving of ship central cooling systems.  
ship central cooling system; energy consumption prediction model; Box-Behnken design (BBD); response surface methodology (RSM); interaction effect; sensitivity analysis
## 3 Beam Steering/Beam Forming Using Reconfigurable Components
### MEMS Switches
It consisted of three metal layers, bound with an adhesive and integrated with two orthogonally oriented slots. This array explores various characteristics of design, fabrication, and test performance. A 2-bit programmable MEMS switches mechanism provides four possible states of phase shifts, including 0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\), and 270\({}^{\circ}\). Moreover, the 2-bit phase resolution reduces the directivity of the transmitarray due to phase quantization loss. The proposed prototype shows an insertion loss that includes 1.4 dB to 1.6 dB for different phase states. AFA 2D beam scanning realized \(\pm\)60 degrees for the azimuth and elevation planes at 32 GHz. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ref**} & **Unit Cell** & **Phase Control** & **Frequency Control** & **Polarization** & **Phase Range** & **Gain** & **Aperture Efficiency** & **Band Width** & **Beam Scanning Capacity** \\ \hline
[100] & FSS & MEMS & 30.2 & CP & 360\({}^{\circ}\) & – & – & 1.6 GHz & – \\
[103] & antenna filter & MEMS & 32 & LP & 2-bit & 26.5 & – & – & \(\pm\)60\({}^{\circ}\) E and H plane \\
[104] & antenna filter & MEMS & 34.8 & LP & 2-bit & 9.6 & 6.2 & – & \(\pm\)40\({}^{\circ}\) E and H plane \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of beam steering reconfigurable transmitarrays with MEMS. Figure 9: (**a**) Proposed unit cell with MEMS bridge; (**b**) unit cell with bias bridge (reprinted with permission of [100]. Copyright 2004 [PERSON], et al. ).
## 3 Results and Discussion  
### Global Analysis  
The global analysis, illustrated through the 3D plots in Figure 4, provides an overview of how three critical factors collectively influence the shear capacity of precast concrete walls. These factors--section height, concrete compressive strength, and normalised axial load--are analysed based on 175 simulations encompassing variations in section height from 2.00 m to 3.00 m (in five increments), concrete compressive strength (fck) from 20 MPa to 50 MPa (in seven strength classes), and normalised axial load (v) ranging from 0.0 to 0.8 (in five levels). Fixed parameters include a section thickness of 0.2 m, an overall wall height (H) of 2.67 m, and the connection with two bars of 25 mm.  
The plots indicate that an increase in section height consistently enhances the maximum shear capacity across all levels of concrete compressive strength and normalised axial load, suggesting that larger sections inherently provide greater resistance to shear forces. Furthermore, higher concrete compressive strength significantly improves shear capacity, particularly when combined with moderate to high levels of axial load. This effect becomes more pronounced in taller sections, such as those with a height of 3.00 m, where the contribution of material strength is especially evident in resisting shear forces.  
Normalised axial load exhibits a more nuanced influence on shear capacity. At low axial load levels, the effect of variations in concrete compressive strength on shear capacity is relatively limited. However, as the axial load increases, its interplay with concrete strength becomes more apparent, particularly in taller sections. For instance, at heights of 2.75 m and 3.00 m, the combination of high axial load (80%) and high compressive strength (50 MPa) produces the highest shear capacities observed in the analysis. Conversely, sections with low height (2.00 m) and low concrete strength (20 MPa) exhibit the lowest shear resistance, regardless of the level of axial load.  
The 3D visualisations show complex interactions between these variables, demonstrating that shear capacity is not determined by any single factor in isolation but rather by their combined effects. This perspective reflects the need for a more focused analysis of each parameter individually to isolate their specific contributions to shear capacity. The following sections will address this by examining the influence of each variable in greater detail.
