esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Two Classical Methods Introduced to Establish PLS Models
#### 3.4.1 GA-PLS Prediction Model Built for Comparison with the Proposed Model  
The main GA parameters were set as follows: population size of 32, window width of 10, maximum generation of 100, and mutation rate of 0.005. Due to the randomness of the GA, 30 modeling repetitions were used in this experiment for selecting the best results. As shown in Figure 7, the corresponding bands above the red dotted line were selected, for a total of 870 bands. As shown in Table 2, \(R_{c}^{2}\) was 0.9318, RMSEC was 0.2617, \(R_{cv}^{2}\) was 0.8908, RMSECV was 0.3287, \(R_{p}^{2}\) was 0.9420, and RMSEP was 0.2421. Due to the complexity of the full-spectrum data, which contained redundant information and noise, the GA left the band closer to the moisture characteristics using the survival of the fittest rule, and the result was optimized and improved compared with the original spectrum. However, compared with the DWT-BOSS algorithm, the result still had redundant wavebands; the proportion of the number of the variables was about 1:16 (V:GA).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable Set** & \(R_{c}^{2}\) & **RMSEC** & \(R_{cv}^{2}\) & **RMSECV** & \(R_{p}^{2}\) & **RMSEP** & \(n\_\)**VAR** \\ \hline L0 & 0.9266 & 0.2713 & 0.8529 & 0.3855 & 0.9085 & 0.2349 & 3112 \\ L1 + BOSS & 0.9326 + 0.0396 & 0.2588 + 0.0689 & 0.9336 + 0.0019 & 0.2581 + 0.0037 & 0.9412 + 0.0657 & 0.2355 + 0.1064 & 9.8333 + 2.8333 \\ L2 + BOSS & 0.9304 + 0.0125 & 0.2641 + 0.0229 & 0.9236 + 0.0011 & 0.2768 + 0.0020 & 0.9470 + 0.0113 & 0.2247 + 0.0230 & 16 + 4 \\ L3 + BOSS & 0.9250 + 0.0120 & 0.2742 + 0.0213 & 0.9300 + 0.0011 & 0.2650 + 0.0021 & 0.9421 + 0.0055 & 0.2351 + 0.0110 & 10.5000 + 3.5000 \\ L4 + BOSS & 0.9380 + 0.0114 & 0.2494 + 0.0220 & 0.9289 + 0.0014 & 0.2672 + 0.0011 & 0.9431 + 0.0094 & 0.2328 + 0.0199 & 14.1000 + 2.9000 \\ L5 + BOSS & 0.9304 + 0.0133 & 0.2641 + 0.0244 & 0.9271 + 0.0013 & 0.2705 + 0.0012 & 0.9447 + 0.0141 & 0.2292 + 0.0298 & 14.1000 + 2.9000 \\ L6 + BOSS & 0.9212 + 0.0026 & 0.2811 + 0.0047 & 0.9131 + 0.0010 & 0.2953 + 0.0017 & 0.9512 + 0.0021 & 0.2158 + 0.0047 & 14.9333 + 2.9333 \\ V & 0.940 & 0.2404 & 0.9171 & 0.2851 & 0.9513 & 0.2236 & 55 \\ \hline \hline \end{tabular} Note: \(n\_\)VAR, number of variables; RMSEC, root mean square error of calibration; RMSECV, root mean square error of cross-validation; RMSEP, root mean square error of prediction; \(R_{c}^{2}\), correction determination coefficient; \(R_{cv}^{2}\), coefficient of determination of cross-validation; \(R_{cv}^{2}\), coefficient of determination of cross-validation; \(R_{cv}^{2}\), coefficient of determination of test set; statistical results are presented as mean value \(\pm\) standard deviation for 30 runs.  
\end{table}
Table 1: Results of different PLS models by different deposition layers.  
Figure 7: The frequencies of selected variables within 30 runs by the GA. Note: The selected wavelength is above the dotted line.
