esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Real-Time Control Strategy Considering Cost per kWh of Storage Batteries
### Simulation Results and Analysis
#### 3.2.3 Analysis of Economic Benefits  
According to the electricity bill settlement method of \"self-generated self-use, surplus electricity to the utility\" the calculation formula for the total electricity cost of the microgrid system is summarized as:  
\[COST_{t}=M_{g}-M_{ps} \tag{48}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Installed capacity of PV generation & 100 kW \\ Battery energy storage capacity & 210 kWh \\ Cost per kWh of the battery & 0.58 yuan \\ Efficiency of ESS & 0.8 \\ Battery maximum charge power & 60 kW \\ Battery maximum discharge power & 100 kW \\ Maximum utility supply power & 120 kW \\ Maximum grid-connected power & 50 kW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation system parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Peak** & **Valley** & **Flat** \\ \hline Period & (9–12) & (0–8) & (12–18) \\  & (18–23) & (23–0) & (8–9) \\ Price (yuan/kWh) & 0.9402 & 0.6351 & 0.3300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time-of-use electricity price parameters.  
Figure 5: Simulation results: (**a**) without pre-charge strategy; (**b**) with pre-charge strategy.  
where \(COST_{t}\) is the total electricity cost; \(M_{g}\) is the electricity fee that needs to be paid for the utility; and \(M_{\text{ps}}\) is the policy subsidy income for distributed generation. The electricity fee income includes photovoltaic power generation subsidies. The power generation subsidies are 0.37 yuan/kWh and the on-grid tariff is 0.3274 yuan/kWh. Both photovoltaic power generation self-use and grid connection can be subsidized, and grid-connected electricity prices can be directly obtained from grid-connection. Self-use can be reflected in the reduction of grid electricity charges. Because the power of the heating load is large, the overall calculation result of the microgrid still accounts for the payment of the electricity fee to the utility, and the improvement of the operating economy is also reflected in the reduction of the electricity bill.  
Calculated according to Equation (48), the comprehensive electricity bill results for the whole year are 52,830 yuan without the charging strategy, and 45,560 yuan after adding the priority charging strategy. These results show that adding the energy storage priority charging strategy has a good optimization effect in terms of battery life and electricity bill.
## Appendix A Baseline-Known Ambiguity Resolution and the Sequential Exclusion Algorithm  
With the baseline coordinates fixed to the ground truth, making use of the phase observations only, the float ambiguities are estimated first using a least-squares adjustment assuming an undifferenced zenith-referenced phase signal standard deviation of 3 mm and using the elevation-dependent weighting function [48]:  
\[w(\theta^{s})=\frac{1}{\left(1+10\times\exp(-\frac{\theta^{s}}{10^{s}})\right) ^{2}} \tag{1}\]  
where \(\theta^{s}\) denotes the mean elevation angle (in degrees) from the two receivers to satellite \(s\). The subscripts for the reference and the rover receivers are dropped for clarity.  
The integer ambiguities are then resolved with the search-and-shrink LAMBDA method using the ILS estimator [29; 30; 31], and validated with the ratio test [32; 33]. In case that the ratio test does not pass, an algorithm for sequential exclusion is used to enable the resolution of possibly many ambiguities during the current epoch. Within the subset of the phase observations (\(\beta_{\phi}\)) having the ambiguities delivering different values in their first and second candidates, exclusion is performed with the following consequence:  
* The number of the excluded phase observations increases from 1 to \(N_{m}\), where \(N_{m}\) is set to 6 in this contribution.
* For the same number of excluded observations, the observations with low elevation angles have priority in exclusion.
* For the observations with the same elevation angle, the observation on L2 has the priority in exclusion, as in this contribution the observations on L1 are considered as the basis observations to compute, e.g., the position dilution of precision (PDOP). This can be changed to other frequencies depending on the needs of the users.
* In case that exclusion of all the combinations in \(\beta_{\phi}\) mentioned above fail the ratio test, the phase observation with the highest exclusion priority is excluded to re-compute the \(\beta_{\phi}\). The exclusion begins then from the beginning.  
