esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Empirical Research
### Structural Model Test
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hypothesis** & \(\beta\) & **STDEV** & **T Statistics** & \(p\) **Values** & **Result** \\ \hline H1: RA \(\rightarrow\) AI & 0.231 & 0.043 & 5.423 & 0.000 & Support \\ \hline H2: CB \(\rightarrow\) AI & 0.184 & 0.043 & 4.277 & 0.000 & Support \\ \hline H3: CX \(\rightarrow\) AI & \(-\)0.326 & 0.050 & 6.521 & 0.000 & Support \\ \hline H4: OB \(\rightarrow\) AI & 0.192 & 0.046 & 4.169 & 0.000 & Support \\ \hline H5: TR \(\rightarrow\) AI & 0.227 & 0.045 & 5.075 & 0.000 & Support \\ \hline H6: TMS \(\rightarrow\) AI & 0.150 & 0.048 & 3.111 & 0.002 & Support \\ \hline H7: OR \(\rightarrow\) AI & 0.149 & 0.049 & 3.056 & 0.002 & Support \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Assessment of the structural model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fornell-Larker Criterion** & & & & & & & & & & & & \\ \hline AI & CB & CP & CX & GS & OB & OR & RA & TMS & TR & VS \\ \hline AI & 0.827 & & & & & & & & & & \\ \hline CB & 0.228 & 0.818 & & & & & & & & \\ \hline CP & 0.080 & \(-\)0.011 & 0.852 & & & & & & & & \\ \hline CX & \(-\)0.496 & \(-\)0.098 & 0.034 & 0.839 & & & & & & & \\ \hline GS & 0.058 & \(-\)0.011 & 0.055 & \(-\)0.037 & 0.863 & & & & & & \\ \hline OB & 0.239 & 0.006 & 0.059 & \(-\)0.062 & \(-\)0.031 & 0.836 & & & & & \\ \hline OR & 0.207 & \(-\)0.087 & \(-\)0.052 & \(-\)0.138 & 0.006 & 0.003 & 0.849 & & & & \\ \hline RA & 0.325 & \(-\)0.001 & \(-\)0.035 & \(-\)0.101 & \(-\)0.022 & 0.128 & 0.052 & 0.830 & & & \\ \hline TMS & 0.199 & \(-\)0.006 & 0.048 & \(-\)0.060 & 0.194 & \(-\)0.070 & 0.057 & 0.133 & 0.851 & & \\ \hline TR & 0.349 & 0.104 & 0.124 & \(-\)0.199 & 0.108 & \(-\)0.001 & 0.002 & 0.074 & 0.001 & 0.837 & \\ \hline VS & 0.261 & 0.017 & 0.091 & \(-\)0.220 & 0.086 & 0.070 & 0.011 & \(-\)0.065 & 0.003 & 0.073 & 0.859 \\ \hline \multicolumn{10}{c}{**Hetetrotrait-Monotrait Ratio**} & & & & & & & & & & \\ \hline AI & CB & CP & CX & GS & OB & OR & RA & TMS & TR & VS \\ \hline AI & & & & & & & & & & & \\ \hline CB & 0.277 & & & & & & & & & \\ \hline CP & 0.081 & 0.083 & & & & & & & & \\ \hline CX & 0.588 & 0.133 & 0.063 & & & & & & & \\ \hline GS & 0.062 & 0.045 & 0.07  
In this study, model fit is measured by the standardized root mean square residuals (SRMR) value. In PLS-SEM, an SRMR value less than 0.08 is considered to be an acceptable model fit [97].
# Phonon Scattering and Thermal Conductivity of Actinide Oxides with Defects
## 2 Methods
### Sample Preparation for Actinide Oxides with Point Defects  
The unit cell of the stoichiometric sample and the unit cells with different point defects are illustrated in Figure 2. Three different concentrations (0.1%, 0.5%, and 5%) are selected for the study. The defect concentrations are defined as the ratio of the number of defect sites to the number of atoms in the stoichiometric sample. The sizes of simulation structures show a slight difference depending on the defect types and concentrations once the structures are equilibrated. Therefore, we avoided using the physical dimensions of simulation structures. Instead, the sizes of simulation structures are represented by the number of unit cells to avoid any confusion that results from the slight differences in the dimensions.  
