esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Discussion of Codeword Interaction Types in Bilinear Interaction  
The bilinear interaction layer aims to capture interactions between different input fields or features in a model. In this section, we explore the effect of various field types (field-all, field-each, and field-interaction) on the performance of the bilinear interaction layer, with the embedding rate set to 100%. The experimental data and observations  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Steganalysis Method**} & \multicolumn{3}{c}{**Steganography Methods**} \\ \cline{2-4}  & **CNV** & **ACL** & **HPS** \\ \hline \(DMSM_{w}\) & 0.7251 & 0.8580 & 0.7460 \\ \(DMSM_{w/o}\) & 0.7208 & 0.8548 & 0.7404 \\ \(FSMDP_{w}\) & 0.7289 & 0.8624 & 0.7501 \\ \(FSMDP_{w/o}\) & 0.7220 & 0.8590 & 0.7421 \\ \(Stegformer_{w}\) & 0.7304 & 0.8644 & 0.7522 \\ \(Stegformer_{w/o}\) & 0.7232 & 0.8604 & 0.7454 \\ \(Ours_{w}\) & 0.7446 & 0.8692 & 0.7664 \\ \(Ours_{w/o}\) & 0.7387 & 0.8639 & 0.7616 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance Comparison of Methods with and without MGA.  
summarized in Table 3 provide insights into the performance of the steganalysis models with different field types in the bilinear interaction layer.  
From Table 3, there are several conclusions that can be drawn. The first conclusion is that the performance of the field-interaction type was the best among the three types. Specifically, for the CNV [19] steganography method, the field-interaction type achieved a 0.23% improvement compared to the field-each type. This indicates that the field-interaction approach is more effective in detecting hidden information in speech frames when using the CNV [19] steganography method. The second conclusion is that the field-each type performed better than the field-all type, achieving gains across the three steganography methods. This suggests that the field-each approach is more reliable and achieves higher detection accuracy than the field-all type when analyzing speech frames for hidden information.
## 4 Experimental Validation
(2) The tension and compression deformation of the arched part of the combined beam is neglected in the theoretical modeling, while the deformation of the arched part also generates some displacement during the actual oscillation, which results in some errors between the actual displacement and the theoretically calculated displacement. Figure 14: Phase graph, time course graph of the experiment under different \(M_{\text{e}}\): (**a**) \(M_{\text{e}}=4\) g; (**b**) \(M_{\text{e}}=8\) g.
## 5 The Pushover Analysis of the Center Column
### Analysis Results
For example, in working condition UT-A-GE, the ultimate lateral drift ratio of the upper column is only 0.0095, and after adopting the form of the split column section, the ultimate lateral drift rate of the upper column is increased to 0.0121, with an increase rate of about 27%. Figure 10 further shows the lateral force-drift ratio curves of the upper and lower columns when Mode B pushover analysis is used. The comparison between Figures 9 and 10 shows that the lateral stiffness resistance of the center column is significantly increased when the top rotational freedom of the center column is constrained; moreover, the peak bearing capacity of the center column is much larger than that shown in Figure 9. Similarly, after considering both the gravity and vertical inertia forces, the deformation capacity of the traditional column is inferior, and the ultimate lateral drift ratio of the upper and lower columns are only 0.0064 and 0.0067. At this time, the lateral drift ratios of the split column when it is damaged are 0.0121 and 0.0109, respectively. Compared with the traditional center column, the ultimate lateral drift ratio of the split column is improved by more than 60%. In addition, when only the self-weight load is considered, the deformation capacity of the split column is stronger. Figure 9: Lateral force–drift ratio curves of columns by Mode A. (**a**) Upper column. (**b**) Lower column. The seismic design code for urban rail transit structures mentions that the underground structure is considered to reach a damage state when the inter-story drift angle reaches 1/250. Figures 11 and 12 show the concrete compressive and tensile damage graph for traditional and split columns under the combined action of gravity and vertical inertia forces at the lateral drift rate = 1/250 in both pushover modes. Among them, DAMAGEC represents compressive damage and DAMAGET represents tensile damage. It can be seen that the compression and tension damage to the concrete in split columns is much smaller than that of the concrete in monolithic column, which indicates that the split column still has a better load-bearing capacity at this time. In addition, by comparing the damage to the structure in the two pushover modes, it can also be found that the damage to the center column in the case of Mode A is mainly concentrated in the bottom of the column, while it is distributed at the ends of the column in the case of Mode B.  
