esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Localization System
### Online Phase
#### 2.2.1 AP Filtering  
Filtering fingerprints in this study adopts the sensing probability of the RSS of WiFi to select APs. The vector of RSS received from AP \(n\) in grid \(l\) is  
\[\alpha_{l}^{n}=[rss_{l1}^{n};rss_{l2}^{n};\cdots;rss_{lG}^{n}]. \tag{3}\]  
We assume that \(r_{d}\) is the RSS threshold value similarly used with the training set. Let \(N_{\text{th}}\) denote the number of measured RSS values greater than or equal to \(r_{d}\) among the components of \(\alpha_{l}^{n}\). The following WiFi sensing probability \(p_{n}\) serves to represent the stability of the signal from AP \(n\) over the sampling time of one of the grids:  
\[p_{n}=\frac{N_{\text{th}}}{G}. \tag{4}\]  
Let \(\bar{p}\) be a selection threshold, and then APs with \(p_{n}\) values greater than or equal to \(\bar{p}\) are selected. Additionally, our preliminary tests reveal that \(\bar{p}=1\) is superior to the other cases. In this study, we set \(\bar{p}=1\), which means that AP \(n\) is selected when every component of \(\alpha_{l}^{n}\) is greater than or equal to \(r_{d}\), which is thus referred to as _probability-one_ (PONE) AP selection in this study. \(\bar{p}=1\) maximally ensures the stability of APs while the availability can be reduced but managed by setting a proper value of \(r_{d}\). It is shown in the following section that \(r_{d}=-90\) dBm in PONE selection provides sufficiently many qualified APs in the environment of the considered campus buildings.
# Quantitative Neutron Dark-Field Imaging of Milk: A Feasibility Study
## 2 Material and Method
### Data Analyses  
The deuterated milk samples comprised of CM and FG scattering structures, which are both spherical entities of colloidal dimensions, in aqueous solvent. The radius of CM is typically less than 70 nm within a total range of 20-200 nm, and FG are expected to fall into a size range of 100-15,000 nm. Both are highly polydisperse in the solvent [6; 9]. The CM range at the lower limit of the resolved range and provided a lower nSLD contrast than the FG in the used solvent. Thus, they were not expected to be resolved, at least when they were not aggregated by gelation. In particular, the FG provided a high nSLD contrast, as expected. This also manifested in a significant DFI contrast increase for samples of increasing fat content. The scattering of the FG can be modeled assuming hard spheres, a model applied regularly in quantitative DFI analyses [19; 20]. However, it is known from the literature that the FG do not appear as being monodisperse, but rather as being polydisperse, a situation that has, in contrast, not been modelled in the context of DFI so far. \begin{table}
\begin{tabular}{c c c} \hline Effective wavelength (Å) & \(\lambda\) & 4.1 \\ \hline Fractional Talbot order & \(n\) & 1 \\ \hline Inter-grating distance (mm) & G\({}_{0}-\)G\({}_{1}\) (L\({}_{1}\)) & 1420 \\ G\({}_{1}-\)G\({}_{2}\) (L\({}_{2}\)) & 1420 \\ \hline Period of gratings (\(\upmu\)m) & P\({}_{8}\) (P\({}_{0}\) = P\({}_{1}\) = P\({}_{2}\)) & 50 \\ \hline Height of gratings (\(\upmu\)m) & h\({}_{0}\) & 100 (Gadox) \\ h\({}_{1}\) & 34.39 (Silicon) \\ h\({}_{2}\) & 20 (Gadox) \\ \hline \multirow{2}{*}{Duty cycle of gratings} & d\({}_{0}\) & 0.75 \\ d\({}_{1}\) & 0.5 \\ d\({}_{2}\) & 0.5 \\ \hline \end{tabular}
\end{table} 
Table 1: The detailed parameters of the Talbot–Lau neutron-grating interferometer. The polydispersity of spheres can be considered using a model of spheres weighted by a size distribution \(P(r)\), which is expressed by [26]:  
\[G(\xi)=\int_{0}^{\infty}G(\xi,r)P(r)dr. \tag{5}\]  
According to the literature for the FG, a log-normal size distribution can be assumed according to [8] with:  
\[P(r)=\frac{1}{r\sigma\sqrt{2\pi}}e^{-\frac{(\ln r-\mu)^{2}}{2\sigma^{2}}}, \tag{6}\]  
where \(\mu\) is the mean, and \(\sigma\) is the standard deviation of sphere radius \(r\). For the log-normal distribution, the median radius of the sphere is \(r_{0}=e^{\mu}\), and the mean radius of the sphere is \(\langle r\rangle=r_{0}e^{\sigma^{2}/2}\). In addition, particularly for high volume fractions of fat, a structure factor of inter-particle correlations must be accounted for in modelling \(G(\xi)\) of the system. For this, we first consider the Fourier space description of small-angle scattering, where the scattered intensity distribution, \(I(q)\), of a dense system of hard spheres with radius \(r\), sphere volume \(V(r)\), volume fraction \(\varphi\), and nSLD contrast \(\Delta\rho\) is described by:  
\[I(q,r)=\varphi V(r)\Delta\rho^{2}|F(q,r)|^{2}S(q,r), \tag{7}\]  
where \(F\) is the form factor, and \(S\) is the structure factor.
