esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Tests of Soil Reinforcement Inside the Bucket Foundation with Vacuum Electroosmosis Method
## 3 Results and Discussion
### Effect of Pore Water Pressure during Soil Reinforcement  
Figure 9 shows the relationship between the excess pore water pressures PP1, PP2, and PP3 and the vacuum load P for the bucket during the reinforcement process. The negative pressure beneath the bucket foundation top during the reinforcement process is approximately \(-\)50 kPa due to vacuum load loss. As shown in Figure 10, pore water pressure increases with elapsed time. PP2 and PP3 are less than \(-\)5 kPa for all tests, except T5. PP2 near the top of the foundation is \(-\)1 to \(-\)2 kPa higher than PP3. The maximum PP3 in T5 is \(-\)18 kPa, and the maximum PP2 and PP3 are \(-\)20 kPa and \(-\)18 kPa, respectively.  
Compared with T1, PP1 increased by 10 kPa, and PP2 and PP3 increased by \(-\)1.5 kPa after 60 h of soil reinforcement in T2 with an additional electrode. The electrode arrangement increases the longitudinal drainage channel within the soil in the bucket, promoting the transmission of the negative pressure throughout the soil, which contributes to soil reinforcement inside the foundation. Compared with T2, PP2 and PP3 increased by \(-\)2 kPa and \(-\)10 kPa in T3 and T5 after 60 h of reinforcement, respectively. This is primarily due to the pore water in the bucket moving from the anode to the cathode under the action of electroosmosis such that the soil in the bucket generates an electric hydraulic gradient in the horizontal direction, which is superimposed on the negative vertical pressure on the foundation and increases the pore water pressure.  
In T4, due to the formation of through-flow passages on both sides of the bucket wall, PP2 and PP3 in the soil are only 50% of PP2 and PP3 in T3 after 24 h of reinforcement. In T6, electrolyzed water was present inside the bucket foundation, and the moisture content rapidly decreased, forming an impervious water-repellent layer; therefore, PP2 and PP3 were close to 0 kPa at the end of T6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Test Nos**} & \multicolumn{2}{c}{**2.5 h**} & \multicolumn{2}{c}{**10 h**} & \multicolumn{2}{c}{**50 h**} \\ \cline{2-7}  & **Settlement** & **Water** & **Settlement** & **Water** & **Settlement** & **Water** \\  & **(mm)** & **Discharge (kg)** & **(mm)** & **Discharge (kg)** & **(mm)** & **Discharge (kg)** \\ \hline T1 & 2.942 & 0.388 & 6.941 & 0.782 & 15.659 & 1.786 \\ T2 & 5.471 & 0.454 & 10.506 & 0.922 & 17.522 & 2.054 \\ T3 & 8.614 & 0.796 & 14.133 & 1.339 & 26.189 * & 3.143 * \\ T4 & 13.599 & 0.924 & 18.105 & 1.410 & - & - \\ T5 & 9.150 & 0.471 & 13.247 & 0.947 & 21.813 & 2.158 \\ T6 & 11.864 & 0.706 & 20.740 & 1.411 & 31.412 & 5.646 \\ \hline \hline \end{tabular}  
* The soil improvement time is 48.7 h.  
\end{table}
Table 3: Settlement and water discharge of foundation at different reinforcement time.  
Figure 9: _Cont._
## 4 Implementation and Management for a Spanish Elderly Residence
### Implementation for the Case Study
#### 4.2.5 Resource Planning  
Among the resources that must be present in the building and prepared to be used in the event of an emergency are material resources and human resources. The material resources will be made up of the different protection means and installations available in the building, including the inventory of active fire protection means and passive fire protection means.  
Active fire protection includes all means and installations provided for fire detection, alarm, and extinction. The most common installations and elements that can currently be found in buildings for public use are the following: equipped fire hydrants (BIEs), portable fire extinguishers, automatic fire detection and alarm system, emergency lighting, emergency signaling, communication systems and other installations such as closed circuit television or precise systems for specific uses, such as the halon gas fire extinguishing system in highly relevant archives or warehouses and where they are not usually present, personal.  
Passive fire protection includes the design conditions and specifications of the building itself, to minimize the consequences of any fire. It is especially important to consider the sectorization conditions, which allow the confinement of the fire within a fire sector, and the evacuation conditions that allow the partial evacuation of an area or the total evacuation of the building.  
