esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Energy Storage Optimization Operation Schedule in an Island System
## 3 Case Study  
### System Information  
This study used the Kinmen power grid as the target system. Kinmen Island is a small island directly west of Taichung City in Taiwan (R.O.C.), which is very near mainland China. It has a 10 MW PV plant, two PSS with a total capacity of 1.8 MW/10.8 MWh, and two fuel plants on the island. The first fuel plant has ten heavy fuel oil generators grouped into three phases corresponding to the commissioning year of the generators. The second fuel plant has four light fuel oil generators. The capacity, power limits, startup cost, and time constraint constants of each generator in the two plants are shown in Table 1. The quadratic form of the fuel cost constants (a, b, and c) was plotted and divided into three segments. The three-segment piecewise linear fuel cost equations, in Equation (3) form, are listed in Table 2. According to [12], light fuel oil (LFO) fuel constants are about three times that of the heavy fuel; hence, parameters for the LFO were assumed to be thrice that of heavy fuel.  
Figure 2a shows the cost curve of the generators according to their range. Different types of units have different working intervals, as listed in Table 1. Figure 2b shows the average cost of power generation by dividing the cost (from Figure 2a) by the power generation. The left end of each segment is the minimum operating power of the generator, and the right end is the maximum operating power of the generator. The results showed that the generator closest to a full load had higher efficiency. In this case, the Phase III generators would be most efficient as their cost was lowest when operating at a higher output. This is followed by Phase II (orange) and Phase I (blue).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Units** & **DG** & **Minimum** & **Maximum** & **Startup** & **Minimum** & **Minimum** \\
**Capacity** & **Power** & **Power** & **Cost** & **Time of** & **Time of** \\  & **(MVA)** & **(MW)** & **(MW)** & **Cost** & **Startup** & **Shutdown** \\ \hline Plant1\_1\_4 & 10.2 & 4 & _7.7_ & 7 & 4 & 2 \\ Plant1\_5\_8 & 9.7 & 4.1 & 7.8 & 7 & 3 & 2 \\ Plant1\_9\_10 & 13.8 & 5.5 & 10.5 & 10 & 5 & 3 \\ Plant2\_1\_4 & 3.48 & 1.5 & 3 & 7 & 4 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The simulation parameters of the diesel generators.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Units** & **Description** & **a\({}_{\mathbf{i}}\)** & **b\({}_{\mathbf{i}}\)** & **c\({}_{\mathbf{i}}\)** & **Segment 1** & **Segment 2** & **Segment 3** \\ \hline Plant1\_1\_4 & Phase I & 15 & 1.9161 & 0.0661 & 2.53x + 13.62 & 2.69x + 12.76 & 2.85x + 11.71 \\ Plant1\_5\_8 & Phase II & 13 & 1.8518 & 0.0657 & 2.47x + 11.56 & 2.63x + 10.7 & 2.8x + 9.63 \\ Plant1\_9\_10 & Phase III & 12 & 1.7966 & 0.0615 & 2.58x + 9.58 & 2.78x + 8.11 & 2.99x + 6.3 \\ Plant2\_1\_4 & LFO - & - & - & 2.53x + 30 & 2.69x + 21 & 2.85x + 18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters for piecewise linear for the diesel generators.
