esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Quality Detection of Waterproof Curtain  
### Coring and Lab Tests  
To evaluate the quality of the waterproof curtain constructed by the trenching-and-replacing-style TRD technology, the core samples were obtained by drilling for lab tests. Considering that a conventional single-tube drilling tool cannot obtain the complete core samples from the pebble gravel layer, and the borehole below a certain depth may deviate from the wall with low verticality, it was optimized to a single-driving double-tube drilling tool for coring, as shown in Figure 8a. The single-driving double-tube drilling tool has an inner tube and an outer tube, and the drill pipe only drives the outer tube to rotate, but the inner tube does not rotate, which can avoid the mechanical breakage of the core sample caused by rotation-induced friction vibration, thus improving the coring quality.  
A total of 15 boreholes were drilled along the edge of the foundation pit for coring. Figure 8b shows the representative core samples obtained from the borehole with a depth of 45 m in the underground 3-floor basement area. It was found that after using the single-driving double-tube drilling tool, continuous core samples were obtained along the depth of waterproof curtain, and the coring quality in the pebble gravel layer and gravelly sand layer was significantly improved.  
After coring, the strength and particle compositions of the core samples were obtained through lab tests, as listed in Table 2. It was found that the unconfined compressive strength of the core samples in the silty clay was greater than 1.0 MPa, and that in the gravel was even greater than 4.0 MPa, which met the requirements that the wall strength should be not less than 1.0 MPa. The particle size of the core samples in the gravel was obviously smaller than that before the waterproof curtain was built, and the large-particle gravel was removed. This implies that the adopted trenching-and-replacing-style TRD technology effectively overcomes the problems of difficult construction and poor quality of the waterproof curtain in deep and large-particle pebble gravel layers.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Depth \(H\) (m)** & **Strata** &
\begin{tabular}{l} **Number of Core** \\ **Samples \(N\)** \\ \end{tabular} & **Strength \(M\) (MPa)** & **Particle Size** \\ \hline
0–6 & Fill/clay & 96 & 0.8 & Fine particle \\
6–12 & Silty clay & 96 & 1.1 & Fine particle \\
12–18 & Silty clay & 96 & 1.2 & Fine particle \\
18–24 & Pebble & 42 & 2.1 & 2–5 cm \\
24–32 & Pebble & 27 & 1.8 & 2–5 cm \\
32–40 & Gravel & 35 & 1.5 & 1–3 cm \\
40–46 & Gravel & 38 & 3.3 & 1–3 cm \\
46–55 & Gravel/mudstone & 42 & 4.2 & 2–5 cm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Unconfined compressive strength and particle composition of core samples.  
Figure 8: Core samples obtained by drilling for lab tests: (**a**) Drill pipe for coring. (**b**) Core samples of waterproof curtain.
# Edge Federated Optimization for Heterogeneous Data
## 4 System Description
### Data Heterogeneity
#### 4.2.1 FedProx  
In traditional federated learning methods such as federated averaging (FedAvg) [23], while they demonstrate effectiveness in distributed data processing, their performance is often limited when dealing with data heterogeneity. To address these limitations of the FedAvg algorithm, [PERSON]. [22] introduced improvements and proposed the federated proximal (FedProx) algorithm, which aims to solve system heterogeneity while providing theoretical guarantees.  
