esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 1 Introduction
The main issue is that ensembles must be as accurate and diverse as possible, and the diversity control must be performed in the generation stage. In this sense, heterogeneous ensemble approaches are expected to produce more diverse models, considering the different learning algorithms of the base models [19, 21]. In the context of solar irradiation forecasting, several ensemble learning approaches have employed a static ensemble of forecasters through time [13, 22, 23, 24, 25]; however, considering the volatile and dynamic characteristics of solar irradiance, static ensemble approaches can present inaccurate results since the best model could change over time. This dynamic characteristic is expected because solar irradiance may be affected by weather conditions [4]. Therefore, a dynamic model selection strategy [14] could improve the results over static ones. In this work, an ensemble based on dynamic selection is proposed for solar irradiance forecasting. The proposed model, referred to as heterogeneous ensemble dynamic selection (HetDS), chooses the most appropriate set of models from a pool of forecasters comprising ARIMA, support vector regression (SVR), multilayer perceptron neural network (MLP), extreme learning machine (ELM), deep belief network (DBN), random forest (RF), and gradient boosting (GB). The best model in the pool is selected based on a local accuracy procedure, which defines a region of competence of size k. Experiments were conducted using the base models and static ensemble approaches, and the results demonstrate that the proposed method achieves the overall best results, considering the root mean square error (RMSE) and mean absolute percentage error (MAPE), mean absolute error (MAE), average relative variance (ARV), and index of agreement (IA), based on evidence provided by Friedman-Nemenyi [26] hypothesis testing. Moreover, the proposed model presents the following advantages:  
* Reduces the risk of selecting an inappropriate model;
* Dynamically searches for the most suitable forecaster to predict a given local pattern in a solar irradiance series;
* It is an agnostic model since other forecasting models can be explored in the pool;
* Increases the generalization capacity of the system. The rest of the paper is divided as follows: the related works are discussed in Section 2; Section 3 describes the models used to perform solar irradiance forecasting; Section 4 presents our proposal to solve the same problem; Section 5 shows the details of the database used, computational results, and a discussion; and finally, Section 6 presents the main conclusions and future works.
## 5 Results
In the proposed method (GD-LAPD), the reduction in time is around 1:200 with respect to EA-LAVD, and around 1:20 with respect to EA-LAPD. Finally, Figure 10 represents the turbine locations for the optimum solutions, both for the set of GD + GR experiments and for the set of EA experiments. In both cases, the turbines are preferentially located at the CA contour in order to maximize the distances between them. However, the inner turbines are more scattered when the EA is used. In the proposed algorithm, the vertices are more likely to be occupied, and a certain trend is observed to dispose of the inner turbines in a more uniform shape; this is similar for all of the algorithm executions. The results found for Horns Rev 1 show solutions very similar to those found for the same site by the random search algorithm based on continuous formulation [28], which identifies solutions with the majority of turbines packed into the side boundaries and a somewhat regular grid in the interior of the farm. They are also very similar to those found by the SGD method of [16], also on a rectangle but with different wind characteristics. For comparison, the times achieved in these and other works are listed as follows:  
* Ref. [16] 3600 s for SGD \(nt=100\), using PyWake 2.6.4 and TopFarm 2.3.5. These authors used SLSQP as a benchmark, with a variable computation time depending on the CA shape between 1200 s and 7500 s.
* Ref. [29] 7560 s for a GA and 6480 s for PSO, \(nt=18\). * Ref. [28] 3220 s for a random search algorithm, \(nt=80\). * Ref. [30] 500 s using TDA 10 K or OpenWind 200 K, \(nt=100\). * Ref. [31] 930 s using GA, \(nt=40\), orientation discretization \(=10\) deg. With the method proposed in this article, the time is reduced to around 25 s on average, with 360 samples of a wind direction of 1 deg, 23 samples of speed of 1 m/s, in a regular or irregular CA for 111 turbines. Compared to the fastest of the algorithms found, the proposed method is about 20 times faster. Figure 10: Comparison of different layout solutions as given by the evolutionary algorithm and the proposed method, for both the Horns Rev 1 site and the Anholt site. On the right are the actual layouts. As a result, it has been seen that the proposed algorithm obtains better solutions than a traditional GA-based one. The required computation time is drastically reduced in both cases if, during the search, the LAPD method is used to evaluate the power deficits. If, as in the case of GA or EA, only the power deficit is estimated, this time reduction is around 1:20. If gradients are also required, it reaches 1:200.
