esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Opportunities and Challenges  
### Intelligent Cluster  
In the future integrated network, the terminals, base stations, core networks, etc., will evolve towards intelligence and become intelligent network elements. Collaboration among multiple intelligent entities can be aimed at a single task or multiple tasks. Its connotation includes task decomposition and combination, goal analysis and modeling, model training and inference, parameter iteration and sharing, etc. At the same time, for tasks specific to the physical world, resource scheduling and performance optimization are carried out for integrated computation and fusion to ensure the reliability of task execution.  
Traditional mobile communication networks also utilize collaboration mechanisms, but these mechanisms are focused on connected collaboration with limited scope, minimal data volume, and collaboration restricted to same-level network elements. However, in the integrated computation network facing AI, multi-intelligent agent collaboration involves a large scale of collaboration, interactive data volume, and collaboration cost, including the possibility of multi-level horizontal and vertical collaboration. As a result, existing collaboration mechanisms cannot be reused. The challenges include selecting collaboration objects, mining and extracting collaboration information, and determining collaboration modes and mechanisms while controlling collaboration costs.
# Quantum Privacy Query Protocol Based on GHZ-like States
## 4 Functional Comparison and Security Analysis  
In the previous sections, we focused on the content of our protocol and the detailed process with flowcharts. Now, we analyze the functional aspects and security aspects of the protocol. First, we compare the functions of our protocol with some privacy query schemes. Then, we proved that our protocol can resist various attacks.
## 6 Experimentation and Performance Evaluation  
The main functionality of to the developed system is to make 3D reconstructions of a confined dynamic underwater environment while giving accurate metrics based only on acquired images (\(1024\times 768\) pixels). In the validation and evaluation of the developed system accuracy, we use examples of simple geometric shapes with known dimensions. The evaluation tests were made in a specific water pool built in our laboratory of a width of 3 meters. Tests could be made on the pool itself or throughout different tubes (forms) introduced in the pool. In each experimental test, dozens of camera positions are used for evaluation. The obtained pairs of images are processed and the estimated distances are compared to the real ones corresponding to these known camera positions. In Figure 14, we present an example of simulation results performed in the hexagonal pool. The lasers pointing at the cross section of the object are projected in the stereo images (Figure 14a). The binary stereo images, obtained using the thresholding expression, highlights the laser projections with respect to the background (Figure 14b). Only the defined image-regions are covered in order to extract unique 2D coordinates of the laser spot on each region. Laser projections belonging to the same image region are matched (Figure 14c) and sent to the triangulation process to determine the 3D distances corresponding to each laser (Figure 15). The mean error scale of distance measurements, for the different objects used in experimentation, is of 2.5%. For the hexagonal object, the mean square error is about 5.45 mm\({}^{2}\) which is quite satisfactory regarding bad resolution of underwater images and light refraction effects.  
Figure 14: Simulation results (**a**) stereo RGB images; (**b**) Segmented images; (**c**) stereo matching.  
Figure 15: Simulation results: 3D distances measurements and distance error.  
Error between estimated distances and real measurements could be caused either because of calibration error, XSG blocks optimization or because of laser spot center detection errors. Analyses of the spot center accuracy detection influence on 3D distance results were also performed. Figure 16 illustrates distance measurements with respect to the gravity-center positions for different real measurements (up to 480 cm). To be able to evaluate the effect of the error on the spot center detection (in terms of pixel position) on the resulting distance, we can use the curve of Figure 16. This curve gives the distance function of spot center position (polar coordinate \(r=\sqrt{x^{2}+y^{2}}\)). For a reference spot distance, we can determine the distance error by varying the pixel coordinates. Table 2 gives an example of error evaluation for three points with a variation of \(\Delta x\) = 1 pixel and \(\Delta x\) = 10 pixels. For distances measures up to 480 cm, the maximum relative distance error is about 1.3%.
