esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mechanical Characterization  
The mechanical properties of the material are given in Table 1, showing the load direction with respect to the rolling direction, the yield stress \(YS\), the ultimate tensile stress \(UTS\), elongation to fracture \(EI\), the coefficients of strain hardening \(n\) and coefficient of resistance \(k\), corresponding to the power law \(\sigma=k\epsilon^{n}\). They were obtained through the true stress-true strain curve between the yield stress and the ultimate tensile stress (See Figure 6a-c). Additionally, these figures include the true stress-strain curves and the Hollomon fit. Figure 6d shows a schematic of tensile specimens in the three directions analyzed. The plastic strain ratio (R-value) was measured to estimate the potential of the 2024-T3 aluminum alloy sheets. The obtained R-values were \(R_{0}=0.82\), \(R_{45}=1.07\), \(R_{90}=0.93\), \(\overline{R}=0.97\) and \(\Delta R=-0.09\). These anisotropy values indicate a low difference in the material, with the highest R-value at 45\({}^{\circ}\) to the rolling direction and the lower R-value at 0\({}^{\circ}\). The anisotropy values are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample Orientation, \({}^{\circ}\)**} & **YS (0.2\%), MPa (SD)** & **UTS, MPa (SD)** & **EI, \% (SD)** & **n, (SD)** & **k, (SD)** \\ \hline RD & 337 (7.86) & 446 (0.86) & 16.5 (1.33) & 0.14 (0.01) & 540 (10.3) \\ DD & 321 (3.61) & 446 (3.39) & 17.1 (0.71) & 0.15 (0.01) & 537 (1.16) \\ TD & 324 (4.04) & 452 (3.08) & 16.5 (0.67) & 0.16 (0.01) & 563 (1.59) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tensile properties.  
Figure 5: SEM images and EDS analysis; (**a**) as-received material; (**b**) balanced biaxial strain sample; (**c**,**d**) EDX analysis of intermetallic phases white arrows Al\({}_{2}\)CuMg, yellow arrows Al\({}_{2}\)Cu.
## 5 Numerical Investigation on DOP
### Effect of Steel Fiber  
Axial force contours of steel fibers for target U-2 in four different moments, along with internal damages and cracks in the matrix, are listed in Table 7. From \(t\) = 630 \(\upmu\)s to \(t\) = 2700 \(\upmu\)s, the axial force in steel fibers develops synchronously with the expansion of internal damages and cracks. Since damage is accumulated by the plastic strain, the damage parameter can reflect the deformation of matrix elements. This deformation will cause a relative slip between the matrix and steel fibers embedded in the matrix, resulting in the generation of axial force in the steel fibers. The axial force will increase until the maximum relative slip \(S_{max}\) is reached and then decline until total debonding happens. Therefore, taking the regions around the ballistic trajectory at \(t\) = 630 \(\upmu\)s and regions where the cracks develop at \(t\) = 2070 \(\upmu\)s as examples, the axial force of steel fibers in the regions with damage is larger than that in the undamaged regions. The above findings show that the steel fibers can exert a bridging effect by limiting the deformation of matrix elements and the development of cracks, improving the resistance of UHPC targets under projectile penetration.  
Figure 19 shows the DOPs of 140 MPa and 180 MPa UHPC targets with volume percentages of steel fibers of 1.0%, 1.5%, 2.0%, 2.5%, 3.0%, 3.5%, and 4.0%, where the striking velocity is 400 m/s. No matter the UHPC target of 140 MPa or 180 MPa, the DOP decreases with the increasing volume percentage of steel fibers. DOPs into 140 MPa UHPC targets with 1.0% and 4.0% \(V_{f}\) is 44.3 cm and 36.2 cm, respectively, where the 3% increase in the volume percentage of steel fibers results in an 18.3% decrease in the DOP. For the 180 MPa UHPC targets, the corresponding DOPs are 38.7 cm and 33.8 cm, respectively, and the decrease in the DOP is 12.7%. It can be seen from Figure 19 that when \(V_{f}\) increases from 3% to 4%, the reduction in the DOP is not obvious, which demonstrates that there is a limit to reducing the DOP by adding more steel fibers.  
