esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robot Learning Method with Physiological Interface for Teleoperation Systems
## 4 Results and Discussion
### Discussion  
First of all, the proposed LWR method constructs a nonlinear model for a specific task. Compared to traditional robot learning methods [23, 24], the proposed method combines the task trajectories with the muscle stiffness during the demonstration process.  
Moreover, the human operator can regulate the muscle stiffness according to the remote environments in the HRI. That is to say, the variability of muscle stiffness represents the operational characteristics of human operator. Therefore, a model to describe the relationship between the prescribed task, task trajectories, and the muscle stiffness are very useful in the learning process. Thus, the learned model can be used to improve the efficiency of the reproduction of the repetitive task. In the proposed method, we transmitted the learned model, i.e., learned trajectories and learned human stiffness, to the controller of the slave robot of the teleoperation system after robot learning. Then, the slave robot can be executed according to the learned model.  
Noteworthy, the experimental results in this work are preliminary and the experiments were conducted mainly to show the feasibility and effectiveness of the proposed method. The stiffness of the human operator varies from person to person. Moreover, even for the same person, the stiffness property at different time could be different (time-varying). Therefore, it is hard to find a baseline to compare the results under \"with stiffness\" and \"without stiffness\" conditions. Different people may have different stiffness, and thus may show different motion model. Therefore, it is difficult to define fair comparison criteria for different/multiple experiment subjects. There is no problem foreseen to generalize the proposed method to different subjects as the implementation procedures will not change and each step is not affected by the specific human subject. However, surely the obtained specific muscle activation/stiffness pattern(s) would be different from person to person.  
This work makes different contribution from our previous work [39, 48], wherein a hidden semi-Markov model with Gaussian mixture method was proposed to carry out the repetitive tasks with enhanced intelligence. In this work, a locally weighted regression method with DTW was proposed for a prescribed task. Moreover, in our recent study, it was found that the length of demonstration data could significantly impact the performance of the teleoperation. To handle this problem, we employed the DTW method to deal with the issue related to different lengths. However, in our previous work [39], the influence of the length of demonstration data was not taken into account.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### General Wiring Optimization
#### 4.2.1 Multiple Wiring Matrices  
Wiring optimization allows for a recursive procedure. The argument of the computational cost operator \(\mathbb{C}[\cdot]\) is a matrix-vector multiplication itself. It can also benefit from linear computation coding by a decomposition of \(\mathbf{\Omega}\) into a codebook and a wiring matrix via (11). However, it is not important that such a decomposition of \(\mathbf{\Omega}\) approximates \(\mathbf{\Omega}\) very closely. Only the overall distortion of the linear function \(\mathbf{T}x\) is relevant. This leads to a recursive procedure to decompose the target matrix \(\mathbf{T}\) into the product of a codebook matrix \(\mathbf{B}\) and multiple wiring matrices such that the wiring matrix \(\mathbf{W}\) in (6) is given as  
\[\mathbf{W}=\mathbf{W}_{1}\mathbf{W}_{2}\cdots\mathbf{W}_{L} \tag{12}\]  
for some finite number of wiring matrices \(L\). Any of those wiring matrices are found recursively via  
\[\mathbf{W}_{\ell}=\operatorname*{argmin}_{\Omega:\mathbb{C}[\Omega\mathbf{x}] \leq C_{\ell}}\operatorname*{\mathbb{E}}_{x}d(\mathbf{T}x,\mathbf{B}\mathbf{W} _{1}\cdots\mathbf{W}_{\ell-1}\Omega x) \tag{13}\]  
with \(\sum_{\ell=1}^{L}C_{\ell}=C\). This means that \(\mathbf{B}\) serves as a codebook for \(\mathbf{W}_{1}\) and \(\mathbf{B}\mathbf{W}_{1}\cdots\mathbf{W}_{\ell-1}\) serves as a codebook for \(\mathbf{W}_{\ell}\).  
