esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, a novel method to produce LULC maps based on the classification of the three maximum fraction images and three standard deviations derived from annual composites of the 100-m spatial resolution PROBA-V dataset was presented. The innovative idea involves the analysis of the temporal (annual) behavior of the proportions of vegetation, soil, and shade of the main LULC classes to classify them according to their maximum peaks and standard deviations (seasonality), which are closely related to the biophysical and phenological behaviors of those classes.  
This study presented an overall accuracy classification of 92.4% and an agreement of 88% with the LULC map produced by the MapBiomas project. The user accuracy and the producer accuracy of classifying croplands, forestlands, and water bodies were above 90%. The method is suitable for processing in cloud-computing platforms, such as the Google Earth Engine, which helps address the high dimensionality of the data; therefore, it can be easily tested for regional- and global-scale classification purposes.  
One of the main limitations of the method is the low number of observations (pixels) without cloud and cloud shadows in some regions over the year to attribute the maximum and standard deviation into the correct LULC class. Data fusion or data synergism may be a proper initiative to address the lack of temporal information or the mixture complexity of the annual proportion of endmembers in the Cerrado physiognnies or even the spectral differences related to either well-managed or poorly managed pastures.  
**Author Contributions:** Conceptualization, H.L.G.C. and Y.E.S.; methodology, H.L.G.C.; software, H.L.G.C.; validation, T.B.H. and H.L.G.C.; formal analysis, H.L.G.C., E.A., and Y.E.S.; investigation, A.C.D.; resources, Y.E.S.; data curation, E.E.S.; writing--original draft preparation, H.L.G.C. and Y.E.S.; writing--review and editing, E.E.S. and A.C.D.; visualization, A.C.D.; supervision, Y.E.S.; project administration, Y.E.S. and E.A.; funding acquisition, Y.E.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, Process No. 2016/19806-3, 2018/14423-4) and by the Conselho Nacional de Desenvolvimento Cientifico Tecnologico (CNPq, Process No. 303299/2018-5, CNPq, Process No. 380716/2019-4).  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 2 Site and Measurements  
### Measurement Site  
Our measurements have been carried out in Kiruna, Sweden (67.8\({}^{\circ}\) N, 20.4\({}^{\circ}\) E) using the D-ICI, the ground-based in situ instrument described in [PERSON] and Vazquez-Martin [22]. Most of the measurements took place on the roof of the main building of the Swedish Institute of Space Physics (IRF) on the space campus at the height of about 15 m above the ground at an altitude of 425 m above mean sea level. The remaining measurements were done from the roof of the IRF Lidar laboratory located 950 m away from the space campus at the height of 5 m above ground. We consider these two locations close to each other and thus can further regard them as the same Kiruna measurement site.  
Our dataset is from multiple snowfall seasons during the winters of 2014/2015 to 2018/2019. At the Kiruna site, each season lasts from about the beginning of November to the middle of May. Surface temperatures in Kiruna can reach down to \(-43.3\)\({}^{\circ}\)C [24], and our data include temperatures down to \(-27\)\({}^{\circ}\)C. The IRF weather database [25], based on instruments 1.7 km away from the space campus and 2.44 km from our current measurement location, provides temperature and relative humidity used in this study.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Graph-Based Homology Mapping Methods  
The previous alignment methods have mainly been used for the detection of similarities at the DNA level. A new class of alignment methods has emerged to perform genome comparison in terms of gene composition and interspecies evolutionary relationships. These methods are graph-based methods that map the DNA/genetic composition of the genomes and, thus, highlight the evolutionary relationship between species, including syntenic, homologous, orthologous, and paralogous regions.