In relation to human resources, a group of people chosen from among the building's workers will be established who will form part of the emergency personnel. They will be specially organized and trained for the prevention and action in case of accidents that produce any type of emergency, and that may affect the building. For this, they must detect anomalies that cause accidents, be informed of the particular and general risk of the place where they carry out their activity, know the material means of protection available to them, their use and handling, and know the sequence of actions that must be carried out in the event of emergency.  
On the other hand, the rest of the occupants of the center must know the existence of the Emergency Plan, the basic instructions for action and know how to detect anomalies that cause accidents.  
The Emergency team will be made up of at least the following components [23]: Head of Emergency (JE), Head of Intervention (JI), Intervention Team (EI), Alarm and Evacuation Team (EAE) and Responsible for the Command Post (RPM).
# Performance Analysis of the Supercritical Carbon Dioxide Re-compression Brayton Cycle
## 4 Conclusions  
The present study investigated seasonal performance analysis of sCO\({}_{2}\)-based re-compression Brayton power system. From the analysis, the following conclusions were drawn:  
* The thermal performance of the considered system is highly sensitive to the turbine's inlet temperate and split mass fraction. For every set of imposed boundary conditions, there exists an optimal value of split mass fraction that tends to minimize the temperature difference between LTR outlet and HTR inlet. It can be concluded that the optimal values of split mass fraction increase with the increase of turbine inlet temperature. Furthermore, the optimal value of the split mass fraction is found as 0.74, corresponding to the turbine inlet temperature of 700 \({}^{\circ}\)C for which cycle's efficiency (\(\eta_{th}\)) is 49%.
* The maximum of the cycle's efficiency comes at high total heat transfer area coefficient (\(UA_{Total}\)) values that in turn increase the cost of the heat exchanger. On the other hand, the cost of the heat exchanger can be reduced by moving the compressor's inlet temperature away from the critical temperature or by lowering the turbine inlet temperature. However, in both scenarios, the cycle's thermal efficiency shall be compromised. Therefore, an optimal trade-off can be established between \(UA_{Total}\) and \(\eta_{th}\) depending on the design objective.
* Exergy destruction is high for high turbine and main inlet temperatures and compressor inlet pressures. The gas cooler is the biggest source of exergy destruction in the cycle. Moreover, exergy destruction for the summer seasons was higher than the winter seasons. The system exergy destruction (\(I_{Total}\)) has the lowest value of 390 kW at 35 \({}^{\circ}\)C and 24 MPa in the month of January.
* The size parameter (\(SP\)) of the turbine increases with increasing turbine inlet temperature, whereas that of the compressor shows an increase when the main compressor inlet temperature increases. This implies that the total size parameter (\(SP_{Total}\)) value of the system increases with increasing turbine and compressor inlet temperatures, which the increase the cost of the turbomachinery under these conditions.
# Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space
## 3 Results and Discussion
### Examples
#### 3.2.3 Focusing of Realistic Laser Pulses  
We now modify the flat top profile used previously to introduce perturbation of the phase that could stand for the imperfections of an experimental wavefront, in the case of a f-number \(f=1\). The perturbation is described by a sum of Gaussians randomly located in the initial transverse profile, whose individual amplitude varies randomly between \(\pm\varepsilon_{\phi}\), but with fixed width \(\sigma=0.3\theta\), with \(\theta\) the opening angle. The number of individual perturbation scales is \((2\theta/\sigma)^{2}=44\). Figure 7 shows how the maximum intensity at focus varies with the standard deviation of the phase \(\sigma(\phi)\). This clearly indicates that initial perturbations of the wavefront can significantly influence the intensity profile at focus, with a steady decrease of the maximum intensity as the perturbation increases. Thus, one has to give some thoughts to the quality of the laser system when designing experiments, as applications of tight focusing pulses are highly dependent on the maximum intensity.  
Figure 6: The dependence of **(a)** the peak value of cycle-averaged intensity on f-number and **(b)** the coordinate \(x\) at which the peak is reached, at a fixed peak power \(P_{0}=1\) W and wavelength \(\lambda=1\) μm (see the explanation in the text).  