Figure 19: DOP versus volume content of the steel fiber \(V_{f}\) for 140 MPa and 180 MPa UHPC targets, where the striking velocity is 400 m/s.
## 5 Results and Discussion
Note that all of the results are reconstructed using 10 modes, and the calculation times of those methods are almost the same as the data-driven sparse sampling method, because the calculation time of the Amiet method is dominant. The microphone position and Mach number of this test condition were set to be (\(x\), \(y\), \(z\)) = (1.2589, 1.9246, \(-\)1.4921) and \(M\) = 0.1827. Although the relative error of the proposed method tends to be locally high in the regions where the sampling points are not located, the spatial averaged error is \(6.000\times 10^{-3}\%\) for the propagation time, and \(6.650\times 10^{-2}\%\) for the attenuation rate, respectively. In addition, the maximum error is suppressed to \(2.280\times 10^{-2}\%\) for the propagation time and \(1.977\times 10^{-1}\%\) for the attenuation rate, respectively. Table 4 presents the averaged error of those methods. These errors of the proposed method are much smaller than those of the IDW and TIN interpolation methods. Thus, the proposed method can be concluded to reduce the calculation cost while minimizing the estimation error when compared with conventional interpolation methods. It should be noted that the error cannot be perfectly removed, even in the proposed method, and the steering vector might be exhaustively calculated by the Amiet method for more accurate process of the experimental data after the experiments. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Data Driven Method** & **IDW Interpolation** & **TIN Interpolation** \\ \hline Propagation time & \(6.000\times 10^{-3}\%\) & 3.361\% & \(4.754\times 10^{-1}\%\) \\ Attenuation rate & \(6.650\times 10^{-2}\%\) & 2.815\% & \(2.264\times 10^{-1}\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of calculation time. Figure 8: Spatial average of absolute error and calculation time (\(p=r\)). Here, the shaded regions of spatial averaged absolute error shows the range from the minimum to maximum values obtained for 50 runs with different random seeds. Shaded regions of calculation time shows the standard deviation obtained for 50 runs. Figure 9: Distributions of error in the sound source grid estimated by sparse sampling(\(p=r\), 10 sampling points).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 4 Evolution of Communications Technologies for Telemedicine
### Short Range Communication Technologies
#### 4.3.3 Ultra-Wideband (UWB)  
UWB is a short-range wireless technique for fast and reliable data transmission, and provides large bandwidth. It is based on the IEEE 802.15.3 standard. It operates in the unlicensed frequency band of 3.1 GHz-10.6 GHz [103] of the electromagnetic spectrum. It has good energy efficiency and throughput, which this makes it a choice technique for use in Wireless Body Area Networks (WBAN) for remote patient monitoring. A longer sensor operational life and even greater data transmission rate (up to 500 Mbps for 2 m range and 110 Mbps for 10 m range) are enhanced by the lower energy usage. Its use case was demonstrated in the design and implementation by [103] in a textile UWB wearable antenna. The Specific Absorption Rate (SAR) on the human skin was found to be 1.68 W/Kg, which is within safe limits to avoid radiation effects. Other works on UWB technique include in [104; 105; 106; 107; 108; 100; 104; 105; 106; 107; 108].  
Types of UWB  
1. Multi-band (MB) Optical Frequency Division Multiplexing (OFDM) UWB.
2. Impulse Radio (IR) UWB.  
IR-UWB is suitable for WBAN, as it has low complexity, lower energy demand, and operates with a very short pulse duration, on the order of 2-3 ns. However, it has drawbacks due to the complexity of the front-end circuitry design.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 5 Conclusions  
This work presents a CMOS-based reconfigurable reflectarray element designed for terahertz (THz) applications. A custom-designed inductive loop is employed in the THz switch to resonate with parasitic capacitance. Through probe testing, TRL calibration, and parameter extraction, the switch's equivalent impedance is obtained for the element design. By using a single switch, the proposed split-ring element design achieves low-loss amplitude control. A 30 \(\times\) 24-element prototype is fabricated and tested. The test results demonstrate an amplitude difference of 3 dB at 290 GHz. The study validates the feasibility of applying low-frequency RRA design principles to THz regimes using CMOS technology, offering a scalable, low-cost pathway for future high-performance THz beam-steering systems.  
