esa-sceva/satcom-chunk-collection huggingface dataset:
# Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size
## 1 Introduction
When considering the cost-benefit of rockfall modelling [36], it is also important to abide by the availability of DTM data and the computational time needed for modelling. The computational time will be longer with DTMs with a small grid cell size, with the difference being the largest when modelling rockfall propagation and runout areas [35]. Although the above-listed studies have investigated the influence of different grid cell sizes of DTM on rockfall modelling, none of them focused on analyzing the changing protection effect of forest. Accordingly, this paper analyses the impact of grid cell size of DTM on the protective effect of forest in rockfall propagation and runout areas. The aim was to quantify the effect of forest on rockfall runout by considering several DTM grid cell sizes, and to observe how it influences modelling of the rockfall propagation and runout area. For modelling the rockfall propagation area, the Rockyfor3D model was used [21], and modelling was performed at different DTM grid cell sizes (1, 2, 5, 10 m). The model was applied with and without the effect of forest, while the forest stand structure remained the same. Based on this analysis, we investigated: (i) the effect of DTM grid cell size on the modelling of rockfall propagation and runout areas, (ii) the extent to which forest reduces rockfall propagation at different DTM grid cell sizes, and (iii) the influence of forest and DTM grid cell size on maximum kinetic energy and maximum passing height of rocks.
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.1 Network Topology Management  
CRSNs usually exhibit network topologies that best suit their application. These topologies can be ad hoc, clustered, hierarchical or mobile in nature. However, the ever-changing spectrum opportunities of the nodes give CRSNs a dynamic topology because a node can seize the availability of a vacant low-frequency band to reduce the number of hops and to improve latency, as shown in Figure 9. This characteristic calls for topology management combined with dynamic channel selection to select the best routes. Ref [14] addresses the problem of topology management by proposing a backbone architecture with cluster heads and gateway nodes that manage capacity consumption. The authors implemented a reinforcement learning-based joint dynamic channel selection and topology management method. In their architecture, sensing is achieved by deploying specialized spectrum sensing devices called coordinators. However, the nodes along this backbone will always be put into use, which will, in the long term, jeopardize the existence of the entire network. The authors attempted to solve this problem by assuming the presence of high-energy nodes along the backbone route. This assumption, however, is not always practical. In contrast, [49] proposes a multi-layered architecture for CRWSNs to provide energy and spectrum efficiency for smart grid utilities. The main point derived here is that in designing routing protocols, the protocol should have the ability to adequately address the topology management challenge, including tackling the deafness, hidden and exposed terminal challenges.
# Dist-YOLO: Fast Object Detection with Distance Estimation
## 2 Dist-YOLO
### Updating the YOLOv3 Loss Function  
Dist-YOLO extends the original YOLOv3 loss function \(\ell\) of a certain scale into the form [31]:  
\[\ell=\sum_{i=0}^{G^{w}G^{h}}\sum_{j=0}^{n^{a}}\!\!q_{i,j}\Big{[}\ell_{1}(i,j)+ \ell_{2}(i,j)+\ell_{3}(i,j)+\ell_{5}(i,j)\Big{]}+\ell_{4}(i,j),\]  
where \(\ell_{1}(i,j)\) is a loss of bounding box center prediction, \(\ell_{2}(i,j)\) is a box dimensions loss, \(\ell_{3}(i,j)\) is the confidence loss, \(\ell_{4}(i,j)\) is the class prediction loss, and \(\ell_{5}(i,j)\) is the distance loss. Finally, \(q_{i,j}\in\{0,1\}\) is a constant indicating whether the \(i\)-th cell and the \(j\)-th anchor contains an object or not. The loss iterates over \(G^{w}G^{h}\) grid cells and \(n^{a}\) anchors. The parts \(\ell_{1},\ldots,\ell_{4}\) are taken from YOLOv3. Part \(\ell_{5}\) is new and extends YOLOv3 with the functionality of distance estimation. In the following formulas, we use \(\widehat{\cdot}\) to denote predictions of the network. The parts of the loss function are, according to [31], defined as follows:  
\[\ell_{1}(i,j)=z_{i,j}\Big{[}H(c_{i,j}^{x},\widehat{c}_{i,j}^{x})+H(c_{i,j}^{y },\widehat{c}_{i,j}^{y})\Big{]},\]  
where \(c_{i,j}^{x}\) and \(c_{i,j}^{y}\) are coordinates of the center of a box, \(H(\cdot,\cdot)\) is the binary cross-entropy, and \(z_{i,j}=2-w_{i,j}h_{i,j}\) serves for a relative weighting of \((i,j)\)-th box size according to its width \(w_{i,j}\) and height \(h_{i,j}\).  
