esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Optimization Results
#### 4.2.3 The Impact of the ESS Sizing  
In this section, we consider the effect of the ESS sizing on the VPP's optimal reserve capacity. Figures 14 and 15 show the optimal reserve contract of the VPP for three scenarios of reserve activation probability and several ESS sizing. It can be seen that when the rated capacity of the ESS is 20 MWh, and the rated power varies from 10 MW to 30 MW, the obtained results show that the rated power of the ESS does not affect the optimal reserve sizing much. Meanwhile, when the ESS rated power is 10 MW and the rated capacity increases from 10 MW to 40 MW, the optimal reserve capacity also increases for all three scenarios of the reserve probability. However, while the scenario \(p_{1}\) still maintains three contract periods, the number of provisioning periods in scenarios \(p_{2}\) and \(p_{3}\) decreases, and the reserve capacity in each period increase significantly. It can be explained that in scenario \(p_{1}\), most of hours with a high probability of reserve activation also have a relatively high RES power output. By contrast, in scenarios \(p_{2}\) and \(p_{3}\), there are some hours of high RES power output but low probability of reserve activation, so that the high rated capacity of ESS will help VPP to accumulate energy to provide higher reserve capacity during high-probability hours.  
Figure 12: Forecasted wind power and demand with RES’ maximum power output of 20 MW.  
Figure 13: The VPP’s reserve bidding with reserve probability: (**a**) \(p=0.05\) for all hours, (**b**) scenario \(p_{1}\), (**c**) scenario \(p_{2}\), and (**d**) scenario \(p_{3}\) with RES’ maximum power output of 20 MW.  
Figure 14: VPP’s reserve capacity for different ESS power ratings and reserve probability scenarios.  
Figure 15: The VPP’s reserve bidding with different ESS capacity and reserve probability scenarios.
## 3 Finite-Time ANFTSMC Design  
### Controller Design Methodology for HAUV  
This section mainly presents a nonlinear control scheme for the HAUV's cross-domain process, which provides a reliable and robust control method for air and underwater navigation and HAUV water-air traversing maneuvers. The coaxial HAUV studied in this paper is a typical underactuated system that is faced with nonlinear and strong coupling characteristics and complex environmental disturbances. Because sliding-mode variable structure control has strong robustness in the presence of system parameter perturbations and unknown external disturbances, sliding-mode control is used to design the position controller and the attitude controller of the vehicle. Figure 3 shows the block diagram of the coaxial HAUV's control logic. As shown in Figure 3, the control logic consists of two loops: the inner loop (attitude) and the outer loop (position). An ANFTSMC is designed for the outer loop and the inner loop, respectively. The controller achieves a faster convergence rate and can estimate the unknown upper bound of uncertainty by using an adaptive algorithm, which improves the robustness of the coaxial HAUV's control system to lumped uncertainty.  
The main content of this section is to discuss and analyze the design of the controller system outlined for the coaxial HAUV. The controller is designed with NFTSMC technology for the coaxial HAUV's translation subsystem and rotation subsystem. Under this control framework, external disturbances and system uncertainties are estimated online in real time by using an adaptive algorithm. The controller can ensure that tracking errors gradually converge to zero in a finite amount of time.
# Dynamic Prediction of Shale Gas Drilling Costs Based on Machine Learning
###### Abstract  
Shale gas, a significant recoverable natural gas resource trapped in shale formations, represents a significant energy reservoir. Although China has significant recoverable shale gas reserves, the challenge of controlling drilling costs remains a critical barrier to efficient development. This study presents a novel stacked ensemble learning model that integrates support vector machine (SVM) and long short-term memory (LSTM) networks to improve the accuracy of shale gas drilling cost prediction. The methodology consists of three main phases. First, we constructed a comprehensive, multidimensional spatiotemporal dataset of shale gas drilling costs. Second, we used Gradient Boosting Decision Tree (GBDT) modelling to rank the importance of various factors influencing drilling costs. Finally, we developed a stacked ensemble learning model combining SVM and LSTM architectures to achieve superior cost prediction accuracy. Experimental results demonstrate the effectiveness of the model, with the coefficient of determination (R\({}^{2}\)) improving from 0.25189/0.33834 (traditional SVM/LSTM models) to 0.55934. Model validation using selected well investment data from the Changing Block shows promising performance, achieving a Mean Absolute Percentage Error (MAPE) of 6.41%, with optimal prediction accuracy in the medium investment range (60-70 million yuan). This innovative approach provides a reliable tool for predicting shale gas drilling costs and offers new methodological perspectives for cost reduction strategies. The results contribute significantly to the sustainable development of shale gas resources and provide valuable insights for industry practitioners and researchers in the fields of energy economics and resource management.  