Conceptualization, F.Y. and S.X.; validation, Y.R. and M.L., writing and methodology, Y.W.; review and editing, F.Y. and Y.R. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Key Research and Development Program of China under Grant No. 2023 YFB2905000.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
We thank [PERSON] for assistance in the probe test.  
The authors declare no conflicts of interest.
# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 2 Augmentation of Trust Concept in the SIoT
### Definition of Trust in SIoT  
There are plenty of trust definitions in particular situations resulting in difficulty in establishing a standard notation of trust in computer science. In order to define trust in the SIoT environment, we tend to follow a widely-accepted approach from social science that trust is considered as _belief_ which appears in many trust-related literature [11; 17]. A general definition of trust in computer science has been broadly acknowledged as following:  
Trust is defined as a _belief_ of a _trustor_ in a _trustee_ that the trustee will provide or accomplish a _trust goal_ as _trustor's expectation_ within a _specific context_ for a _specific period of time_.  
In SIoT environment, trustors and trustees can be humans, devices, systems, applications and services. Measurement of trust as the belief (called trust value) can be absolute (e.g., probability) or relative (e.g., level of trust). The trust goal is in a broad understanding. It could be an action that the trustee is going to perform (trust for action); it could also be information that the trustee provides (trust for information). Trustor's expectations are deliberately considered to include specific requirements for well performing (in some degree) the trust goal. All of the terms in this definition will be described and explained in detail in the next sections.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 2 Cubesat Specifications and Constraints
### On-Board Computer  
The On-Board Computer operates and controls the satellite by handling multiple parallel tasks and functions, depending on the operation modes. The OBC interfaces between the subsystems and controls the data flow. Typical data bus architectures like I2C, UART, and CAN are often used for the communication between OBC and subsystems. High-speed bus architectures like CAN are preferred for missions requiring high data rate and high data volume communication with the OBC.
## 3 Materials and Methods  
### Downlink Signal Spectrum Structure  
Currently, recommended as ranging pseudo-codes by the CCSDS [30], T4B and T2B are both generated by combining six identical codes with different logistics. The primary distinction between these two lies in that the weight of the clock component of the T4B code is double that of the T2B code. The frequency of the PN code ranging clock tone component is about 0.5 MHz or about 1 MHz when the PN code rate is 1 Mchips/s or 2 Mchips/s. In addition, according to the CCSDS standard, only one group of DOR beacon signals is set in the S-band, and the frequency spacing between the DOR signal and the carrier is 1/600 of RF frequency, which is about 3.7 MHz when the RF frequency is 2.25 GHz. Two sets of DOR beacon signals are allocated in the X-band, and the frequency spacings are 1/2200 (corresponding to DOR\({}_{1}\)) and 1/440 (corresponding to DOR\({}_{2}\)) of RF frequency, respectively, which are about 3.8 MHz and 19.2 MHz when the radio frequency is 8.45 GHz. The X-band downlink signal spectrum construction of a typical deep spacecraft is shown in Figure 3.
# Integrated Sensing and Communication Using Random Padded OTFS with Reduced Interferences
## 7 Simulation Results
### Additional Pilot Repetition-Based Pilot-to-Data Interference Reduction  
In the next scenario, sinc extrapolation-based data-to-data interference compensation is always enabled and we compare only the system performance with and without pilot-to-data interference compensation (by the introduced pilot repetition in the Doppler direction of the DD grid), proposed in Section 5 and graphically explained in Figure 12. At present, we simulate a strong clutter reflection. We use Doppler reflection parameters the same as in the previous scenario, but pick only one magnitude \(-18\) dB. However, we also add a strong reflection on zero Doppler, the magnitude of the reflection being \(-3\) dB. The magnitudes of Doppler reflections are normalized to the main line of sight (LOS) propagation path. The maximum delay of the clutter reflections does not exceed \(L-2\). Here, it is important to notice that for moving the transmitter or receiver, the reflections from static objects will also have Doppler. For this case, the parameters of the pilot repetitions \(K\) and \(i\) should be chosen to exclude the influence of these strong reflections in the DD domain. In the cases  
Figure 14: BER for channels with 10 reflections for different modulation types and channel coefficient variances **without** sinc extrapolation-based data-to-data interference compensation. The type of modulation and variance of the channel coefficients are indicated in the legend of the figure.  