The number of unit cells in \(y\) (\(N_{y}\)) and \(z\) (\(N_{z}\)) directions are fixed to be 6, while the number of unit cells in \(x\) (\(N_{x}\)) is increased progressively. Since 1D heat conduction is assumed by exchanging heat energy between the hot bath and the cold baths slowly, the 1D thermal transport is not likely to be altered by the surface boundaries (\(x-y\) and \(x-z\) planes). In their previous study [4], the authors  
Figure 1: Actinide oxide structure with vacancy defects during RNEMD. RNEMD induces a temperature gradient by imposing a heat flux between the hot bath and the cold bath.  
already observed that thermal transport property is not affected much by the cross sectional areas (\(N_{y}\times N_{z}\)). This implies that 1D heat conduction is well-maintained in the present RNEMD simulations. The same phenomenon has been identified by other researchers [19; 20; 21]. Therefore, the authors decided not to examine the effect of cross-sectional area on the 1D heat conduction in the present study.  
As mentioned earlier, two similar temperature profiles are obtained from the RNEMD simulation in the left and right segments of the simulation structure. Therefore, the sample length must be defined by the half of the total simulation structure length; \(N_{x}\) is the number of unit cells of the half size of the simulation structure in \(x\)-direction, which is the direction of the thermal conductivity calculation. Thermal conductivities are estimated for PuO\({}_{2}\) or UO\({}_{2}\) with various point defect types and concentrations for seven different sample lengths in the direction of thermal transport (\(N_{x}\) = 10, 20, 30, 40, 50, 75, and 100)
## 4 Experiments
### Results and Analysis
#### 4.2.2 Attack-Specific Analysis  
To better understand the framework's performance across different attack types, we conducted a detailed analysis of the response effectiveness for each attack category. Figure 2 illustrates the performance comparison across different attack types, showing that ARCS maintains consistent superiority across all categories, with particularly strong performance in handling complex attack patterns such as multi-stage APTs and ransomware.  
Analysis of the performance across different attack types revealed several key insights about ARCS's strengths and limitations. The framework shows exceptional performance in detecting and responding to DDoS attacks, with a 92.3% success rate and an average response time of 18.7 s. This performance can be attributed to the dual-stream architecture's ability to quickly identify traffic patterns and implement appropriate countermeasures. In contrast, for more sophisticated attacks like ransomware, which typically involve longer attack durations and complex evolution patterns, ARCS achieves an 87.8% detection rate with a slightly longer average response time of 26.4 s. This variation in performance demonstrates both ARCS's strengths in handling well-defined, pattern-based attacks, as well as areas where the system requires more processing time and sophisticated analysis for complex threats. The increased response time for complex attacks reflects the necessary thoroughness in threat analysis and response validation, particularly when dealing with multi-stage attack patterns that require both immediate response and strategic planning through the dual-stream architecture.
## 2 Networking Architectures
### Multi-Layer UAV Ad-Hoc Network  
Another architecture in the form of networking multiple groups of heterogeneous UAVs is the multi-layer UAV ad-hoc network, as illustrated in Figure 2d. In this architecture, the networking between member UAVs within a specific group form an ad-hoc UAV network, which corresponds to the lower layer of the network. The backbone UAVs of all groups are connected to each other and come under the upper layer. However, only one backbone UAV from a group is further connected to the GS. In addition, only the GS holds the information that is routed to it so as to reduce communication and the computation load on the GS. Therefore, this architecture is suitable for missions that involve a large number of heterogeneous UAVs.  
In summary, a decentralized communication architecture is suitable for connecting multiple UAVs in FANET. With the help of its multi-hop schema, this communication architecture can provide extended coverage for data transmission. Moreover, the UAV ad-hoc network architecture is suitable for UAVs that are homogeneous and less in number. On the other hand, multi-group and multi-layer architectures can be deployed in situations where a large number of heterogeneous UAVs are required. In addition, a multi-layer UAV ad-hoc network may be preferred to a multi-group counterpart due  
Figure 2: Communication architectures. (**a**) Centralized architecture; (**b**) unmanned aerial vehicle (UAV) ad-hoc network; (**c**) multi-group UAV ad-hoc network; (**d**) multi-layer UAV ad-hoc network.  
to its fully decentralized nature. A multi-layer ad-hoc UAV networking architecture, furthermore, is more flexible in providing \"on-the fly\" communication network, chiefly due to its robustness against a single point of failure.
