esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential and Limitations of the New European Ground Motion Service in Landslides at a Local Scale
## 2 Materials and Methods  
### Setting  
La Miera landslide is in Asturias (NW Spain) to the East of the town of Pola de Lena (lat: 43.155212, long: \(-\)5.821555) (Figure 1). This landslide was previously mapped by the Spanish Geological Survey at 1:50,000 scale in 1973 [43; 44] and by the Institute of Natural Resources and Land Planning (INDUROT) of the University of Oviedo at a scale of 1:25,000 between 1989 and 1998 [45]. As deduced from this previous work this landslide is characterized by an extent of 0.48 km\({}^{2}\), a perimeter of 2.7 km, and an average slope of \(<\)30\({}^{\circ}\). The minimum and maximum altitudes of the La Miera landslide are 300 and 465 m a.s.l and its main orientation is W-SW. The entire landslide is covered by forest, meadows, and pastures together with two settlements, La Miera and Los Campos, which are composed of dozens of houses and other buildings. To the North of the landslide boundary is El Valle village, with several dozen houses and buildings (Figure 1). The houses and buildings of these three villages were constructed between the end of the 19 th century and the 2010s [46]. In addition, there is a paved road that connects Pola de Lena with these villages and two power lines that cross the landslide area from South to North (Figure 1).  
From a geological point of view, the La Miera landslide is located in the Central Coal Basin (Cantabrian Zone) [47] (Figure 1A-C). The bedrock consists of Upper Carboniferous lutite, limestone, sandstone, and the scarce coal layers of the Lena Group [48]. The most important geological structure in the area is a reverse fault following a mainly N-S direction, conditioning the trend of the alluvial plain (Figure 1D). Landscape evolution in the area is conditioned by the fluvial activity of the Lena River, which runs at the foot of the landslide (Figure 1D), and gravitational mass movement processes with a variety of sizes: slides, flows, and rockfalls [4]. The Lena River, due to the high mobility of its channel (of the order of hundreds of meters), was channelized in 1955 so that at present, there is no channel migration. La Miera landslide is an ancient mass movement mainly composed of heterometric fragments of lutite, limestone, and sandstone embedded in a clayey matrix [43; 44; 45] (Figure 1D). The thickness of the landslide is unknown because no drilling or geophysical techniques have ever been applied to this mass movement. As mentioned above, previous studies were only focused on the landslide contour mapping. There are no previous investigations in which motion rates have been measured [43; 44; 45]. Currently, the landslide exhibits signs of geomorphological activity and evidence of building damage. The age proposed by the geological mapping conducted in 1973 is inaccurate, with an estimation ranging between the Pleistocene and the Holocene [43; 44].
# Artificial Intelligence and Employment: A Delicate Balance Between Progress and Quality in China
## 2 Literature Review, Theoretical Analysis, and Research Hypotheses
### Global Trends and Debates on AI and Employment  
The global discourse on AI and employment highlights dual narratives of opportunity and disruption. While developed economies like the U.S. and Germany report AI-driven productivity gains and high-skilled job creation, these benefits often coincide with skill polarization and wage stagnation for low-skilled workers [2, 3, 4, 5, 6, 7, 8, 9, 10, 11]. In developing economies, such as India and Brazil, rapid AI adoption exacerbates regional disparities, displacing labor-intensive roles while struggling to upskill workforces. China's experience mirrorsthese trends but offers unique insights due to its vast regional heterogeneity, where coastal hubs like Shenzhen integrate AI into advanced manufacturing, while inland regions lag in technological infrastructure.  
In developed economies, the adoption of AI has been associated with the creation of high-skilled jobs, even in traditionally low-skilled industries [12]. However, this trend has also led to concerns about skill polarization, where high-skilled workers benefit from AI-driven productivity gains, while low-skilled workers face job displacement and wage stagnation [13]. These challenges are particularly relevant in developing economies like China, where rapid technological adoption coexists with significant regional economic disparities and varied industrial structures [14].  
