esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Due to the effects of reservoir level fluctuations, there exist unstable seepage flows in the dam body [22]. [PERSON] [23] used the ADALINE network to simulate rainfall infiltration effects and applied it to the processing of dam data. [PERSON] [11] established a diurnal variation monitoring model of dam seepage elements, where a lag effect function based on unstable seepage theory was introduced. [PERSON] [24] used the finite element method to analyze the rainfall and water level changes' influence on Hualianshu landslide seepage by using the saturated-unsaturated seepage theory. [PERSON] [25] calculated the positional changes of the water table line within the dam body and the corresponding seepage field parameters during the rise in the reservoir water level, discovering the effect of rising levels on upper loose accumulations, which was fairly obvious. [PERSON] [26] simulated the dissipation process of pore water pressure during the rapid decline in the reservoir level and drew relevant security conclusions. [PERSON] [27] discovered the relationship between slope stability and hydraulic conductivity based on Three Gorges Dam in China. The inverse research based on monitoring data and numerical simulation results demonstrates economy and efficiency. In the inversion analysis, optimization algorithms are widely applied for iterative searches over a range of permeability coefficient values. The optimal value is determined by the objective function minimum [28]. [PERSON] [29] constructed an inverse model for the permeability coefficient of a high core rockfill dam based on Particle Swarm Optimization. [PERSON] [30] proposed an improved greedy sampling method based on model reduction technology to rapidly estimate the hydraulic conductivity of a steady state problem. In recent years, with the rapid development of machine learning, many scholars have attempted to use support vector machines [31] and back propagation artificial neural networks [32] for applications in inverse fields, while a BP network is a multi-layer feedforward network trained by the error backpropagation algorithm and is also one of the most widely used network models. Its learning principle is using the steepest descent method to adjust thresholds and weights to make the network's sum of squares of errors the minimum. It has been proven that BP networks have great accuracy in predicting parameters on the basis of observed value in various engineering research areas [33; 34; 35]. The marine predator algorithm is a nature-inspired metaheuristic, applying Levy and Brownian movements in design, and is confirmed to be a high-performance optimizer [36]. Due to its excellent performance, it can be used to search for the optimal solution during the inversion process to minimize the objective function. The main contributions of this study are as follows: (1) A method for seepage parameters inversion based on an advanced optimization named the Marine predator algorithm is developed; (2) Three different methods for seepage parameters inversion are compared, and the advantage of the Marine predator algorithm in seepage parameters inversion is proposed.
## 3 Results
### NDVI-Biomass Comparisons  
Comparison of NDVIG and the different metrics of biomass for all sampling dates and both sites yielded significant non-linear, exponential relationships (Figure 6). The correlations between NDVIG and the green fraction of the biomass were much stronger than the total biomass and total standing biomass. Of all the biomass metrics, green standing biomass had the strongest correlation with NDVIG (\(R^{2}=0.85\)).  
The NDVI-biomass relationship was also examined for each month. The peak-season results yielded a stronger correlation (\(R^{2}=0.97\), Figure 7) than the comparable figure using all-season data (\(R^{2}=0.85\), Figure 6D). The cause of this improved correlation is evident in the seasonal progression in the NDVI-biomass relationship (see Figure S1, Supplementary Materials), and selection of peak-season (July) data greatly improved the overall NDVI-biomass fit (Figure 7). When the biomass data were log-transformed, the relationship between NDVI and green standing biomass became linear (cf. Figure 7A,B).  
Figure 5: Time series of NDVI derived from ground spectrometry (NDVIG) (**A**) and harvested green fraction of the standing biomass (**B**) from both grazing reserves as well as the time series of NDVI from ground spectrometer data, MODIS data from the Terra and Aqua platforms, and SPOT data from the Pinhorn grazing reserve (**C**) and Sounding Creek grazing reserve (**D**) during the summers of 2009 and 2010. Note that SPOT coverage was missing in June and August 2010 for Sounding Creek. For comparison, average NDVI values for bare soil were approximately 0.1 at these sites. Error bars denote the standard error of the mean.
## 3 RPL Routing Attacks
### Rank Attack  
One of the main RPL design aspects is the ranking mechanism which utilizes the rank property to ensure loop-free routing. It is based on having the rank values increase in the downward direction from the root to the leaf nodes. Accordingly, preferred parent selection is performed on neighbor nodes of lower ranks and better positions only. However, such a mechanism can be exploited by a malicious node to launch the so-called rank attack. Without strict adherence to the above rule, a node can increase its rank value and deceive its neighbor nodes at some point after joining an RPL network. The target is to create a sub-optimal topology causing data traffic to traverse network paths of lower QoS performance. It also can incur unwanted routing loops and drain available resources, particularly when considering large-scale RPL deployments.  
