esa-sceva/satcom-chunk-collection huggingface dataset:
# Incentive Price-Based Demand Response in Active Distribution Grids
## 5 Simulation Studies
### Case 2: Simulation of BESS Control without Proposed IDR Method  
In this simulation case, BESS are connected to the 10 nodes of the LV grid as specified in Figure 3. The BESS are controlled to maximize the self-power consumption at each node i.e., the excess power generated is stored in the BESS during peak PV power production and the BESS is discharged during off-peak hours. The power setpoints of the BESS are computed by solving the optimization problem defined in (11) whose objective is to minimize the cost of net power consumption at each node. The prediction horizon is set to 4 h (\(N_{p}=4\)). The choice of prediction horizon depends on many factors such as the computational burden and memory storages of the LVGC and BESS controllers, duration of the available forecasted powers, and the communication bandwidth. Although choosing a higher prediction horizon improves the cost savings slightly, in this study a 4 h prediction horizon which is fair enough to address the over-voltage issues due to PV power production. The lower and upper limits of SOCs of the BESS are set to be 0.1 and 0.9 respectively.  
The simulation results can be seen in Figure 6. The plot (a) shows the power setpoints of BESS every 10 min. The SOC of the BESS at end nodes (R7, R13 and R17) are shown in the plot (b) while the node voltage profiles are shown in the plot (c). It can be seen that the BESS is fully utilized for minimizing the power export to the grid and their SOCs reach the upper limit of 0.9 pu. However, the node voltages are slightly above the limit of 1.1 pu during noon and it is below the limit of 0.9 pu in the evening.  
Figure 5: Plot of node voltages without battery energy storage systems (BESS) connected to the LV grid.  
Figure 6: Plot of case 2: (**a**) setpoints of BESS, (**b**) State of Charge of the Battery energy storage systems (BESS) of BESS, and (**c**) node voltages at end of the feeder.  
The BESS charging and discharging routine in this case study are independent of the grid voltages, hence there are over-voltage and under-voltage events similar to case 1, although the magnitude and duration of these voltage violation are less. If the storage capacity of the BESS is increased, the voltage violations can be indirectly managed better. However, the above solution is expensive and there is no guarantee that the node voltages are always maintained within the limits.
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods
### Calculation of River Velocity and Discharge  
After calculating the river surface velocity, the total discharge and mean velocity are calculated according to the velocity-area method. As shown in Figure 7, the arrow direction represents the flow of water. Suppose that the surface flow velocity of the measuring point \(i\) is \(V_{i}\), and the width between the measuring points \(i-1\) and \(i\) is \(d_{i}\), and \(h_{i-1}\) and \(h_{i}\) denote the actual water depth (m) corresponding to the vertical line of the  
The partial mean velocity \(\overline{V_{i-1,i}}\) between the two measuring points is shown in Equation (10).  
\[\overline{V_{i-1,i}}=\frac{V_{m(i-1)}+V_{mi}}{2} \tag{10}\]  
The partial mean velocity near the bank is shown in Equations (11) and (12), where \(\varepsilon\) denotes velocity coefficient on the bank; its value usually follows the Chinese national measurement specifications standard GB 50179-2015 [22]. When on slopes where the water depth gradually becomes shallower to zero on banks, \(\varepsilon\) ranges from 0.67 to 0.75, and \(\varepsilon\) is 0.8 and 0.9 on the steep banks of an uneven riverbank and even riverbank and \(\varepsilon\) is 0.6 at the river bank or in a stagnant water area.  
\[\overline{V_{1}}=V_{m1}\cdot\varepsilon \tag{11}\]  
\[\overline{V_{i}}=V_{mi}\cdot\varepsilon \tag{12}\]  
After calculating the partial mean velocity of each part, the product of it and the partial area is the discharge of each part, and then the total discharge \(Q\) is the sum of the discharge of all parts, as shown in Equation (13).  
\[Q=\sum_{i=1}^{n}\overline{V_{i}}\cdot S_{i} \tag{13}\]  
The mean velocity \(\overline{V}\) is calculated according to the total discharge \(Q\) and the cross-section area \(S\), as shown in Equation (14).  
