esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results Using Test Data Obtained with FEM Model
### Test of Four-, Five-, and Six-Layered Soil Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Subarea** & \multicolumn{4}{c}{**Soil Model**} \\ \cline{3-5}  & & **Three Layers** & **Four Layers** & **Five Layers** & **Six Layers** \\ \hline \multirow{3}{*}{AD} & B & 2.8489 & 2.8021 & 2.6606 & 2.3731 \\  & W & 3.3519 & 3.2576 & 3.2597 & 3.4385 \\  & M & 3.0170 & 3.0324 & 3.0298 & 3.0313 \\  & SD & \(1.4260\times 10^{-1}\) & \(1.0472\times 10^{-1}\) & \(1.1070\times 10^{-1}\) & \(1.8770\times 10^{-1}\) \\ \hline \multirow{3}{*}{AE} & B & 1.0326 & 1.0229 & 0.9435 & 0.7258 \\  & W & 1.3074 & 1.3441 & 1.3186 & 1.4264 \\  & M & 1.1256 & 1.1919 & 1.1502 & 1.2290 \\  & SD & \(8.1256\times 10^{-2}\) & \(7.3115\times 10^{-2}\) & \(1.0621\times 10^{-1}\) & \(1.2140\times 10^{-1}\) \\ \hline \multirow{3}{*}{AF} & B & 1.1974 & 1.0378 & 0.8037 & 0.8057 \\  & W & 1.3651 & 1.3703 & 1.4633 & 1.5694 \\  & M & 1.2462 & 1.1984 & 1.2058 & 1.2461 \\  & SD & \(4.0584\times 10^{-2}\) & \(8.2700\times 10^{-2}\) & \(1.1100\times 10^{-1}\) & \(1.6308\times 10^{-1}\) \\ \hline \multirow{3}{*}{BD} & B & 2.8106 & 2.7237 & 2.5355 & 2.4723 \\  & W & 3.1828 & 3.2663 & 3.2140 & 3.2300 \\  & M & 2.9380 & 2.9697 & 2.9199 & 2.8683 \\  & SD & \(9.7723\times 10^{-2}\) & \(9.6893\times 10^{-2}\) & \(1.6189\times 10^{-1}\) & \(1.8302\times 10^{-1}\) \\ \hline \multirow{3}{*}{BE} & B & 1.1973 & 0.9274 & 0.8895 & 0.8382 \\  & W & 1.4790 & 1.2444 & 1.2860 & 1.3274 \\  & M & 1.2340 & 1.1509 & 1.0734 & 1.1341 \\  & SD & \(4.5144\times 10^{-2}\) & \(9.8441\times 10^{-2}\) & \(1.2563\times 10^{-1}\) & \(1.2988\times 10^{-1}\) \\ \hline \multirow{3}{*}{BF} & B & 1.1658 & 0.9808 & 0.63834 & 0.8348 \\  & W & 1.4157 & 1.2943 & 1.2521 & 1.4245 \\  & M & 1.2230 & 1.1359 & 1.0909 & 1.1736 \\  & SD & \(5.1185\times 10^{-2}\) & \(5.9807\times 10^{-2}\) & \(1.0602\times 10^{-1}\) & \(1.1372\times 10^{-1}\) \\ \hline \multirow{3}{*}{CD} & B & 4.9306 & 4.5902 & 4.8376 & 4.9021 \\  & W & 6.1153 & 6.4191 & 6.5768 & 6.5309 \\  & M & 5.2753 & 5.4091 & 5.4652 & 5.4593 \\  & SD & \(2.9209\times 10^{-1}\) & \(4.2134\times 10^{-1}\) & \(4.5797\times 10^{-1}\) & \(4.4843\times 10^{-1}\) \\ \hline \multirow{3}{*}{CE} & B & 3.5846 & 3.2722 & 3.2329 & 3.4347 \\  & W & 5.3712 & 5.2403 & 4.9603 & 5.4556 \\  & M & 4.0532 & 3.9115 & 3.9175 & 4.0276 \\  & SD & \(4.1656\times 10^{-1}\) & \(3.8760\times 10^{-1}\) & \(3.1435\times 10^{-1}\) & \(4.0025\times 10^{-1}\) \\ \hline \multirow{3}{*}{CF} & B & 3.3198 & 2.9987 & 3.3901 & 2.7103 \\  & W & 5.1539 & 5.1060 & 5.0355 & 5.2276 \\  & M & 3.7941 & 3.8153 & 3.7752 & 3.9706 \\  & SD & \(4.0815\times 10^{-1}\) & \(4.5375\times 10^{-1}\) & \(3.9664\times 10^{-1}\) & \(5.4033\times 10^{-1}\) \\ \hline \end{tabular}
\end{table} 
Table 17: Test of three-, four-, five-, and six-layered models using 50 independent runs for each model and for each area. Figure 12: Mean OFs for different soil models in different subareas.