\[\ell_{2}(i,j)=0.5z_{i,j}\Bigg{[}\left(log\Bigg{(}\frac{w_{i,j}}{a_{j}^{w}} \Bigg{)}-\hat{w}_{i,j}\right)^{2}+\left(log\Bigg{(}\frac{h_{i,j}}{a_{j}^{h}} \Bigg{)}-\hat{h}_{i,j}\right)^{2}\Bigg{]},\]  
where \(a_{j}^{w}\) and \(a_{j}^{h}\) are the width and height of the \(j\)-th anchor.  
\[\ell_{3}(i,j)=q_{i,j}H(q_{i,j},\hat{q}_{i,j})+(1-q_{i,j})H(q_{i,j}\hat{q}_{i, j})I_{i,j},\]  
where \(\hat{q}_{i,j}\) is the predicted confidence, and \(I_{i,j}\) is a mask, which excludes the part of a loss for the \(i\)-th cell if \(q_{i,j}=0\), but its prediction has IoU \(>0.5\).  
\[\ell_{4}(i,j)=\sum_{k=0}^{c}H(C_{i,j,k}-\hat{C}_{i,j,k}),\]  
where \(C^{i,j,k}\) is \(k\)-th class probability in \(i\)-th cell. The distance loss is defined as  
\[\ell_{5}(i,j)=\omega(\hat{d}_{i,j}-d_{i,j})^{2}\]  
Figure 1: The figure illustrates how the YOLO architecture is modified to estimate the distance of objects. The part enriching YOLO by distance estimation is visualized by the black box in the prediction vector (marked by orange color).  
for the class-agnostic version and  
\[\ell_{5}(i,j)=\omega\sum_{k=0}^{c}C_{i,j,k}(\hat{d}_{i,j,k}-d_{i,j,k})^{2}\]  
for the class-aware version, where \(\omega\) is a weighting constant preventing the distance loss being preferable than the other losses. In our experiment, we set \(\omega=1\times 10^{-2}\).
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 2 Optical Methods and Platforms of Remote Sensing of Plants
### Some Platforms for the Plant Remote Sensing
Satellites are the final group of the flying platforms for plant remote sensing. Using satellites for plant remote sensing is based on global programs (e.g., programs of NASA and European Space Agency) [49] because the cost of these investigations is very high [33]. Satellites can be used for the regular remote sensing of plant ecosystems [49]. The analysis of data from satellites plays a key role in the estimation of the global primary productivity [54], influence of temperature [55], evapotranspiration [56], and air CO\({}_{2}\) concentration changes on this productivity [49], estimation of forests areas [57] and their changes under anthropogenic activity [49], and solutions to other global problems. On the other hand, plant remote sensing on the basis of satellites can be limited by the low spatial and time resolutions and the constant trajectories of the flight of these platforms [33].
## 5 Conclusions  
A two-layer RDH scheme for VQ index table based on de-clustering and IFSOC is proposed. After data embedding by de-clustering, IFSOC is applied to compress the marked index table and embed the second layer of data at the same time. The total embedding capacity is satisfactory. In addition, the compression after de-clustering embedding increases the security of the first layer data, while the indicator free SOC secures the second layer data.  
Experimental results show that the proposed RDH scheme outperforms state-of-the-art schemes under the overall evaluation of embedding capacity and image bit rate. Hybrid RDH scheme is a promising approach. By compressing VQ index followed by appending secret bits can successfully camouflage the original VQ index and create a high embedding capacity.  
Conceptualization, C.-C.C.; Methodology, C.-C.C.; Software, J.-F.C. and W.-J.K.; Validation, J.-H.H. and C.-C.C.; Formal analysis, J.-H.H.; Investigation, J.-H.H.; Resources, C.-C.C.; Data curation, J.-F.C. and W.-J.K.; Writing--original draft preparation, J.-H.H.; Writing--review and editing, C.-C.C.; Visualization, J.-H.H.; Supervision, C.-C.C.; Project administration, C.-C.C.; Funding acquisition, J.-H.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology of Taiwan, grant number MOST 110-2221-E-507-003.  