## 3 Interaction between Plasma Sheath and HPM
### The EM Properties of Plasma Sheath
When the node data of the plasma sheath are transferred to the computational EM mesh, the computational fluid mesh node data are allocated to the computational EM mesh nodes contained in the mesh to complete the data transmission of multi-physics coupling. According to the vehicle model shown in Figure 1a, the circular center of the vehicle nose cap is taken as the origin of the \(x\)-axis. On the stagnation line [30], the distribution graph of plasma frequency and collision frequency is presented in Figure 4.  
As the frequency of EM waves exceeds that of the plasma, EM waves can travel through the plasma without being obstructed, according to plasma characteristics. The EM waves are reflected and absorbed by the plasma when their frequency is lower than that of the plasma. When the EM waves' frequency approaches that of the plasma, the plasma absorbs the EM waves to the greatest extent possible [18; 20]. By irradiating the plasma sheath with EM waves in the 0.1-100 GHz frequency range, the plasma sheath transmission, reflection, and absorption are investigated. As shown in Figure 5, it can be seen that the characteristic frequency of the plasma sheath in this example is about 24 GHz, and the absorption of the plasma sheath near this frequency reaches the maximum. As the frequency of EM waves increases, the plasma's transmittance gradually increases, and the absorptivity gradually decreases.
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 5 Conclusions  
This study introduces an image recognition method tailored specifically for carriage linear array images, which are characterized by extreme aspect ratios, ultra-high resolution, and local nonlinear scale distortions. These inherent properties significantly limit the recognition accuracy of many existing algorithms, particularly because they require substantial computational resources and memory to handle such complex image data. Traditional methods, such as feature-based approaches, often struggle with the sheer scale and variability of these images, resulting in inefficient training and poor performance.  
To address these challenges, we reformulated the recognition problem into a template matching task focused on sparse point set alignment. A coordinate transformation using weighted radial basis functions was proposed to handle the local nonlinear scale distortions typically present in linear array images. Additionally, we designed a unique objective function to enhance the similarity between mixed Gaussian distributions of point sets, ensuring accurate parameter determination for the transformation equation, even in cases where point set correspondences are ambiguous or the point sets vary in size.  
Extensive experimental data validate the effectiveness of our method: under local nonlinear scale distortions of up to 15%, the carriage image recognition accuracy reaches 100%. Even with a 10% increase or decrease in the number of detected targets, the recognition accuracy remains unaffected. Moreover, identifying the target category from 79 templates takes only 24 ms. This demonstrates the algorithm's exceptional performance in terms of recognition accuracy, processing speed, and robustness. Our method allows for the direct identification of carriage models from line-scan images, eliminating the need for traditional AEI equipment.  
However, we acknowledge that as the number of template categories increases, the image recognition processing time will also increase accordingly. This limitation will be addressed in future research as we explore ways to further improve the algorithm's efficiency.  
