esa-sceva/satcom-chunk-collection huggingface dataset:
# Affective Stroking: Design Thermal Mid-Air Tactile for Assisting People in Stress Regulation
## 3 Results
### Qualitative Results
#### 3.3.2 Users Recalled Positive Experience for Stress Regulation  
Regarding whether the mediated stroking evoked positive experiences, the majority of participants (20/25) reported recalling positive and pleasant experiences during relaxation. Among them, seven participants mentioned feeling relaxed before bedtime. P02: _\"It felt like the state before sleep, receiving a new kind of hypnotic effect\"_. Seven participants felt that the simulated touch closely resembled a gentle touch from a real person, evoking experiences from intimate relationships, such as touches from parents or other caregivers during childhood, or emotional touches from romantic partners. P01: _\"It reminded me of being stroked as a child and because I am a mom, I usually stroke my baby similar to this sensation\"_. P22: _\"It felt like someone stroking me to sleep, thinking of my grandmother\"_. P24: _\"It felt like when my mother used to hold and pat and stroke me as a child\"_. P34: _\"it feels like my wife is flirting with me\"_. Two participants reminisced about past experiences with nature. P18: _\"When I was a child, there was no air conditioning at home. Lying on the sofa by the window, the breeze would blow the curtains, and they would brush over me. It's a pleasant memory. The stroking was light, barely noticeable.\"_ P42: _\"It reminded me of standing on a gnsysy hillside, with the wind blowing, lifting my hair\"_. One participant felt it was similar to a spa experience, which was very pleasant. Three participants found it fun and intriguing, wanting to know what was stroking them. The remaining five participants had neutral emotions, with no significant emotional fluctuations.  
Regarding whether the mediated stroking helped with stress regulation, almost all participants (23/25) mentioned that it provided emotional comfort. Concerning their stress regulation and relaxation methods during the relaxation phase, twelve participants mentioned zoning out or dozing off. Ten participants recalled pleasant memories and experiences related to intimate relationships. Four participants focused on the stroking itself. However, two participants felt it had little effect. P16 said: _\"my personality tends to be unaffected by external factors\"_. P12 mentioned: _\"He couldn't fully relax because they were preoccupied with thinking about the previous math problems\"_.  
According to the interviews mentioned above, the positive and enjoyable sensory experiences, combined with the recalled positive memories, counterbalanced the negative feelings of stress and anxiety induced by the math test. Affective stroking helped participants to modify the situation, divert attention, and achieve cognitive reappraisal, as part of the emotion regulation strategy [30], alleviating negative emotions. It might contribute to further stress relief and relaxation.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
###### Abstract  
Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accuracy and prolong end-to-end delay when eliminating transmission overhead. In this paper, we propose a data aggregation scheme based on overlapping rate of sensing area, namely AggOR, aiming for energy-efficient data collection in wireless sensor networks with high information accuracy. According to aggregation rules, gathering nodes are selected from candidate parent nodes and appropriate neighbor nodes considering a preset threshold of overlapping rate of sensing area. Therefore, the collected data in a gathering area are highly correlated, and a large amount of redundant data could be cleaned. Meanwhile, AggOR keeps the original entropy by only deleting the duplicated data. Experiment results show that compared with others, AggOR has a high data accuracy and a short end-to-end delay with a similar network lifetime.  
Keywords:wireless sensor networks; data aggregation; overlapping rate of sensing area; data accuracy +
Footnote †: journal: Sensors
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
###### Abstract  
This article reviews the past and current statuses of time-domain near-infrared spectroscopy (TD-NIRS) and imaging. Although time-domain technology is not yet widely employed due to its drawbacks of being cumbersome, bulky, and very expensive compared to commercial continuous wave (CW) and frequency-domain (FD) /NIRS systems, TD-NIRS has great advantages over CW and FD systems because time-resolved data measured by TD systems contain the richest information about optical properties inside measured objects. This article focuses on reviewing the theoretical background, advanced theories and methods, instruments, and studies on clinical applications for TD-NIRS including some clinical studies which used TD-NIRS systems. Major events in the development of TD-NIRS and imaging are identified and summarized in chronological tables and figures. Finally, prospects for TD-NIRS in the near future are briefly described.  
