esa-sceva/satcom-chunk-collection huggingface dataset:
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 2 PV Generation Scheduling Problem in the Incentive Program
### PV Curtailment and ESS Operation  
Generation schedules can have a significant impact on the PV site's daily revenue. Therefore, the PV site should strategically schedule its generation, including ancillary resources. Typically, PV generators offer their PV generation as a generation schedule without any adjustments. However, strategically modifying the generation schedule before submission has the potential to increase incentives.  
Figure 2 shows a concept of adjusting the generation schedule based on PV curtailment and ESS operation to maximize incentives. The PV scenarios are examples of under-forecasted and over-forecasted for the following day. Two PV scenarios lie outside the permissible range for energy deviation in the incentive program; both scenarios are ineligible for incentives.  
Considering the characteristics of PV energy deviation, the generation schedule can be adjusted to be lower than forecasted PV generation in day-ahead scheduling. In this  
Figure 1: Incentive unit price according to energy deviation.  
Figure 2: A concept of adjusting the generation schedule based on PV curtailment and ESS operation.  
situation, it is necessary to consider as many PV scenarios as possible to incorporate the characteristics of the energy deviation. Subsequently, in real-time operation, ESS discharge can be performed for generation levels below the range of energy deviation eligible for incentives, and PV curtailment and ESS charging are performed for generation levels above the range. In general, PV curtailment can reduce electricity sales revenue. However, mitigating the energy deviation by considering incentives can increase revenues. Indeed, operating ESS or curtailing PV alone can mitigate energy deviation. However, the proposed method is expected to enhance the flexibility of PV operation during real-time operation by expanding the controllable output range.  
In the scheduling problem, the main objective is maximizing the economic benefit from MP and incentives. In order to maximize the total revenue, ESS charging is likely to occur more frequently than PV curtailment to mitigate errors. In this situation, PV curtailment will reduce the daily power generation sales revenue. In addition, the ESS discharge may occur even when MP is low to mitigate the energy deviation, reducing daily power generation sales revenue. Therefore, it requires optimized ESS operation, PV curtailment, and a strategically adjusted generation schedule. Considering these simultaneously makes it possible to maximize the economic benefit from MP and incentives efficiently.
# Explainable Artificial Intelligence for State of Charge Estimation of Lithium-Ion Batteries
## 3 Methodology
### Dataset  
The data available from AVILOO [38] include a significant number of different e-Golf cars in several thousand sessions; see Table 2. A session is defined as the totality of signal data points between the start of a drive or charging event and its end. Sessions can be differentiated based on whether the battery was charging or discharging. They can also be differentiated by whether the session is a full cycle or a partial cycle. A full cycle is when the difference between the MAX SOC DISPLAY signal value and the MIN SOC DISPLAY signal value is greater than 90% within the session. Within this work, we focus on full cycle discharging sessions.  
Recognizing the frequent inaccuracies in the original SoC reported by the vehicles, we carried out a thorough verification process and corrected the SoC whenever a mistake was found. We achieved this by developing precise Open-Circuit Voltage (OCV) curves; see Figure 3. The curves serve as a benchmark to align the cell voltages of the cars with established lookup tables, thus ensuring the SoC values to be as accurate and reliable as possible.  
**Dataset composition:** The data comprise multiple sets of individual e-Golf cars. One car set contains one or more full-cycle discharged sessions. A car set containing only one full-cycle discharge session is referred as a single-labeled session. A car set containing more than one full-cycle discharge session is referred as a multi-labeled session. The sessions are labeled with SoC. The dataset used consisted of 4 multi-label sessions and 196 single-label sessions.  
Figure 3: Automated SOC corrections based on OCV curves.  
Figure 2: Splitting data into windows: Creation of windows using a stride parameter set to 1.  
**Data inspection:** The features and the labels were checked to identify any anomalous signal behavior or outliers. Every sub-session (an uninterrupted driving session inside of an individual car set) was visualized; this allowed for a visual assessment of signal distribution. Subsequently, domain experts analyzed outliers effectively. The noise and layout of signals that could lead to false conclusions were excluded from the dataset. For example, one of the multi-SoC sessions was excluded from the analysis due to its lack of monotonically decreasing SoC behavior.
