esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Optimisation for Cooperative Target Tracking with Passive Mobile Sensors
## 3 Reward Functions
### Remarks on the Reward Functions  
Both the Expected RID and the Determinant of FIM involve approximations: the former using linearisation, the latter using the predicted target state in place of the true state. The following analysis highlights the difference between the two reward functions after approximation.  
To compare the performance difference of the reward functions, we fix the location of Sensor 1 at \(S_{1}=(50,300)\), and plot the reward of one step ahead versus the location of Sensor 2. In the simulation, \(\mathbf{P}\) is set to be a constant, \(\sigma_{1}=0.175\) (rad) and \(\sigma_{2}=0.2618\) (rad). The contour plots for the Expected RID and Determinant of FIM are in Figure 2a,b, respectively.  
Figure 2: Reward function performance comparison—Normalised values of reward functions for the location of sensor 2 when the locations of sensor 1 and target are fixed. (**a**) expected RID; (**b**) determinant of FIM; (**c**) trace of FIM. The normalised value of colorbar indicates the relative amount of Fisher information gained by the sensor system.  
In view of these figures, we can make the following observations:  
* While Figure 2a shows an increasing reward as Sensor 2 approaches the target, there is no information change observed when Sensor 2 moves around the target in a circle. This indicates that the Expected RID changes only with respect to the distance from individual sensors to the target rather than with respect to the relative locations of the two sensors. This is in contrast to the value of the Determinant of FIM shown in Figure 2b.
* Figure 2b shows a straight line across the two sensor locations, where the values of FIM are singular. This indicates a subspace corresponding to the singular points on which the target state is unobservable. On the other hand, neither the expected RID nor the trace of the FIM take this singular subspace into account.
* We observed that the contour plot of the Expected RID in Figure 2a is virtually the same as that of the Trace of FIM shown in Figure 2c. The latter is independent of the two sensor locations as long as their distances to the target remain unchanged and this is verified by (23). This suggests that the Expected RID does not preserve the observed information structure with respect to the two sensor locations after linearisation.  
Note that we fix the location of sensor 1 in Figure 2 for a clear visualization of the difference in comparison, given that both sensors are movable in the sensor trajectory optimisation.
## Appendix A  
**Dataset--Legend**:  
* _ISO_ stands for ISO-3166 Country Code ([[https://www.iso.org/obp/ui/#search&3166](https://www.iso.org/obp/ui/#search&3166)]([https://www.iso.org/obp/ui/#search&3166](https://www.iso.org/obp/ui/#search&3166)));
* Rapid Mapping (R) Activation Code ([[https://emergency.copernicus.eu/mapping](https://emergency.copernicus.eu/mapping)]([https://emergency.copernicus.eu/mapping](https://emergency.copernicus.eu/mapping)));
* _BB_TL_LON_ and _BB_TL_LAT_ is the couple of coordinates (LONgitude, LATitude) of the Bounding Box (BB) for the Top Left (TL) corner;
* _BB_BR_LON_ and _BB_BR_LAT_ is the couple of coordinates (LONgitude, LATitude) of the Bounding Box (BB) for the Bottom Right (BR) corner;
* _PRE Date_ and _POST Date_ stand for Pre Fire acquisition Date, and Post fire acquisition Date, respectively. They specify the date in which the Sentinel-2 satellite registered information related to the specified bounding box, which is compliant with the constraints of availability and cloud coverage specified in Section 2.2. * _FOLD_ indicates in which fold the product belongs to, according to the colors represented in Figure 1.  
