esa-sceva/satcom-chunk-collection huggingface dataset:
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 4 Results
### Validation of S1 SSM
The correlation values showed greater variability over the four seasons in the Salamanca region, which is partly due to the higher number of stations in this region. In addition, some of the stations in Salamanca showed no correlation between observed and satellite-derived SSM products. These stations are located in an agricultural area where irrigation may be used. One consequence of irrigation is that the SSM increases regardless of precipitation events. The S1 SSM may be sensitive to irrigation, while in situ observations are not affected by irrigation. Considering the entire study period (see \"All\" box plots in Figure S1), a good agreement was observed for SMOSMANIA and REMEDHUS, with mean correlation coefficients of 0.63 and 0.51, respectively. This indicates that S1 SSM is able to capture the characteristics of in situ measurements. When the analysis was performed for the S1 SSM products at 06:00 UTC, the statistics between these products and the in situ measurements showed a reduced accuracy. The correlation coefficient and unbiased root mean square difference (ubRMSD, Equation (1)) values between the SMOSMANIA, Meteopole-Flux, and REMEDHUS stations and the S1 SSM products for the whole study period are listed in the Supplementary Materials (Table S1). It is shown that good correlation coefficients were obtained for most of the stations (F-test \(p\)-value \(<\) 0.01), with the exception of Las Brozas in the Salamanca region, which showed a poor value of 0.09 and a \(p\)-value = 0.1. For all the other stations, \(p\)-values lower than 0.01 were obtained, indicating that the correlation was significant. Regarding the ubRMSD, typical values ranging from 0.030 to 0.120 m\({}^{3}\) m\({}^{-3}\) were found for all stations. The availability of S1 SSM products is better for Salamanca than for the two regions in France, leading to a larger number of observations for Salamanca, ranging from 180 to 361. Note that a better correlation of OL WG2 with in situ SSM observations was systematically observed for all stations (Table S2). Among the 26 stations considered in Table S2, OL WG2 \(R\)-values less than 0.6 were observed for only 4 REMEDHUS stations: El Coto, Las Brozas, Las Tres Rayas, and Las Victorias (\(R\) = 0.44, 0.54, 0.59, and 0.58, respectively). S1 SSM presented smaller ubRMSD values than OL WG2 for only six stations: LGC, Carretero, El Coto, El Tomillar, Las Victorias, and Paredinas.
## 4 Methodology  
The overall framework of the proposed DSTGFCN is shown in Figure 2. This framework employs an encoder-decoder structure to facilitate multi-step prediction. Inspired by the research in [13], we substituted all the linear layers in the GRU with graph convolutions to construct GC-GRU as the fundamental unit for spatio-temporal modeling. During the encoding stage, the dynamic graph fusion module extracts spatial information for each time step based on the traffic state, time information, and learnable spatial node embeddings to model dynamic spatial correlations. Then, it fuses the dynamic spatial features of adjacent time slots to generate a dynamic graph. The GC-GRU receives the dynamic adjacency matrix to model dynamic spatio-temporal correlations. Simultaneously, the spatial node embeddings adequately learn the dynamic and latent spatial information from historical traffic data to construct an adaptive adjacency matrix. Since future traffic states cannot be observed during the decoding phase, the decoder utilizes an adaptive adjacency matrix to model spatio-temporal correlations and achieve multi-step traffic prediction. Furthermore,residual connections are added between layers of the GC-GRU to harness the capacity of the multi-layer network in a stable training process.
## 2 Materials and Methods
### Shielding Parameters  
If the attenuator is located between the detector and the source, the amplitude of the primary gamma-ray decreases exponentially according to the Beer-Lambert law [23; 24]:  
\[I=I_{o}e^{-\mu x} \tag{1}\]  
\(I_{o}\) is the intensity of primary gamma, while \(I\) is the intensity of transmitted gamma through the glass. In addition, \(\mu\) indicates the linear attenuation coefficient of the energy of interest. The definition of \(x\) is the attenuator thickness. When it comes to a compound, the mass attenuation coefficients are calculated as [25; 26]:  
\[MAC=\sum_{i}w_{i}\left(MAC\right)_{i} \tag{2}\]  
The term \(w_{i}\) represents weight fraction of the \(i\)th constitute elements.  