Multiple wiring matrices are useful, if the codebook matrix \(\mathbf{B}\) is computationally cheap, but poor from a distortion point of view. The product of a computationally cheap codebook matrix \(\mathbf{B}\) with a computationally cheap wiring matrix \(\mathbf{W}_{1}\) can serve as a codebook \(\mathbf{B}\mathbf{W}_{1}\) for subsequent wiring matrices that performs well with respect to both distortion _and_ computational cost.  
Multiple wiring matrices can also be useful if the hardware favors some serial over fully parallel processing. In this case, circuitry for multiplying with \(\mathbf{W}_{\ell}\) can be reused for subsequent multiplication with \(\mathbf{W}_{\ell-1}\). Note that in the decomposition phase, wiring matrices are preferably calculated in increasing order of the index \(\ell\), while in the inference phase, they are used in decreasing order of \(\ell\), at least for wide matrices.
# Internet of Things (IoT) Cybersecurity: Literature Review and IoT Cyber Risk Management
## 2 Literature Review
### Cybersecurity in IoT Architecture
#### 2.1.4 Cybersecurity at the Application Layer  
Monitoring and control, big data and business analytics, and information sharing and collaboration are widely used enterprise IoT applications [36]. Different application areas such as smart homes, smart transportation, smart health, and smart grids require different security management approaches [37]. For example, smart health deals with highly personalized data and requires high-level security and privacy protection [38]. Since many IoT applications may be owned by third-party service providers, cyberattacks on these applications may affect the security of other interrelated applications [39].  
Some security issues include the security of protocols such as CoAP, MQTT, and XMPP [40], improper patch management [41], inadequate authentication [42], and insufficient audit mechanisms [43]. The above-mentioned security issues have been addressed with various solutions such as key management, access control, heterogeneous network authentication, private information protection, and data security protection [37].
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 7 Conclusions  
This paper presents the C-FSS as a strong solution for dual-resonance applications within the LTE frequency bands, utilizing asymmetry and passband filtering techniques. This design greatly improves wireless communication efficiency by minimizing PIM. The unique design enables dual-resonance behavior within a single-layer, single-element structure, setting it apart from traditional second-order filtering techniques typically used for comparable applications. The proposed C-FSS showcases remarkable performance, featuring reflection coefficients of around \(-29.59\) dB and \(-31.11\) dB at \(1.92\) GHz and \(2.08\) GHz, respectively, which makes it an excellent choice for LTE band communication. Moreover, the C-FSS demonstrates its angular stability by delivering reliable results across a range of incidence angles, reaching up to \(80^{\circ}\). The analysis is supported by ECM, and the precision of the simulated results is confirmed through measurements of a constructed \(4\times 4\) array structure utilizing a VNA. This research underscores the distinct benefits of the C-FSS, yet it also reveals various challenges and avenues for improvement that still exist. Future work can focus on prioritizing the development of a tuneable model, which would enhance its selectivity by refining the filter's precision and improve its overall performance by optimizing its functions across different operational conditions.  
Conceptualization: V.D., R.M. and A.K.; methodology: V.D., R.M. and A.K.; validation: V.D., R.M. and A.K.; formal analysis: R.M. and A.K.; investigation: V.D., R.M. and A.K.; writing--original draft preparation, V.D.; writing--review and editing: R.M. and A.K.; supervision: [PERSON] and A.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors are sincerely thankful to the reviewers for their valuable comments for improving the quality of the manuscript. The authors declare no conflicts of interest.
