esa-sceva/satcom-chunk-collection huggingface dataset:
# An Exploration of Robot-Mediated Tai Chi Exercise for Older Adults
## 2 Materials and Methods  
### Apparatus  
#### 2.1.1 Robot Tai Chi Tutor Development  
The robot tutor was developed using a programmable humanoid robot, NAO (Figure 1, left) [35]. It is 58 cm-tall and weighs 11 lbs. Its size and weight allow users to see its movement clearly without feeling intimidated. NAO has 25 degrees of freedom and can smoothly move its body parts. It also has two speakers and a text-to-speech engine that allows the robot to speak designed scripts in a gender-neutral robotic voice. For over a decade, NAO has been widely applied for older adult-robot interaction studies due to its attractive humanoid appearance, high controllability, and robustness [36; 37]. Thesestudies mainly include social interaction [15], simple physical training [38], psychological well-being [39], and cognitive training [22]. NAO's mechanical and electrical functions enable it to perform Tai Chi movements well [33; 34].  
We designed six movements based on elemental postures in the Yang Style 24-Posture Short Form [40]. Considering the participants' age, only postures with low levels of difficulty that require a limited range of stretch, moderate limb strength, and easy balance were selected. Each movement started and ended by slightly separating the two feet and evenly distributing the body weight between the two feet. Some movements were also modified for safety, such as the \"Ma Bu\", which is similar to a squat, and \"Sideway Stepping\", which resembles a side squat, were reduced to a third to a half of the original range. More detailed information about each movement is described in Figure 2.  
Although NAO has been one of the best humanoid robots on the market, its balancing and stepping are not as stable as those of a human, so we did not include continuous stepping and turning. The robot does not have a waist joint, and thus motions with waist turn were not included, either.  
All robot movements were first designed, configured, and adjusted using the Chore-graphe software (version 2.8.5) to ensure correct posture positions, balance, and motion ranges. Then, the movements were converted as robot dynamic parameters and programmed into a robot control program powered by the Naoqi Python API (version 2.5). We further adjusted the smoothness and speed of the motions. These were examined and approved by a certified Tai Chi instructor. A set of scripts were designed to verbally explain the movements. The text-to-speech engine of the robot was used for the robot to speak out those explanations. The speech engine and the motion control were designed to work in parallel so that the robot's movements and speeches were synthesized and coordinated, enabling the robot to demonstrate movements along with simultaneous verbal instructions, as would be performed by a human tutor.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 6 Evaluation of Algorithm Performance  
This study constructs a number of network simulation environments to verify suitability of the PCEB-MS protocol for use in the long-strip structure of the coalmine tunnel. The simulation environment is also used to conduct a comparative analysis of the PCEB-MS protocol and the CNP algorithm, with reference to their connectivity and clustering interference. In addition, the influence of an increasing number of sinks on the network's performance using the PCEB-MS protocol, is also compared in relation to delay and network lifetime.  
The simulation platform in the experiment is constructed with OMNET++4.2 as the simulation tool, using the C++ language in the Windows operating system [33; 34]. Simple modules are used for the nodes and sinks in the experiment, which are defined as node.ned and Sink.ned, respectively. Table 2 shows the other parameters.
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Tomography Experiments and Results
### Influences of Multi-GNSS Observations  
To test the effect of multi-GNSS observations in tropospheric tomography, two cases were carried out and compared, namely, CS02 with GPS-only SWD and CS03 with multi-GNSS SWD as described in Table 1.  
