esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Comparison of Ice Velocity Products
#### 3.3.4 Mosaic Difference  
To assess the difference between Sentinel-1a/b and Landsat-8 velocity results, we compare Antarctica mosaics for year 2015-2016 produced from each sensor (Figure 7). We find that the mean differences (Sentinel-1 minus Landsat-8) for the speed in x- and y- directions are \(-0.7\) and \(-0.3\) m/year, with a standard deviation of 18.9 and 18.0 m/year, respectively. These values are consistent with the  
Figure 6: (**A**) Lognormal distribution of the standard deviation of the antarctic ice velocity mosaics produced for each sensor and each repeat interval. For Landsat-8 (top-left panel), the 6 distributions correspond to repeat intervals of 368 (red), 80 (orange), 64 (green), 48 (turquoise), 32 (light blue) and 16 days (blue), respectively. SAR distributions are color-coded with grey getting darker with the length repeat cycle. Sentinel-1 (top-left panel) has 3 distributions for 6 (lightest), 12 and 24 (darkest) day repeat cycle. (**B**) Mode of the standard deviation distributions in (**A**) versus repeat interval. The grey vertical bars correspond to the 1-sigma deviation of the lognormal distributions shown in (**A**). Note: RADARSAT-2 point is shifted by 0.5 days to avoid overlapping with Sentinel-1 (24 days).  
standard deviation maps for the annual mosaics produced in Figure 2. Overall we conclude that the results from both sensors are in excellent agreement. Absolute differences in the interior are generally less than 10 m/year, but can be locally larger because of the ionosphere streaking [39] or low correlation in Sentinel-1a/b. Along the coast, larger differences (<50 m/year) are found, which are caused by stronger weathering, ionospheric noise, and ongoing velocity changes.  
We also compare our mosaics (Figure 3) with the ENVEO's ([[http://www.enveo.at/](http://www.enveo.at/)]([http://www.enveo.at/](http://www.enveo.at/))) ice velocity mosaic products of Greenland published by [19] for year 2014-2015 and 2015-2016 from Sentinel-1 acquisitions. We find that the ENVEO's velocity maps are in good agreement with our Landsat-8 and Sentinel-1 mosaics. The distribution of the difference between the absolute ice speed from ENVEO minus the results from this study is shown in Figure 8. Typically, the mean differences between ENVEO and this study for year 2015-2016 are 0 \(\pm\) 11 and 0 \(\pm\) 7 m/year for Sentinel-1 and Landsat-8, respectively.  
Figure 7: Distribution of the difference between the velocity maps from Sentinel-1 and Landsat-8 for year 2014–2015. The velocity component \(v_{x}\) and \(v_{y}\) are displayed on the bottom and top panels, respectively.
## 3 Materials and Methods
### Sediment Sampling  
The studied stream originates in the highlands southwest of Elazig province and flows in a general northeast direction, ultimately discharging into the Keban Dam Lake. The sampling transect extended over approximately 35 km, from upstream station K1 (37\({}^{\circ}\)S 50\({}^{\prime}\)72.67\({}^{\prime\prime}\) E, 42\({}^{\circ}\)57\({}^{\prime\prime}\)51.2\({}^{\prime\prime}\) N) to downstream station K15 (37\({}^{\circ}\)S 53\({}^{\prime}\)08.42\({}^{\prime\prime}\) E, 42\({}^{\circ}\)72\({}^{\circ}\)59.8\({}^{\prime\prime}\) N) (Station K15). The sampling was conducted on a single day in July 2024, during a dry-weather period to ensure hydrological stability and minimise runoff influence. Stations K1 to K5 were located in rural and agricultural settings, K6 to K10 in peri-urban transitional zones, and K11 to K15 in urban-influenced areas near the outskirts of Elazig city. Along its course, the stream traverses rural, agricultural, and peri-urban areas, including cultivated fields and settlements located on the outskirts of Elazig city. Field observations during the sampling indicated an average stream width of 3-6 m and a depth ranging from 0.3 to 0.8 m, depending on local morphology and seasonal flow conditions. Although official discharge data are unavailable, the stream appears to have a moderate flow regime, fed by seasonal runoff, small tributaries, and potentially anthropogenic discharges such as untreated wastewater in certain downstream sections. This hydrological setting plays a crucial role in the transport and accumulation of pollutants, particularly metals, along the sediment continuum.  