If the ratio test passes after excluding certain phase observations, the successfully resolved ambiguities are held fixed until cycle slips or large data gaps are detected.  
For ambiguities that are not successfully fixed, the float solutions are treated as pseudo-observations in the next epoch as long as no cycle slip or large data gap is recognised. The variance-covariance matrix of these float ambiguities is used without adding any system noise, i.e., the ambiguities are constrained as constant in time. During the sequential exclusion of ambiguity resolution, the option not considering the float ambiguities from the previous epoch is also tested with the different exclusion possibilities.  
The fixed ambiguities are validated again through backward processing.
# Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model
## 2 Literature Review
However, the computational time required for the model is not considered here. Ref. [41] introduced a random forest regressor for the prediction of responses in a CNC milling operation. The model's robustness makes it suitable for generalizing different machining-related applications. However, the RF regressor performs poorly in sparse data where certain expected values do not exist at all. It also faces difficulties regarding interpretability. Ref. [42] presented an error-trend-seasonal framework for secular seasonality and trend forecasting of the tuberculosis incidence rate in China. The ETS model provided a pronounced improvement for the long-term seasonality and trend forecasting in TB incidence rate over the SARIMA models. However, this model cannot obtain detailed information (age and sex) concerning TB cases. [PERSON] and [PERSON] (2021) introduced a long short-term memory (LSTM)-convolutional neural networks (CNN)-grid-search-based deep neural network model for sentiment analysis. The incorporation of grid search as a hyperparameter optimization technique minimizes pre-defined losses and increases the accuracy of the model. However, the model works only in small data. Also, the training cost of the model is too expensive. Ref. [43] presented a recurrent BiLSTM for German named entity recognition. The recurrent model achieves the highest performance gains. However, the model suffers from increased computational complexity. Ref. [44] introduced a nonlinear autoregressive network with exogenous input (NARX) for the prediction of global solar radiation. The model can be used to predict solar data in locations where measuring instruments are not employed or radiation data are unavailable. However, the model assumes that the future output depends only on past inputs, which may not hold in all cases. Even though these existing approaches show considerable performance in demand forecasting, they still face various limitations like inaccurate predictions, increased computational complexity, interpretability issues, generalization issues, and difficulties in training complex methods. In order to resolve these limitations, a deep learning model is developed in this research.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 2 Background to Interference Monitoring  
This section provides a background for interference detection (Section 2.1), classification (Section 2.2), localization (Section 2.3), and mitigation (Section 2.4).
# Structural Detection and Restoration Proposal for the Mardin Sahkulubey Cupola
## 5 Discussion
### S.E.M. and E.D.S. Analysis  
When the chemical test results were examined, the S.E.M. imaging determined that the material generally has sharp corners and a rectangular and compact structure. The E.D.S. analysis revealed that the material was mainly composed of Ca, Mg, Si, and Fe. As a result of this study, it was observed that the material reflects the characteristics of the limestone used in the Mardin region, and it is registered geographically as \"Mardin Stone\". These stones stand out because they are easy to cut and, therefore, easy to process. For this reason, it is possible to come across rich ornaments on the stone. It is a stone that has been used since it was discovered.  
According to the results of the microchemical E.D.S. analysis,  
* In the southern EDS-1 region: Ca, Mg, O;
* In the southern EDS-2 region: Si, Mg, O;
* In the southern EDS-3 region: Fe, Si, O;
* In the southern EDS-4 region: Mg, Ca, O;
* In the southern EDS-5 region: Si, N, O;
* In the northern EDS-1 region: Fe, Si, O;
* In the northern EDS-2 region: Ca, Mg, O;
* In the northern EDS-3 region: Si, Ca, O;
* In the northern EDS-4 region: Ca, Mg, O;
* In the northern EDS-5 region: a-Si, Ca, O.  
The predominant structure was detected.  