time-domain spectroscopy; near-infrared spectroscopy; radiative transfer equation; diffusion equation; biological tissue; time-domain instruments; light propagation in tissue; optical properties of tissue; diffuse optical tomography; fluorescence diffuse optical tomography +
Footnote †: journal: Medical Imaging
## 5 Results and Discussion
### Effects of the AFG Material  
This section investigates the effects of the AFG materials on the dynamic response of the beam. In the analysis, the non-uniformly distributed load model and the parabolically varying foundation are adopted. The following parameters are used: \(v_{\text{load}}=100\) m/s, \(\bar{I}=0.2L\), \(\lambda_{1}=\lambda_{2}=0.5\). The effects of the material gradient index \(n\) on the mid-point displacement of the beam are shown in Figure 16, considering both types of AFG materials. It shows that the AFG materials change the forced vibration response of the beam considerably. The maximum displacements of the mid-point and the value of the first peak, are reduced, compared with the case of homogeneous material (i.e., \(n=0\)). However, the values of the other peaks are increased. Furthermore, the vibration periods of the AFG beams are longer than those of the homogeneous beam. The variation of the maximum displacement of the whole beam as the material gradient index increases is shown in Figure 17. The maximum displacement decreases as the index increases, initially rapidly and then slowly. When \(n>4\), its value is nearly unchanged as the index increases. These results indicate that the AFG materials can reduce the maximum displacement of the beam. This conclusion can be further confirmed by Figure 18, which shows the variation of the maximum displacement of the beam as the velocity of the moving load changes. Compared with the beam made of homogeneous material, i.e., \(n=0\), the AFG beams made of the two components have smaller maximum displacements. The material gradient increases the overall stiffness of the system, so that the peak value of the vibration amplitude decreases, while the other peak values decrease because, under the same foundation, the material gradient reduces the amplitude of the first vibration and weakens the damping effect of the foundation. The above results inspire the idea that the FGMs can be used to optimize  
Figure 14: Effects of the foundation variation coefficients \(\lambda_{1}\) and \(\lambda_{2}\) on the dynamic response at the midpoint. (**a**) Power material, linear foundation; (**b**) Power material, parabolic foundation; (**c**) Exponential material, linear foundation; (**d**) Exponential material, parabolic foundation.  
Figure 15: Effect of inhomogeneous foundation on maximum displacement, linear foundation, and power AFG material. (**a**) \(\lambda_{2}=0\); (**b**) \(\lambda_{1}=0\).  
the dynamic response of beams under moving loads, especially to reduce the maximum displacement of the beams as the loads travel through.
## Materials and Methods
### Hyperspectral Image Acquisition  
Hyperspectral images of the rice samples were acquired by using a line-scan (push broom) HSI system as shown in Figure 1. The system consisted of an electron multiplying charge-coupled device camera (EMCCD: Luca R DL-604M, 14-bit, Andor Technology, South Windsor, CT, USA), visible/near-infrared imaging spectrograph (Headwall photonics, Fitchburg, MA, USA), programmable linear stage (translation table) with stepping motor, and light sources. The camera was coupled with a C-mount objective lens (F1.9 35-mm compact lens, Schneider Optics, Hauppauge, NY, USA). The HSI system was constructed to cover visible (VIS) to near-infrared (NIR) wavelengths for reflectance measurements. The lighting sources used were two 150 W halogen lamps with DC power supplies which enabled control of light intensity. Light was transmitted via two optical fibers to the sample surfaces to provide near-uniform illumination. The detailed information of system was described by [PERSON] et al. [28].  
Hyperspectral images of rice samples were collected by placing the sample plate onto the programable translation table unit and obtaining spectral/spatial data line-by-line as the translation table moved the sample plate under the instantaneous field of view (IFOV) of the HSI system. The exposure time was set at 16 ms and the samples on the translation table were advanced at 0.3 mm/scan. Thus, to cover the spatial shape of samples (15 cm plate holding 100 samples), a total of 500 steps for advancement of the plate was required. The hyperspectral reflectance images of the rice were stored for further processing and analyses. The white and dark reference images were also acquired after collecting hyperspectral data for individual sample plates. A white reference was obtained using a Spectralon (\(\sim\)99% reflectance), and the dark reference was obtained by capping the objective lens.