Not applicable.  
The authors confirm that the data supporting the findings of this study are available within the article.  
The authors declare no conflicts of interest.  
Figure 20: Comparisons with SOC-based RDH schemes. EC (**a**), file size (**b**), total bitrate (**c**), image bitrate (**d**).
## 4 Conclusions  
This paper analyzes changes in the soil water content of slopes after the end of rainfall under the condition of natural evaporation. Changes in soil water content over time at different depths in both bare and vegetated slopes were analyzed. The effects of vegetation, slope ratio, and meteorological factors on the dynamic changes in slope water under natural evaporation conditions were revealed. The main conclusions of this study can be summarized as follows:  
1. Soil water dissipation from the bare slope was more significant on the first day after the rainfall ended but became milder in the following 14 days. The slope ratio has little impact on the dynamic change in water content in bare slopes under natural evaporation conditions. The main influencing factors are soil depth and evaporation time. A greater soil depth leads to a lower rate of soil water dissipation.
2. The greater the slope ratio of vegetated slopes, the greater the amount of soil water dissipation within a 40 cm range. However, there is no significant difference in soil water dissipation at depths of 60 cm and 100 cm for different slope ratios of vegetated slopes. Since the soil water dissipation rate decreases with time, the change in soil water dissipation over time can be modeled by the logarithmic function \(\Delta w=a\times\ln(\Delta t)+b\).
3. Pearson correlation analysis revealed that slope conditions, such as slope ratio and vegetation, are the primary factors influencing soil water dissipation under natural evaporation conditions after rainfall. Meteorological factors have less influence on soil water dissipation, although wind speed, humidity, and temperature have a significant impact on soil water dissipation in the 20-40 cm, 0-40 cm, and 0-20 cm soil layers, respectively. Solar radiation, on the other hand, has little effect on soil water dissipation in each soil layer.  
Conceptualization, H.X. and J.W.; methodology, H.X. and J.W.; formal analysis, Z.L. and J.C.; data curation, J.C. and Y.S.; writing--review and editing, Z.L., J.W. and J.C.; funding acquisition, H.X. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research was substantially funded by the Joint Funds of the National Natural Science Foundation of China (No. U22A20232) project supported by the National Natural Science Foundation of China (No. 52078195) and the Innovation Demonstration Base of Ecological Environment Geotechnical and Ecological Restoration of Rivers and Lakes (2020 EJB004).
## 5 Experiments and Results
### Quantitative Learning Performance  
The results depicted in Figure 3 illustrate the total reward obtained by four intelligent agents per episode (episode reward) over 5000 training episodes utilizing different observation methods and DRL algorithms (DQN and IQN based). The solid blue and red lines in Figure 3 represent the learning performances of eDA3-X agents with relative and local view methods, respectively. Conversely, the dashed blue and red lines depict the baseline method performances. Table 1 provides a quantitative comparison of the observation methods, including the number of objects collected, collisions between agents, collisions with walls, and episode reward. Each value in Table 1 is the average value over the final 100 episodes with a range of one standard deviation (\(\pm\sigma\)).  
Based on the results presented in Figure 3 and Table 1, it can be observed that all the intelligent agents were able to successfully learn how to collect objects within the environment, as indicated by the steadily increasing episode rewards and low collision counts. Moreover, it was found that performance improvements were more notable when agents used the relative view to observe the environment rather than the local view, regardless of the DRL algorithms employed. This is likely due to the additional global positional information provided by the relative view.  
Moreover, the results presented in Figure 3 and Table 1 suggest that the eDA3-X agents outperformed the baseline methods in terms of building more efficient policies in the environment. Specifically, when using the local view method, eDA3-DQN and eDA3-IQN agents collected 18.17 (26.56%) and 29.19 (41.68%) more objects than their DQN and IQN counterparts, respectively. On the other hand, when using the relative view method, the eDA3-DQN and eDA3-IQN agents achieved an improvement in episode reward by 43.24 (21.57%) and 16.07 (6.86%) compared to the baseline methods. The following sections present a detailed discussion on the performance improvement by using the relative view method.
