esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design of Wearable Micro Thermoelectric Generator Working in a Height-Confined Space
## 3 Results and Discussion
### Verification of Simulation Results by Micro-TEG  
In order to determine the credibility of the simulation results, several sets of controlled experiments were conducted. The existing thermoelectric leg size in the laboratory is \(w\) = 0.85 mm. To make it convenient and cost-effective, a device with \(N\) = 2 and heat sinks with different fin heights were designed. As shown in Figure 6, the measured values of the open-circuit voltages of three devices with different thermoelectric leg heights (\(l\) = 0.49 mm, \(l\) = 1.38 mm, \(l\) = 1.88 mm) are basically consistent with the simulated values under the condition of no heat sink and the heat sink with fins of corresponding height. With the addition of heat sinks, the performance of the device was significantly improved. When \(N\) = 2, \(l\) = 0.49 mm, \(H\) = 3.74 mm, \(w\) = 0.85 mm, and \(P_{\rm m2}\) reaches 0.44 mW/g, which is 11 times higher than that before adding the heat sink. Since the heat loss between the radiator and the device is ignored in the simulation process, the calculated output power of the device is slightly higher than the measured value, while the error is within the allowable range. It can be seen that the simulation results are credible enough.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Authors** & **Output Power Density** & **Condition** & **TEG Location** & **Ref.** \\ \hline [PERSON] et al. & 0.3 \(\mu\)W/cm\({}^{2}\) & No heat sink, \(\Delta\)T = 5 K & Wrist & [25] \\ [PERSON] et al. & 2.28 \(\mu\)W/cm\({}^{2}\) & No heat sink & Wrist & [44] \\ [PERSON] et al. & 4.07 \(\mu\)W/cm\({}^{2}\) & No heat sink & Wrist & [45] \\ [PERSON] et al. & 20 \(\mu\)W/cm\({}^{2}\) & Very large heat sink & Wrist & [46] \\ [PERSON] et al. & 20 \(\mu\)W/cm\({}^{2}\) & Pin Radiator with no air flow & Wrist & [47] \\ [PERSON] et al. & 25.1 \(\mu\)W/cm\({}^{2}\) & No heat sink, under cold condition & Forearm & [48] \\ [PERSON] et al. & 30.5 \(\mu\)W/cm\({}^{2}\) & Finned Radiator, natural convection & Wrist & This Work \\ \hline \end{tabular}
\end{table}
Table 1: Comparison between different TEG systems mentioned in the literature.
## 3 Research Methodology  
The process of data collection and analysis in this study can be described as two-fold. In the first instance, a cross-sectional survey research design was adopted for data elicitation from a population of professionally registered QS and CPM practitioners operating in Nigeria. The choice of this research design was predicated on its flexibility, ethical advantages, efficiency, cost-effectiveness, and utility in covering geographically spread samples [69]. As such, questionnaires were distributed electronically to a cohort of pre-selected respondents. A juxtaposition of quota, purposive, and snowballing sampling techniques were deployed in identifying and recruiting respondents for this phase of the study. The respondents were drawn from the population of the database of the Quantity Surveyors Registration Board of Nigeria (QSRBN). At the point of engagement with the QSRBN database (2019), it was established that 3588 Quantity Surveyors were professionally registered and, practicing in Nigeria. Given the difficulty of identifying and accessing a similar database for CPM, it was decided to rely on data from QS professionals who had the experience of performing CPM functions on construction projects. Accordingly, respondents were asked to indicate if they had CPM experience or otherwise before engaging with subsequent sections of the questionnaire. However, this did not imply that the survey was only available for QS professionals with CPM experience. Considering the sample size of the population (3588), a margin of error of 5% and a confidence level of 95%, a total of 400 questionnaires were administered with an expectation of securing responses from at least 348 respondents. However, 135 completed responses were received from the respondents, thereby indicating a response rate of 33.75%. The minimal experience of each the respondents was over five years, and the combined linearity of the years of experience was 15 years. 54% of the respondents are QSs, and 43% of them have CPM experience in addition to their primary professional experience. At the moment, there are no Universities offering construction project management as a course and most construction project managers are from already established professions such as QS, Architecture and Building. The emphasis on CPM and QS professionals was borne out of the understanding that these professionals were mostly responsible for the determination of the post-contract cost controlling techniques to be deployed on construction sites and projects. Therefore, these professionals have a significant impact on the overall cost performance of a construction project. Consequently, the research approach extracted the data using a 5-point Likert scale. The Likert scale ratings of 1 to 5 were used to extract their perception of the importance of the post-contract cost controlling techniques. Five being highly important; 4 is important; 3, moderately important; 2, not important and 1, ignore. The second phase of this two-fold research methodology is described subsequently.
