esa-sceva/satcom-chunk-collection huggingface dataset:
# Gravity Field Recovery Using High-Precision, High-Low Inter-Satellite Links
## 3 Simulation Environment
The impact of ocean tide model errors is assessed by taking the difference of two tide models, EOT11a [42], and GOT4.7 [43]. The total stochastic model for the observations is approximated individually for both satellite formations by means of a cascade of digital Butterworth ARMA filters [44; 45]. Filter coefficients are chosen in such a way that the cascade's frequency response optimally matches the inverse of the amplitude spectrum of the previously generated pre-fit residuals. They are estimated as a result of the computation of the linearized normal equations, which include differences between the \"true\" (only the static GOCC03s gravity field model and sensor noise are included) and the reference observations (only the static GOCC03s gravity field model is included), such that the error sources from the sensors are considered exclusively. Assuming uncorrelated high-low and low-low SST observations, weighting matrices are set up for all observation components separately. The goal is the retrieval of all spherical-harmonic (SH) coefficients up to a maximum SH degree of 100 from observations sampled every 5 seconds for the first 30 days of the year 2001. Due to the fact of non-linear observation equations, the \"reference\" observations are reduced from the \"true\" observations as a result of the linearization process. The gravity field parameters are estimated by solving full normal equations of a least squares system based on a standard Gauss-Markov model using weighted least squares with stochastic models in accordance with the simulated instrument noise levels. The resulting gravity field coefficients are analyzed and compared regarding quality and performance in terms of retrieval errors by removing a monthly average of the true mass transport model from the recovered signal. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **“True” World** & **“Reference” World** \\ \hline Static gravity field model & GOCC003s & GOCC003s \\ Non-tidal gravity field model & ESA AOHIS & - \\ Ocean tide model & GOT4.7 & EOT11a \\ Noise model & Laser interferometer & - \\ Noise model & Accelerometer & - \\ Noise model & Star camera & - \\ Noise model & GNSS (orbit accuracy) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Force and noise models of the “true” and “reference” world used in the simulations.
## 5 Results
### Prediction Model Establishment
#### 5.2.2 Training Process and Results of BPNN  
BPNN requires corresponding hyperparameter adjustments based on different data to ensure that the BPNN model achieves optimal performance in the task. Hyperparameter adjustments include but are not limited to learning rate, number of hidden layer nodes, number of iterations, and regularization parameters. By adjusting hyperparameters, the performance of the model is optimized, leading to improved accuracy and generalization ability in the task. To expedite the convergence speed of the model, data preprocessing was performed to normalize it within the range of 0 to 1 and then divided into training, validation, and test sets in a ratio of 7:1.5:1.5.  
The final training process of the BPNN is illustrated in Figure 7. The data shown is sampled, with each epoch in the plot being formed every 10 iterations. During the iterations, the best performance of the validation set occurred at the 886 th iteration, with a \(MSE\) of 0.000793 and an \(R^{2}\) of 87.74%, indicating that the model has converged. In the subsequent models depicted in the \(MSE\) and \(R^{2}\) graphs, the three lines representing the training, validation, and test sets exhibit minimal fluctuations during the training process,  
Figure 6: Response surface plots and contour map plots of surface roughness for combinations of three consecutive variables.  
indicating good convergence stability. This effectively avoids overfitting and underfitting phenomena, resulting in a stable predictive model.
## 8 Conclusions  
In conclusion, the modified PPS-MOEA/D method in this article shows significant superiority in terms of the Hypervolume, Spacing, and number of feasible solutions across 15 different combinations of scenarios. Specifically, on average, the Hypervolume metric is 26.62% higher compared to the outstanding algorithm (ANSGA-III). In terms of Spacing, our approach reduces the spacing by an average of 19.08% compared to the best-performing alternative (CTAEA). Regarding the number of feasible solutions, our algorithm performs on par with the top-performing algorithms (ANSGA-III and NSGA-II).  
The simulation results suggest that, to balance efficiency and risk coverage, the following scenarios are recommended: when n = 6 and \(\delta\) = 2, or when n = 8 and \(\delta\) = 2. From the perspective of minimizing the transfer distance while maintaining a relatively high risk coverage, the recommended scenarios are n = 4 and \(\delta\) = 1, or n = 6 and \(\delta\) = 1.  