###### Abstract  
The performance of wireless networks can be greatly influenced by the radiation pattern and polarization of the antennas at the nodes, especially when they are under non-line-of-sight (NLOS) channel environments. In this study, we designed a planar quasi-isotropic antenna based on the combination of a meandered electric dipole and an electrically small loop at a frequency of 2.45 GHz. Its electrical size (\(ka\)) is 0.47 and shows a gain deviation of 3.01 dB with radiation efficiency of 82.6% per the simulation. The performance of a wireless link under the line-of-sight and NLOS channels in an indoor environment was measured using the proposed quasi-isotropic antenna as a receiving antenna after validating its radiation and impedance properties experimentally (the measured gain deviation: 5.2 dB, the measured radiation efficiency: 79.2%). This study demonstrates that better properties are achieved using the quasi-isotropic antenna. The quasi-isotropic antenna shows an improved packet delivery ratio (PDR) and received signal strength indicator (RSSI) compared to the results using omni-directional antennas as a transmitting and receiving pair in the NLOS channels. To the best of our knowledge, the experimental validation of the enhancement of wireless link reliability using a quasi-isotropic antenna has not been reported before, and was first carried out in this study.  
isotropic radiation pattern; electrically small antennas; radiation pattern synthesis; mission-critical wireless network +
Footnote †: journal: Applied Sciences
# A Case Study on the Rehabilitation of a Fire-Damaged Structure
## 2 Visual Inspection of Damaged Structural Components
As shown in Figures 5-8 the condition of the girders and beams can be summarized as follows:  
* Girder #1: One longitudinal rebar at the bottom corner was exposed by 1-2 m, and 7 stirrups were exposed due to the spalling of concrete both at side cover and inside the stirrups. * Girder #2: No marked exposure of the longitudinal rebar was observed. Concrete spalled on the side of Slab #3 at a slight depth. Two stirrups were partly exposed toward the end of the span at the location of Column #3. * Girder #3 to #6: These girders comprise the longitudinal edge of the room. No marked damage was observed on these girders except that a longitudinal bar at the corner of Girder #6 was exposed by 3 m.
* Beam #1: Longitudinal rebar was exposed at each corner by about 4 m. Sides of the beam are not deemed to have been affected by fire. * Beam #2: Extensive spalling of the concrete cover both at bottom and sides was observed. Longitudinal rebar was exposed at the corners, and stirrups were slightly visible at some areas. In spite of the spalling of the concrete cover and the exposure of reinforcing bars, the girders and beams were deemed to still retain their designed structural capacity, judging from the fact that neither flexural cracks at mid-span nor diagonal cracks toward the span-ends were found, and deflection at the center of the element was not detected with the naked eye. However, a possible degradation in  
Figure 4: (**a**) Damage to top surface of Slab #3 and (**b**) pileup of debris under Slab #3 and #4.  
the load-carrying capacity must be retrofitted by testing the strength of the damaged concrete and the exposed reinforcing bars, and then by providing the required amount of reinforcement to compensate for the degraded capacity. In the battery room, there are four columns and six non-bearing walls made of blocks. None of the columns showed signs of damage in the visual inspection. The walls showed no signs of cracks on  
Figure 5: Damage to Girder #1. (**a**) Exposed stirrups in the north region. (**b**) West side of girder seen from the north. Figure 6: Damage to Girder #2. (**a**) East side of girder seen from the north. (**b**) West side of girder seen from the north. Figure 7: Damage to Beam #1. (**a**) East side of beam seen from the north. (**b**) West side of beam seen from the north. their surfaces except for the spalling of plasters and paint on the surface of Walls #1 and #3, and the extensive contamination of the surface due to smoke from the fire. No specific damage was observed on four columns, _i.e._, no sign of cracks, spalling of the concrete, or exposure of the reinforcing bar. Therefore, no special action is necessary to restore their structural capacity.
## 3 System Model
### Virtual Networking  
In this section, we will describe the detailed implementation of virtual networking. In the absence of a centralized control server and a centralized index server, the application layer of the network uses the overlay network approach, which can make any node in the network routable, including reachable and unreachable nodes, based on the logical ID of the node, independent of the IP address. The following sections describe the details related to virtual networks separately.