Figure 7: Evolution of the peak amplitude \(a_{0}\) at focus as a function of the standard deviation of the initial phase (\(\sigma(\phi)\) in radian, solid line) and standard deviation of \(a_{0}\) over 20 runs (dashed lines). Inlets exhibit examples of the normalized transverse intensity profile obtained at focus for two different values of \(\sigma(\phi)\). Transverse directions are normalized to \(\lambda\).  
Another useful feature is that the described method allows for a fast computation of the laser focal spot given the initial beam profile and wavefront measured in an experiment. In Figure 8a the beam profile of the red spectral range (700-1000 nm) of the Light Wave Synthesizer 20 (LWS-20) at Umea is plotted, while (b) shows the wavefront on the focusing optics, which was originally corrected at the end of the laser and collected extra aberrations during transport in the vacuum compressor and beam line including about 10 mirrors. The calculated LWS-20 focal spot with \(f=3\) without further corrections is highlighted in Figure 8c. If applied directly in an experiment such a spot is applicable for simulation purposes. Note that other methods can also be used to retrieve beam profiles using experimental inputs, such as the Gerchberg-Saxton algorithm [55] which was, for example, used for laser wakefield simulations with realistic laser profiles [56]. Alternatively, the experimental focus can be optimized with the help of our method coupled with machine learning techniques and using the measured beam profile before focusing and focal spot. This compensates for all added aberrations from the beam transport as well as the focusing optics.
## 4 Results
### Performance Analysis by Video Type
#### 4.2.2 Target Scale (TS)  
This group comprises images in which the target scale varies from small to large throughout the video, representing 10% of the dataset.  
GELAN and DETR exhibit superior performance in this category, with GELAN demonstrating a slight advantage in terms of overall accuracy (see Table 5). The capacity of these models to manage significant variations in target scale indicates that they are more robust in scenarios with diverse target sizes. While YOLO is efficient, it displays slightly lower performance, which may limit its applicability in situations where the target scale  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **IoU** & **AP** & **F1-Confidence** \\ \hline YOLO & 84.4\% & 84.2\% & 87\% at 0.102 \\ \hline GELAN & 81.63\% & 87.2\% & 91\% at 0.323 \\ \hline DETR & 95.30\% & 90\% & 92\% at 0.411 \\ \hline ViTDet & 78.6\% & 70.4\% & 76\% at 0.137 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for video type ’UAV’.  
Figure 4: Detection results for “UAV”.  
varies considerably. Figure 5 illustrates the results of this category for both small and larger objects.
# A Short-Term Wind Speed Forecasting Model Based on EMD/CEEMD and ARIMA-SVM Algorithms
## 1 Introduction
[18] propose an EMD-SVM model, with error compensation to reduce the accumulated errors and improve the prediction accuracy of short-term wind speed forecasting. [PERSON] et al. [19] present a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. [PERSON] et al. [20] propose a hybrid EMD-RNNs-ARIMA model for wind speed and wind power prediction. [PERSON] et al. [21] combine the improved sparrow search algorithm (ISSA) and least squares SVM (LSSVM) to improve the convergence accuracy and shorten the prediction time of the wind prediction model. [PERSON] et al. [22] introduce a gray wolf algorithm (GWO) and SVM wind prediction model to predict the wind speed accurately. Therefore, it is clear to see that the hot point of the wind speed forecasting models is mainly aiming at the development of hybrid models. A hybrid model has the advantage of higher accuracy, better stability, and greater adaptability. Mode decomposition technology, such as CEEMD and EMD, is an effective algorithm for processing nonlinear and non-stationary signals, which has been widely used in the wind speed forecasting model. It favors reducing the influence of unfavorable factors such as randomness and volatility of the original wind speed on the prediction errors. In this paper, hybrid short-term wind speed forecasting models combined with CEEMD/EMD-ARIMA-SVM are proposed. The linearity of ARIMA and the globe nonlinearity of SVM are fully exploited in the algorithm to improve the accuracy of the forecasting model. The validity of the model is verified based on the measured typhoon wind speed data; results show that the proposed prediction model has sufficient accuracy and great stability.