This study provides practical guidance for the generation of police patrol schemes, yet certain limitations remain to be addressed in future research. Although the model incorporates considerations such as dynamic risk variations and the minimization of transfer distances using the Amap platform, the random and sporadic nature of real-world incidents introduces inherent unpredictability. No predictive technology can ensure 100% accuracy in forecasting risks. When discrepancies arise between actual and predicted risk levels, the police patrol scheme may deviate from its optimal performance. Thus, the model requires the capability to dynamically adjust based on real-time conditions.  
Additionally, during actual patrol operations, the time required for handling incidents can vary depending on the severity and complexity of the situation. This variability poses a challenge to the fixed patrol time intervals designed in the model. While one approach is to expand the patrol intervals to allow for additional buffer time, this is not the most optimal solution.  
To address these challenges, our next steps involve integrating online modeling methods into the system. These methods will aim to optimally allocate and dispatch police resources while preserving the integrity of the existing patrol scheme as much as possible. By doing so, we seek to enhance the adaptability and efficiency of police patrol operations in dynamic and unpredictable environments.  
Conceptualization, P.C. and J.S.; methodology, P.C.; software, J.S. and H.J.; validation, H.J.; formal analysis, P.C.; investigation, H.J. and P.C.; data curation, J.S.; writing--original draft preparation, J.S.; writing--review and editing, P.C.; visualization, [PERSON]; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Fundamental Research Funds for the Central Universities under grant [number 2023]KF01 ZX12].  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks
## 3 Proposed Method
### Fuzzy Logic System
#### 3.2.2 Link Residual Time  
Link Residual Time (LRT) is the time duration of vehicles when vehicles are in the communication range. The LRT is calculated by using a physical layer and distance parameters [33] like receiver power, transmission power and SNR signal-to-noise ratio. We can also optimize the efficiency of data transmission by using these LRT [34], where SNR  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Meaning** \\ \hline Criterion & Suitability criterion for each cluster head (distance/energy). \\ \hline Energy & Node residual energy at every moment \\ \hline Terminologies CM & The cluster member node responsible for receiving the environmental data. \\ \hline CH & The cluster head node responsible for aggregating the received environmental data and sending them to the road side unit. \\ \hline SCH & The substitute cluster head replaces the cluster head if there is lack of energy. \\ \hline CHL & Cluster head leader is responsible for sending the aggregated information to road side unit. \\ \hline BS & Road side unit. \\ \hline R & Number of rounds. \\ \hline Mod(r,3) & Shows the clustering method used by the division remainder function. \\ \hline Compare & Is used to compare two values. \\ \hline Criterion & Suitability criterion for each cluster head (distance/energy). \\ \hline Energy & Node residual energy at every moment. \\ \hline Competition Radius & Competition radius to get the cluster head leader. \\ \hline FND & The first dead node. \\ \hline HND & Half of the dead nodes. \\ \hline LND & The last dead node. \\ \hline E\({}_{\text{dec}}\) & Amount of energy consumption. \\ \hline \(\epsilon_{\text{fs}}\) & More than one route operations. \\ \hline ERX & Required energy. \\ \hline Elex & Energy derived. \\ \hline ERC & Receiving one bit data. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._is the received signal and noise ratio, SNRth is the threshold value of signal-to-noise ratio, d is the distance, and \(\lambda\) is the path loss.  
\[\text{LRT}=\frac{\sqrt{10^{(\text{SNR}_{0}-\text{SNR}_{\text{th}})/5\lambda}-d}}{ \text{Relativesepeed}}-(\text{t}_{1}-\text{t}_{2}) \tag{4}\]  
The FLS (fuzzy logic system) contains main three parts: (i) fuzzy logic engine, (ii) fuzzifier, and (iii) defuzzifer. The fuzzification system receives three input parameters, namely speed, distance, and LRT (Link Residual Time). We used the member function, which is responsible for converting input data into fuzzy logic data. The value of distance in member function is [less, Medium, High], the value of speed in member function is [slow, Medium, High], and the value of LRT in member function is [Poor, Good, Average]. The member functions are defined inside triangular functions with value ranges between 0 and 1 [35].