## 2 Materials and Methods
### Proposed Unified Performance Metric
#### 2.3.3 Integration of Lagrange Interpolation  
Lagrange interpolation has been widely used in the literature for various purposes, such as in network security and efficient data transmission in WSN [34; 35; 36; 37], optimizing resource distribution [38; 39], and error detection and correction in WSN [40]. In this paper, we proposed using Lagrange interpolation to create a function that can evaluate the performance of a WSN at different rounds using the fitness function discussed in Equation (12). Lagrange interpolation allows us to represent the relationship between the network's overall performance and the number of rounds in the simulation. Given \(Fit_{j}\) as the overall performance of the WSN at round j, we can express the Lagrange-interpolating polynomial \(P(x)\) in Equation (13).  
\[P(x)=\sum_{j=0}^{n}(Fit_{j}\prod_{i=0,\ i\
eq j}^{n}\frac{x-x_{i}}{x_{j}-x_{i}}) \tag{13}\]  
where  
\(n\) represents the number of data points or the number of rounds;  
\(x\) is the independent variable representing the number of round;  
\(x_{i},\ x_{j}\) are the known data points that represent the number of round.  
The integration of Lagrange interpolation, which combines different metrics into a unified metric, allows us to evaluate WSN's performance over time or as the number of rounds progresses. This allows us to comprehensively measure the network's behavior throughout the simulation and predict its performance for unknown data points.
## 4 Results and Discussion
### Adaptive Genetic Algorithm  
Using the improved genetic algorithm to solve the scheduling model for battery-swapping trucks, the transportation scheduling plan for 30 battery-swapping trucks in a single work shift is shown in the following Figure 5.  
The multi-objective weighted evaluation score for this approach is 122.17. The single work shift incurs total transportation costs of CNY 13,713.89, with trucks waiting a combined total of 22.96 h, covering 2496.3 km, and undergoing 73 battery swaps. On average, each battery-swapping truck has transportation costs of CNY 457.13, waits 0.76 h, travels 83.21 km, and swaps batteries 2.43 times. Specifically, shovel point A executed 126 loading operations, moving 7560 tons; shovel point B executed 128 operations for 7680 tons; shovel point C executed 120 operations for 7200 tons; shovel point D executed 147 operations for 8820 tons; shovel point E executed 124 operations for 7440 tons.
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 6 Experimental Results and Analysis
### Performance of CSI Fall Detection Algorithm Based on Improved LSTM Model
#### 6.2.1 Comparison of Training Performance  
The CSI data were put into the original LSTM model for training, and the training performance is shown in Figure 20. As shown, the performance of the model begins to stabilize after the first 2000 iterations in training; the fluctuation in the root mean square error (RMSE) and loss is large, fluctuating greatly at about 0.2. Further, CSI data were put into the improved LSTM model for training, and its training performance is shown in Figure 21. It can be seen that the improved model begins to stabilize after 200 training iterations, becoming stable after about 1000 training iterations with less variance fluctuation. These results show that the improved model is more stable, is more accurate, has a faster convergence speed, and shows lower computational complexity than the original model.  
Figure 20: The training performance of the original LSTM model.
## 2 Application and Methodology  
### Study Area and Problem Definition  
The southern Caspian Sea basin is located in the Middle East, north of Iran, where several rivers provide the habitat of many native fish species such as trout. The Tajan Riveris one of the most important rivers in this basin, which originates from the upstream mountains in the province of Mazandaran. The increasing amounts of pollutants draining off extensive cultivated areas to rivers pose a serious threat to aquatic habitats. In fact, the increases in water pollutants such as nitrate might jeopardize the survival of aquatic species. When the nitrate concentration is above the maximum threshold, it prevents the vital biological activities such as reproduction. Thus, environmentalists are concerned regarding the increasing the nitrate concentration, particularly in the downstream areas of the catchment.  