# Remote Monitoring and Diagnosis for Building Maintenance Units Based on Internet of Things System
## 2 System Design and Application
### Overall System Architecture Design
By introducing a predefined diagnostic rule set \(R_{diag}=\{r_{1},r_{2},\cdots,r_{m}\}\), each rule \(r_{j}\) is associated with a specific fault phenomenon \(S_{j}\in\{s_{1},s_{2},\cdots,s_{m}\}\) and defines a mapping from the real-time data space to a Boolean output. A rule \(r_{j}\) is activated, and the corresponding fault phenomenon \(s_{j}\) is triggered if and only if the data vector \(D_{t}\) satisfies the logical conditions defined by \(r_{j}\). These rules reflect domain expert knowledge, device operating safety thresholds, or anomaly patterns learned from historical data. Specifically, under a particular real-time data state \(D_{t}\), the rule \(r_{j}\) can identify and activate the corresponding fault phenomenon \(s_{j}\) by satisfying its logical conditions, i.e., \(r_{j}\left(D_{t}\right)=1\), as shown in \[L_{n}:D_{p}^{\prime}(t)\xrightarrow{\Pi_{\text{TCP/IP},\Pi_{\text{SC/WIFI}}} }D_{dp}(t) \tag{2}\]1 The result of this process is the formation of a set of active fault phenomena \(S_{active}\left(t\right)\), which contains all the fault phenomena corresponding to the triggered rules. This set can be expressed using Formula (8). \[L_{n}:D_{p}^{\prime}(t)\xrightarrow{\Pi_{\text{TCP/IP},\Pi_{\text{SC/WIFI}}} }D_{dp}(t) \tag{2}\]2

\[L_{n}:D_{p}^{\prime}(t)\xrightarrow{\Pi_{\text{TCP/IP},\Pi_{\text{SC/WIFI}}} }D_{dp}(t) \tag{2}\]3 Schematic diagram of the correlation between operational data and fault phenomena. The schematic in Figure 3 provides a visual representation of the decision result matrix of rule set \(R_{diag}\) under different real-time data inputs, reflecting the process of identifying system-level fault symptoms from real-time data. This process not only achieves the recognition of fault phenomena but also provides a foundation for subsequent fault cause analysis. Upon identifying an active fault phenomenon \(S_{j}\in S_{active}(t)\), the next step is to infer the most likely cause from the predefined set of potential root causes \(F=\{f_{1},\cdots,f_{z}\}\), as outlined in \[L_{n}:D_{p}^{\prime}(t)\xrightarrow{\Pi_{\text{TCP/IP},\Pi_{\text{SC/WIFI}}} }D_{dp}(t) \tag{2}\]4

Due to the often ambiguous nature of the relationship between phenomena and causes, which may exhibit \"one-to-many\" or \"many-to-many\" characteristics, probabilistic methods are introduced to quantify the inference process. The methodology employs Bayesian inference, integrating statistical correlations extracted from historical data or expert knowledge. The strength of the relationship between phenomena and causes is modeled as conditional probabilities. Formally, a phenomenon-cause probability matrix I is constructed, where its dimensions are \(Z\times M\), corresponding to \(Z\) causes and \(M\) phenomena. This matrix is explicitly defined in Formula (9):
# Multi Ray Model for Near-Ground Millimeter Wave Radar
## 3 Four-Ray Model for Near-Ground Radar
\begin{split}&\frac{P_{r}(\text{Four rays})}{P_{r}(\text{LOS})}=\frac{\left| \widetilde{E}_{Total}\right|^{2}}{\left|\widetilde{E}_{\text{LOS}}\right|^{2}} \cong\Big{|}1+\Big{(}2+\rho_{\text{ground}}\cdot e^{-j\frac{4\pi f}{c}\frac{ h_{\text{radar}}h_{\text{target}}}{d}}\Big{)}\cdot\rho_{\text{ground}}\cdot e^{-j \frac{4\pi f}{c}\frac{h_{\text{radar}}h_{\text{target}}}{d}}\Big{|}^{2}\\ &=1+4\Big{|}\rho_{\text{ground}}\Big{|}^{2}+\Big{|}\rho_{\text{ ground}}\Big{|}^{4}+4\Big{[}1+\Big{|}\rho_{\text{ground}}\Big{|}^{2}\Big{]} \text{Re}\bigg{\{}\rho_{\text{ground}}\cdot e^{-j\frac{4\pi f}{c}\frac{h_{ \text{radar}}h_{\text{target}}}{d}\bigg{\}}\bigg{\}}\\ &+2\text{Re}\bigg{\{}\rho_{\text{ground}}\!^{2}\cdot e^{-j\frac{8 \pi f}{c}\frac{h_{\text{radar}}h_{\text{target}}}{d}}\bigg{\}}\end{split}  \tag{7}\]  