Figure 15: BER for channels with 10 reflections for different modulation types and channel coefficient variances **with** sinc extrapolation-based data-to-data interference compensation. The type of modulation and variance of the channel coefficients are indicated in the legend of the figure.  
where the pilot repetition was enabled, the column of the DD grid where the interferences were concentrated was excluded from processing, for both types of signal, ZP-OTFS and RP-OTFS. This simulates the case of interference between MTs described in Section 5.  
The results of the simulation are shown in Figure 16 and are denoted as \"with pilot repetition\" and \"without pilot repetition\", respectively. In the case of strong reflections, the side lobes significantly reduce the performance, even in cases when sinc extrapolation is enabled. The additional pilot-to-data interference compensation technique allows efficient elimination of pilot side-lobes in DD grid regions used by data and reduction in the BER.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
## 3 Review on Optimization Methods under Continuous Phase Shift
### CR Method  
The idea of the CR method is that, while the constraint set \(\mathcal{F}\) in C1 is nonconvex, it can be relaxed to a Euclidean unit ball, which is a convex set. Therefore, problem (5) under C1 can be relaxed into  
\[\min_{\mathbf{e}}\;\;f\Big{(}\mathbf{x}^{(n)},\mathbf{e}\Big{)},\;\;\text{s.t. }\;|e_{m}|^{2}\leq 1,\quad\forall m. \tag{10}\]  
Since (10) has a convex set, it can be solved via convex tools, such as CVX. Afterward, the solution of the relaxed problem is projected to the nearest point in \(|e_{m}|^{2}=1\) to obtain a feasible solution.  
A variant of the above method is replacing the interior point method with the projected-gradient (PG) method, which alternates between gradient steps and projection steps. Although this variant has not been employed in the existing literature involving RISs, it has a linear computational complexity compared to the cubic complexity of the interior point method, and thus is promising for large-scale systems.  
Notice that this method is applicable to model C2. For model C2, where \(\mathcal{F}\) is already in the form of \(|e_{m}|^{2}\leq c\), there is no relaxation involved and the solution is directly obtained from solving (10). Furthermore, unlike other methods applying to C2, there is no need to optimize \(\beta_{m}\) and \(\theta_{m}\) separately since the optimization of \(\beta_{m}\) is incorporated in (10).
## 2 FSW Experiments Considering Productivity  
### The FSW Experimental Environment  
The structure used in this study is aluminum alloy 6063-T4 and the chemical composition is shown in Table 1 below.  
In addition, we fabricated test pieces with the same thickness as the aluminum battery housing structure to conduct experiments under various welding conditions. The dimensions of the test piece and welding tool are shown in Tables 2 and 3.  
The rotation speed and traveling speed of the tool were used as welding variables. We increased the rotation speed by 200 RPM from 1600 RPM to 2400 RPM, and the traveling speed by 100 mm/min from 600 mm/min to 1000 mm/min. This study performed a total of 25 experiments by combining the variables above. Table 4 summarizes the experimental conditions.  
During the experiments, the plunge depth of the tool was fixed at 0.1 mm and the tilting angle was set at 0\({}^{\circ}\). We collected temperature data using K-type thermocouples, as shown in Figure 4, to calculate the friction coefficient and welding efficiency for the analysis.  