Figure 1 displays the land use and river network map of the Tajan River basin. As displayed, most of the cultivated land is located downstream of the river. We selected one of the sub-basins in this downstream area as the target sub-basin in the present study. The total area of this sub-basin was approximately 3000 Ha. The downstream river area in this catchment is an important habitat for the fishes, as this is mainly where they reproduce. There is an ongoing conflict between the Department of the Environment and the Department of Agriculture regarding the management of this catchment, which has a total cultivated area of 2010 Ha. On the one hand, the Department of Agriculture would like the cultivation pattern in this area to be appropriate, based on agricultural considerations. On the other hand, the Department of he Environment would like to protect the valuable river habitat in terms of the water quality parameters. Here, nitrate is one of the important pollutants. It should be noted that four main crops, including four varieties of rice and wheat, were considered in this study to optimize the cropping pattern considering nitrate contamination. One year was selected as the simulation period in the case study to analyze various cropping patterns. These crops were cultivated in some sub-basins, providing possibility of calibrating and validating the model for each crop. The available data for the most recent 14 years, including recorded river flow and nitrate concentration at the downstream hydrometric station (shown in Figure 1), were used for calibration and validation of the model. Based on the general recommendations, 80% of the  
data were applied for the calibration process, and rest of the data were used to validate the model.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Multi-Channel MAC Protocols
#### 4.3.1 Aht mac  
An adaptive high-throughput MAC protocol was designed for resource reservation and sharing, adapting to rapid node density changes. AHT MAC follows the CCH and SCH mechanisms similar to IEEE 802.11p. Each node is equipped with GPS for proper time synchronization to avoid collision during transmission. Two ranges are included in this protocol for efficient transmission, i.e., transmission range (TR) and interference range (IR). The nodes transmit in their TR only, and IR is present to detect interference when nodes transmit in their TR and avoid it. Distributed TDMA is used for the periodic broadcast of information such as node ID, location, and velocity of nodes. As in IEEE 1604.9, the SCH is divided into SCH intervals (SCHs) consisting of payload intervals and guard intervals; furthermore, the PI is divided into service resource blocks (SRBs) as depicted in Figure 6. SRB management eliminates the hidden terminal and exposed terminal problem.  
SRBs are fully utilized, and the nodes show high performance by transmitting and receiving even when the density is increased. The handshake process in DTR/DTA is secured by a request conflict resolution mechanism [77]. The protocol has less contention  
Figure 6: SCH intervals [77].  
time and, thus, improves throughput and minimizes delay. The demerit of this protocol is the broadcast overhead due to DTR/DTS in dense and large networks with packet loss.
# Few-Shot Federated Learning: A Federated Learning Model for Small-Sample Scenarios
## 2 Related work
### Federated Learning  
Federated learning, a specialized form of distributed machine learning, aims to address the challenge of data silos. Initially proposed and designed by Google, the FedAvg algorithm [19] distinguishes itself from traditional centralized machine learning algorithms by distributing the model from the server to multiple clients. Clients locally train the model using their private data and then upload the training results to the server. The server aggregates the uploaded learning parameters or gradients, iterating this process until the global model converges. Numerous classical works in this field [19; 20; 21], with FedAvg widely applied across industries, demonstrate exceptional performance in scenarios with large and high-quality datasets. However, these methods exhibit significant performance degradation when faced with small-scale, small-sample datasets, especially those requiring data to be independently and identically distributed (iid). Unfortunately, in practical applications, datasets often deviate from ideal conditions in terms of size, distribution, and quality, leading to a substantial decrease in the accuracy of the final trained global model.
# Degradation State Recognition of Piston Pump Based on ICEEMDAN and XGBoost
## 5 Degradation State Recognition with XGBoost  
### Parameter Optimization for the XGBoost Model  
In order to improve the performance of the model, it is necessary to adjust the XGBoost parameters. The XGBoost algorithm mainly includes three types of parameters: general parameters, Booster parameters, and task parameters. Each type of parameter includes several specific parameter values, among which the booster parameters have a greater impact on the algorithm performance. In this paper, on the basis of fixing other parameters, changing the booster parameters: max depth (the maximum depth of the tree), the min child weight (minimum sum of instance weight needed in a child) and \(n\) estimators (number of base classifiers) to find the optimal parameters, which can avoid the over-fitting of the model and improve the model accuracy.  
The maximum depth of the trees and the min child weight are two parameters that affect each other. If the parameters are optimized in turn, they will only become disadvantages of local optimization. Moreover, the amount of data in this paper is small; therefore, we can use the grid search to select the maximum depth of the trees and the min child weight at the same time. The results are shown in Figure 9.  
When the maximum depth of the tree is 3 and the weight sum of leaf nodes is 1, the average state recognition accuracy is the highest, which is 0.9917. Thus, the max depth = 3 and the min child weight = 1. Fixing the above two parameters, the number of decision trees was selected by logarithmic loss function to evaluate the probability output of XGBoost classifier [53]. The logarithmic loss function is defined as:  
\[\sigma=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{M}y_{ij}\log(p_{ij}) \tag{12}\]  
where \(N\) is the input sample size, \(M\) is the number of categories, \(y_{ij}\) represents the real category of the input data points, and \(p_{ij}\) is the probability that the \(i\)-th data point predicted by the XGBoost classifier belongs to the \(j\)-th class. The results are shown in Figure 10.  