## 4 Discussion
### Vegetation Bias
[PERSON] et al., 2017 [4] used a combination approach for VB removal. For cells where ICESat observations were available, canopy heights were estimated as the difference between the lowest and highest ICESat signal returns. For other cells, the regionalised look-up maps provided VB for given canopy density and canopy height, as shown in Figure 17b (Figure S6 in [4]). The look-up maps were computed based on ICESat observations, as shown in Figure 17a. As can be observed, not all possible variations of tree density and tree heights are available. The VB reaches about 16 m for canopy heights of 35 m (\(\approx\)50%). It should be noted that the canopy model FCHM2005 [6] tends to overestimate canopy heights as compared to GCHM2019. Therefore, it could be reasonable to expect a smaller fraction would be used with GCHM2019. In Figure 17b, the highest tree bias of 16 m does not appear in the look-up map, and the maximum value of VB is about 13 m for canopy heights of 30 m (\(\approx\)43%). For comparison to the VB3 model, the equivalent to a look-up  
Figure 16: Fraction of canopy height that is removed by: (**a**) Functions investigated in [PERSON] et al., 2016 [2] in Figure 2. (**b**) The equivalent for VB3 as \(\mu_{\text{Conopy}}\), plotted over the same axes. table is plotted in Figure 17c. The VB computed a VB3 = \(0.585H_{\text{canopy}}\rho_{\text{canopy}}\) plotted for canopy heights ranging from 0 to 49 m and canopy densities from 0 to 100%. Visually, the linear analytical VB3 fits the DEM-ICESat observations in Figure 17a well. For example, for a canopy height of 35 m and density of 95%, the VB3 model gives a VB of 19.4 m, whereas in (a), the largest VB is 16 m.  
In this study, the VB model is linear and independent of additional observations (e.g., ICESat) and does not consider vegetation or land use types. Nevertheless, the proposed approach provides a simple, logical, and analytical method to remove VB from DSMs that showed considerable success in the study area in Costa Rica. From the presented discussion, it could be concluded that the VB3 approach, which scales canopy heights by canopy density and a coefficient \(a\), is a logical consequence of the SRTM side-looking and penetration into the canopy, which differs with canopy heights. We believe that further improvements are possible by tuning the coefficient \(a\) for different vegetation types in training areas. This could be done using LiDAR or LVIS training datasets and a map of vegetation, land use types, and climate zones and then applied for global modelling of VB.