## 3 Components of VPN/Overlay Architecture
### TLS Based VPN  
TLS VPN, also known as Secure Sockets Layer VPN [8; 24; 25], offers a standard VPN solution that operates within the Transport Layer and can be accessed through a web browser or specific client/server applications. Communication between the sender and receiver takes place through socket connections. Two common SSL/TLS VPN deployments are referenced in [23]. In traditional SSL/TLS communication, two keys are utilized for data encryption: a public key shared among all users and a private key unique to each endpoint. To enhance security, two-factor authentication or one-time passwords (OTP) may be employed. Unlike IPsec VPNs, where authenticated clients have full access to the private network, SSL/TLS VPNs offer more granular control over access. This allows for creating specific tunnels for applications via sockets, rather than granting access to the  
Figure 1: Generic VPN/Overlay stacking technology.  
entire network, and enables the implementation of specific _access roles_ with distinct rights for different users.
# Plant Disease Diagnosis in the Visible Spectrum
## 2 Objective and Work Plan
### Work Plan  
In order to reach the objective, we divide the task of generating a diagnosis into two steps. First, there is the segmentation of the plant in order to separate it from the background, and then, there is quantification of the presence of colors associated with the scope of diseases considered to generate the diagnosis expressed as the portion of the surface of the plant exhibiting a specific disease. Then, we analyze a set of images to provide the sufficient evidence to evaluate whether the objective is attained.  
For the segmentation step, we adapted two techniques depending the type of background: for low-noise GMM segmentation and for high-noise probabilistic saliency segmentation.  
Once the segmentation is achieved, the color quantification is done by using thresholds for the considered colors in this work; the colors not satisfying those thresholds are labeled as unidentified, the result of the identification and quantification of colors is the diagnosis.  
In order to verify if the objective is achieved, a total of 4977 images are analyzed, a special set of 23 images, which were selected because of the challenging background, complement 4954 images of the public dataset PlantVillage, which is used to compare the performance of the proposal to a benchmark. The analysis of the 23 images has the purpose of evaluating the performance when subject to challenging backgrounds, and the analysis of the PlantVillage dataset has the purpose of comparing the effectiveness of the proposal against previous works.  
Lastly, as the final part of the work plan, a discussion of the results in order to identify the weaknesses and strengths of the proposal was performed coupled with the conclusions in order to reflect on the most important aspects of the study and the attainment of the objective.
## 3 Results and Discussion
### Measurement Results
#### 3.2.5 Specific Absorption Rate  
SAR measurement is a necessary element for health safety in WBAN systems. The SAR depicts how electromagnetic radiation is absorbed by tissues, causing a temperature increase. As a result, the SAR levels are limited to protect safety the human body from thermal radiation. Therefore, SAR is linked to E-fields and tissue conductivity [43]:  
\[\text{SAR}=\frac{\sigma|\text{E}|^{2}}{\rho}\bigg{(}\frac{\text{W}}{\text{Kg}} \bigg{)} \tag{7}\]  
where \(\sigma\) shows the tissue conductivity and \(\rho\) is its density. Alternatively, SAR shows the time derivative of the dissipated absorbed energy (dW) in mass (dm) in a volume (dV) of a provided density (\(\rho\)).  
\[\text{SAR}=\frac{\text{d}}{\text{dt}}\bigg{(}\frac{\text{dW}}{\text{dm}} \bigg{)}\Longrightarrow\frac{\text{d}}{\text{dt}}\bigg{(}\frac{\text{dW}}{\rho (\text{dV})}\bigg{)} \tag{8}\]  
\[\text{SAR}=\frac{1}{\rho}\bigg{(}\frac{\text{dW}}{\text{dt}}\bigg{)}\;. \tag{9}\]  
The SAR values are calculated for an average of 10 g of tissues and should be less than 2 W/Kg based on the \"International Commission on Non-Ionizing Radiation Protection\" (ICNIRP, European) standard. The SAR values will be higher if the BSA is located near the body's skin. In measurement, the gap between the body and the bottom of the BSA was at 5 mm to be comparable to the simulation. The air gap and the ground plane reduced the SAR. Similarly, for a 1 g tissue average, the \"Federal Communication Commission\" standard (FCC, and US standard) limit is 1.6 W/kg. The SAR values in 1 g and 10 g tissue average for lower and upper bands, were calculated, and they were lower than the standard at an input power of up to 0.5 W (100 dBm). Maps of 1 g and 10 g average SAR values for various situations at the maximum input power of 0.1 W or 0.5 W for the on-body  
Figure 27: Measured RSSI in dB in on-body scenarios (Distance: 2 m to 40 m).