# Elastic Modulus Prediction Model of Foamed Concrete Based on the Walsh Formula
## 5 Parameter Sensitivity Analysis
### Poisson's Ratio of Foamed Concrete Matrix  
The elastic modulus of foamed concrete with Poisson's ratio of matrix material in the range 0.1-0.28 were calculated, and the corresponding curves drawn, as shown in Figure 15. It can be seen from Figure 15 that when the matrix Poisson's ratio increases from 0.1 to 0.28, the elastic modulus of foamed concrete with porosity 0.59, 0.62, 0.65, 0.68, and 0.71 decreased by 18.7%, 19.7%, 20.6%, 21.5%, and 22.4%, respectively. This shows that the elastic modulus decreases continuously with the increase in Poisson's ratio. This is mainly because the material is not prone to lateral deformation when Poisson's ratio is small; on the contrary, when the Poisson's ratio is large, the material is prone to lateral deformation, and the stiffness of the material is large. Therefore, the elastic modulus is negatively correlated with the matrix Poisson's ratio.
# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 3 Denoising Based on the Speech Segment Detection
### Smoothing  
We perform the smoothing process for all the presented speech segment detection [42]. The smoothing process considers multiple contiguous time points before and after a time point and determines to detect the speech data considering those time points as the unit. Here, we use 200 audio samples before and after each time point, which corresponds to smoothing on a running window of 25 ms, and determine the time point is a speech or non-speech segment in which a larger number of time points belong.  
Figure 20 illustrates the result of the speech segment detection if the smoothing process is applied or not when we use fuzzy clustering. Specifically, Figure 20 illustrates the result of the speech segment detection before smoothing; Figure 20 illustrates the result of it after smoothing. The blue-filled areas show that the speech and non-speech segments are sliced into multiple time points. By the smoothing process, we can determine whether a time point is in a speech or non-speech segment by considering adjacent time points in a time frame as depicted in Figure 20. As a result, we note that some segments that have been labelled as non-speech are now correctly labelled as speech. Here, we show only the result where fuzzy clustering is used for the speech segment detection, but for all the other methods, we apply the same smoothing process into the result of the speech segment detection.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
### Paper Focus and Outline  
Within the context of the research field, the driving motivation for this research was to conduct some further real condition jamming experiments and analyse the results in order to gain a better understanding of the performance of geodetic GNSS receivers when faced with an intentional degradation of signals. The focus of this paper is based on an estimation of the vulnerability of geodetic GNSS receivers under various L1/E1 jamming scenarios. More specifically, the research focused on two different issues: (a) the impact of static jamming where the distances between the jammer and receivers varied, and (b) the impact of short-term kinematic intentional disturbances, both on signal quality and positional (horizontal and vertical) accuracy.  
The outline of the paper is as follows. The second section (Section 2) discusses the theory. Basic facts are presented on the GNSS positioning and ambiguity fixing algorithms, followed by an introduction to the carrier-to-noise-ratio. This section also covers the localisation of moving jammers and the dispersion of receiver coordinates while jamming. The next section (Section 3) describes the setup and measurement campaign and the receivers and jammer used in the experiment, followed by a description of the experiments (Section 4). Section 5 contains data analysis, results and discussion, followed by a summary of the main findings and a description of further ideas for testing (Section 6).
# An Emergency Message Routing Protocol for Improved Congestion Management in Hybrid RF/VLC VANETs
## 1 Introduction
### Background
This helps in the routing and emergency response. **Supplements RF gaps:** The VLC can relay the messages between the vehicles/ infrastructure in situations where the RF signals may be obstructed, such as tunnels and underground parking structures. However, the directional nature of VLC poses some challenges for its application, especially at road intersections, where signal propagation in diverse directions is required. Therefore, hybrid RF/VLC systems that leverage the advantage of both the RF and VLC technologies are widely investigated [18]. While RF has broader coverage and is suitable for usage beyond LoS, VLC provides valuable redundancy with low interference for short-range transmissions, where a direct signal path exists. Hence, hybrid RF/VLC networks can complement each other in VANET applications [19]. They have the potential to improve the reliability, throughput, and coverage, for time-critical safety-related messaging in VANETs [20; 21].