# Multitask Learning-Based Affective Prediction for Videos of Films and TV Scenes
## 2 Related Work
### Visual Emotional Features  
Visual affective prediction is classified from the perspective of features and involves methods based on low-level visual features, high-level semantic features, and deep learning features.  
Affective prediction methods based on low-end visual features extract low-level visual features from video images. Commonly used bottom-level feature extraction methods for the prediction of emotions in video images include GLCM [22] texture features, SIFT [23] features, and Tamura [24] features. [PERSON] et al. [25] combined psychology and art theory in a method that contains color, texture variations, and simple semantic information. [PERSON] et al. [26] investigated the influence of shape features, such as straight lines and curves, on sentiment classification.  
Semantic feature-based affective prediction methods translate high-level semantic information into affect features so as to establish a connection between video image sentiment and semantics such as objects and scenes. [PERSON] et al. [27] screened 1200 adjective-noun pairs (ANPs) and constructed a 1200-dimensional feature vector to detect concepts in images and indirectly express image sentiment. [PERSON] et al. [13] used a topic model to extract the emotional features of a movie.  
The machine learning-based approach performs affective analysis by extracting visual features of video images and analyzing the differences between visual low-level features and high-level semantics, which effectively reduces the neglect of affective information. The convolutional neural network [12] has subsequently become an important technique in computer vision due to features such as weight sharing and translation invariance and has been widely used for visual emotion feature extraction in recent years. [PERSON] et al. [28], proposed the use of a convolutional neural network (CNN) to extract spatial features and long short-term memory for feature extraction of time series.  
Most emotions are embedded within the images, but factors such as the pace of motion, changes in camera angles, and the narrative structure also influence the emotional response during viewing. In the extraction of video features related to emotions, [PERSON] et al. [6] used a CNN to process video data for visual feature extraction, focusing on analyzing facial expressions and body language. In recent years, researchers have begun to apply Transformers to computer vision tasks to improve the quality of feature representation by capturing long-distance dependencies between frames. [PERSON] et al. [3] use a transformer encoder for video vision feature extraction to capture the dependencies between frames.  
Traditional affect analysis work considers either video dynamic information or static information of video keyframes and does not fully exploit the joint visual expression of video. We use video-image fusion features to improve video affective value prediction, obtain more complete visual information, and enhance the accuracy of fine-grained affective prediction.
## 2 Basic Theory of the TPOD Method and the Rotor Model with Coupling Faults
### The Effects of Systematic Parameters  
We will discuss the effects of systematic parameters of the rotor system to the dynamical characteristics of the coupling fault. The systematic parameters include cracked depth, clearance between rotor and stator, stator stiffness, eccentricity. The bifurcation and amplitude-frequency behaviors will be provided as the values of the systematic parameters vary.
## 3 Antenna Characteristics
### Effect of the Length of the Ground Plane on the Bandwidth  
The graphical representation in Figure 4 indicates that the antenna with a full ground plane exhibits a narrow bandwidth, while the design goal is to have an antenna that operates across the full UWB frequency range. This antenna shows three resonance frequencies: 8.68 GHz, 10.20 GHz, and 12.73 GHz. The first resonance frequency offers a bandwidth of 0.96 GHz, the second resonant frequency offers a bandwidth of 0.35 GHz, and the latter provides a bandwidth of 0.53 GHz. By reducing the length of the ground plane, the antenna's bandwidth is increased, and its impedance matching improves, as is evident in Figure 4. Upon reducing the ground plane to 8 mm, the bandwidth increases to 1.32 GHz, and reduction to 7.6 mm increases the bandwidth to 1.75 GHz. A further reduction to 7 mm results in significant bandwidth increment to 5.69 GHz. However, this adjustment moves the lower cut-off frequency further away from the desired design frequency. Subsequently,  
Figure 3: Effect of the slots in radiating patch.  
reducing the ground plane to 6 mm causes the lower cut-off frequency to fall below the design frequency, with a bandwidth of 5 GHz and a reduced reflection coefficient, as observed in Figure 4. At 6.7 mm, we achieve a bandwidth of 5.59 GHz and a lower cut-off frequency of 3.21 GHz, which is remarkably close to the design's lower cut-off frequency. The trade-off lies in moving the lower cut-off frequency away from or below the desired design frequency. Fine-tuning the ground plane length to 6.7 mm achieves a broader bandwidth while maintaining proximity to the desired cut-off frequency.