The FedProx algorithm is an enhanced version of FedAvg, with a key innovation being the introduction of a proximal term during the local update process. The introduction of this regularization term is primarily aimed at mitigating model bias arising from data heterogeneity and plays a crucial role in reconciling differences in model updates across different devices. Through this approach, FedProx not only enhances the stability of the model in data-heterogeneous environments but also accelerates convergence, effectively reducing the adverse effects of data heterogeneity. The local model update formulas of FedProx are described as follows:  
\[min_{w}h_{k}\big{(}w;w^{t}\big{)}=F_{k}(w)+\frac{\mu}{2}\big{\|}w-w^{t}\big{\|} ^{2} \tag{1}\]  
where \(min_{w}h_{k}(w;w^{t})\) means \(\gamma_{k}^{t}\)-intexact solution and \(\gamma\) is a variable for different devices and at different iterations. \(F_{k}(w)\) means the local objective function for device \(k\), \(\mu\) represents the regularization parameter for the proximal term, controlling the extent of the proximal term's impact. \(\frac{1}{2}\big{\|}w-w^{t}\big{\|}^{2}\) means the proximal term itself, which is half the squared norm of the difference between the current model parameters \(w\) and the model parameters \(w^{t}\) after the last global update. The purpose of this term is to prevent the model parameters updated locally from deviating too far from the global model parameters \(w^{t}\).  
In this system, each participating node (such as Jetson Nano) performs model training locally and then uploads the model updates to the central server. After collecting all the model update data from the nodes, the FedProx algorithm is applied to construct a more accurate and updated global model on the server. This design not only improves the overall model accuracy but also ensures the privacy and security of user data, as the data does not need to leave the local device to complete the training process.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **MIT-BIH** & **St Petersburg INCART** \\ \hline N & 16,696 & 18,568 \\ A & 3160 & 3406 \\ V & 3489 & 3943 \\  & 5.28:1:1.1 & 5.45:1:1.15 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The proportion distribution of N, A, and V data.
## 2 Design of the Shaking-Table Test of the Pile-Soil-Layer Interval Seismic Structure  
### Test Prototype Structure and Similar Relationship  
A typical and engineering planar-rule building was used, as shown in Figure 1a. Next, experimental models were designed and developed after simplification. In this study, we analyzed the stress characteristics of this type of architecture under the non-isolated and interlayer-isolated conditions under the horizontal (x) and bidirectional seismic actions. The lower substructure was a three span in horizontal and longitudinal directions, the column grid was 7.2 m \(\times\) 7.2 m, and the substructure had two floors with a two-story height of 4.8 m. The upper substructure was also a three span in the horizontal and longitudinal directions, with a column grid of 7.2 m \(\times\) 7.2 m, and six floors with a two-story height of 3.3 m. The total height of the building was 29.4 m. The upper and lower substructures have a 1:1 plane area ratio. The aspect ratio of the upper substructure was 1:2.75. The building's seismic-fortification intensity was 8\({}^{\circ}\) (0.2 g), the construction site was classified as a class III, and the earthquake types are grouped into the second group. The standard values of the live load of the lower and upper substructures were 3.5 and 2.0 kN/m\({}^{2}\), respectively. The concrete strength grade of the building ranged from C30 to C35. The cross-sectional dimensions of the lower and upper substructure frame columns were 600 mm \(\times\) 600 mm and 500 mm \(\times\) 500 mm, respectively, and the frame beam was 300 mm \(\times\) 700 mm. The thickness of the isolation plate was 160 mm, and the thickness of the other slab was 120 mm. As the maximum board size of the vibration table was 4 m \(\times\) 4 m, the maximum bearing capacity was only 22 t. Therefore, the size and quality of the prototype structure should be simplified; however, the dynamic response characteristics of the simplified structure should be kept constant. The simplified structure is shown in Figure 1b.  
The parameters of the test model could not fully satisfy or were not the same as the parameters of the prototype structure system because of the use of various materials. Therefore, the main parameters of the soil-isolation structure system were the same as those used in the prototype structure system, and the secondary parameters were almost similar. Combined with the test conditions and material performance, the principle of the vibration stress of the isolation and the dynamic responses of the soil were met. The acceleration, length, and elastic modulus were the basic similar parameters, with the similarity ratios of the length and acceleration ratio being S\({}_{l}\) = 1/12 and S\({}_{a}\) = 1, respectively, and similarity ratios of the structure and soil being S\({}_{E}\) = 1 and S\({}_{E}\) = 1/4, respectively. In addition, the similarity ratios of the other physical quantities were derived according to [PERSON]'s \(\pi\) theorem. The similarity coefficients of the model are listed in Table 1.  