\[\overline{V}=\frac{Q}{S}=\frac{Q}{S_{1}+\sum_{i=2}^{i}S_{i-1,i}+S_{i}} \tag{14}\]
# Four-Port MIMO Antenna System for 5G n79 Band RF Devices
## 2 Antenna Design
### MIMO Configuration  
MIMO antenna systems are widely applicable to 5G infrastructure since they easily come over fading and lossy environments while providing diversity in performance characteristics [30]. Figure 3 shows the overview of the proposed four-port antenna, both the designed and the fabricated prototype. The MIMO elements are arranged in a perpendicular manner in order to ensure the maximum possible isolation. The total dimensions of the proposed antenna array are 40 \(\times\) 40 mm\({}^{2}\). This MIMO configuration is reported the in mmWave [31] and sub6 GHz system [32].  
Figure 3: \(Cont.\)  
Figure 2: Parametric modelling with respect to (a) h (b) i (c) g (d) h.  
The S-parameters are presented in Figure 3. The reflection co-efficients are shown in Figure 4a, while the ports isolation is shown in Figure 4b. Due to the symmetry of the structure, the S-parameters of only two antennas (i.e., Ant1 and Ant2) are shown. The minimum isolation achieved at the desired frequency was 25 dB, which indicated that the proposed MIMO antennas were highly isolated.
## 2 Literature Review
However, mesoscopic models in the transportation field are still lagging in practical application and findings are still limited to the laboratory tests [25]. For more information regarding the basis of different modelling scales please see the reference [26]. In order to minimize fatalities and financial loss, it is vital to understand the flood conditions that cause vehicles to become uncontrollable. The traction of full-scale vehicles in varying static water was investigated by [PERSON] et al. [27] using a small vehicle (Toyota Yaris) and a typical large 4-wheel drive (4 WD, Nissan Patrol). The latter was considered because of the increased number of 4 WD-related fatalities in Australia [28]. The experiments indicated that deeper standing-water depths result in less traction. The Toyota Yaris floated entirely at 0.6 m standing water depth, whereas the Nissan Patrol floated at 0.95 m, according to the tests. Most authors agree that the stability criteria for still-water depth should be 0.3 m for small passenger vehicles and 0.5 m for 4 WD vehicles [27; 29]. Previous studies have addressed the potential impacts for transportation networks during flood events, but the impacts of flood disruption may persist long after the flood has subsided, which also needs to be considered. This paper, as an extension of the published investigations of the authors regarding this case study [30], contributes to the existing literature by applying a deeper look and valid alternative modelling into the flooded road network. The mesoscopic simulation was used to build the road network model, assigning trip paths with the impact of road closures and speed reductions, and evaluating travel time and travel distance after the failure of infrastructure components such as roads and bridges because of a flood event in a specific case study. Moreover, a novel approach for accurate quantification of the flood disruption effects on the road network is developed considering both the dynamic situation of flooding on the day of the flood and one following day, when the flood has subsided but its disruption effect remains in the traffic network. For this purpose, by combining a flood model and a mesoscopic traffic model, phenomena of queueing during the flooding were simulated. This can provide insight into how the transportation system behaves under time-varying flood conditions and traffic supply reductions that facilitate the identification of vulnerable links.
