esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Fiber-Shaped Electronic Devices for Wearable Applications
## 3 Device Applications
### Light-Emitting Fibers
Despite this progress, developing wearable displays composed of luminescent textiles still needs to be performed to achieve high electroluminescence performance, robust interconnection of lighting fibers, and a durable passivation layer. To address the aforementioned issues, [PERSON] et al. reported a feasible strategy for OLED textile displays that  
Figure 6: (**a**) Schematic illustration of a pixel structure in the electroluminescent fabric. (**b**) Schematic illustration and photograph of the electroluminescent fabric showing the pattern “N”. (**c**) Schematic illustration of the smart electroluminescent fabric being functionalized by connecting Bluetooth. (**a–c**) Reproduced with permission Ref. [65]. Copyright 2021, American Chemical Society. (**d**) Schematics of the simultaneous extrusion process and the light-emitting fiber (**e**) Photograph of an electroluminescent textile displaying numbers from 0 to 9. (**f**) Photograph of the real-time brain-interfaced camouflage of the fiber under green illumination. (**d–f**) Reproduced with permission Ref. [66]. Copyright 2018, Wiley-VCH. (**g**) Photograph of the RGB fiber PELDs, which are woven into clothes. (**h**) Left: schematic illustration of the textile display and a magnified schematic showing the contact region, the emission region, and the interlocking fiber. Right: photograph of the resulting textile display, which is integrated into the textile, visualizing the letter information “A”. (**g.h**) Reproduced with permission Ref. [15]. Copyright 2021, Wiley-VCH. (**i**) Schematic illustration of the woven OLED textile display consisting of orthogonally arranged arrays of interconnectable OLED fibers and conductive fibers. (**j**) Photograph of the working woven textile device comprising 10 \(\times\) 10 fiber arrays by the passive matrix scheme. (**i,j**) Reproduced with permission Ref. [67]. Copyright 2021, American Chemical Society. comprise interconnectable phosphorescent OLED fibers and a polyurethane (PU)-based passivation layer (Figure 6i) [64]. The green OLEDs, in a 1D OLED pixel array, were thermally deposited onto the ITO/PET rectangular fibers with a patterned PU side barrier [67]. They exhibited a high brightness of \(\sim\)4300 cd m\({}^{-2}\) at 5 V, and an efficiency of \(\sim\)46 cd A\({}^{-1}\) (\(\sim\)58 lm W\({}^{-1}\)), with environmental robustness [67]. The woven OLED network and perpendicularly aligned conductive fibers were driven by a passive matrix driving process, and, moreover, operated even under the water and under stretch deformation along the diagonal axis (Figure 6j) [67]. However, further research on light-emitting fibers needs to progress to realize low diameter, 360\({}^{\circ}\) light emissions, and a simple manufacturing process developed for practical wearable displays.
###### Abstract  
The aim of this study was to use near-infrared time-resolved spectroscopy (TRS) to determine the absolute values of cerebral blood volume (CBV) and cerebral hemoglobin oxygen saturation (ScO\({}_{2}\)) during the immediate transition period in term neonates and the changes in optical properties such as the differential pathlength factor (DPF) and reduced scattering coefficient (\(\mu_{\text{s}}\)*). CBV and ScO\({}_{2}\) were measured using TRS during the first 15 min after birth by vaginal delivery in term neonates who did not need resuscitation. Within 2-3 min after birth, CBV showed various changes such as increases or decreases, followed by a gradual decrease until 15 min and then stability (mean (SD) mL/100 g brain: 2 min, 3.09 (0.74); 3 min, 3.01 (0.77); 5 min, 2.69 (0.77); 10 min, 2.40 (0.61), 15 min, 2.08 (0.47)). ScO\({}_{2}\) showed a gradual increase, then kept increasing or became a stable reading. The DPF and \(\mu_{\text{s}}\)' values (mean (SD) at 762, 800, and 836 nm) were stable during the first 15 min after birth (DPF: 4.47 (0.38), 4.41 (0.32), and 4.06 (0.28)/cm; \(\mu_{\text{s}}\)': 6.54 (0.67), 5.82 (0.84), and 5.43 (0.95)/cm). Accordingly, we proved that TRS can stably measure cerebral hemodynamics, despite the dramatic physiological changes occurring at this time in the labor room.  