Appendix B Dataset Land use--Legend: Land use details for the areas of interest considered in the dataset Tables A2 and A3. They are specified in the grading maps cartouche, using the hectare (ha) as unit of measurement. The land use types are specified as follows (Land use types used in this work refer to the official Copernicus EMS notation, available at [[https://emergency.copernicus.eu/mapping/ems/domains](https://emergency.copernicus.eu/mapping/ems/domains)]([https://emergency.copernicus.eu/mapping/ems/domains](https://emergency.copernicus.eu/mapping/ems/domains))):  
* Residential/Industrial: urban areas involving residential or industrial buildings;
* Arable land: also specified as cropland, non-irrigated arable land areas, permanently irrigated land, and rice fields;
* Grassland: natural grassland;
* Forests: broad-leaved forest, coniferous forest, mixed forest;
* Heterogeneous agricultural areas: annual crops associated with permanent crops, complex cultivation, land principally occupied by agriculture, agro-forestry areas;
* Open spaces with little or no vegetation: beaches, dunes, sand plains, bare rock, sparsely vegetated areas, and glaciers;
* Pastures: ground covered with grass or herbage, used or suitable for the grazing of livestock;
* Permanent Crops: vineyards, fruit trees and berry plantations, olive groves;
* Shrub and/or herbaceous vegetation association: natural grassland, moors, and heathland, Sclerophyllous vegetation, transitional woodland shrub;
* Inland wetlands: inland marshes, peatbogs;
* Woodland shrub: transitional woodland shrub. For sake of space the table is split in two parts: the first reports land use attributes from \"Arable land\" to \"Open spaces with little or no vegetation\", the other from \"Pastures\" to \"Woodland\".
## 6 Conclusions  
### Theoretical Contribution  
This study offers meaningful theoretical contributions by extending the TAM into the cultural domain of privacy concerns related to ARSGs. While prior studies have widely applied the TAM to explain technology adoption [11, 45, 48], few have examined how cultural differences in privacy sensitivity interact with the TAM's constructs. Our findings reveal that privacy concerns do not exert uniform effects across cultures but instead operate in culturally specific ways that reshape core TAM relationships.  
In the U.S., consistent with previous research, privacy concerns significantly dampened attitudes toward ARSGs, ultimately reducing behavioral intention to adopt the technology [47, 48]. However, in South Korea, despite higher overall privacy concerns, the negative influence on attitudes was minimal, and behavioral intention remained strong. This demonstrates that privacy concerns may function more as a negotiable factor than a fixed barrier in collectivist societies, particularly when social conformity and perceived societal benefits are involved [15, 42]. These findings confirm that the TAM's predictive power is deeply shaped by cultural values such as collectivism, technological optimism, and the regulatory context.  
Additionally, this study builds on [PERSON] et al. [42] by empirically validating the call to incorporate cultural moderators--like privacy concerns--into the TAM, offering a more context-sensitive framework. Our research also suggests that perceived usefulness consistently drives behavioral intention across cultures, but ease of use may play a smaller role in emerging technologies like ARSGs, where familiarity remains limited.  
By integrating privacy concerns into the TAM within a cross-cultural context, this study advances theory by demonstrating how sociocultural norms and legal frameworks reshape the cognitive evaluation of new technologies. These insights offer a valuable foundation for future cross-cultural studies on privacy-sensitive technologies and call for a more dynamic, culturally informed application of the TAM.
# mmSight: A Robust Millimeter-Wave Near-Field SAR Imaging Algorithm
## 3 System Design
### Data Capturing
#### 3.2.1 FMCW Signal Model  
The radar part is responsible for signal transceiving. The chirp signals transmitted by the FMCW (Frequency-Modulated Continuous Wave) radar used in this paper are:  
\[m(t)=A\cos\left[2\pi(f_{0}t+Kt^{2}/2)+\varphi\right] \tag{1}\]  
The transmitted signal is reflected from the target after a round-trip delay \(\tau\), at which point the received signal is:  
\[b(t)=A\cos\left[2\pi(f_{0}(t-\tau)+K(t-\tau)^{2}/2)+\varphi\right] \tag{2}\]  
Figure 2: System Model (The motion trajectory of SAR in this paper is shown on the top; the synthesized virtual array is shown on the left below, and the monostatic is shown on the right. The interconnection between them is indicated by arrows).  