The psychological impacts of AI on employees vary, including job insecurity and anxiety, optimism, job satisfaction, resistance to AI adoption, and motivation for skill enhancement [12; 13; 14; 15]. These psychological responses significantly influence workforce dynamics and employment quality. They underline the necessity for targeted interventions, such as reskilling programs, trust-building initiatives, and mental health support, to mitigate adverse effects and enhance the workforce's adaptability. Some individuals experience anxiety or hesitation due to job insecurity, while others demonstrate optimism and job satisfaction resulting from improved working conditions and opportunities for skill enhancement. Targeted interventions, including reskilling programs, trust-building activities, and mental health assistance, are crucial to alleviate these consequences and improve staff adaptability.
## 4 Trusted Network Secure Routing Algorithm
### Node Reputation
#### 4.1.3 Comprehensive Reputation Value  
By combining the results from the sections above, the final formula for the reputation value of a node is obtained as follows:  
\[R_{ij}=\lambda R_{ij-d}+(1-\lambda)R_{ij-in} \tag{11}\]  
In Equation (11), \(R_{ij,d}\) represents the direct reputation-evaluation value of node \(i\) with respect to node \(j\), \(R_{ij,in}\) represents the indirect reputation value of node \(i\) with respect to node \(j\) obtained through node \(k\), and \(\lambda\) represents the weight of the direct credit value; the greater the value of \(\lambda\) is, the greater will be the influence of the direct credit value on the total credit value. This paper assumes that all nodes in the sensor network have the ability to detect the communication abilities of other nodes. Node \(i\) has direct trust relationships with multiple nodes and can accept the recommendations of adjacent nodes for trusted nodes \(j\). Trust is based on observations and evaluations of other nodes collected over a period of time to evaluate the node credibility.
## 3 Case Studies
### Dams with a Low Groundwater Level
#### 3.1.2 Proposed Calculation Method  
According to the engineering design information, the values of the variables needed for calculations are depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Positions**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Design Flood Level**} & \multicolumn{2}{c}{**Check Flood Level**} & \multirow{2}{*}{**RMSE**} \\ \cline{3-4} \cline{6-7}  & & **Leakage (m\({}^{3}\).d\({}^{-1}\))** & & **RE (\%)** & **Leakage (m\({}^{3}\).d\({}^{-1}\))** & **RE (\%)** & \\ \hline \multirow{4}{*}{Blanket \(Q_{t}\)} & Proposed method & \multirow{2}{*}{2716.46} & \multirow{2}{*}{4.20} & \multirow{2}{*}{3896.64} & \multirow{2}{*}{4.36} & \multirow{2}{*}{138.73} \\ \cline{2-4} \cline{6-7}  & USBR method & & & & & \\ \cline{2-4} \cline{6-7}  & Numerical method & \multirow{2}{*}{3173.71} & \multirow{2}{*}{21.7} & \multirow{2}{*}{4021.82} & \multirow{2}{*}{7.71} & \multirow{2}{*}{449.51} \\ \cline{2-4} \cline{6-7}  & Numerical method & & & & & \\ \hline \multirow{4}{*}{Blanket \(Q_{t}\)} & Proposed method & \multirow{2}{*}{2606.97} & \multirow{2}{*}{/} & \multirow{2}{*}{3733.84} & \multirow{2}{*}{/} & \multirow{2}{*}{/} \\ \cline{2-4} \cline{6-7}  & Proposed method & & & & & \\ \cline{2-4} \cline{6-7}  & Casinader method & & & & & \\ \cline{2-4} \cline{6-7}  & [PERSON] method & & & & & \\ \cline{2-4} \cline{6-7}  & [PERSON] method & & & & & \\ \cline{2-4} \cline{6-7}  & Numerical method & & & & & \\ \hline \multirow{4}{*}{Total \(Q_{total}\)} & Proposed method & \multirow{2}{*}{3080.63} & \multirow{2}{*}{7.92} & \multirow{2}{*}{4674.23} & \multirow{2}{*}{6.84} & \multirow{2}{*}{265.19} \\ \cline{2-4} \cline{6-7}  & Numerical method & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the calculated results between the calculation method and the numerical simulation for the Fukang composite geomembrane slope-wall rock-fill dam.  
