esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Based on the results contained in this article, in which the mechanical behavior of concretes containing recycled carbon fibers has been compared to that of concretes containing polypropylene fibers and with conventional concretes, it has been possible to draw the following conclusions:  
* Despite displaying worse compressive behavior than concretes without added fibers, those containing recycled carbon fibers do not differ greatly from concretes containing polypropylene fibers, achieving strengths of more than 25 N/mm2 and thus making them viable for use in building construction.
* Adding fibers to concrete reduces the spalling effect; concretes that do not contain fibers display greater premature cracking as well as greater flaking of surface fragments during testing.
* The flexural tests offered encouraging results for recycled carbon fibers, as these obtained the best results for maximum strengths and good results for residual strengths, with a minimum number of fibers present in the region of rupture.
* The impact resistance test results for concretes containing recycled carbon fibers displayed great uniformity in addition to substantially improving the energy absorption capacity when compared to conventional concretes and those containing polypropylene fibers.  
Figure 8: Symptomatology of the concrete: (**a**) compression tests; (**b**) flexural tests; and (**c**) impact resistance tests.  
The results obtained have revealed the good performance of these fibers despite their reduced number in the region of rupture of some of the specimens. Ensuring a larger quantity of fibers in the areas experiencing greater strain by reducing their size or adding a larger amount of fibers per m\({}^{3}\) makes it possible to increase the potential that these fibers can offer.  
For all these reasons, it may be concluded that recycled CFRP fibers in quantities of 3 kg/m\({}^{3}\) and 6 kg/m\({}^{3}\) represent an alternative to the use of polypropylene fibers, which constitutes progress from both a mechanical and an environmental point of view.  
Conceptualization, A.C. and M.I.P.; methodology, M.I.P. and D.A.; software, D.A.; validation, M.d.l.N.G. and D.A.; formal analysis, A.C. and M.I.P.; investigation, D.A.; resources, M.d.l.N.G.; data curation, D.A.; writing--original draft preparation, M.I.P.; writing--review and editing, A.C. and M.d.l.N.G.; visualization, D.A.; supervision, A.C.; project administration, A.C. All authors have read and agreed to the published version of the manuscript.  
This research study received no external funding.  
Not applicable.  
Data available on request.  
The authors thank the company Sika S.A.U. for the supplied materials.  
The authors declare no conflict of interest.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 2 Materials and Methods
### Deep Learning-Based Plant Disease Detection Technology
#### 2.1.1 Anchor-Based Plant Disease Detection Algorithm  
The anchor-based plant disease detection algorithm adopts the detection idea of an anchor plus a priori box, setting a priori boxes (anchor-box) with different aspect ratios at each feature point of the plant feature map for screening and adjustment to obtain the final prediction box. Due to the redundant computation of the anchor-box, anchor-based plant disease detection is slow.  
Anchor-based plant disease detection frameworks can be divided into two categories: two-stage detectors and one-stage detectors [23], as shown in Figure 3.  
Figure 1: Methodology implemented for the recognition of the damage caused by the cogollero worm [14].  
Figure 2: Deep learning-based plant disease detection technology.  
The main plant disease algorithms based on a two-stage detector are RCNN [3], SPPNet [24], Fast R-CNN [4], Faster R-CNN [25], etc. The two-stage detector utilizes two networks to implement classification and regression, respectively. A feature extractor (backbone) is used to generate a series of proposal boxes that may contain plant disease targets to be detected, and then some filtering rules are applied to filter the proposal boxes and identify the disease targets.  
Taking Faster-RCNN as an example, firstly, the images are fed into the feature extractor for feature extraction, and the extracted features are fed into the region proposal network (RPN) to generate candidate frames. Secondly, the final proposal boxes are filtered according to the results of the classification and regression. Next, features are extracted from the suggestion frames in the feature map, and each feature is input to the pooling layer of the region of interest (ROI) and unified into a \(7\times 7\) size. Then, it is transformed into a one-dimensional vector by a fully connected layer. Finally, a classification and regression task is carried out to further correct the proposal box and determine the specific class of targets. The two-stage detector is more advantageous in the detection accuracy and classification precision of plant diseases.  
One-stage detector-based plant disease detection algorithms mainly comprise YoLo [26], SSD [27], and RetinaNet [28]. A one-stage detector accomplishes the classification and localization of plant disease targets in a network and extracts features directly from the network for plant disease category and location prediction.  