## 3 Experimental Test Program and Results
### Corrosion Damage Localization in a Small Mortar Sample  
A cylindrical reinforced mortar sample with a diameter of 36 mm and a height of 60 mm was continuously monitored with the AE technique during an accelerated corrosion process. A smooth rebar with a diameter of 6 mm of which 2 cm was exposed to corrosion was positioned in the middle of the sample. The corrosion process was accelerated by imposing a direct current with a current density of 100 \(\upmu\)A/cm\({}^{2}\). The rebar was connected to the positive side acting as the anode, whereas a stainless steel plate was connected to the negative side acting as the cathode. The sample was partially immersed in a 5% sodium chloride solution.  
Six piezoelectric broadband sensors (50-2000 kHz) were attached to the sample with hot melt glue. The AE signals were amplified with preamplifiers with a gain of 34 dB. A Vallen AMSY-6 system was used for acquisition. The detection of background noise was prevented by setting an amplitude threshold of 38.5 dB. The pre-trigger time, duration discrimination time, and rearm time were set to 20 \(\upmu\)s, 400 \(\upmu\)s, and 3 ms, respectively. The sampling rate was set to 10 MHz. With 2048 samples being stored, the length of the stored signal was 204.8 \(\upmu\)s. The digital frequency filter was set between 230 and 850 kHz. X-ray images were made at intermediate stages during the accelerated corrosion process, aiming at 3D X-ray microfocus Computed Tomography (3D-XCT) to visualize the internal damage sources.  
A schematic representation of the setup is shown in Figure 3. More details of the setup can be found in [PERSON] et al. [33].  
Unfiltered localization results are shown in Figure 4. The localized AE events are plotted on X-ray images taken during the corrosion process. It allows a visual confirmation of the AE source localization results. The initial result is fairly well localized when compared to the observed crack and corrosion products. As monitoring was performed at higher frequencies, the error between the threshold crossing and actual TOA will be small, therefore resulting in a fairly good localization result even without additional post-processing and filtering.  
Figure 5 shows the result after application of the post-processing protocol. The events that were localized by hits recorded by 4 sensors are indicated with a different marker as the events localized by hits recorded by 5 or 6 sensors. For the former, a unique solution exists, and no information is thus available on the localization error. For the latter, the error ellipsoids could be calculated. Here, the radius of the error ellipsoid was chosen to be maximum 2.5 mm in every direction. After post-processing, the localization result has been improved resulting in more events being localized near the crack and corroding zone,  
which could again be verified with the X-ray scans. Most events are localized at the bottom side of the rebar which was closest to the sodium chloride solution.
# Study on the Effect of Anchor Cable Prestress Loss on Foundation Stability
## 4 Comparison and Analysis of Calculation Results and Monitoring Data  
### Analysis of Land Surface Settlement  
The calculated results of land surface settlement under the condition that the prestress loss rate in the numerical calculation is 0% and 10% are extracted and compared with the field measured values, as shown in Figure 12. In the process of excavation, the measured results of surface settlement are in good agreement with those of the numerical simulation without prestress loss. At the end of excavation, the maximum measured surface settlement is 2.46 mm, which is about 7.5% less than the calculated maximum settlement without prestress loss (2.66 mm).
## 6 Experimental Validation  
### Experimental Setup  
The experimental testbed for our proposed system prototype is shown in Figure 11; we use this prototype to generate a real optical Morse encoded signal and verify the feasibility of our proposed automatic recognition approach.  
At Tx, an unstable Morse encoded signal is first produced by an arbitrary waveform generator (AWG 7051). Then, it is modulated to the LED (XLamp(r) XP-L2) driven by the self-designed driving circuit packed inside the lamp. Note that a signal lamp consisting of LED arrays is employed in our system for the purpose of simulating the maritime optical communication scenarios. At Rx, we use an optical lens (ZLKC-KM5012 MP8) with a focal length of 50 mm to narrow the field-of view (FOV) of the receiver, thereby mitigating the incoming ambient light noise. The emitted light signal is captured by a self-designed optical receiving module including an avalanche photo-diode (S8664-50K) and a transimpedance amplifier (LTC6268-10) with a high gain factor of 2 million times. Then, after analog-digital conversion by ADS8866 (16-bit resolution), the signal is applied to the STM32F446 embedded system [30] and processed based on our proposed approach. The sampling period is \(T_{s}\) = 2.7 ms, and the sampling rate of the ADC is set at \(F_{s}\) = 375 Hz to guarantee that there are at least 20 C30 samples during the period of a basic dot unit.  