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 4 Proposed Fast CU Size Selection Algorithm
### Level 2 SVMs (L-2): Features and Optimal Weight Calculation
#### 4.2.2 Feature Selection  
Let \(F_{j}^{i}\) be the set of features extracted for the \(i\)th CU depth level in \(j\)th level. Thus, for level 2, \(F_{j=2}^{i}\) is defined as  
\[F_{j=2}^{i}:=\{\theta^{i},\pi^{i},\tau^{i}\}, \tag{10}\]  
where \(\theta^{i}\), \(\pi^{i}\), and \(\tau^{i}\) refer to texture information, context-information, and coding information, respectively, for the \(i\)th CU depth level [10]. In this case, the texture complexity is calculated with the mean absolute difference of the block (\(\theta_{1}\)). The context information for L-2 includes the sum of the CU depths (\(\pi_{1}\)) and the average of the sum of CU and PU depth levels (\(\pi_{2}\)) from the left and above neighbouring CTUs. The coding information for L-2 includes the RD cost (\(\tau_{1}\)) and the number of bits(\(\tau_{2}\)). Since the current depth level is already considered in the first level, data that is directly related to the CU are available in the second level. Similar to L-1, different combinations of the features [10] are used at the depth levels, as shown in the Table 2.
# A Novel Method to Mitigate the Multipath Error for BDS-2 Dam Deformation Monitoring
## 3 Results
### MHGM for Deformation Monitoring  
Corresponding to the strategies shown in Table 2, Figures 3 and 4 show the results of the MHGM using DOY 44-50 observation data at all the stations, and the hemispherical modeling results of DOY 50 and 53 are shown as examples. The horizontal and vertical axes represent the azimuth angle and elevation angle, respectively.  
Figure 3: MHGM modeling results for DOY 50 at the C0210, C0100, C0012 and C8894 stations. The horizontal and vertical axes represent the azimuth and elevation angle, respectively. Different colors indicate different multipath error values.  
Figure 4: _Cont._  
Since the observation environment of all stations is similar, the hemispherical modeling results in Figures 3 and 4 of all stations are similar. It can be found in Figures 3 and 4 that the maximum value of multipath error can be up to 4 cm. The most serious areas of multipath error in Figures 3 and 4 for different day are approximately symmetrical, which indicate that it is caused by static objects. In the dam observation environment, larger multipath error are usually caused by the water surface reflection and the mountain slope. As shown in rectangle 1 of Figure 3, the maximum value of multipath error appears between 120\({}^{\circ}\) and 150\({}^{\circ}\) in DOY 50, and it can be noticed from Figure 2 that this direction is facing the mountain slope. The same result also appears in Figure 4 for DOY 53. The azimuth direction of between 300\({}^{\circ}\) and 330\({}^{\circ}\) is affected by water reflection. Even if the elevation angle is greater than 50\({}^{\circ}\), the multipath error can still reach the maximum value in some directions. During the dam monitoring period, the surrounding environment is stable. Multipath distribution map is consistent with the surrounding environment, and the MHGM can reflect the observation environment of the station. This model can therefore be used to evaluate the possible multipath error sources around the station, and give some guidance to eliminate the influence of multipath error sources in the physical sense. In additoin, it does not require any hardware modification and could be easily applied to different kinds of hardware and software systems of a deformation monitoring environment.
## 4 Experiments and Results
### The Power Consumption in Sensing Part (\(P_{sense}\))  
We examine the difference in delay methods between the deep sleep of the microcontroller unit (MCU) with the deep_sleep() function and the simple delay (polling method). The dependent variable is delaying tactics. We examine the differences in power usage between deep sleep and polling. Moreover, we also evaluate the power consumption in the idle state of the microcontroller unit (MCU). This scenario consists of sense/delay actions and idle state power usage. The code's setup() and loop() functions are idle when they do not include instructions. Bootstrapping codes call the begin() and loop() functions even when the setup() and loop() functions have empty bodies (no instructions).  
In addition, we specify three trial times (\(N\), \(T\), and \(K\)). Conversely, when polling or deep sleep techniques are implemented, the sleep period (\(t_{a},t_{c},t_{s},t_{w}\)) will be 1.00, 0.10, 1.00, and 1.00 s, respectively. Finally, we compare the current consumption of the network deactivation period in terms of delay time in any scenario, as shown in Table 2, while the independent variable is current consumption.  