Figure 9: The relationship between recognition accuracy and max depth, min child weight.  
The closer the value of logarithmic loss is to 0, the higher the accuracy of the classifier is. In this paper, when the number of trees is 167, the logarithmic loss value is \(-0.0061\), which is the largest, so the number of the trees is equal to 167. The parameters for XGBoost are shown in the Table 4, and the remaining parameters are the default values.
## 4 Discussion  
Our literature review has shown that there are currently quite a few different models (both static and dynamic) that summarize the temperature distribution in the PV module and/or the output power of the PV module. The models are presented dynamically in most cases, but very few models consist of a dynamic thermal and electrical part. Therefore, this paper aims to present a novel dynamic thermo-electric model of the PV module based on known equations involving different parameters given in various dependencies. In most studies, the temperature distribution in the PV module was presented for only one or three layers, while our study takes into account all five layers (glass, EVA, PV cell, EVA and tedlar). Optical losses of the thermal model are presented as a function of the incidence angle of the Sun's rays, which is very important for fixed PV modules. The RMSE and MAE values for cloudy and overcast days are in the same range or even better compared to the results of other studies [10; 22; 30], while more significant deviations are observed in the  
Figure 9: Comparison of the static and dynamic electric model for RMSE and MAE under different weather conditions.  
case of sunny days due to measurement error. As described in Section 2.2, the calculation of the output power of the PV module is based on the double diode model. Some specific parameters are presented as a function of the solar radiation and the operating temperature of the PV module, which gives additional adaptability and accuracy to the model. The double diode model has already been very well presented in specific studies and represents an exact way of determining the output power of the PV module. From this point of view, the paper aimed to use the accuracy of a double diode model in combination with the dynamics of the thermal model to present a novel dynamic thermo-electric model of the PV module. Compared to the results of other studies [10], the double diode model results represent a more accurate assessment of the output power of the PV module, despite the measurement errors.
# Predicting Compressive Strength of 3D Printed Mortar in Structural Members Using Machine Learning
## 6 Conclusions  
Based on the present study, ANN can be used to predict the compressive strength of 3 DP concrete. However, the difficulty of this method is that the accuracy of the model depends on the number of patterns. Unfortunately, there are still limited studies and patterns concerning 3 DP concrete's compressive strength. In contrast to the low number of patterns in the field, proposed model has acceptable results and by increasing the number of studies through time more accurate models can be developed. * Seven different networks with different complexities and accuracies are presented. From these algorithms, users can choose which model suits their project the most based on their limitations. It is obvious that if they need more accurate results they need to choose a network with a more complex structure. * Since the maximum number of hidden layers in this study is three, it is proved that for predicting 3 DP concrete's compressive strength, with a well-developed method, the results of a network with only one hidden layer can be accurate enough and there is no need for more complex net-works. * The correlation coefficient of three out of seven networks (ANNMOGOA-1, ANNMOGOA-4, and ANNMOGOA-5) is more than 0.96, which is accurate enough to be accepted. * Based on SI, four networks have good performance in predicting the compressive strength of 3 DP concrete. * Considering MAPE, the accuracy of ANNMOGOA-1 is about 92%, which is a high value of accuracy. Figure 16: Models’ statistical indicators.