## 2 Description of the Herringbone Retaining System (HRS)  
### Structural Components  
The new retaining and protection structure is formed by installing batter piles in front of vertical piles (to give a herringbone-like structure) which helps the vertical piles resist the earth pressure acting on their backs. The full HRS consists of vertical piles, batter piles, capping beams, wales, and a curtain (for cutting off drains)--the basic configuration is shown in Figure 6.  
Cast-in-situ bored piles are adopted for the vertical piles and they are distributed along the edge of the excavation. A capping beam, made of reinforcement concrete, is built on top of the vertical piles to make them form a unit that increases their integrity. A wake is designed as a reinforcement concrete beam or a steel beam, and it is added to the design (below the capping beam) to link the vertical piles and batter piles together. This transfers some of the load acting on the vertical piles to the batter piles. The batter piles are made of precast (PC) piles or steel piles which are paced into soil by a pre-boring method. They tilt to the inner side of the excavation and are offset with respect to the vertical piles so that they lie in between them. Clearly, their presence will act to limit the horizontal displacement of the vertical piles together with the excavation wall. There are three types of connection between the wale and batter piles: (i) direct connection method; (ii) wedge plate method; (iii) concrete filling method. That is to say, the heads of the batter piles can be supported on the wake directly if there exists no gap between them, but the wedge plate or concrete is required for filling the gap if one exists (see Figure 6b).The lower parts of the batter piles are buried in the soil below the bottom of the excavation. The curtain is a supplementary method for cutting off drains when the groundwater level is high; it is constructed by the method of high-pressure jet grouting or cement mixing piles behind the vertical piles.
## 4 Discussion  
Previous studies have reported that at smaller spatiotemporal scales, the linear relationship between SIF and GPP did not apply to certain plant functions. Therefore, we intended to use continuous ground observation data to analyze the applicability of the linear relationship in a cork oak plantation canopy. In this study, we collected continuously ground-observed canopy SIF, GPP, and meteorological data in the growing season from June to September in 2019, during which divergences in canopy structure and chlorophyll content are negligible. Furthermore, the change patterns in SIF, relationship with GPP, and the influence of environmental factors during different timescales were elucidated.
# Atmospheric Effects on Satellite-Ground Free Space Uplink and Downlink Optical Transmissions
## 3 Results
### Variation of the Zenith Angle  
Variation of the zenith angle has a significant impact on the coherence length in both uplink and downlink channels. In Figures 8 and 9, at different wavelengths, we illustrate through simulation how the Fried parameter (which measures the spatial extent across which the phase of the optical beam is kept) varies with the zenith angle in both uplink and downlink channels.  
The observations from Figures 8 and 9 clearly show that the coherence length decreases as the zenith angle increases. The observations also illustrate that longer wavelengths result in a longer coherence length. In Figure 8, for a 1550-nanometer wavelength at turbulence strength of 3.5 \(\times\)\(10^{-16}\) m\({}^{-2/3}\), the coherence length decreased rapidly from its initial value of almost 0.45 m to about 0.15 m when the zenith angle increased by little more than 80 degrees. As a result, smaller zenith angles and longer wavelengths are preferred to obtain longer coherence lengths. Moreover, at the same wavelength, the coherence length at a weaker turbulence of 3.5 \(\times\)\(10^{-16}\) m\({}^{-2/3}\) was always greater than the stronger turbulence value of 3.5 \(\times\)\(10^{-14}\) m\({}^{-2/3}\). Hence, weak turbulence is always preferred in order to have a greater degree of coherence.  
Figure 8: Variation in the Fried parameter in an uplink channel.
## 2 System Model
### Spectro-Spatial Information-Guided T-F Domain Masking for GSC-MVDR Beamformer
Figure 3 shows the operation of the proposed neural framework during testing phase. The sequence of blocks at the lower half of this figure illustrates the implicit dependence of the neural processing on the spatial information of the target source. As the spatial information corresponding to the target source is utilized in the training of the proposed neural framework, the trained neural model incorporates the spatial information in addition to the spectral information. As such, the overall neural speech enhancement can be described as a spectro-spatial information processing framework. Hence, this methodology can also be referred to as spectro-spatial neural speech enhancement. Beamforming leads to an improvement in the SNR of the source-of-interest (SOI), resulting in a subsequent improvement in intelligibility under noisy and reverberant conditions. With the inclusion of T-F masking, a further improvement in the intelligibility of the SOI is achievable even in the presence of multiple interfering speakers. The training label generation procedure can be compactly explained in terms of subsections \(A\) and \(B\). Figure 2: T-F mask prediction using a spectro-spatial feature set (constructed from input multichannel spectrogram and location information obtained by MS-GLMB filtering) and proposed residual dense convolutional graph-U-Net framework (training phase).