Figure 10: Lateral force–drift ratio curves of columns by Mode B. (**a**) Upper column. (**b**) Lower column.
# Structure-Aware and Format-Enhanced Transformer for Accident Report Modeling
## 5 Experiments
### Baseline Models  
To evaluate the effectiveness of SAFE-Transformer in modeling long-form accident reports, we adopt a multi-label classification task focused on accident intelligence extraction. A typical multi-label text classification model consists of two components: an encoder, which transforms the input document into a vector representation, and a classifier, which predicts one or more target labels based on this representation [27]. Since the primary focus of this study is on the encoder design, we select the following models as baselines: RoBERTa [65], Longformer [32], BigBird [31], and ERNIE-SPARSE [30]. Configurations of these baseline models are summarized in Table 3. We intentionally exclude SOTA models  
\begin{table}
\begin{tabular}{c c c} \hline
**Code** & **Label Name** & **Description** \\ \hline L1 & Accident Profile & Summarizes basic accident information, including casualty statistics, operational context, causative agents, and accident classification. \\ L2 & Emergency Response & Describes the immediate on-site handling of the incident, including rescue actions and emergency response measures. \\ L3 & Accident Liability & Indicates the attribution of responsibility to individuals or organizations involved in the incident. \\ L4 & Management & Refers to shortcomings in safety management systems, such as inadequate supervision, training, or procedural enforcement. \\ L5 & Actions and Prevention & Covers both corrective measures implemented post-incident and proactive strategies to prevent similar future occurrences. \\ L6 & Legal Provisions & Cites specific laws, regulations, or legal clauses used as the basis for accident handling and accountability. \\ L7 & Unsafe Acts & Captures human-related safety violations, such as unauthorized operations, procedural breaches, or negligence. \\ L8 & Unsafe Conditions & Refers to hazardous physical states of tools, equipment, or materials that contributed to the accident. \\ L9 & Unsafe Environment & Describes environmental risks present at the worksite, such as poor weather, unstable terrain, or inadequate lighting. \\ \hline \end{tabular}
\end{table}
Table 2: Presents the set of accident intelligence labels used in this study.  
Figure 4: Example of the original report structure and its annotated counterpart. (**a**) Partial structure of the original report (**b**) Partial structured annotation information.  
that specifically optimize the classification head, as our emphasis is on evaluating encoding performance rather than classification architecture.
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 4 Simulation Experiment and Result Analysis  
In order to test whether or not the DBO-BiGRU model described in this chapter is able to achieve the design goals, this section conducts simulation experiments, data comparison, and results analysis. Comparison experiments are designed in this section to observe the performance of the DBO-BiGRU model and the comparison models on the public dataset, respectively. The comparison models are classical deep neural network temporal prediction models RNN, LSTM, and Temporal Convolutional Network (TCN).
## 1 Introduction
Thus, the demultiplexing effects of the devices can be tuned by changing an external bias electric field that can adjust the tilt angle of the LC infiltrated in the MIM waveguide. Such a device in micrometer scale can provide an effective and flexible way of demultiplexing signals in the terahertz range.