The total effective atomic cross-section was determined by taking into account the effective atomic number, effective electron density depending on the total molecular cross-section (\(\sigma_{t}\)), and the total electronic cross-section (\(\sigma_{a}\)) [27; 28; 29].  
\[\sigma_{t}=\frac{1}{N_{A}}\sum_{i}n_{i}A_{i}\left(MAC\right)_{i} \tag{3}\]  
\[\sigma_{a}=\frac{1}{N_{A}}\sum_{i}f_{i}A_{i}\left(MAC\right)_{i} \tag{4}\]  
\[\sigma_{e}=\frac{1}{N_{A}}\sum_{i}\frac{f_{i}A_{i}}{Z_{i}}\left(MAC\right)_{i} \tag{5}\]  
Figure 1: MCNPX simulation setup for gamma-ray transmission studies (MCNPX Visual Editor).  
\[Z_{eff}=\frac{\sigma_{a}}{\sigma_{e}} \tag{6}\]  
\[N_{eff}=\frac{(MAC)}{\sigma_{e}} \tag{7}\]  
The used parameters are as follow:  
\(n_{i}\): number of atoms  
\(A_{i}\): atomic weight  
\(Z_{i}\): atomic number  
\(f_{i}\): fractional abundance of ith element  
\(N_{A}\): Avogadro number  
A certain attenuator thickness will decrease the absorbed gamma strength by a factor of two; this is called the half-value layer, and it is a function of distance from the source [9; 30]:  
\[HVL=\frac{ln(2)}{LAC} \tag{8}\]  
The term one mean free path (MFP) describes an absorption of 0.368 of the initial gamma radiation [31; 32]:  
\[MFP=\frac{1}{LAC} \tag{9}\]  
As a vital gamma ray shielding parameters exposure buildup factor (EBF), energy absorption buildup factor (EABF) is also a remarkable shielding parameter to obtain total contributions to gamma attenuation rays in a material environment. Currently, exposure buildup factor (EBF) and energy absorption buildup factor (EABF) parameters were also calculated using the Geometry-Progressive (G-P) fitting method. A thorough explanation of the above mechanism can be found in our previous reports.
# Experimental Investigation of Mechanical Properties of Clay-Cement Slurry Containing Graphene Oxide
## 3 Results
### Anti-Permeability Performance of Grouting Reinforcement  
Under the action of an air compressor, a certain amount of high-pressure gas is injected into the water tank. The pressure of the inflow pipe is kept constant by adjusting the valve of the outflow pipeline. The pressure at the outflow end can be obtained through a pressure gauge, and the outflow time and pressure can be recorded. Combined with parameters such as the size of the test piece and the water head pressure applied, the permeability coefficient of the specimen can be calculated. According to [PERSON]'s law, the seepage flow is calculated using the following formula:  
\[Q=KAJt=KA\frac{\sigma_{1}-\sigma_{2}}{\rho gL}\Delta t \tag{2}\]  
where \(\Delta t=t_{2}-t_{1}\), \(\Delta\sigma=\sigma_{1}-\sigma_{2}\); the permeability coefficient can be expressed as  
\[K=\frac{QL\rho g}{A\Delta\sigma\Delta t} \tag{3}\]  
where \(Q\) represents the permeation flow rate; \(K\) is the permeability coefficient of the specimen; \(A\) is the cross-sectional area of the specimen; \(j\) is the hydraulic gradient; \(\sigma_{1}\) is the pressure at the inlet of the grouting pipe at \(t_{1}\); \(\sigma_{2}\) is the pressure at the inlet of the grouting pipe at \(t_{2}\); \(L\) is the length of the specimen; \(\rho\) is the density of water; and \(g\) is the acceleration due to gravity.  
In this experiment, three levels were investigated for each factor under consideration. An orthogonal experimental design table with three factors and three levels was utilized. Additionally, three parallel samples were prepared for each mixture ratio. The specimen ratio of gravel grouting test is shown in Table 4.  
Table 5 presents the results of the permeability coefficient and compressive strength tests of the slurry-stone body after 28 days under different slurry ratios, grouting pressures, and formation drainage velocities. Analysis of the test results in Table 3 shows that the compressive strength of the stone body is above 1.5 MPa at 28 days. The permeability coefficient of the stone body is significantly affected by the grouting parameters and formation drainage velocity. There are differences in the permeability coefficients of the slurry-stone bodies under different parameters, and each group's permeability coefficient is around \(1\times 10^{-6}\mathrm{cm/s^{-1}}\). This indicates that the clay-solidified slurry can fully meet the requirements of water-rich karst formations for reinforcement and impermeabilization by filling and grouting.