shale gas; drilling costs; spatio-temporal datasets; machine learning +
Footnote †: journal: applied sciences
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 5 SRv6-Based Lossless Migration
### Supporting Buffering and SRv6 Behavior  
To guarantee service continuity in the case of workload migration, we leverage both standard SRv6 behaviors and newly defined behaviors. Concerning the standard behaviors, we exploit the H.Encaps.Red, the End with the USD defined in [23] and the End.M.GTP6.D defined in [13]. Taking advantage of the programmability and flexibility of SRv6, we extend the list of behaviors by the definition of custom ones: End.Buffer and End.M.GTP6.D.Buffer.  
Both behaviors update the packet headers and store them in the buffer. When the packet flush is triggered, the stored packets are submitted to the IPv6 routing table hosted on the device. We highlight that the packet can be immediately forwarded after release from the buffer without the need for any additional modification. This approach speeds up packet delivery and reduces the overhead on the device. As a final remark, note that the packet buffer can be used to handle packets that are targeted at different services; all the information to reach the service is embedded in the packets.
## 3 Results
### Analysis of NDBI  
In this study, the resultant NDBI values ranged between \(-\)0.39 and 0.35 (Table 5). The negative values represent an absence of built-up areas (mostly vegetated areas), while the positive values reflect the presence of built-up locations within the study area across all years. As stated by [PERSON] et al. [65], a lower value of NDBI reflects scattered built-up areas, while higher values indicate a concentration of built-up areas. A hot spots analysis was conducted in order to detect any spatial autocorrelation (clustering) between the hot and cold spots, in addition to any outlier or non-significant spots for the NDBI values throughout the study site. The maximum value of NDBI (0.35) was found in 2020, and the lowest value (0.17) was found in 2010.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Year** & \multicolumn{4}{c}{**Total LST Pixels**} & \multicolumn{4}{c}{**Urban LST**} & \multicolumn{4}{c}{**Rural LST**} \\ \hline  & **min** & **max** & \(\mu\) & \(\delta\) & \(\mu\)**+0.5\(\delta\)** & **min** & **max** & \(\mu\) & **min** & **max** & \(\mu\) & **UHI-intensity** (\({}^{\circ}\)**C)** \\ \hline
1990 & 31.6 & 52.4 & 40.8 & 4.1 & 42.8 & 42.8 & 52.4 & 47.6 & 31.6 & 42.8 & 37.2 & 10.4 \\
2000 & 26.4 & 48.3 & 38.2 & 3.6 & 39.9 & 39.9 & 48.3 & 44.1 & 26.4 & 39.9 & 33.2 & 10.9 \\
2010 & 31.7 & 47.8 & 39.4 & 3.5 & 41.2 & 41.2 & 47.8 & 44.5 & 31.7 & 41.2 & 36.4 & 8.1 \\
2020 & 36.3 & 53.7 & 45.6 & 3.1 & 47.1 & 47.1 & 53.7 & 50.4 & 36.3 & 47.1 & 41.7 & 8.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The assessment of intensity of UHIs over the multiple surface covers of the study area.  
Figure 5: The extracted UHIs over the study area for the periods (**a**) 1990, (**b**) 2000, (**c**) 2010, and (**d**) 2020.  
Moreover, in terms of mean values, in 1990, the area had the lowest mean for NDBI, while in 2000 and 2010, the mean values were the highest. The highest values of NDBI were concentrated in the east and the west portions of the study area, while the central and northern portions had the lowest NDBI values due to vegetation cover (mostly palm trees) in these areas. The highest value of the maximum LST (53.7 \({}^{\circ}\)C) was found in 2020, and the lowest value (48.3 \({}^{\circ}\)C) was found in 2000.  
