esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 2 Design Method of Proposed Dual-Band Phase Shifter
### Parasitic Susceptance Compensation
#### 2.1.2 Susceptance Compensation at High-Band Operation  
Likewise, in an \(f_{\rm H}\) operation, the co-operating band can be represented by \(f_{\rm L}\). The \(\lambda/4\) shunt open stub for \(f_{\rm L}\) provides a short-circuited condition at node B. However, a \(\lambda/4\) shunt open stub will act as an inductive component at \(f_{\rm H}\) because \(f_{\rm H}>f_{\rm L}\). The capacitor was therefore connected in parallel to compensate the effect of the \(\lambda/4\) shunt open stub, as  
Figure 2: Compensation method according to operating band: (**a**) Low-band operation; (**b**) High-band operation.  
shown in Figure 2b. The input admittances of the \(\lambda/4\) shunt open stub and shunt capacitor (\(C_{\rm c}\)) from the perspective of node B can be expressed using Equations (4) and (5):  
\[Y_{\rm inB}^{\rm stub}=jY_{\rm Ls}\tan\biggl{(}\frac{\pi f_{\rm H}}{2f_{\rm L}} \biggr{)} \tag{4}\]  
\[Y_{\rm inB}^{\rm capacitor}=j2\pi f_{\rm H}C_{\rm c} \tag{5}\]  
The parasitic compensating \(C_{\rm c}\) is given as (6):  
\[C_{\rm c}=-\frac{Y_{\rm Ls}\tan\biggl{(}\frac{\pi f_{\rm H}}{2f_{\rm L}} \biggr{)}}{2\pi f_{\rm H}} \tag{6}\]
## 3 Coal Pillar Load and Stress Distribution Law of Working Face in End-Mining Stage
### The Width of Limit Plastic Zone of RCP in Working Face  
After the RR is formed, the support pressure of the surrounding rock in the roadway undergoes redistribution, and the coal pillar's load-bearing capacity and the support pressure are in a state of limit equilibrium, as shown in Figure 4. The width of the plastic zone on the side of the coal pillar is denoted as \(R_{1}\), while that in front of the working face under the influence of mining disturbance is referred to as \(R_{2}\). Therefore, when the width of the RCP in the working face is reduced to \(R_{1}+R_{2}\), the stress within the coal pillar reaches its maximum, and the surrounding rock enters the plastic deformation stage. At this point, it is necessary to implement equalization and pressure relief measures to mitigate roof pressure when the working face and the RR are interconnected.  
According to the limit equilibrium theory of rock mass [25], the width of the plastic zone R1 on the side of the RR can be calculated using Equation (1).  
\[R_{1}=\frac{MA}{2\tan\varphi}\ln\left(\frac{k\gamma h+\frac{C}{\tan\varphi}}{ \frac{C}{\tan\varphi}+\frac{P_{z}}{\Lambda}}\right) \tag{1}\]  
where \(M\) represents the average mining height of the working face, which is 2.9 m; \(A\) is the lateral pressure coefficient, 0.5; \(\varphi\) is the internal friction angle of the coal, 30\({}^{\circ}\); \(C\) is the cohesion within the coal, 0.9 MPa; \(k\) is the stress concentration coefficient, 2.5; \(\gamma\) is the weight of the overlying strata, 25 kN/m\({}^{3}\); \(h\) is the depth of the coal seam, 155 m; \(P_{z}\) is the support force of the roadway coal side, 0 MPa.  
Figure 4: Stress distribution of coal pillar in working face. \(R_{1}\)—RR side plastic zone; \(R_{2}\)—working face side plastic zone; \(k_{1}\), \(k_{2}\)—stress concentration factor; \(\gamma\)—overburden rock bulk density; \(h\)—coal seam buried depth.  
The width of the plastic zone on the side of the RR is determined to be \(R_{1}=2.5\) m using Equation (1). Considering the impact of mining on the plastic zone in the coal pillar, an excavation disturbance factor d = 1.2-3 is introduced, leading to the widths of the plastic zone on both sides of the coal pillar in the goaf being \(R_{2}=1.2\) and \(R_{1}=3\) m, respectively. Therefore, when the width of the coal pillar at the working face is 5.5 m, the coal pillar enters the yield zone.