Figure 5: Potential distributions of the seepage field at a typical cross-section of the dam body: (**a**) in the designed flood level condition; (**b**) in the check flood level condition.  
We conclude the following:  
\(t=1\) mm, \(\delta=1\) mm, \(\gamma=16.29,\ m_{1}=0.82,\ m_{2}=0.52,\ L_{0}=142.54\) m, \(B_{t}=205.26\) m  
\(H_{1}=upstream\ water\ level-1795.8\) m.  
By substituting the above values into Equation (15), the seepage flux of the Fukang slope-wall rock-fill dam can be calculated as shown in Table 2.
## 4 Analysis of the Experimental Results
### Comparison of MPC and Multi-Path Fronthaul with Duplication (MPD)
Thus, in this case, where the extra delay is simulated in path 2, MPC is more affected than MPD because MPC needs to wait for more encoded blocks to be received to successfully decode the blocks. Here again, taking the maximum acceptable RTT latency of 500 \(\upmu\)s on the fronthaul for the MAC-PHY split [14], which is shown by the vertical black line in Figure 9, MPC can achieve an error probability of \(10^{-5}\) at latency 158 \(\upmu\)s earlier than the maximum allowed RTT. Aside from the benefit of MPC in enhancing reliability while the latency remains below a strict latency bound, another aspect to consider is the load on the fronthaul network introduced by MPC and MPD. For each data point of size \(S\) bits, MPD duplicates a full copy of the data into \(n\) blocks of size \(S\) bits. On the other hand, an \((n,k)\) MPC encodes the data into \(n\) blocks of size \(S/k\) bits. For example, if 1 packet of size \(S\) bits is to be transmitted every \(t\) milliseconds on average, the load on the fronthaul generated by MPC and MPD, denoted as \(\text{MPC}_{\text{load}}\) and \(\text{MPD}_{\text{load}}\) respectively, can be calculated as:  
Figure 9: UDP latency functions for MPC with \(k=2\) and MPD when adding a delay to fronthaul path 2. _The dashed line represents the maximum latency to be supported by the MAC–PHY split_. \[\text{MPC}_{\text{load}} =(S\times 1000/t)\times n/k\;(\text{bits per second}) \tag{4}\] \[\text{MPD}_{\text{load}} =(S\times 1000/t)\times n\;(\text{bits per second}).\]  
Equation (4) demonstrates the potential benefits of MPC over MPD in reducing the load on the fronthaul network, allowing for a significant reduction in the required bandwidth by a factor of \(k\) (\(1<k<n\)), resulting in a reduction in fronthaul cost. The results obtained under the particular system parameters set out in this paper show that MPC performs well despite the delay that is introduced into the fronthaul network. The budget latency, however, can be lowered by considering some aspects of the experiment deployed in Section 3, such as the deployment of specific devices such as accelerators for the encoder/decoder and RAN functions that are computationally intensive, and the use of the Data Plane Development Kit for fast packet processing.
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 3 Methods
### Principal Component Analysis  
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation. As a result of applying PCA, a set of data of possibly correlated variables is converted to a set of values of linearly uncorrelated variables called principal components. The first component has the largest variance, and each following component in turn has the largest variance among the rest. Therefore, it is expected that only the first few principal components are able to express the data in a new set of orthogonal coordinates approximately, but quite accurately. As a consequence, PCA decreases the dimension of the data and thus reduces the computation time [18]. In this study, we applied PCA to the dataset, and we selected features that can explain 90% of the total variance. As a result, the new dataset consisted of more efficient and representative variables. We applied PCA in all of the experimental cases including the real wave power station experiment.