Comparisons of wet refractivity error in CS02 and CS03 are presented in Figure 6 where we can find that there are only very slight improvements (about 0.7%) after the introduction of multi-GNSS observations. This may be due to the fact that the tomographic voxel is relatively flat with much wider horizontal range (0.5\({}^{\circ}\)) than the vertical range (12 km), resulting in negligible impacts on the percentage of voxels with signal passed (28.59% and 28.60% in CS02 and CS03, respectively). Another reason is  
Figure 7: Horizontal cross-section at (**a**) 250 m layer in CS02; (**b**) 2000 m layer in CS02; (**c**) 250 m layer in CS03; (**d**) 2000 m layer in CS03. Triangles denote the to-be-estimated grid nodes. Red dots represent the puncture points of GNSS signals.  
that the introduction of multi-GNSS has small impacts on the spatial dispersion of puncture points although the puncture points become denser as can be seen from Figure 7.
## 3 Proposed UDS-Based SSI
### Estimation of the Prediction Rules  
A DNN was used to build the prediction rules \(\mathcal{F}_{Y}\) and \(\mathcal{F}_{\Gamma}\), which map the features derived from the UDS to the magnitude spectrum of the speech signal and the filter coefficients of HE, respectively. The optimum prediction rules were obtained by minimizing the PD between the original power spectra \(\mathbf{p}_{n}\) and the predicted power spectra \(\mathbf{\hat{p}}_{n}\). The predicted power spectrum is given by  
\[\mathbf{\hat{p}}_{n}=\mathbf{\hat{y}}_{n}\odot\mathbf{H}^{(bt)}(P_{n},\Gamma_ {n}) \tag{18}\]where \(\mathbf{\hat{y}}_{n}=[\hat{Y}_{0,n},\ldots,\hat{Y}_{M-1,n}]\) denotes the magnitude spectra of the predicted speech. \(\mathbf{H}^{(hc)}(P,\Gamma)\) is the magnitude response of the HE filter for a given pitch period \(P\) and the HE filter coefficients \(\Gamma=\{\gamma_{k}\}_{k=-K}^{K}\), which are given by  
\[H^{(hc)}(\
u;P,\Gamma)=\left|1+\sum_{k=-K}^{K}\gamma_{k}\exp\left\{j\pi\
u(P+k )\right\}\right| \tag{19}\]  
where \(\
u\) is the normalized frequency index, that ranges from 0 to 0.5. With \(\mathbf{W}_{Y}\) and \(\mathbf{W}_{\Gamma}\) as the DNN weights of the predictors \(\mathcal{F}_{Y}\) and \(\mathcal{F}_{\Gamma}\), respectively, the updated estimate of the DNN weights for each predictor can be computed iteratively, and are given by  
\[\mathbf{W}_{Y}^{(n+1)} = \mathbf{W}_{Y}^{(n)}-\lambda_{Y}\
abla\mathbf{W}_{Y}D_{PD}\] \[\mathbf{W}_{\Gamma}^{(n+1)} = \mathbf{W}_{\Gamma}^{(n)}-\lambda_{\Gamma}\
abla\mathbf{w}_{ \Gamma}D_{PD} \tag{20}\]  
where \(\lambda_{Y}\) and \(\lambda_{\Gamma}\) are the learning rates for the weights \(\mathbf{W}_{Y}\) and \(\mathbf{W}_{\Gamma}\), respectively.
## Appendix A Details of Proposed AI Model Architecture and Process
### Computational Complexity  
The computational complexity of our proposed model is derived from its architectural components and their associated operations. The model comprises two identical cGAN network units: one operating in the frequency domain and the other in the spatial domain. Each unit includes a generator and a discriminator, both of which are crucial for the adversarial training process. Additionally, the model incorporates fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations to transition between the spatial and frequency domains.  
The total computational complexity of the model is analyzed in terms of floating-point operations per second (FLOPs), providing a precise measurement of the computational workload [77; 78]. This complexity is calculated by summing the FLOPs of its generators, discriminators, and domain transformation operations (FFT and IFFT). Below, we outline the FLOPs for each component.