## 6 Field Test of the WSN Platform
### Field Deployment of the WSN Nodes
#### 6.1.1 Field Deployment of Gateway Nodes  
The gateways were deployed in the locations shown in Figure 6. Gateways are always deployed before sensor nodes for star topologies to allow checking the connectivity of the nodes during installation.  
While the sensor node deployment procedure described in Section 5.1 provides the means to check the quality of its connectivity within the network, no such provision was made for the gateways, because of the short time we had to prepare the field test. The assessment of the quality of a gateway deployment would include cellular coverage for the GPRS modem, light exposure for energy harvesting and the coverage of the sensor nodes in the field.  
For this test deployment, we checked manually in the server log that the gateways were able to successfully connect. Furthermore, we monitored the field messages in real time for reports of success (or errors and retries) of the connection steps performed by the gateways: register with the cellular operator, access the GPRS service and open a socket with the server over the Internet.  
The deployment places for the gateways were chosen so that one solar panel was roughly directed towards south or on the east-west axis, and the sunlight was reaching them without major obstructions. Figure 8(b) shows an overview of part of the test field and the deployment position of Gateway 2.
## 3 The Proposed ReSiNet for Low-Memory Devices
### Construction of ReSTiNet
#### 3.2.1 Tiny-YOLO  
A popular technique called \"Tiny-YOLO\", which is the smaller version of \"You Only Look Once (YOLO)\", was formulated to create a single step procedure that involved both the detection as well as the classification process. Upon a single appraisal of the input image, both the bounding box and class predictions are produced.  
The distinguishing feature of this technique as opposed to the conventional models is that the class as well as bounding box predictions are performed at the same time. The procedure is as follows: Firstly, the image that is considered as the input is split across the \(S\times S\) grid. Secondly, every single grid cell is assigned with a confidence score, which contains the respective bounding box. The probability or chances that the object is present in every bounding box is referred to as the confidence score and is mathematically given by the following:  
\[C=\Pr(Object)*IOU_{pred}^{truth} \tag{1}\]  
where term IOU (\"intersection over union\") is defined to be a fraction that numerically lies within the limits of [0, 1]. The overlapped area in between the ground truth as well as the bounding box predictor is termed as the intersection. The entire region between the ground truth and the predictor is known as the union. In ideal terms, the IOU must be closer to 1, which implies that the ground truth is approximately equal to the bounding-box predictor.  
Similarly, the conditional class probability C is also predicted by individual grid cells while the bounding boxes are created. Thus, for every cell, the class-specific probability function is expressed as follows.  
\[\begin{split}\Pr(Class_{i}|Object)*\Pr(Object)*IOU_{pred}^{truth }\\ =\Pr(Class_{i})*IOU_{pred}^{truth}.\end{split} \tag{2}\]
# Earth Observations for Sustainable Development Goals
## 3 Applications in Ecosystems and Forestry  
Ecosystem health can be monitored from remote sensing using Gross Primary Productivity (GPP). While there are global products derived from remote sensing, such as the MODIS GPP global product (MOD17), the resolution of those products does not allow for precise local studies. Deriving GPP from Sentinel 2 MSI data was tested in the Doriana National Park (DNP) as a contribution to the SDG targets 6.6 and 15.1 [5]. Results show the potentiality of Sentinel-2 data for the estimation of GPP at a finer scale. High spatial resolution products allow more detailed description of the distribution of GPP over the heterogeneous ecosystems, improving the understanding of ecosystem functions, which are highly correlated to their health condition. While some papers in this Special Issue focus on the calculation of indicators for SDGs, [6] goes beyond the SGD indicators for forest ecosystem (e.g., indicators 15.1.1 and 15.2.1) to look for ways to measure the ecosystem health in forests. This review found that the major stressor for the forest ecosystem is \"climate change\", followed by \"insect infestation\", while, for grasslands, it is \"grazing\", followed by \"climate change\". \"Biotic interactions, composition, and structure\" is the most important ecological attribute for both ecosystems. \"Fire disturbance\" is the second most important for forests, while, for grasslands, it is \"Soil chemistry and structure\".