Then, within the radar, the transmitted and received signals are mixed and low-pass filtered to produce an IF signal, which can be expressed as:  
\[r_{I}(t)=A\cos\left[2\pi(f_{b}t+f_{0}\tau-K\tau^{2}/2)\right] \tag{3}\]  
where, \(f_{b}=K\tau\) is the frequency of the IF signal, and the last term \(K\tau^{2}/2\), as the residual video phase, is generally neglected due to its relatively small value in near-field conditions. In addition to transmitting one way I (In-phase) signal, the radar also transmits one way Q (Quadrature) signal that is orthogonal to the I signal. Thus, the obtained IF IQ (In-phase and Quadrature) signal can be expressed as:  
\[r(t)=r_{I}(t)+jr_{Q}(t)=Ae^{2\pi(f_{b}t+f_{0}\tau)}. \tag{4}\]
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 4 Experimental Evaluation
### Comparison Study and Discussion  
Within this section, we conduct a comparative analysis of the ACE-GCN alongside other cutting-edge models using the NTU-RGB+D 60, NTU-RGB+D 120, and NW-UCLA datasets. The empirical findings presented in Tables 2-4 illustrate that the ACE-GCN can attain a Top-1 identification accuracy of 92% (X-Sub) and 96.3% (X-View) for the NTU RGB+D 60 dataset. The ACE-GCN achieves a Top-1 recognition accuracy of 88.6% (X-Sub) and 90.1% (X-Set) for the NTU RGB+D 120 dataset. The ACE-GCN achieves 96.6% Top-1 recognition accuracy for the NW-UCLA dataset.  
The ACE-GCN outperforms the ST-GCN in terms of Top-1 recognition accuracy, achieving a 10.5% improvement on the X-Sub benchmark and an 8.3% improvement on the X-View benchmark of the NTU RGB+D 60 dataset. The parameter count is decreased by 1.2 million. Thanks to the adaptive channel topology and the multi-branch aggregation method formed by Edgeconv under the refined attention mechanism, the ACE-GCN effectively converges important global and local information in the spatial and temporal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Param.**} & \multicolumn{2}{c}{**NTU RGB+D 60**} \\  & & **X-Sub (\%)** & **X-View (\%)** \\ \hline ST-GCN [5] & 3.1 M & 81.5 & 95.1 \\
2s-AGCN [6] & 9.94 M & 88.5 & 94.5 \\ SGN [32] & 0.69 M & 89 & 96.5 \\ Shift-GCN [25] & 2.76 M & 90.7 & 96.2 \\ MS-G3D [31] & 6.4 M & 91.5 & 96.6 \\ MST-GCN [33] & 12 M & 91.5 & 96.1 \\ SMotif-GCN [34] & - & 90.5 & 95.8 \\ DD-GCN [35] & - & 88.9 & 95.7 \\ EfficientGCN-B4 [26] & 1.1 M & 91.7 & 94.83 \\ SARGCN [36] & 1.09 M & 88.9 & 96.3 \\ ACE-GCN (ours) & 1.9 M & 92 & 95.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Top-1 classification accuracy comparisons for the NTU RGB+D 60 dataset.  
Figure 10: Comparison of the recognition accuracy rate between the ACE-GCN and baseline across action classes in the NTU-RGB+D 60 X-sub dataset. The red dash line indicates the accuracy rate of the ACE-GCN in each action class, and the blue bar graph indicates the accuracy rate of the baseline model in each action class.  
domains, thereby achieving the comprehensive capture and fusion of channel and spatio-temporal features. Therefore, the ACE-GCN's human action recognition Top-1 accuracy for the NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA datasets is significantly better than that of other cutting-edge methods. In addition, the ACE-GCN has fewer parameters than other cutting-edge models and achieves better performance on the four benchmarks.
# Convex-Hull Algorithms: Implementation, Testing, and Experimentation
## 2 Preliminaries
### Definitions  
Recall that in the two-dimensional convex-hull problem we are given a multiset \(S\) of points, where each point \(p\) is specified by its Cartesian coordinates \((p.x,p.y)\), and the task is to compute the _convex hull_\(\mathcal{H}(S)\) of \(S\) which is the boundary of the smallest convex set enclosing all the points of \(S\). More precisely, the goal is to find the smallest set--in cardinality--of vertices describing the convex hull, i.e., the output is a convex polygon \(P\) covering all the points of \(S\). In particular, the set of vertices in \(P\) is a subset of the points in \(S\). A normal requirement is that in the output the vertices are reported in the order in which they appear when traversing around the convex hull. Throughout the paper, we use \(n\) to denote the number of points in the input \(S\) and \(h\) the number of vertices in the output \(P\).  