Finally, the output recognition results are analyzed on the computer in real time using the algorithm mentioned in Section 4.3, and the accuracy performance is determined. The key parameters of the devices used in this system are listed in Table 1.
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
###### Abstract  
The position prediction of marine moving targets based on historical trajectories is an important assistance procedure for marine reconnaissance and surveillance. Limited by satellite access period, space-based historic trajectory data have sparse and uneven intervals. However, most current time-series prediction methods require uniform time intervals. For non-uniform time series data, common processing methods first use the interpolation algorithm to fit historical data, and then carry out predictions based on equal interval data after the uniform sample. The disadvantage is that the accuracy of the interpolation data will limit the prediction accuracy. In addition, the time-series prediction methods represented by the grey model (GM) and autoregressive model (ARM) can only deal with equal-interval time prediction, in which it is hard to satisfy the prediction demand of non-equidistant time. Aiming at the limitations of most time series prediction methods and meeting the requirement of long-term variable duration prediction, a novel trajectory prediction method for sparse and non-uniform time series data based on deep neural networks is proposed. Firstly, to maximize the mining of the original data features, the moving behavior features are extracted from the raw historical track data by calculating the information of position, velocity, and position change for feature extension. Then, because of the temporal coherence of the track data, and inspired by the design idea of local correlation of the convolutional neural network (CNN), the CNN model is used to excavate the navigation rules to achieve position prediction. Finally, training of the network model is accomplished based on historical track samples. The experiments are carried out based on the space-borne automatic identification system (AIS) observation data. Experimental results illustrate that the method behaves better than other methods with the superiority of lower requirements for sampling, stronger adaptability to data characteristics, and higher forecasting accuracy for long-term prediction. When applied to the satellite search of marine moving targets, the track prediction has the potential to reduce the uncertainty of target location and guide satellite searching missions, thereby significantly improving the searching efficiency of targets.  
s +
Footnote †: journal: Applied sciences  
1 Laboratory of System Simulation and Concurrent Design Technology, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China 1  
2 Beijing Remote Sensing Information Institute, Beijing 100192, China 2  
3 China Academy of Aerospace Aerodynamics, China Aerospace Science and Technology Corporation, Beijing 100074, China 3
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
###### Abstract  
In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clustering scheme to overcome the said limitations and thereby minimize the effect of the hot-spot problem. The fuzzy logic is applied to capture the underlying network conditions. In re-clustering, an important question is when to perform next clustering. To determine the time instant of the next re-clustering (i.e., number of nodes depleted--energy drained to zero), associated fuzzy membership functions are optimized using genetic algorithm. Simulation experiments validate the proposed scheme. It shows network lifetime extension of up to 3.64 fold while preserving detection capacity and energy-efficiency.  
wireless sensor networks; fuzzy logic systems; genetic algorithms; optimization; en-route filtering; network lifetime; re-clustering
# Application of the MiL and HiL Simulation Techniques in Stewart Platform Control Development
###### Abstract  
During the integration phase of a system development, we are often concerned as to whether the designed control algorithm could be performed on the selected controller in real-time. One of the tools to test and validate the control scheme is the Hardware-in-the-Loop (HiL) simulation technique, which is a part of a model-based design methodology. This approach requires a simulation model of a controlled system running in a real-time loop with an intended controller and a control algorithm, which are objects of interest in this method. To perform the test, the control algorithm must be deployed to the controller such as a PLC. This paper presents a use case of the HiL technique in the design of the Stewart platform control, where the controller is PLCnext from Phoenix Contact. The control algorithm was first verified in the Model-in-the-Loop simulation (MiL) and then generated as a code from the Matlab/Simulink environment and deployed to the PLCnext, which resulted in a smoother transition from the design to the integration and testing phase. The presented method is also applicable to other controllers that support code generation.  
