esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Unidirectional Chloride Diffusion Test  
The obtained chloride content profiles after 90 days of unidirectional Cl\({}^{-}\) diffusion are shown in Figure 4. Mean values of the determined Cl\({}^{-}\) contents (three specimens tested for each concrete mix) along with the corresponding deviation bars are depicted in Figure 4.  
The obtained Cl\({}^{-}\) content profiles are fitted to [PERSON]'s second law of diffusion [33].  
\[C(x,t)=C_{\text{S}}\text{erfc}\bigg{(}\frac{x}{2\sqrt{D_{\text{\tiny{Hss}}}t}} \bigg{)}\]  
where \(C(x,t)\,(\%)\) is the chloride concentration as a function of depth \(x\) (m) and time \(t\) (s), \(\text{erfc}(x)=1-\frac{2}{\sqrt{\pi}}\int_{0}^{x}e^{-t^{2}}du\) is the complement of the error function, and the fitted parameters are the surface chloride concentration \(C_{\text{S}}\) (%) and the non-steady state diffusion coefficient \(D_{\text{\tiny{Hss}}}\) (m\({}^{2}\)/s). The obtained fitted parameters are shown in Table 6.  
The mean chloride profiles of Figure 4, and also the values of their fitted parameters (Table 6), are very close for both concretes. This means that the presence of the admixture does not affect the chloride ingress by diffusion in the experimental conditions of this work. This result is in good agreement with findings of previous works [17].
# Enhancements and Challenges in CoAP--A Survey
## 5 Qualitative and Quantitative Analysis of Congestion Control Schemes
### Qualitative Analysis  
In this subsection, we discuss the enhanced congestion control schemes qualitatively. Various congestion control schemes are evaluated in different network topologies and different network traffic scenarios. Some schemes including CoCo-RED, CoCoA, 4-state estimator, adaptive congestion control scheme, and genetic CoCoA++ consider the continuous traffic scenarios, whereas some other schemes including improve adaptive, CACC, message loss feedback-based, and content freshness-based consider the constant traffic pattern. Finally, there are only a few schemes that consider the burst traffic pattern which include CoCo-RED, CoCoA, and CoCoA+. Compared to the default CoAP congestion control, the enhanced versions achieve better quality of network (QoN) in terms of various performance parameters. Based on our analysis, we can infer that the enhanced congestion control schemes such as message feedback-based, CoCoA, CACC, content freshness based, 4-state estimator, and adaptive congestion control scheme have higher throughput than the default CoAP congestion control with message feedback-based being the lowest among them and adaptive congestion control scheme being the highest one. In terms of settling time, CoCo-RED, CoCoA, and CoCoA+ analyze this metric and achive lower settling time than default CoAP. Therefore, CoCoA+ is suitable for the application where one requires lowest settling time. Another parameter i.e., packet delivery ratio can be used to identify the suitable scheme for the scenario where one needs to have higher packet delivery ratio. For example, if CoAP is to be applied in the IoUT environment, the CoCoA+ is better candidate then default CoAP as it achieves higher packet delivery ratio than default CoAP. Depending upon the requirement of application, different schemes can be implemented to improve the QoN. However, there is always a tradeoff between different performance parameters. For instance, if CACC is applied in the underwater sensor network, it will be suitable in terms of saving energy as it consumes less energy as compared to other schemes; however, the throughput will not be as high as that of adaptive congestion control scheme.