Furthermore, in terms of mean values, in 2000, the area had the lowest mean LST, while in 1990 and 2020, the mean values were the highest (Table 5). The highest LST was at the eastern and western portions of the study site, whereas the central and northern parts had the lowest LST values, also as a result of date palm trees at these sites of the study area.
## 3 Method
### Hardware Adaptability  
Ensuring hardware adaptability requires that the acquisition application should not embed a list of predefined sensors to interface with but should be able to discover those that are available in the target smartphone and in the paired smartwatch. If available in the smartwatch, the automatically populated list should include not only classical inertial sensors (e.g., accelerometer, gyroscope, and compass), but also different sensors for the monitoring of, for example, the ambient temperature, ambient level of humidity, heart rate, and others.  
To achieve this goal, we used the Android API. In particular we exploited the SensorManager class that provides the getSensorList (int type) [47] method, through which it is possible to obtain a list of the available sensors in a device. In this way, the acquisition application can adapt to the smartphone in which it is executed and offers the possibility to choose all the sensors or some of them. Moreover, if the smartphone is paired with a smartwatch, the application can also adapt to the smartwatch, thus also making it possible to select the sensors available in the smartwatch itself, increasing the information associated to the activities carried out by the subject.  
This ensures extreme flexibility in the choice of the data type that can be recorded. Moreover, the subject can select only the sensors that are hosted by the smartphone or the smartwatch.  
Figure 2: **(Left)** The folder organization in the acquisition application. **(Right)** The folder organization in the labeling application.
# Multimodal Emotion Recognition Using Visual, Vocal and Physiological Signals: A Review
## 4 Human Emotional Expression Recognition
### Single Modality
#### 4.1.2 Speech Modality
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Database** & **Features** & **Classifier** & **Average Accuracy** \\ \hline
[171] & 2018 & EMO-DB & Combine & & 91.82 \\  & & SAVEE & WLPCCVQ and & RBFNN & 93.67 \\  & & & WMFCCVQ & & \\  & & & AM–FM modulation & & & \\  & & features (MS, MFF), & & & \\
[172] & 2019 & EMO-DB & cepstral features & SVM & 86.22 \\  & & & (ECC, EFCC, & & \\  & & SMFCC) from THT & & & \\  & & EMO-DB & Combine either & & 77.08 \\
[173] & 2020 & SAVEE & MFCC, HFCC, & & SVM & 55.83 \\  & & TBFCC-B or & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Speech emotion recognition methods using handcrafted features. [PERSON] et al. [178] used feature engineering for upstream and ECAPA for downstream processes and reported an accuracy of 77%. [PERSON] et al. [184] used a speech dataset, namely sustained emotionally colored machine-human interaction using a nonverbal expression project (SEMAINE), to test two models: one utilizing a basic neural network, and a method based on a CNN to analyze speech emotions. The CNN exhibited better accuracy and allowed for learning throughout the operation, thus minimizing the risk of forgetting and giving a better performance. [PERSON] et al. [185] used CNNs and reported an accuracy of over 70%. [PERSON] et al. [186] used CNNs that took a raw speech waveform and identified the individual's emotional state with a greater accuracy than other established methods. [PERSON] et al. [187] recently designed speech emotion recognition using LSTM and restricted Boltzmann machines (RBMs). Unlike facial affect recognition, voice affect recognition depends on the language and dialects of the speakers. Voice is affected by some external noise. The models for speech emotion recognition are trained using datasets that are appropriate to the applications they are designed for [188]. Further studies showed that acoustic parameters change due to oral variations and depend on personality traits [166,167]. Therefore, the data must be diversified for methods to deal with different language dialects (and speech accents). The speech emotion recognition methods must consider the scenario of multiple speakers.
## 3 Proposed Methodology
### Characterization of the Root Cause of Fault
#### 3.1.2 Voltage Dip  
The RMS voltage dip of fault is evaluated based on two cycle window length which is equivalent to 200 samples. These features indicate a degree of unbalance during fault occurrences. Maximum voltage which is half cycle before the fault initiated is deducted with the minimum voltage value at the last point at which the 2 nd cycle is calculated. The percentage of voltage dip value is then evaluated. Figure 8 illustrates an example of voltage dip with the minimum voltage indicated. The minimum voltage dip during the fault is calculated relatively with the maximum value as:  
\[Vd=\left(Vmax-Vmin\right)_{rms}\times 100 \tag{17}\]
# Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
## 2 Materials and Methods
### In Situ Measurements
#### 2.2.2 Biophysical Variables  
Agricultural crop variables were measured at the exact same location where spectral signatures were recorded shortly before. The time offset between the variable sampling and the spectral sampling was 45 min on average and 60 min at maximum.  