# High-Precision Tea Plantation Mapping with Multi-Source Remote Sensing and Deep Learning
## 3 Methods
### Semantic Segmentation-Based Tea Classification Models
#### 3.3.3 Swin-UNet
It introduces a sliding window along with relative position encoding, allowing multiple calculations of W-MSA to cover the entire sequence. This enhances the model's perceptual range, enabling it to extract more global information from the sequence. SW-MSA partially addresses the limitation of window size when dealing with long sequences. The calculation of multi-head self-attention in W-MSA can be computed using the formula defined in Equation (15). \[Attention(Q,K,V)=Softmax\left(\frac{QK^{T}}{\sqrt{d}}+B\right)V \tag{15}\]  
where \(Q,K,V\in R^{M^{2}\times d}\) represent the query, key, and value vectors, respectively, \(M^{2}\) and \(d\) denote the number of window patches and vector dimensions, and \(B\) is taken from the bias matrix \(B\in R^{(2M-1)\times(2M+1)}\). Figure 12: The structural diagram of the Swin-UNet model. In the case of the Sentinel-1 + DEM dataset, for instance, the input remote sensing image data size was 224 \(\times\) 224 pixels with 69 bands. After passing through the linear embedding layer, the feature dimension became \(C\), and the spatial size became 56 \(\times\) 56. Following three consecutive downsampling steps, the resolution reduced to 7 \(\times\) 7, with the feature dimension being 8\(C\). Upsampling was then performed to restore the original image resolution of 224 \(\times\) 224 \(\times\) 4\(C\), followed by linear projection to obtain one-dimensional classification results. The total loss function was constructed by computing the binary cross-entropy loss and weighted Dice loss compared with the ground truth label data. Error backpropagation was utilized to update parameters across layers for training completion.
## 3 Results and Discussion
### FTIR Analysis  
FTIR spectroscopy was used to identify the functional groups that exist in the _M. spicata_ extract that may act as reducing agents in the process of ZnO NPs biosynthesis. In the current study, it was found that the secondary metabolites of _M. spicata_ were responsible for the reduction of zinc acetate dihydrate into ZnO NPs. The FTIR spectrum of biosynthesized ZnO NPs exhibited several peaks at 3942, 3898, 3858, 3744, 3245, 2362, 2158, 2107, 2048, 1636, 1559, 1514, 1395, 1087, 1033, 932, 840, 774, 714, 487, and 412 cm\({}^{-1}\) (Figure 2). The broad peak at 3245 cm\({}^{-1}\) indicated the presence of hydrogen-bonded groups and could be corresponded to hydroxyl groups (O-H) stretching out from the phenolic compounds that were existed in the plant extract [52; 53; 54]; likewise, the peaks at 3942-3744 cm\({}^{-1}\) representing stretching vibration of O-H group [55; 56]. The band at 2362 corresponded to N-H bond of the primary and secondary amides. The peaks at 1636, 1559, 1514, and 1395 cm\({}^{-1}\) have been assigned to C=C stretch of alkenes or C=O stretch of amides [52; 53]. The bands at 1087 and 1033 are attributed to stretching vibrations of the C-O bond from biomolecules that existed in the leaf extract [54; 56]. Additionally, peaks at 932, 840, 774 and 714 could correspond to C-N stretching amine groups [57]. The major absorption band at 487 cm\({}^{-1}\) was allotted and characteristic to the Zn-O bond [52; 56; 58; 25].  
Figure 1: X-ray diffraction (XRD) patterns of zinc oxide nanoparticles biosynthesized by _Mentha spicata_ leaf extract as reducing agents.
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 3 Experiment and Analysis  
### Implementation Details  
The QCIS-Net algorithm and other experiments were trained and tested on the PyTorch framework using an NVIDIA Tesla V100 GPU. The QCIS-Net was trained for 50 epochs with an initial learning rate of \(1\times 10^{-4}\) based on the pretrained weights of the MS COCO dataset, which is reduced by a factor of 10 at the 40 th epoch. The Adam optimizer with a momentum of 0.9 was used to train the network. Data augmentation, such as left-right flip, was used during the training phase, while no data augmentation was applied during the inference phase. It is worth noting that, during the experiment, the long and short edges of the image were randomly adjusted to be between 800 and 1333 for both training and testing.