# Study on Electromagnetic Radiation Interference Caused by Rocket Fuel
## 1 Introduction
It was also found that the energy of the electromagnetic radiation was positively correlated with the quality of fuel, and the electromagnetic pulse was delayed for hundreds of microseconds and continued for a long time after combustion ended [10; 11; 12]. In 2014, [PERSON] summarized and analysed the experimental results of electromagnetic radiation generated during energetic material deflagration. It was believed that the movement of ionized atoms, ions and electrons was the cause of the electromagnetic waves caused by deflagration. The expansion of detonation products caused a strong vibration in the surrounding air, forming a strong thermal wave (T\(\sim\)11,000 K) with a duration of 20 us, which caused clear ionization in the air and movement of ion plaques, thereby generating a current [13]. Figure 1: Recovery process of the first-stage rocket of a Falcon 9. According to the existing public literature, a complete theoretical system has not been formed for related research on electromagnetic radiation generated by the detonation of energetic materials. Most of the related experimental studies focused on a small mass (below 10 kg), which is of little practical significance. The experimental setup generally used traditional rod antennas and the data recording equipment was generally an oscilloscope, which resulted in a low sampling rate and a short data recording time. Because the experimental data processing method was relatively simple, the signal analysis was not comprehensive, so it is difficult to guarantee the accuracy of the research results. In this paper, an electromagnetic radiation measurement system for energetic materials represented by rocket fuel is established, which has a higher sampling rate and longer recording time. By measuring the electromagnetic signals generated by the deflagration of aviation fuel under different conditions, this paper analyses the characteristics and laws of electromagnetic radiation, constructs a mathematical model, and forms a complete theoretical system. Combined with the electromagnetic radiation interference of electronic equipment on spacecraft, this paper assesses the threat of electromagnetic radiation, provides help for the anti-electromagnetic interference design of spacecraft electronic equipment, and ultimately improves the safety of space missions.
## 3 Materials and Methods  
Our methodology aims to significantly enhance exam accessibility for BVI students by integrating advanced assistive technology and specialized design features. This approach is designed to improve student autonomy and streamline exam experience.  
Figures 2 and 3 illustrate that the ExamVoice system operates through a coordinated process involving three key roles: the main instructor, other instructors, and BVI students.  
The process starts with the main instructor using the ExamVoice system to create and record the exam content. This system involves preparing other essential materials, such as Bluetooth-enabled earphones and a customized answer sheet designed explicitly for BVI students. Once these materials are arranged, the recorded exam content, earphones, and answer sheets are either handed over to other instructors for further administration and setup or provided to BVI students, who use these resources to complete their exams with appropriate support and supervision.  
Other instructors are responsible for managing the distribution of these materials and setting up the exam environment. They ensure that BVI students receive all the necessary tools for a successful exam experience, including the proper distribution and setup of earphones and answer sheets. They also oversee the application of these materials during the exam.  
BVI students use the provided earphones and answer sheets to access and respond to the recorded exam questions. The Bluetooth-enabled earphones allow students to navigate the pre-recorded questions independently using voice control buttons. Crucially, BVI students take the exam in the same hall as their sighted peers, ensuring that all students face consistent testing conditions.  
Overall, the ExamVoice system ensures equal access to exam content, allowing BVI students to participate in the exam process with greater independence and fairness.  
Figure 2: Complete overview of the ExamVoice system.
## 2 Materials and Methods
### Methods
The Servohydraulic Test System (Instron 8505) was used. For all prisms, a notch of 12.5 mm depth was cut in the middle of the specimen. The test layout of the specimens is shown in Figure 4. The loading speed was 0.05 mm/min until the crack mouth opening displacement (CMOD) reached 0.1 mm, and 0.2 mm/min thereafter, until the CMOD value reached 4 mm and the test was stopped. To calculate the limit of proportionality \(f_{LOP}\) and the residual flexural tensile strength \(f_{R,j}\) for CMODs of 0.5, 1.5, 2.5, and 3.5 mm, Equation (1) was used:  
\[f_{R,\,j}=\frac{3F_{l}l}{2 bh_{sp}^{2}} \tag{1}\]  
where  
\(f_{R,\,j}\) is the residual flexural tensile, MPa;  
\(Fj\) is the load corresponding to \(CMOD\) = \(CMOD\)\({}_{j}\), N;  
\(l\) is the span length, mm;  
\(b\) is the width of the specimen, mm;  
\(h_{sp}\) is the distance between the tip of the notch and the top of the specimen, mm. The RA and RF were obtained from the parent PPFRCs. The parent concretes were crushed, sieved, and washed to obtain the aggregates and fibers. The concrete was crushed by a laboratory jaw crusher with a fixed target output particle size of 8 mm, as the Figure 5 (left) and sieved by an electromagnetic sieve shaker (CISA BA 400N), as the Figure 5 (right). In order to obtain a consistent sieved aggregate, the mass of each sieve is 3 kg and the sieving time is 2 min, 1 min clockwise, and 1 min counter-clockwise. The RF was obtained using a flotation method, whereby the sieved RA was placed in a basin and water was poured in to completely submerge it in water; during the mixing process, the RF floated to the surface, due to its density, and collected. The recycled aggregate was sieved into 0/4, 4/12, 12/20, and \(>\)20 mm fractions. Aggregate fractions of 4/12 and 12/20 mm were retained to test the particle size distribution, absorption, and density [29]. The shape distribution of the collected RF as well as the recovery rate were characterized and analyzed. For the recovery rate, a 5 kg sample of  
Figure 4: Experimental testing: stress–strain relationship (**left**); residual tensile strength (**right**). crushed PPFRC was submerged in water, and the PP fibers were recovered by flotation. The weight of the fibers recovered from the 5 kg samples was expressed as g/kg and related to the theoretical fiber content in the parent concrete (3 or 9 kg/m\({}^{3}\)) through the density of the hardened concrete (i.e., 5 kg is approximately 0.002 m\({}^{3}\)). The RF was divided into three types according to their shape. A long RF was defined as one that is more than half the length of the original fiber, a short RF was one that is less than half of the original length, and the rest were classified as bent RF; Figure 6.