# Short-Term Effects of Pine Plantations on Vegetation and Soil in Northern Mongolia
## 3 Results
### Understory Vegetation Composition and Its Change
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**BBS**} & \multicolumn{3}{c}{**MGS**} & \multicolumn{3}{c}{**BDS**} \\ \cline{2-13}  & **SR** & **PC** & **AGB** & **BGB** & **SR** & **PC** & **AGB** & **BGB** & **SR** & **PC** & **AGB** & **BGB** \\ \hline pH & 0.23 & 0.266 & \(-0.645\) * & \(-0.744\) **\(-0.034\)** & \(-0.033\) & \(-0.212\) & \(-0.099\) & \(-0.392\) & \(-0.396\) & \(-0.297\) & \(-0.195\) \\ SOC & \(-0.111\) & \(-0.155\) & 0.302 & 0.442 & \(-0.149\) & \(-0.148\) & 0.083 & 0.232 & \(-0.273\) & \(-0.272\) & 0.341 & 0.471 \\ BD & 0.23 & 0.266 & \(-0.645\) * & \(-0.744\) **\(-0.096\)** & 0.095 & \(-0.078\) & \(-0.23\) & 0.34 & 0.333 & \(-0.382\) & \(-0.45\) \\ SM & \(-0.135\) & \(-0.172\) & \(-0.149\) & \(-0.013\) & \(-0.157\) & \(-0.156\) & 0.165 & 0.317 & \(-0.25\) & \(-0.25\) & 0.339 & 0.497 \\ \hline \hline \multirow{2}{*}{Variables} & \multicolumn{3}{c}{NRS} & \multicolumn{3}{c}{NFM} & \multicolumn{3}{c}{STP} \\ \cline{2-13}  & SR & PC & AGB & BGB & SR & PC & AGB & BGB & SR & PC & AGB & BGB \\ \hline pH & \(-0.294\) & \(-0.616\) * & 0.122 & \(-0.094\) & \(-0.425\) & \(-0.252\) & \(-0.126\) & \(-0.238\) & 0.162 & 0.268 & \(-0.227\) & \(-0.371\) \\ SOC & \(0.657\) * & 0.447 & \(-0.222\) & \(-0.082\) & 0.517 & 0.235 & 0.241 & 0.376 & \(-0.095\) & \(-0.217\) & 0.315 & 0.439 ** \\ BD & \(-0.036\) & \(-0.749\) ** & \(-0.529\) & \(-0.627\) * & \(-0.57\) & \(-0.297\) & \(-0.25\) & \(-0.41\) & \(-0.036\) & 0.074 & \(-0.123\) & \(-0.243\) \\ SM & 0.35 & 0.683 * & 0.184 & 0.309 & 0.609 But in the naturally regenerated and natural plots, the IV values and dominant species were a little bit different. _Elymus sibricus_ and _Phlomis tuberosa_ species had high IV values and dominated in the naturally regenerated stand (NRC). _Allium bidentatum_, _Carum carvi_, _Fragaria orientalis_, and _Phlomis tuberosa_ species dominated in the natural forest edge (NFM), but _Cleistocene squarrosa_, _Festuca valesiaca_, _Patrinia rupestris_, _Linum sibricum_, _Allium linare_, and _Carangana microphylla_ species dominated and had a high IV in the steppe area, STP (Table 5). There were significant differences among plots in the diversity index (H\({}^{\prime}\)), evenness index (J), and Simpson index (D), as shown in Table 6. The diversity Shannon index in BBS has the highest value (0.8156), indicating the most diverse plot, MGS has the lowest value (0.172), showing the least diversity. The evenness index for MGS has the highest evenness (0.4966), suggesting a more balanced species distribution. NFM has the lowest evenness (0.0869), indicating dominance by a few species. The Simpson index for BBS has the highest value (1.1638), showing the least dominance and highest diversity. NRS
###### Abstract  
Due to the great success of the CYclone Global Navigation Satellite System (CYGNSS) mission, the follow-on GNSS Reflectometry (GNSS-R) missions are being planned. In the perceivable future, signal sources for GNSS-R missions can originate from multiple global navigation satellite systems (GNSSs) including Global Positioning System (GPS), Galileo, GLONASS, and BeiDou. On the other hand, to facilitate the operational capability for sensing ocean, land, and ice features globally, multi-satellite low Earth orbit (LEO) constellations with global coverage and high spatio-temporal resolutions should be considered in the design of the follow-on GNSS-R constellation. In the present study, the particle swarm optimization (PSO) algorithm was applied to seek the optimal configuration parameters of 2D-lattice flower constellations (2D-LFCs) composed of 8, 24, 60, and 120 satellites, respectively, for global GNSS-R observations, and the fitness function was defined as the length of the time for the percentage coverage of the reflection observations reaches 90% of the globe. The configuration parameters for the optimal constellations are presented, and the performances of the optimal constellations for GNSS-R observations including the visited and the revisited coverages, and the spatial and temporal distributions of the reflections were further compared. Although the results showed that all four optimized constellations could observe GNSS reflections with proper temporal and spatial distributions, we recommend the optimal 24- and 60-satellite 2D-LFCs for future GNSS-R missions, taking into account both the performance and efficiency for the deployment of the GNSS-R missions.  