# Reducing Nervousness in Master Production Planning:
## 3 Proposed PDS
### Measurement of System Nervousness  
Nervousness measures the difference in the quantity of product \(i\) to be produced in period \(t\) during production cycle \(k\) compared to the previous cycle and period. The calculation is based on two parameters (the magnitude of change and the frequency of changes), so significant changes or a high frequency of changes in production imply high values of nervousness. Two metrics express the nervousness per cycle and period. Letbe the number of schedule changes of product \(i\) in cycle \(k\) and \(C_{ti}\) be the number of schedule changes of product \(i\) in period \(t\). Furthermore, let \(Q_{ti}^{k}\) be the production quantity for product \(i\) in period \(t\) in cycle \(k\). Then, in Equation (6), \(N_{cki}\) is the nervousness in cycle \(k\) for product \(i\), and in Equation (7), \(N_{pti}\) is the nervousness in period \(t\) for product \(i\). Equation (8) presents the measure of nervousness \(N\).  
\[N_{cki}=C_{ki}*\left\{\sum_{t=0}^{n-1}|Q_{i(t+1)}^{k}-Q_{ti}^{k}|\right\},\forall k,\ \forall i \tag{6}\]  
\[N_{pti}=C_{ti}*\left\{\sum_{t=0}^{n}|Q_{it}^{(k+1)}-Q_{it}^{k}|\right\},\forall t,\ \forall i \tag{7}\]  
\[N=N_{cki}+N_{pti} \tag{8}\]  
Thus, \(Q_{it}^{k}\) represents the quantity of production for product \(i\) in period \(t\) in cycle \(k\). For the example in Table 1, in period 5, nervousness is measured between \(Q_{i2}^{2}\), \(Q_{i3}^{2}\), \(Q_{i4}^{2}\), and \(Q_{i5}^{2}\) for \(N_{cki}\) and \(Q_{i5}^{2}\), \(Q_{i5}^{3}\), \(Q_{i5}^{4}\), and \(Q_{i5}^{5}\) or \(N_{pti}\).  
The parameter \(\Phi_{k}\) quantifies the ratio between cost and nervousness for each cycle \(k\). In cycle \(k\), \(c(k)\) represents the cost and \(N(k)\) represents the nervousness. This parameter identifies the magnitude of the change in each cycle by calculating the area under the curve of cost and nervousness. In addition, let \(c=[c_{1},\ldots,c_{k}]\) and \(N=[N_{1},\ldots,N_{k}]\) be two vectors to update \(\Phi_{k}\); thus, \(\Phi_{k},k=1,2,\ldots,60\) is given by Equation (9).  
\[\Phi_{k}=\left|\frac{\sum_{k=1}^{k}c(k)}{\sum_{k=1}^{k}N(k)}\right|,\forall k \in\left\{k=1,\ldots,k=60\right\} \tag{9}\]  
The following numerical example for \(k=10\) illustrates the updating of Equation (9). Considers values for \(c\) and \(N\) as follows:  
\(c=[12.1,\ 12.44,\ 12.85,\ 13.34,\ 13.97,\ 14.6,\ 15.39,\ 16.3,\ 17.12,\ 18.05]\),  
\(N=[1.79,\ -3.47,\ -8.63,\ -14.07,\ -19.13,\ -24.43,\ -29.74,\ -34.54,\ -38.87,\ -43.37]\).  
Such values indicate that a program costing 12.1 monetary units has a nervousness of 1.79 for \(k=0\). Thus, the calculations of \(\Phi\) are exemplified below for the vectors \(c\) and \(N\).  