## 2 Methods  
### Model Based Methods  
#### 2.1.1 ATPRK  
Area-To-Point Regression Kriging (ATPRK) is a geostatistical approach to super-resolution that was first suggested for use with MODIS data in [32]. It was applied to pansharpening of WorldView-2 and Landsat data in [33] and adapted for S2 images in [8].  
ATPRK uses regression-based overall trend estimation and area-to-point-based residual upsampling. Here, trend is the geostatistical term for the spatially varying mean of a spatial process, and residual refers to the variation remaining after removal of the trend. ATPRK accounts explicitly for size of support (pixel), spatial correlation, and Point Spread Function (PSF) of the sensor [8]. While the original code only performed \(20\to 10\) m super-resolution, extending it to perform \(60\to 10\) m super-resolving in exactly the same way is fairly simple.  
ATPRK consists of two parts: regression and area-to-point kriging (ATPK). The final ATPRK estimate \(\hat{\mathbf{\Upsilon}}_{20m_{l}}^{10m}(\mathbf{x})\), is given by  
\[\hat{\mathbf{\Upsilon}}_{20m_{l}}^{10m}(\mathbf{x})=\hat{\mathbf{\Upsilon}}_{20m_{l}1}^{10 m}(\mathbf{x})+\hat{\mathbf{\Upsilon}}_{20m_{l}2}^{10m}(\mathbf{x}),\;l\in\{1,\dots,6\}, \tag{1}\]  
where \(\hat{\mathbf{\Upsilon}}_{20m_{l}1}^{10m}(\mathbf{x})\) is the regression prediction and \(\hat{\mathbf{\Upsilon}}_{20m_{l}2}^{10m}(\mathbf{x})\) is the ATPK prediction.
# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 4 Materials and Methods
### Datasets
#### 4.1.2 KAIST Datasets  
The KAIST Multi-Spectral Day/Night dataset is a large-scale dataset designed for automated driving and advanced driver assistance system (ADAS) visual perception tasks. It encompasses a diverse range of drivable scenarios, spanning urban and residential areas, and includes data collected across various time periods (day and night) as well as finer temporal divisions (dawn, morning, afternoon, dusk, and night) to facilitate all-weather visual perception research. The dataset was acquired using a multisensor platform comprising RGB/thermal imaging cameras, RGB stereo cameras, 3D LiDAR, and inertial sensors (GPS/IMU). Optical alignment and calibration techniques were employed to ensure the precise synchronization of RGB and thermal imaging data. The KAIST dataset supports multiple visual perception tasks, including target detection, drivable area detection, image enhancement, depth estimation, and thermal image pseudo-coloring. It serves as a crucial benchmark for addressing visual perception challenges under low and extreme lighting conditions.
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 3 System Model
### Modulation  
Along with illumination, LEDs can be also used for data communications. Here, we have adopted the most common data format of on-off keying (OOK) for intensity modulation of LEDs. However, other modulation formats could also be used. The information bits of an LED are denoted by \(\{b^{j}\}_{j=-\infty}^{\infty}\) where \(b^{j}\) is a uniformly distributed sequence of {0,1}. The LED is 'on' when \(b^{j}=1\) and is 'off' when \(b^{j}=0\). Let \(rect(t)\) be a unit amplitude rectangular pulse of duration \(T\) (i.e., data rate \(R_{d}=T^{-1}\)). The transmitted optical signal is given by:  
\[s(t)=P_{p}\sum_{j=-\infty}^{\infty}b^{j}rect(t-jT) \tag{8}\]  
where \(P_{p}\) is the peak optical power of the emitted light wave. The received electrical signal at the photodiode is given by:  
\[y(t)=\gamma h(t)\otimes s(t-\tau)+n(t) \tag{9}\]  
where \(n(t)\) is the additive white Gaussian noise (AWGN) and \(\tau\) denotes the transmission delay.  