## 5 Simulation Study
### Impact of Varying Queue Length  
We also vary the queue length of sensor nodes in our simulations, with the results presented in Figure 9. The queue length here indicates the maximum messages the sensor can hold. With an increase of queue length, the delivery ratio increases for all protocols because messages can then stay in the memory for a longer time before they are dropped. It is also noticed that ARP achieves higher gain than other protocols with the increase of queue length. As shown in Figure 9(b), with the increase of maximum queue length, the message copies in all protocols rise. This is reasonable for more copies can be accomodated in the queue before being dropped when the queue length is large. However, the ARP can always control well its transmission overhead even when the available queue size is small. Figure 9(c) depicts that the average data delivery delay decreases with larger queue length while the delivery delay in the flooding protocol is more sensitive to the variation of the queue length.  
Figure 8: Impact of sensor node density: **(a)** Average delivery ratio; **(b)** Average copies; **(c)** Average delay.
# _remote sensing_
## 3 Results
### Crop Diseases Results
#### Orchards Diseases Mapping
Thus, a new analysis with new image acquisitions was programmed for the incoming seasons to validate and confirm these results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Confusion Matrix with 12 Initial VIs**} \\ \hline Output class 1 & 58.5\% & 4.8\% & 92.5\% \\ Output class 2 & 3.1\% & 33.7\% & 91.6\% \\  & 95\% & 87.6\% & 92.2\% \\ \hline \multicolumn{3}{c}{**Confusion Matrix with Extended List of VIs**} \\ \hline Output class 1 & 59.7\% & 2.5\% & 96.0\% \\ Output class 2 & 1.6\% & 36.0\% & 95.1\% \\  & 97\% & 93.6\% & 95.7\% \\  & Target class 1 & Target class 2 & OA \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained from the application of the neural network, trained with VIs computed on PRISMA images, on the two regions in New Zealand. As in the previous cases, the characteristics of the neural network included 60% of the samples for training, 20% for validation, and 20% for testing, with 20 hidden neurons. Figure 10: Results of the application of the trained NN on kiwi fields in Italy. The legend shows the probability of a field infection (in this case, representing similar behavior as those fields validated in other regions of interest). It can be concluded that the methodology described above can be applied for the monitoring of kiwifruit anomalies, considering the availability of satellite images with the required spectral, radiometric and spatial resolution. However, the results cannot be directly applied to any kind of orchard, as further detailed in the Discussion section. Concerning the use of hyperspectral images, it needs to be noted that, for performing this test, the images were collected in dates different from those of the RapidEye and Sentinel-2 images and, also, with a smaller number of fields, due to the limited time span of the archived PRISMA images, and the limited coverage of the areas of interest. Therefore, with the limitation recalled above, the result, not obvious because satellite images of different spatial resolution (in the range of 5 m to 30 m) and number of spectral channels were used, is that, in this case, in the range of spatial resolution from 5 m to 30 m, higher spectral information provides better results.
###### Abstract
Prior research has indicated that the %Rep derived from individualized %Rep-%VL equations exhibits high consistency in the short term (6-7 days) in the Smith machine bench press at a 60%1 RM load [5], and over a longer period (12 weeks) in the pull-up exercise using body mass [16]. Additionally, it has been found that the %Rep linked with varying %VL (from 15% to 75%) remained consistent (mean difference <3%) following a RT program of 12 weeks, even though participants significantly increased their maximum repetitions to failure (pre-test: 15.1 \(\pm\) 4.2 repetitions; post-test: 17.0 \(\pm\) 4.1 repetitions) [16]. Despite these positive results, no research has yet assessed the precision of generalized and individualized %Rep-%VL relationships in predicting %Rep during a typical training session that includes multiple sets. It is likely that the fatigue from performing multiple sets has different impacts on the pattern of repetition velocity loss and the maximum repetitions achieved to failure [17]. Fatigue may also be reflected in a reduced mean velocity of the fastest repetition in a set (MV\({}_{\text{fastest}}\)) [17; 18]. Moreover, %VL can be calculated using either a fixed (MV\({}_{\text{fastest}}\) of the first set) or a flexible (MV\({}_{\text{fastest}}\) specific to each set) reference repetition velocity. Yet, there is little information regarding whether the choice of reference velocity for calculating %VL influences the accuracy of %Rep estimates at different %VL levels. To address some gaps related to the use of %VL to quantify proximity to failure, the current study aimed to (i) compare the goodness-of-fit between the individualized and generalized %Rep-%VL relationships obtained during a single set of repetitions to failure in the Smith machine parallel back-squat exercise against the 75%1 RM load, (ii) elucidate whether the generalized and individualized %Rep-%VL equations modeled from the data collected in the first set can provide accurate estimations of the %Rep when a moderate %VL is achieved during the successive sets (second, third, and fourth sets) of the training session, and (iii) determine whether the reference repetition (MV\({}_{\text{fastest}}\) specific to each set vs. MV\({}_{\text{fastest}}\) of the first set) used for computing the %VL affects the magnitude of the errors when estimating %Rep. It was hypothesized that there would be a greater goodness-of-fit(higher coefficient of determination [R\({}^{2}\)] and lower standard error of the estimate [SEE]) for individualized compared to generalized %Rep-%VL relationships [15]. However, since no study has previously examined the effect of fatigue on the precision of the %Rep-%VL equations, no specific hypotheses were formulated regarding our second and third aims.
