esa-sceva/satcom-chunk-collection huggingface dataset:
# Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller
###### Abstract  
We propose a hands-free control system for a human-guided smart stroller. The proposed method uses real-time peer-to-peer localization technology of the human and stroller to realize an intuitive hands-free control system based on the relative position between the human and the stroller. The control method is also based on functional and mechanical safety to ensure the safety of the stroller's occupant (child) and the pilot (parent) during locomotion. In this paper, first, we present a preliminary investigation of the humans' preference for the relative position in the context of hands-free guided strollers. Then, we present the control method and a prototype implemented with an electric wheelchair and UWB sensors for localization. We present an experimental evaluation of the proposed method with 14 persons walking with the developed prototype to investigate the usability and soundness of the proposed method compared to a remote joystick and manual operation. The evaluation experiments were conducted in an indoor environment and revealed that the proposed method matches the performance of joystick control but does not perform as well as manual operation. Notably, for female participants, the proposed method significantly surpasses joystick performance and achieves parity with manual operation, which shows its efficacy and potential for a smart stroller. Also, the results revealed that the proposed method significantly decreased the user's physical load compared to the manual operation. We present discussions on the controllability, usability, task load, and safety features of the proposed method, and conclude this work with a summary assessment.  
peer-to-peer localization; hands-free control; motorized stroller; human-machine interface; human-centered design +
Footnote †: journal:  
[PERSON], [PERSON], [PERSON]; [PERSON] Peer-to-Peer Ultra-Wideband Localization for Hands-Free Control of a Human-Guided Smart Stroller.
## 2 Theoretical Descriptions  
### The Basic Principle of Second-Order Total Variation Denoising Algorithm  
Given that the original signal is expressed as \(\mathbf{x}=[x_{0},x_{1},x_{2},...,x_{N-1}]\), where \(N\) is the length of original time series. Then, the bidiagonal matrix \(\mathbf{D}_{1}\in R^{(n-1)\times n}\) is defined as:\[\mathbf{D}_{1}=\begin{bmatrix}-1&1&&&\\ &-1&1&\\ &&\vdots&\vdots&\\ &&&-1&1\end{bmatrix} \tag{1}\]  
The expression of \(l_{p}\)-Norm (\(p\geq 1\)) is as follows:  
\[\left\|\mathbf{x}\right\|_{p}=\left(\|x_{1}\|^{p}+\|x_{2}\|^{p}+\cdots+\|x_{N}\|^{p }\right)^{\frac{1}{p}} \tag{2}\]  
when \(p=1\) and \(p=2\), Equation (2) can be presented as Equations (3) and (4), respectively.  
\[\left\|\mathbf{x}\right\|_{1}=\left(|x_{1}|+|x_{2}|+\cdots+|x_{N}|\right) \tag{3}\]  
\[\left\|\mathbf{x}\right\|_{2}=\sqrt{\left|x_{1}\right|^{2}+\left|x_{2}\right|^{2} +\cdots+\left|x_{N}\right|^{2}} \tag{4}\]  
Then the first-order total variation term can be noted by Equation (5):  
\[\mathbf{TV}(\mathbf{x})=\left\|\mathbf{D_{1}x}\right\|_{1}=\sum_{n=1}^{N-1}\left|\mathbf{x}(n )-\mathbf{x}(n-1)\right| \tag{5}\]  
Since the traditional total variation using first-order difference has poor performance in signal reconstruction, the second-order total variation is introduced in this paper, which is defined in Equation (6).  
\[\mathbf{TV}_{2}(\mathbf{x})=\left\|\mathbf{D_{2}x}\right\|_{1}=\sum_{n=1}^{N-1}\left|\mathbf{x }(n+2)-2\mathbf{x}(n+1)+\mathbf{x}(n)\right| \tag{6}\]  
In Equations (5) and (6), \(\mathbf{TV}(\mathbf{x})\) and \(\mathbf{TV}_{2}(\mathbf{x})\) is represented as the operator of first-order and second-order total variation, respectively. Generally, the noisy signal \(\mathbf{y}(n)\) was composed of useful signal \(\mathbf{x}(n)\) in additive Gaussian white noise \(\mathbf{w}(n)\), which can be expressed as follows:  
\[\mathbf{y}(n)=\mathbf{x}(n)+\mathbf{w}(n)(n=0,1,\cdots,N-1) \tag{7}\]  
Therefore, the one-dimensional second-order total variation denoising model can be attributed to the following convex optimization problem:  
\[F(\mathbf{x})=\arg\min_{x}\frac{1}{2}\left\|\mathbf{y}-\mathbf{x}\right\|_{2}^{2}+\lambda \phi\left(\mathbf{TV}_{2}(\mathbf{x})\right) \tag{8}\]  
In Equation (8), \(F(\mathbf{x})\) is the objective function: \(R^{N}\to R\); \(\lambda\) is the regularization parameter which is to control the degree of smoothing; \(\phi\): \(R\to R\) is a sparsity-promoting penalty function (regularizer). The second term in the expression reflects the degree of signal variation.