## 4 Kinematic Model
### Actuation Time  
When using a single mobile actuator (MA) to control the joints of the serial arm, each joint can be actuated individually. The total time needed to reconfigure the joint angles and reach a specific target is composed of the time required to travel along the links, engage the joints, and rotate them. Assuming a constant linear velocity of the MA, denoted by \(\dot{d}\), and a constant rotational velocity denoted by \(\dot{\theta}\), the time required to reach a target can be calculated as follows:  
1. The time, \(\tau_{d}\), required for the MA to move from its initial position, \(d_{init}\), to the position of the target, \(d\), which can be calculated as shown below: \[\tau_{d}=\frac{\left|d_{f}-d_{init}\right|}{\dot{d}}\] (4)  
Figure 8: Schematic diagram of 3D MASR. A top/side view of the robot and definition of the geometric parameters.  
2. The time, \(\tau_{\theta}\), required to rotate the joints to from the initial arm angles, \(\theta_{init}\), to the desired angles, \(\theta_{f}\), which can be calculated as shown below: \[\tau_{\theta}=\frac{\left|\theta_{f}^{T}\cdot\theta_{init}\right|}{\dot{\theta}}.\] (5) Therefore, the approximation of the total time is dependent on the configuration of \(\mathbf{q}_{init}\) and \(\mathbf{q}_{f}\), and is given by \[\tau\Big{(}\mathbf{q}_{init},\mathbf{q}_{f}\Big{)}=\tau_{d}+\tau_{\theta}.\] (6)
## 4 Results and Discussion
### Parameter Optimization
#### 4.2.3 Batch Size Optimization  
The batch size setting significantly affects the optimization of the constructed model and the memory usage of the computer. If the batch size is set too small, the gradient will be unstable, and it will be challenging for the model to converge. If the batch size is set too large, the speed of processing the same data will be accelerated, but the epoch required to achieve the same accuracy will also increase, and the model will quickly fall into a local optimum. Four different batch sizes (32, 64, 128, and 256) were compared, and the classification results are shown in Figure 13.  
Figure 11: Window size comparison.  
Figure 13 shows that, as the batch size increased, the overall classification accuracy and training time of the model gradually decreased. When the batch sizes were 32 and 64, respectively, the overall classification accuracy of the model performed well. Compared to the former, the overall classification accuracy decreased by 0.065% when the batch size was 64, but the training efficiency of the model increased by nearly 51.2%, which is in line with the demand from a practical point of view. Therefore, the batch size of the model input was selected to be 64.
# Computer-Vision-Based Product Quality Inspection and Novel Counting System
## 3 Methods and Result
### Image-Based Product Quality Inspection and Feature Matching Techniques
#### 3.1.2 Defect Detection Using a Combined SSIM, PSNR, and MSE Evaluation
Finally, the similarity score \(S_{\text{combined}}\), reflecting the weights of SSIM, PSNR, and MSE, is calculated as follows:  
\[S_{\text{combined}}=\omega_{\text{SSIM}}*S_{\text{SSIM}}+\omega_{\text{PSNR}}* S_{\text{PSNR}}+\omega_{\text{MSE}}*\frac{1}{S_{\text{MSE}}} \tag{9}\]  
The weights \(\omega_{\text{SSIM}}\), \(\omega_{\text{PSNR}}\), and \(\omega_{\text{MSE}}\) in the \(S_{\text{combined}}\) formula sum to 1 and are adjusted based on the characteristics that each metric evaluates. This adjustment is not just about evaluating the images but also about focusing on the specific defects and characteristics of the product. First, if the overall appearance or structure of the product is important, the weight of the SSIM metric, \(\omega_{\text{SSIM}}\), is increased. SSIM evaluates the structural similarity of the image, focusing on structural elements such as the patterns, edges, and textures of the product. This is suitable in situations where structural defects are critical, such as in the bending of metal products or the consistency of patterns in textiles. Second, when noise or distortion on the product's surface is the focus, the weight of the PSNR metric, \(\omega_{\text{PSNR}}\), is increased. PSNR plays a significant role in examining surface scratches or the finishing of lens surfaces. Third, when fine pixel-level defects are of particular importance, the weight of the MSE metric, \(\omega_{\text{MSE}}\), is increased. MSE precisely calculates the differences between pixels, making it ideal for processes that need to detect very small defects. In conclusion, the weights in the \(S_{\text{combined}}\) formula are set according to the product's characteristics and the type of defects being emphasized. By adjusting the weights according to the specific features of each metric, the efficiency and accuracy of defect detection can be improved.