# Cross-Entropy Method in Application to the SIRC Model
## 3 Description of the Numerical Results
### CE Method Version 2  
In the second case, it was necessary to change slightly the algorithm from Section 2.3 to be able to use it for two control functions. In point 1 of the algorithm a variable is generated \(C_{m}\sim N(\mu_{k},\sigma_{k}^{2})\), which serves to designate \(u_{C_{m}}\), where \(\mu_{k},\sigma_{k}\) are requested by the user. Here, 2 \(\mu\) will need to be initialized \(\mu_{k}^{(u)},\mu_{k}^{(v)}\) and 2 \(\sigma\): \(\sigma_{k}^{(u)},\sigma_{k}^{(v)}\). Then designate \(C_{1}^{(u)},\ldots,C_{N}^{(u)}\sim N(\mu_{k}^{(u)},\sigma_{k}^{(u)2})\) and similarly \(C_{1}^{(v)},\ldots,C_{N}^{(v)}\sim N(\mu_{k}^{(v)},\sigma_{k}^{(v)2})\). Now \(J(u,v)\) can be count without problems. Further the algorithm goes the same way, and in point 6, all four parameters \(\mu_{k}^{(u)},\mu_{k}^{(v)},\sigma_{k}^{(u)},\sigma_{k}^{(v)}\) are updated. The Figure 5 presents these functions.  
In this case, we have very similar results as in the previous section. This can be seen in both the charts and the cost index values (0.003044 and 0.006451). The only problem here is the need for a large sample to bring the results closer.
# A Visual Object Tracking Algorithm Based on Improved TLD
## 3 The Improved TLD Tracking Algorithm
### Optimize the Detecting Strategy of the Detection Module  
In the TLD framework, all possible scales and shifts of the initial bounding box are generated as a scanning-window grid. Deciding the presence or absence of the object for each window by cascading classifiers results in a great amount of calculations and slows down the tracking speed. In order to reduce the number of windows so as to improve the tracking speed, we proposed that the detection module just detects the windows around the estimated location by the tracking module, which is called \"local detection\" in this paper. If the tracking module fails to track the object, then the detection module detects the target in a full frame, called \"global detection\" in this paper. The difference between global detection and local detection is shown in Figure 10.  
In this work, the range of local detection was centered on the estimated position of the object, and the size was 2.5 times the bounding box's size on the last frame. That means the detection module only needs to detect windows, the coordinates of which are within this range. If the tracking module loses the target, the detection module needs to detect the target in a full frame. After global detection, if the detector finds the object, the tracking result is used to correct and initialize the KCF tracker. Otherwise,  
Figure 8: The peak values and the threshold during a video sequence.  
Figure 9: The bounding boxes of KCF and ground truth. In the 491 st frame, KCF tracked incorrectly and the peak value was lower than the threshold according to Figure 8 in the 250 th and 550 th frames.  
the improved TLD will not output the bounding box. To summarize, KCF is quick and local detection reduces many windows, thus improving tracking speed.