An interesting case is that of grazing incidence, when the angle \(\theta_{0}\) becomes close to 90 degrees. The reflection coefficients of both polarizations then approach:  
\[\rho_{\text{ground}}\cong \begin{cases}-1=e^{j\pi}&TE\\ +1=e^{j0}&TM\end{cases}  \tag{8}\]  
and Equation (7) can be approximated by:  
\[ \begin{split}&\frac{P_{r}(\text{Four rays})}{P_{r}(\text{LOS})}= \frac{\left|\widetilde{E}_{Total}\right|^{2}}{\left|\widetilde{E}_{\text{LOS}} \right|^{2}}\cong 6\mp 8\text{Re}\bigg{\{}e^{-j\frac{4\pi f}{c}\frac{h_{\text{radar}}h_{ \text{target}}}{d}\bigg{\}}+2\text{Re}\bigg{\{}e^{-j\frac{8\pi f}{c}\frac{h_{ \text{radar}}h_{\text{target}}}{d}\bigg{\}}\bigg{\}}\\ &=6\mp 8\cos\Big{(}\frac{4\pi f}{c}\frac{h_{\text{radar}}h_{\text{ target}}}{d}\Big{)}+2\cos\Big{(}\frac{8\pi f}{c}\frac{h_{\text{radar}}h_{\text{target}}}{d}\Big{)}\\ &=4\Big{[}1\mp 2\cos\Big{(}\frac{4\pi f}{c}\frac{h_{\text{radar}}h_{\text{ target}}}{d}\Big{)}+\cos^{2}\Big{(}\frac{4\pi f}{c}\frac{h_{\text{radar}}h_{\text{ target}}}{d}\Big{)}\Big{]}\\ &=4\Big{[}1\mp\cos\Big{(}\frac{4\pi f}{c}\frac{h_{\text{radar}}h_{\text{ target}}}{d}\Big{)}\Big{]}^{2}=\left\{\begin{array}{cc}16\sin^{4}\Big{(}\frac{2\pi f}{c}\frac{h_{ \text{radar}}h_{\text{target}}}{d}\Big{)}&TE\\ 16\cos^{4}\Big{(}\frac{2\pi f}{c}\frac{h_{\text{radar}}h_{\text{target}}}{d} \Big{)}&TM\end{array}\right.\end{split}  \tag{9}\]  
The received power in the direct line-of-sight path is described by the radar formula [16]. Consequently, the total received power calculated using the four-ray model is:\[P_{r}(\text{Four rays})\cong G_{r}\cdot\underbrace{\frac{\lambda^{2}}{\left(4\pi \right)^{3}d^{4}}\cdot\frac{P_{r}(\text{Four rays})}{P_{rh}(\text{LOS})}}_{Path- loss}\cdot RCS\cdot\underbrace{G_{t}\cdot P_{t}}_{EIRP} \tag{10}\]  
Inspection of the last derivation reveals that, for grazing incidence, the path loss of the vertical _TM_ polarized radar link, grows in proportion to \(d^{4}\) (where \(d\) is the distance between the radar and the target), while for _TE_ the path loss grows in proportion to \(d^{8}\).
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
## 4 Design of the TSR-DARDA Scheme
### Tsr-Darda Scheme
#### 4.2.2 Distributed In-Network Aggregation
However, the proposed TSR-DARDA scheme is for spherical networks with tiered structure. The time complexity is \(T(k)=O(k\cdot k)=O(k^{2})\). However, the partition of circular tiers and cells can be calculated off-line._
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### Supporting Functions
#### 3.1.1 Key Derivation Function KDF (H\({}_{v}\), Z, klen)  
Given a hash function \(H_{v}\) with output bit length \(v\), bit string \(Z\), and an integer _klen_ (that denotes the required bit length of the secret keys, where _klen_ \(<\) (232-1) \(v\)). The output is a bit string \(K\) of length _klen_. The pseudocode for this function is given in Algorithm 2.  
```
1\(ct\leftarrow\)\(0x00000001\)
2 For\(i=1\) to \([klen/v]\) Do
3\(Ha_{i}\gets H_{v}(Z||ct)\)
4\(ct\gets ct+1\)
5 EndFor
6 output the first _klen_ bits of \(Ha_{1}\parallel Ha_{2}\parallel\ldots\parallel Ha_{[klen/v]}\)
```  
**Algorithm 2**\(KDF(H_{v},\ Z,\ klen)\)
# Analysis and Applications of GlobeLand30: A Review
## 5 Discussion and Outlook  
Based on the data downloading records and published literature, this paper has reviewed the analysis and applications of GlobeLand30 since its release in 2014. GlobeLand30 has been recognized by international scientific and user communities, such as being the world's first wall-to-wall 30 m GLC data product [85], \"a milestone achievement in the Earth observation and open geo-information access.\" [13]. However, GlobeLand30 is far from satisfying all user requirements, and the system has considerable room for improvement, such as more comprehensive validation, continuous updating, and more value-added applications.