Average Leaf Inclination Angle (ALIA) was measured with a Suunto PM-5/360 inclinometer held along the leaf petiole to display its slope against the horizontal plane [45]. The measurement was repeated at different positions of the leaf and for different leaves within the canopy. Additionally the Leaf Inclination Distribution Function (LIDF) was noted down for a more detailed description of the canopy architecture [46]. Leaf chlorophyll content (LCC) was measured with a Konica-Minolta SPAD-502 handheld device at different heights with focus on the upper canopy layer. The chlorophyll meter had been individually calibrated in a preceding field campaign against destructive measurements of winter wheat leaf chlorophyll content from different senescence states. Coefficients of [47] were used to derive LCC from the samples. Leaf senescence (Cbr) was estimated as the fraction of brown leaf parts in the foliage. This variable varies between zero (no brown spots = 100% fresh vegetation) and one (no green spots = 100% senescent vegetation). For a proper estimation of Cbr, the approach of [48] was slightly adapted to incorporate the non-linearity of the vertical distribution of brown leaves. The dissociation factor between upper and lower layer thus created consistent results. This was achieved by applying a cosine function of the brownness in the upper layer to the power of two. Cbr can be written as Equation (2):  
\[\text{C}_{\text{br}}=\frac{\text{br}_{\text{u}}+\text{br}_{\text{1}}}{2}-\frac {\left|\text{br}_{\text{u}}-\text{br}_{\text{1}}\right|}{2}\cdot\cos(90^{ \circ}\cdot\text{br}_{\text{u}}) \tag{2}\]  
with br\({}_{\text{u}}\) as the fraction of brown leaf parts in the upper and br\({}_{\text{1}}\) in the lower layer of the canopy. For LAI measurements, a LI-COR Biosciences LAI-2200 instrument was used that had been upgraded with the ClearSky Kit to obtain functionalities of the advanced LAI-2200C. Equipped with a GPS sensor and a white diffuser cap, the device allows for nondestructive measurements of leaf area index under sunlight conditions. To obtain green LAI, the measured LAI value was multiplied with the factor \(1-\text{C}_{\text{br}}\) to exclude the impact of non-photosynthetic vegetation on LAI measurements. Multiplicationof leaf variables with the LAI value allows their interpretation on canopy level, e.g., canopy chlorophyll content (CCC).
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 2 Related Works  
### Combination Models Based on Weighted Averaging  
In order to apply the combination prediction model to the field of dam deformation, [PERSON] et al. (2018) used the Arima model to predict the deformation data of a dam, and the ANN model to predict the error. Finally, the predicted value of the two parts was added together to obtain the final predicted value, which proved that the prediction accuracy was higher than the ARIMA model. [PERSON] et al. (2018) proposed a combined prediction algorithm based on ARIMA-BP, where ARIMA was used to predict the sequence to obtain the residual value, then BP was used to predict the residual value to obtain the final result by addition, compared with the single model, the prediction accuracy is improved. However, the above models all use ordinary addition operations, which cannot fully describe the complex relationship between the predicted values of each model. Therefore, it is necessary to improve the combination method in traditional combination models to improve the prediction accuracy of the combination model.
## 5 Performance Testing
### Deployment Density Test  
To identify a balance between iBeacon number and accuracy of positioning, different numbers of placement were tested. Twelve iBeacons were deployed in Room ZB214 on the ceiling and their positions were recorded. Fingerprint data were then collected, and the test was repeated, with one iBeacon removed each time until there were only three iBeacons left. The initial setup of the iBeacon is presented in Figure 8.  