As shown in Figure 4, we machined holes (diameter: 1 mm) 1 mm deep from the surface at distances of 10 mm, 15 mm, 25 mm, and 35 mm from the center, and inserted thermocouples (diameter: 0.9 mm) into the holes to measure the temperature transferred through the base metal during welding.  
\begin{table}
\begin{tabular}{c c} \hline
**Component** & **Percentage [\%]** \\ \hline Silicon & Min. 0.2–Max. 0.6 \\ Iron & Max. 0.35 \\ Copper & Max. 0.10 \\ Manganese & Max. 0.10 \\ Magnesium & Min. 0.45–Max. 0.9 \\ Chromium & Max. 0.10 \\ Zinc & Max. 0.10 \\ Titanium & Max. 0.10 \\ Aluminum & Remainder \\ \hline \end{tabular}
\end{table}
Table 1: The chemical composition of aluminum alloy 6063-T4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Width [mm]** & **Length [mm]** & **Thickness [mm]** \\ \hline
120 & 300 & 2.5 \\ \hline \end{tabular}
\end{table}
Table 2: The test piece dimension for experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Shoulder Diameter [mm]** & **Pin Diameter [mm]** & **Pin Height [mm]** \\ \hline
12 & 4.5 & 2.2 \\ \hline \end{tabular}
\end{table}
Table 3: The FSW tool dimension for experiments.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **1600 RPM** & **1800 RPM** & **2000 RPM** & **2200 RPM** & **2400 RPM** \\ \hline
600 mm/min & \# 1 & \# 6 & \# 11 & \# 16 & \# 21 \\
700 mm/min & \# 2 & \# 7 & \# 12 & \# 17 & \# 22 \\
800 mm/min & \# 3 & \# 8 & \# 13 & \# 18 & \# 23 \\
900 mm/min & \# 4 & \# 9 & \# 14 & \# 19 & \# 24 \\
1000 mm/min & \# 5 & \#
## 2 Description of Specimens  
To demonstrate the advantages of the scanning inductive thermography, two rail pieces with rolling contact fatigue (RCF) defects were used. As described in a recent publication, head checks and squats are common defects on rails, stemming from rolling contact fatigue (RCF) [9].  
Head checks are a material fatigue phenomenon caused by the high contact stresses between the wheel and the gauge corner of the rail that occur in sections of track with a curve radius between 500 m and 3000 m. They appear as fine surface cracks that occur at discrete intervals that grow into the rail head with a very shallow entry angle (10-30 degrees). Squats are rolling contact fatigue (RCF) defects that occur in straight or (gently) curved track within the running band on the rail head, where the rail is heavily sheared. This specific defect phenomenon is thought to be initiated via small parts of hard material (such as pieces of track ballast) that are caught between the rail and wheel and cause certain cracks to grow. The crack then preferentially grows in the direction of the heavily sheared surface layer; this generates the characteristic round shape of the squat. For a more detailed description of head checks and squats, one can refer to [9] and the references therein.  
A 250 mm long piece of rail with head checks on the gauge corner (RP01; see Figure 1) was used for the scanning in stop-and-go technique. The rail piece was provided from voestalpine Rail Technology GmbH and it is a cut out piece of a decommissioned pearlit rail from the Austrian railway system. A characterization of the head checks on this specific rail piece was published in [9]. The average distance between the head checks was determined as 2.54 mm and an average length of head checks as 17.1 mm.  
The specimen used for continuous scanning is a 300 mm long pearlitic rail piece with squats (RP02; see Figure 2). The specimen has two large squats, which are clearly visible and some small and shallow cracks in the earlier stages of crack progression. These ones are not or only barely visible. The specimen is also a cut-out part of a decommissioned rail piece found in the Austrian railway system, provided by voestalpine Rail Technology GmbH.  
Table 1 presents a short summary of both rail pieces and shows the short names that will further be used in this work to reference the specimens.  
Figure 1: (a) A 250 mm long rail piece with head checks (RP01); (b) detailed view of the head checks on the gauge corner; area with head checks marked in red.  
Figure 2: (a) A 300 mm rail piece with squats (RP02); (b) detailed picture of a large squat on the specimen; area with squats marked in red.