\[\Phi=\left[\left|\frac{12.1}{1.79}\right|=6.76,\ \left|\frac{24.54}{-1.68} \right|=14.61,\left|\frac{37.39}{-10.3}\right|=3.63,\ \ldots,\left|\frac{128.1}{-171.1}\right|=0.75,\ \ \left|\frac{146.2}{-214.5}\right|=0.68]\]
# Artificial Intelligence Algorithm-Based Arrangement Optimization of Structural Isolation Bearings
## 5 Discussion
### Total Cost  
Compared with the initial stage, when the weight coefficient of total cost \(\gamma\) varies between 0.8 and 0.0, the total cost of isolation bearing in the stable stage is reduced by 15.25%, 13.8%, 12.95%, 11.35%, and 8.55%, respectively, as depicted in Figure 13. The greater the weight coefficient of total cost \(\gamma\), the larger the decrease percent is. Moreover, fixing the weight coefficient of total cost \(\gamma\) at 0.8, when weight coefficient of the seismic decrease coefficient \(\alpha\) increases from 0.0 to 0.2, the decrease percent of total cost rises from 14.3% to 16.2%. If fixing weight coefficient \(\gamma\) at 0.4, when weight coefficient \(\alpha\) increases from 0.2 to 0.4, the decrease percent of total cost rises from 12.6% to 13.3%. While fixing weight coefficient \(\gamma\) at 0.2, when weight coefficient \(\alpha\) increases from 0.0 to 0.8, the total cost of bearings decreases by 11.3%, 10.9%, 12.1%, 11.1%, and 12.6%, respectively.  
When weight coefficient \(\gamma\) is greater than 0.4, the reduction of total cost tends to increase with the increment of weight coefficient \(\alpha\). While weight coefficient \(\gamma\) is less than 0.4, there is no obvious correlation between the decrease percent of total cost and the variation of weight coefficient \(\alpha\). Therefore, when focusing on total cost of bearings during optimization, it would be better if the weight coefficient of total cost \(\gamma\) is larger than 0.4. The greater the weight coefficient of total cost \(\gamma\) is, the larger the decrease magnitude of total cost is. Meanwhile, it would be better if the weight coefficient of the seismic decrease coefficient \(\alpha\) is as large as possible, which would bring 1-2% decrease percent of total cost for every increase of 0.2 in weight coefficient \(\alpha\).
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Lanthanum Sulfate Films  
Preparation of lanthanum sulfide films took place on a stainless steel substrate by using lanthanum (III) chloride (LaCl\({}_{3}\)) and Na\({}_{2}\)S solution [45]. Several phases, such as the tetragonal (20 = 28.7\({}^{\circ}\)) and monoclinic structure (20 = 20.7\({}^{\circ}\) and 31.1\({}^{\circ}\)), could be detected based on the XRD patterns. The crystallite size was 1.4 nm in each sample. FESEM micrographs showed sponge-like porous morphology with grain sizes smaller than 0.5 \(\upmu\)m. Other experimental results, including a small semicircle arc, were observed in the middle-frequency region; the contact angle was 47.6\({}^{\circ}\).
## 2 Materials and Methods
### Open Surgery Experiment  
(1) Animals and procedures  
A total of three abdominal aortas from three adult male rabbits were used. All the rabbits were New Zealand white rabbits (Oryctolagus cuniculus) and all weighed 3.6 kg. Anesthesia was performed using an intramuscular injection of glycopyrrole (0.01 mg/kg) and intravascular injection of propofol (1.5 mg/kg). Animals were positioned in dorsal recumbency and prepared for aseptic surgery. The abdomen was incised to expose the abdominal aorta. Vascular ligation was performed by applying LigaSure(tm) or QMR device to the abdominal aortas of the anesthetized rabbits in the distal and proximal directions. A total of 11 ligations was conducted, six times using LigaSure(tm) and five times using the QMR energy generator.  