Figure 1: Structural plan (unit: mm). (**a**) Prototype structure; (**b**) Reduced structure.
# A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
## 3 Infra-Structure Analysis
### AC and DC Power Distribution
Numerous complex factors, such as fault type, system topology, grounding configuration, component specifications, size, and more, affect the protection coordination in DC-connected systems. When working with bidirectional chargers, the problem gets much more complicated. With minimal inertia, a DC-connected system is susceptible to disruptions and may become unstable if errors are not promptly fixed. Thus, a critical factor in system restoration is the speed at which problems are found and isolated. Studying current DC power distribution systems, including low-voltage DC microgrids, can help one understand how DC-connected charging stations are protected from one another. For example, the model describes a low-voltage DC microgrid protection plan coordinating several preventive measures. An alternative method for safeguarding DC systems using a loop-style bus is described. Isolated faults can be recognized, and the proposed plan keeps the power supply intact. In a DC-connected system, it is essential to install DC meters to measure energy generation and consumption associated with EV chargers, battery energy storage, and RES [10]. In addition to being useful for future station development, these data are essential for the appropriate billing of EV station consumers. Despite being readily available, DC meters need standardized accuracy, calibration, and testing protocols that permit their use in metering applications. For systems that are connected to DC, the development of such standardized and approved DC meters is required. Figure 4: Block diagram of DC charging unit.
## 3 Simulation and Results
### Robustness Test  
Robustness testing is any quality assurance methodology focused on testing the consistent accuracy of software. In this section, we test the algorithms of aIMF, WT, EKF and PF which function as noise reduction models. The testing strategies used different inputs other than the EUR-USD exchange rates with the added noise, which are created from 1 Hz sine wave at 10% amplitude of the original datasets with the 10% random distribution. Those different inputs are EUR-JPY with the added noise, EUR-CHF with the added noise, finally, EUR-GBP with the added noise. Later, we used loss estimators to measure the prediction performances of the proposed aIMF, WT, EKF and PF, _i.e._, MSE, MAE, MAPE, R\({}^{2}\), AIC, BIC and Accuracy count. Having simulated with all the loss estimators indicated in Tables 7-9 under the same conditions used to test for EUR-USD as input, the results shared the same trend with few deviations from each other. This served to confirm that the aIMF algorithm performed significantly better when filtering a nonlinear nonstationary time series, _i.e._, EUR-JPY, EUR-CHF and EUR-GBP exchange rates, followed by WT and EKF algorithms. Moreover, we rejected using the PF algorithm to reduce the noise for the nonlinear nonstationary time series data. Additionally, we have discovered that the EKF and WT algorithm must be optimised in the areas of resampling and building up mother wavelet, respectively.  
Figure 11: _Cont._  
The following configurations were used to perform all the simulations:  
1. Intel(R) Xeon(R) server with \(2\times 2.4\) GHz E5620 CPUs, 3.99 GB RAM and a 64-bit Microsoft Windows Operating System is configured as the main processor.
2. Sony Visio, Sony L2412M1 EB Desktop with an Intel Core i5, 2.5 GHz, 8 GB RAM, and a 64-bit Microsoft Windows Operating System is used as the front-end connection to the data terminal from Bloomberg via web access using a Citrix client.
3. Application programs written using R programming scripts and some amendments to suit the requirements.  
The simulation results showed that there were no bugs in the software scripts, and an average execution time of 3 s for all the Ordinary Least Square (OLS) models.