# A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks
## 5 Evaluation
### Emulated IoT-Blockchain
#### 5.2.2 Mobile Scenario  
In this section, we investigate blockchain recovery when the receiver moves. The receiver gradually starts from a position close to the sender and has a speed of 1 m per second. The yellow arrow indicates the direction of movement in Figure 9, where Figure 9a shows the starting point and Figure 9d shows the final destination. When the receiver is within the signal range of the sender, they can communicate directly. However, when the receiver moves outside the sender's signal range, the first communication failure occurs, and the routing protocols need to recalculate and find a new one-hop relay node. Moreover, the second communication failure occurs when the receiver continues to move outside the communication range of the first-hop relay node. At this point, the routing protocol looks for a new second-hop relay node from the sender to the receiver again. Finally, the third communication failure occurs when the receiver moves outside the communication  
Figure 8: Recovery time of emulated IoT-blockchain in static scenario. (**a**) Recovery time; (**b**) Average recovery time with components.  
Figure 7: Throughput and RTT of emulated IoT-blockchain system. (**a**) Throughput; (**b**) RTT.  
range of the sender's second-hop relay node. The IoT-blockchain system performs a final communication recovery, finding a third-hop relay node from the sender to the receiver. The recovery time results for the different occasions in the emulated mobile IoT-blockchain system are shown in Figure 9(a). From the results, we observe that, in our nine-node IoT-blockchain system, the receiver's movement causes three occasions of communication recovery. The result of each communication recovery shows that BATMAN is the least time-consuming. On the first occasion, BATMAN takes 6.22 s to recover, which is 72.3% and 72.1% faster than OLSR and BABEL, respectively. For the second occasion, the recovery time of BATMAN is 5.92 s, which is 71.35% quicker than OLSR and 70.4% faster than BABEL. On the last occasion, BATMAN is 5.95 s, 73.3% and 72.36% faster than OLSR and BABEL, respectively  
send OGM packets to the sender. Even if the number of OGM packets received from other next-hops is one larger than the number received from the failed best next-hop, BATMAN will immediately switch the route and choose the one with better link quality.  
For BABEL, the recovery time is the second smallest among the three. This is, firstly, because it does not send only one type of packet to probe its neighbors to establish routes like BATMAN. Instead, it sends two packages, HELLO and IHU, and it takes time to enable the nodes and neighboring nodes to recognize each other's existence and establish connections. BABEL has a faster recovery time than OLSR because, when the sender realizes a failure with the selected link, it requests new routing information instead of waiting to receive new topology information about the network, as in OLSR.
# Joint Multi-Scenario-Based Earthquake and Tsunami Hazard Assessment for Alexandria, Egypt
## 3 Multi-Scenario Physics-Based Seismic Hazard Analysis
### Data and Methods
This limitation arises from the need for a more comprehensive understanding of seismic source heterogeneity, the physical properties of rock/soil, and the attenuation parameters, which cannot be realistically resolved at this time. This statement aligns well with the conclusion drawn by [PERSON] [49], who suggested that the field of strong motion seismology does not require the consideration of frequencies higher than approximately 10 Hz. The NDSHA approach accommodates different magnitude scales from various agencies or magnitude scales assigned to the same event by different authors. To ensure consistency, we homogenized the earthquake magnitudes to moment magnitude (Mw) using the magnitude scaling relationships proposed by [PERSON] et al. [50]. Additionally, we adopted the magnitude-distance thresholds recommended by [PERSON] et al. [8] for the computations. We employed smoothing procedures to reduce uncertainty in the location and magnitude of earthquakes. However, relying solely on discretized cells for smoothing often proves insufficient. Therefore, we also evaluated a centered smoothing window, allowing us to analyze earthquake magnitudes in the central cell and neighboring cells. For a more comprehensive understanding of these procedures, refer to the work of [PERSON] et al. [43]. A smoothing procedure for defining earthquake location and magnitude, M, was then applied to account for spatial uncertainty and source extension. After smoothing, only the cells (earthquake sources) located within the seismogenic zones were retained. The  
Figure 6: (**a**) Seismogenic zones adopted for computing the NDHSA maps for Alexandria, represented by blue polygons along with their numbers; a representative fault plane solution for each seismogenic source is shown in the center of each source, and the distribution of smoothed seismicity is represented by small circles with different colors according to magnitude. (**b**) Seismic velocities (Vp and Vs) and density (\(\rho\)) of the regional bedrock model adopted from [PERSON] et al. [6]. smoothing process makes NDSHA robust and prevents it from possible uncertainties in the earthquake catalog, which is not required to be complete for M < 5.