Hardware-in-the-Loop; model-based design; Model-in-the-Loop; programmable logic controller; Stewart platform
## 3 Results
### Test Description  
The purpose of this work is to analyze the performance of Doppler-based and TDCP-based algorithms with different grades of GNSS receivers: a geodetic receiver, a consumer-grade receiver, and a smartphone chip. Specifically, the used devices are as follows:  
* Novatel geodetic receiver [33], hereinafter briefly referred to as \"Novatel\";
* uBlox ZED-F9P GNSS receiver [34], hereinafter briefly referred to as \"uBlox\";
* Broadcom BCM47755 chipset [35] (embedded in Xiaomi Miss smartphone), hereinafter briefly referred to as \"Xiaomi\".  
The test was carried out in a rural area in the suburbs of Naples, where the mentioned devices simultaneously acquired about \(1\) h of GNSS measurements. The test was static, in order to have a null velocity as ground truth. The location of the data collection is shown in Figure 1a. The equipment arrangement is shown in Figure 1b. The adopted antennas were placed nearby on the same plane; a geodetic antenna was connected to the Novatel device, a patch antenna was connected to the uBlox device, and the antenna of the Xiaomi smartphone was embedded in the device.  
The data collected by the devices were processed with specifics MATLAB software, i.e. PANGNAV for the Doppler part [36], and by an ad-hoc TDCP algorithm published in [15].  
In Figure 1c, the probability distribution of the multipath errors on L1 for uBlox is shown; from that, it can be noted that the multipath errors are small and compliant with an open-sky scenario.  
It should be noted that the performance of the uBlox device could be affected by the used setup (shown in Figure 1b); indeed, the adoption of a ground plane, as suggested in [37], could improve the performance of such a device.
## 2 Materials and Methods
### Random Patch Extraction
#### 2.2.2 Training Pipeline  
In this study, we combined CBS with RPE to alleviate class imbalance and enhance patch diversity during training. Whenever we loaded a parcel image for training, we first used CBS to select a farmland class at uniform probability, then retrieve the parcels belonging to that class in sequence. Because every class is effectively chosen at the same rate, minority classes receive greater sampling opportunities, thereby mitigating skewed class distributions.  
Next, we applied RPE to the chosen parcel image, extracting multiple patches by drawing random \((x,y)\) offsets. If the valid area of a patch fell below a user-defined threshold, we discarded that attempt and simply drew a new \((x,y)\) offset to try again. Even for the same parcel, these random offsets capture subtle variations in texture, background masking, or illumination, allowing the network to see multiple different views during training. The flowchart shown in Figure 7, represents the overall training pipeline, including class sampling, patch extraction, and model input stages.  
Figure 6: Comparison of patch extraction patterns used in RPE and RPE. In RPE (**b**), patch positions vary randomly at each epoch, as indicated by the differently colored boxes.  
Figure 7: Training flowchart of our proposed method. CBS selects parcels according to class balance, and RPE extracts patches from randomly sampled positions (red boxes) that satisfy predefined conditions.  
Finally, the extracted patches were passed to a CNN for softmax probability calculation and loss computation, followed by weight updates via back-propagation. Repeating this procedure over multiple epochs progressively reduces bias toward majority classes and increases patch diversity. Detailed parameters--such as the total patch count, valid-area cutoff, and batch size--are provided later in Section 2.3.
# Integrating Multi-Access Edge Computing (MEC) into Open 5G Core
## 5 Performance Evaluation
### Environment  
A validation environment was designed for the proposal, looking for a MEC framework and a 5 GC to evaluate and validate the final product. Some MEC platforms were initially considered, but a containerized solution was chosen using Kubernetes due to a lack of support from these other solutions. The 5 GC chosen was free5 GC, which was containerized to facilitate the execution of network functions. Deployments were carried out on virtual machines hosted on a dedicated server.  