# Effect of Microwave-Assisted Processing of Acorn Flour on Muffin Quality
## 3 Results and Discussion
### Effect of Microwave Treatment on Antioxidant Properties of Acorn Flow
Specifically, the greatest decrease in tannin content in dry microwave-treated samples was observed in the sample treated at 1850 W for 1 min (a 36.9% reduction) and in the sample treated at 925 W for 3 min (a 31.9% reduction). In contrast, the reduction in tannin content in wet microwave-treated samples was lower, with a maximum decrease of 28.6% observed in the sample treated at 1850 W for 1 min. These results align with those reported by [PERSON] et al. [62], who found a reduction in tannin content with increasing microwave treatment duration in plum kernels. Similarly, [PERSON] et al. [64] confirmed the effect of microwave heating on tannin reduction in various grains, including pearl millet (45.5%), finger millet (45.2%), and sorghum (77.5%) following microwave treatment at 900 W for 40-100 s. [PERSON] et al. [34] also reported an 88% reduction in tannin content in soybean flour after exposure to 0.9 kW microwaves for 1.5 min. According to [PERSON] et al. [33], the reduction in tannin content during microwave treatment may result from the heat generated within the product due to increased molecular motion, which can disrupt hydrogen bonds. The observed changes in antioxidant capacity were directly related to variations in the content of antioxidant compounds, exhibiting trends similar to those observed for total free phenolics and flavonoids. The highest antioxidant capacity was recorded in the dry microwave-treated sample for 2.0 min at 925 W (a 32.3% increase) and in the wet microwave-treated sample for 1.0 min at 925 W (a 19.9% increase). In general, wet microwave-treated samples exhibited lower antioxidant capacity compared to their dry counterparts. According to [PERSON] et al. [57], the antioxidant capacity may be influenced not only by phenolic compounds and flavonoids but also by compounds formed during browning reactions, such as melanoidins. [PERSON] et al. [62] further suggest that the increase in antioxidant capacity may be attributed to the breakdown of cellular structures, which enhances the release of pigments and phenolic compounds into the matrix.
# Short-Term Wind Speed Prediction Based on Principal Component Analysis and LSTM
## 3 Case Study
### Prediction Result Analysis
From Table 6 and Figure 7, the proposed model outperformed the other four competitors for short-term wind speed forecasting with the smallest mean  
Figure 5: Forecast results in Case I. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Statistical Indicators** & **RMSE** & **MAPE** & **MAE** & \(\mathbf{R^{2}}\) \\ \hline \multirow{3}{*}{Feature-BPNN} & Min & 0.2425 & 0.0702 & 0.1832 & 0.9650 \\  & Mean & 0.3352 & 0.0975 & 0.2498 & 0.9806 \\  & Max & 0.4575 & 0.1418 & 0.3218 & 0.9902 \\ \hline \multirow{3}{*}{Feature-GPR} & / & 0.1898 & 0.0535 & 0.1379 & 0.9546 \\ \hline \multirow{3}{*}{Feature-LSTM} & Min & 0.1577 & 0.0429 & 0.1057 & 0.9592 \\  & Mean & 0.1745 & 0.0488 & 0.1212 & 0.9749 \\  & Max & 0.1926 & 0.0535 & 0.1334 & 0.9942 \\ \hline \multirow{3}{*}{Feature-RNN} & Min & 0.1693 & 0.0471 & 0.1232 & 0.9066 \\  & Mean & 0.2077 & 0.0613 & 0.1549 & 0.9648 \\ \cline{1-1}  & Max & 0.2613 & 0.0871 & 0.1937 & 0.9997 \\ \hline \multirow{3}{*}{Feature-SVR} & / & 0.9976 & 0.1726 & 0.5726 & 0.704 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of prediction results using meteorological characteristics model.
## 5 Optimal Path Selection
### Lane-Changing Efficiency Cost Function  
During the lane-changing process of intelligent semi-trailers, efforts should be made to shorten the lane-changing time as much as possible to improve lane-changing efficiency. We use the time difference between the initiation and completion of the lane change to represent the lane-changing efficiency cost. The lane-changing efficiency cost function is defined as:  
\[J_{t}=k_{5}(t_{e}-t_{0}) \tag{32}\]  
where \(k_{5}\) is the weight parameter.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Evaluation Index** & _MSE_ & _RMSE_ & _MAE_ & _MAPE_ \\ \hline Training set & 0.00104 & 0.03225 & 0.02493 & 0.02827 \\ Test set & 0.00105 & 0.03246 & 0.02509 & 0.02848 \\ \hline \end{tabular}
\end{table}
Table 3: Evaluation indexes of Transformer.  