In the study titled \"Investigation of Mardin Limestone as a Building Stone\", the chemical properties of the limestones taken from the quarry in the Mardin Babol region, the Kasmiye Madrasa, and Abdullatif Mansion are compared with the samples taken from the Sahkulubey Cupola. The chemical component of the samples in the study consists of calcite crystals and contains iron and magnesium in its structure. The chemical composition of the samples taken from the Sahkulubey Cupola is similar to that of the samples in the study [32]. In contrast, in the SEM-EDS analyses of the Kumbet samples, a silicate mineral was encountered in the sections with fibrous structure or in sections where the structure was changed from a tight structure to a fibrous structure.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 4 Design and Deployment
### User Interface and Data Presentation
#### 4.4.1 Dashboard Development  
Our dashboard application is an important part of \"in the wild\" research, gaining insights into what it means to deploy in the natural environment with all the complexities that this brings (different geographies, exposure to the elements, and extreme events). The dashboard application makes use of the data streams collected through the IoT infrastructure that have been stored on our MongoDB database. The purpose of this dashboard is to enable environmental scientists to quickly make sense of the data collected, informing them of the status of the sensed environment and of potential related hazards detected through the system and providing room for decision making.  
The data in the MongoDB comprises of readings from the soil sensor nodes and the sheep trackers, all deployed in the Hiraethlyn field, as mentioned earlier. Whilst the soil sensor readings contribute towards environmental parameters such as soil temperature, soil moisture, air humidity/air temperature, and surface flow, the sheep trackers provide the GPS locations of sheep in the said area. A user-driven application has been developed in order to present the readings in a dashboard-style, as shown in Figure 10.  
The dashboard consists of different windows, namely an operations dashboard, a soil dashboard, and a sheep dashboard, as indicated in the left pane of Figure 10. The operations dashboard is the main window and shows the number of sites where the IoT infrastructure has been deployed, the number of  
Figure 10: Environmental IoT dashboard application.  
soil nodes and sheep trackers used in each site, the status of the nodes (whether they are working), and the kinds of readings being received from each type of node. The application also enables the user to select a node and to analyse the readings obtained from this particular node, as shown in Figure 11. This figure shows the readings from the air temperature/humidity sensors from one soil node over a one-week period. Each of these windows is populated using pluggable \"widgets\" which can be individually developed to add to the dashboard. This is another key element of configurability to support a highly flexible set of current and future sensor modalities. An example of an open-source IoT dashboard can be found here: [[https://thingsboard.io/](https://thingsboard.io/)]([https://thingsboard.io/](https://thingsboard.io/)).
## 3 Results and Discussion  
### Computational Setup  
Table 5 presents detailed information about the computational resources used in this study. The table includes the total and average training time for each variant of the model (GRU, LSTM, and Transformer), the device configuration (including GPU model, CUDA version, and the number of GPUs), and the number of CPU workers for data loading. Such details are crucial for reproducibility and for understanding the computational environment in which the experiments were conducted.  
Table 5 contains the main configuration and timing statistics for each model type training. The Total Run Duration (seconds) and Avg. Epoch Duration (seconds) columns provide insight into how long it took to the models under identical experimental conditions. The Device Used and GPU Name rows specify which hardware was applied, while the Num GPU Used row indicates how many GPUs were running in parallel. The GPU CUDA Version row shows the CUDA environment version, and the Num Dataloader Workers (CPU) row reports how many CPU processes or threads were used to load the data efficiently during training.  
Figure 2: GRU final t-SNE visualization (1000 triplets). Blue points correspond to Job Description, green points to Positive Resume, and red points to Negative Resume.