# Usability Test for an Over-Ground Walking Assistance Robotic Device Based on the Mecanum Wheel
###### Abstract  
Robotic walking assistance devices support the rehabilitation of patients with neurological impairments. However, most commercialized systems rely on treadmill-based walking, which may not reflect real-world environments. This study aimed to evaluate the usability of a newly developed over-ground walking assistance robot (OWAR-MW) based on mecanum wheels compared with a commercial system (Andago) from the perspectives of physical therapists and patients with stroke. Nine physical therapists and nine stroke patients participated. Each participant walked 100 m using both the OWAR-MW and Andago systems. Subsequently, a satisfaction survey was conducted across three categories--safety, operability and functionality, and convenience--using a questionnaire adapted from the standard usability testing guidelines for walking assistive devices. Additionally, in-depth interviews were conducted to explore user experience and improvement needs. In both participant groups, the OWAR-MW showed a tendency for lower satisfaction scores than Andago across all categories. Stroke patients reported significantly lower scores in all three categories (safety: 4.90 vs. 4.04, operability and functionality: 4.83 vs. 4.33, convenience: 4.87 vs. 4.49, \(p\) < 0.05), whereas therapists noted a significant difference only in safety (4.02 vs. 3.37, \(p\) < 0.05). Key issues identified included a lack of handles, delay in actuator response, low motion detection sensitivity, non-intuitive controls, and discomfort caused by the harness, particularly the thigh straps. OWAR-MW demonstrated usability limitations in its current prototype form. Technical improvements in user interface, control accuracy, and harness design are necessary before clinical application. This study provides valuable feedback for the future development of user-centered rehabilitation robotics.  
over-ground walking; robotic assistive device; stroke; usability test +
Footnote †: journal: Applied sciences
## 3 Method
### Constrained Energy Minimization Algorithm Incorporating the Feature Bands  
A \(m\times n\) multi-spectral image \(S\) with \(r\) bands can be expressed as  
\[S=[s_{1},s_{2},s_{3},\cdots,s_{mn}] \tag{1}\]  
where \(s_{i}\) is the spectral vector of the pixel \(i\),\(s_{i}=[s_{i1},s_{i2},s_{i3},\cdots,s_{ir}]\).  
To highlight the information on the burning area, we normalized the difference between the SWIR1 band and SWIR2 band in Sentinel-2A/B and Landsat-8/9 images and the NIR band, respectively, to construct two kinds of normalized difference fire indexes (\(NDFI_{i}\)) [44; 45]. The reason for choosing the near-infrared band is that there is usually smoke when the burning event occurs in the conflict zone. Longer wavelength electromagnetic waves can reduce the influence of scattering to a certain extent.  
\[NDFI_{1}=\frac{\rho_{SWIR_{1}}-\rho_{NIR}}{\rho_{SWIR1}+\rho_{NIR}} \tag{2}\]  
\[NDFI_{2}=\frac{\rho_{SWIR_{2}}-\rho_{NIR}}{\rho_{SWIR_{2}}+\rho_{NIR}} \tag{3}\]  
where \(\rho_{NIR}\), \(\rho_{SWIR_{1}}\) and \(\rho_{SWIR_{2}}\) correspond to the bands whose central wavelength is about 0.865 \(\upmu\)m, 1.610 \(\upmu\)m and 2.200 \(\upmu\)m in Sentinel-2A/B and Landsat-8/9 images, respectively.  
\(NDFI_{i}\) was inserted into the image as the feature bands \(\lambda_{1}\) and \(\lambda_{2}\), and after magnifying them 5000 times, a finite impulse response linear filter \(W=[w_{1},w_{2},w_{3},\cdots,w_{r},w_{\lambda_{1}},w_{\lambda_{2}}]\) was designed to minimize the average output energy \(WRW^{T}\) of the background sample through the filter by using the known target spectral vector \(X=[x_{1},x_{2},x_{3},\cdots,x_{r},x_{\lambda_{1}},x_{\lambda_{2}}]\) as the constraint. In this state, the description of constraints can be followed; that is, while ensuring a high response to the target spectral vector, excessive attention to background information can be reduced so as to effectively extract the target. The objective function and constraint equation is shown in formula (4):  
\[\left\{\begin{aligned} &\min\{WRW^{T}\}\\ & s.t.XW^{T}=1\end{aligned}\right. \tag{4}\]  
where \(R=(1/\text{mn})SS^{T}\) represents the autocorrelation matrix of \(S\).  
Under the constraint of \(s.t.XW^{T}=1\), the Lagrange multiplier method is used to solve the optimal filter coefficient:  
\[W_{\text{CEM}}=\frac{R^{-1}X^{T}}{XR^{-1}X^{T}} \tag{5}\]  
where \(R\) represents the autocorrelation matrix of \(S\), \(X\) is known target spectral vector.  
Ultimately, \(W_{CEM}\) was applied to each pixel in the image to obtain the distribution of the target in the image and realize the detection of the target.  