# Treatment Strategies in Neutrophil Dermatoses: A Comprehensive Review
## 3 Pydedema Gangrenosum
### IL-1 Inhibitors  
**Anakinra**  
Anakinra acts by inhibiting IL-1 \(\alpha\) and \(\beta\); it plays a fundamental role in the treatment of monogenic autoinflammatory syndromes, such as CAPS (cryopyrin-associated periodic syndromes) and Still's disease in adults [51]. If we delve into the pathophysiology of PG, the expression of IL-1\(\beta\) and its receptor has been observed in PG lesions, hence its use in this pathology [1; 22; 26]. Based on these data, anakinra could play a role in recalcitrant PG and autoinflammatory syndromes, such as PASH and PAPA syndrome, with adequate clinical responses to treatment [45; 46; 47; 48; 52]. The dose used was 100 mg daily subcutaneously, in combination or not with glucocorticoids, which can be increased to 200 mg daily (2 mg/kg day) in the case of an inadequate clinical response.  
**Canakinumab**  
The use of canakimumab is based on the inhibition of IL-1\(\beta\), widely used in CAPS and adult-onset Still's disease [51]. Regarding the evidence supporting its use, a clinical trial published by [PERSON] et al. presented the selection of five subjects diagnosed with steroid-refractory PG. The intervention consisted of receiving 150 mg of canakimumab subcutaneously at week 0, with a possible extra dose after two weeks in case of refractoriness, as well as an optional dose of 150-300 mg at week 8 based on clinical assessment. At week 16, 80% of the patients presented improvements and 60% exhibited complete remission [44].
# CGM: Copy Mechanism GPT with Mask for Ellipsis and Anaphora Resolution in Dialogue
## 4 Experimental Results
### Experimental Results and Analysis  
#### 4.3.1 Evaluation Metrics  
In our experiments, we used BLEU [21], Rouge-L [22], simCSE [23], and BERTScore [24]. The BLEU score evaluates the fluency by calculating n-gram precision between candidate and reference sentences, with a brevity penalty (\(BP\)):  
\[\text{BLEU}=BP\cdot\exp(\sum_{n=1}^{N}w_{n}\log p_{n}), \tag{8}\]  
where \(p_{n}\) is the n-gram precision and \(w_{n}\) is the weight for each n-gram precision. The brevity penalty penalizes short translations:  
\[BP=\begin{cases}1&\text{if }c>r\\ e^{1-r/c}&\text{if }c\leq r,\end{cases} \tag{9}\]  
where \(c\) is the length of the candidate sentence and \(r\) is the length of the reference sentence. ROUGE-L evaluates the longest common subsequence (LCS) between a candidate sequence and reference sequence. Given a reference sequence \(X\) of length \(m\) and a candidate sequence \(Y\) of length \(n\), Rouge-L is calculated as:  
\[R_{lcs}=\frac{\text{LCS}(X,Y)}{m}, \tag{10}\]  
Figure 2: Comparing models.  
\[P_{lcs}=\frac{\text{LCS}(X,Y)}{n}, \tag{11}\]  
\[\text{Rouge-L}=\frac{2R_{lcs}P_{lcs}}{R_{lcs}+P_{lcs}}, \tag{12}\]  
where LCS(\(X\), \(Y\)) is the length of the longest common subsequence between \(X\) and \(Y\). As another metric to evaluate the model's restoration capability, we calculate F1 through exact matching between the reference sequence and candidate sequence. We evaluated how well the restoration target was reconstructed using BLEU, Rouge-L, and F1, which indicate how accurately words match, and assessed how well the intent expressed in the user's current utterance was maintained using simCSE and BERTScore, which represent semantic similarity between two sentences. simCSE computes the cosine similarity between sentence embeddings to measure semantic similarity:  
\[\text{simCSE}(x,y)=\frac{\text{embed}(x)\cdot\text{embed}(y)}{\|\text{embed }(x)\|\|\text{embed}(y)\|}. \tag{13}\]  
BERTScore computes token-level cosine similarity between BERT embeddings of two sentences, with importance weighting:  
\[\text{BERTScore}_{\text{P}}=\frac{1}{|x|}\sum_{x_{i}\in x}\max_{y_{i}\in y}x_{i} ^{T}y_{j}, \tag{14}\]  
\[\text{BERTScore}_{\text{R}}=\frac{1}{|y|}\sum_{y_{i}\in y}\max_{x_{i}\in x}x_{ i}^{T}y_{j}, \tag{15}\]  
\[\text{BERTScore}_{\text{F}}=2\cdot\frac{\text{P}\cdot\text{R}}{\text{P}+ \text{R}}, \tag{16}\]  
where \(x\) and \(y\) are the reference and candidate sentences, respectively.