###### Abstract  
The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humul areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis (VPMchl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001-2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models' validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C\(\cdot\)m\({}^{-2}\cdot\)yr\({}^{-1}\), respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002-2003, 2011-2013 and 2015-2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.  
g 2022 14
# An Efficient Probe-Based Routing for Content-Centric Networking
## 4 Performance Evaluation
### Metrics  
In this paper, eight performance measurements were adopted for evaluation. They are defined as follows:  
* _Effectivity of probe_ is represented by the accuracy of the content provider and the routing cost. The accuracy of the content provider is defined as the ratio of data packets received by _I router_ that is consistent with its expected content provider. The routing cost is defined as the number of hops a data packet passed by to reach _I router_. This metric helps to select a more efficient _probe_.
* _Total number of forwarded interests_ is the amount of interests forwarded by routers. In other words, consumer-issued initial interests are not counted. This metric presents the network congestion.
* _Number of timeout interest_ is the amount of PIT entries missed deadline. Each PIT entry has a deadline. The router selects another content provider in FIB as the best content provider for the time-out entry in PIT and then re-sends the interest packet. This metric is used to demonstrate the accuracy of routing table.
* _Average response time (Latency)_ is the average time that takes for _I router_ to get a data packet response. This metric is used to compare the routing efficiency.
* _Throughput_ is the rate at which network data is successfully transmitted in a certain amount of time, and its units include bits, bytes, and packets per second [15]. In this paper, throughput is defined as the sum of all routers' received packets divided by the simulation duration.
* _Packet Loss_ indicates that the packet is dropped before reaching its designated destination [15]. It is measured as a rate of packets loss compared to packets sent. This metric allows observing network collisions and congestion. The formulation is \[Packet\ loss=\frac{(\textit{Sent packets}-\textit{Received packets})}{\textit{Sent packets}}\times 100\%\] (1)
* _Delay_ is the total nodal time for a packet to be transmitted from one communication endpoint to another over the network, including node processing delay, queuing delay, transmission delay, and propagation delay [42]. The more the network traffic, the longer the delay. The equation is \[Average\ Delay=\frac{\textit{Sum of packets Delay}}{\textit{Total number of packets}}\] (2)
* _Jitter_ is used to describe the degree of network delay variation [15]. The formulation is \[Jitter=\sqrt{\frac{\sum(Delay-\textit{Average Delay})^{2}}{\textit{Received packets}-1}}\] (3)
## 3 Results
### Results and Analysis
#### 3.3.3 Training and Testing of the Target Task Network  
Based on the feature-specific target task networks described in Section 2.2.4, the training and validation sets from the target task sample library were utilized to train the corresponding feature extraction networks for the three features. The validation curves derived from the training process of each feature-extracting network are illustrated in Figure 16.  
Figure 14: A schematic diagram of the verification process.  
Figure 15: Validation accuracy curves in meta-learning: (**a**) meta-learning validation curve for the spatial feature P3 SF; (**b**) meta-learning validation curve for the time–frequency feature ETFF; (**c**) meta-learning validation curve for the Doppler feature DFSDF.  
In Figure 16, each step on the horizontal axis represents a training iteration with 20 samples, and the entire process encompasses two complete training epochs. As shown via the curves, when meta-knowledge is utilized, the validation accuracy rates of the target task networks for spatial, time-frequency, and Doppler features after three steps of training are 87.3%, 91.2%, and 84.8%, respectively. Upon the completion of training, the test set samples were employed for evaluation. The test accuracy rates of the target task networks for spatial, time-frequency, and Doppler features are 93.6%, 90.2%, and 87.8%, respectively. The relationship between the test accuracy rates for each feature and the target relative horizontal orientation angle is depicted in Figure 17.  
As shown in Figure 17, the spatial feature P3 SF achieves a classification accuracy of 87% at a relative horizontal orientation angle of approximately 15\({}^{\circ}\). This successfully addresses the challenge of distinguishing between multiple-source interferences and volume targets at small relative horizontal orientation angles in conventional spatial spectrum estimation. The time-frequency feature ETFF demonstrates a marked decrease in classification accuracy for samples with a relative horizontal orientation angle exceeding 75\({}^{\circ}\); moreover, the Doppler feature DFSDF shows relatively low classification accuracies for samples with a relative horizontal orientation angle below 30\({}^{\circ}\). The two features, ETFF and DFSDF, exhibit significant complementary advantages in relation to the relative horizontal orientation angle.