A standard matched filter is adopted at the Rx in order to recover the transmitted data. The impulse response of the filter at the Rx is a rectangular pulse of a unity amplitude and duration \(T\). Let us assume \(\tau=0\), i.e., the matched filter of the Rx is synchronized to the arrival signal transmitted by an LED as in [24].
# Entropy-Regularized Federated Optimization for Non-IID Data
## 3 Methodology
### Convergence Analysis
For practical finite \(T\) values, one can show that the final error term is proportional to \(\lambda_{T}\) (which is zero) plus higher-order terms that vanish as \(T\) grows. Thus, ERFO-Decayed achieves unbiased convergence to the optimum. Theorem 2 implies that using a decaying regularization schedule allows ERFO to enjoy the benefits of entropy regularization during the early and mid stages of training (improving robustness to heterogeneity and guiding the optimization trajectory) while still asymptotically converging to the exact minimizer of the original federated objective. In other words, the entropy term acts like a vanishing perturbation that does not affect the final solution when scheduled to zero. The convergence speed is essentially unaffected by the entropy term in the long run, aside from the constant-factor impact of slightly larger gradients in early iterations (which our assumptions and choice of \(\eta\) account for). Practical implications: Convergence theory suggests that if one is concerned with reaching the best possible accuracy (unbiased solution), the decayed variant of ERFO is preferable, as it guarantees eventual convergence to \(w^{*}\). On the other hand, if training time is limited or if a small bias is acceptable in exchange for stability, a fixed small \(\lambda\) can be used, and training can be stopped early. In our experiments, we indeed observe that ERFO-Decayed matches the final accuracy of standard FedAvg on UNSW-NB15 (since \(\lambda_{T}=0\)), while ERFO-Fixed on PneumoniaMNIST achieves better intermediate results than FedAvg and reaches a comparable final accuracy (with a negligible gap attributable to the regularization). Entropy regularization does not dramatically alter the theoretical requirements for convergence; it mainly adds a controllable bias which decays to zero if scheduled appropriately.
# A Wideband Terahertz Transmissive Polarization Manipulator Based on Metasurfaces
## 3 Results and Discussion
### Physical Mechanism for LCPC Operation
Similarly, the frequency-dependent impedance for the unit cell with a y-polarized incident wave as shown in Figure 2b can be expressed as:  
\[\mathrm{Z_{y}(w)=\frac{1}{jwL1}+\frac{1}{\frac{2}{jwL5}+\frac{1}{2 jwL4}+\frac{ 1}{jwC3}+\frac{2}{jwC4}}+\frac{1}{jwC3}} \tag{14}\]  
To investigate the transmitted wavefront trajectory, we introduce the vector wave Equation as:  
\[\frac{x^{2}}{t_{xx}}\ +\frac{y^{2}}{t_{xy}}-2\frac{xy}{t_{xy}t_{xx}}\cos\varphi_{ yx}=\sin^{2}\varphi_{yx} \tag{15}\]  
The wave-front trajectory curves at different frequencies are as shown in Figure 9. It is clearly shown that transmitted waves at 1.085 THz and 1.175 THz are circular, whereas waves at 1.387 THz and 0.995 THz are elliptically polarized. This limitation is because at some frequencies, the transmitted x- and y-components are unequal. This results in an axial ratio greater than 0 dB, hence indicating that the transmitted waves are not exactly circularly polarized. However, the performance criterion for the circular polarization is considered to be within 3 dB of axial ratio where the nonzero value of the axial ratio corresponds to slight elliptical polarization which can be considered as partial circularly polarized waves. Figure 8: Electric field distribution considering the four cells of the metasurface array at: (**a**) 1.085 THz; (**b**) 1.454 THz.