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 1 Introduction
### Mode Shapes by Demodulation of Uniform--Rate Scanning CSLDV Output Signals
#### 2.3.2 Surface Fitting of the Undamaged Mode Shapes  
In practical applications, mode shapes of the undamaged structure are usually non-existent or difficult to obtain. Most of the damage only causes local changes of the mode shape--the overall effect on the mode shape is not obvious. Based on the above reasonable assumption, we can use the binary polynomial function to fit the overall mode shape using measured shape data, and it can be regarded as the mode shape of undamaged state. Assuming the measured mode shape of damaged state can be expressed by binary polynomial functions approximately:  
\[V_{R}(x,y)\ =\ \sum_{m\ =\ 0,n\ =\ 0}^{p,q}W_{(m,n)}\overline{x}^{m}\overline{ y}^{n} \tag{13}\]  
where, \(p,q\) are the determined polynomial function orders, \(\overline{x},\overline{y}\) are the central normalized coordinates of the measured plate to avoid the ill condition problem, \(W_{(m,n)}\) is the corresponding polynomial coefficient. The polynomial coefficients can be solved using the least-square method  
\[\mathbf{W}_{(p+1)\times(q+1),1}\ =\ \big{(}\mathbf{U}_{N,(p+1)\times(q+1)}\big{)}^{-1}V_{R} \tag{14}\]  
where, \(\mathbf{U}\) is the Vandermonde matrix of the central normalized coordinates, \(N\) is the point number of the measured mode shape. The Vandermonde matrix can be expressed as  
\[\mathbf{U}\ =\ \left[\begin{array}{ccc}1&\cdots&\overline{x}_{1}^{p}\overline{ y}_{1}^{q}\\ \vdots&\ddots&\vdots\\ 1&\cdots&\overline{x}_{N}^{p}\overline{y}_{N}^{q}\end{array}\right] \tag{15}\]  
However, the number of measured points using CSLDV are huge. In most cases, it is larger than hundreds of thousands or more. In order to avoid too much computation and over fitting, a proper order of the polynomial function needs to be determined. In order to evaluate the similarity between the fitting result and the original result, a fit value is defined as  
\[fit(p,q)\ =\ \frac{rms(\mathbf{V}_{R})}{rms(\mathbf{V}_{R})+rms\big{(}\mathbf{e}_{p,q} \big{)}}\times 100\% \tag{16}\]  
where \(\mathbf{e}\ =\ \mathbf{U}_{N,p\times q}\mathbf{W}_{p\times q,1}-\mathbf{V}_{R}\) is the deviation between the measured mode shape and fitted mode shape using the polynomial function, and \(rms()\) means the root-mean-square value. In order to determine the polynomial order, a convergence index is defined as  
\[\left\{\begin{array}{ccl}con(p)&=\ fit(p,q)-fit(p-2,q)\\ con(q)&=\ fit(p,q)-fit(p,q-2)\end{array}\right. \tag{17}\]  
When the convergence index is less than the threshold, the polynomial order is determined. In this paper, the threshold value is set to be 0.1% to keep the best fitting effect.
## 2 Antenna Design
\[\rho=\frac{\left|S_{ii}*S_{ij}+S_{ji}*S_{jj}\right|^{2}}{\left(1-\left|S_{ii} \right|^{2}-\left|S_{ij}\right|^{2}\right)\left(1-\left|S_{ji}\right|^{2}- \left|S_{jj}\right|^{2}\right)} \tag{10}\]  
Multiple antennas increase the signal-to-interference ratio in a MIMO system; the diversity gain quantifies this increase. The DG also measures how much transmission power can be reduced without a performance loss. The more the DG, the better the performance of a MIMO antenna array, and its values range from 0 to 10. Calculated using  
Figure 8: Comparison of 2D Radiation pattern with and without MTM in (**a**) Electric Field and (**b**) Magnetic Field. Figure 9: Comparison of Gain 3D Radiation pattern of each port at 3.6 GHz (**a**) with MTM and (**b**) without MTM. the formula in Equation (11), the DG of the proposed MIMO antenna is plotted on the left \(y\)-axis of Figure 10b. \[DG=10\sqrt{1-\left|\rho\right|^{2}} \tag{11}\]  
In a multi-port MIMO antenna system, the overall input impedance matching and mutual coupling effects are characterized by a single parameter: TARC. For a MIMO antenna to function correctly, TARC values must remain less than \(-10\) dB for the band of concern. Figure 10a (right \(y\)-axis) gives a comparison of TARC values in both cases. TARC of a MIMO antenna is calculated from S-parameters using the formula in Equation (12). \[\text{TARC }=-10\log\sqrt{\frac{\left(S_{mm}+S_{mm}\right)^{2}+\left(S_{mm}+S_{ nm}\right)^{2}}{2}} \tag{12}\]  
To estimate the expected loss in data throughput of a MIMO antenna communication system, CCL is calculated from scattering parameters using mathematical Equations (13) to (15). Lower the loss in throughput to improve the overall performance of the MIMO antenna. While the right \(y\)-axis of Figure 10b displays CCL curves with and without the use of the MTM superstrate, its value remains less than \(0.2\text{ bits}/\text{sec}/\text{Hz}\) for the band of concern. Figure 6.0  