## 3 Model and Scenario Selection
### Selection of Prior Dataset and Experimental Dataset Conditions  
The quantitative boundary conditions for both the prior dataset and experimental dataset are listed in Table 1, wherein the primary air velocity serves as the variable. For the prior dataset, the primary air velocity starts from 26.5 m/s, with each incremental change of 0.5 m/s representing one prior condition. In particular, a total of 20 prior conditions are simulated, with each condition containing temperature field distributions at four different heights. Consequently, this simulation results in a total of 20 temperature field distributions for various prior conditions. As shown in Figure 3, the positions of the acoustic temperature measurement points, together with the 18 measurement points arranged on each boundary, form a total of 1944 measurement paths. These paths are used to collect 3D TOF data for the four sectional temperatures of the 20 sets of conditions, constituting the prior dataset _TR_.  
In addition, five experimental conditions are selected with the boundary conditions listed in Table 2 to simulate the temperature field distributions. These five sets of experimental conditions are used to collect 3D TOF data, forming the experimental dataset. Since the boundary conditions for the experimental conditions differ from the prior conditions, the final sampling results in a prior dataset with dimensions of (1944 \(\times\) 4) \(\times\) 20 and an experimental dataset with dimensions of (1944 \(\times\) 4) \(\times\) 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Secondary Air** & **Primary Air** & **Secondary Air** & **Tertiary Air** & **Over-Fire Air** \\
**Velocity (m/s)** & **Temperature** & **Temperature** & **Velocity (m/s)** & **Temperature** \\  & **(K)** & **(K)** & **(K)** & **(K)** \\ \hline
40 & 400 & 600 & 40 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative boundary conditions for a priori datasets.  
Figure 3: Positions of acoustic temperature measurement points and paths.  
Furthermore, two additional conditions are chosen as validation conditions, with primary air velocities of 36.75 m/s and 37 m/s. These two conditions exceed the range of the prior dataset and are used to test the applicability of the reconstruction algorithm to such conditions.
## 3 Materials and Methods
### IoT Power Quality Sensor  
The main hardware device is an embedded IoT PQ sensor (see Figure 2). This sensor has been thoroughly described in the work of [15], and it is an owned design that provides information related to PQ and energy consumption about a smart appliance within any facility. Here, the IoT PQ sensor is used to provide information about voltage, current, real power, apparent power, and other important electrical parameters. The power consumed in an AC load is called true power, also known as active or real power. On the other hand, the product of the two RMS values, voltage and current, is known as apparent power. While real power does the actual work in the load, apparent power is the total power in an AC circuit, both circulating and dissipated. Apparent power must be considered when designing and operating power systems, as, although all the current associated does no work at the load, it still must be supplied by the power source. Therefore, conductors, transformers, and generators must be sized to carry this total current. The ratio of active power to apparent power in a circuit is called the power factor. This is the reason to include both measurements in this IoT sensor. A system with a low power factor will have high circulating currents owing to energy that returns to the source from energy storage in the load. The consequences for those higher currents are losses and reduced efficiency.  
All this information will lead to a proper analysis of operational parameter patterns as well as knowledge of the power supply and load conditions of the multiple appliances in the building. These data will be useful for equipment maintenance and support, and ultimately for user energy awareness.  
Figure 1: The overall architecture of the energy augmented reality (AR) application. IoT, Internet of Things; PQ, power quality.
# Corrosion Susceptibility and Microhardness of Al-Ni Alloys with Different Grain Structures
## 1 Introduction  
The importance of aluminum-based alloys in the field of materials is associated with the great relevance that these systems have in the automotive industry. This is mainly a consequence of their low density, which, associated with the wide range of other properties of technological relevance that they present, make them very versatile systems suitable for use in a variety of applications. However, it has been found that many of these alloys used to make engine parts suffer a decrease in performance when used at elevated temperatures [1]. Therefore, in recent years, interest in aluminum-based alloys that have good properties at high temperatures has grown. It has been established that suitable alloys could be those that present the eutectic transformation at temperatures above 600 \({}^{\circ}\)C [2, 3] and whose second-phase precipitates are stable at these temperatures.  
Al-Ni alloys, which present a eutectic at 639.9 \({}^{\circ}\)C, are currently considered suitable candidates for use in high-temperature services [4, 5, 6]. The adequate performance of the Al-Ni system at high temperatures has been associated with the presence of Al\({}_{3}\)Ni intermetallic particles resistant in high-temperature environments [2, 3, 5, 7]. It is also expected that the presence of such particles improves the mechanical resistance of alloys.  