# Combining Higher-Order Statistics and Array Techniques to Pick Low-Energy P-Seismic Arrivals
## 5 Results
### False Detection Robustness: Comparison with STA/LTA
We remark that only the STS was used here. The application of the MCS step potentially further improves the reliability of predictions, possibly discarding some of the false detection by the STS. Figure S2 shows, in detail, the application of STS for each sub-trace deriving from Trace A analysis. The results of this analysis demonstrate HOSA's robustness against false detections. When applied to two 50 s noise-only traces, HOSA produced only one false detection compared to six false detections by STA/LTA, reducing the number of false alarms by a factor of six. Figure 11: Trace A analysis: Robustness to false detection for STA/LTA and single-trace stage (STS) of HOSA. Panel (**a**) shows the noise-only seismic trace used, along with the 3 false detections assigned by the STA/LTA technique (red vertical lines). Panel (**b**) represents the STA/LTA function along with the triggering thresholds, represented by blue and red dashed lines, respectively. Panel (**c**) depicts the same trace, along with the pick assigned by STS, marked by a red line (note that in a real application, this pick would undergo a secondary selection step performed by the MCS). To apply the STS of HOSA, the whole trace was divided into five 10 s sub-traces, each marked by vertical dashed grey lines in Panel (**c**). The blue arrows between 25 and 35 s highlight the window with the HOSA false detection. Panels (**d**,**e**) provide a detailed analysis of the STS application on the window that resulted in the HOSA false detection. Panel (**d**) zooms on the time interval between 25 and 35 s  
of Panel (**c**). Here, we indicate with green lines the different potential onsets determined by STS (only 2 are visible because they overlap). Panel (**e**) shows the corresponding fourth-order statistic (\(\gamma_{4}^{\prime}(t)\)), along with the 5 thresholds used for the multi-threshold criterion of STS (refer to the main text for a complete explanation). This onset is accepted by STS due to the value \(\sigma_{o}=30\) ms \(<\) 75 ms, as reported in the upper-left part of the panel. Zoomed-in views of the other sub-traces are provided in Figure S2.
## 2 Bidirectional FSI Numerical Model
### Fluid Domain Solution
#### 2.2.2 Boundary Conditions  
The inner and outer diameters of the drill string are 61 mm and 71 mm, respectively, and the diameter of the wellbore is 76 mm. The coupling relationships include the drill string-internal fluid coupling and the drill string-annular fluid coupling. The particle size of the fluid can have a significant impact on the analysis results. In order to balance the analysis accuracy and the solution speed, the relationship between the particle size and the total wall pressure was explored. The density of the drilling fluid is 1000 kg/m\({}^{3}\), the kinematic viscosity is 1 \(\times\) 10\({}^{-6}\) m\({}^{2}\)/s, and the flow rate is 50 L/min. The total pressure of the fluid domain mapped to the inner wall surface of the drill string within a 1000 mm long section is shown in Figure 4. When the particle size is less than 4 mm, the total pressure fluctuation of the drill string wall is small, and the total pressure of the wall corresponding to 4 mm is approximately 3.5% different from that in the case of 1 mm. However, when the size is greater than 4 mm, the total wall pressure increases sharply; at this point, due to the larger particle dimensions, the particle geometry penetrates the drill string wall during contact, resulting in significantly higher wall forces exerted on the drill string. Comprehensively considering the solution accuracy and speed, the particle size is set to 4 mm.  
The drill string and wellbore wall models are shown in the Figure 5. In order to enable the particles to pass through the annular gap, the particles in the annular gap are refined, and the size of the refined particles is 0.3 mm. To improve the solution efficiency, the internal fluid and annular fluid are solved, respectively. The schematic diagram of the coupling model is shown in Figure 6.  
Figure 4: Relationship between particle size and total pressure.