\[\psi^{R}=\left[ \begin{array}{cccc}\varphi_{11}&\varphi_{12}&\varphi_{13}& \varphi_{14}\\ \varphi_{21}&\varphi_{22}&\varphi_{23}&\varphi_{24}\\ \varphi_{31}&\varphi_{32}&\varphi_{33}&\varphi_{34}\\ \varphi_{41}&\varphi_{42}&\varphi_{43}&\varphi_{44}\end{array} \right] \tag{14}\]  
\[\varphi_{mm}=1-\left|\sum_{n=1}^{4}S_{nm}^{*}S_{nm}\right| \tag{15}\]
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 5 Experiment Analysis  
### Locating Environment  
We divided the localization area into 400 grids, each grid representing a reference point. The number of APs is 6, and due to irregularities in the experimental environment in the mine, APs are placed irregularly. When deploying an AP, we deploy the APs in an irregularly-shaped form on the premise that each location could receive 6 AP signals. All of the locations were positioned by accepting RSSI from APs. In this paper, experiments were performed in a dynamic indoor environment, and there were impaired APs in the experimental environment. When carrying out the experiment, we collect the RSSI of AP with the mobile phone, establish the fingerprint database and carry out the emulation experiment with the computer. When collecting RSSI fingerprints, we control the acquisition height of 1 m from the ground position, and the AP is placed 2 m away from the ground position. When performing the fingerprint update, the greater the number of times of updating, the larger the energy consumption and the more accurate the positioning accuracy. Because RSSI are highly susceptible to the environment, the frequency of updating the fingerprint database in this article is set to hourly. We choose a bomb shelter as shown in Figure 7. In the experimental environment, we placed 6 APs and 10 reference anchor nodes. The spatial location in the experimental environment was converted into two-dimensional coordinates. Then, we randomly selected a number of locations, used the algorithm proposed in this paper to find the coordinates of the location to be positioned and calculated the localization error. When conducting the experiments, there were people moving around in the lab environment to simulate a dynamically-changing environment. We randomly selected 1 to 2 APs out of 6 APs, removed their antennas and interfered with the power they access, simulating impaired APs.  
When there are impaired APs in the environment, in order to fully verify the security performance of the localization algorithm, we carried out the following two types of experiments. The first type of experiment is for the same APs to take different impaired conditions and then observe the resistance of the algorithm when APs are impaired to varying degrees. The second type of experiment is to set up a different number of impaired APs and then observe the resistance of the algorithm when multiple APs are impaired.
## 2 Methodology
### Bogie Frame Model and Its Mesh Generation Scheme  
Figure 3 shows that tetrahedron elements were used for the mesh generation of the bogie frame model. In the calculation process, an overall model with a coarser mesh was used at first, then sub-models with a more refined mesh were established in regions near the filled welds of traction rod brackets. Taking the model without defects as an example, the grid size of the overall bogie model was 15 mm, and the model contained 905,282 elements and 278,475 nodes. Elements with a side length of 4 mm were applied to the region around the welds in the sub-models, while those measuring 0.5 mm were used to represent defects and weld roots.
## 3 Results
### Analysis of Single Factors  
The effect of the single factor on the response value was analyzed by the dimensionality reduction method. In the regression equation of the response value, other factors were fixed at zero levels, and the curves of the single factor and response value for the cohesion and internal friction angle are shown in Figure 2. According to the analysis in Table 4 and Figure 2, soil cohesion decreased with the increase in soil moisture content and increased with higher clay content. However, it increased slightly at first but then decreased when wet bulk density reached 2.3 g cm\({}^{-3}\). Among the three factors, clay content had the greatest impact on cohesion with the largest slope of the regression line. The internal friction angle showed similar responses to the changes in clay content, moisture content and wet bulk density, which all increased until the three factors reached the central level and then dropped when the factors continued increasing. Soil moisture content was the primary factor that affected the internal friction angle among the three factors.