In other cases, the rank attack can be launched by a malicious node, decreasing its advertised rank value. This would attract most of its neighbor nodes as a well-ranked parent candidate. As a result, multiple neighbor nodes would then make changes to their current preferred parents. This then can help in initiating further attacks such as blackhole attacks and cause the situation to be even worse. The main target in both modes of rank attack is undermining network stability.
## 2 System Model
### Considered Scenario  
We consider a single base station (BS) deployment with a circular coverage area of radius \(r\), see Figure 1a. We assume that network slicing is utilized to deliver \(K\) services having different QoS requirements. Further, for brevity, we use \"\(k\)-type session\" to mean \"user's request for service of type \(k^{\prime}\), \(k=1,\ldots,K\). The geometric locations of user equipment (UE)-generating sessions are assumed to be uniformly distributed in the cell coverage. The height of BS and UE is assumed to be \(h_{BS}\) and \(h_{UE}\), respectively.  
The considered system is formalized in terms of queueing theory, see Figure 1b. We consider a queueing system with \(K\) types of traffic assumed to be partially elastic with the minimum and the maximum resource requirements, \(1\leq b_{k}^{\min}\leq b_{k}^{\max},k=1,\ldots,K\), expressed in primary resource blocks (PRB). The resource requirements correspond to the rates \(c_{k}^{\min}\) and \(c_{k}^{\max}\) and are derived below by utilizing the radio part models. We also denote \(\mathbf{b}_{\min}=(b_{1}^{\min},\ldots,b_{K}^{\min})^{T}\), \(\mathbf{b}_{\max}=(b_{1}^{\max},\ldots,b_{K}^{\max})^{T}\). Requests for service of type \(k\) arrive according to the homogeneous Poisson process with intensity \(\lambda_{k}\). The session service time is exponentially distributed with the mean \(\mu_{k}^{-1}\). We assume that \(g_{k},k=1,\ldots,K\), PRBs from \(C\) PRBs are reserved for each type of service requests, \(\sum_{k=1}^{K}g_{k}<C\). Then \(c=C-\sum_{k=1}^{K}g_{k}\) represents the shared pool of resources available for any type of traffic.
## 2 Materials and Methods
### Implementing Digital Signal Processing Techniques for CSI Data Analysis  
We adapted the approach described in [32] to capture real-time CSI data using an ESP32 at the receiving end. The original CSI data are complex numbers and can be represented in terms of either phase or amplitude. Given that CSI data take the form \(h=y/x=v+wi\), we process this data to extract amplitude as \(\sqrt{v^{2}+w^{2}}\) and phase as \(\arctan 2(v,2)\). In this study, we focus on amplitude analysis. The amplitude of the CSI data is given by Equation (1).  
\[\text{Amplitude}=\sqrt{v_{sc}^{2}+w_{sc}^{2}},sc\in[1,52] \tag{1}\]  
where \(sc\) represents the subcarrier index ranging from 1 to 52. The ESP32 identifies 52 active subcarriers, with most showing some degree of offset. For simplicity, we select the 1 st subcarrier for analysis.  
Figure 4: Antennae placement in our experiment for RR monitoring.
## 4 PCA-IWOA-XGBoost Model Construction and Prediction Result Analysis
### PCA Dimensionality Reduction  
Using the occurrence time (\(x_{1}\)), magnitude (\(x_{2}\)), focal depth (\(x_{3}\)), epicentral intensity (\(x_{4}\)), fortification intensity (\(x_{5}\)), \(\beta\) (\(x_{6}\)), population density (\(x_{7}\)), building codes (\(x_{8}\)), topography (\(x_{9}\)), building quality (\(x_{10}\)), and urban-rural classification (\(x_{11}\)) from Table 6 as factors influencing earthquake fatalities, principal component analysis (PCA) was conducted. The data were processed and analyzed using the factor analyze module in SPSS 24 software. First, the data were standardized, and then correlation coefficients between the influencing factors were calculated, with the results shown in Figure 8. From Figure 8, it can be observed that there was a certain degree of correlation between different influencing factors, making them suitable for principal component analysis. Figure 8: Heatmap of correlation analysis for factors influencing earthquake fatalities. To further investigate the degree of representation of each principal component for the original data, the contribution rates and cumulative contribution rates of each principal component are calculated in Figure 9. The cumulative contribution rate of the first six principal components reaches 87%, indicating that these six principal components capture most of the information from the original data and can be used for analysis. Through dimensionality reduction, the number of model features was reduced from 11 to 6, which helped improve the model's computational speed.
# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 3 Methodology
### Data-Preprocessing Module  
The data-preprocessing module of LTS-SEI adopts the simplest processing method for the source domain signal \(\mathbf{X}_{S}\) and target domain signal \(\mathbf{X}_{T}\). We assume that the length of each signal vector of \(\mathbf{X}_{S}\) and \(\mathbf{X}_{T}\) is \(L\). The source domain sample \(X_{S}\) and target domain sample \(X_{T}\) can be obtained through the following three steps.  
(1) We slice the source domain signal \(\mathbf{X}_{S}\) and target domain signal \(\mathbf{X}_{T}\) as follows:  
\[X=\begin{bmatrix}x_{1}&x_{2}&x_{3}&\dots&x_{k}\\ x_{1+s}&x_{2+s}&x_{3+s}&\dots&x_{k+s}\\ x_{1+2s}&x_{2+2s}&x_{3+2s}&\dots&x_{k+2s}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ x_{1+\lfloor\frac{k-s}{s}\rfloor\cdot s}&x_{2+\lfloor\frac{k-s}{s}\rfloor \cdot s}&x_{3+\lfloor\frac{k-s}{s}\rfloor\cdot s}&\dots&x_{k+\lfloor\frac{k-s} {s}\rfloor\cdot s}\end{bmatrix} \tag{2}\]  
where \(k\) represents length of each slice, \(s\) represents slice step size, \(\lfloor\cdot\rfloor\) rounding down, and \(X\) represents sample matrix.
2. We use the following equation to standard-normalize the source domain sample \(X_{S}\) and target domain sample \(X_{T}\). \[X_{i}=\frac{X_{i}-\text{mean}(X_{i})}{\text{std}(X_{i})},i=0,1,\dots,\left\lfloor \frac{L-k}{s}\right\rfloor\] (3)  
Figure 2: Framework of LTS-SEI.  
where \(\text{mean}(\cdot)\) and \(\text{std}(\cdot)\) are used to calculate the mean and standard deviation, respectively.
3. We label all source domain samples \(X_{S}\) based on label set \(\textbf{Y}_{S}\).
## 4 Case Study and Discussion
### Case Study  
#### 4.2.1 Scenarios  
The proposed MILP (mixed-integer linear programming) optimization parameters were implemented in MATLAB Optimization Toolbox. The branch and bound algorithm was terminated after reaching a 0% duality gap. The purposes of the study were to determine the effects of various cases on profit maximization, considering the presence and absence of the criteria presented in Table 7.  
The cases in the study consisted of scenarios in the presence and absence of flexibility, incentive, and reliability criteria. The flexibility scenario demonstrated the effectiveness of the scheduling algorithm, compared with the previous operations. The scenario with the incentive demonstrated the effect of the incentives on the gross revenue. The purposes of the reliability criteria were to determine whether they influenced the gross revenue, as opened in 2020 by the current AOs in South Korea.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Medium-Range Communication Technologies
#### 5.2.3 Limitations  
The DSRC technology has several benefits such as low latency, secure transmission and fast network acquisition [163]. The QoS of DSRC is much better than Wi-Fi but the technology itself faces some technical difficulties [42]. Some technical challenges in DSRC technology are mentioned below.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Wi-Fi** & **DSRC** \\ \hline Specifications & IEEE 802.11a & IEEE 802.11p \\ \hline Width in channel & 20 & 10 \\ \hline Signalling & OFDM & OFDM \\ \hline Data Rate (Mbps) & upto 54 & upto 27 \\ \hline Modulation type & upto 64 QAM & upto 64 QAM \\ \hline Symbol duration (micro sec.) & 4 & 8 \\ \hline Guard Time (micro sec.) & 0.8 & 1.6 \\ \hline FFT Size & 64 & 64 \\ \hline FFT period (micro sec.) & 3.2 & 6.4 \\ \hline Preamble duration (micro sec.) & 16 & 32 \\ \hline Sub carrier spacing (MHz) & 0.3125 & 0.15625 \\ \hline Frequency spectrum (GHz) & 5 & 5.9 \\ \hline Latency (msec) & 50 & 100 \\ \hline Range (m) & 100 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of medium-range technologies.  