neonate; vaginal delivery; cerebral blood volume; cerebral hemoglobin oxygen saturation; near-infrared time-resolved spectroscopy
# Research on Airspace Conflict Detection Method Based on Spherical Discrete Grid Representation
## 4 Algorithm Model Construction
### Conflict Detection Model on the Base of Airspace Analysis  
The airspace data is inputted into the database, and the airspace attribute includes time attribute T+ height attribute H+ range attribute R. Starting from the highest-level airspace group, judge the time attribute of airspace A and airspace B, C... If there is a conflict, set the airspace where there is a conflict to a collection of airspace. Judge the height attribute of airspace B and airspace C, D in turn. If there is a conflict, set the airspace where there is a conflict to another airspace collection. And so on, starting from the high-level airspace, the conflict is detected to the low-level airspace in turn, and the airspace where the conflict exists is set to a separate set of airspace. The CD process is shown in Figure 12:  
* Step 1 time attribute CD. Compare the start time and end time of airspace A with other airspace. If there is a conflict with the intersection of the empty time, put the airspace An and the airspace with which there is a conflict into an airspace set;
* Step 2 height attribute CD. Logical relationship and time attribute detection: compare whether there is an intersection in the airspace altitude layer. After the CD of time and  
Figure 12: Airspace CD process.  
height dimensions is completed, several airspace sets are obtained. There are both time conflicts and high conflicts in the airspace within these airspace sets;
* Step 3 range attribute CD. Obtain the data matrix of airspace A and the data matrix of airspace B. Carry on the Hadamard product operation \(matA\circ matB\) to obtain the \(matC\) of the same dimension. At that time \(matC=O_{(i\times j)}\), it is determined that there is no conflict between airspace A and airspace B; \[\begin{bmatrix}1&1&\cdots&0&0\\ 1&1&\cdots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 1&1&\cdots&0&0\\ 1&1&\cdots&0&0\end{bmatrix}\circ\begin{bmatrix}0&0&\cdots&1&1\\ 0&0&\cdots&1&1\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&\cdots&0&0\\ 0&\cdots&1&1\end{bmatrix}=\begin{bmatrix}0&0&\cdots&0&0\\ 0&0&\cdots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&\cdots&0&0\\ 0&0&\cdots&0&0\end{bmatrix}\]
* Step 4 outputs the airspace CD results. Starting from the high-level airspace, it is detected in accordance with steps 1-3, and cycle to determine whether all airspace CD is completed.
# A Comparative Study of Effective Domain Adaptation Approaches for Arabic Sentiment Classification
## 5 Discussion
### Arabic vs. English  
To assess the behavior of DANN and mSDA on Arabic texts compared with English texts, we used the reported in-domain [18] and adaptation [10] results to calculate the average transfer loss of each method, as shown in Figure 4. Although these methods worked well in English (low transfer loss) [10; 11], they seemed to suffer when handling the challenges of the Arabic language. As shown in Figure 4, the transfer loss of each method was doubled when tested in Arabic. However, DANN showed more degradation than mSDA. While domain dependency is a problem for any cross-domain sentiment classification task, the impact is typically even worse for Arabic, due to the challenges of dialect variety (as discussed in the previous section), resource scarcity and morphological richness. The problem of resource scarcity is noteworthy, since it prevents progress being made at the same pace as for other resource-rich languages. Given the diversity of Arabic dialects compared with the unified English language, existing resources are barely sufficient to reach an acceptable performance for DA, since the Arabic resources are distributed among various dialects. Even with all the current Arabic resources, there is still an urgent need to acquire a large amount of new labeled data from a specific dialect to achieve good performance in that dialect. This highlights the importance of exploiting transfer learning and domain adaptation to overcome this limitation. The last dimension of Arabic complexity is its morphological richness. A single word can express different meanings depending on its suffixes or prefixes. Leaving words in their original form increases the number of features and therefore leads to sparseness [19]. This makes it difficult to find sufficient common features for adaptation. At the same time, words may lose their meaning after stemming (i.e., reducing words to their roots), which in turn affects task performance. A balance between semantic loss and feature sparsity that is widely recommended for Arabic involves the application of light stemming [47]. However, data  
Figure 3: Average transfer loss of each method on MSA and DA datasets.  
scarcity and the variety of dialects again introduce obstacles when seeking to build powerful morphological analyzers.