Using more iBeacons can result in greater coverage, with the following caveats. Deployment density can be increased by walls, furniture, distance and other obstacles, as they could attenuate the signals. With the deployment of different numbers of iBeacons, ranging from 12 to 3 iBeacons in this test, the CDF of RMSEs were plotted in Figure 9. The results show that positioning accuracy was enhanced by increasing the number of iBeacons from 3 to 12 (Figure 9). The RMSE was highest with three iBeacons and diminished gradually until eight iBeacons were deployed. The CDFs of the RMSE in using more than eight iBeacons were very similar in Figure 9. Figure 10 presents the difference in RMSE using different numbers of iBeacons. The results indicate that there was a significant improvement in accuracy from 3.44 m to 1.80 m, when the number of iBeacons was increased from three to eight. Using more than eight iBeacons resulted in a minimal increase in accuracy. The improvement of RMSE from 8 iBeacons to 12 iBeacons was 0.26 m, which was reduced from 1.80 m to 1.54 m. [PERSON] et al. (2018) explained that when iBeacons are close to each other (for instance, when there are too many iBeacons), the receiving mobile phones have difficulties detecting the exact location of the signal sources. Our results suggest that having too many iBeacons not only fails to improve the positioning accuracy significantly, but could also result in unnecessary overlaps of coverage areas.  
Figure 8: Deployment plan of 12 iBeacons and the locations of fingerprint collection.  
From the experimental results, eight iBeacons were appropriate for the deployment in balancing the iBeacon hardware required and the RMSE error, but this number was dependent upon the room size. In considering the testing environment of a classroom with an area of 152 m\({}^{2}\), the average spacing between iBeacons was 4.4 m, the context whereby this spacing was recommended in consideration of deployment density.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 2 System Model
### Received Signal Model  
Throughout this paper, we consider a non-coherent intensity modulation with direct-detection (IM/DD) scheme since it is a practical and widely extended technique for FSO communications [38]. With this scheme, the intensity of the emitted light is employed to convey the information. Therefore, the PD output current can be expressed as follows [39; 40]  
\[i=hRx+n, \tag{1}\]  
where \(x\) is the transmitted intensity in W, \(R\) is the detector responsivity in W/A, \(h\) is the channel coefficient and \(n\) represents the noise term at the receiver side. We assume that the shot noise induced by the ambient light is the dominant noise source in our analysis. Thus, we model \(n\) as a signal-independent zero-mean Gaussian noise with variance given by  
\[\sigma_{n}^{2}=2 qRB_{n}P_{amb}, \tag{2}\]  
Figure 1: Possible 5G + network scenarios where FSO will extend or complement existing deployments.  
where \(q\) denotes the electron charge, \(B_{n}\) is the receiver noise bandwidth, and \(P_{amb}\) is the ambient light power. This latter term can be obtained as \(P_{amb}=\pi r^{2}S_{n}B_{o}\Omega_{FOV}\), where \(r\) is the receiver aperture radius, \(S_{n}\) is the ambient light spectral radiance, \(B_{opt}\) is the optical bandwidth in nm and \(\Omega_{FOV}\) is the receiver field of view (FOV) in srad.  
The transmitted intensity is taken as symbols drawn with equal probability from the OOK constellation such that \(x\in\{P_{1},P_{0}\}\), where \(P_{1}\) and \(P_{0}\) represent the power corresponding to the transmission of a '1' and a '0', respectively.  
In this respect, we can cite three primary atmospheric phenomena that affect optical wave propagation and that constitute the total channel coefficient \(h\): (i) the deterministic path loss, \(h_{l}\) characterized by absorption and scattering; (ii) the geometric spread and pointing errors \(h_{p}\); and (iii) the refractive-index fluctuations (i.e., the atmospheric turbulence), \(h_{a}\), leading to irradiance fluctuations. Thus, the total channel attenuation is modeled as the product of these aforementioned channel factors as:  
\[h=h_{l}h_{p}h_{a}, \tag{3}\]  
where \(h_{p}\) and \(h_{a}\) are random variables (RVs). In the next sections, we describe the underlying distribution that model those RVs. Finally, we define the signal to noise ratio as [41]:  
\[\text{SNR}=\frac{\left(RhP_{av}\right)^{2}}{\sigma_{n}^{2}}, \tag{4}\]  
where \(P_{av}=\left(P_{0}+P_{1}\right)/2\) represents the average transmit power.