# VDMS: An Improved Vision Transformer-Based Model for PM\({}_{2.5}\) Concentration Prediction
## 4 Results  
### Model Validation and Comparison  
Cross-validation (CV) is a widely used method for evaluating the predictive capability and robustness of models. In this study, the performance of the VDMS model was validated using ten-fold CV based on both samples and sites. The dataset was randomly partitioned into ten subsets; for sample-based CV (Sam-CV), each subset comprised 10% of the samples, while for station-based CV (Sta-CV), each subset consisted of 10% of the ground monitoring stations. The model was fitted over ten iterations, ensuring that all data were tested: in each iteration, one subset was designated as the test set while the remaining subsets served as the training set. Evaluation metrics included the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), and mean absolute error (MAE). During the experiments, the learning rate was fine-tuned, and an early stopping strategy was employed to ensure that the spatial RMSE reached a local minimum. A small batch  
Figure 3: Meteorological data fusion.  
size was utilized because larger batch sizes were observed to lead the model to converge to sharp local minima [36]. Given the overparameterization of the model, a stochastic gradient descent (SGD) optimizer with momentum was selected, with a weight decay of 0.1 [37]. Following the relationship between batch size and momentum coefficient proposed by [PERSON] and [PERSON] [38], the momentum coefficient was set to 0.6 to further optimize the training process.
## 2 Materials and Methods  
The purpose of this study was to provide a method to evaluate the effect of selection of interpolator parameters on the quality and precision of the (interpolated) toolpath for the Heidenhain iTNC 530 CNC system. Specifically, the effects of corner jerk settings on geometric precision and toolpath quality in corner neighborhoods were assessed in dependence on the selected limit frequency and desired machining tolerance.  
This study focused primarily on the effects of the settings of the CNC parameter values of contour tolerance, limit frequency, and corner/axial jerk limits on the quality and accuracy of the toolpath output of the interpolator module (in contrast to the focus on the effects on the end result of the interpolator-control loop-dynamics-machining process chain, see [28; 29; 30]).  
The reasons behind this choice can be summarized as follows: if the output of the interpolator itself does not satisfy the set criteria of toolpath quality or precision due to a suboptimal choice of CNC parameter values, the subsequent cumulative influence of the position control loop, machine dynamics, and machining process is likely to cause additional deterioration in both toolpath quality and precision. The quality of the interpolator output (as measured by the selected criteria) is thus a necessary (but not sufficient) prerequisite to achieve the desired level of workpiece quality.  
Another compelling reason to study the influence of CNC parameter selection on the properties of the interpolator output is that the particular commercial implementation of the interpolator module is a common feature across a wide span of machine tools. Thus, information on the influence of CNC parameters on interpolator output quality can serve as a basis for subsequent optimization methods in a range of practical settings. This contrasts with the effect of position loop and machine dynamics, which can differ significantly based on the properties of the individual machine tool.  
While the Heidenhain technical manual contains rules (both exact and rule-of-thumb) on parameter settings for many individual parameters, no such rule is provided for the axis-jerk limit at corners. The only advice given in the technical manual on this matter is the following [32], p. 833.  
_\"In order to achieve the optimum results for your machine or application, test the various filter settings with a test part consisting of short, straight paths.\"_  
This study shows that this parameter has a significant impact on surface quality and precision in corner neighborhoods and provides a possible methodology to quantify this influence.
# Age Estimates from Name Characters
## 4 Experiment and Results  
### Experimental Setup  
The model was trained on Taiwanese names, and then the generalizability was verified on bilingual names and ethnic-Chinese Malaysian names. For Taiwanese real names, OCR was used to scan the downloaded images from college admission lists in newspapers from 1958 to 1994. The newspapers from 1988 to 1992 were missing; therefore, a total of 31 years of Taiwanese names were collected. Meanwhile, the electronic versions of the college admission lists were available from 1994 to 2012, in which the file of 2009 was missing. Additionally, student names from over 50 school yearbooks were acquired. For Taiwanese social media names, FB names and all the posts of fortune-telling games were collected until September 2017. In total, there were 4.04 million messages from 48 fanpages. The statistics of the data are presented in Tables 12 and 13.  