Conceptualization, Z.F. and X.P.; methodology, Z.F.; software, Z.F.; validation, Z.F. and X.P.; formal analysis, Z.F.; investigation, Z.F.; resources, Z.F.; data curation, Z.F.; writing--original draft preparation, Z.F.; writing--review and editing, Z.F.; visualization, Z.F.; supervision, X.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Science Fund for Youth Science Foundation of China under Grant 62105015; in part by the Aviation Science Foundation 2023 under Grant 20230046051003; and in part by the Beihang University Frontier Cross Fund 2023.  
Not applicable.  
Data Availability StatementData available on request due to restrictions, e.g., privacy or ethical restrictions.  
The authors declare no conflicts of interest.
# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 4 Results and Discussion
### Moderator Analysis for VR-Supported Online Learning
#### 4.2.1 Research Context in VR Studies
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Moderator** & \(k\) & \(g\) & **95\% CI** & \(Q_{B}\) & \(p\) \\ \hline Sample size & & & & 3.399 & 0.065 \\ Small class & 19 & 0.657 & [0.314, 1.000] & & \\ Regular-sized class & 18 & 1.181 & [0.742, 1.621] & & \\ \hline Discipline & & & & 17.934 *** & 0.000 \\ Natural science & 3 & 4.565 & [2.751, 6.378] & & \\ Humanities and social sciences & 9 & 0.689 & [0.050, 1.328] & & \\ Medical science & 3 & 0.818 & [0.024, 1.611] & & \\ Engineering and technology sciences & 22 & 0.619 & [0.371, 0.868] & & \\ \hline Grade level & & & & 0.217 & 0.642 \\ K–12 & 5 & 0.625 & [\(-\)0.807, 2.058] & & \\ College & 32 & 0.973 & [0.673, 1.273] & & \\ \hline Equipment & & & & 0.514 & 0.474 \\ HMD & 11 & 1.049 & [0.736, 1.362] & & \\ Desktop & 26 & 0.866 & [0.474, 1.258] & & \\ \hline Pedagogy & & & 12.830 * & 0.012 \\ Collaborative learning & 5 & 1.098 & [0.515, 1.681] & & \\ Direct instruction & 9 & 0.370 & [\(-\)0.307, 1.047] & & \\ Experiential learning & 12 & 1.704 & [1.069, 2.339] & & \\ Game-based learning & 7 & 0.960 & [0.620, 1.301] & & \\ Inquiry-based learning & 4 & \(-\)0.239 & [\(-\)1.355, 0.878] & & \\ \hline Learning outcome & & & 2.888 & 0.409 \\ Affective outcome & 13 & 0.711 & [0.187, 1.235] & & \\ Behavior & 4 & 2.348 & [0.190, 4.506] & & \\ Knowledge & 10 & 0.984 & [0.372, 1.595] & & \\ Skill & 10 & 0.687 & [0.408, 0.965] & & \\ \hline \hline \end{tabular} Note: \(k\) = number of independent studies; \(g\) = Hedge’s \(g\); CI = confidence interval; \(Q_{B}\) = between-group heterogeneity; \({}^{*}p\) < 0.05, *** \(p\) < 0.001.  
\end{table} 
Table 4: Moderator analysis results for VR-supported online learning. Regarding students' educational stage, 86.5% of the studies were conducted at the college level, while only 13.5% were conducted at the K-12 level. There was no significant variance in effect size across different grade levels (\(Q_{B}\) = 0.217, \(p\) = 0.642). This may be due to the fact that VR is an innovative technology that appeals to both K-12 and college students. Its use in online learning may initially generate a novelty effect [13; 55], which could lead to increased student interest and engagement. It thus appears that online learning supported by VR is not moderated by grade level; however, it is worth noting that a moderate effect (\(g\) = 0.625) was reported for the K-12 stage, while a large effect was reported at the college level (\(g\) = 0.973). One possible explanation is that college students may be better able to adapt to and use VR to enhance their learning than K-12 students in terms of cognitive development.