###### Abstract  
The popularity of patch antennas in magnetic resonance imaging (MRI) has reduced because of the large size required for patch antennae to resonate. Since the size of the patch antenna is associated with the wavelength and the wavelengths that are used in MRI are substantially large, large antennas are used. Methods of reducing patch antenna sizes have been proposed; however, these methods reduce the penetration depth and uniformity. In this study, we reduced the area of the patch antenna by 30% by folding the ground and patch planes in a zigzag pattern. The patch antenna produced two main resonant modes. The first mode produced a uniform magnetic field that was used for MRI. The second mode produced a strong and focused electric (\(1\,\mathrm{E}1\))-field, which was used for radiofrequency (RF) heating. Furthermore, we explored the use of a combination of two patch antennas aligned along the \(z\)-axis to provide a circular uniform magnetic flux density (\(1\,\mathrm{B}1\)) field at 300 MHz, which corresponds to the Larmor frequency in the 7 MRI system. In addition, the patch antenna configuration will be used for RF heating hyperthermia operating at 1.06 GHz. The target object was a small rat with insertion of colon cancer. Using the proposed configuration, we achieved \(1\,\mathrm{B}_{1}\) -field uniformity with a standard deviation of 3% and a temperature increment of \(1\,\mathrm{\SIUnitSymbolCelsius}\) in the mimic cancer tissue.  
MRI; patch antenna RF heating hyperthermia; full body MRI +
Footnote †: journal: Applied Sciences
## 2 Background and Literature Review  
As networking technologies advance, cyberattacks have proliferated worldwide. What were once largely exploratory hacks causing minimal damage have evolved into lucrative operations for attackers, inflicting substantial losses on governments, corporations, and even individual smartphone users [1; 2; 3; 4]. Adversaries continuously refine their methods, leveraging everything from novel exploits to botnets to target any device connected to a network. The exponential growth of interconnected systems, the surge in data traffic, and the relentless innovation in attack strategies have made real-time detection and mitigation ever more difficult [8; 9; 10]. As network attacks have grown more complex, integrating ML into IDSs has become significantly more effective than traditional methods [8; 9; 10]. Earlier implementations, such as the concept, first introduced by [PERSON] in 1980, required system administrators to manually inspect logs for anomalies [10]. In contrast, modern IDS solutions increasingly rely on intelligent automation through ML techniques [12; 13; 14; 15; 16]. Over the past decade, a wide range of ML approaches, including supervised learning, unsupervised learning, pattern recognition, and deep learning (DL) approaches such as ANN and Recurrent Neural Networks (RNNs), have been explored to enhance IDS capabilities [5; 10; 11; 13; 14; 15; 16; 17]. In this regard, each method offers unique advantages, and selecting the right combination of algorithms and datasets remains essential for effective implementation. This has led to a surge in research aimed at exploring how ML and DL can improve intrusion detection, with a growing emphasis on large-scale, high-quality datasets to train and validate these models. The ongoing pursuit of greater accuracy, performance, and autonomy continues to drive innovation in this domain [18; 19; 20; 21; 22]. The introduction of DL further expanded the potential of IDS by minimizing the need for manual feature engineering. Due to their layered structure, DL models are capable of automatically extracting complex patterns from raw data, enabling deeper insights and more accurate threat detection [12]. As a result, both ML- and DL-based intrusion detection solutions have evolved continuously, from early research in the 1980s to the sophisticated, self-adaptive systems we see today. Real-time network data capture represents one of the key contributions of this study, serving as the primary input for the anomaly detection model. As networking technologies continue to evolve, the structure and behavior of data flows across networks have undergone substantial transformations. These changes, driven by advances in communication protocols and transmission mechanisms, have made it increasingly critical to capture network traffic during the precise window when intrusions occur, namely, during the exchange of data between systems [4; 10; 11]. Accurate and timely capture of network data during these transmission windows is essential for identifying malicious activity. To support this capability, a range of packet capture tools, such as pcap, ncap, and dashcap, have been