## 2 Materials and Methods
### Problem Statement and Solution Overview
\\ \hline \(C(v)\to L\) & \\ \hline \(C(n\
eq v)\to C_{n}\subseteq\left\{L\setminus D_{n}\right\}\) & \\ \hline \(S_{n}\) & Successor set of lines for the node \(n\). \\ \hline \(S(n)\) & Function (algorithm) to obtain \(S_{n}\). \\ \hline \(S(v)\to L\) & \\ \hline \(S(n\
eq v)\As can be seen in Figures 1 and 2, the mutually acceptable interfering line sets can be constructed sequentially, using only pairwise mutually acceptable interfering lines. We search for mutually acceptably interfering line sets using the tree construction technique (Figure 3). We construct the tree using a breadth-first approach:  
1. \(\forall l\in L\) find \(A_{l}\) with \(A(l)\). 2. Create root node of tree with empty line \(\mu\). 3. Keep creating the next layer of the tree until the next layer is empty. 4. For each leaf node \(f\) obtain the line set \(\{D_{f}\setminus\mu\}\); this line set represents the set of lines with mutual interference lower than the acceptable threshold. Add line set to the set of line sets. The set of nodes of the same depth we call a layer. The way to create the next layer for each node \(n\) of the current layer is as follows:  
1. Find \(C_{n}\) using \(C(n)\). Figure 1: Mutually acceptable interfering line sets illustration. Figure 2: Pairwise mutually acceptable lines illustration. \begin{tabular}{l l}
2. & Find \(S_{n}\) using \(S(n)\). \\
3. & Create nodes based on \(S_{n}\) and add them to the tree as children nodes of node \(n\). \\ \end{tabular}   
Let us illustrate the example of constructing the tree of mutually acceptable interfering line sets (Figure 3). First, we start with the root node with an empty line. Then we create layer 1 nodes from the successor set which consists of all of the lines (lines with identifiers from 0 to 9). Nodes with lines 8 and 9 of layer 1 have no lines in their mutually acceptable line sets \(A_{8}\) and \(A_{9}\) with identifiers higher than 8 and 9, correspondingly, so they have no children (we will explain later why we create children nodes with higher value identifiers than the line identifier of the parent). \(A_{0}\) has lines \(\{3,4,5,8,9\}\) the identifiers of which are higher; so, assuming function \(I()\) always returns true, we obtain five children for node 0 of layer 1. The node with line 3 of layer 2 (which is a child of the node with line 0 of layer 1) has two children with lines 8 and 9. Lines of those children are elements from the set intersection \(\{A_{0}\cap A_{3}\}\) whose identifiers are higher than 3. Traversing from the leaf node, we obtain the set of mutually acceptable interfering lines, for example, line set \(\{8,3,0\}\). Now, let us define the functions to find mutually acceptable lines \(A()\), to find candidate sets \(C()\), and to find successor sets \(S()\).
## 3 Problem  
In the training process of wireless FL, a significant portion of the system's time consumption comes from communication overhead, while the local computation time of clients generally accounts for only about one-tenth of the communication time. Figure 1 shows the synchronous FL training process of three clients. Starting training at the same time, due to the considerable differences in computing capability among the clients, when using the same size data samples for training, clients with higher computing capability (such as Client 1) complete the training first, while clients with weaker computing capability (such as Clients 2 and 3) lag behind. Since each client occupies the same proportion of bandwidth exclusively, the communication time with the edge after completing local training is equal. In this case, high-computing-capability clients send their local models to the edge first, but the edge needs to wait to collect local models sent by all clients before aggregation. Hence, high-computing-capability clients (such as Clients 1 and 2) experience some waiting time. Although this does not impact the overall delay in a single round, if this phenomenon is scaled to all rounds globally and with a large number of clients sharing limited bandwidth, it inevitably results in a significant amount of time wasted.
# A Deep Learning Approach to Estimate Soil Organic Carbon from Remote Sensing
## 3 Materials and Methods
### The EO-Learn Land Cover  
To train the first stage of our pipeline, we used ground truth LC data that are accessible for the territory of Slovenia and can be accessed here [51]. The entire territory of Slovenia is divided into 940 tiles, each sized at 500 \(\times\) 500 px, with each pixel corresponding to an area of 10 \(\times\) 10 m\({}^{2}\) (Figure 2). The core of the dataset consists of satellite imagery collected by the Sentinel-2 satellites. The country-wide reference for land use/land cover is provided. It is available in the form of a geopackage, which contains polygons and their corresponding labels. The labels represent the following 7 classes:  
Although the classes here represented are important for the training of the LULC stage of the model, they were not actually used as the results in our pipeline. It is also worth noting that not all classes have the same representation in the training dataset, which is especially evident if the geography of the region (Slovenia) is taken into account (i.e., mostly mountainous region, with not very many wetlands). It is, therefore, expected that the performance of the segmentation model itself will not be balanced with regard to the reported classes. However, we are only interested in the \"knowledge\" of the model that gets encapsulated in the latent feature vectors, and do not care too much about the actual classes.  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**LULCID** & **Name** & **Representation (\%)** \\ \hline
1 & Cultivated land & 21.6 \\ \hline
2 & Forest & 38.2 \\ \hline
3 & Grassland & 24.8 \\ \hline
4 & Shrubland & 1.9 \\ \hline
5 & Water & 0.6 \\ \hline
6 & Wetlands & 0.01 \\ \hline
7 & Artificial surface & 12.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Land use/land cover classification.  