# Emotion Recognition from Videos Using Multimodal Large Language Models
## 2 Related Works
### Emotional Reaction Intensity Estimation  
The MuSe 2022 (Multimodal Sentiment Analysis Challenge) research challenge [5] first employs Hume-Reaction, a benchmark for ERI estimation from videos. The task organizers invite researchers to explore the complementary role of multimodal information in the emotion recognition task. The same video corpus has been used in further competitions, such as the ABAW 2023 (Affective Behavior Analysis in the Wild) research challenge [21]. Task participants mainly adopt transformer-based architectures [6] and focus on facial details to address the issue. The main limitation of transformer-based models is the need for large-scale training data that are, unfortunately, not always available in several domains and scenarios. Some research efforts have been devoted to exploiting modality fusion layers [7; 8], each one relying on visual [22] and audio [23] encoders. Adopting separate per-modality encoders limits the potential of attention-based classifiers as they neglect cross-modality interactions.  
The authors of [20] propose a dual-branch network that processes both visual and acoustic information, employing spatial (for vision only) and temporal (for both modalities) transformer-based encoders. They also propose a modality dropout fusion layer to combine modalities, proving its effectiveness with respect to simple concatenation. The approach described in [24] is based on the PosterV2-ViT model, a transformer-based architecture designed to extract features from the Hume-Reaction dataset. The authors also combine these visual features with the precomputed DeepSpectrum audio features to further improve the performance. In [9], the authors propose ViPER, a multimodal architecture designed to combine features from an arbitrary number of sources. All the information is extracted at the frame level and concatenated across modalities before feeding a Perceiver model, a transformer-based modality-agnostic architecture [25]. Beyond visual and acoustic features, it includes Facial Action Units and textual features to enhance the results. Particularly, textual features are obtained using the CLIP [26] model to align video frames with pre-defined templates.  
Unlike ViPER [9], this work focuses on exploring the capabilities of visual and video LLMs in video emotion recognition. It proposes both a probing network tailoring LLMs to the emotion recognition task and an extension of the state-of-the-art ViPER [9] architecture integrating LLMs to generate textual video- and frame-level textual descriptions.
## 3 The Hierarchical Steel Surface Detection Methodology  
Many previous studies have employed object detection methods for steel surface defect detection [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. They reported that the results showed its inability to detect the scratch defect. The other defects were precisely detected. The reason behind the model's incompetence in detecting scratches is its features and the visibility of the defect in an image. The scratch defect look is a thin elongated line, whose clarity is affected by the luminosity of the image, which further makes it difficult to distinguish the defect from the background. In some images, the shape of the scratch was the whole image horizontally or vertically, due to which the limited anchor boxes in the object detection model led to lower detection of the scratch. The bounding boxes of ground truth (GT) and the anchor boxes were not coordinating correctly. According to the literature review, the image segmentation methods show good results in detecting scratches among steel surface defects [33; 34; 35]. Most of the defects of steel surfaces, such as pitted surfaces, inclusion, patches, rolled-in scale and crazing, can be considered objects because they satisfy most of the characteristics of object definition, i.e., closed boundary, different appearance and uniqueness. However, scratches are not included in the general category of objects.  
To overcome the limitations of object detection and image segmentation approach for steel surface defect detection, a proposed hybrid architecture for defect detection on the steel surface is in this study. The proposed approach has a hierarchical structure of the binary classifier on the top layer, the image segmentation algorithm for scratch detection and object detection algorithm for detecting other types of defects on the second layer. The image classifier of the top layer classifies the input image into scratch images and other defect images. If the input image is classified as a scratch image, it is input to the image segmentation algorithm of the second layer, that is, UNet, and the location of the scratch is found through object segmentation. If the input image is classified as a different kind of defect images, it is fed into an object detection algorithm of the second layer, i.e., RetinaNet, and the defect is located. In the final phase, the result is evaluated with an evaluation metric. The proposed architecture is shown in Figure 1.