# Quantitative Optical Coherence Elastography for Robust Stiffness Assessment
## 2 qOCE System  
Details about the qOCE technology have been described in our previous publications [13; 14]. The qOCE system (Figure 1) operates on a spectral domain OCT (SD OCT) engine at 1310 nm based on a fiber-optic Michelson interferometer. The imaging system has a 2.5 mm imaging depth. The sample arm of the interferometer is interfaced with the novel qOCE probe that has a built-in Fabry-Perot (FP) force sensor and also acquires a signal from the sample underneath the probe. A common path OCT signal is generated due to the interference between optical fields reflected from two end facets of the FP cavity with a length of \(L_{\text{FP}}\). The signal peak (\(I_{\text{FP}}\)) from the FP cavity is localized at \(L_{\text{FP}}\). With a force (\(F\)) excreted through the probe, the length of the FP cavity changes with the amount of \(\Delta L_{\text{FP}}\) that is proportional to \(F\): \(F=k\Delta L_{\text{FP}}\) where \(k\) is a parameter quantifying the stiffness of the probe shaft. We track the Doppler phase shift of \(I_{\text{FP}}\) (\(\Delta\Phi_{\text{FP}}=\int_{start}^{t_{end}}a\tan[I_{\text{FP}}(t+\delta t){I_ {\text{FP}}}^{*}(t)]dt\)) where \(t_{\text{start}}\) and \(t_{\text{end}}\) indicate the starting and ending time of the indentation process and \(\delta t\) indicates the time interval between signals involved in Doppler analysis. With \(\Delta\Phi_{\text{FP}}\), we are able to quantify \(\Delta L_{\text{FP}}\) (\(\Delta L_{\text{FP}}=(\lambda_{0}\Delta\Phi_{\text{FP}})/(4\pi)\) with \(\lambda_{0}\) indicating the central wavelength of the light source) and the force \(F\) (\(F=[k\lambda_{0}/(4\pi)]\Delta\Phi_{\text{FP}}=\alpha\Delta\Phi_{\text{FP}}\)). Light exiting the probe also illuminates the sample. Light backscattered from the sample (\(E_{\text{s}}\)) couplers back into the qOCE probe and interferes with reference light (\(E_{\text{r}}\)) from the reference arm to generate a depth resolved OCT signal \(I_{\text{s}}(z)\). Doppler analysis is applied to \(I_{\text{s}}(z)\): \(\Delta\Phi_{\text{s}}(z,t)=a\tan[I_{\text{s}}(z,t+\delta t){I_{\text{s}}}^{*} (z,t)]\). Depth resolved sample displacement is thus obtained: \(d(z)=\frac{\lambda_{0}}{4\pi}\int_{start}^{t_{end}}\Delta\Phi_{\text{s}}(z,t)dt\). By choosing the reference arm optical path length appropriately, the OCT signal for simultaneous probe deformation tracking (\(I_{\text{FP}}\)) and tissue deformation tracking \(I_{\text{s}}(z)\) can be multiplexed in the same Ascan without spatial overlap.  
To generate qOCE data, the probe is translated to compress the sample and OCT signals are acquired during the compression process for the quantification of reaction force and the tracking of sample displacement. Notably, we performed M-mode scanning in this study. The probe acquires signals from the same spatial location over a period of time. Spatially resolved qOCE imaging is beyond the scope of this study. Imaging results can be found in our previous publications [12; 14; 14].  
Figure 1: Configuration of the quantitative optical coherence elastography (qOCE) system.
# Line of Sight and Image Motion Compensation for Step and Stare Imaging System
###### Abstract  
In recent years, applications such as marine search and rescue, border patrol, etc. require electro-optical equipment to have both high resolution and precise geographic positioning abilities. The step and stare working based on a composite control system is a preferred solution. This paper proposed a step and stare system composed of two single-axis fast steering mirrors and a two-axis gimbal. The fast steering mirrors (FSMs) realize image motion compensation and the gimbal completes pointing control. The working principle and the working mode of the system are described first. According to the imaging optical path, the algorithm and control flow of the line of sight (LOS) and image motion compensation are developed. The proposed method is verified through ground imaging and flight tests. Under the condition of flight, the pointing accuracy of the target can be controlled within 15 m. The proposed algorithm can achieve effective motion compensation and get high-resolution images. This achieves high resolution and accurate LOS simultaneously.  
line of sight (LOS); motion compensation; step and stare; gimbal; fast steering mirror (FSM) +
Footnote †: journal: Applied