Here, two observations can be identified regarding the two datasets. First, people tend to use fake names or nicknames instead of real names on social media. Even with a fake-name filtering step on social media names, stacked first names (such as \") and single-character first names (such as \") appear more often compared to real names. According to the statistics, the ratio of stacked first names on social media is 3.4%, compared to 0.27% on real names; while the ratio of single-character first names is 8.14%, compared to 1.4% on real names. Second, the age distributions of the two datasets are different, as indicated in Figure 6, which refers to the unbalanced data issue.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **\# of Names** & **Unique Names** & **Year Range** \\ \hline Taiwanese Real Name & 1,635,533 & 280,274 & 1940–2000 \\ Taiwanese Social Media Name & 731,137 & 173,209 & 1940–2010 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Collected Taiwanese datasets.  
Figure 6: Distribution of names of two datasets.  
In the following experiments, each age interval was set of five years, starting with 1945. To solve the data imbalance issue, the continuous years with sufficient data sizes were selected such that balanced data was reached for each age interval, shown as Table 14.  
For Malaysian names collected from the university graduation lists, the age may be varied for a person with a doctorate degree. Thus, the names with \"Doctor of\" were removed from the Malaysian name dataset. Additionally, the same sampling approach was adopted to generate a balanced dataset. Only experiments for age-interval prediction were conducted because the gender label is not available for Malaysian names.
## 3 Verifications and Results
### Effectiveness of AID Tags

Table 4: Mean Jaccard Index (MJI) Comparison: Noisy vs. Clean Data (SNR = 3 dB). Figure 5: Jaccard similarity coefficient heatmap of 3570 material combinations under 3 dB SNR (sampling interval = 20). Diagonal elements (blue) represent self-similarity between clean/noisy signals (<90%), while off-diagonal values (yellow-red) indicate cross-talk between distinct configurations. Circled red regions highlight interference from material pairs with low impedance (e.g., PMMA-PVC). \begin{table}
\begin{tabular}{c c} \hline
**Structural Steel MJJ (\%)** & **Aluminum MJJ (\%)** \\ \hline
98.54 & 98.76 \\ \hline
98.31 & 98.24 \\ \hline
98.97 & 98.60 \\ \hline
98.64 & 98.73 \\ \hline
97.17 & 96.98 \\ \hline
97.61 & 97.92 \\ \hline
98.21 (mean) & 98.21 (mean) \\ \hline \end{tabular}
\end{table} 
Table 5: Detection robustness analysis with metallic backing plates (thickness 0.002 m). Structural steel and aluminum substrates maintain MJJ > 97.5% across all configurations, confirming minimal acoustic coupling between AID tags and AUV hull materials. of the heatmap. The analysis utilized a sample size matching the original dataset and 10,000 resamples, with results summarized in Table 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Material Layers** & **Cylinder Radius** & **Quantity** \\ \hline
3 & 0.25 & 210 \\ \hline
4 & 0.26 & 840 \\ \hline
5 & 0.27 & 2520 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Calculation content. Figure 6: Average broadband power reflection coefficients under different material arrangements. Figure 7: Jaccard similarity coefficient heatmap with a sampling interval of 10 (excluding interfering material arrangements; color mapping is a change from blue to cyan, from yellow to magenta, and then to red, and it is proportional to the magnitude of the values). The global confidence interval for all combinations in Table 6 is [94.70%, 94.82%], confirming the statistical reliability of the system over a wide parameter range. To further quantify the impact of the aforementioned material arrangements on the detection accuracy of AID tags, error rates with and without these arrangements were repeatedly calculated under a 3 dB SNR. Higher error rates indicate a stronger influence of material configurations on target recognition. The results are presented in Figure 8.  
As shown in Figure 9, the detection error remains around 12% when using the aforementioned material combinations. In contrast, avoiding these materials significantly reduces the error rate, with an average detection error rate of merely 0.41%. Interfering material arrangements that disrupt target recognition are substantially minimized. Furthermore, systematic screening can exclude specific combinations to further reduce errors.