# Evaluation of Fatigue Life of Asphalt Concrete Mixtures with Reclaimed Asphalt Pavement
## 4 Tests Results
### Results of Fatigue Tests
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mixture**} & \multicolumn{2}{c}{**Fatigue Curve Parameter**} & \multicolumn{2}{c}{**Fatigue Resistance**} \\ \cline{2-5}  & \(A\) & \(b\) & \(\mathbf{R^{2}}\) & \(\varepsilon_{6}\), \(\mu\)**m/m** \\ \hline AC22P 35/50 50\%RAP & \(1.7\times 10^{19}\) & \(-6.2\) & 0.91 & 134 \\ AC22P 35/50 ref. 1 * & \(4.7\times 10^{20}\) & \(-7.1\) & 0.88 & 117 \\ AC22P 35/50 ref. 2 ** & \(3.2\times 10^{18}\) & \(-6.1\) & 0.91 & 116 \\ ACWMS16 25/55-60 50\%RAP & \(5.3\times 10^{18}\) & \(-5.7\) & 0.86 & 176 \\ ACWMS16 25/55-60 ref. 1 *** & \(5.9\times 10^{22}\) & \(-7.4\) & 0.95 & 185 \\ ACWMS16 25/55-60 ref. 2 * & \(7.9\times 10^{20}\) & \(-6.4\) & 0.94 & 214 \\ \hline \hline \end{tabular}  
* The results of tests under the research project RID 1/25, ** the results of tests under the research work described in [25], *** the results of tests under the research work described in [26].  
\end{table} 
Table 2: Results of the mixture fatigue test. Figure 3: Grading curves. direction of longer fatigue lives. The obtained results can also be referred to slightly older results of tests of asphalt concrete BA 0/25 D50 [28], where the results of \(\varepsilon_{6}\) from ca. 100 to 130 \(\upmu\)m/m were obtained for the six variants of the mixture with various asphalt contents and asphalt binder origins (suppliers). On this basis, it was possible to state that the results for the AC22P 35/50 mixture and the results described above are the typical and expected results. Taking this into account, the results for the mixture with reclaimed asphalt look positive and do not allow conclusions to be drawn about the deterioration of the fatigue life. The high modulus asphalt concrete is the only mixture in Poland for which the requirements of fatigue life are specified in the recommendations [24]. The value of the \(\varepsilon_{6}\) parameter of the ACWMS16 25/55-60 mixture with reclaimed asphalt is much higher than the required 130 \(\upmu\)m/m, which allows for positive evaluation of its properties in terms of fatigue life. The other two ACWMS16 25/55-60 reference mixtures without the RAP exhibit better fatigue properties in terms of the \(\varepsilon_{6}\) parameter and the fatigue characteristics. This difference is due to the use of \"fresh\" polymer-modified asphalt and RAP containing unmodified asphalt binder. As we know, the use of modified binders and the extent of modification have a beneficial effect on the fatigue resistance of the mixtures. The mixture with reclaimed asphalt combines the unmodified asphalt binder and polymer-modified binder. Therefore, a lesser extent of modifications of the polymer contents was obtained than in the case of reference mixtures without RAP.
## 1 Introduction
The limitation arises from: (1) the uncertainties of meteorological reanalysis used as input data, (2) the realism of LUE attributed to a given pixel derived from a biome properties look-up table (BPLUT) and more crucially (3) the ability of both scalars to correctly represent the abiotic controls for any particular ecological conditions. In MODIS, estimates of LUE are obtained from lookup tables based on vegetation type, which may contain errors either in the assignment of vegetation type to a pixel or in the initial estimates of \(\mathrm{LUE_{max}}\). We tested for the accuracy of this simple model on an evergreen forest largely dominated by _Quercus__i_x where flux data are available. _Q.__i_x is an emblematic Mediterranean tree species growing over more than 6 Mha around the Mediterranean Sea [15]. For testing we will use long-term GPP derived from an eddy-flux tower (further called EC GPP) set up in our research site. This site underwent recurrent summer droughts and demonstrated the key-role of soil water deficits on its carbon exchanges [16]. We suspect biased estimates of the active season GPP impacted by summer drought because VPD seems an insufficient surrogate of the soil dryness in such MT climate. The main objectives of this work are: (1) to compare the time course of EC GPP and to those derived from MODIS, (2) to assess the anomalies of MODIS GPP to between-year variations in drought severity; and (3) to quantify the limitations of such model.
