esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Method
### System Architecture Design
#### 5.1.3 System Function  
Rescue areas usually deploy drones, unmanned vehicles, robotic dogs, and other vital-sign detection devices according to mission needs and field environments, with drones and other devices realizing remote control signaling based on the control plane of the 5G network. Meanwhile, the collected intelligence data are transmitted from the user plane, offloading the rescue area's imagery, audio, acoustic patterns, geographic environment, and other information within the local private network, and shunting to the local area, based on preset rules as needed, the edge, or the rear center. Among them, delay-sensitive tasks or simple solving tasks are solved and distributed locally in the rescue area via the on-board server to provide information support for on-site rescue tasks. Lightweight computing tasks are shunted to the edge side for processing through the private network, and then the results are distributed to the rescue area through the 5G downstream network. Large-scale tasks or data fusion tasks, such as disaster situation analysis, situation prediction, public opinion judgment, risk assessment, etc., are transmitted back to the rear command center and processed based on the pre-trained large language model and sufficient arithmetic resources, and the results are distributed to the edge side or the rescue area via the downstream network.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Further Physical Layer Security Approaches
#### 5.3.1 Leaky Wave Antenna (LWA)  
The antenna structure does not necessarily have to be an array to have physically secure behavior. The so-called leaky wave antennas can also be used for this purpose and have angle-dependent frequency characteristics [100]. Thus, beam-forming happens via the carrier frequency change, which gives an additional level of security, having not only spatial beam-forming but carrier frequency differences between separate users ([PERSON], [PERSON]). In doing so, the secrecy capacity is forced to be high when [PERSON] and [PERSON] are at different angles. However, when the angles of [PERSON] and [PERSON] are approaching, then [PERSON] can gain a higher SNR than [PERSON], which would result in low secrecy capacity. Moreover, for the edge frequencies (max. and min. angles), the secrecy capacity is lower for small angle differences between [PERSON] and [PERSON]. Although this phenomenon also appears with conventional antenna arrays, in the case of the LWA this less secure edge behavior applies for significantly different angles of [PERSON], which makes this system more resilient to single [PERSON] attacks. Hence, such antennas have great potential in realizing a secure transmission due to these unique properties [136].
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 4 Results and Evaluation
### Block Test  
The focus of this test is the ratio of ones and zeros in the sequence. It begins by partitioning the sequence into blocks, as indicated in Equation (9), where \(M\) is the block length, \(n\) is the length of the sequence, and \(N\) is the number of blocks [14]. \(\pi_{i}\), which indicates the proportion of ones, is calculated using Equation (10) for each M-bit block, where \(1\leq i\leq N\). Equation (11) calculates \(X^{2}(obs)\) to measure how well the ones within a given M-bit block match the expected 50% value. The \(p\)-value is finally computed using Equation (12), where _igamc_ is a function as explained by [10]. A \(p\)-value of less than 0.01 is considered to indicate failure (i.e., a non-random sequence). This test can be viewed as a restricted version of frequency test, where each block of the sequence is examined individually; if one block fails the test, then the overall success rate for the sequence is degraded.  
\[N=\frac{n}{M} \tag{9}\]  
\[\pi_{i}=\frac{\sum_{j=1}^{M}X_{(i-1)M+j}}{M} \tag{10}\]  
\[X^{2}(obs)=4M\sum_{i=1}^{N}\biggl{(}\pi_{i}-\frac{1}{2}\biggr{)}^{2} \tag{11}\]  
\[P-value=igamc\biggl{(}\frac{N}{2},\frac{X^{2}(obs)}{2}\biggr{)} \tag{12}\]  
Figure 5 shows the block test success rate achieved by LAES 128, Present 128, Clefia 128, and AES on files of different sizes. The x-axis identifies the rate of success for each crypto algorithm on a specific size of file, and the y-axis identifies the size of the files. Figure 5 shows that the randomness trend lines for the algorithms are above 40%. The trend lines for the LAES show a higher success rate for larger files, almost similar to the AES trend line. This indicates that despite the higher number of rounds in the LAES counterparts, it was able to produce a higher degree of diffusion on files consisting of a higher number of blocks.  
Figure 5: Block test.