Figure 2: The division of Slovenia into tiles.  
For each tile, general, static, and dynamic data are available. General data include the tile dimensions, geographical coordinates, and timestamps for when the satellite images were captured. Static data encompass information on land cover, categorized into 11 possible classes (cultivated land, forest, grassland, shrubland, water, wetlands, and artificial surface). It is assumed that the land cover either remains stable or changes very little over the one-year interval for which the data were acquired. Dynamic data consist of satellite imagery. Depending on the tile, there were approximately between 50 and 100 satellite images collected during the year 2019. In our training, we used a single image per its corresponding area, selecting those that correspond to the time frame of the existing ground truth LULC map. This time frame is the entire year of 2019; however, we selected the month of July, because it had the least amount of cloud coverage.
# Reward Function and Configuration Parameters in Machine Learning of a Four-Legged Walking Robot
## 2 Materials and Methods
### The Course of the Study  
At the outset, it should be noted that the maximum number of steps in the learning process has been set at 5,000,000 steps. The comparison will be within the given range because it was found that after this time, the trend of the process stabilises.  
As intended, the first study focused on modifications of the reward function in the context of its impact on the learning quality and speed. For this purpose, three learning cycles were performed, each with a different form of the reward function, as follows:  
* The first cycle included a balanced distribution of the reward awarded to the agent both for achieving the goal and for behaving in accordance with the assumptions during the process, as represented in green on the charts;
* The second focused on a significant increase in the value of the reward awarded for achieving the goal alone, while leaving the rewards awarded for the correct behaviour during learning, as marked in orange below;
* The third cycle had the same value for achieving the goal as the first award, and the value for behaviour during the process was doubled, as characterised by the navy-blue colour.  
The second study used the best of the above variants in terms of the learning efficiency; and, on this basis, the impact of three key hyperparameters defined in the configuration file of the learning process was checked, with the remaining values unchanged, as follows:  
* During the first cycle of the second series, the gamma parameter changed from 0.995 to 0.8. This is a factor in how much an agent will consider potential future rewards. It must be lower than 1; otherwise, the learning process may be degraded, as represented in brown on the charts;
* The second cycle focused on the change in the number of hidden layers present after introducing the observations made by the agent and ranged from three to one--as marked below in light blue;
* The last, third, cycle consisted of changing the number of units in each layer of the neural network from 512 to 32, as characterised by the pink colour.  
Figure 4: The learning environment.  
All the changes were made within the ranges provided in the documentation of the ML-Agents module to prevent the learning process from being carried out under unreasonable conditions.
# Short-Term Power Prediction of Wind Turbine Applying Machine Learning and Digital Filter
## 3 Time Series Forecasting Experiments
### Evaluation Standard  
Mean Absolute Error (_MAE_), Mean Absolute Error (_MAE_), Symmetric Mean Absolute Percentage Error (_SMAPE_), Mean Squared Error (_MSE_), Root Mean Squared Error (_RMSE_), and Coefficient of Determination (\(R^{2}\)) are commonly used to evaluate the accuracy of forecasting methods: where \(\hat{y}_{i}\) and \(y_{i}\) are the predicted and actual values, and \(n\) is the total sample size.  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{|\hat{y}_{i}-y_{i}|} \tag{16}\]  
\[SMAPE=\frac{1}{n}\sum\limits_{i=1}^{n}{\frac{|\hat{y}_{i}-y_{i}|}{{(|g_{i}|+|y_ {i}|)}/2}}\times 100\% \tag{17}\]  
\[MSE=\frac{1}{n}\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2} \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2}} \tag{19}\]  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(y_{i}-\hat{y}_{i})}^{2}}{\sum\limits_{i= 1}^{n}{(y_{i}-\overline{y}_{i})}^{2}} \tag{20}\]  
Figure 8: Wind speed–orientation distribution map.  
Figure 9: Diagram of wind distribution and wind speed and power output corresponding to wind direction.