TC1 and TC2 are to compare the two delay techniques as the baseline of this experiment. TC3 and TC4 add the sense module to evaluate and compare the sensing state with the baseline. TC5 and TC6 evaluate the sending module, which is the full function of IoT devices. For each test case, the IoT devices process for 30 s per round and repeat for ten rounds.
###### Abstract  
Drought is a major environmental constraint, affecting agricultural productivity worldwide. Allelopathic hormesis, the low-dose stimulatory effect of allelochemicals, offers a pragmatic solution in alleviating the adverse effects of drought in plants. This study, therefore, is conducted to evaluate the potential of a Brassica water extract (BWE) in enhancing drought tolerance in wheat. The experiment was based on three factors, viz, drought with three levels (100%, 60% and 30% field capacity; FC), different concentrations of a Brassica water extract (control, water spray, 0.5%, 1.0%, 1.5%, 2.0%, 2.5% and 3.0%) and two wheat cultivars, Ihsan-2016 (drought tolerant) and Galaxy-2013 (drought-sensitive). Drought stress, particularly at 30% FC, decreased the morpho-physiological attributes of both wheat cultivars; nevertheless, the application of Brassica water extract, particularly at 2.0%, effectively enhanced tolerance against drought stress. Compared with the control, the application of 2.0% Brassica water extract increased the morphological attributes, such as seedling length and the fresh and dry weights of both wheat cultivars in the range of 2-160% under 30% field capacity. In addition, the 2.0% Brassica water extract triggered the activities of antioxidant enzymes, including superoxide dismutase, catalase and peroxidase (11-159%), decreased the hydrogen peroxide content (14-30%) and enhanced chlorophyll a and b and carotenoid contents (19-154%), as compared to the control, in both wheat cultivars under 30% field capacity. The vigorous growth and higher drought tolerance in wheat cultivars with Brassica water extract application were related to improved chlorophyll contents and physiological attributes, a better antioxidant defense system and a reduced H\({}_{2}\)O\({}_{2}\)-based damaging effect.  
Keywords:antioxidants; allelopathic water extract; reactive oxygen species; wheat +
Footnote †: journal: _Scienced Journal of Applied Sciences_
## 5 Performance Evaluation
### Travel Time Comparison After Learning  
RL algorithms are typically assessed using two key metrics: \"evaluation after learning\" which evaluates a fixed policy after training, and \"evaluation during training\" calculated throughout the training process [49]. The evaluation of RL agents is crucial, especially when implemented in real-world scenarios, where challenges may arise. In this section, we focus on \"evaluation after learning\", as it aligns with our goal of identifying reward faults, ensuring the models learn the intended behavior. Testing aims to evaluate agent correctness, addressing reward faults (not reaching intended rewards) and functional faults (taking actions leading to unsafe states). Based on the special reward mechanism, our scenario prioritizes reward faults, evaluating model learning against expectations. To assess agent performance, we save the model weights for both DQN and FLDQN during training. After learning, we load these weights to evaluate their learning outcomes. Testing involved 1000 episodes in the SUMO simulation environment for comprehensive evaluation.  
Results depicted in Figure 9 demonstrate that FLDQN outperforms DQN, with FLDQN achieving an average travel time of 33.6 s, compared to 51.4 s for DQN. This equates to a 34.6% reduction in travel time, calculated as in Equation (9):  
\[\text{Percentage Reduction}=\frac{\text{DQN time}-\text{FLDQN time}}{\text{ DQN time}}\times 100 \tag{9}\]  
\[=\frac{51.4-33.6}{51.4}\times 100=34.6\%\]  
This significant difference highlights the superior learning capabilities of the proposed FLDQN model. The substantial improvement in travel time suggests its potential for real-world applications where efficient traffic management is crucial. In conclusion, the superior performance of FLDQN can be attributed to its unique strengths compared to DQN. The weight sharing mechanism and collaborative learning approach enable agents to leverage collective knowledge and improve individual decision-making, resulting in  
Figure 8: A comparative analysis of average running time between DQN and FLDQN. The results underscore the efficiency gains achieved by FLDQN through parameter sharing, leading to reduced computation costs and accelerated training.  
significantly faster travel times and demonstrating the potential of FLDQN for effective traffic management applications.