## Materials and Methods
### Experimental Design  
Experiments were carried out to evaluate the impacts of Li-ion battery storage on the system performance when incorporating fluctuations and intermittency (different \"solar days\"), focusing on the impact on water production and SEC. Particularly, on the \"partly cloudy day\", the final tests were conducted with SCs to enable comparisons with the results of the batteries. The experiments conducted are specified below:  
1. Operation on the \"partly cloudy day\": The system performance using the BW30 membrane (5-g/L NaCl feedwater) on the \"partly cloudy day\" was determined to examine the directly coupled system performance (no energy storage) when subjected to real weather conditions. Comparisons of the system performance--in particular, the permeating production and SEC--were made between the reference (directly coupled without energy storage) and fully charged Li-ion batteries on that day.
2. Operations on other \"solar days\": The experiments (using the BW30 membrane and 5-g/L NaCl feedwater) were conducted with and without fully charged batteries (100% SOC) on the \"very cloudy\" and \"sunny\" days to evaluate the impacts of batteries on the dynamic characteristics of the PV system when subjected to different solar conditions.
3. Operation with different battery capacities: The initial SOC varied over a wide range (70%, 50%, 40%, 30% and 20%) and was tested on the \"partly cloudy day\" to investigate how the PV-membrane system would respond if it was equipped with a smaller capacity battery bank--in particular, with respect to the SEC, permeating EC and production. The varied initial SOCs correspond to the energy storage capacities of 1.7, 1.2, 1, 0.7 and 0.5 kWh of the Li-ion batteries.
4. Comparison between Li-ion batteries and SCs: To examine the impacts of different energy storage technologies on the PV-membrane system, the system performances were compared when equipped with SCs and a charge controller and fully charged Li-ion batteries on the \"partly cloudy day\" with the same PV power rating.
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Timing Measurements
#### 2.1.2 System Configuration  
All tests were performed on the same hardware platform, a usual hardware server-like set up with the main requirement--CPU capability to include Intel-VT/AMD-V Virtualization Technology (VT) support [18]. For the test, Intel(R) Core(TM) i7-860 @ 2.80 GHz (Lynnfield) with 4 physical cores along with 8 GB of RAM and Broadcom BCM5751 Netxtreme Gigabit NIC were used. All measurements on both host and guest operating systems were performed using Ubuntu 18.04.2 LTS 4.15.0-46-generic kernel with standard scheduling configuration. Measurements were performed on the system with _invariant TSC counter_ capability with a constant rate which guarantees a constant clock frequency even in the presence of power saving means and also a consistency of clock tick among different CPUs. Hyper-threading mode was disabled to provide a strictly serialized execution of CPU instructions. Virtual machines were configured with enabled VT technology to replicate physical CPU and were provided with two CPUs and 1 GB of RAM each. The following virtualization platforms were used due to being the most popular: Citrix XenServer Host 7.0.0-125380c, Virtual Box 4.3.36, QEMU/KVM, and VMware ESXi.
###### Abstract  
Stress wave-based communication has great potential for succeeding in subsea environments where many conventional methods would otherwise face excessive difficulty, and it can benefit logging well by using the drill string as a conduit for stress wave propagation. To achieve stress wave communication, a new stress wave-based pulse position modulation (PPM) communication system is designed and implemented to transmit data through pipeline structures with the help of piezoceramic transducers. This system consists of both hardware and software components. The hardware is composed of a piezoceramic transducer that can generate powerful stress waves travelling along a pipeline, upon touching, and a PPM signal generator that drives the piezoceramic transducer. Once the transducer is in contact with a pipeline surface, the generator integrated with an amplifier is utilized to excite the piezoceramic transducer with a voltage signal that is modulated to encode the information. The resulting vibrations of the transducer generates stress waves that propagate throughout the pipeline. Meanwhile, piezoceramic sensors mounted on the pipeline convert the stress waves to electric signals and the signal can be demodulated. In order to enable the encoding and decoding of information in the stress wave, a PPM-based communication protocol was integrated into the software system. A verification experiment demonstrates the functionality of the developed system for stress wave communication using piezoceramic transducers and the result shows that the data transmission speed of this new communication system can reach 67 bits per second (bps).  