The development of Al-Ni alloys with a controlled microstructure is a relevant topic [1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14]. However, studies that relate macro- and microstructures with the electrochemical behavior of Al-Ni alloys are scarce [15, 16, 17]. It is vitally important to consider that the development and optimization of new alloy systems also require studies thatevaluate the compatibility of the materials with the environment with which they will be in contact. Corrosion, that is, the degradation of metallic materials, is an inevitable problem that must be faced in almost all industrial and technological environments.  
In general, the electrochemical response of the systems will be the result of the combination of the different structures and compositions of the alloys. It has been shown that the corrosion behavior of aluminum-based alloys will depend on the nature of the secondary phases, their distribution as a result of the different microstructures, their electrochemical behavior and their interaction with the matrix [12; 18; 19].  
With the aim of obtaining a complete overview regarding the performance of Al-Ni alloys, in the present work, the influence of thermal parameters on the structures resulting from the directional solidification process of Al-Ni alloys with different alloy content has been studied. It has also been evaluated how the resulting macrostructures and microstructures and the distribution of second phases affect the microhardness and corrosion behavior of the alloys.
## 4 Calibration Methodology
### Building Calibration
#### 4.1.3 Example: The MixedUse Building  
Calibration of the envelope parameters for the MixedUse building has been performed with free oscillation data as described in Section 4.1.1. The HVAC system of the MixedUse building is made of two independent main technical systems: a Variable Refrigerant Volume Unit (VRV) and an air-to-water HP. The initial performance curves of these two systems have been fitted with linear estimations to reproduce the suppliers' technical documentation with the performance equations of the corresponding EnergyPlus objects. For the MixedUse VRV system a total of 14 different curves were required, from cooling and heating capacity for low/high outdoor conditions including its boundary curves to piping length correction and defrosting; see Figures A5 and A6 in Appendix C.1.  
Key parameters were then estimated in the next step (nominal power, design airflow, design supply temperature, efficiency,...) via the optimization process with the NSGA-II genetic algorithm in order to find the combination of parameter values that results in the best fit of energy consumption while maintaining the indoor climate of the building. The results for the VRV system are displayed in Table A7 and the results for the HP system in Table A8 in Appendix C.1.  
Finally, the evaluation period took place during Summer 2020, between the months of June and August (i.e., on data not used for identification). Results are displayed in Table 3. It should be noted that during the evaluation period the HP underwent a series of malfunctions and had to be repaired. This is why evaluation results for the HP are not presented here.
# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 4 Results and Discussion
### Performance Evaluation
#### 4.2.3 Bigram Keystroke Dynamics
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Exp** & **Cls** & **EER** & **ZM-FAR** & **R** & **P** & **Acc** & **FRR** & **FAR** \\ \hline \multirow{4}{*}{Freq} & AdaB & 0.516 & 0.966 & 0.893 & 0.488 & 0.480 & 0.926 & 0.107 \\  & DT & 0.505 & 1.000 & 0.823 & 0.503 & 0.492 & 0.833 & 0.177 \\  & RF & 0.501 & 0.911 & 0.906 & 0.504 & 0.496 & 0.908 & 0.094 \\  & SVM & 0.505 & 0.957 & 0.864 & 0.496 & 0.493 & 0.873 & 0.136 \\ \hline \multirow{4}{*}{Same} & AdaB & 0.510 & 0.930 & 0.915 & 0.491 & 0.487 & 0.934 & 0.085 \\  & DT & 0.509 & 0.971 & 0.869 & 0.494 & 0.488 & 0.888 & 0.131 \\ \cline{1-1}  & RF & 0.497 & 0.858 & 0.944 & 0.500 & 0.499 & 0.939 & 0.056 \\ \cline{1-1}  & SVM & 0.500 & 0.917 & 0.917 & 0.500 & 0.497 & 0.917 & 0.083 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Average results achieved by each of the four classifiers on the bigram subset when training on Arabic and testing on English. Results labelled “freq” compare the frequent bigrams “th” and “N”, and results labelled “same” compare the same bigrams “th” and “|�”. Cls: classifier; R: recall; P: precision; Acc: accuracy; AdaB: AdaBoost. likely language-specific. This holds for the Chinese [7] and Arabic languages, where both languages are significantly different from the English language.