## 3 Gene Discovery and Gene Expression Analysis in the Pre-NGS Era
### Other Plant Metabolites Produced by Endophytic Fungi
#### 4.6.6 Polyphenols, Flavonoids, Terpenoids, Isoquinoline Alkaloids  
An interesting attempt towards elucidation of multiple pathway genes was made recently by [PERSON] et al. [83]. The maidenhair tree (_Ginkgo biloba_) is a natural resource for flavonoids and terpen. However, limited insights have considered the role of endophytic fungi in this plant, and it is unclear whether a genetic exchange has eventually occurred between _G. biloba_ and its endophytic associates. In a study considering functional gene profiles and repetitive sequences carried out to focus on this issue, 25 endophytic fungal and bacterial strains were isolated from roots, which included two _Aspergillus_ spp. The discovery that several genes from polyphenolic, flavonoid, terpenoid, and isoquinoline alkalioid biosynthetic pathways are present in both _Ginkgo_ and the endophytic _Aspergillus_ strains, suggests that the latter may participate in plant metabolism in a complementary manner.  
The fungal endophytic strains _Penicillium citrium_ TDPEF34, and _Geotrichum candidum_ TDPEF20, respectively from healthy and brittle leaf diseased date palms, proved to be very effective in assays against some pathogenic fungi and bacteria [84]. Ethyl acetate extract of _G. candidum_ exhibited strong antioxidant activity, probably due to high polyphenol and flavonoid content. Four publicly available genomes of _P. citrium_ and _G. candidum_ were also screened to identify secondary metabolite gene clusters, and 48 putative secondary metabolite gene clusters resulted. However, none of them showed significant similarity to known secondary metabolite clusters. This led to the conclusion, that there is a potential for the discovery of novel metabolites in these endophytic fungi.
# A Topology Map Generation Algorithm for Optimal Path Finding for Image-Based Maps
## 2 Related Works
### Comparing the Related Works  
The A* algorithm performs better than other path-finding algorithms because it uses heuristic estimations. However, the amount of computation increases rapidly when the resolution of an image-based map increases. Several researchers have sought to improve the heuristics or to increase efficiency via preprocessing.  
The Jump Point Search (JPS) [20] algorithm reduces the number of nodes by creating jump points in an A* algorithm that uses image-based maps. JPS creates a jump point when a specific condition is met. JPS searches all directions that can be taken from a reference point and creates a connection by defining the start node as a parent. However, a disadvantage is that nodes can be created at dead ends (independent of destination guidance) because jump points are nominated when it is possible to navigate in all directions from a specified point despite the existence of obstacles. Conversely, as CNA creates nodes employing obstacles (not spaces) as start points, it is possible to prevent node creation on paths that lead to dead ends.  
The regions discovery algorithm (RDA) [21] automatically creates boundaries and then divides maps into clusters by reference to specific conditions. RDA finds a start node from which it is possible to search a region from the top left of the map, from left to right and top to bottom, and then move to the right. If an obstacle is encountered or another area attained, a boundary is created; this process is repeated until all tiles are assigned to areal clusters. The A* algorithm can also be used in this manner, and it has the advantage that nodes are unnecessary. However, such algorithms are inappropriate for AR devices with relatively low specifications, because a large priority queue size must be allocated to memory. In contrast, for the CNA proposed in this paper, the amount of memory required for preprocessing is minimal because the number of nodes can be reduced to the number of corners of all obstacles if A* is employed.
# Salt-Specific Suppression of the Cold Denaturation of Thermophilic Multidomain Initiation Factor 2
## 2 Results
### Salt-Dependent Thermodynamic Parameters for Denaturation of the G and C Fragments  
From the dependence of thermodynamic parameters on salt concentrations, we quantified how strongly these parameters depend on salt. In particular, we defined the slope of the salt dependencies (e.g., \(\Delta\Delta H/\Delta\)[salt]) as a suitable measure to determine the extent to which salt influences the denaturation process. By comparing different salt types, one can potentially learn about the molecular mechanism behind the salt-specific effects on cold denaturation (Figure 5a-c).  
Salt-specific enthalpy change: Overall, the G domain showed much higher sensitivity to salts than the C fragment. In absolute values, the G fragment had a higher slope (red versus blue in Figure 5a). Under all conditions, the slopes were negative and dependent on the salt type. The largest negative slope was observed for CsCl, while NH\({}_{4}\)Cl and NaClO\({}_{4}\) had the lowest slope values. The profile of salt-specific slopes was similar for G and C fragments. In summary, the analysis showed that the change in denaturation enthalpy was dependent on the type of cation and anion, and denaturation enthalpy was more highly dependent on salts for the G fragment than the C fragment.  