Consequently, the overall cost function can be represented as:  
\[J=J_{c}+J_{r}+J_{e}+J_{t} \tag{33}\]  
Each path within the feasible path cluster is analyzed, and the path with the lowest total cost function is chosen as the optimal option.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 4 Simulation-Based Performance Metrics  
### Case Study 1: 802.11 az IoT Enabler, Simulation-Based Results, Time Domain  
In 802.11 az, a position estimate is obtained by solving the hyperbolic location based on the measured TOA \(t_{A_{a}}\) at the mobile side from several ANs, where \(a\) is the AN index, \(a=1,\ldots,N_{AN}\).  
\[t_{A_{a}}=t_{s}+\frac{d(Tag,AN_{a})}{c}+\sum_{a=2}^{N_{A}N}\bigg{(}\frac{d(AN_ {a-1},AN_{a})}{c}+t_{f}\bigg{)}, \tag{8}\]  
where \(t_{s}\) is the starting time of transmission from one AN in the network, taken arbitrarily as the first AN (\(AN_{1}\)), \(c\) is the speed of light, \(d(Tag,AN_{a})\) is the geometric distance between the mobile device and the \(a\)-th AN, \(a=1,\ldots,N_{AN}\), \(t_{f}\) is the forwarding time of the signaling message between two access nodes, and \(d(AN_{a-1},AN_{a})\) is the geometric distance between the \(a-1\)-th AN and \(a\)-th AN. With several noisy observations of the measured time of arrivals, the IoT device can compute its position (as well as the unknown \(t_{s}\)). It is assumed that the AN positions and the forwarding time are known and transmitted in the signaling message. In addition to that, a minimum of four synchronized access points are needed to estimate the four unknowns \((x,y,z,t_{s})\), with the \((x,y,z)\) the device location.  
To understand an achievable location performance, we defined a simulation over a square area of 0.4 km\({}^{2}\) at the highest bandwidth available (160 MHz). We observe in Figure 6 that this solution would be able to offer sub-meter accuracies 80% of the times when at least seven ANs are available.  
Figure 6: Example of 802.11 az performance at various number of access nodes at signal to noise ration (SNR), SNR = 10 dB.
## 3 Fully Intelligent Accident Liability Determination Method Based on Collision Detection  
This section introduces an advanced methodology for determining accident liability utilizing fully intelligent systems. The input for the proposed method is video captured by traffic road cameras; the overall process plan in our study is outlined as follows:  
**1. Vehicle recognition and tracking:** Utilize the YOLOv8 algorithm for high-precision vehicle recognition, followed by real-time target tracking with the MOSSE filter.  
**2. Accident detection:** Calculate the amplitude change in the global optical flow for each frame at the time of the accident and establish a threshold to extract features indicative of violent or abnormal motion patterns. These features are subsequently input into a trained SVM (support vector machine) to detect traffic accidents.  
Liability determination: Extract key frames of the accident using the K-means algorithm. These key frames, along with descriptive information, are passed into a large language model for in-depth analysis. The system then automatically generates an accident liability determination report.  
The modular process of the fully intelligent accident liability determination method, based on collision detection, is depicted in Figure 2.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Challenges
#### 8.2.10 Additional Resources  
In KDN, all application policy updates, network monitoring and configuration, and control plane decisions are knowledge-driven. In order to understand knowledge or rules produced from the knowledge plane, all application, management, and control planes must allocate additional resources to implement rule engines. A rule engine's purpose is to execute rules or make inferences from knowledge and make decisions based on the rule/knowledge evaluation. Rule engines infer from knowledge/rules using techniques such as forward chaining and backward chaining, which require considerable software and hardware resources for implementation. Furthermore, the whole knowledge plane, which includes knowledge generation models, knowledge bases, ontology editors, rule generators, etc., needs both software and hardware resources for knowledge or rule generation, storage, and sharing. On the other hand, in SDN, none of these additional resources are required, as it does not have a knowledge plane.
## 3 Results and Discussion
### Influence of Current Density on the Migration Number of Y(III) Ions  
In this section, AlF\({}_{3}\)-(Li,Na)F-3 wt.%Y\({}_{2}\)O\({}_{3}\) molten salt is studied at a temperature of 950 \({}^{\circ}\)C, electrolysis time of 1 h, and current densities of 1.0-2.0 A/cm\({}^{2}\). The relationship between the current density and the ion migration number is displayed in Figure 5. In the current density range of 1.0-2.0 A/cm\({}^{2}\), the migration number of Y(III) ions increases from 0.65 to 0.85 almost linearly (Equation (16)). As the current density increases, the cathode potential, electrochemical reaction rate, polarization intensity of active complexed Y(III) ions, current-carrying capacity, and ion migration rate increase. Furthermore, the  
Figure 4: Variation of the ion mobility number with the (Li,Na)F/AlF\({}_{3}\) molar ratio.  