## 2 Methods
### Graph Neural Network (GNN) Approach
#### 2.3.4 Implementation Details  
The hidden dimension of the GNN layers was set to 64, and the Adam optimizer was used with a learning rate of 0.001, a commonly used value in neural network training with the Adam optimizer, to balance convergence speed and stability. The training process was conducted for 100 epochs to ensure convergence and sufficient learning of the graph representations.  
All analyses and model training were performed using Python 3.12.6 and PyTorch 2.5.1. Additional libraries included NumPy, Pandas, and PyTorch Geometric for graph-related computations.
## 2 Materials and Methods
### Numerical Verification  
To validate the reliability of the NMS model, four representative material combinations from Table 1 (e.g., Al-AA, PMMA-PVC-PETG) were used to construct multilayer cylindrical shells with a radius of 0.25 m and a layer thickness gradient of 0.01 m (innermost structural steel layer: 0.002 m).  
A finite element model of a multilayered elastic cylindrical shell was developed using COMSOL Multiphysics 6.2 software. Acoustic-structure interaction analysis was implemented through multiphysics coupling between the Acoustic Module and Structural Mechanics Module. The geometric modeling employed a 2D axisymmetric approximation to simulate an infinitely long cylindrical shell, with the outermost radius set at 0.25 m and uniform thickness of 0.01 m for each polymer layer. In the physical settings, the external water domain (density 1000 kg/m\({}^{3}\), sound speed 1476 m/s) was connected with the internal air cavity through an acoustic-structure interaction interface. Material properties strictly followed Table 1 data, using frequency-independent isotropic parameters to define elastic layer characteristics (e.g., PMMA's Young's modulus 2.8 GPa, Poisson's ratio 0.38). [PERSON] adopted the \(\lambda/10\) accuracy criterion, as illustrated in Figure 2.  
Computational results from both the Normal Mode Series (NMS) and Finite Element Method (FEM) for a cylindrical shell with radius \(r=0.25\), constructed using selected materials from Table 1, are shown in Figure 3.  
Figure 3: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material Names** & \begin{tabular}{c} **Density** \\ **(\(\rho\)) (kg/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Young’s Modulus** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Longitudinal** \\ **Velocity** \\ **(\(c_{L}\)) (m/s)** \\ \end{tabular} &
\begin{tabular}{c} **Characteristic** \\ **Impedance** \\ **(\(Z=\rho c_{L}\))** \\ **(Megarayleighs)** \\ \end{tabular} \\ \hline Water & 1000 & / & / & 1480 & 1.48 \\ \hline Air & 1.2 & / & / & 344 & \(0.41\times 10^{-3}\) \\ \hline Polymethyl Metacrylate (PMMA) & 1180 & 2.8 & 0.38 & 2108 & 2.49 \\ \hline Polyvinyl Chloride (PVC) & 1400 & 3 & 0.38 & 2003 & 2.80 \\ \hline Polytetrafluoroethylene (PTFE) & 2200 & 0.4 & 0.37 & 567 & 1.25 \\ \hline Polyethylene Terephthalate (PETG) & 1270 & 2 & 0.37 & 1669 & 2.12 \\ \hline High-Density Polyethylene (HDPE) & 970 & 1.5 & 0.4 & 1820 & 1.77 \\ \hline Low-Density Polyethylene (LDPE) & 910 & 0.1 & 0.45 & 646 & 0.59 \\ \hline Acrylic Acid (AA) & 1190 & 3.2 & 0.35 & 2078 & 2.47 \\ \hline Aluminum (Al) & 2700 & 70 & 0.33 & 4032 & 10.89 \\ \hline Structural steel & 7850 & 200 & 0.3 & 5856 & 45.97 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of commonly used materials.  
Figure 2: Finite element model.
# Sensitivity Analysis and Filtering of Machinable Parts Using Density-Based Topology Optimization
## 1 Introduction
### Density-Based Topology Optimization  
The density-based approach starts from a design domain \(\Omega\) that is discretized using nodes and elements. This design domain is the maximum space (line, surface, or volume) where the final design could exist. A density value is assigned to each element of the discretized design domain; hence, it is parametrized by this density. The density value is not binary, so it can vary from 0 (void) to 1 (solid), governed by an interpolation function [8; 9]. Historically, the use of only black and white elements easily leads to an ill-posed result [10]. Many solutions have been proposed [11; 12; 13], but none are totally satisfactory. The proposal to include not only binary values for the density of each element was presented by [PERSON] and later by [PERSON] in their studies [8; 9]. Those values are calculated following a power rule [14], which will be explained later in this manuscript.  