## 1 Introduction
The innovative communication model developed in this work mimics the communication infrastructure between the primary and secondary control layers; it enables the examination of MG performance as a cyber-physical system, including analyzing the impact of communication latency and DDoS attacks on frequency stability. To summarize, the main contributions of the proposed work are as follows:  
* Implementing an innovative cyber-physical co-simulation platform that integrates OPAL-RT, the network simulator ns3, and Docker containers into a unified, sophisticated platform, providing a sustainable tool to pursue various research studies within cyber-physical power systems applications. * Proposing an experimental study targeting the sustainability challenges in cyber-physical inverter-based microgrids through an innovative way of assessing the frequency stability in different real-world scenarios. * Developing an innovative ns3-based communication model that adequately represents the communication infrastructure between the primary and secondary control layers within the islanded inverter-based microgrid. * Proposing a distributed denial of service (DDoS) model in real-time operation within the ns3-based communication surface for assessing sustainable MG operation in different cyber scenarios regarding communication delays and DDoS attacks. The remainder of the paper follows this organization: Section 2 presents the proposed architecture of the cyber-physical inverter-based microgrid, including the power system and communication network modeling. Section 3 demonstrates the modeling of Distributed Denial of Service (DDoS) attacks in real-time operation. Section 4 presents the implementation of the proposed cyber-physical co-simulation platform for assessing the operation of an inverter-based MG. Section 5 delineated four subsections to assess the MG performance under normal operation, communication delay, and DDoS attacks--the conclusion in Section 6.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
## 3 UAVs Deployment Methods for Disaster Scenarios of Different Scales  
According to the requirement in disaster emergency response that UAVs must be deployed precisely and timely at the target point, this paper designs SAIBFGS, which is suitable for small-area UAV deployment, and LAESGA, which applies to large area UAV deployment.
# Transformer Fault Diagnosis Method Based on Incomplete Data and TPE-XGBoost
## 3 Transformer Fault Diagnosis Method
### Missing Data Filling Method  
In order to verify the filling effect of the RFR algorithm on incomplete data, the sample values are artificially and randomly removed. The distribution of 20% missing values in the sample data is shown in Figure 3. The black part represents that the feature value is available, while the white part represents missing data. The larger the white areas in the graph, the less complete the feature. The C represents the integrity of the data.  
For continuous features, zero or mean value is often used to replace missing values in the sample data. However, given the intrinsic relationship between dissolved gases in oil, the regression algorithm is able to learn from the sample data to achieve prediction and filling of missing values.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Features** & **No.** & **Features** \\ \hline
1 & \(M\)(H\({}_{2}\)) & 9 & \(C\)(C\({}_{2}\)H\({}_{4}\)/C\({}_{2}\)H\({}_{6}\)) \\
2 & \(M\)(CH\({}_{4}\)) & 10 & \(K\)(H\({}_{2}\)/(H\({}_{2}\) + TH)) \\
3 & \(M\)(C\({}_{2}\)H\({}_{6}\)) & 11 & \(K\)(C\({}_{2}\)H\({}_{4}\)/TH) \\
4 & \(M\)(C\({}_{2}\)H\({}_{4}\)) & 12 & \(K\)(C\({}_{2}\)H\({}_{6}\)/TH) \\
5 & \(M\)(C\({}_{2}\)H\({}_{2}\)) & 13 & \(K\)(C\({}_{2}\)H\({}_{2}\)/TH) \\
6 & \(M\)(TH) & 14 & \(K\)((CH\({}_{4}\) + C\({}_{2}\)H\({}_{4}\))/TH) \\
7 & C(C\({}_{2}\)H\({}_{2}\)/C\({}_{2}\)H\({}_{4}\)) & 15 & \(K\)((C\({}_{2}\)H\({}_{4}\) + C\({}_{2}\)H\({}_{6}\))/TH) \\
8 & C(CH\({}_{4}\)/H\({}_{2}\)) & 16 & \(K\)((C\({}_{2}\)H\({}_{2}\) + CH\({}_{4}\))/TH) \\ \hline \end{tabular}
\end{table}
Table 2: Derivation results of DGA features.  
Figure 3: Distribution of missing values in the sample dataset.  
The main process of numerical filling using the RFR model is as follows:  
1. Analyze missing sample data and construct a sample set based on missing values and treat-filled features as labels.
2. Select the sample data with the least number of missing values for filling-in priority. The missing values of features other than the feature to be filled are temporarily replaced by their mean.
3. Predict the values of the missing data using RFR and insert the predicted results into the original sample set.
4. Repeat steps 1-3 until the last feature with the highest number of missing values is predicted by a final regression on it using the original sample after filling.