Anchor-based plant disease algorithms have been dominating the field of plant disease detection, beginning with the initial RCNN, with the rapid development of algorithms with decreasing numbers of parameters and an increasing speed and accuracy of plant disease detection.
# Poly(lactic acid)-Based Electrospun Fibrous Structures for Biomedical Applications
## 2 PLA Synthesis  
Poly(lactic acid) (PLA) is a synthetic biopolymer derived from natural renewable resources (such as rice, sugar cane, and starch). Lactic acid (LA) is the constituent monomer for the synthesis of PLA. Two common synthesis ways of direct polycondensation or ring-opening polymerization of lactic acid are applicable for producing PLA. For industrial production, ring-opening polymerization is mostly employed [4; 28; 29].  
The chiral molecule of LA exists in both forms of L and D isomers. The isotactic homopolymers of PLLA, PDLA, or racemic poly(dl-lactide) (PDLLA) can be obtained from the polymerization of pure L- or D-lactides. This offers possibilities for tuning the ultimate properties of the polymer depending on its final application [4; 28; 30; 31; 32]. Figure 2 represents the stereoisomers of the polymer from D-lactide and L-lactide [28].  
For the L or D stereoisomer as starting compounds, the synthesized polymer is semicrystalline with a melting temperature (T\({}_{\rm m}\)) around 180 \({}^{\circ}\)C. The racemic form of PDLLA is quite amorphous. The synthesis of stereoregular PLA is an effective approach to alter its properties. Using this strategy, by changing the chain length and/or the block succession, it is possible to tailor the mechanical and physical behavior of the PLA-based material [28].  
Low molecular weight PLA was firstly polymerized by Carothers in 1932. Through subsequent attempts by DuPont, higher molecular weight PLA was synthesized and patented in 1954. For biomedical applications, for the first time, LA-based polymers were commercially produced as fiber materials for resorbable sutures; further, the mass production was followed by the development of different prosthetic devices [30].
## 3 Transfer Reinforcement Learning with Space Decomposition
### Space Decomposition Based Reinforcement Learning  
RL is a commonly used machine learning technique, which can acquire new knowledge in a dynamic environment via interaction. Here, the famous RL called Q-learning is adopted to learn the MPPT knowledge. However, if a system needs a high control accuracy, the searching space of the continuous control variable should be divided into a large number of selected actions (e.g., \(10^{6}\) actions for a continuous control variable between 0 to 1). As a result, the conventional Q-learning [28] easily encounters the curse of dimension and a low-efficiency learning rate for selecting an optimal action of a continuous control variable [29].  
To handle this problem, the space decomposition is introduced to decompose the large original searching space into multi-layered smaller searching subspaces. As illustrated in Figure 3, the optimization space of the \(i\)th controllable variable \(x_{i}\) can be decomposed into \(J\) smaller searching subspaces in each layer. If the \(j\)th action \(a_{i}\)\({}^{\text{1j}}\) is selected in the first layer's searching space, then the agent will seek a more accurate searching space in the corresponding second layer's searching space. Therefore, the optimization accuracy of the control variable \(x_{i}\) can be calculated as  
\[OA_{i}=\frac{x_{i}^{\text{ub}}-x_{i}^{\text{lb}}}{cJ} \tag{4}\]  
where \(c\) represents the number of decomposition layers; and \(x_{i}^{\text{lb}}\) and \(x_{i}^{\text{ub}}\) are the lower and upper bounds of the \(i\)th controllable variable, respectively.  
Based on Equation (4), if the number of actions in each layer is set to be 10 (i.e., \(J=10\)), then the same accuracy (\(10^{-6}\)) can be achieved for a continuous control variable between 0 to 1 when \(c=6\). This means that the number of selected actions can be significantly reduced from \(10^{6}\) to 10. Therefore, the learning rate and control accuracy of Q-learning can be considerably improved, based on the space decomposition.  