# Synchrotron X-ray Radiation (SXR) in Medical Imaging: Current Status and Future Prospects
## 3 Synchrotron X-ray Radiation in Medical Imaging
### Bone and Joint Imaging
#### 3.4.4 Incudostapedial Joint  
Synchrotron-radiation phase-contrast imaging (SR-PCI) was used in visualizing the ultrastructure of the incudostapedial joint (ISI) of the middle ear at submicron voxel size. For each sample, a total of 900 projections spanning 180\({}^{\circ}\) of rotation were acquired, covering a total 3.6 mm \(\times\) 2.4 mm field of view throughout the sample. Total scan time per sample was 2 h with exposure times ranging from 400 to 600 milliseconds per frame and four-frame averaging for a projection using X-ray energy of 30 keV. Each sample was placed 0.16 m away from the detector and the source-to-sample distance was 55 m [14]. In this study, ISJs of three human cadaveric were dissected and scanned using SR-PCI at 0.9 \(\upmu\)m isotropic voxel size and compared with previous images at 9 \(\upmu\)m voxel size. The study demonstrated that the contrast to noise ratio of bone to soft tissue for 0.9 \(\upmu\)m was higher than the 9 \(\upmu\)m scan indicating better discernibility of bone from soft tissue (Figure 7).  
Figure 7: (**A**) 3D rendered volume of the incus and stages from the 9 \(\upmu\)m scan. Axial slice through the IS] at 9 \(\upmu\)m scan (**B**) and 0.9 \(\upmu\)m scan (**C**). The 0.9 \(\upmu\)m clearly demonstrates high contrast between the bone and the surrounding soft tissue. Reprinted with permission from [14].
## 2 Radio Communication Technologies
### The Main Characteristics of LPWAN Technologies
#### 2.2.5 Modulation and Handover  
The spread spectrum and narrowband techniques are the two main classes of modulation techniques used with LPWAN technologies to increase the range of the radio network [7]. LoRaWAN uses a bidirectional communication protocol provided by the chirp spread spectrum (CSS) modulation, which at low power works well with the channel noise, multipath fading, and the Doppler effect, enabling high interference resilience. LoRaWAN uses different spreading factors (SF7 to SF12), whereby the lower spreading factor enables a shorter range at the expense of a higher data rate, and vice versa [14; 21].  
NB-IoT uses narrowband modulation techniques to encode the signal at a low bandwidth, providing an elevated linking capacity. Using single-carrier frequency division multiple access (FDMA) in the uplink and orthogonal FDMA (OFDMA) in the downlink as the modulation techniques, the data rates for NB-IoT are limited to 200 kbps in the downlink and 20 kbps in the uplink [18].  
SigFox end devices also use a narrowband modulation technique called the differential binary phase-shift keying or D-BPSK. This technique reduces the signal to 100 Hz frequencies and reduces the channel noise while increasing the number of devices that are supported per unit bandwidth. In this way Sigfox also reduces the power consumption while increasing the receiver sensitivity [7; 18; 21].  
Another feature to consider when choosing the radio communication technology is the capability to pass the messages of mobile end devices from one server to another.  
This feature, known as handover, is managed by the external radio access network (RAN) in 3 GPP technologies, while non-3 GPP technologies manage the handover themselves without external support [22]. In LoRaWAN, the handling of messages over the nearest servers for mobile devices is avoided by the ability for message reception from numerous base stations.  
Therefore, no handover is needed between the network's base stations if the end device is mobile. That is, the end devices in LoRaWAN are not associated with a determined gateway. Handover is also enabled for mobile devices in Sigfox IoT networks [8; 10; 18]. NB-IoT is designed based on the existing LTE functions; however, many segments have been removed to keep NB-IoT simple, reducing the costs and minimizing its power consumption. This optimization occurs at the cost of removing the handover, so NB-IoT has the disadvantage of not being suitable for mobile IoT end devices [8; 23].