A point \(q\) of a convex set \(X\) is said to be an _extreme point_ if no two other points \(p\) and \(r\) exist in \(X\) such that \(q\) lies on the line segment \(\overline{pr}\). So, the vertices of \(P\) describing the convex hull \(\mathcal{H}(S)\) are precisely the extreme points of the smallest convex set enclosing the points of \(S\).  
Let \(S\) be a multiset of points. Of these points, a point \(p\)_dominates_ another point \(q\) in the north-east direction if \(p.x>q.x\) and \(p.y>q.y\). Since the requirement is to be strictly larger in both \(x\) and \(y\), then for a pair of points as \((1,1)\) and \((1,2)\), we have that neither of them dominate the other in the north-east direction. A point \(p\) is _dominant_ in the north-east direction if it is not dominated in this particular direction by any other point in \(S\). That is, the first quadrant centred at \(p\) has no other point in it. Analogous definitions can be made for the south-east, south-west, and north-west directions. A point is said to be _maximal_ among the points in \(S\) if it is dominant in one of the four directions. Clearly, every extreme point is maximal (for a proof, see [14]), but the opposite is not necessarily true.  
An algorithm operates _in place_ if the input is given in a sequence of size \(n\) and, in addition to this, it uses \(O(1)\) words of memory. An in-place convex-hull algorithm (see, for example, [15]) partitions the input into two parts: (1) The first part contains all the extreme points in clockwise or counterclockwise order of their appearance on \(P\) and (2) the second part contains all the remaining points that are inside or on the periphery of \(P\). Further, an algorithm operates _in situ_ if it uses \(O(\lg n)\) words of extra memory. For example, quicksort[16] is an in-situ algorithm if it is implemented recursively such that the smaller subproblem is always solved first.
# A New Parameter-Free Slope Unit Division Method That Integrates Terrain Factors
## 2 Methods and Study Area  
### Study Area  
Dongchuan District, under the jurisdiction of Kunming city, Yunnan Province, is located on the northern edge of the Yunnan-Guizhou Plateau (China). Its longitude ranges from 102\({}^{\circ}\)47\({}^{\prime}\) E to 103\({}^{\circ}\)18\({}^{\prime}\) E, and its latitude ranges from 25\({}^{\circ}\)57\({}^{\prime}\) N to 26\({}^{\circ}\)32\({}^{\prime}\) N, covering a total area of approximately 1858.79 km\({}^{2}\). Its specific location is shown in Figure 1. The region features high mountains and deep valleys, with dramatic differences in elevation. Influenced by the Xiaojiang fault zone, it is a seismic zone with fractured surface rocks and poor stability. Climatically, this area has a subtropical monsoon climate, contributing to a fragile geological environment. The area is rich in mineral resources, but excessive mining and cultivation have severely depleted forest resources. Owing to its unique geological and topographical features, coupled with long-term human activities, Dongchuan District frequently experiences natural disasters such as landslides, mudslides, and mountain collapses, which pose significant threats to ecological security and socioeconomic development.  
Figure 1: Location and DEM map of Dongchuan District.
## 4 Discussion
### AF and AFB Microbial Community at the End of the Start-Up  
_Frimicutes_ dominate in the overwhelming majority of microbial communities in DF systems of various wastes, including those processing CW [21], with _Clostridia_ being the largest producer, and _Thermoanoaterobacterium_ being an important representative of thermophilic communities [17; 47].  
Interestingly, _Actinobacteria_ are not so common in DF systems, while relatively recently it was shown that their representative _Streptomyces rubiginosus_ can produce hydrogen from sugarcane molasses [48].  
During the course of DF of CW, _Lactobacillus_ accounted for 50% of the microbial community [49]. Even though the hydrogen yield when using _Lactobacillus acidophilus_ as biohydrogen producer was rather low (2 mol/mol lactose), the DF of CW was accompanied by the accumulation of valuable metabolites in the form of organic acids, including pyruvate, propionate, acetate, lactate, formate, and butyrate [24].  