## 4 Experimental Setup and Results
### Evaluation Metrics
The Alpha follows at 0.8652, with Synaptic and Lapicque trailing at 0.7819 and 0.6228, respectively. Performance falls off sharply at 15 neurons and below. The 15n LIF is the only model with a respectable F1 Score of 0.7942. The Synaptic, Lapicque, and Alpha models are all severely imbalanced, with extremely low Recall or Precision. The 10-neuron models are the weakest overall, with the LIF having the highest F1 at 0.7846. The Lapicque fails to make correct positive predictions, while the Alpha is heavily biased towards Recall over Precision. As can be seen, in summary, the 100-neuron Alpha model achieves the best overall performance based on the F1 Score, followed closely by the 100n LIF and 80n Alpha. In general, model performance improves with higher neuron counts, with most models seeing \begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **Recall** & **Precision** & **F1 Score** \\ \hline
100n-LIF & 0.851 & 0.9366 & 0.8918 \\
100n-Synaptic & 0.8484 & 0.9274 & 0.8861 \\
100n-Alpha & 0.8738 & 0.9421 & 0.9067 \\
100n-Lapicque & 0.5233 & 0.7866 & 0.6285 \\
80n-Alpha & 0.8958 & 0.9158 & 0.9057 \\
80n-Synaptic & 0.8894 & 0.9131 & 0.9011 \\
80n-LIF & 0.7416 & 0.9267 & 0.8239 \\
80n-Lapicque & 0.7458 & 0.5965 & 0.6628 \\
50n-LIF & 0.7956 & 0.88852 & 0.838 \\
50n-Synaptic & 0.8692 & 0.8904 & 0.8796 \\
50n-Alpha & 0.8332 & 0.9139 & 0.8717 \\
50n-Lapicque & 0.7803 & 0.5567 & 0.6498 \\
30n-LIF & 0.879 & 0.8986 & 0.8887 \\
30n-Synaptic & 0.8358 & 0.8598 & 0.8476 \\
30n-Alpha & 0.8543 & 0.836 & 0.845 \\
30n-Lapicque & 0.4543 & 0.7479 & 0.5652 \\
25n-LIF & 0.859 & 0.9202 & 0.8886 \\
25n-Alpha & 0.76 & 0.8705 & 0.8114 \\
20n-LIF & 0.8966 & 0.8878 & 0.8922 \\
20n-Synaptic & 0.6967 & 0.891 & 0.7819 \\
20n-Alpha & 0.866 & 0.8643 & 0.8652 \\
20n-Lapicque & 0.7978 & 0.5108 & 0.6228 \\
15n-LIF & 0.7346 & 0.8643 & 0.7942 \\
15n-Synaptic & 0.0784 & 0.9294 & 0.1446 \\
15n-Lapicque & 0.0079 & 1 & 0.0157 \\
15n-Alpha & 0.8224 & 0.3234 & 0.4643 \\
10n-Lapicque & 0 & 0 & 0 \\
10n-Alpha & 0.8224 & 0.3234 & 0.4643 \\
10n-Synaptic & 0.6802 & 0.803 & 0.7366 \\
10n-LIF & 0.6987 & 0.8947 & 0.7846 \\ \hline \end{tabular}
\end{table} 
Table 2: Recall, Precision, and F1 Score metrics across the models for the final epoch (index 9). a drop-off below 20 neurons. The Alpha models tend to have the most balanced Recall and Precision, while the Lapicque consistently underperforms due to low Recall. LIF and Synaptic fall in the middle of the pack. The ideal neuron count is in the 80-100 range for this dataset and model architecture.
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 3 Basic Principles of Servomotor Control
### Feedback Control Principle in Servomotors  
Feedback control systems are closed-loop control systems that compare the actual output signal with the desired one. Based on the difference between these values, they adjust the control settings, thereby changing the system's behavior to minimize the deviation of the output signal. In the case of servomotors, feedback control systems provide a specified value for the position, speed, or any other output parameter of the motor. Based on [63; 64; 65; 66; 67], it is possible to identify the key aspects in the construction of this principle and draw conclusions about its advantages and disadvantages.  
A feedback control system consists of the following components:  
* Sensor. In the case of servomotors, encoders or potentiometers are mainly used to continuously track the speed or position of the motor's output shaft.
* Controller. This is the part responsible for processing feedback signals and generating control actions through an integrated controller to the motor. Most control systems use a PID controller for its speed and minimization of output error.
* Desired output signal. This is the target value that the motor control system aims to achieve. Any parameter can be taken as the desired value, forming the basis for the control system.  