* Hidden Terminal Problem: As the MAC layer consists of two parts, the IEEE 802.11p forms the lower layer while as IEEE 1609.4 contains the upper MAC layer. In DSRC technology, when the packets are transmitted, the ACK mechanism does not exist. In addition, The clear-to-send mechanism is not set by default in carrier-sense multiple access with collision avoidance (CSMA/CA) medium access, which results in the hidden terminal problem [164].
* Congestion Issue: As the number of AVs increase, the communication load also increases, which causes congestion [164]. This congestion leads to delay and packet loss that affects the performance of the network. The increased number of AVs also increases the intensity of the channel will result is severe degradation of performance.
* Handover issue: A vehicle that travels with high speed has to change several network topologies over time. Two types of handover occur: (1) horizontal handover; and (2) vertical handover. Horizontal handover is the handover of the moving AV from one RSU to another, whereas vertical handover is handover of an AV between different cellular LTE base stations. A vertical handover takes longer time compared to the horizontal handover [30]. This will result into the above-mentioned hidden terminal problem.
* Doppler Spread: The Doppler spread occurs due to the relative motion of transceivers. The relative velocities shift the signal frequency and the signal at the receiver is at a different frequency compared to the broadcast signal. The transmitted signal in DSRC technology faces this Doppler shift phenomenon due to vehicle motion on road which will affect predicting the exact location and position of the AV [35].
## 5 Problem Solution  
Since value-based functions are suitable for solving discrete space problems, and next-hop relay selection is a discrete action, we choose a value-based reinforcement learning approach for policy optimization. Our proposed scheme has a large number of channel states and queuing states, which leads to a high dimension of the Q-table, and makes the Q-learning [37] algorithm difficult to coverage during training. However, the DQN algorithm can solve this problem, featuring a combination of a deep neural network (DNN) and Q-value. DQN does not directly select the action with the highest Q-value in the Q-table, but fits the \(Q_{\pi}(s_{t},a_{t},\theta)\) through the neural network [38]. Compared to recording the Q-value for an all-action-state situation, DQN just needs to store the weights of each neuron to calculate the Q-values for all policies \(\pi(s_{t},a_{t})\), which greatly reduces the storage space and makes the algorithm converge faster [39; 40].  
Since each node needs to make the decision to select the next-hop node, we use the multiagent dueling DQN (MADDQN) approach. MADDQN treats each node as an independent agent, and all the agents are trained centrally. When making the next-hop selection, the trained network parameters are shared with all nodes, and each node selects the next-hop node individually. The specific process of MADDQN is shown in Figure 2.
## 3 Results and Discussion
### BLUF Proteins for Optogenetic Tools
The BLUF domain associated with the TetR or AcrR domain may be exploited for the light-dependent regulation of pathways (as mentioned earlier) in bacteria. The BLUF domain was also analyzed with the endonuclease 3c and endonuclease_NS domains for their optogenetic potential. The BLUF domain endonuclease 3c could be engineered and used as an optogenetic tool for the light-dependent regulation of the DNA repair process. On the other hand, the BLUF domain associated with the endonuclease_NS domain could be exploited for the light-dependent regulation of processes like hydrolase activity, nucleic acid binding and metal ion binding [126]. The modular architectures comprised of BLUF associated with the AraC and abhydrolase domains could also be harnessed for the light-dependent regulation of the arabin metabolism, as well as the diverse group of hydrolytic enzymes that include proteases, lipases, peroxididases, esterases, epoxide hydrolases, and dehalogenases, respectively [131]. BLUF in combination with the RhoGEF domain is also considered an important modular architecture, which could be used as an optogenetic tool for the light-dependent regulation of a diverse array of cellular processes [142; 143; 144; 146; 148; 149].