During the start-up of the reactor for the combined DF of vegetable and fruit waste and CW, _Lactobacillus_ constituted the overwhelming part (72%) of the microbial community with a minor content of _Bifidobacterium_ (6%). However, after adaptation to a high concentration of lactose, _Bifidobacterium_ (85.4%) began to dominate in the microbial community, almost eliminating _Lactobacillus_ (0.97%) [50].  
The dominance of _Bifidobacterium_ and _Clostridium_ was observed during DF of starch, where _Bifidobacterium_ represented 40-60% of the total microbial population, while _Clostridium_ accounted for about 40% [51]. The authors hypothesized the existence of a symbiotic relationship, in which _Bifidobacterium_ members first break down starch into monomers, which are then used by _Clostridium_ species to produce biohydrogen [51].  
We have also noted that at the end of the start-up period, the relative number of methanogenic archaea in AFB (0.1%) was lower than in AF (0.4%). This data correlated well with a higher specific hydrogen yield (30% more in AFB) (Figure 6) and VHPR (32% more in AFB) (Figure 5) at the end of the start-up period. At the same time, the hydrogen content in biogas in AFB was 31% lower than in AF. Thus, better mixing in the reactor increased the efficiency of biohydrogen production from CW, which was explained by better mass transfer and less formation of stagnant zones where hydrogen-scavengers such as methanogens could develop. At the same time, the hydrogen content in biogas decreased with a higher recirculation rate.
# Exploiting Online Services to Enable Anonymous and Confidential Messaging
## 5 Discussion
The proposed PoC also allows protection against specific attacks such as replay attacks, Chosen-Ciphertext Attacks (CCAs) [54, 55, 56, 57, 58], and Chosen-Plaintext Attacks (CPAs) [54, 55, 55, 59, 60, 61]. Although an attacker without access to the channels' _password_ and _salt_ is unable to insert new messages in a channel, the Dolev-Yao intruder model assumes that previous messages can be sent, performing a replay attack. The proposed PoC adopts the per-message use of a securely generated random value that can only be used once (_nonce_). Each user stores previously exchanged _nonce_ values, rejecting messages for which this value is repeated. Moreover, the _nonce_ is encrypted when exchanged, and, thus, it is assumed that it is impracticable for the attacker to alter the _nonce_, making replay attacks infeasible. In a CCA, the attacker must be able to request the decryption of ciphertext of their own choosing. In a CPA, the attacker must obtain the encryption of plaintexts of their own choosing. The only way to generate a valid ciphertext, or to decrypt it, is by having the correct _password_ and _salt_, which are assumed to have been securely exchanged between users. Moreover, the used KDF and SRG are assumed secure, plus the adoption of a fresh IV for each per message ensures that these attacks do not break the system.
## 4 Numerical Results  
### Verification of Finite Element Model  
To verify the finite element model, the experimental and analytical horizontal load-slip curves are compared on Figure 10. The first and last horizontal loading cycles of the actual test are plotted together with the analytical horizontal load-slip (+direction) curve. The averaged maximum horizontal load from experimental result is 3.70 kN as mentioned above, and the maximum horizontal load from the FEA is 2.94 kN, which represents an error below 4.3%. The analytical result shows that the relative slip at the maximum load is reached earlier than in actual test. The difference in the relative slip is caused by the occurrence of large slip in the actual test when the horizontal load was applied. The error in the maximum horizontal load and relative slip seems to be caused by the manufacturing tolerance of the ceramic friction material and stainless-steel plate. The finite element model employs the idle conditions, which lead to more conservative results. In addition, the ceramic is modeled with an elastic condition. Nevertheless, the error is small enough to assume the finite element model describes accurately the actual test.  
Figure 10: Horizontal load–slip relationships (only +direction, 10 kN vertically loaded).  
Figure 9: Interactions and load conditions in FEA model.  
Figure 11 shows the deformation of the sliding plate. Comparing Figures 5d and 11, it can be seen that the sliding plate is deformed by the ceramic friction material as the center point of the specimen moves away in the horizontal direction, which is similar in the analysis result for all of load cases. Accordingly, the finite element model is accurate enough for investigating the frictional behavior of the ceramic friction material.