The working principle of a feedback control system is quite simple and operates on a clear algorithm. The sensor continuously monitors the output value in real time and sends data to the controller. The error is then calculated by comparing the signals, and a control action is output. Using the controller and its components, a control signal is generated to minimize the deviation and increase the stability of the control system.  
Advantages of feedback systems include:  
1. Accuracy. Continuous control of the output value allows feedback systems to achieve high levels of maintaining the desired output signal.
2. Dynamic response. Feedback control systems provide a quick response to changes in load, disturbances, or noise, allowing servomotors to be used in changing conditions.
3. Reduction in static error. The controllers used in these systems minimize static error and reduce the transient process time.
4. Stability. The closed-loop control increases the overall stability of the control system.
## 3 Specific Absorption Rates and Radiated Power Density in the Vicinity of the Structures Analyzed
For this study, we had as a guideline the exposure limits from the USA (10 W/m\({}^{2}\)) and from countries such as Poland, China, Italy and France (1 W/m\({}^{2}\)), and the radiated power density was calculated with Formula (1)  
\[P_{d}=\frac{P_{t}\cdot G_{t}}{4\cdot\pi\cdot R} \tag{1}\]  
where \(P_{d}\) is the radiated power density in W/m\({}^{2}\), \(P_{t}\) is the transmitter power in Watts, \(G_{t}\) is the gain of transmitting antenna and \(R\) is the distance from the antenna in meters. The results after varying \(R\) from 0.01 m to 1 m can be observed in Table 8. It can be determined that the antenna exceeds the limits if the distance between the antenna and the human tissues is less than 0.1 m. If the distance is 0.5 m, the limits are exceeded only for Poland, China, Italy and France, while at a distance of 1 m, the limits imposed are in accordance with the results obtained. Thus, the same conclusions can be drawn: namely, that the antenna must be placed a further from the human body.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### Meta-Heuristic Algorithms
#### 3.4.4 Artificial Bee Colony (ABC)  
Artificial bee colony (ABC), presented in [54], is one of the most recently defined algorithms by [PERSON] in 2005, motivated by the intelligent behavior of honey bees. Similar to PSO and differential evolution (DE) algorithms, ABC employs common control parameters, such as the size of the colony and the maximum number of cycles. ABC operates as a population-based search algorithm, where individuals, referred to as food positions, are iteratively modified by artificial bees over time. The primary goal of the bees is to locate food sources with high nectar levels, ultimately aiming to identify the one with the highest nectar content. In this system, artificial bees navigate a multidimensional search space, with employed and onlooker bees selecting food sources based on their experience and the experiences of other bees within their nest. They adjust their positions accordingly. In contrast, scouts randomly explore and select food sources without relying on previous experience. Whenever a new food source offers a higher nectar amount than the one stored in their memory, bees update their position accordingly, discarding the previous source. As a result, the ABC algorithm combines local search methods, performed by employed and onlooker bees, with global search methods, managed by onlookers and scouts, to balance the exploration and exploitation processes. ABC for the IoD path planning is explained in Algorithm 3.  
```
Limit: Threshold of trail, Max_Iteration: maximum iteration  INITIALIZATION:,  Position\(\leftarrow\) initialize positions; begin  iter=0  Calculate fitness value and obtain BestCost, BestPath whileIter\(<\)Max_Iterationdo for\(i\gets 1\)to PopSizedo  NewPosition(i)\(<\)- new solutions in the neighbourhood of position(i) for the  employed  Calculate fitness value and obtain BestCost, BestPath ifsolution is inactivethen  Increment its trail else  Clear its trail, Update BestCost, BestPath  end for for\(i=1:\)to PopSizedo iftrail(i)\(>\)-limitthen  Replace inactive solution by new fresh one,  Clear its trail  end if  end for  Increment Iter  end for  Repeat until ideal best is obtained or run out of time  end for
```  
**Algorithm 3**ABC algorithm for IoD path planning
###### Abstract  
Adopting cognitive radios (CRs) having multiple antennas in blind non-cooperative and cooperative spectrum sensing (CSS) under fading channels has gained attention due to higher detection performances provided by the spatial diversity gain of multi-sensors in different geographical locations and lower complexity, respectively. However, most studies do not consider sensing scenarios of more practical significance: for example, sometimes adopting only uncalibrated antenna arrays and sometimes only correlated signals at antenna arrays of CRs, but almost always, none of these impairments. Therefore, this paper studies these combined impairments on the performances of two blind techniques in centralized CSS with decision fusion (DF) and data fusion for different numbers of CRs and antennas per CR, both under frequency selective fading channels. One is the circular folding cooperative power spectral density split cancellation (CFCPSC), and the other is the generalized likelihood ratio test (GLRT). Extensive numerical results show, for instance, that with sample fusion (SF) and calibrated antennas, GLRT outperforms CFCPSC independently of correlation, numbers of antennas, or CRs. However, uncalibrated antennas severely penalize GLRT while surprisingly benefiting CFCPSC. Correlation is detrimental to GLRT and CFCPSC with SF but may help CFCPSC in DF and GLRT in DF or eigenvalue fusion. Generally, CFCPSC outperforms GLRT.  