## 2 Related Work
### Techniques for Converting Time Series Data into 2D Representations
#### 2.3.1 Methods Based on Data Rearrangement
The rearranged two-dimensional signal matrix can be expressed as \(D_{row}\in R^{N\times L}\) and \(D_{column}\in R^{L\times N}\) in Equation (3) and Equation (4), respectively. \[D_{\text{row}}= \begin{bmatrix}S_{11}&\cdots&S_{1L}\\ \vdots&\ddots&\vdots\\ S_{N1}&\cdots&S_{NL}\end{bmatrix}  \tag{3}\]  
\[D_{\text{column}}= \begin{bmatrix}S_{11}&\cdots&S_{1N}\\ \vdots&\ddots&\vdots\\ S_{1L}&\cdots&S_{LN}\end{bmatrix}  \tag{4}\]  
For multi-source raw vibration signals, each set of vibration signals can be rearranged according to the single-source data rearrangement techniques described previously. The resulting two-dimensional matrices are utilized as multi-channel inputs to the model. This methodology harnesses the temporal sequence information inherent in each vibration signal channel and capitalizes on potential correlations among different channels. Such an approach enhances the model's capacity to extract features from the vibration signals, thereby improving the accuracy of fault diagnosis [33]. The preceding discussion outlines two primary methods for rearranging raw vibration signals and converting them into two-dimensional matrices, along with strategies for managing both single-source and multi-source vibration signals. By transforming one-dimensional data into a two-dimensional matrix format, these methods enable the utilization of deep learning models, such as two-dimensional convolutional neural networks, which are adept at extracting features and patterns from time series data. For multi  
Figure 1: The window sampling processes. (**a**) Fixed-window sampling. (**b**) Overlapping window sampling. source vibration signals, the data from each channel are individually converted into a two-dimensional matrix and subsequently employed as multi-channel input for the model, thereby leveraging inter-channel correlations to enhance performance further. However, the conversion of time series data into a two-dimensional matrix may lead to some information loss, particularly for sequences with strong long-term dependencies, where crucial time-related information can be obscured during rearrangement. Additionally, the choice of time window size and the parameters for overlapping sampling during segmentation significantly affect the final results [34], necessitating careful tuning and optimization to achieve optimal conversion outcomes. Furthermore, the data rearrangement process increases computational complexity and overhead, potentially diminishing training and inference efficiency, especially when managing large-scale datasets.
After 10 min MW pretreatment, the peroxide value (PV) of rapeseed increased from 0.99-1.14 meq O\({}_{2}\)/kg to 2.23-2.27 meq O\({}_{2}\)/kg, which is within the Codex Alimentarius limits (PV < 15 meq O\({}_{2}\)/kg) [18]. The acid value (AV) of extracted oil from MW-treated hazelnuts increased from 1.56 mg KOH/g oil to 1.83 mg KOH/g, which was attributed to triacylglycerplycerols hydrolysis [9]. As a contrary result, MW pretreatment caused a significant decrease in the AV of black seed oil (4.85-3.03 mg KOH/g), which can be attributed to lipase inactivation due to the thermal pretreatment [15]. The effect of MW on micronutrients content and oxidative stability of oil has been reported in other research [17; 19]. Rapeseed with moisture levels of 9-15% had individual and total tocopherols in the extracted oils which first increased, then decreased according to the period of microwave radiation. With 7 min MW treatment of rapeseed with 9% moisture, the phytosterols and polyphenols in extracted oil reached maximum values (922.48 mg/100 g and 96.91 TA.100 g), which were 18.04% and 176.88% higher, respectively, than the untreated sample. On the contrary, the reduction of locopherol contents compared with the control group was observed in oil extracted from MW-treated chia seed [16]. The induction period of extracted oil increased from 7.46 h to 22.80 h due to the increased micronutrients with antioxidant activity [20]. The effect of MW treatment on the flavor quality of oil has been investigated. The pyrazine compounds in the rapeseed oil appeared after 6 min of microwave pretreatment, this may be the main reason for giving a pleasant roasting flavor when compared to crude oils [21]. Studies on the microwave treatment effect on the quality of peanut oil are still lacking. The object of this study was to investigate the changes in extraction yield, physicochemical properties, micronutrients content, oxidative stability, and flavor quality of peanut oil extracted by cold pressing after microwave pretreatment. The results of this study will be used to evaluate the feasibility of using microwaves pretreatment as an improvement method for cold-pressed peanut oil processing.