## 4 Conclusions  
In this study, we used GPS kinematic PPP to estimate the vertical OTL displacements of eight semidiurnal and diurnal tidal constituents with 10-year GPS data from 12 GPS reference stations in Hong Kong. The systematic and residual phasor differences between the GPS estimates and nine OTL models were obtained by least squares methods in the complex domain. Our experimental results suggest that:  
For the M2, N2, O1, and Q1 tidal constituents, the RMS residual between the GPS KPPP estimates and the different OTL models is mainly due to the uncertainties of the OTL models, which exist as a systematic component. When the systematic differences are removed, the residual RMS is basically within 0.2 mm, which indicates that the uncertainty of the four KPPP-estimated tidal constituents is at the 0.2 mm level. The K1, P1, and K2 tidal constituents are affected by significant GPS-related common-mode error. After removing the systematic differences, the residual RMS was still significant, which indicates that the three tidal constituents are also affected by GPS-related residual error. The RMS residual of the S2 tidal constituent between the GPS estimates and the OTL models is mainly caused by GPS-related error. Overall the uncertainty of the GPS-estimated S2 tidal constituent is at the 0.5 mm level. These results indicate that GPS KPPP can provide constraints for the Earth's vertical tidal displacement at the 0.2 mm level for the M2, N2, O1, and Q1 tidal components, and the 0.5 mm level for the S2 tidal component.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] processed data; [PERSON] and [PERSON] carried out the experimental analysis.  
This research was funded by the National Natural Science Foundation of China, grant number U1531128 and the Fundamental Research Funds for the Central Universities of Central South University, grant number 2018 zzts680.  
We thank the Lands Department of Hong Kong for providing GPS observations. We are grateful to the providers of the Bernese software, the International Mass Loading Service and Ocean Tide Loading Provider. We thank the two anonymous reviewers for their careful reading and thorough comments.  
The authors declare no conflict of interest.
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 3 Results
### Effects of PAM on Cell Migration  
The results of scratch healing test were shown in Figure 4a,b. After 24 h of culture by PAM containing 1% serum, the scratch area in the control group was reduced by \(22.38\pm 1.55\%\), the scratch healing rate was increased compared with that at 0 h, and the difference was statistically significant (\(p<0.05\)). The scratch area in the 15 s, 30 s, 45 s and 60 s-PAM groups decreased by \(64.72\pm 2.83\%\), \(37.25\pm 2.1\%\), \(12.23\pm 1.72\%\) and \(7.56\pm 1.05\%\), respectively, the scratch healing rate increased compared with that at 0 h, and the difference was statistically significant (\(p<0.05\)). After 24 h of culture, the cell scratch healing ability of 75 s-PAM group decreased to \(-2.11\pm 0.22\%\), compared with that at 0 h. The number of cells was few, the cell connection was loose, and the convergence was significantly decreased (\(p<0.05\)).  
Figure 4: \(Cont.\)The results of single-layer penetration experiment were shown in Figure 4c,d: the number of cells passing through the subcompartment in the 15 s-PAM group was 122 \(\pm\) 2.68, which was significantly increased compared with that in the control group (103 \(\pm\) 3.54). The number of cells passing through the subcompartment in the 30 s-PAM group was 97 \(\pm\) 1.93, which had no significant difference compared with that in the control group. In the 45 s, 60 s and 75 s-PAM groups, the number of cells passing through the subcompartment was 83 \(\pm\) 2.83, 60 \(\pm\) 3.75 and 24 \(\pm\) 2.10, respectively, which was significantly reduced compared with that in the control group, and the difference was statistically significant (\(p<\) 0.05).  
Figure 4: **(a)** Typical pictures of the effects of scratch healing test (40\(\times\)) at different time (0 h, 12 h and 24 h). **(b)** Cell healing rate * \(p<\) 0.05 compared with control group (0 h). **(c)** Typical photos of the cells on the lower surface of the Transwell insert membrane (40\(\times\)). **(d)** Results of single-layer penetration experiment. The number of cells was increased in the 15 s-PAM group and decreased in the 45 s, 60 s and 75 s-PAM group. * compared with control group (0 s) \(p<\) 0.05.