# CRCSI: A Generic Block Interleaver for the Next Generation Terrestrial Broadcast Systems
## 1 Introduction
The basic idea is that by setting the corresponding sorting vectors in the time and frequency domain, the adjacent elements distances are expanded. Specifically, through permuting the row according to a certain spanning rule, and then cyclically shifting the row elements so as to achieve the reordering of columns, the ability of the data block to resist continuous errors is enhanced. Numerical analysis and simulation results for fixed and mobile reception scenarios are provided to validate the effectiveness of the proposed block interleaver schemes. The remainder of this paper is organized as follows. The system model and problem formulation are introduced in Section 2. Section 3 elaborates the design of the proposed interleaver and the numerical evaluations are shown in Section 4. Finally, conclusions are drawn in Section 5.
# Coordinated Multi-UAV Reconnaissance Scheme for Multiple Targets
## 4 Proposed Method
### Improved Dynamic Task Planning Algorithm Based on Market Auction Mechanism
#### 4.4.2 Task Area Removal Mechanism  
When reconnaissance missions in certain task areas are abruptly canceled, they need to be removed from the reconnaissance sequence \(w_{i}\) of the UAV. This article uses a simple and effective removal mechanism to address this issue.  
If the task area to be removed has already been scouted, it has no impact on the remaining tasks and does not require any further processing. However, if the task area to be removed has not been scouted, it can be directly removed from the corresponding reconnaissance sequence, and the UAV will skip over the task area and proceed to the next one for reconnaissance. After adjusting the reconnaissance sequence, the UAV reconnaissance time \(t\) is redistributed by the method described in Section 4.4.3 to optimize the UAV's reconnaissance revenue \(f_{1}\). The rest of the unaffected UAVs do not change the task sequence and reconnaissance time.
## 4 Results and Discussion
### Baseline Particle Classification Methods  
#### 4.2.1 Cut-Off  
The data set contains 18,000 samples for each of the three types of clusters, and 3628 of them, or 20.16%, are low-pt EM clusters, EM clusters with pt lower than maximum PU cluster pt that would be lost, using these criteria. Compared with QCD clusters, there are 8159, or 45.33% low pt clusters. In the case of the third quartile cut-off value, 45 EM clusters (0.25 %) are lost in the case of the EM/PU classification and 87 (0.48 %) in the EM/QCD classification. On the other side, using a cut-off value lower than the maximum PU (QCD) cluster pt, the number of wrongly classified PU (QCD) clusters grows rapidly, resulting in unnecessary storage of useless data.  
Therefore, simply accepting/rejecting clusters based on cluster pt value is not an acceptable option for the L1T selection method.
# A Combined Method for Preparation of Landslide Susceptibility Map in Izmir (Turkije)
## 3 Materials and Methods
### Statistical Analysis
#### 3.3.5 Combined Method (CM)  
Various methods have been developed in many landslide susceptibility studies, and countless studies have been conducted using these methods. Investigating these studies, it is clear that each method has advantages and disadvantages. The combined method was developed to minimize the errors that these disadvantages might cause. The logic behind the method is that superimposing LSMs created using various methods will result in a susceptibility map with the highest reliability. The combined method procedure involves assigning each pixel the highest landslide susceptibility score among all the data obtained for that pixel from the LSMs prepared using different methods for a specific geographical point (Figure 6). The aim here is to conduct more reliable studies and minimize the risk of errors by choosing the higher susceptibility value as the landslide susceptibility of a certain pixel where method \"A\" indicates low susceptibility and method \"B\" indicates high susceptibility. In order to prepare an LSM with the combined method, it is necessary to prepare LSMs based on between two and four methods and overlay these maps. The results obtained indicate that the combined method yields the greatest reliability when the highest number of susceptibility maps prepared with different methods are overlaid. However, using more than four methods is considered not to lead to higher reliability, therefore a maximum of four methods for preparing LSMs should be used and the combined method should be applied with these maps.  
LR, AHP, FR and IOE are the most frequent four methods selected in recent landslide susceptibility mapping studies. Four different LSMs were prepared using these methods and overlaid using ArcGIS Spatial Analyst Tools. Then, all pixels were reclassified in the study area after choosing the pixel value with the highest landslide susceptibility among to the four methods (Figure 6). As a result of this combined method classification process, the final LSM was classified according to five groups, from lowest susceptibility to highest (Figure 6).  
Figure 6: Details of the combined method, highest susceptibility class out of four methods is assigned.