Salt-specific heat capacity change: Again, the concentration dependence of the heat capacity was found to be higher for the G domain than the C fragment. The only exception is CsCl, where both fragments had similar slopes. The highest negative slope was observed for NaCl and the G fragment, and the lowest negative slope for LiCl and the C fragment in the case of chloride ions. The slope became slightly positive in the presence of NaClO\({}_{4}\) for the heat capacity of the C fragment, while the G domain heat capacity had a significantly negative slope. As previously observed, the slopes were dependent on the type of salt, and the profiles observed for the G and C fragments were similar. In addition, the C fragment heat capacity showed high anion dependence when comparing the slopes of sodium chloride with those of sodium perchloride anion. The change in the slopes was much larger compared with that observed for the G fragment.  
Salt-specific transition temperature changes \(T_{ts,\,hot}\): Transition temperatures have only been discussed on the hot side, where we have enough data points at different salt concentrations. For the cold transition, we were not able to reliably estimate the slope due to technical limitations. The G fragment transition temperatures showed high positive slopes for CsCl, while the C fragment transition temperatures showed the highest slope in NaCl. The profiles of the slopes for different salts were similar, even though differences in magnitude were present. In the case of perchlorate, both slopes were negative; however, the C fragment transition temperatures had a larger negative slope than those of the G fragment.
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 3 Results and Discussion  
### Evaluation Metrics  
From the perspectives of fairness and comprehensiveness, this paper adopts accuracy (ACC), precision (P), recall (R), mean intersection over union (mIoU), and the F\({}_{1}\) score--to assess the performance of various methods on the dataset in the experiments. For a detailed introduction to the types of indicators, refer to [30; 31]. Their calculation equations are as follows:  
\[ACC\big{(}Y,\hat{Y}\big{)}=\frac{1}{n}\sum_{i=1}^{n}\delta(y_{i},\hat{y}_{i}), \tag{12}\]  
Here, \(Y\) represents the true labels, \(\hat{Y}\) denotes the identified frontal surfaces, \(n\) indicates the total number of frontal surfaces in all labels, and the Dirac delta \(\delta(.)\) is an indicator function where \(\delta(.)=1\) when \(y_{i}=\hat{y}_{i}\), otherwise \(\delta(.)=0\).  
\[P=\frac{TP}{TP+FP}, \tag{13}\]  
\[R=\frac{TP}{TP+FN}, \tag{14}\]  
\[F_{1}=2\frac{P\times R}{P+R}, \tag{15}\]  
\[IoU_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}}, \tag{16}\]  
\[mIoU=\frac{1}{N}\sum_{i=1}^{N}IoU_{i}, \tag{17}\]  
Among them, \(i\) represents the frontal category, and \(N\) denotes the total number of frontal categories (including non-frontal classes), which is five in this study. \(TP\) represents the total number of accurately predicted fronts, while \(FP\) indicates the total number of fronts misidentified as non-fronts. \(FN\) denotes the total number of non-fronts misidentified as fronts.  
The evaluation in this study targets frontal objects, considering an identification as correct when the distance between the labeled and recognized results is within nine grid points. This criterion accounts for errors arising from both the expansion of network labels during data preprocessing and the post-processing of network identification results.