## 3 Results and Discussion
### Histograms of Pore-Volume Distribution  
As seen in the previous subsection, small pores are dominant in number, but represent a less important volume. Alternatively to the histograms shown in Figure 19, it is possible to obtain histograms of pore distribution according to pore volume. These represent, for each volume range, the total volume of those pores whose individual volume is within that range. Again, this information can be displayed in absolute values or in relative values, i.e., showing for each volume range the percentage of the total volume.  
Similar to the previous case, it is often interesting to represent nested histograms. The results of the specimens considered in this work are shown below (Figure 20).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **CUB-01** & **CUB-02** & **CUB-03** & **CUB-04** & **CUB-05** & **CUB-06** \\ \hline Maximum Pore Size & 5.51 & 2.48 & 2.73 & 5.96 & 3.09 & 7.11 \\ (mm) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum Pore Size.  
Figure 19: Histograms of pore distribution (in volume) for different ranges, from the largest to the smallest (**a**–**d**).  
Figure 20: Histograms of pore-volume distribution (in volume) for different ranges, from the largest to the smallest (**a**–**d**).  
The results reveal some interesting points. Again, most of the total porosity of the specimens is distributed in pores less than 1 mm\({}^{3}\). However, significant differences are observed between specimens, varying from 50% to 75%. While Figure 17 shows that almost 100% of the pores are less than 1 mm\({}^{3}\), this represents \"only\" between 50% and 75% of the total porosity.  
In a more detailed analysis, we observe that most of the total porosity belongs to pores smaller than 0.1 mm\({}^{3}\), with many differences between specimens (from 15% to 27%). Comparing Figure 20 with Figure 19, we can see that the pores smaller than 0.1 mm\({}^{3}\), which represent approximately 90% of the total pores, only contain between 15% and 27% of the total porosity.  
These morphological analyses can be of great interest to understand the intrinsic dispersion associated with the mechanical response of the specimens. It is expected that these specimens show different behavior against certain external actions (for example, different response against freeze-thaw cycles, or different behavior against mechanical or thermal fatigue) and that this can be explained by the different morphology of their microstructure.
# Robust Background Subtraction via the Local Similarity Statistical Descriptor
## 2 Local Similarity Statistical Descriptor
### Local Similarity Statistical Descriptor
Second column: the intensity values of the small red box in the input images. Third column: the corresponding LSSD feature images. from the fall sequence, (a\({}_{1}\)) is the first frame and (b\({}_{1}\)) is the thirteenth frame; (a\({}_{2}\)) and (b\({}_{2}\)) show the intensity values of \(p(212,69)\) and its neighboring pixels. Comparing (a\({}_{2}\)) and (b\({}_{2}\)), we can find that the pixel values near \(p\) change dynamically due to the swinging leaves, thus, the foreground detection based on pixel values will make a false segmentation. However, as the LSSD feature describes local similarity, its value keeps unchanged during this process, which made it more suitable for coping with dynamically changing scenes. Compared with other texture features like LBP [21], SILTP [22] and LBSP [26], the proposed texture feature LSSD not only has the merit of dealing with illumination variations, but it also has strong robustness to complex background motions. For example, if we concatenate all the compared results of Equation (8) into a binary string like other texture features do, then the new LSSD feature of Figure 3(a\({}_{2}\)) can be represented as: 00000 11110 11111 11111 11111 and Figure 3(b\({}_{2}\)) can be represented as: 00000 11110 11111 11111 11111. Taking the bitwise XOR operation to get the Hamming distance, we can see that the result is the same with the LSSD distance. However, in the case of dynamic background motions shown in Figure 4, the new LSSD feature of (a\({}_{2}\)) can be represented as: 01100 01000 00100 0000 11000, and (b\({}_{2}\)) can be represented as: 00000 0000 00100 11001 01001. The Hamming distance is 8, while the LSSD distance is 0, which is what we expect. In Figure 5, we also show how the color and LSSD feature values change between consecutive frames. Firstly, in the top subgraph, we give the feature variation plot of the pixel \(p(512,391)\) from the first frame to the 300 th frame of the PETS2006 sequence with illumination changes. Then, in the bottom subgraph, we give the feature variation plot of the pixel \(p(212,69)\) from the first frame to the 300 th frame of the fall sequence with dynamic background motions. The blue line represents the color feature variation and the red line represents the LSSD feature variation. We can see that the variation of the LSSD feature is much smaller, which indicates the robustness of the LSSD feature against different challenges. In Section 4.4, we will give a more detailed comparison between LSSD and other texture features. Figure 4: An LSSD encoding example under dynamic backgrounds. First column: input images. Second column: the intensity values of the small red box in the input images. Third column: the corresponding LSSD feature images.