## 4 Results and Discussion
### Influence Analysis of the Manipulation Parameter  
The transitional flight process of the vertical take-off and landing fixed-wing UAV is the intermediate process connecting the vertical take-off and landing fixed-wing flight process, and it is an important flight mode of the vertical take-off and landing fixed-wing UAV. During the transition flight, the ducted lift-increasing system deflects continuously, the aerodynamic configuration changes accordingly, and the aerodynamic force also changes accordingly. Transition flight is also a dangerous flight mode. The larger the declination angle-speed envelope of the vertical take-off and landing fixed-wing UAV's ducted lift-increasing system, the easier it is to achieve the transitional flight process. Within the flight envelope, the flight attitude of the UAV and the speed of power deflection have a certain impact on the transition process.  
In the transition process, on the premise of ensuring longitudinal balance, the transition at different pitch angles will have different transition state characteristics (see in Figures 10 and 11). From the calculation of flight data, it can be seen that in the ducted deflection data, when transitioning at a large attitude angle, the duct deflection can be completed at a lower flight speed, while for transition flight at a small attitude angle, a higher flight speed is required, as it allows for the flight transition to be completed.  
When deflection rates of the duct are different, fixed-wing vertical take-off and landing UAVs have different trim-flying capabilities. Within the allowable range of the power system, the deflection rate of the duct can vary from 1\({}^{\circ}\)/s to 29\({}^{\circ}\)/s (see in Figure 12). When the deflection rate of the duct exceeds 29\({}^{\circ}\)/s, transition flight will exceed the lift fan trim capability of the aircraft, and, therefore, it cannot be completed.  
The simulation result is calculated under the stable equilibrium state of the UAV. In this state, it is found that when the transition time is the target, the smaller the pitch attitude angle, the faster the completion time of the transition flight. Taking the dynamic characteristics of the power system as the target, it is found that the transition flight can be completed stably when the pitch angle is 3\({}^{\circ}\), the duct deflection rate is 5\({}^{\circ}\)/s, and the deflection rate of the small wing surface is 1.5\({}^{\circ}\)/s.
### Background and Motivation  
As VANET is a powerful technology with a very high mobility pattern, an uncomplicated and agile mechanism to make a rapid routing decision remains indispensable.  
According to the literature, routing protocols based on geographical location are the most interesting when approaching VANET, as is the greedy perimeter stateless routing (GPSR). The latter allows vehicles to acquire accurate movement information from on-board global positioning system (GPS) receivers. The transmitter forwards the data packet to its neighbor with a short distance from the destination. The deal is that only one parameter is taken into account for the transmission decision: the position information.  
An enhanced variant of GPSR, called fuzzy geographical routing (FzGR), was proposed in [9]. This variant adopts the concept of fuzzification [10] and employs a set of resources to send packets over the link with the best quality, rather than a distance-based link. This technique provides much better quality of service (QoS) performance.  
FzGR gathers two proposed approaches (Figure 1). The first approach computes the score of each one-hop from three QoS measures to support the choice of the optimal next-hop, hence meeting the needs of users. The three quality measures calculated for this purpose are delay, throughput, and the size of the buffer. The second approach uses the relevant score beside the principal metric on which the GPSR is based, i.e., distance between each neighbor and the final destination, in order to comply with the standards of this protocol. The output of each approach is calculated by the fuzzy logic controller (FLC).  
In the context of multimedia data transmission over vehicular networks, the instability and the dynamism of communicating vehicles (nodes) impacts significantly the quality of the video during the transmission [11; 12]. The FzGR protocol improves the streaming quality, either in terms of quality of service (QoS) or quality of experiment (QoE). However, the transmission delay must be further enhanced while keeping good performance.  
Alternatively, where neighboring nodes along a path may move out of transmission range, the stability along the path in the existence of expiring links must be considered. Another major reason for a connection to break down is when an intermediate node or destination node becomes inaccessible. This is due to multiple factors, like node failure or when a node becomes unresponsive.  
To this end, a new form of routing protocol has taken strides in this area: multipath routing protocols [13; 14]. In fact, their implementation in VANET aims to improve several criteria, namely load-balancing, reliability, fault tolerance, security, and QoS.  
Figure 1: General model: FzGR best next-hop election algorithm [9].