The density-based approach is extensively described in several studies [6; 15]. It follows the simplified flowchart shown in Figure 1, where every step is numbered for future reference.  
This study gives special attention to levels 4 and 5 of the flowchart in Figure 1, as these processes involve the parameters under study. Specifically, for the interpolation in level 4, the Solid Isotropic Material with Penalization method (SIMP) is utilized. Additionally, a density filter is employed as the filtering technique in level 5.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 2 Experimental Section
### Methods
#### 2.2.3 Phenolic Compound Mixture (PCM)  
Wastewater usually contains more than one phenolic compound, so to study the feasibility of the ILs as extraction agents for a mixture of phenolic compounds in water, an equal amount of each compound was used to prepare the samples by weighing, and the extraction efficiency was determined using the experimental procedure described in the previous section.
## 2 Methodology
### Method for Analyzing Traffic Flow Characteristics at Accident Sites Compared to Node-Link Systems  
This study introduces a geohash-based traffic flow segmentation method designed to accurately reflect current traffic characteristics, as previously outlined. This method offers valuable insights for assessing the impact zones of traffic incidents, thus enabling drivers to make informed decisions about actions such as reducing speed, changing lanes, or selecting alternate routes. Additionally, this segmentation approach is adaptable for providing real-time information to drivers navigating the dynamic conditions in merging and diverging traffic zones.  
As illustrated in Figure 5, this method is applied to analyze merging/diverging sections and accident impact zones through a direct traffic analysis approach specific to this research. Traditional node-link information systems, which segment data into larger areas, such as L1, L2, and L3, typically provide only average speed data within these broader segments, such as within L2. However, the application of this novel segmentation approach allows for detailed, fine-grained analysis across smaller spaces, divided into sub-segments S1 through S6. For instance, if S5 is identified as a non-converging space, it becomes feasible to pinpoint the exact location of speed changes and assess the impact zone of an accident. This segmentation also allows for an in-depth examination of speed variations across lanes in merging and diverging sections.  
This refined granularity significantly enhances the utility of the traffic information available to drivers, enabling more precise and proactive decision making on the road. Consequently, the geohash-based segmentation methodology offers a transformative improvement in traffic data resolution and applicability, with potential for broad implementation in advanced driver assistance systems (ADASs) and real-time traffic management applications.  
Figure 5: Advantages and applications of homogeneous spaces in the provision of traffic information.
## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Preemptive Prevention
#### 4.1.1 Redundant Equipment Planning
In [85], in order to solve the problem of insufficient state estimation measurements in distribution networks, a data processing method is designed to handle the phase measurement data and advanced metering system data of distribution networks, which increases the redundancy level of distribution network measurements and improves the ability of state estimation to resist FDIA. In [86], the weaknesses of the distribution system are analyzed, and synchronized phase measurement units are arranged at vulnerable nodes to improve the ability of the system to recognize network attacks. In addition, in order to prevent all kinds of attacks intruding from the communication link, in [87], a redundant gateway planning method is proposed, and artificial noise signals are also added to mitigate the impact of information attacks during system operation. In [88], a blockchain-based multi-control architecture is proposed to improve the likelihood of detecting and recognizing deliberate attacks through redundant controllers. In [89], a defense method for deploying time-slotted channel hopping devices is proposed for time synchronization attacks, where time-slotted channel hopping is utilized to establish a trust model to ensure high-precision synchronization of all communication nodes. Although redundant equipment planning can enhance the system's ability to detect and resist deliberate attacks, it inevitably leads to an increase in the initial investment cost. This increase is a non-negligible factor for small-scale integrated energy systems, on par with system security. Therefore, balancing investment costs and security performance is the focus of the study of redundant equipment planning for small-scale IECPS. In [90], an optimization model for determining the number and location of PMUs is developed based on the network-wide observability of the power system, which is achieved by using as few PMU devices as possible. In [91], the authors proposed a two-stage robust optimization model for energy storage allocation to study the optimal investment strategy for energy storage with consideration of economics. In [92], the researchers analyzed the methods and effects of redundancy planning for integrated energy systems using the k-out-of-n model, and concluded that a win-win situation for both economy and resilience can be achieved within a certain redundancy level. Therefore, through rational redundancy design based on the consideration of risk aversion benefits, it can bring a greater return than the initial investment cost.