In the feature bands images, the pixel value in the burning area is much larger than that in the non-burning area. Therefore, after the introduction of the feature bands, the response value of the filter \(W\) to the burning area is amplified, while the response value of the non-burning area is suppressed, and the two can be better distinguished.
## 1 Introduction
Results reveal that the high accuracy of detecting certain classes exhibited on the test set (originated from the same data collection as the one from which the training set comes) is, in some cases, followed by low accuracy obtained on the data from other collections. To support and complete the above analysis, we apply a data visualization of classes co-occurring in different datasets. The t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction method [10] is applied to reduce the original multidimensional feature space into easy-to-visualize two dimensions. Obtained visualizations explain the low accuracy of specific training and test dataset combinations caused by intra-dataset diversity within co-occurring classes. The paper is organized as follows. Section 2 contains a short survey of related works. Section 3 focuses on the traffic data used in the current study. Section 4 describes the methodology. Section 5 presents the results obtained and a discussion on them. Finally, Section 6 concludes the paper.
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 5 Experimental Results  
This section evaluates the frequency synthesizers based on the passive fourth-order RC and RLC filters. Experiments evaluate the two circuits' phase noise, loop capture time, and spur suppression. The test results are analyzed for comparison.
## 4 Random Analysis
### Effect of Coefficient of Variation \(Cov_{e}\)  
Figure 12 illustrates the changes in the mean values of the normalized maximum tunnel responses with increasing \(\text{COV}_{E}\) under different \(\theta_{E}\) (\(\theta_{E}=1D\), \(3D\), \(10D\)). The effect of the COV of the soil elastic modulus on the mean values of the normalized responses is not very obvious. The increasing \(\text{COV}_{E}\) induces slight decreases in the mean values of \(w_{\text{m}}\) and \(M_{\text{m}}\). Furthermore, it is observed that the increase in \(\text{COV}_{E}\) amplifies the effect of the variation in \(\theta_{E}\) on the mean values of the normalized maximum tunnel responses. The variations in the COVs of the normalized maximum tunnel responses with increasing \(\text{COV}_{E}\) under different \(\theta_{E}\) (\(\theta_{E}=1D\), \(3D\), \(10D\)) are shown in Figure 13. The increase in \(\text{COV}_{E}\) results in an approximately linear increase in the COVs of the normalized responses. It is worth noting that the maximum slope is observed at the maximum \(\theta_{E}\) (\(10D\)), whereas the minimum slope can be seen at the minimum \(\theta_{E}\) (\(1D\)). For example, the slope of variation in \(M_{\text{m}}\) with increasing \(\text{COV}_{E}\) under \(\theta_{E}=10D\) is about 1.7 times larger than that under \(\theta_{E}=1D\). Therefore, the results imply that the increasing \(\theta_{E}\) strengthens the effect of the variation in \(\text{COV}_{E}\) on the variability of the tunnel responses.  
Figure 12: Effect of \(\text{COV}_{E}\) on mean values of normalized maximum tunnel responses under different \(\theta_{E}\) values: (**a**) Normalized maximum tunnel settlement, (**b**) Normalized maximum bending moment; (**c**) Normalized maximum shear force.
# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 4 Experimental Setting, Results and Discussions  
### Experimental Setting and Training Strategy  
We performed the experiments in the MATLAB-R2020a environment using Windows 10 with a Core i7 processor at 3.0 GHz CPU and Nvidia T1660 Ti GPU, and Kaggle, the web-based data science environment. The image preprocessing, lung segmentation and image augmentation were carried out using MATLAB-2020a. Deep learning-based classification and performance evaluation were conducted using Tensorflow and Keras libraries on the Kaggle platform. For the classification of COVID-19 from normal and pneumonia, we used a public dataset containing 10,192 normal, 3616 COVID-19, and 1345 pneumonia images. As the dataset is large and contains more than 15,000 CXRs, we utilized the hold-out strategy to train and evaluate the models. As depicted in Figure 15, we initially fragmented the dataset into training and testing datasets in an 80:20 ratio. Then, a 20% of the training set is randomly allocated as the validation set. The training set contained a total of 2893 COVID-19, 8154 normal, and 1076 pneumonia CXRs which were used to train and validate the models using the training process. The testing set contains a total of 3030 CXRs with 723 COVID-19, 269 pneumonia and 2038 normal CXRs. It was used to judge the classification performance of the models. The training and testing sets are disjoint so that the same images do not represent both sets. The models are trained using the training set and its suitable parameters are selected using the validation set. Once training of the models is accomplished, the testing set is used to check how well each model performs.  