The Kubernetes cluster proposed to simulate the MEC platform comprises three virtual machines, one for deploying free5 GC and the other two for the MEC platform. All virtual machines ran on a server called NECOS-02. VM1, used to deploy free5 GC, has 8 GB RAM, 100 GB disk, 4 vCPUs, and Ubuntu Live Server 18.04 LTS operating system. VMs 2 and 3 each have 8 GB RAM, 120 GB disk, and 4 vCPUs, with Ubuntu Live Server 18.04 LTS operating system. Figure 4 shows the validation structure.  
The free5 GC architecture used for validation involves three UPFs: Branching UPF (B-UPF), MEC UPF (M-UPF) and Internet UPF (I-UPF). The B-UPF acts as an intermediary between the user's connection and the destination network and is not associated with any slice.  
To validate the proposal, a MEC application was developed to simulate an end-to-end environment where the traffic between the UE and the application uses the integration service. In addition, a SR was created to provide registration tokens for applications wishing to use the MTS API services. Once the applications and services had been developed, they  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Application** & **Latency** \\ \hline Smart Home & 1 ms–1000 s \\ \hline Remote Surgery & 200 ms \\ \hline Remote Medical Consultation & 1 ms–100 s \\ \hline Paramedic Support & 200 ms \\ \hline Critical Health Event & 200 ms \\ \hline Smart Farm & \(\sim\) hours \\ \hline Vital Signs Patches & 1000 ms \\ \hline Locatable Tags & 1000 ms \\ \hline \hline \end{tabular}
\end{table}
Table 2: KPIs used for comparison.  
were deployed in a production environment, using the Waitress server to run the Python applications. The deployments were coordinated, with the SR being executed first to allow the applications to be registered. The validation phase was divided into three stages: the first ensured the correct execution of the test environment, the second involved registering the services in the SR, and the third consisted of running the tests and collecting and evaluating the results.
# Effects of Shading on the Growth and Carbon Storage of _Enhalus acoroides_
## 4 Discussion
### Effect of Shading on the Carbon Storage of Seagrass Beds  
Seagrass accumulates organic matter via photosynthesis, and light reduction decreases its photosynthetic rate [32], which in turn affects productivity [33] and even causes biomass loss [34]. In the present study, as the shading intensity increased, the amount of effective light that _E. acoroides_ could receive decreased, thus inhibiting its growth and biomass accumulation. The loss of aboveground biomass reflects both the growth and development of seagrasses in low-light environments and suggests that seagrasses are able to avoid being shaded by their own leaves by reducing their leaf area [35]. The loss of leaves reduces photosynthesis in seagrasses to some extent, and the reduction in oxygen delivery to the rhizome may cause anaerobic conditions in the subsurface tissues, thus causing a reduction in the biomass of the subsurface [36].  
The seagrass canopy capture of organic matter from the surrounding habitat is the main source of sediment carbon, and the underground biomass usually has a high lignin content and relatively low decomposition rate, so the reduction in the seagrass biomass disrupts its CO\({}_{2}\) uptake process [37]. There is a distinct lack of research on the effects of reduced light on the sediment carbon storage of seagrass beds, but existing studies suggest that the degradation and loss of seagrass beds may result in the loss and release of sediment carbon [38]. For example, the large-scale loss of _Posidonia oceanica_ has been shown to lead to an 11-25% reduction in sediment carbon stocks [39]. In _Thalassia testudinum_, shading treatments have also been shown to result in a loss of up to 47% of sediment carbon stocks in the surface layer alone [40]. In the present study, the sediment carbon was lower in the shading treatment than in the control, and it decreased significantly with increasing depth. This may be due to the loss of aboveground biomass caused by reduced light, resulting in a decrease in its efficient trapping of allochthonous carbon and reducing the deposition of aboveground tissues to the detritus layer [37]. In addition, a reduction in root biomass would decrease the flux of the root exudation of the dissolved organic carbon into the sediment [41]. Therefore, biomass loss due to light reduction would further lead to a reduction in sediment carbon stocks. However, _Zostera nigricaulis_ showed no significant change in the carbon content of sediment during up to two years of intense disturbance [42]. Possible reasons for this difference in results include the contribution of seagrass itself to the sediment carbon pool [43], the hydrological conditions of the study area [44], and the influence of the composition and activity of the microbial community on sediment mineralization [45].