multi-GNSS; GNSS reflectometry; constellation design; particle swarm optimization algorithm +
Footnote †: journal:  
1
# Assessing the Opportunity Offered by Electric Vehicles in Performing Service Trips to End Consumers
## 4 Results
These findings underscore the feasibility and effectiveness of the delivery vehicles used, affirming their suitability for urban delivery operations. Figure 3: The services performed by an electric LCV for _Service pattern 3_. Figure 2: The services performed by ICE or electric LCVs for _Service patterns 1_ and 2. Further analysis pointed out the request for parking spaces considering that in urban areas, there are high amounts of competition between private cars and commercial vehicles. The car parking space occupancy has been used as a reference. According to the characteristics of the LCVs and cargo e-bikes used, and that usually the studied service is performed in areas where the supply of loading and unloading on street bays is not so high, it is assumed that an LCV needs two units of car parking space while one is needed for a cargo e-bike. Therefore, referring to the driving and service time, which is one of the main headings of costs supported by city users in terms of parking space availability, the suggested _service \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \begin{tabular}{c} **Service Pattern 1** \\ **ICE LCV** \\ \end{tabular} & \
\begin{tabular}{c} **Service Pattern 2** \\ **Electric LCV** \\ \end{tabular} &
\begin{tabular}{c} **Service Pattern 3** \\ **Electric LCV** + \\ **Cargo e-Bike** \\ \end{tabular} \\ \hline \multirow{4}{*}{_Tailpipe Emissions [g]_} & CO & 0.155 & & \\ \cline{2-4}  & CO\({}_{2}\) & 64,285.00 & & \\ \cline{2-4}  & NOx & 57.760 & - & - \\ \cline{2-4}  & PM & 0.394 & & \\ \cline{2-4}  & VOC & 8.590 & & \\ \cline{2-4}  & _Energy consumption [kWh]_ & - & 91.77 & 89.37 * \\ \cline{2-4}  & _Emissions from electricity production [g]_ & - & CO\({}_{2}\) 28,357.00 *** & CO\({}_{2}\) 27,615.00 *** \\ \cline{2-4}  & _Requested time for parking [equivalent_ \\ _parking hours]_ & 49.60 & 49.60 & 31.75 ** \\ \hline \hline \end{tabular}  
*–2.61% compared to _service pattern 2_. Energy consumption [kWh]: 87.42 (electric LCV) 1.95 (cargo e-bike). ***–25% compared to _service pattern 1_ and 2. ***–The calculation was carried out considering the Italian energy mix. 1 kWh = 0.309 kg of CO\({}_{2}\)[68].  
\end{table} 
Table 4: The result indicators of test application. Figure 4: The services performed by cargo e-bikes for _Service pattern 3_. pattern 3_ could allow for a significant reduction, estimated in the case study to be about 25% compared to the status quo. Additionally, the use of electric vehicles allows for significant benefits to be obtained in terms of pollutant emissions (_environmental_ sustainability) with a contained use of energy. In addition to this, through the inclusion of further ancillary services performed by cargo e-bikes (e.g., the collecting of packing materials), the benefits for a city raise further.