## 2 Results and Discussion
### Adsorption
#### 2.5.3 Adsorption Kinetics of the EDA-MPMs  
In order to better illustrate the adsorption mechanism of EDA-MPMs for Cr(VI), adsorption kinetics is essential and important for evaluation of the adsorption behavior. The pseudo-first order and the pseudo-second order kinetic models are employed to evaluate the experimental data obtained from batch Cr(VI) removal experiments. The two kinetic models equations are given as follows:  
Pseudo-first-order Equation [38]:  
\[\text{ln}(q_{e}-q_{t})=\text{ln}q_{e}-k_{1}t \tag{4}\]  
Figure 6: Effect of sorption time on the Cr(VI) adsorption onto MPMs and EDA-MPMs at different concentrations.  
Pseudo-second-order Equation [39]:  
\[\frac{t}{q_{t}}=\frac{1}{k_{2}q_{e}^{2}}+\frac{t}{q_{e}} \tag{5}\]  
where \(q_{e}\) and \(q_{t}\) are the amounts of solute adsorbed at equilibrium and at time \(t\) (min), respectively; \(k_{1}\) (1/min) and \(k_{2}\) (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)) are the kinetic rate constants for the pseudo-first-order and the pseudo-second-order models, respectively.  
All the corresponding parameters obtained from the linear form of pseudo-first-order and pseudo-second-order are listed in Table 1 and Table S1. The fitting curves obtained from the linear plots of \(t/q_{t}\)_versus_\(t\) are plotted in Figure 7 and Figure S1. It can be seen that the linear correlation coefficients (\(R^{2}\)) for pseudo-second-order kinetic model are all over 0.9990. Therefore, the pseudo-second-order kinetic model is able to describe properly the kinetic behavior of Cr(VI) on the EDA-MPMs. Furthermore, the \(k_{2}\) value decreased from 25.539 \(\times\) 10\({}^{-4}\) to 2.295 \(\times\) 10\({}^{-4}\) g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\) with an increase in the initial Cr(VI) concentration. The theoretical simulated curve fitted quite well with the experimental data in Figure 6. More importantly, the \(q_{e}\) values calculated by pseudo-second-order model were in agreement with experimental values. Analogous results had also been reported by many researchers for different systems [40,41].
## 2 Materials and Methods
### Data Sources
#### 2.2.3 Influencing Factor Data  
To further explore the factors affecting the spatial and temporal variations in NPP, TEM, PRE, SM, slope, and elevation were selected as the influencing factors of NPP. Meteorological data were obtained from the National Meteorological Information Center of China ([[http://www.nmic.cn/](http://www.nmic.cn/)]([http://www.nmic.cn/](http://www.nmic.cn/)) (accessed on 7 August 2023)), involving PRE (mm), TEM (\({}^{\circ}\)C), SUN (h), and EVA (mm) for 40 meteorological stations in and around the Songnen Plain from 2001 to 2020. SM information was acquired from the National Oceanic andAtmospheric Administration of the United States ([[http://www.esrl.noaa.gov/psd/data/gridded/data.pcssoil.html](http://www.esrl.noaa.gov/psd/data/gridded/data.pcssoil.html)]([http://www.esrl.noaa.gov/psd/data/gridded/data.pcssoil.html](http://www.esrl.noaa.gov/psd/data/gridded/data.pcssoil.html)) (accessed on 7 August 2023)) using a global observed PRE- and TEM-driven land surface process model. The spatial resolution is 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), the temporal resolution is at monthly and annual scales, and the vertical layer is 1 layer (0-160 cm). The information has a long time series and a good simulation of the seasonal and interannual variability characteristics of SM. The considered terrain feature data were collected from NASA Earthdata ([[https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)]([https://search.earthdata.nasa.gov/](https://search.earthdata.nasa.gov/)) (accessed on 7 August 2023)), which provides 30 m spatial resolution digital elevation model (DEM) data covering land globally. The considered terrain feature data mainly include two types: elevation data and slope data. According to the spatial resolution of the NPP data, all influencing factors are uniformly resampled to 500 m after pre-processing, such as projection transformation, spatial interpolation, clipping, and shearing. All the influencing factor data were resampled to a uniform resolution of 500 m.