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 4 Experiments
### Experimental Conditions
For the sake of unified measurement, the evaluation index of Relative Time Interval Mean Error (RTIME) is proposed. The calculation formula is expressed as Equation (21):\[\left\{ \begin{array}{l}RTIME_{\lambda}=\sqrt{\frac{1}{n}\sum\limits_{i-1}^{n} \left(\frac{\lambda_{i}-\lambda_{i}}{\Delta t_{i}}\right)^{2}}\\ RTIME_{\varphi}=\sqrt{\frac{1}{n}\sum\limits_{i-1}^{n}\left(\frac{\phi_{i}- \phi_{i}}{\Delta t_{i}}\right)^{2}}\\ RTIME_{Dis}=\sqrt{\frac{1}{n}\sum\limits_{i-1}^{n}\left(\frac{D\text{is}_{i}} {\Delta t_{i}}\right)^{2}}\end{array} \right. \tag{21}\]  
where \(n\) is the number of test samples, \((\lambda_{i},\varphi_{i})\) and \((\lambda_{i},\phi_{i})\) are, respectively, the observation position and the prediction position of the \(i\)th sample, \(Dis_{i}\) is the prediction distance error computed as Equation (19) of the \(i\)th sample, and \(\Delta t_{i}\) indicates the prediction interval of the \(i\)th sample in units of hours.
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
###### Abstract  
Vehicular ad hoc network (VANET) is a special form of mobile ad hoc network (MANET), which plays a key role in the intelligent transportation system (ITS). Though many outstanding geographic routing protocols are designed for VANETs, a majority of them use parameters that only affect routing performance. In this article, we propose an intersection routing based on fuzzy multi-factor decision (IRFMFD), which utilizes several features. The scheme is divided into two parts, namely vehicular decision management and intersection decision management. In the vehicular component, candidate vehicles between two static nodes (SNs) located at two intersections derive potential routing paths considering distance, neighbor quantity, and relative velocity. In the intersection component, the candidate SN was chosen from the current intersection's 2-hop neighbors which were connected with the current intersection by a route that was decided on in part one. To get the best scheme, we also introduced other factors to estimate the number of hops in each link and link lifetime. The simulation shows that the IRFMFD outperforms on delivery ratio and end-to-end delay compared with AODV (Ad hoc on-demand distance vector), GPSR (Greedy perimeter stateless routing) and GeOpps (Geographical opportunistic routing).  
VANET; MANET; ITS; fuzzy number; routing protocol +
Footnote †: journal: Applied  
Article
## 4 Experiment Set up and Results
When the microfiber waist radius is \(r\) = 1.45 \(\upmu\)m, the temperature sensitivity response of the five characteristic wavelengths are \(\text{S}_{\text{T-dip1}}\) = \(-\)993.3 pm/\({}^{\circ}\)C, \(\text{S}_{\text{T-dip2}}\) = \(-\)1007.4 pm/\({}^{\circ}\)C, \(\text{S}_{\text{T-dip3}}\) = \(-\)933.1 pm/\({}^{\circ}\)C, \(\text{S}_{\text{T-dip4}}\) = \(-\)963.2 pm/\({}^{\circ}\)C, and \(\text{S}_{\text{T-dip5}}\) = \(-\)950.4 pm/\({}^{\circ}\)C. The minimum dynamic range of dip 2 wavelength is 17.7 nm, and the allowable temperature variation range is 17.6 \({}^{\circ}\)C. Under the condition of the 0.01 nm resolution of the OSA, the temperature resolution of the this OMCI sample is less than 0.01 \({}^{\circ}\)C, reaching the performance of current mainstream commercial CTD [25]. Figure 7: (**a**) Output spectra of OMCI sample 1 with \(l\) = 7 mm, \(r\) = 1.55 \(\upmu\)m, \(l_{a}\) = 6 mm at different temperatures. (**b**) Relationships between the dip wavelengths drift and the temperatures. Experimental results verify that the sensitivity can be improved by reducing the radius \(r\) of the waist region. In addition, the above analysis shows that, the dynamic range is reduced with the increasing sensitivity, so there is a contradictory relationship between the sensitivity and the dynamic range. In practical applications, the sensor device parameters should be reasonably selected according to needs. The sensing characteristics of OMC sensing unit conform to the aforementioned theoretical analysis. Due to the OMCI sample is made of ordinary single-mode optical fiber. From the foregoing analysis, it can be seen that the temperature sensitivity of the interferometer of this experimental sample is less than 0.1 pm/\({}^{\circ}\)C, and the highest resolution of the OSA used in the experiment is 10 pm, so the interference variation cannot be detected. In practical applications, we can use the higher precision OSA for demodulating signal, or optimize the structure (such as using special optical fibers) to increase the temperature response of the interferometer arm difference. Compared with the structures of OMC and OMC-sagnac loop, the temperature sensing sensitivity of the OMCI structure designed in this paper is similar. However, this structure enriches the temperature sensing demodulation scheme. It is able to realize the temperature self-correction function, and reduce the difficulty of multi-parameter demodulation of the sensor in theoretically. In addition, the structure is more compact and easy to manufacture and package.