# Hot-Pressing of Ti-Al-N Multiphase Composite: Microstructure and Properties
###### Abstract  
This study focuses on the development and characterization of a bulk Ti-Al-N multiphase composite enriched with BN addition and sintered through hot pressing. The research aimed to create a material with optimized mechanical and corrosion-resistant properties suitable for demanding industrial applications. The composite was synthesized using a powder metallurgy approach with a mixture of AlN, TiN, and BN powders, processed under a high temperature and pressure. Comprehensive analyses, including microstructural evaluation, hardness testing, X-ray tomography, and electrochemical corrosion assessments, were conducted. The results confirmed the formation of a multiphase microstructure consisting of TiN, Ti\({}_{2}\)AlN and Ti\({}_{3}\)AlN phases. The microstructure was uniform with minimal porosity, achieving a hardness within the range of 500-540 HV2. Electrochemical tests revealed the formation of a passive oxide layer that provided moderate corrosion resistance in chloride-rich environment. However, localized pitting corrosion was observed under extreme conditions. The study highlights the potential of a BN admixture to enhance mechanical and corrosion-resistant properties and suggests directions for further optimization in sintering processes and material formulations.  
A 2025 15 1341 +
Footnote †: journal: Applied sciences
###### Abstract
However, reports on the impact of operator manipulation on NiTi instrumentation are limited, primarily owing to the difficulty in evaluating outcomes within standardized experimental systems. Consequently, criteria for proper operational manipulation have not been clearly established. Studies have investigated the effect of the up-and-down speed [25] and insertion pressure [26] on force and torque generation and canal-centering ability. Pecking motion is considered essential for the safe operation of NiTi instruments because it alleviates pushing stress, distributes bending stress, and reduces fracture risk [27]. However, few studies have investigated the effect of the amplitude of upward and downward motion (pecking amplitude) on NiTi instrumentation [27; 28; 29; 30]. Reducing pecking amplitude may lower the screw-in force, while its effect on cyclic fatigue resistance varies with the NiTi instrument used [27; 28; 29; 30]. Moreover, the effect of pecking amplitude on the torque generation and root canal-shaping ability has not been investigated. For the Reciproc Blue instrument, it is recommended that the pecking amplitude does not exceed 3-4 mm [31]. However, the optimal pecking amplitude for this instrument is still not well defined, although determining it could enhance the safety and effectiveness of root canal instrumentation with this instrument. This study assessed the impact of varying pecking amplitudes on the force and torque generation, canal-centering ability, and cyclic fatigue resistance of the Reciproc Blue instrument. The null hypothesis was that the pecking amplitude does not affect the tested properties of the instrument.
## 2 Application of Membranes in Contamination Situations
### Surface Water Contamination
The wastewater streamsgenerated during the processes taking place in this industry are highly polluted and contain solvents, oils and greases, organic compounds, and heavy metal ionic compounds, such as chromium (Cr), copper (Cu), zinc (Zn), lead (Pb), cadmium (Cd), nickel (Ni), and iron (Fe), in addition to other different cations and anions [34]. Significant pollutants of surface waters, consider ed in the context of their negative impact on aquatic organisms, due to limited dissolution in water, are substances such as oils, including crude oil. In the event of infrastructure failure, tank leakage, and uncontrolled release of oils into waters, it is essential to take action in a place of contamination. One of the actions taken is the use of a barrier, including membrane barriers (Table 1), which allow the separation of the oils substance and the purification of the water. Various polymer membranes are most commonly used to remove such contaminants, and their operational and practical design and manufacturing aspects are important, ensuring high separation efficiency and purification efficiency. Particular attention should be paid to the careful removal of nickel, chromium, and zinc, due to the harmfulness of these metals and their ions. In the process of removing this type of contamination, it is important to select not only high-performance polymer membranes, but also to select an appropriate treatment process. The most commonly used are reverse osmosis (RO), nanofiltration (NF), ultrafiltration (UF), complexation-ultrafiltration (CUF), microfiltration (MF), polymer inclusion membranes (PIMs), electro-membranes (EMs), hybrid processes,