Figure 15: Data partition and training of CNNs using holdout strategy.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 4 Results
### Discussion and Potential Improvements  
One avenue for improving robustness is establishing better criteria for rejecting poor position estimates. [PERSON]'s distance is a commonly used estimate of the influence of a data point when doing least-squares regression. [PERSON]'s distance measures the effect of deleting a given observation. Another technique is to use [PERSON]'s M-estimation [25] to handle outliers due to bad measurements.  
Time-Separated Measurements  
Another possible improvement would be to combine two sets of relative codephase and Doppler measurements taken from two samples of L1-band data a short time, \(\Gamma\), apart. Assuming \(\Gamma\) is large enough for the satellites to have moved by \(\sim\)1 km, yet small enough for the receiver to have moved by less than the desired spatial resolution, then the following relative codephase estimator should be more robust,  
\[L_{\delta\phi}(t,\mathbf{r}_{0}) = \sum_{i}\left(\delta_{\phi m}^{ij}-\delta_{\phi}^{ij}(t,\mathbf{r} _{0})\right)^{2}+\] \[\sum_{i}\left((\delta_{\phi m}^{ij}-\delta_{\phi}^{ij}(t+\Gamma, \mathbf{r}_{0})\right)^{2}.\]  
Taking multiple samples would, however, increase the power required.
## 4 Time-Varying Filter
### Coefficient Sets  
In order to choose the right sets of coefficients, one must first determine the horizon, before which the parameters should vary. Our experimental research proved that the most efficient horizon lies between the settling time, calculated as a 2% border around the steady state and 1.5 times that value.  
As for the rest of the parameters, we designed an optimization routine to propose the sets of coefficients that reduce the settling time of the given system. The fitness of each candidate in the mentioned routine is evaluated as the time value needed to achieve the 2% border around the steady state of the response of the time-varying filter prototype to the step signal. In this paper, we assume that all parameters (\(k\), \(\omega_{0}\), and \(\zeta\)) can vary in time. Of course, as stated earlier, the parameters settle down to the pre-determined values, according to the time-invariant design, after reaching the defined horizon.  
The first stage of the optimization process assumes that the parameters can change linearly through a given horizon. The starting value of the linear change is picked randomly and the objective is the reduction of the settling time value. One can choose the number of random steps in this stage. Figure 6 presents the first stage of the optimization routine.  
Once the starting point for each linear set of coefficients is determined, the second stage of optimization starts. During our research, we noticed that, often, the linear change of parameters can be further improved, in terms of reducing the settling time of the whole time-varying design. The next step of our optimization routine introduces Bezier-like curves as a basis for the parameter sets. The freedom they offer in shaping the dynamics of those sets is very useful. The mentioned curves are calculated according to the following equation:  
\[B(t)=\sum_{i=0}^{n}\binom{n}{i}(1-t)^{n-i}t^{i}P_{i}, \tag{5}\]  
where \(n\) is the degree of the polynomial, \(P(i)\) is the control point, \(i\) is the auxiliary index, and \(t\in[0,1]\). The first control point is the one calculated in the first optimization stage. The last point is given by the final, pre-determined by the time-invariant design value at the time equal to the chosen horizon. The time basis of the curve is also re-calculated, according to the mentioned horizon. In this paper, we use a random placement of one middle control point. In this stage, one can also choose the number of steps. The aim is the same as before, which is the reduction of the settling time of the system. Figure 7 presents the second stage of the optimization routine.  
As one can notice, introducing the Bezier curves results in a non-linear change of the coefficients in the chosen horizon. In most test cases, it allowed for an additional 10% improvement of the settling time, in comparison to first stage optimization alone.