# Generation of Orbital Angular Momentum Modes Using Fiber Systems
## 3 Basic Concepts and Theories of OAM Beams Generation and Detection
### Detection of OAM Beams
We are able to judge different TCs through this interference method. In physical angle-independent processes, people generally define the number of interference line in the upper part \(l\) more than that in the lower part. In other words, a right-tilted reference beam is usually assumed if not caring about the sign of the TC. For an angle-dependent physical process, the tilted degree between the OAM mode and fundamental mode must be given. As for Figure 6c, the OAM beam interferes with itself with a translation. If setting the propagating direction of the left-translational OAM beam as the reference axis (Figure 6a), the other OAM beam operates as a left-tilted reference beam related to the signal OAM beam. These two interference beams have the same divergence (wavevector). Thus, in the left area of arbitrary subfigure in Figure 6c, the TC is recognized as \(+l\) for a downward open fork-shaped pattern and \(-l\) for upward open fork-shaped pattern, according to the judging method given in Figure 6a. For the right area, the result is opposite, where the TC is \(+l\) for upward-opening-fork shape pattern and \(-l\) for downward-opening-fork shape pattern. To integrate these two methods, when comparing Figure 6b,c, we just need to focus on the right part in Figure 6c and use the same judgment process as that used for the tilted plane light wave (Figure 6b). In addition, the two rows patterns in Figure 6b,c show the effect of initial phase difference on the interference pattern. This simply leads to a translation of the patterns but not disturb the judgment about the TC.
# Lateral Dynamic Response of Helical Pile in Viscoelastic Foundation Considering Shear Deformation
## 5 Parametric Study
### Space Response Analysis of Helical Pile  
#### 5.1.1 Influence of Helix Inclination Angle  
According to the common design parameters of the helical piles, the helix inclination angle is set as \(15^{\circ}\), \(30^{\circ}\), and \(45^{\circ}\), respectively. Figure 8 shows the influence of the helix's inclination angle on the LDD, BM, and SF of the helical pile with space response. It can be found that the LDD of the pile top increases as the helix inclination angle increases. The BM of the pile end and the SF of the pile middle part increase as the helix inclination angle decreases. The reason for these phenomena may be that the decrease in the helix's inclination angle increases the equivalent diameter and thus increases the stiffness of the helical pile. It is also found that, the smaller the helix's inclination angle, the greater the degree of the above changes.  
Figure 8: _Cont._
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 3 Transmitter Design  
This section introduces the proposed GFDM transmitter. First, the transmitter model is presented, followed by two analog precoders based on different levels of CSI knowledge. In the first one, we assume that the UT has no CSI knowledge, while in the second one, partial CSI knowledge (the average AoD of each cluster) is assumed.
## 3 Methodology
### HSAN Model Building
#### 3.4.2 Word SAM  
A GRU was used to estimate the hidden features for each word; however, each word exhibits different importance to the meaning of the sentence. Thus, a SAM was used to estimate the weight of each hidden feature of a word. If the SAM calculated the weighted sum of the hidden features \(h_{t}\) of each word, the final sentence vector \(s_{i}\) was obtained as follows:  
\[u_{t}=tanh(W_{w}h_{t}) \tag{7}\]  
\[l_{t}=\frac{\left\|u_{t}\right\|^{2}}{1+\left\|u_{t}\right\|^{2}}\frac{u_{t}} {\left\|u_{t}\right\|} \tag{8}\]  
\[\alpha_{t}=\left\|l_{t}\right\| \tag{9}\]  
\[s_{i}=\sum_{t}\alpha_{t}h_{t} \tag{10}\]  
where \(W_{w}\) is a learnable weight, and \(u_{t}\) is used to measure the importance of each word \(h_{t}\). \(l_{t}\) uses squashing to compress \(u_{t}\) so that its vector length falls between 0 and 1. The length of \(l_{t}\) is used as the importance weight of the word \(\alpha_{t}\), and the weighted sentence vector \(s\) is obtained. The squashing-attention used is different from that of previous studies on the attention mechanism. For the weight value, a vector was used as an estimate instead of normalizing the value.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 2 UAV Tracking System Overview
### Communication Link  
Communication link uses a radio-frequency (RF) medium to transmit and receive information packet to and from the UAV. There are various frequencies currently deployed. The deployed frequencies are based on UAV brand as well as functionality of the UAV. As seen in certain UAVs, frequencies, such as 433 MHz and 915 MHz, are used for telemetry, while 1.2 GHz and 2.4 GHz are used for video transmission [10; 15]. The 433 MHz and 915 MHz are used because of their longer transmitting range capability. However, they suffer from low bandwidth and, hence, are not ideal to transmit video signals. The major disadvantage of using 1.2 GHz and 2.4 GHz is that they are simply designed for short range.