coli_, ice nucleation protein (INP) homologs from ina\({}^{+}\) bacteria (e.g., _Pseudomonas syringae_), and protein A from _Staphylococcus aureus_ as anchoring motifs [3; 6; 7]. Among these, the Lpp-OmpA fusion remains the most popular [35; 36] and has been successfully used to display a wide range of active enzymes, including \(\beta\)-lactamases and lipases, on the surface of _E. coli_[37; 38]. The typical fusion comprises the N-terminal sequence (Met1 to Gln29) of the major lipoprotein (Lpp) and a transmembrane region of OmpA spanning five \(\beta\)-sheets (\(\beta\)3-\(\beta\)7: Ans67 to Asn180). Two major features support its pertinence (after proper signal processing and membrane embedding): the N-terminal residue (Lpp: Cys21) binds to OM lipids [39], and the C-terminal stretch (OmpA: Trp164 to Asn180) extends toward the extracellular space [37; 40]. In a previous study, _E. coli_ was successfully engineered to display the catalytic domain of EhCHT1 (an amoebic chitinase) on the bacterial surface using the eLpp-OmpA variant, resulting in a whole-cell (WC) biocatalyst with functional endochitinase activity [41]. The eLpp-OmpA variant includes an elongated transmembrane region of OmpA, spanning\(\beta\)1-\(\beta\)7 sheets (Ala22 to Asn180), which structurally resembles the native conformation and favors \(\beta\)-barrel stability [42; 43]. To our knowledge, the display of _E. coli_ phytase on whole-cell bacterial surfaces has not been investigated previously. Therefore, this study aimed to fuse eLpp-OmpA with AppA to produce a microbial WC biocatalyst with phytase activity (MWCB-phytase). In addition to the advantages associated with the carrier and protein passenger (described above), MWCB-phytase is environmentally friendly and promises competitive reaction rates. Moreover, it avoids cell lysis and enzyme purification techniques, which are costly, time-consuming, and often difficult to scale-up. Here, we report the surface engineering of _E. coli_ to display its phytase using a homologous expression approach. The results of different analytical assays employing the engineered bacteria are also discussed.
# RF Energy Harvesting Wireless Communications:
## 2 Urban and Semi-Urban RF Environment
### RF Distribution Features  
According to measurement results, the RF power density in urban and semi-urban areas has two important features: (a) it has nonuniform spatial and spectrum distributions; and (b) its strength is relatively stable over time [23].
## 3 Materials and Methods
### Ionospheric Phase Delay Analysis and Correction
(**a**) The electron content (\(TEC\)) of the Xi’an ionosphere fluctuated over three days. (**b**) Uplink C-band (4.75 GHz), downlink S-band (3 GHz). (**c**) Uplink Ku-band (14.3135 GHz), downlink Ku-band (12.5635 GHz). (**d**) Uplink Ka-band (31.75 GHz), downlink Ka-band (30 GHz).
## 2 Global Appearance Descriptors  
As mentioned in the previous section, global appearance descriptors constitute an interesting alternative for mapping and localization. In this work, the robot moves along the floor plane, and it captures images using a hyperbolic mirror, which is mounted over a camera along the vertical axis. This section details three methods to describe the global appearance of a set of panoramic scenes \(IM=\{im_{1},im_{2},...,im_{N}\}\) where \(im_{j}\in\mathbb{R}^{M_{x}\times M_{y}}\). After using each description method, a descriptor for each image is calculated; thus, the database is composed of a set of descriptors, \(D=\{\overrightarrow{d_{1}},\overrightarrow{d_{2}},...,\overrightarrow{d_{N}}\}\) where each descriptor is \(\overrightarrow{d_{j}}\in\mathbb{C}^{l\times 1}\) and corresponds to the image \(im_{j}\).  
Figure 2: Two main methods to extract the most relevant information from the images for mapping and localization purposes. (**a**) Detection, description, and tracking of some relevant landmarks along a set of scenes. (**b**) Building a unique descriptor per image that contains information on its global appearance.  
The remainder of the section presents the global appearance techniques used throughout the paper and the homomorphic filter, which is used as a pre-treatment for the images.