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Comparison of Different FD-Physical Layer-Based UWAC Systems
#### 2.5.4 Signal-to-Noise Ratio  
As the SNR approaches higher values, the AUA-CCS [40] and A-DTWR [19] show better BER outputs in the time invariant channel case \(f_{d}T_{s}=0\). The SNR output of A-DTWR [19] is substantially better than AUA-CC [40] in the time variant case \(f_{d}T_{s}=10^{-3}\), \(f_{d}T_{s}=10^{-4}\). The OFDM-based IBFD-UWAC systems such as A-IBFD [24] and ML-SC-IBFD [23] attain higher SNR values of 30 dB and 20 dB, respectively, while TRDC [22] achieves the lowest SNR of 2.5 dB. The hybrid-SS [33] technique is used in a full-duplexacoustic modem to effectively eliminate local transmission interference and improve the SNR of the received signal.
## 2 Concept of Micro-Grid Systems
### Distributed Generation Sources
However, the results have shown that these generators are not able to supply power during peak periods, but they may meet peak demand and improve the power generation reliability, as they can provide standby capabilities when the power grid fails [33]. * Diesel Generators: A diesel generator (also known as a genset) is composed of an internal combustion engine and a synchronous generator connected to the same shaft. Diesel generators are generally utilized as backup or emergency power systems in commercial and industrial installations. They are also widely utilized in new areas where connection to the distribution grid is unfeasible or expensive to perform [34]. Their principal disadvantage is the negative impact that they can have on the environment due to their high NOx.
## 2 Study Area
### Hydrogeological Setting  
The synthesis of geological, hydrological, and topographical data as well as drilling data obtained from the Draa-Oued Noun Water Basin Agency, has led to the conclusion that the nature of aquifers in the region is fractured type [5; 6; 7]. The location of most of the positive drillings coincides with the passage of faults or fractures [45; 46; 47]. Recent work of [PERSON] et al. [50] has shown that areas of very high groundwater potential are mainly found in the southern, eastern, and north-eastern plains of the Ifnii Basin, particularly at the intersection of the hydrographic network with the hydrogeological lineaments. These areas generally lie in granitic formations, volcanic sedimentary formations, and alluvial plains,  
Figure 1: (**a**) Location of the Anti-Atlas in Morocco; (**b**) Location of the study area on a simplified geological map of the Anti-Atlas showing the main Precambrian inliers (Simplified by [PERSON] et al. [39]; [PERSON] et al. [40]).  
with high porosity and permeability in the low-lying topographic areas. The Tangarfa spring, with a flow rate of 16 L/s, is a good example because it emerges in the contact zone between volcanic rocks and carbonates, facilitated by a network of NE-SE and NW-SE oriented faults [5; 7; 50]. The high potential areas mainly surround the tributaries of the main river as well as the faults. The Larba-Msiu well and the Mesti spring, with high flow rates of 8.33 L/s and 5.66 L/s, respectively, illustrate the synergistic effects of multiple factors favorable to groundwater infiltration. Their presence in highly permeable alluvial deposits, situated above a well-developed drainage network that interconnects with fault systems, highlights the complex interaction of lithologic, hydrologic, and structural controls on groundwater flow dynamics. In the vicinity of the city of Sidi Ifni, granitic and granodioritic formations were studied through 15 boreholes, revealing a positive correlation between water occurrence and fractured levels recorded at variable altitudes [8]. Measured flow rates showed a wide range of variability and in some cases reached significant levels (up to 3 L/s) [44].  