###### Abstract  
A new algorithm named PolyCISAMR is introduced to automatically generate high-fidelity conforming finite element (FE) meshes for two-dimensional polycrystalline microstructures. PolyCISAMR extends the capabilities of the Conforming to Interface Structured Adaptive Mesh Refinement (CISAMR) algorithm, which transforms a structured grid overlaid on the domain geometry into a high-quality conforming mesh. The PolyCISAMR approach uses a segregated meshing strategy, where CISAMR is used to discretize each grain independently and the resulting matching meshes are merged to form the final FE model. In addition, this article presents a set of integrated algorithms for processing low-resolution images of a polycrystal, reconstructed using DREAM.3D software (Version 6.5.121), to generate NURBS characterizations for each grain prior to mesh generation. Example problems demonstrate the effectiveness of PolyCISAMR in creating high-quality meshes for various polycrystalline metallic microstructures along with corresponding crystal plasticity finite element (CPFE) simulations.  
polycrystal; mesh generation; CISAMR; finite element method; DREAM.3D
# Remediation of Heavy Metal Contaminated Farmland Soil by Biodegradable Chelating Agent GLDA
## 2 Experimental
### Leaching of Soil with Different Leaching Times and Different Dosages of GLDA  
GLDA-4 and GLDA-Na were selected as leaching solutions with a concentration of 5 mmol/L, and leaching was carried out at four oscillation times of 30 min, 60 min, 120 min, and 240 min. The rest was the same as above.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **pH** & **CEC** & **OM** & **Fe** & **Pb (S = 406.9)** & **Cd** & **Cu** & **Zn** \\ \hline  & & **cmol/kg** & **g/kg** & **\#g/g** & **\#g/g** & **\#g/g** & **\#g/g** & **\#g/g** \\ \hline Dabaoshan SB & 5.04 & 8.87 & 13.26 & 50855.02 & 287.26 & 0.58 & 210.02 & 249.83 \\ Huaqiao HQ & 5.01 & 10.07 & 41.06 & 12364.12 & 42.87 & 0.71 & 25.53 & 71.41 \\ Tielong TL & 6.01 & 8.54 & 30.32 & 18826.91 & 146.08 & 3.30 & 27.49 & 198.72 \\ Liantang LT & 7.17 & 13.63 & 29.63 & 38993.00 & 947.63 & 1.87 & 43.56 & 954.50 \\ \hline \end{tabular}
\end{table}
Table 1: Basic physical and chemical properties of the soil sample.  
The concentrations of the GLDA-4 and GLDA-Na solutions were set to 2.5, 5, 10, and 20 mmol/L as the leachate, shaken for 120 min, and the rest was the same as above.  
The removal rate of heavy metals was calculated according to the equation:  
Removal rate of heavy metals (%) = removal amount of heavy metals/total amount of heavy metals in the soil \(\times\) 100%.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 2 Datasets  
The dataset is an important part of any OCR system to validate the results of OCR. It is especially challenging for the Arabic language because of the cursive nature of the Arabic language, diacritics, different writing styles in which each word's overall shape changes, text sizes, and other reasons. The collection of the Arabic dataset is also very limited due to the low-resource nature of the Arabic language. Previously, refs. [10,11] shared some commonly used datasets, as shown in Table 1. They presented the shared datasets for Arabic, Urdu, and Persian, both publicly available and otherwise.
## 3 Methods
### Data Partitioning and Parallelization
#### 3.3.2 Shape Complexity Partitioning and Parallelization  
In this paper, a data partitioning method based on polygon shape complexity is designed and implemented. After partitioning, the complexity for each group of data is similar. The process of this method is as follows: 1 The vector data are read, the polygons in the layer are traversed, the six indicators of NOV, Ampl, NOH, ANN, Conv, and ER of  
Figure 4: Polygon multicore parallel computing based on polygon number division.  
Figure 3: Scatter plots and linear fitting of calculation time versus shape complexity.  
each polygon are calculated according to the calculation formula, the shape complexity model obtained above is brought in to calculate the complexity value of each polygon in the vector data, and it is stored in the table. 2 The sum of the complexity values of all polygons in the table is calculated. The sum value is denoted by \(M\); the number of groups, which is determined by the number of CPU threads, is denoted by \(n\); and the threshold on the complexity that can be assigned to each thread is denoted by \(L\). For example, when the number of threads is selected to be 16, the data will be divided into 16 groups, and the complexity threshold of each group of data is \(1/16\) of the sum of the complexity values of the vector data. This complexity threshold is calculated as shown in Equation (3).  
\[L=M/n \tag{3}\]  
3 The vector data are grouped in accordance with the calculated complexity threshold to ensure that the shape complexity of each group of data does not exceed the threshold and that the complexities are roughly equal. 4 Each CPU thread reads the data of a completed group and calls the polygon overlay analysis algorithm for calculation. 5 The data for each group are output after overlay analysis, and the results for each group are combined. Figure 5 shows a schematic diagram of the proposed multicore parallel computing method based on shape complexity. This method can account for the influence of polygon shape and size on the computational efficiency of the algorithm and, thus, achieve a better load balance among CPU threads while avoiding complex element cutting and stitching operations.