# Soil Moisture Retrieval from Multi-GNSS Reflectometry on FY-3E GNOS-II by Land Cover Classification
## 4 Results and Discussion
### Discussion  
Generally, there are two types of retrieval methods for multi-GNSS reflectometry observations: First, use different retrieval models for different systems; Second, calibrate observables from different systems to a same level and use a general retrieval model for them. The first type is simple but not good for data fusion and cross-satellite validation. This study utilized the second type of method which requires careful intercalibration among GNSS systems. The observations under full coherent reflection from different systems should be consistent as the received power only depends on the size of first Fresnel Zone which should be almost same as the GNSS signals (at least for GPS L1, BDS B1 and GAL E1) have similar carrier frequency. However, observations under incoherent scattering depend on the scattering area which is related to the chip length and signal modulation, resulting a discrepancy of observations between GNSS systems. This paper empirically calibrated reflectivity observations of the three systems to a same level in order to apply a common retrieval model. This method can be further improved if coherent and incoherent components can be accurately distinguished.  
So far, the empirical model in this study was constructed on 36 km ease grid. However, due to the high heterogeneity of land surface, model with higher spatial resolution is essential. The ongoing study will attempt to establish the model on 9 km or 6.25 km ease grid with more observational data. Due to the incoherent integration time of 1 s, the along-track resolution for FY-3E GNOS-II observations is restricted to 6 km. In order to increase spatial resolution for terrestrial applications, it is planned to reduce the incoherent integration time in subsequent GNSS-R missions.  
The soil moisture retrieval model requires auxiliary data sets to compute the attenuation effects of vegetation and roughness (topography). The optical thickness of vegetation could be estimated with SMAP vwc ancillary data. However, it is difficult to find appropriate ancillary data for roughness (topography) attenuation estimation because only a small-scale RMS height was considered in most soil roughness ancillary data, whereas large-scale roughness (topography) has a larger influence on spaceborne GNSS observations. The extrapolated topography attenuation has obvious regional characteristics and could be related to the distribution of global topography. By assuming that the roughness/topography are constant for a certain region, and only soil moisture and vegetation vary with time, GNOS-II soil reflectivity was linearly fitted to the SMAP reference soil moisture for each ease grid of 36 km.  
Figure 12: The histogram of ubRMSE (**a**) GNOS vs. in situ (**b**) SMAP vs. in situ.
# Dynamic Modeling and Optimization of Tension Distribution for a Cable-Driven Parallel Robot
###### Abstract  
Cable-driven parallel robots (CDPRs) have been gaining much attention due to their many advantages over traditional parallel robots or serial robots, such as their markedly large workspace and lightweight design. However, one of the main issues that needs to be urgently solved is the tension in the distribution of CDPRs due to two reasons. The first is that a cable can only be stretched but not compressed, and the other is the redundancy of the parallel robot. To address the problem, an optimization method for tension distribution is proposed in the paper. The structural design of the parallel robot is first discussed. The dynamics model of the parallel robot is established by the Newton-Euler method. Based on the minimum variance of cables' tension, an optimization method of tension distribution is presented for the parallel robot. Furthermore, the tension extreme average term is introduced in the optimization method, and the firefly algorithm is applied to obtain the optimal solution for tension distribution. Finally, the proposed approach is tested in the simulation case where the end-effector of the robot moves in a circular motion. Simulation results demonstrate that the uniformity and continuity of tension are both outstanding for the proposed method. In contrast with traditional solving methods, the efficiency of this method is largely improved.  
cable-driven parallel robot; dynamics modeling; cable tension optimization; firefly algorithm +
Footnote †: journal: Applied Sciences