Sediment samples were collected along the urban sediment stream (Figure 2) using a hand trowel. The starting point of the stream is outside the city centre. Fifteen samples of river sediment (0-5 cm from the surface) were collected. A portable global positioning device recorded the coordinates of every sampling location. The samples were placed in polyethene bags, transported to the laboratory, and kept in dark conditions below 4 \({}^{\circ}\)C until analysis. To minimise contamination, the hand trowel was cleaned with a detergent, rinsed, and dried before each use.  
Before being analysed, the samples were allowed to air-dry in the lab. Additionally, an agate mill was used to crush the samples to about 0.075 mm (200 mesh) size after they had been homogenised and divided into subsamples.
# An Image Segmentation Method Based on Improved Regularized Level Set Model
## 4 Experimental Results
### Segmentation of Medical Images
The other is that segmenting images of cerebral infarction is not a simple target and background segmentation problem. The medical images always incorporate various organizations and backgrounds. Thus, the traditional level set algorithms are not appropriate for brain images. Hence, the objective of the following portion of our experiments is to demonstrate the effectiveness of our IRLS-IS method on a complex cerebral infarction image. Our IRLS-IS model is compared with the DRLSE model [27] and the LSACM algorithm [51]. Figure 8 presents the results of the segmentation experiment, where the original cerebral infarction image can be downloaded at [[https://medpix.nlm.nih.gov/home](https://medpix.nlm.nih.gov/home)]([https://medpix.nlm.nih.gov/home](https://medpix.nlm.nih.gov/home)) (Picture Name: Posterior Reversible Leukoencephalopathy, PRES), and the image size is 369 \(\times\) 351. Figure 8b,c show that the DRLSE and LSACM algorithms cannot segment the cerebral infarction image. The segmentation results of our IRLS-IS method are demonstrated in Figure 8d. Since the IRLS-IS algorithm initializes the level set function to a constant value and breaks away from the dependence on the initialization position, our IRLS-IS method can analyze complex medical images very well. Figure 7: Comparisons of the four models in segmenting the heart image: (**a**) The original image; (**b**) The segmentation results of the LSACM model; (**c**) The segmentation results of the CEM model; (**d**) The segmentation results of the LHIF model; (**e**) The segmentation results of the IRLS-IS model.
## 3 Results and Discussion
### Performance Parameters (Merits) and Model Discrimination
The comparison of correlation indices, their features, and idiosyncrasies were first summarized in ref. [37]. It should be noted that adjusted (multiple) correlation coefficients should be used for model discrimination when the degrees of freedom are available (present case). What remains is the residual error. Its examination reveals whether the modeling is adequate or not. It is suitable for comparing linear and nonlinear methods; even statistical testing is possible (\(F\) test can be applied as a variance ratio test). Three variants of \(F\)-tests might be feasible (practicable) [38]. (i) Ratio of residual variances (sum of squares, if the degree of freedom is equal, or known):  
\[F_{c}=\frac{s_{1}^{2}}{s_{2}^{2}} \tag{7}\]where \(s_{1}^{2},s_{2}^{2}\) are the residual variances for model 1 and model 2 to be compared, respectively. The residual variance is to be calculated using the degree of freedom (\(v=N-p\)), where \(p\) is the number of parameters (two to four, \(cf.\), Figure 3):  
\[s^{2}=\frac{RSS}{v}=\frac{\sum_{l}^{N}(y_{i}-\hat{y}_{i})^{2}}{N-p} \tag{8}\]  
\(F_{c}\) should be compared to the tabulated \(F\) value: \(F_{tab}(v_{1},v_{2},0.95\) ), if the \(F_{c}\) is larger than the tabulated \(F\) value, then the null hypotheses should be rejected (\(H_{0}\), _i.e._, no significant differences are in the variances of the models). The power of \(F_{c}\) is \"small\", _i.e._, only large differences are detected (also called conservative estimation) so, a better option might be the following:  
(ii) Partial \(F\) test:  
\[F_{p}=\frac{(RSS(extended)-RSS(simpler))}{s^{2}(simpler)} \tag{9}\]  
where \(RSS\) is the residual sum of squares (nominator of Equation (8)). Strictly speaking, the partial \(F\) test has been developed to add in and remove variables from a linear model. Formally, linear, and nonlinear models can also be compared (if the numbers of parameters are the same) as if \"curvature\" was introduced in the model. \(F_{p}\) should be compared to the tabulated \(F\) value: \(F_{tab}(1,v_{2},0.95\) ), where \(v_{2}\) is the degree of freedom of the simpler model. (iii) The termination criteria were suggested for models having nonlinearity [39]. Two models termed 2 and 1 can be compared using the sequential probability ratio:  
\[SPR=\left[\frac{s_{2}}{s_{1}}\right]^{N} \tag{10}\]  
The \(SPR\) value must be compared with the numbers \(A\) and \(B\) from critical tables. If \(SPR\geq A\), then model 1 is to be accepted. If \(SPR\leq B\), then model 2 is to be accepted. If \(B<SPR<A\), then no decision can be made (more information is needed, _i.e._, more data and more measurements). For a double 95% significance level, an approximation is highly useful: \(A\approx 19\) and \(B\approx 0.0526\)[39].