(2) Measurements  
The outer diameters of the aortas were measured before the ligations. During the ligations, duration to seal and peak temperature of surrounding tissue were measured. While applying energy, all the procedures were recorded using a thermal imaging camera (FLIR-T62101, FLIR Systems, USA), then the stored images were analyzed using a temperature analysis program (FLIR R & D software 3.3, FLIR Systems, Wilsonville, OR, USA). After the ligations, the abdominal aortas of anesthetized rabbits were extracted and subjected to a burst pressure measurement and a histological analysis. Among the three rabbits, burst pressures were measured using the aortas of two rabbits and the other aorta was histologically analyzed. The burst pressure was measured by instilling normal saline into both ends of the vessel lumen. Using a previously described technique [6], pressure was slowly increased until the vessel leaked. Histological analyses were performed on both proximal and distal portions of the sealing site (Figure 1) with an optical microscope (Bx 50, Olympus, Tokyo, Japan). Each sealed vessel was processed for staining with Masson trichrome stain and hematoxylin and eosin stain. Histological analysis was performed on the following items (Figure 2): seal width (width of ligation site), adventitial collagen denaturation (length of denatured collagen in the total collagen surrounding the vascular outer membrane of both ligature sites), wall layer cleavage (portion of the whole blood vessel walls on both sides of the ligation site where layer separation occurred, as indicated by qualitative evaluation), and wall injury (ligation site including the length of the entire vessel wall length of damaged blood vessels, expressed as numerical qualitative value).
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 5 Discussion  
In this study, a positioning system based on computer vision was proposed. The experimental results show that the system is capable of detecting transmission lines with approximately 91.44% accuracy for each studied case. The system analyzed the geometric patterns of transmission lines. For this reason, the previous training is not necessary. The main disadvantage was related to data acquisition and the camera resolution, since it affects the detection of the wires.  
Once that three-phase system (three conductors) was segmented, the system was capable of computing the UAV position respect to three-phase system. We presented two approaches: simple and hybrid methods. Based on RMSE, the positioning errors were less than 66.02 cm (x estimation) and 26.29 cm (z estimation) for the simple method, and 25.77 cm and 10.26 cm for the hybrid method. These results are adequate for aerial applications.
## 2 Materials and Methods
### Multichannel Orthogonal Correlation Detection Method  
The detection of the Alpha navigation signal is the first step in signal processing in the receiving equipment, with the purpose being to find the starting position of the expected station of the three frequency signals [28]. To accurately identify the three frequency signals of each station in an Alpha navigation system under the influence of noise and mutual interference, this paper presents a multichannel orthogonal correlation method by which the recognition and signal detection of Alpha navigation stations can be realized. Figure 2 illustrates the implementation process of the proposed method. The proposed method is realized through three processes: digital filtering, orthogonal correlation, and station identification.
# Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector
## 1 Introduction  
The ability to learn and adapt has made machine learning techniques mainstream in cybersecurity. Training a model with a comprehensive dataset having multiple attack types is a key to improve anomaly detection performance [1]. However, issues like high dimensionality of datasets pose a significant threat for most of these techniques hindering real-time response and deployment on legacy systems. Manually reducing feature size is a key solution to lower the computational complexity. Removing insignificant features while retaining the important ones is a trade-off which can reduce complexity but compromise predictive performance of the model to some extent. Traditional feature selection models classified into two groups mainly wrappers and filters can be useful in featurefiltering [2]. Wrappers generate several feature subsets and select the one which generates the best result. Wrapper methods however require significant computation time to generate good results. The filter method employs various informative features such as entropy or information gain to decide which features yield the best output [3]. The drawback is the heuristic method of selecting these features. Datasets with certain degree of non-linearity may suffer from the possibility of reduced performance after feature filtering. In this paper, we explore a dynamic feature selector (DFS) for processing cybersecurity datasets. We combine several measurement indicators along with a meta-learner bagging ensemble-based approach to generate group of features that achieve the best performance in malware detection and deliver superior accuracy across datasets pertaining to the cybersecurity domain.