## 2 Distortion and Perceptual Measures
### Perceptual Measures in Information Theory
#### 2.2.4 Common Assumptions  
In the more general case, several assumptions are made regarding perceptual measures across different settings, which we briefly discuss here.  
**Assumption 1** (Non-negativity).: _For arbitrary distributions \(P_{X}\) and \(P_{Y}\) defined on the \(\sigma\)-algebra \((\mathcal{X},\mathcal{F})\), the perceptual measure satisfies_  
\[\mathcal{D}(P_{X},P_{Y})\geq 0\;\;\text{and}\;\;\mathcal{D}(P_{X},P_{Y})=0\; \;\text{if and only if}\;\;P_{X}\stackrel{{ d}}{{=}}P_{Y}.\]  
**Assumption 2** (Convexity in the second argument).: _For \(\lambda_{1},\lambda_{2},\ldots,\lambda_{n}\in\mathbb{R}_{\geq 0}\) such that \(\sum_{i}\lambda_{i}=1\),_  
\[\mathcal{D}(P,\sum_{i=1}^{n}\lambda_{i}Q_{i})\leq\sum_{i=1}^{n}\lambda_{i} \mathcal{D}_{\text{TV}}(P,Q_{i}). \tag{4}\]  
This convexity is a property satisfied by the \(f\)-divergence family, including the TV distance and the KL divergence, as well as the 1-Wasserstein distance.  
Some members of the \(f\)-divergence family, such as the Hellinger distance and the \(\chi^{2}\)-divergence, exhibit tensorization. This means that, for product distributions, these divergences can be easily expressed in terms of the marginal distributions. Tensorization, or additivity under products, is crucial for establishing converse proofs.  
**Assumption 3** (Tensorization).: \[\mathcal{D}(\otimes_{i=1}^{n}P_{X_{i}},P_{Y^{n}})\geq\sum_{i=1}^{n}\mathcal{D}(P_{ X_{i}},P_{Y_{i}}).\] (5)  
Notably, the KL divergence and \(W_{2}\) distance are tensorizable, in the sense that  
\[\mathcal{D}_{\text{KL}}(\otimes_{i=1}^{n}P_{X^{i}}\|\otimes_{i=1}^ {n}P_{Y^{n}}) =\sum_{i=1}^{n}\mathcal{D}_{\text{KL}}(P_{X_{i}}\|P_{Y_{i}}), \tag{6}\] \[W_{2}(\otimes_{i=1}^{n}P_{X^{i}}\|\otimes_{i=1}^{n}P_{Y^{n}}) =\sum_{i=1}^{n}W_{2}(P_{X_{i}}\|P_{Y_{i}}). \tag{7}\]  
The TV distance, however, is not tensorizable.  
We may also consider a sequence of perceptual measures to analyze performance over coding blocks. The sub-decomposable assumption introduced in [32] ensures that coding over longer sequences is advantageous.  
**Assumption 4** (Sub-decomposability).: _A sequence of perception measures \(\mathcal{D}_{n}(\cdot,\cdot):\operatorname{Prob}(\mathcal{X}^{n})\times \operatorname{Prob}(\mathcal{X}^{n})\mapsto\mathbb{R}\) is called sub-decomposable if_  
\[\mathcal{D}_{n}(\otimes_{i=1}^{k}P_{X_{i-1}^{l_{i}}},\otimes_{i=1}^{k}P_{X_{ i_{i-1}^{l_{i}}}^{l_{i}}})\leq\sum_{i=1}^{k}\mathcal{D}_{n_{i}}(P_{X_{i_{i-1}^{l} }^{l_{i}}},P_{Y_{i_{i-1}^{l}+1}}) \tag{8}\]  
_for any positive integers \(n_{1},n_{2},\ldots,n_{k}\) such that \(\sum_{i=1}^{k}n_{i}=n\), where \(l_{i}:=\sum_{j=1}^{i}n_{i}\) with \(l_{0}:=0\)._  
By definition, any tensorizable measure, such as the KL divergence, is also sub-decomposable. The non-tensorizable TV distance is sub-decomposable as well, as shown in Equation (3).