stress wave; stress wave-based communication; piezoceramic transducer; pulse position modulation +
Footnote †: journal: Sensors
## 3 Results
### Calculation of the Effective Coefficient of Electromechanical Coupling
#### 3.3.1 Calculation of \(k_{eff}\) for Fully Electroded Resonators  
We calculate \(k_{eff}\) for fully electroded resonators from the resonance and antiresonance frequencies obtained  
* Computationally analytically;
* Experimentally;
* Through the FEM model for mesh variants (a) and (b) as shown in Figure 3. Analytical calculation of the resonance and antiresonance frequencies is performed according to the relations in [1]. The calculated values are \[f_{rt} = 994,\text{270}\,\text{Hz},\] \[f_{at} = 1,\text{121},\text{000}\,\text{Hz}.\]  
By substituting these values into the relation (1) we obtain the value of the effective coefficient of the electromechanical coupling  
\[k_{eff}=0.4619.\]  
The experimentally determined values are taken from [1]  
* Resonance frequency \(f_{re}=1,\text{001},\text{600}\,\text{Hz}\);
* Antiresonance frequency \(f_{ac}=1,\text{112},\text{500}\,\text{Hz}\).  
By substituting these values into the relation (1) we obtain the effective coefficient of the electromechanical coupling  
\[k_{effc}=0.4352.\]  
The values of the resonance and antiresonance frequencies for the finite element mesh variants (a) and (b) according to Figure 3 were determined in Section 3.1. By substituting  
Figure 5: Shape of the oscillation in the electrode symmetry plane, for the electrode structure (a).  
into the (1) relation, we obtain the value of the effective coefficient of the electromechanical coupling  
\[k_{effa}=0.4631\]  
for the mesh variant (a) and  
\[k_{effb}=0.4630\]  
for the mesh variant (b).  
The obtained result is consistent with the fact that the values of resonance and antiresonance frequencies are both shifted by \(~{}0.6\%\) compared to the analytical solution. The calculation of \(k_{eff}\) given by the relation (1) can then be expected to be almost identical in value for both the analytical and numerical solutions.
## 2 Cost of Reactive Power Production from DERs
### Wind Turbines
#### 2.2.1 Reactive Power Capability of a WT  
In [21], the capability curve for a WT with a full power back-to-back converter together with its reactive power cost model have been well addressed; the presented results are utilized and modified in this paper. The active power generation of a WT is a function of the wind speed, as given in [22]. The maximum reactive power provision capability of the WT is given as:  
\[Q_{WT}=min\{Q_{c}\,,Q_{v}\} \tag{12}\]  
where:  
\[Q_{c}=\sqrt{\left(V_{g}I_{c,max}\right)^{2}-P_{WT}^{2}} \tag{13}\]  
\[Q_{v}=\sqrt{\left(\frac{V_{g}V_{c,max}}{X}\right)^{2}-P_{WT}^{2}}-\frac{V_{g} ^{2}}{X} \tag{14}\]  
where \(X\) represents the total reactance of the WT transformer, the grid filters, and the reactance of the transformer adapting the WT's voltage to the grid voltage. Due to the deviation of the actual wind power from the forecasted value, delivery of the reactive power given in Equation (12) based on the predicted active power value (\(P_{WT}^{h}\)) cannot be uniformly ensured by the WT over the entire hour of operation. It is assumed that the maximum hourly variation of wind power from the forecasted value (\(\Delta P_{max}\)) is estimated based on the previous meteorological data. Hence, the available reactive power of the WT at the \(h\)th hour (\(Q_{WT}^{h,av}\)) can be determined as follows [21]:  
\[Q_{WT}^{h,av}=\min\ \left\{Q_{c}^{h,av},Q_{v}^{h,av}\right\} \tag{15}\]  
where  
\[Q_{c}^{h,av}=\sqrt{\left(V_{g}I_{c,max}\right)^{2}-\left(P_{WT}^{h,max}\right) ^{2}} \tag{16}\]  
\[Q_{v}^{h,av}=\sqrt{\left(\frac{V_{g}V_{c,max}}{X}\right)^{2}-\left(P_{WT}^{h, max}\right)^{2}}-\frac{V_{g}^{2}}{X} \tag{17}\]  
\[P_{WT}^{h,max}=min\{\left(P_{WT}^{h}+\Delta P_{max}\right),P_{WT}^{rated}\} \tag{18}\]