# Eco-Friendly Preservation of Pharaonic Wooden Artifacts using Natural Green Products
## 3 Results
### Preservation Effects of Essential Oils on Experimental Wooden Blocks  
The antifungal effects of the most efficient essential oils (thyme, geranium, and cinnamon) were evaluated on the most deteriorated experimental aged _Acacia nilotica_ wooden cubes inoculated with the four highly frequent fungal species (_Aspergillus niger_, _A. fumigatus_, _A. flavus_, and _A. terreus_, Supplementary Materials, Table S12). Thyme oil showed remarkable antifungal effects, with complete inhibition of the growth of all fungal species at 0.5 \(\upmu\)L/mL. _A. flavus_ showed some resistance against thyme oil at 0.125 \(\upmu\)L/mL with a mark 2 growth retardation. Geranium oil showed complete fungal growth inhibition against the tested fungal species at the higher concentration (0.75 \(\upmu\)L/mL). At low concentrations (0.125, 0.25 \(\upmu\)L/mL), _A. niger_ and _A. fumigatus_ showed resistance to the geranium oil with retardation marks 2 and 1, respectively. Conversely, cinnamon oil impeded fungal growth at 0.75 \(\upmu\)L/mL. The present study revealed that thyme oil was the most efficient essential oil against the tested fungal species, so it was selected for the subsequent experiments.
## 4 Control System Design
### Finite-Time Convergent Disturbance Observer Design  
For the sake of promoting the design of FCDO [24], facilitate (4) as follows:  
\[M\vartheta+Cv+Dv=\tau+d \tag{26}\]  
Firstly, define accessory variable \(p=M\upsilon\), where \(\hat{p}\) denotes the estimate of \(p\). Furthermore, \(\hat{p}\) denotes the estimation error of accessory variable \(p\):  
\[\hat{p}=\hat{p}-p \tag{27}\]  
Differentiating Equation (28),  
\[\begin{split}\dot{\hat{p}}=&-Cv-Dv+\tau-D_{1}\tilde {p}-D_{2}|\tilde{p}|^{\gamma}\operatorname{sign}(\tilde{p})\\ &-\bar{d}\operatorname{sign}(\hat{p})\end{split} \tag{28}\]  
where \(D_{1}=\operatorname{diag}\{D_{11},D_{12},D_{13}\}\) and \(D_{2}=\operatorname{diag}\{D_{21},D_{22},D_{23}\}\) represent FCDO parameter matrices; \(D_{1i}\) and \(D_{2i}\) (\(i=1,2,3\)) denote positive design parameters; \(\bar{d}=[\bar{d}_{u},\bar{d}_{v},\bar{d}_{v}]\) denotes upper bound vector of disturbance; \(0<\gamma<1\); \(\hat{d}\) denotes the estimate of \(d\) and \(\hat{d}\) can be further acquired by (29):  
\[\begin{split}\hat{d}=\hat{p}+Cv+Dv-\tau\\ =-D_{1}\tilde{p}-D_{2}|\hat{p}|^{\gamma}sign(\tilde{p})-\bar{d} sign(\tilde{p})\end{split} \tag{29}\]  
Consider the following Lyapunov function candidate:  
\[V_{p}=\frac{1}{2}\tilde{p}^{T}\tilde{p} \tag{30}\]  
Differentiating Equation (31),  
\[\dot{V}_{p}=\tilde{p}^{T}\tilde{p}=\tilde{p}^{T}(\dot{p}-M\vartheta) \tag{31}\]  
Figure 3: The block diagram of USV path following.  
Substituting (27) and (29) into (32) results in  
\[\dot{V}_{p} =\tilde{p}^{T}\big{(}-D_{1}\tilde{p}-D_{2}|\tilde{p}|^{\gamma}sign( \tilde{p})-\tilde{d}sign(\tilde{p})-d\big{)}\] \[\leq-\tilde{p}^{T}D_{1}\tilde{p}-\tilde{p}^{T}D_{2}|\tilde{p}|^{ \gamma}sign(\tilde{p}) \tag{32}\] \[\leq-2D_{1\min}V_{p}-2^{\gamma+1}D_{2\min}V_{p}^{(\gamma+1)/2}\]  
where \(D_{1\min}=\min\{D_{11},D_{12},D_{13}\}\) and \(D_{2\min}=\min\{D_{21},D_{22},D_{23}\}\).  
According to Paper [37], \(\tilde{p}\) converges to 0. Further acquired from (28)-(30) is the following:  
\[\tilde{d}= d-\hat{d}\] \[= D_{1}\hat{p}+D_{2}|\hat{p}|^{\gamma}sign(\hat{p})+\tilde{d}sign( \hat{p})\] \[+M\hat{v}+Cv+Dv-\tau\] \[= \hat{p}-\hat{p}=-\hat{p} \tag{33}\]