# Semiconductor Laser Linewidth Theory Revisited
## 8 Impact of a Passive Section, Chirp Reduction Factor and the Fabry-Perot Case
### Linewidth of a Laser Consisting of a Gain Chip Subject to Feedback from an External Cavity
Similarly, we obtain  
\[D_{f^{*}f}=\frac{\int_{0}^{l}\|\Phi\|^{2}D_{\text{sp}}\,dz}{\left|2\int_{0}^{ l}\Phi^{+}\Phi^{-}\frac{\partial\beta}{\partial\omega}\,dz\right|^{2}}\frac{1}{| \chi|^{2}}. \tag{98}\]Hence, the product of (97) and (98) is given by  
\[D_{f^{*}f}\Big{(}1+\alpha_{\text{H,eff}}^{2}\Big{)}=\frac{\int_{0}^{l}\|\Phi\|^{2 }D_{\text{sp}}\,dz}{\Big{|}2\int_{0}^{l}\Phi^{+}\Phi^{-}\frac{\partial\beta}{ \partial\omega}\,dz\Big{|}^{2}}\frac{\big{(}1+\alpha_{\text{H}}^{2}\big{)}}{ \text{Im}^{2}(\alpha_{\text{H}}\chi+i\chi)} \tag{99}\]  
and the spectral linewidth can be written as  
\[\Delta\
u=\frac{\Delta\
u_{\text{FP}}}{F^{2}} \tag{100}\]  
with the chirp reduction factor  
\[F =\text{Im}(\alpha_{\text{H}}\chi+i\chi) \tag{101}\] \[=1-\frac{c}{2n_{\text{g}}l}\,\text{Im}\Big{(}\frac{\partial\text {ln}(r^{+})}{\partial\omega}\Big{)}+\alpha_{\text{H}}\frac{c}{2n_{\text{g}}l} \,\text{Re}\Big{(}\frac{\partial\text{ln}(r^{+})}{\partial\omega}\Big{)}\]  
and  
\[\Delta\
u_{\text{FP}}=\frac{\int_{0}^{l}\|\Phi\|^{2}D_{\text{sp}}\,dz}{2\pi \langle|f|^{2}\rangle\Big{|}2\int_{0}^{l}\Phi^{+}\Phi^{-}\frac{\partial\beta}{ \partial\omega}\,dz\Big{|}^{2}}\Big{(}1+\alpha_{\text{H}}^{2}\Big{)} \tag{102}\]  
being the linewidth of a FP laser having the cavity length \(l\) and the reflection coefficient \(r^{+}(\Omega)\) at the rear facet. Equation (101) agrees with [53; 54] as well as [16; 55] if the differently chosen harmonic time dependence (\(\text{e}^{i\omega_{0}t}\) as used here versus \(\text{e}^{-i\omega_{0}t}\)) is observed. The relation to the static frequency chirp is given in Appendix C. Introducing an effective length of the passive cavity  
\[l_{\text{p}}=-\frac{c}{2n_{\text{g,p}}}\,\text{Im}\Big{(}\frac{\partial\text {ln}(r^{+})}{\partial\omega}\Big{)}, \tag{103}\]  
the chirp reduction factor can be written as  
\[F=1+\frac{n_{\text{g,p}}l_{\text{p}}}{n_{\text{g}}l}+\alpha_{\text{H}}\frac{ c}{2n_{\text{g}}l}\,\text{Re}\Big{(}\frac{\partial\text{ln}(r^{+})}{ \partial\omega}\Big{)}. \tag{104}\]  
Thus the second term on the right-hand side of (101) can be interpreted as the ratio of the round-trip times in the passive and active sections. The chirp reduction factor allows an estimation of the reduction of the linewidth by a passive section or an external cavity for given group index, length and Henry's \(\alpha\)-factor of the active section. \[1+a_{\text{H,eff}}^{2}=(1+a_{\text{H}}^{2})\frac{|\chi|^{2}}{\text{Im}^{2}( \alpha_{\text{H}}\chi+i\chi)} \tag{97}\]0 Schematic illustration of a two-section DBR laser featuring an active gain section and a passive Bragg grating as considered in Section 8.