# Analysis of Vehicle-Bridge Coupling Vibration Characteristics of Curved Girder Bridges
## 4 Spatial Vibration Analysis of the Coupled Time-Varying System of Vehicle-Curved Grider Bridge
### Impact Analysis of Curvature Radius  
Based on the above bridge and car models, according to the common radius values of highway and urban interchange ramp bridges, the curvature radii were selected as 35m, 40 m, 500 m, 100 m, and 200 m, respectively. With the same bridge length, the Timoshenko curved beam element was used to discretize the bridge structure. Considering the good bridge top deck irregularity level, the self-written program Cmck was used to calculate the coupling dynamic responses when a stranded truck with a load of 1 ton travelled through the curved girder bridge at a speed of 40 km/h and explored the influence of curvature radius on the spatial vibration.  
The comparison of the displacement and the acceleration time-history responses at the bridge mid-span with different radii of curvature are shown in Figures 6-8. The peak values at the mid-span are shown in Table 2 and Figure 9.  
Figure 5: Comparison of the acceleration time-history curves at mid-span.  
Figure 6: Time-history curve of mid-span vertical displacement under different curvature radii.  
Figure 8: Time-history curve of mid-span torsional displacement under different curvature radii.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Radius** & **Vertical** & **Vertical Acceleration** & **Torsional** \\
**(m)** & **Displacement (mm)** & **(m/s2)** & **Displacement (rad)** \\ \hline
35 & 2.153 & 0.158 & \(0.114\times 10^{-3}\) \\
40 & 1.966 & 0.173 & \(0.944\times 10^{-4}\) \\
50 & 1.890 & 0.124 & \(0.753\times 10^{-4}\) \\
100 & 1.712 & 0.117 & \(0.364\times 10^{-4}\) \\
200 & 1.668 & 0.123 & \(0.183\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of dynamic response peak values of bridges with different curvature radii.  
Figure 7: Time-history curve of mid-span vertical acceleration under different curvature radii.  
Figure 9: Peak dynamic response of bridge mid-span under different curvature radii: (**a**) vertical displacement; (**b**) vertical acceleration; (**c**) torsional displacement.  
From Figures 6-9 and Table 2, as the radius of curvature increased, the dynamic responses showed a gradual decrease. The peak vertical displacement decreased from 2.153 mm to 1.668 mm, a decrease of 22.3%; the peak vertical acceleration decreased from 0.173 m/s\({}^{2}\) to 0.123 m/s\({}^{2}\), a decrease of 28.9%; the peak torsional angular displacement decreased from 0.114 \(\times\) 10\({}^{-3}\) rad to 0.183 \(\times\) 10\({}^{-4}\) rad, a decrease of 83.9%. Within the radius of curvature less than 100m, the decrease in the dynamic responses was more pronounced. Compared with the vertical dynamic response, the torsional response was more sensitive to the radius of curvature.
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Ablation Study  
To evaluate the contributions of key components in SS3 DNet-AF, we conducted an ablation study using the ShapeNet dataset, with Chamfer distance (CD) as the primary evaluation metric. This study investigates two major aspects: the impact of the attention mechanism in feature fusion and the significance of the initial geometry representation.
# Performance of 5G Trials for Industrial Automation
## 2 Industrial Use Cases and Requirements  
A range of use cases were identified and described in, e.g., 3 GPP [1], 5G-ACIA [7], and the EU project 5G-SMART [8]. These use cases represent the typical characteristics of today's industry applications in terms of cycle times and packet sizes as well as the requirements for latency, reliability, and survivability.  
The evaluation undertaken in this article was carried out for the 5G system deployments at the 5G-SMART project trial sites. For each of the trial sites, a variety of use cases was considered for evaluating and validating 5G for smart manufacturing. These use cases are described below, where their benefits as well as their key requirements are outlined.
# An Internet of Things (IoT) Application on Volcano Monitoring
## 3 IoT Thermometers Network
### Software
#### 3.2.2 Repeater Software  
The Repeater.cpp program manages the repeater operation (Figure 8). The default status is receiving mode (Section 3.2.3), meaning that it is always waiting for a message from the gateway. Depending on the command received, two different actions are executed. On the one hand, if the final recipient is an end device, the repeater relays the message and keeps waiting for the answer during a  
Figure 7: Gateway flow chart.  
fixed time period, which depends on the number of end devices that compose the network. Then, it packages all the information received from the end devices, adds some extra information such as each end device RSSI, sends it to the gateway, and returns then to receive mode. On the other hand, if the final recipient is the repeater itself, it automatically answers the requested information and returns to receive mode.