# A Dual-Gas Sensor Using Photoacoustic Spectroscopy Based on a Single Acoustic Resonator
###### Abstract  
A dual-gas photoacoustic spectroscopy (PAS) sensor based on wavelength modulation spectroscopy (WMS) was developed and experimentally demonstrated. Distributed feedback (DFB) laser diodes, emitting at 1512 and 1653 nm, were utilized as the excitation sources for the simultaneous measurement of NH\({}_{3}\) and CH\({}_{4}\), respectively. The PAS signal was excited by modulating the DFB laser at the first longitudinal resonant frequency of a cylindrical acoustic resonator. Absorption lines for NH\({}_{3}\) and CH\({}_{4}\) were simultaneously recorded during one frequency scan of the DFB lasers without using any optical switch. The interference of NH\({}_{3}\) and CH\({}_{4}\) on each other was investigated for accurate detection. The limits of detection (LoDs) of the PAS sensor for NH\({}_{3}\) and CH\({}_{4}\) for an integration time of 100 s were determined to be 0.1 and 0.3 ppm, respectively. The present PAS sensor provides a new scheme for multi-gas analysis with the advantages of cost-effectiveness, a simple structure and multi-wavelength operation.  
photoacoustic spectroscopy; absorption; multi-gas sensing; diode laser +
Footnote †: journal: Applied Sciences  
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Footnote 0: _Appl. Sci._**2021**, _11_, 5224. [[https://doi.org/10.3390/app11115224](https://doi.org/10.3390/app11115224)]([https://doi.org/10.3390/app11115224](https://doi.org/10.3390/app11115224))
# A Review of Test Stimulus Compression Methods for Ultra-Large-Scale Integrated Circuits
## 3 Encoding-Based Test Stimulus Compression Methods
### Fixed-Length to Fixed-Length  
The typical encoding method for fixed-length to fixed-length is dictionary encoding. In the test stimulus compression method based on dictionary encoding, the data in the original test set are divided into segments of n bits each. These segments are then encoded using b-bit code words, where \(b<n\). The b-bit code words act as indices in a dictionary, while the n-bit segments correspond to the content of the dictionary entries, with each index corresponding to a unique entry. This indexing relationship is stored in the decoder. During testing, the ATE sends the index to the decoder, which then outputs the corresponding content to the scan chains within the CUT. As the number of test vectors increases, not every possible segment is indexed in the dictionary. If all segments in the original test set can be indexed in the dictionary, the dictionary encoding is considered complete.  
[PERSON]'s [36] method based on multi-scan chain structure for complete dictionary encoding is shown in Figure 3. The scan chains for testing consist of n chains, and every clock cycle inputs an n-bit segment into the scan chains. In dictionary encoding, each scan slice is filled according to the principle of the fewest segment types and stored in the dictionary. The index count in the dictionary is \(b=\log_{2}N\), where N is the number of segment types after the scan slice is filled. The more scan chains, the more bits in the scan slices and the shorter the test time. However, the complete dictionary encoding method is limited by the number of segment types after filling, and the more types there are, the more indexes there are and the higher the design complexity of the decoder, thus occupying more hardware resources.  
In response to these limitations, [PERSON] [37] and [PERSON] [38] proposed a partial dictionary encoding compression method. The segments after the scan slices are filled are sorted by their occurrence frequency, and the high-frequency segments are stored in the dictionary while the low-frequency ones are not encoded and are input directly to the scan chains as a bypass. An additional flag bit is added to distinguish whether the segment has been compressed. On this basis, to allow more segments to be encoded into the dictionary under strict control of the dictionary size, [PERSON] [39] and [PERSON] [40] utilized masking technology to encode more segments through masking operations. The physical implementation of the masking technology is simply an XOR network, which is easy to implement and has minimal overhead. Additionally, [PERSON] [41] proposed an index reuse method to increase the compression efficiency of dictionary encoding.