higher diffusion rate of Y(III) ions and number of compounds participating in the electrode reaction also increase the migration number of Y(III) ions.  
\[N_{\text{Migrate}}=0.452+0.2T \tag{16}\]  
The obtained data are fitted using the least squares method, and the linear relationships between the Y(III) ion migration number and temperature, (Li,Na)F/AlF\({}_{3}\) molar ratio, and current density are represented by Equations (17) and (18).  
\[n=0.572+1.05\times 10^{-4}T+0.13I \tag{17}\]  
\[n=0.852+1.12\times 10^{-4}T-0.22M+1.15I \tag{18}\]  
where \(n\) is the migration number of Y(III) ions; \(T\) is the temperature, 900-1000 \({}^{\circ}\)C; \(M\) is the (Li,Na)F/AlF\({}_{3}\) molar ratio, 2.0-3.0; \(I\) is the cathode current density, 1.0-2.0 A/cm\({}^{2}\).
## 5 Experimental Results
### Evaluation of the CGFace Model  
First of all, we examined the performance of CGFace on two generated datasets. For PCGAN, the original size 256 \(\times\) 256 of the input image is resized to 64 \(\times\) 64. After that, the faces are detected, rotated, and aligned to frontal using dlib library to eliminate the pose changes. After that, four-fold cross-validation is conducted; it randomly divides the data into four subsets, in which each subset contains 5000 images. Each time, three subsets are fed into the model to train the proposed CNN, while the other subset used for testing purpose. Then, for the training set, 75% of the training data is used by the convolutional neural model to learn and update the weight, whereas the remaining 25% is used to validate data to fine-tune the parameters. The complete validation accuracy and loss for each fold are given in Figure 7. The optimization algorithm used in CGFace is Adam optimization [25] with a learning rate of 0.001, a batch size of 32, and 50 epochs.  
As shown in Figure 7, the training/validation accuracy increases dramatically to over 80%, while the training/validation loss drops significantly to under 30% after the first few epochs. For the  
Figure 5: Differences in the generated images when the number of iterations increases.  
Figure 6: Images with the size of 64 \(\times\) 64 generated by the BEGAN model.  
rest of the training process, the training/validation accuracy gradually increases and reaches a peak at nearly 98%. In contrast, the training/validation loss slowly decreases and hits bottom at 3%. Among four folds, fold four yields the best results regarding validation accuracy and validation loss as well as the model stabilization. Fold 4 yields the best results because the model in fold 4 is well trained without the overfitting problem. On the other hand, the other folds have fluctuations in the training loss and validation loss.  
Next, we performed a similar process as the PCGAN's experiment for the BEGAN dataset. As shown in Figure 8, the training/validation accuracy leaps to over 90%, whereas the training/validation loss plummets to 10% after the third epoch. For the rest of the training process, the training/validation accuracy gradually increases and reaches a peak at nearly 97%. In contrast, the training loss and validation loss slowly decreases and hits bottom at 3%. Within four folds, fold three yields the best results concerning validation accuracy and validation loss as well as the model stabilization.
## 3 Results and Discussion
### Mip  
MIP has been widely used to quantitatively characterize the pore structures in HCP, which can obtain the porosity and the pore size distribution of HCP in the measurement range of 0.01 \(\upmu\)m\(\sim\)1000 \(\upmu\)m. According to the previous research [40], the pores in HCP can be classified into gel pores (\(<\)0.01 \(\upmu\)m), medium capillary pores (0.01-0.05 \(\upmu\)m), and large capillary pores (0.05-10 \(\upmu\)m) based on the pore size. The gel pores have no negative effect on the mechanical performance of HCP, and an improved hydration degree is expected with the increased proportion of gel pores in HCP. Regarding the capillary pores, the medium capillary pore strongly affects the volume stability of HCP [41; 42], while the large capillary pores are closely related to the mechanical strength and impermeability of HCP [43; 44]. As the hydration products of OPC and OPC-CSA blended cement under the effect of NC and AS after 3 d hydration were observed to be significantly different, only the pore structures of the hardened OPC and OPC-CSA cement pastes after curing for 3 d were measured by MIP, and the results are shown in Figures 8 and 9.  