centralized cooperative spectrum sensing; multiantenna sensor; correlation; uncalibrated antennas; GLRT; CFCPSC; decision fusion; data fusion +
Footnote †: journal: Electronics  
02222111719[[https://doi.org/10.3390/electronics1111719](https://doi.org/10.3390/electronics1111719)]([https://doi.org/10.3390/electronics1111719](https://doi.org/10.3390/electronics1111719))
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 4 Simulation Setup and Result Analysis
### Simulation Results and Discussion
#### 4.3.2 End-To-End Delay  
Figure 6 illustrates that DMJSR outperforms GSR, E-GyTAR and TFOR in terms of end-to-end delay as well. This is because DMJSR avoids pre-determining an end-to-end routing path before  
Figure 4: Packet delivery ratio vs. the number of nodes (@5 packets/second). GSR, Geographic Source Routing Protocol; DMJSR, Dynamic Multiple Junction Selection based Routing Protocol; TFOR, Traffic Flow Oriented Routing Protocol; E-GyTAR, Enhanced Greedy Traffic Aware Routing Protocol.  
Figure 5: Delivery ratio as a function of the packet-sending rate (350 nodes).  
dispatching data packets without considering connectivity as GSR does: the route in DMJSR is discovered progressively based on multiple junction mechanism by considering network connectivity when relaying data packets from source to destination. In E-GyTAR only directional density is used to find the path, but in urban scenarios with a two-lane road, there are a lot of streets having non-directional density and shortest distance to the destination that can play a key role for relaying packets towards the destination during routing. The speed based greedy forwarding approach of E-GyTAR results in incurring more hops while relaying the packet from source to destination resulting in an increase in end-to-end delay. In DMJSR, there is a reduction in the number of hops required to deliver data packets due to use of the forwarding mechanism that maintains one-hop neighbor information based on position and direction instead of the speed of vehicular node to forward packets between two junctions. Although, TFOR uses directional and non-directional traffic density to accomplish routing path but still suffer from the local optimum problem at junction level which causes more delay. DMJSR selects those multiple junctions that provide enough connectivity as compared to E-GyTAR and TFOR and is less likely to be affected by local optimum. For GSR, E-GyTAR and TFOR, the likelihood of packets staying more time in suspension buffer of a vehicular node is more as compared to DMJSR, which results in an increase in end-to-end delay for GSR, E-GyTAR and TFOR. The novel combination of multiple junction selection mechanism with position and direction based one-hop forwarding mechanism in DMJSR leads to a considerable reduction of end-to-end delay in comparison to the other protocols.
# Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
## 5 Application Framework
### Protein-Protein Interaction Analysis Using EAIT  
There have been many successes in EAIT in recent years, especially in the field of biological sequence analysis. These techniques expand our understanding of the mechanisms underlying natural and artificial systems to offer new insights. Algorithmic information dynamics (AID) is at the intersection of computability, algorithmic information, dynamic systems, and algebraic graph theory to tackle some of the challenges of causation from a model-driven mechanistic perspective, in particular, in application to behavioral, evolutionary, and molecular reprogramming.  
A recent exploration of this is in understanding the protein-protein interaction (PPI) map [60] between the SARS-CoV-2 proteins in human cells (the coronavirus responsible for the Covid-19 pandemic) and human proteins. 332 high-confidence PPI were experimentally identified using affinity-purification mass spectrometry. However, the mechanistic cause behind these specific interactions is not a well understood phenomena yet. A recent work [61] tries to explore the Kolmogorov complexity estimates of these PPI and found a positive correlation in the BDM values of the interactions. Such studies will help us predict in silico the biological dynamics, helping us find drug targets for therapeutics.  
The BDM used for the study is based on the ACSS database, limited to the block length of 13. Extending ACSS to larger block lengths with help bridge the causal gap, which for longer strings such as proteins can be considerable, limiting its advantage over traditional entropy-based methods. The quantum framework described in this paper can potentially extend the ACSS database to empower the BDM more toward the actual CTM value.