# Predictive Energy-Aware Routing Solution for Industrial IoT Evaluated on a WSN Hardware Platform
## 5 PEAR Solution: Hardware Integration and Results Evaluation
### Scenarios Description
#### 5.2.2 Deployment Setup  
To make the tests relevant, replicable and comparable, each test starts from the same initial state and follows the same deployment setup. In the initial state of each test, the gateway and all field devices are turned off. Moreover, the deployment setup is characterized by (1) the configuration of the scenario parameters and (2) the sequential powering up of the field devices on a profile cluster basis starting with the higher energy profile clusters. The field devices from the lower energy profile clusters are powered up only when 50% of field device from the higher energy cluster have joined the network. The first field devices joining the network are expected to be placed on lower topology layers and to have a higher energy consumption compared to the field devices joining the network after them. This deployment setup creates favorable inter-cluster energy balancing conditions, thus eliminating the deployment bias in energy balancing and relevantly emphasizing the PEAR performance and impact.
# New Trends in 4D Printing: A Critical Review
## 7 Application and Case Studies
### Electronics  
Electrical devices are usually designed with a rigid form, offering user-friendly and easy solid interfaces that simplify the tabletop or portable arrangement. This strategy proved to be the best way to preserve general device efficiency as well as dependability. The employment of SMMs in smart electronics can enable more adaptable and accommodating systems for a wider range of applications. For example, wearable and implantable electronics are better suited to soft, flexible, and stretchy electronics [189]. Soft electronics and wearable devices' capacity to accept the natural deformation of biological tissue can significantly increase the comfort, portability, and convenience of continuous physiological monitoring. They can be set inside stretchable/flexible products to develop extremely flexible soft robots. They can serve as modern sensors due to the 4 DP structures' responsiveness to stimuli such as pH, humidity, temperature, stress, and strain. SMAs may be used to assess temperature and strain as well as detect wear and damage inside a structure in addition to acting as actuators [15].  
In Ref. [178], a sensor-actuator was created with 4 DP using nanocarbon black/PLA composites. The device achieved independent heat stimulation and strain sensing capabilities. Consequently, it could effectively interact with thermally stimulated objects, while simultaneously detecting changes in resistance to self-assess their contact status.  
As discussed in [190], SMMs can be used to harvest energy and can be self-powered sensors. SMP-based sensors can harvest energy and act as self-powered, wearable, bio-mechanical sensors. These multi-functional electronic controllers can be employed to improve the flexibility of soft robots [15].  
In Ref. [191], conductive nanocomposites with electro-responsive shape-changing capabilities were used. These shape-changing liquid sensors were manufactured using modified composite ink via DIW. The sensor showed adaptability to various situations and high detection precision.  
The authors of [192] proposed the use of strained-tailored, multi-stable, shape-morphing 3D structures for integrating 3D electronics and adaptable wearable sensors. A composite film was created using a silicone elastomer-based material with phase-changing wax microparticles. It exhibited various buckling modes when subjected to strains in different directions and formed a 3D architecture. Silver nanowires were sprayed on a silicone composite sheet to demonstrate functionality through an electrical circuit connection while switching on some LEDs.
## 1 Introduction
Many factors (e.g., the slope surface morphology, distribution of internal rock joints, and inclination angle of the rock stratum for the layered rock slope) also have important influences on the stability of anti-dip slopes. For example, due to the non-uniformity of slope materials and an unreasonable slope excavation design, a major slope collapse accident occurred in an open-pit mine in Inner Mongolia, resulting in 53 deaths and an economic loss of \(\sim\)204.325 million yuan. Moreover, the simulations of anti-dip interbedded rock slopes should also consider the different rock types. The stability and failure mechanisms of anti-dip interbedded rock slopes warrant more in-depth study. As the largest open-pit coal mine in Asia, in the Fushun West mine, toppling-sliding failures occur in some slopes. Due to the complexity of the geological conditions, slopes always have a high sliding risk, which endangers mine workers. Based on the true geological conditions of the Fushun West open-pit mine, DEM numerical modeling was performed, and the influences of embedded rock joints, inclination angles of the rock stratum, and the slope angles of anti-dip interbedded rock slopes on the overall stability and crack propagation were investigated in this study. The failure mechanisms of anti-dip interbedded rock slopes with the aforementioned influencing factors were revealed. These results can serve as a foundation for disaster prevention of interbedded rock slopes.