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### WSN Physical Network Setup for Node Distribution on the Ground
#### 3.3.3 Large-Scale Physical Ground Network  
The data gathering method using the softwarization concept for a ground network [34] is used in the physical network implementation. Herein, the orchestration data gathering phase is validated using a real-world implementation that can be manipulated to be used in applications such as trap monitoring applications. The factor that reflects the dispersing aspect of elected gateway nodes within the ground network after each reconfiguration election process is called sparsity, obtained as in Equation (3):  
\[\sigma=\sqrt{\frac{\sum_{i=1}^{N}\left(q_{0}^{i}-\mu\right)^{2}}{N}} \tag{3}\]  
wherein \(\sigma\) is the sparsity factor after the election process of \(i\), \(q_{0}^{i}\) are the coordinates of the elected gateway nodes, \(\mu\) is the average coordinate of the elected gateway nodes, \(N\) is the number of gateway nodes. The simulated post-orchestration data gathering phase is structured to observe the impact of varying the density spread factor parameter on the network's performance. In this section, the same network parameters are used to analyze the physical ground network's performance. Hence, out of 20 nodes distributed on the ground, 17 SensorTags are programmed as leaf nodes, while 3 development boards are programmed as coordinator nodes. Each development board is connected via a USB cable to the RPi node as a gateway. The network organization is based on star network structure. Also, the TI 15.4 Stack communication protocol is used for interaction between the SensorTags and the development board. To validate the softwarization concept in a real-world scenario, 3 coordinator nodes along with 17 leaf nodes were distributed in scenarios with varying densities of gateway nodes, as shown in Figure 23 (\(\sigma\) = 1) and Figure 24 (\(\sigma\) = 2). The data gathering time for each density scenario was set to 60 s. The message rate was also increased from 1 to 20 msg/s. The packet delivery and power consumption were utilized to evaluate the communication performance of the proposed physical network model.  
Figure 23: The physical ground network formation for a large-scale network with \(\sigma\) = 1.
## 4 MLA-Assisted Higher Magnification Imaging  
Previously, solid immersion lens (SIL) microscopy usually used single microlens to enhance the magnification [38, 39]. However, SIL microscopy has a limitation in the difficulty of aligning between an objective lens and microlens. The MLA-assisted MCM can provide numerous observation regions through pinhole arrays, which facilitate aligning the lenses and finding the region of interest (ROI) of an object. An enlarged virtual image of a target object can be observed through MLA when the target object is located closer than the focal length of MLA, as shown in Figure 8a. The MLA-assisted MCM can also increase the magnification of the imaging system by capturing a virtual image of MLA.  
Figure 6: High dynamic range (HDR) imaging of MCM. (**a**) An algorithm flow chart of HDR reconstruction. (**b**) Sectional images of monocotyledonous plant stem according to exposure times. (**c**) A reconstructed HDR image by integrating the various contrast images. The scale bar is 100 \(\upmu\)m.  
Figure 7: The corresponding histogram measured from images before and after image reconstruction. The area of HDR image histogram is increased by 1.36 times compared with the single exposure image (0.5 ms).  
However, image overlap can occur through adjacent microlenses; thus, a pinhole mask was formed between a target and MLA. The experimental results show that the MLA-assisted MCM without the pinhole mask offers an overlapped image, whereas the system with the mask provides a clear, magnified image, as shown in Figure 8b,c. The MLA-assisted MCM observes the group 7 element 5 of USAF 1951, which represents that the resolving power of MLA-assisted MCM demonstrates 2.46 \(\upmu\)m, as shown in Figure 8d.  
A scratch pattern with a dimension of 1 \(\times\) 16 \(\upmu\)m (Scratch & Dig Target, ISO 10110, Grade# 0.004) was observed through the MLA-based MCM, as shown in Figure 9. The MLA-based MCM observes a clear pattern compared with the digital zoom image of MCM, which offers a blur edge pattern due to the artificially-enlarged pixels. The magnification of MLA-assisted MCM is 8.43\(\times\), which is 3.92 times higher than that of MCM.
# Flexible 5G New Radio LDPC Encoder Optimized for High Hardware Usage Efficiency
## 2 Encoding of LDPC Codes
### Straightforward and RU Hybrid Encoding  
In order to avoid multiplication with a dense matrix \(\left(-\textbf{ET}^{-1}\textbf{B}+\textbf{D}\right)^{-1}\) from (10), [PERSON] and [PERSON] [28] proposed a hybrid encoding of IEEE 802.3 an codes, which instead uses generator matrix for calculation of first \(g\) parity bits (\(\textbf{p}_{1}\)) and then uses RU's relation from (11) to calculate remaining parity bits (\(\textbf{p}_{2}\)). Such an approach allows easier transformation of the PCM since the constraint for nonsingularity of the matrix \(-\textbf{ET}^{-1}\textbf{B}+\textbf{D}\) is removed. Additionally, the critical path can be shortened, which can lead to faster hardware implementation. The hybrid encoding has also been used for LDPC codes in space applications [29].