# Waveform Design for Space-Time Coded MIMO Systems with High Secrecy Protection +
## 2 Related Works
### Linear Decoding QO-STBC Scheme  
After [PERSON] first proposed the orthogonal STBC scheme with two transmit antennae [15], efforts were made to invent full-rate STBC schemes with a larger number of transmitting antennae. In an effort to increase the antenna size and achieve the full data rate, by loosening the orthogonality condition, several QO-STBC schemes have been proposed [16; 17]. These QO-STBC schemes could achieve the full rate, but at the cost of higher decoding complexity, due to the non-perfect orthogonality of the encoding matrix.  
In order to solve this problem, a new QO-STBC scheme providing a simple linear detection capability was proposed [14]. By applying the Givens rotation to the detection matrix of a conventional QO-STBC scheme, new encoding matrices for three and four antenna systems were derived. The encoding matrix, \(\mathbf{X}\), for four transmit antennae can be expressed as follows:  
\[\mathbf{X}=\left[\begin{array}{cccc}s_{1}+s_{3}&s_{2}+s_{4}&s_{3}-s_{1}&s_{4}- s_{2}\\ -s_{2}^{*}-s_{4}^{*}&s_{1}^{*}+s_{3}^{*}&s_{2}^{*}-s_{4}^{*}&s_{3}^{*}-s_{1}^{* }\\ s_{3}-s_{1}&s_{4}-s_{2}&s_{1}+s_{3}&s_{2}+s_{4}\\ s_{2}^{*}-s_{4}^{*}&s_{3}^{*}-s_{1}^{*}&-s_{2}^{*}-s_{4}^{*}&s_{1}^{*}+s_{3}^{* }\end{array}\right]. \tag{6}\]  
As shown above, a signal transmitted for each time slot at each antenna has now become a linear combination of two modulation symbols. By assuming a single receive antenna, the received signal during four time slots can be expressed as  
\[\left[y_{1}\,y_{2}\,y_{3}\,y_{4}\right]^{T}=\mathbf{X}\left[h_{1}\;h_{2}\;h_{3 }\;h_{4}\right]^{T}+\mathbf{n}^{T}, \tag{7}\]  
where \(h_{i}\) is the channel gain from the \(i\)-th transmit antenna to the receive antenna, and \(\mathbf{n}=\left[n_{1}\;n_{2}\;n_{3}\;n_{4}\right]\) is an AWGN vector for the four time slots. For detection, the received signal vector can be represented by using the equivalent channel matrix, \(\mathbf{H}\), as follows:  
\[\left[y_{1}\,y_{2}^{*}\;y_{3}\,y_{4}^{*}\right]^{T}=\mathbf{H}\left[s_{1}\;s_ {2}\;s_{3}\;s_{4}\right]^{T}+\left[n_{1}\;n_{2}^{*}\;n_{3}\;n_{4}^{*}\right]^{ T}, \tag{8}\]  
where  
\[\mathbf{H}=\left[\begin{array}{cccc}h_{1}-h_{3}&h_{2}-h_{4}&h_{1}+h_{3}&h_{ 2}+h_{4}\\ h_{2}^{*}-h_{4}^{*}&h_{3}^{*}-h_{1}^{*}&h_{2}^{*}+h_{4}^{*}&-h_{1}^{*}-h_{3}^{* }\\ h_{3}-h_{1}&h_{4}-h_{2}&h_{1}+h_{3}&h_{2}+h_{4}\\ h_{4}^{*}-h_{2}^{*}&h_{1}^{*}-h_{3}^{*}&h_{2}^{*}+h_{4}^{*}&-h_{1}^{*}-h_{3}^{* }\end{array}\right]. \tag{9}\]  
The channel matrix \(\mathbf{H}\) is orthogonal, although the encoding matrix \(\mathbf{X}\) is quasi-orthogonal. For this reason, maximum likelihood (ML) decoding can be achieved via simple linear detection, as given by:  
\[\mathbf{\hat{s}}=\mathbf{H}^{H}\left[y_{1}\;y_{2}^{*}\;y_{3}\;y_{4}^{*}\right] ^{T}. \tag{10}\]