## 3 The Proposal
### Validation and Benefit of the Framework
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Instruments for Policy and Regulation** & **Explanation** & **Grid\({}^{*}\)** \\ \hline \multirow{3}{*}{1.} & \multirow{3}{*}{Infrastructure sharing.} & From passive infrastructure to open access for telecommunications networks, operators are expected to share their network capacity with other operators in a fair manner. & **\#1** & **\#2** & **\#3** & **\#4** \\ \cline{3-5}  & & This neutral host could be created by local entreprene in partnership with the local government (_Pemda_) because, based on the number of entreprene in Jakarta, developing a network in this region may be deemed unprofitable due to the national consumer profile. & **\#2** & **\#3** & **\#4** \\ \cline{3-5}  & & The local businesses or local governments who are more concerned with the requirements of the local community would have a different perspective, and it is believed that the on-net revenue from cellular operators or ISPs that rent their network capacity is sufficient. & **\#2** & **\#3** & **\#4** \\ \hline \multirow{3}{*}{3.} & Reorientation of development & As of now, the development commitment is still based on the MNOs’ business strategy. It emphasizes economic factors in terms of demand potential. Governments should determine the places to be developed, notably in grid \#3 and grid \#2’s priority grid. & **\#3** & **\#2** & **\#3** \\ \cline{3-5}  & & Since the development of most national MNOs is concentrated in metropolitan cities, rural frequency permits have not been optimized. Under such conditions, a spectrum leasing program for neutral hosts at the local level temporarily utilizing the allocation of frequencies of national cellular operators based on government-approved business-to-business (2B2) agreements is feasible. & **\#3** & **\#2** \\ \cline{3-5}  & & In this spectrum leasing model, the frequency right-of-use fee (_BHP_) is not required to be billed twice, as the frequency permit is still associated with the national mobile operator. & **\#3** & **\#4** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Recommendations for Policy and Regulatory Instruments.
# A Finite Regime Analysis of Information Set Decoding Algorithms
## 3 A Finite Regime Analysis of Information Set Decoding Techniques
### Lee-Brickell
It then exploits the following equation

\[\sum_{i^{\prime}\in\mathcal{I}^{\prime}}v_{\text{up}\ i^{\prime}}=\tilde{s}_{ \text{up}}\Leftrightarrow\tilde{s}_{\text{up}}=\sum_{i\in\mathcal{I}}v_{ \text{up}\ i}+\sum_{j\in\mathcal{J}}v_{\text{up}\ j}\Leftrightarrow\tilde{s} _{\text{up}}+\sum_{i\in\mathcal{I}}v_{\text{up}\ i}=\sum_{j\in\mathcal{J}}v_{ \text{up}\ j}\]

to precompute the value of \(\tilde{s}_{\text{up}}+\sum_{i\in\mathcal{I}}v_{\text{up}\ i}\) for all possible \({k/2\choose p/2}\) choices of \(\mathcal{I}\), and store them into a lookup table \(\mathcal{L}\), together with the corresponding choice of \(\mathcal{I}\). The algorithm then enumerates all possible \({p\over 2}\) sized sets of indexes \(\mathcal{J}\), computing for each one \(\sum_{j\in\mathcal{J}}v_{\text{up}\ j}\), and checking if the result is present in \(\mathcal{L}\). If this is the case, the algorithm has found a candidate pair \((\mathcal{I},\mathcal{J})\) for which \(\sum_{i\in\mathcal{I}\cup\mathcal{J}}v_{\text{up}\ i}=\tilde{s}_{\text{up}}\) holds, and thus proceeds to check if \(\sum_{i\in\mathcal{I}\cup\mathcal{J}}v_{\text{down}\ i}=\tilde{s}_{\text{ down}}\). This strategy reduces the cost of computing an iteration quadratically at the price of increasing the number of iterations with respect to [PERSON] and [PERSON]'s approach, and taking a significant amount of space to store the lookup table \(\mathcal{L}\) which contains \({k/2\choose p/2}\) elements. We note that [PERSON]'s variant of the ISD is the first one to exhibit non-polynomial memory requirements, due to the size of the set \(\mathcal{I}\), which should be memorized and looked up. [PERSON]'s algorithm is summarized in Algorithm 5. ```
Input:\(s\): an \(r\)-bit long syndrome (column vector) \(H\): an \(r\times n\) binary parity-check matrix \(t\): the weight of the error vector to be recovered Output:\(e\): an \(n\)-bit binary row error vector s.t. \(He^{T}=s\), with \(\textsc{weight}(e)=t\) Data:\(P\): an \(n\times n\) permutation matrix \(\widehat{e}=eP\): the error vector permuted by \(P\) \(p\): the weight of the first \(k\) bits of \(\widehat{e}\), \(0\leq p\leq t\), \(\ell\): length of the run of zeroes in \(\widehat{e}=[\widehat{\mathcal{O}}_{1\times k}\;0_{1\times\ell}\;\widehat{ \mathcal{O}}_{1\times r-\ell}]\) \(\widehat{s}\): an \(r\)-bit long binary column vector, equal to the syndrome of \(e\) through \([V\;I_{r}]\), \(s= \begin{bmatrix}\tilde{s}_{\text{up}}\\ \tilde{s}_{\text{down}}\end{bmatrix} \) \(V\): an \(r\times k\) binary matrix \(V= \begin{bmatrix}v_{0}&\ldots&v_{k-1}\end{bmatrix} = \begin{bmatrix}V_{\text{up}} \\ V_{\text{down}}\end{bmatrix} = \begin{bmatrix}v_{\text{up}\;0}&\ldots&v_{\text{up }\;k-1}\\ v_{\text{down}\;0}&\ldots&v_{\text{down}\;k-1}\end{bmatrix}