Figure 2: (a) Geological map of the Ifni inlier (Extracted from a 1:100,000 map of Sidi Ifni); (b) Geological section AA\({}^{\prime}\) across the Ifni inlier.
## 2 System Description
User \(k\) sends the vector \(\overrightarrow{\mathbf{s}}^{(k)}\) via two radio connections to concurrently reach the relay and the destination terminals during the first time period. We denote \(\mathbf{h}_{R}^{(k)}=\left[h_{R}^{(k)}(0),\cdots,h_{R}^{(k)}(L-1)\right]^{\dagger}\) and \(\mathbf{h}_{D}^{(k)}=\left[h_{D}^{(k)}(0),\cdots,h_{D}^{(k)}(L-1)\right]^{\dagger}\) as the radio links that exist between user \(k\) and relay, and user \(k\) and destination, respectively, with \(L\) being the number of link taps. The collected signals at the relay and destination are described as  
\[\mathbf{y}_{R}=\sum_{k=0}^{K-1}\overrightarrow{\mathbf{s}}^{(k)}\otimes \mathbf{h}_{R}^{(k)}+\mathbf{n}_{R}, \tag{8}\]  
\[\mathbf{y}_{D}^{(1)}=\sum_{k=0}^{K-1}\overrightarrow{\mathbf{s}}^{(k)}\otimes \mathbf{h}_{D}^{(k)}+\mathbf{n}_{D}^{(1)}, \tag{9}\]  
where \(\otimes\) is the convolution action, and \(\mathbf{n}_{R}\) and \(\mathbf{n}_{D}^{(1)}\) are the noise vector at the relay and destination nodes, respectively. Considering the impact of of IQM on \(\mathbf{y}_{R}\) and \(\mathbf{y}_{D}^{(1)}\), we obtain  
\[\mathbf{\bar{y}}_{R}=\eta_{R}\mathbf{y}_{R}+\mu_{R}\mathbf{y}_{R}^{\dagger}, \tag{10}\]  
\[\mathbf{\bar{y}}_{D}^{(1)}=\eta_{D}\mathbf{y}_{D}^{(1)}+\mu_{D}{\mathbf{y}_{D }^{(1)}}^{\dagger}, \tag{11}\]  
where \(\eta_{R}\), \(\mu_{R}\), \(where \(\mathbf{h}_{RD}=[h_{RD}(0),\cdots,h_{RD}(L-1)]^{\dagger}\) is the radio link between relay and destination terminals, \(\mathbf{n}_{D}^{(2)}\) is the noise samples, and \(\Delta\) is the scaled factor. The IQM effect on \(\mathbf{y}_{D}^{(2)}\) yields  
\[\overline{\mathbf{y}}_{D}^{(2)}=\eta_{D}\mathbf{y}_{D}^{(2)}+\mu_{D}\mathbf{y }_{D}^{(2)^{\dagger}}. \tag{13}\]  
The goal of this study was to create a data detection technique while making simultaneous forecasts for all unidentified parameters of \(\mathbf{h}_{R}^{(k)}\), \(\mathbf{h}_{D}^{(k)}\), \(\mathbf{h}_{RD}\), \(\eta^{(k)},\mu^{(k)}\), \(\eta_{R}\), \(\mu_{R}\), \(\eta_{D}\), and \(\mu_{D}\) for \(k=0,1,\cdots,K-1\).