## 4 Case Study: Analysis of a Device
### Application of the Methodology
#### 4.2.10 Phase 10: Data Analysis Using UFED Physical Analyzer  
In this phase, the data acquired with UFED Touch 2 were analyzed using the software UFED Physical Analyzer Version 7.32.0.16. The extraction was indexed without errors, and the results were consistent, including the credentials for services in the _cloud_, which can be used in applications with acquisition capacity specifically aimed explicitly for such services, such as the UFED Cloud Analyzer.  
Although the objective of this work is not to compare the capabilities of forensic suites, the acquired data were also analyzed with the AXIOM Examiner and XRY XAMN tools, all presenting consistent results from the perspective of forensic analysis. It should also be noted that data acquisitions can be carried out with one tool, and the analysis with another, being limited only to the format generated by the acquisition tool. Figure 18 shows the list of recovered files that were analyzed, including files deleted by the user, and that had not yet been overwritten. The files from instant messaging applications, such as WhatsApp, were also recovered.  
Figure 16: Possibility of physical acquisition of the SM-A105M/DS.  
Figure 17: Physical acquisition of the SM-A105M/DS.
# Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks
## 2 Main Contribution
### Link and Node State Monitoring  
In the OSPF protocol, the instability of links or nodes can trigger excessive LSA flooding and frequent routing updates, leading to routing oscillations. This situation not only results in a significant increase in routing overhead but also places a greater burden on the node's CPU. In severe cases, it may even lead to network storms, resulting in network failures. To address this issue, this section introduces link and node states to monitor the statuses of links and nodes in real time, thereby providing data support for subsequent algorithms.  
A network topology can be represented as \(G(N,E)\), where \(N\) is the set of network node nodes and \(E\) is the set of communication links. Assume there exists a link \(l(l\in E)\) between nodes \(u\in N\) and \(v\in N\), which can be denoted as: \(l=uv\). In this case, the link state is evaluated based on the absence of Hello packets in link \(l\) during the assessment period, as expressed in the following formula:  
\[\eta(uv)=\frac{T-t_{d}}{T} \tag{5}\]  
where \(\eta(uv)\) is the link state evaluation function, \(T\) is the assessment period, and \(t_{d}\) represents the time segment formed by the absence of Hello packets during the assessment period. This is defined as the difference between the expected time of receiving the next Hello packet and the actual last received time, as illustrated in Figure 3.  
Figure 2: OSPF-ICSA architecture diagram.  
Additionally, based on the aggregated average link state of all interfaces on node \(v\), the node state is assessed using the following formula:  
\[\gamma(v)=\frac{\sum_{w\in E}\eta(uv)}{|E|} \tag{6}\]  
where \(\gamma(v)\) is the node state assessment function, and \(|E|\) represents the cardinality of \(E\), which is the number of links associated with node \(v\).  
Building on this, this paper introduces weight parameters \(\alpha\) and \(\beta\) for a comprehensive assessment of link and node states. The specific evaluation formula is:  
\[\varphi(u)=\alpha*\eta(vu)+\beta*\gamma(u) \tag{7}\]  
where \(\varphi(u)\) is the comprehensive assessment function used to evaluate the overall state of the link between node \(v\) and its neighboring node \(u\). The link state is denoted by \(\eta(vu)\), indicating the condition of the link between nodes \(v\) and \(u\), while \(\gamma(u)\) represents the state of node \(u\). The parameters \(\alpha\) and \(\beta\) are weight coefficients for link and node states, respectively, which adjust their influence on \(\varphi(u)\).
# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 2 System Framework Design
### Data Optimization and Dimensionality Reduction  
Because most human actions occur within 1-3 s, to completely cover the entire human action cycle in this paper, we set the acquisition period of action as 4 s, and the millimeter-wave radar collects 15 frames of point cloud data per second. In our experiments, we found that the number of superimposed frames needs to be more significant to overcome the sparsity of millimeter-wave radar, and the number of superimposed frames is too large to lose a large number of spatial and temporal features. Because each action has 60 frames of data in a 4 s acquisition period, to ensure that the data can be evenly distributed, we need to ensure that the number of superimposed frames can be divided by 60. Ultimately, we choose to aggregate six frames of point cloud data into one frame of point cloud data to generate a more dense point cloud of the human body, as shown in Figure 3. After aggregation, a single action will generate 10 aggregated point clouds so that the entire human body action can be accurately depicted without significantly losing information due to the small number of frames. Since the variations in human actions in most daily activities are mainly manifested in the variations in human height (e.g., standing, bowing, sitting, squatting, etc.) and the variations in human limbs (e.g., boxing, kicking, etc.), these human actions can be accurately described in both the \(x\)-axis and \(z\)-axis dimensions. Thus, we round off the \(y\)-axis component to obtain the point cloud projection in the xz plane, as shown in Figure 4. This approach retains most of the critical information and dramatically reduces the amount of data.  
Figure 3: Point cloud of human body after superimposing 6 frames.
# Single Image Super-Resolution Method Using CNN-Based Lightweight Neural Networks
## 3 Proposed Method  
Although more complicated deep neural network models have demonstrated better SR performance than conventional methods, it is difficult to implement them on low-complexity, low-power, and low-memory devices, due to the massive network parameters and convolution operations of deeper and denser networks. In case of SR-DenseNet, it is difficult to implement this model to the applications for real-time processing even though its SR performance is superior to that of other neural network models. To address this issue, we considered two lightweight network structures at the expense of unnoticeable quality degradation, compared to SR-DenseNet. The purpose of the proposed two lightweight neural networks for SISR is to quadruple the input images the same as SR-DenseNet. Firstly, SR-ILLNN learns the feature maps, which are derived from both low-resolution and interpolated low-resolution images. Secondly, SR-SLNN is designed to use only low-resolution feature maps of the SR-ILLNN for a few more reducing the network complexity.
## 3 Results and Discussion  
### Particle Size Distribution  
No significant differences were found comparing sieve results performed by dry or wet procedures. For both procedures, the material that passed the No. 200 sieve (0.076 mm openings) was 89% and 87%, for the dry and wet method, respectively. Despite the similar results obtained, based on practical experience, it is recommended to use the wet method to determine the fine fraction. It should be noted that the dried fine particles tend to agglomerate, complicating the separation process, and thus extending the washing time for several hours.
## 3 Results and Discussion  
### Q-System Method  
To estimate the support system it is necessary to establish an equivalent diameter for the tunnel, \(\mathrm{D_{e}}\), which is the width/height of excavation divided by the ESR (excavation support ratio), which for transversal tunnels can be established at 1.3, so \(D_{e}\) = 5.0. For the central tunnel, a value of ESR of 1.0 has been estimated, obtaining an equivalent diameter for the tunnel, \(D_{e}\) = 6.5. The evaluations of rock masses in sandstone and shale rock masses by using the Q-system and the GSI are summarized in Table 4. By applying Equation (1), in sandstone formation a value of Q of 1.27 is obtained, while in the shale rock mass the value of Q is 0.23.  
The suggested support systems based on the Q-system for the central tunnel and the transversal tunnels are given in Table 5 following the recommendations proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (1971) using an equivalent diameter of 5.0 and 6.5 for transversal tunnel and central tunnel, respectively.
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Hybrid VLC/RF Systems  
About 80% of all wireless data traffic is thought to be generated by indoor applications. Therefore, we envision VLC as a complementary technology that provides various benefits over RF, particularly in indoor situations, such as utilizing the existing lighting infrastructure to construct a low-cost system that enables ultra-high data rate [156]. While VLC has several distinct benefits, it also has a few downsides that might greatly hinder the efficiency of the VLC system if they are not accounted for. In contrast to RF, which may make good use of the possibilities presented by NLOS communication, the primary difficulty with VLC is the considerable performance decrease that occurs when LOS connectivity is unavailable. The uplink transmission situation in VLC systems is inefficient because of the undesired irradiance, unlike the downlink transmission situation. In this regard, creating a hybrid VLC/RF heterogeneous network (HetNet) that could integrate the benefits of both systems to boost users' mobility and offer ubiquitous coverage [157] is a potential route to solve these shortcomings. Hybrid RF/VLC networks have gained a lot of interest as of late due to the fact that they are a practical option that can boost indoor communication efficiency. Furthermore, NOMA can improve connectivity and spectral efficiency in these hybrid networks.