# Development of a GNSS Buoy for Monitoring Water Surface elevations in Estuaries and Coastal Areas
## 2 Methodology
### VBS-RTK Positioning Technology
Furthermore, GPSNET utilized the filtered state vector for the complete network to calculate a virtual reference station dataset at a location near the rover [19]. Rover station: The rover station is a GNSS buoy with a GNSS receiver and a GNSS antenna attached to it. According to [PERSON] et al. [20], the procedure of VBS positioning can be summarized as follows:  
1. Pre-process network observations: Establishing the network database and completing coordinate adjustments for each reference station. 2. Calculating data from regional stations: Collecting continuous observations and the accurate coordinate from each GNSS reference station, thereby establishing the Area Correction Parameters database. 3. Generating VBS data for the rover: The rover station reports approximate coordinates in National Marine Electronics Association format to the VBS-RTK control center. The VBS-RTK control center calculates the systematic error by interpolation and combines the error with the GNSS observations from the nearby reference station to produce VBS data. Then the VBS data are subsequently transmitted to the rover station in RTCM format. 4. Calculating the coordinate of the rover station: The rover station receives the VBS data and processes ultra-short-baseline RTK positioning. In this work, a dual-frequency receiver was installed on the GNSS buoy as the rover station. The receiver receives signals from both GPS and GLONASS. The specifications of the GNSS receiver are listed in Table 2. The circular error probability is 1.2 times the root-mean-square value. [PERSON] et al. [21] provided detailed information on the technology and precision of the VBS-RTK system operated by the NLSC. They found that for the GPS satellite positioning reference network and VBS-RTK, the total accuracies were approximately 2 cm and 5 cm in the horizontal and vertical directions, respectively. Notably, although the inclusion of a GLONASS into the GNSS network may not improve positioning accuracy, the transmission of accurate data for RTK computation is improved. Because the elevation from the water surface to the location of the GNSS receiver installed on the buoy is known, once the position of the receiver is obtained, the water surface elevation can be easily determined. \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Specification** \\ \hline RTK & horizontal: 10 mm + 1.0 ppm (parts per million) \(\times\) baseline length \\  & vertical: 15 mm + 1.0 ppm \(\times\) baseline length \\ \hline Velocity & 0.02 m/s (CEP) \\ \hline \end{tabular}
\end{table} 
Table 2: RTK GNSS receiver accuracy specifications.
## 2 Research Area Overview and Data Sources
### Data Sources  
The 2020 land use data were obtained from the China Land Cover Dataset (CLCD), publicly released by the team led by Professors [PERSON] and [PERSON] at Wuhan University [19], featuring a spatial resolution of 30 m \(\times\) 30 m. According to the Land Use Classification Standard of the Ministry of Land and Resources (GB/T21010-2017 [20]), land use in the study area is categorized into six types: cultivated land, forest land, grassland, water bodies, constructed land, and unused land. The 30 m Digital Elevation Model (DEM) topographic data were sourced from the Shuttle Radar Topography Mission (SRTM) elevation data, a collaborative effort between NASA (National Aeronautics and Space Administration) and NIMA (National Imagery and Mapping Agency), available at [[http://srtm.csi.cgiar.org/srtmdata/](http://srtm.csi.cgiar.org/srtmdata/)]([http://srtm.csi.cgiar.org/srtmdata/](http://srtm.csi.cgiar.org/srtmdata/)) (accessed on 19 May 2024). The geological disaster data for the year 2020 were provided by the Geological Environment Monitoring Institute of Hebei Province, drawn from their geological disaster risk census materials.  
Figure 1: Research area geographical location and land use type diagram.  
Figure 2: Precipitation distribution map for 2020.