# Low-Frequency Expansion Approach for Seismic Data Based on Compressed Sensing in Low SNR
## 2 CS Theory
When measurement matrix \(\mathbf{\Phi}\) satisfies RIP, Formula (5) can be transformed into a convex optimization problem that is numerically easy to deal with the I1 constraint:  
\[ \begin{split}\min&\|\mathbf{\Psi}^{T}\mathbf{E}\|_{1}\\ s.t.\mathbf{A}^{CS}\mathbf{E}&=\mathbf{\Phi}\mathbf{\Psi}\mathbf{E}=\mathbf{D} \end{split}  \tag{6}\]  
The method of solving Equation (6) can be roughly divided into three categories: a greedy algorithm, convex optimization algorithm and other algorithms (such as Bayesian class, iterative threshold, minimum total variation and related improved algorithms). The greedy algorithm [34; 35; 36] selects a local optimal solution in each iteration of the solution process, and finally obtains the global optimal solution. The most important feature of this algorithm is its fast solving speed. The disadvantage of the algorithm is that it requires a great deal of input data, and the accuracy of the optimal solution is low. A convex optimization algorithm [37; 38] is a more global optimization to improve the inefficiency in the greedy algorithm. It converts the non-convex optimization problem into a convex optimization problem to calculate the approximate value of the signal and minimizes it under the global criterion. This algorithm has a high calculation accuracy and requires less measurement data. The disadvantage of this algorithm is its slow calculation speed. Iterative threshold technology is often used in sparse optimization algorithms [39; 40; 41]. For example, when processing high-dimensional data, this technology can reduce the computational complexity and improve computational speed in each iteration.
## 4 Dynamic Response Analysis of the RPC Frames Under Coupled Fire and Explosion
### Recommendations for the Design of Frame Structures Under the Coupled Fire and Explosion  
The numerical simulation results of this study can provide a basis for the design of frame structures. It can be found that the stresses of the frame structure in fire and explosions are as follows. Firstly, the beams connected with the floor slabs undergo bending and shear damage, which causes the floor slabs to fracture or even completely shatter under the explosion loads, leaving only the beam and column structures. Secondly, the bending and shear damage to the beams and columns caused the bottom columns to be removed from the overall structure, and finally, the frame structure collapsed due to the redistribution of the internal forces in the structure as a result of the removal of the bottom columns.  
Through numerical simulation analyzing the damage process of RC frame and RPC frame under the action of fire and explosions, we can obtain a method to improve the explosion resistance of the frame. Considering the economy, it is found that the method of local replacement of RPC materials and local reinforcement of RC frames can be used to enhance the blast resistance of frame structures in fire and explosions. The probability of fire and explosions on the ground floor of the structure is very high, so the blast resistance of the frame structure and the risk of continuous collapse can be improved by reinforcing the important load-bearing members on the ground floor. For example, the ground floor columns can be designed as RPC columns or RPC reinforcement can be added to the ground floor RC columns. In addition, under the coupled effect of fire and explosions, the beams of the concrete frame structure closest to the source of the explosion are susceptible to bending damage. At the same time, the beams at both ends of the restraint produce shear damage, which leads to damage to the adjacent floor slabs. Therefore, large-span beams and main beams can be replaced by RPC materials, which can improve their local resistance. Finally, the blast resistance of the frame can also be improved by increasing the hoop ratio of the ground floor columns and the reinforcement ratio at beam-slab intersections and by installing blast-resistant walls and blast-resistant windows.
# New Space Engineering Design: Characterization of Key Drivers
## 2 Methodology
### Literature Research  
The literature research was conducted using established databases, including Scopus and Google Scholar. Given the novelty and complex dynamics of the New Space field, additional sources, such as news articles, trade newsletters, and the websites of key manufacturers, were consulted. This approach enabled the creation of an extensive preliminary database, offering a robust foundation for subsequent analysis.