# Dynamic Proxemic Model for Human-Robot Interactions Using the Golden Ratio
## 3 Materials and Methods
### Dynamic Comfort and Safety Zone Distribution
* **Public zone** (3.08 m or more): Robots at this distance have minimal social interactions with humans, which correlates with Hall's public zone and partially with the social zone. A robot at this distance can maintain its normal navigation speed. The calculation of the variable zone sizes _zone size_\(i\) is based on the calculation of the safety zone \(d_{\textit{safety}}\) calculated in Equation (1), where the variability comes in the form of changing relative velocity \(v_{\textit{rel}}\). The default safety zone is equal to 0.45 m. All subsequent comfort zones are variable in the same ratio according to Equation (2), where \(\varphi\) is the constant of the golden ratio (1.618) and \(i\) represents the order of the comfort zone from the safety zone. \[zone\;size_{i}=d_{\textit{safety}}\cdot\varphi^{i} \tag{2}\]
## 2 The Principle of Angle Measurement Based on Direct THG
### The Basic Principle of Angle Dependence of Direct THG in Centrosymmetric Crystal
However, as the crystal is rotated at an angle \(\Delta\theta\) away from the phase-matching angle \(\theta_{\rm m}\) shown in Figure 2c, d, the transmitted THG will be weakened because the generated THWs in different positions are reduced by the destructive interference where the \(\Delta k(\theta_{\rm m}+\Delta\theta)\) and \(\Delta n(\theta_{\rm m}+\Delta\theta)\) are not equal to zero. For the birefringent phase matching, there are three types for both the negative uniaxial crystal and the positive uniaxial crystal. Here, we only discuss the type I phase-matching of positive uniaxial crystal and the type II phase-matching of negative uniaxial crystal, taking into consideration that the high-efficiency THG in the centrosymmetric crystals has been demonstrated in the literature. The concept is similar in both the type I phase-matching of positive uniaxial crystal and the type II phase-matching of negative uniaxial crystal. The only difference is that the phase-matching angle \(\theta_{\rm m}\) should meet the condition of \(n_{\rm 1o}+n_{\rm 1o}+n_{\rm 1e}(\lambda_{1},\,\theta_{\rm m})=n_{\rm 3e}( \lambda_{3},\,\theta_{\rm m})\), and the incident fundamental wave should contain both the o-wave and the e-wave for the THG process. Combined with Equation (1) and the concept of phase-matching, the calculation of \(\theta_{\rm m}\) can be easily induced, and they are displayed in Equation (2) and Equation (3). Equation (2) is for the type I phase-matching, and Equation (3) is for the type II phase-matching. It should be noted that Equation (3) only shows an implicit relation, which should be solved by numerical methods. \[\theta_{\rm m}=\arcsin\left(\sqrt{\frac{\left[N_{\rm o}(\lambda_{3})\right]^{- 2}-\left[N_{\rm o}(\lambda_{1})\right]^{-2}}{\left[N_{\rm e}(\lambda_{1}) \right]^{-2}-\left[N_{\rm o}(\lambda_{1})\right]^{-2}}}\right)\,\text{Type I }\left(\text{Positive crystal}\right) \tag{2}\]  
\[\left(\frac{\cos^{2}\theta_{\rm m}}{\left[N_{\rm o}(\lambda_{3}) \right]^{2}}+\frac{\sin^{2}\theta_{\rm m}}{\left[N_{\rm e}(\lambda_{3})\right] ^{2}}\right)^{-1/2}=\frac{1}{3}\left\{2N_{\rm o}(\lambda_{1})+\left(\frac{ \cos^{2}\theta_{\rm m}}{\left[N_{\rm o}(\lambda_{1})\right]^{2}}+\frac{\sin^{ 2}\theta_{\rm m}}{\left[N_{\rm e}(\lambda_{1})\right]^{2}}\right)^{-1/2}\right\} \tag{3}\]  
Generally, a small incident angle deviation \(\Delta\theta\) can cause a large change in the THG intensity, which is especially beneficial for the small-angle measurement. Next, we propose to utilize the angle dependence of direct THG for angle measurement. Two measurement methods will be explained in detail. Figure 1: (**a**) The schematic of THG by \(\mathbf{\chi}^{(3)}\) in centrosymmetric crystal; (**b**) The refractive index ellipsoid of uniaxial crystal.