Regarding the OPC system, it is found that, along with the hydration to 3 d, the addition of NC in OPC slightly refined the pore structure by shifting the peak leftward. Besides, an obvious increase in the cumulative pore volumes was found. In the sample with the combined addition of NC and AS, a similar pore size distribution and cumulative pore volume were found in both samples, regardless of the addition of NC or not. It indicates the addition of NC has limited capability to densify the microstructure of OPC. Besides, the presence of AS can, in some way, relieve the negative effect of NC on the pore structures of HCP.  
Regarding the OPC-CSA binary blended cement, an obvious rightward shifted peak was found compared to the OPC system, which indicates that the partial substitution of OPC by CSA resulted in enlarged pore structures in HCP, especially the massively generated large capillary pores. It could be attributed to the hindered silicate phase hydration, as observed in Figure 6. The rapid aluminate-contained phase hydration depressed the silicate phase hydration, thereby reducing the formation of C-S-H gel and resulting in enlarged pore structures [45]. Besides, the addition of NC slightly refined the pore structure (by shifting the peak leftward) and increased the cumulative pore volume, which complies with the findings in the OPC system (Figure 8). Through the combined addition of NC and
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 4 Capacity Bounds Analysis
### Multiservice Capacity Bounds
#### 4.2.1 Capacity Bounds Due to Interference  
The number of active users in any given cell is interference-limited, and the maximum number of active users is primarily influenced by the non-orthogonality factor, \(\varepsilon\), as well as the requested service classes. Therefore, from (37) for any service class, the capacity bounds \(k\) are limited as follows:  
\[S_{k}\leq\frac{1}{F}\cdot\left[-V_{k}\cdot\varepsilon_{k}+\sum_{l\
eq k,\ l=1}^{s}n_{l} \cdot V_{l}\cdot\varepsilon_{lk}\right] \tag{38}\]
## 4 Results and Discussions
### Validation of FEM Model Using Field Measurements
#### 4.3.2 Comparison of Dynamic Response Magnitude at Different Vehicle Speeds and Loads  
The full-scale NE-ALT system was applied to conduct field tests to measure the dynamic responses of asphalt pavement in dry and saturated conditions. According to the adjustable ranges of the full-scale vehicle simulator's speed and applied load, it could be found that parameter combinations controlled in field tests mainly corresponded to the low-speed group of that in numerical simulation. Variation trends of indicator magnitudes with different vehicle loads and speeds were established and evaluated, respectively, which included vertical stress, strains in vertical and transverse directions, and pore water pressure, as shown in Figures 12 and 13. These variation trends of dynamic response indicators measured from field tests were compared with those obtained from numerical simulation for the purpose of further validating the FEM models.  
Figure 12: Variations of dynamic response magnitudes with vehicle load obtained from field tests (controlling vehicle speed at 20 km/h): (**a**) Pore water pressure; (**b**) Vertical stress; (**c**) Vertical strain; (**d**) Transverse strain.  
It was found that the magnitudes of strains in two directions and vertical stress increased slowly with the increase in single-wheel grounding pressure, which inversely decreased with the increase in vehicle speed. Moreover, signals obtained in saturated conditions exhibited slightly larger magnitudes than those measured in dry conditions. For pore water pressure, it also showed slight uptends with single-wheel grounding pressure but increased with the growth of vehicle speed. Pore water pressure was sensitive to vehicle speed compared with vehicle load according to Figures 12 and 13. In particular, some unavoidable \"imperfections\" existed during the field measurements, such as the measured pore water pressure at a vehicle load of 0.780 MPa in Figure 12a. It might be induced by comprehensive factors such as the controlled accuracy tolerance of the full-scale vehicle simulator, the unrecoverable structural integrity of asphalt pavement embedded with sensors, and the relatively small interval of loading parameter settings. Variation trends of dynamic responses in the bottom of the upper layer of asphalt pavement obtained from numerical simulation were compared with those measured from field tests, both of which showed similar trends. As vehicle speeds in field tests were controlled at relatively low levels compared with those in numerical simulation, several characteristics of variation trends were not coincident for strains in vertical and transverse directions. More systematic field tests need to be conducted in the future for the purpose of verifying the variation trends of dynamic responses at a relatively high vehicle speed.