# LightMFF: A Simple and Efficient Ultra-Lightweight Multi-Focus Image Fusion Network
## 3 Method
### Network Architecture  
We propose ULU-Net, an ultra-lightweight U-Net variant specifically designed for MFF, as shown in Figure 3. The network employs an encoder-decoder architecture that takes source images, initial decision maps, and edge maps as input to generate refined decision maps. Unlike conventional U-Net architectures that typically use large channel numbers (e.g., 64\(\rightarrow\)128\(\rightarrow\)256\(\rightarrow\)512), we carefully design a minimal channel progression (8\(\rightarrow\)16\(\rightarrow\)24\(\rightarrow\)32\(\rightarrow\)48\(\rightarrow\)64) that significantly reduces parameters while maintaining essential feature extraction capability. The encoder consists of six stages with this progressive channel expansion, while the decoder has five stages for feature reconstruction. To further enhance parameter efficiency, we employ element-wise addition instead of channel concatenation for skip connections, preventing channel number expansion and thus reducing the parameters in subsequent layers.  
The input layer concatenates source images (\(I_{A}\), \(I_{B}\)), initial decision maps (\(\text{IDM}_{A}\), \(\text{IDM}_{B}\)), and edge maps (\(\text{Edge}_{A}\), \(\text{Edge}_{B}\)) into a multi-channel tensor:  
\[X_{\text{input}}=\text{Concat}(I_{A},I_{B},\text{IDM}_{A},\text{IDM}_{B},\text {Edge}_{A},\text{Edge}_{B}) \tag{8}\]  
To achieve parameter efficiency while maintaining feature extraction capability, we introduce InceptionDWConv2d [48], a lightweight module inspired by the Inception archi  
Figure 3: The designed U-shaped ultra-light network ULU-Net.  
tecture. The module splits input channels with a branching ratio of 0.125 and processes them through parallel pathways:  
\[\begin{split} Y_{\text{out}}=\text{Concat}(X_{\text{identity}},DWConv_ {3\times 3}(X_{\text{branch1}}),\\ DWConv_{1\times 11}(X_{\text{branch2}}),DWConv_{11\times 1}(X_{\text{ branch3}}))\end{split} \tag{9}\]  
where \(DWConv\) represents depthwise convolution with specified kernel sizes. This design effectively captures multi-scale and directional features while minimizing computational cost.  
The entire network contains only 15K parameters, which is merely 1/200 of MobileNetV3's parameter count [49], making it remarkably lightweight compared to existing methods. This ultra-compact design is achieved without sacrificing performance through our careful architectural choices and the effective utilization of focus property and edge information as prior guidance.
## 4 Discussion  
### Effect of One-Time Biochar Dose  
There was no statistical significance to the mitigation effect between the two one-time biochar dosages. The 2 kg/m\({}^{2}\) (Treatment 1) and 4 kg/m\({}^{2}\) (Treatment 2) showed very similar percentage reduction in gaseous emissions from swine manure. Both treatments significantly (\(p<0.05\)) reduced NH\({}_{3}\) and skatole with a very similar percentage reduction (33% and 25%; 42% and 48%, respectively). These results are consistent with [PERSON] et al., where HAP biochar treatments at a lab-scale showed a significant (two out of three trials) percentage reduction of NH\({}_{3}\) emissions by 18-21% and a significant (all three trials) percentage reduction of skatole by 74-95% [18].  
For the rest of the targeted gases, no statistical significance was found (Table 1). The 2 kg/m\({}^{2}\) (Treatment 1) showed a 28% reduction of phenol, a 31% reduction of p-cresol, a 38% reduction of indole, and a 66% reduction of 4-ethyl phenol; the 4 kg/m\({}^{2}\) (Treatment 2) showed a 49% reduction of phenol, a 40% generation of p-cresol, a 36% reduction of indole, and a 53% reduction of 4-ethyl phenol. This lack of statistical significance to mitigation was observed in CO\({}_{2}\) and N\({}_{2}\)O, whereas both dosages generated CH\({}_{4}\) emissions by 15% and 16%, respectively.  
The practical significance of this finding is that a lower dose of HAP biochar treatment could be as effective as a higher dose. A lower than 2 kg/m\({}^{2}\) dosage could be evaluated in future experiments to explore the possibility of reducing the cost and improving the sustainability of this proposed treatment.