## 4 Case Study
### Experimental Design
#### 4.1.1 Apparatus  
As an experimental device, a smart 55 inch TV was used to generate UXs. The manufacturer of the TV was Samsung (UNF8000), and the TV was available in Korea. Considering that UX varies over time and a user's perceived UX depends on previous experiences, an experimental device for the case study should have comparatively unfamiliar functions in order not to be affected by previous experiences. What this study cared about to do research was controlling experience rather than using new devices. That is why the experimental focus of this study was gesture interaction with smart TV. Although smart TVs are fairly common nowadays, gesture interactions with smart TV are less familiar.
## 5 Evaluation and Discussion
### Sentiment Analysis Model Development  
Developing the sentiment analysis model involved creating a lexicon-based approach that utilized a compilation of positive and negative terms in both Arabic and English, with each term assigned a specific sentiment score. This model operated through a series of steps. Initially, feedback texts underwent tokenization, breaking down the content into individual terms for detailed analysis. Subsequently, each term received a sentiment score based on the lexicon, with the collective scores of the terms used to ascertain the overall sentiment of the feedback. Finally, aggregate scores were categorized into three sentiment classes.  
* Positive (score > 0)
* Neutral (score = 0)
* Negative (score < 0)
## 3 Results
### Binary Logistic Regression  
In order to verify a relevant statistical relationship between the presence of PH and other factors (especially investigated biomarkers), a univariate and a multivariate binary logistic regression analysis were performed (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**sPAP \(\geq\) 40 mmHg**} & \multicolumn{2}{c}{**Univariate**} & \multicolumn{2}{c}{**Multivariate**} \\ \multicolumn{2}{c}{**Binary Logistic Regression**} & \multicolumn{2}{c}{**Hazard Ratio (95\%)**} & \multicolumn{2}{c}{**\(p\)-Value**} & \multicolumn{2}{c}{**Hazard Ratio (95\%)**} & \multicolumn{2}{c}{\(p\)**-Value**} \\ \hline \multicolumn{2}{c}{Age} & 1.066 (0.978\(-\)1.161) & 0.145 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Univariate and multivariate binary logistic regression analysis detecting predictors of PH via sPAP \(\geq\) 40 mmHg. sPAP: systolic pulmonary artery pressure; BMI: body mass index; CVD: cardiovascular disease; PAD: peripheral artery disease; COPD: chronic obstructive pulmonary disease; LVEF: left ventricular ejection fraction; LVEDD: left ventricular end-diastolic diameter; IVSd: interventricular septal thickness at diastole; AV Vmax: maximal velocity over aortic valve; AV dpmcan: mean pressure gradient over aortic valve; AV dpmcan: maximal pressure gradient over aortic valve; Tapse: tricuspid annular plane systolic excursion; AVI: aortic valve insufficiency; MVI: mitral valve insufficiency; TVI: tricuspid valve insufficiency; BNP: brain natriuretic peptide; Chri: cardiac troponin; Hkt: hematocrit; Hb: hemoglobin; CK: creatine kinase; sST2: soluble suppression of tumorigenicity-2; GDF-15: growth differentiation factor-15; H-FABP: heart-type fatty acid binding protein; IGF-BP2: insulin-like growth factor binding protein 2; suPAR: soluble urokinase-type plasminogen activator receptor. Bold numbers: \(p\leq\) 0.050.  
Figure 2: AUROC curves, cut-off values, and Youden Index for prediction of presence or absence of PH up to 12 months after TAVR. **(A)**: sST2; **(B)**: GDF-15; **(C)**: H-FABP; **(D)**: IGF-BP2; **(E)**: suPAR. sST2: soluble suppression of tumorigenicity-2; GDF-15: growth differentiation factor-15; H-FABP: heart-type fatty acid binding protein; IGF-BP2: insulin-like growth factor binding protein 2; suPAR: soluble urokinase-type plasminogen activator receptor.  
In the univariate analysis, the STS score, presence of atrial fibrillation, low tricuspid annular plane systolic excursion (TAPSE), creatinine, brain natriuretic peptide (BNP), cardiac troponin (cTNI), hemoglobin, sST2 at baseline, H-FABP at baseline, and IGF-BP2 at baseline showed a relevant association (\(p<0.100\)); thus, multivariate analysis was performed with these variables. Only the atrial fibrillation, TAPSE, and sST2 at baseline were found to have a significant \(p\)-value \(<0.050\).