# Data-Augmented Hybrid Named Entity Recognition for Disaster Management by Transfer Learning
## 4 Results
### Recognition of Named Entities  
The augmented model is a hybrid NER approach that combines handcrafted grammar rules through data augmentation and Bi-LSTM-CRF from the reference model. Compared with purely rule-based NER, the hybrid NER does not require a well-built database that records all potential outcomes. The augmented model can infer named entities from sentence patterns because of the built-in Bi-LSTM-CRF framework. Accordingly, in the case of foreign words, this approach is still a feasible solution.  
To demonstrate how the augmented model recognizes named entities, the following illustrates two cases. In the first case, all named entities are available in the database. In the second case, some named entities do not record in the database. These cases illustrated in Figures 6 and 7.  
In Case 1, the sentence \"Ceiling in Pu-Tong Building 307 cracks seriously\" gives a clear statement. The module can fully recognize the entities within the sentence. Thus, the model pairs \"product_names\" with \"ceiling\", \"location\", with \"Pu-Tong Building 307\", and \"event\" with \"cracks seriously\".  
In Case 2, the sentence \"The tiles and steel structure of the College of Social Sciences Building 308 damaged severely\" states the problem generally. Unlike in Case 1, this sentence consists of two subjects, a general description and a set of unknown product names. The result given by the model shows that both subjects, \"tiles\" and \"steel structure\", are recognized as \"product_name\" despite being unknown to the module. The location \"College of Social Sciences Building 308\" is also recognized successfully, and the general response \"damaged severely\" is classified as \"event\".  
The two cases above illustrate the process of the augmented model and how it copes with unknown entities. Through these cases, we can understand the ability of the augmented model and how it functions. With the hybrid NER approach, the recognition process that requires much human labor can now accomplish itself. With the surge of diverse report messages, our solution expected to be flexible and promising in information gathering and analysis in disaster management.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Precision** & **Recall** & **FB1** \\ \hline Location & 93.33\% & 94.30\% & 93.81\% \\ product\_name & 87.90\% & 90.58\% & 89.22\% \\ person\_name & 70.25\% & 78.83\% & 74.29\% \\ time & 77.90\% & 74.48\% & 76.15\% \\ Event
## 2 Materials and Methods
### Methods for Manufacturing and Testing Samples
#### 2.2.6 Short-Beam Shear-Testing Method for ILSS  
The SBS (short-beam shear) test (ASTM D2344/D2344M\(-\)16) was used to determine the ILSS values of all the samples. The SBS test is a three-point bending test, with a span width of 4 times of the sample thickness; theoretically, the setting creates the highest shear stresses on the midplane of the specimen, resulting in shear failure in the interlaminar region. A TINIUS OLSEN Super \"L\" Universal Testing Machine with a 12,000 lbf (53,379 N) load cell and crosshead-loading rate of 1.0 mm/min was used to perform tests with 5 or more sample specimens for each different type of laminate. The 5 best data values were retained for results and analysis. As shown in Figure 9, the sample was centered on two 3-mm-diameter supports, each with a 10-mm span width. The supports and loading nose were set to be parallel and level. The ILSS value was calculated using Equation (1), where \(P_{\text{max}}\) is the maximum loading in N; \(w\) and \(t\) denote the sample width and thickness in mm, respectively.  
\[\text{ILSS}=0.75\times P_{max}/(w\times t) \tag{1}\]  
Note that the ILSS test measured the apparent shear strength value rather than the pure shear strength, since the sample was also subjected to other stresses, strains, and stress concentrations due to the metal fixtures; however, it is a widely used method to relatively  
Figure 8: Cross section cut of a no-pressure-3 DP CFRP laminate.  
Figure 9: SBS testing-fixture setup with a sample.  
compare composite laminate samples with similar material compositions and identical dimensions, using identical machine and fixture settings.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
###### Abstract  
Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Doppler signals based on Hilbert-Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.  
micro-Doppler; feature extraction; time-frequency; HHT; AMD; signal decomposition Article