After selecting all the actions in all the layers, the solution of the controllable variable can be identified as  
\[x_{i}=x_{i}^{c,\text{lb}}+a_{i}^{cJ}\Big{(}x_{i}^{c,\text{ub}}-x_{i}^{c,\text {lb}}\Big{)}/J \tag{5}\]  
\[x_{i}^{\text{J,lb}}=\left\{\begin{array}{ll}x_{i}^{\text{lb}},&\text{if }l=1\\ x_{i}^{l-1,\text{lb}}+a_{i}^{l-1,j},\frac{\big{(}x_{i}^{l-1,\text{ub}}-x_{i}^{ l-1,\text{lb}}\big{)}}{J},&\text{otherwise}\end{array}\right. \tag{6}\]  
\[x_{i}^{\text{J,ub}}=\left\{\begin{array}{ll}x_{i}^{\text{ub}},&\text{if }l=1\\ x_{i}^{l-1,\text{ub}}+a_{i}^{l-1,j},\frac{\big{(}x_{i}^{l-1,\text{ub}}-x_{i}^{ l-1,\text{lb}}\big{)}}{J},&\text{otherwise}\end{array}\right. \tag{7}\]  
Figure 3: Knowledge transfer of TRL between two adjacent tasks for maximum power point tracking (MPPT) with PSC.  
where \(x_{i}^{J,\text{lb}}\) and \(x_{i}^{J,\text{ub}}\) are the lower and upper bounds of the \(l\)th layer's searching space, respectively; while \(a_{i}^{l,j}\) is the \(j\)th action in the \(l\)th layer's searching space.
## 6 Result and Discussion
### Qualitative Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Models** & **Mean IoU** & **Recall** & **Precision** & **F1 Score** & **Specificity** & **Params(M)** & **Inf Time (s)** & **Avg Inf Time (s)** \\ \hline \multirow{4}{*}{**CHASE**} & Res U-Net & 0.7853 & 0.6715 & 0.8617 & 0.7548 & 0.991 & 8.225 & 0.4019 & 0.0502 \\ \cline{2-9}  & Attention U-Net & 0.7872 & 0.6721 & **0.8679** & 0.7575 & 0.991 & 10.085 & 0.4736 & 0.0592 \\ \cline{2-9}  & DeepLabv3+ & 0.7314 & 0.6191 & 0.7454 & 0.6764 & 0.9822 & 11.249 & 0.7077 & 0.0884 \\ \cline{2-9}  & Proposed & **0.8047** & **0.7254** & 0.8492 & **0.7824** & 0.9892 & **2.168** & **0.3975** & **0.0497** \\ \hline \multirow{4}{*}{**DRIVE**} & Res U-Net & 0.7871 & 0.7269 & 0.80225 & 0.7627 & 0.9828 & 8.225 & 0.5069 & 0.0253 \\ \cline{2-9}  & Attention U-Net & 0.7746 & 0.6540 & **0.8602** & 0.7431 & **0.9898** & 10.085 & 0.5767 & 0.0288 \\ \cline{2-9}  & DeepLabv3+ & 0.7234 & 0.6706 & 0.6757 & 0.6731 & 0.9697 & 11.249 & 0.7622 & 0.0381 \\ \cline{2-9}  & Proposed & **0.8027** & **0.7619** & 0.8073 & **0.7840** & 0.9826 & **2.168** & **0.4679** & **0.0233** \\ \hline \multirow{4}{*}{**HRF**} & Res U-Net & 0.7655 & 0.6675 & 0.8026 & 0.7288 & 0.9856 & 8.225 & 0.4106 & 0.0342 \\ \cline{2-9}  & Attention U-Net & 0.7662 & 0.7046 & 0.7605 & 0.7315 & 0.9805 & 10.085 & 0.4768 & 0.0397 \\ \cline{2-9}  & DeepLabv3+ & 0.6681 & 0.5645 & 0.5913 & 0.5776 & 0.9657 & 11.249 & 0.6569 & 0.0547 \\ \cline{2-9}  & Proposed & **0.7713** & **0.6771** & **0.8991** & **0.7372** & **0.9859** & **2.168** & **0.3954** & **0.0329** \\ \hline \multirow{4}{*}{**LES-AV**} & Res U-Net & 0.8153 & 0.7431 & 0.8509 & 0.7934 & 0.9908 & 8.225 & 0.3867 & 0.0645 \\ \cline{2-9}  & Attention U-Net & 0.7996 & 0.7026 & 0.8554 & 0.7715 & **0.9916** & 10.085 & 0.4003 & 0.0667 \\ \cline{2-9}  & DeepLabv3+ & 0.7384 & 0.6707 & 0.6979 & 0.6841 & 0.9794 & 11.249 & 0.6669 & 0.1111 \\ \cline{2-9}  & Proposed & **0.8194** & **0.7456** & **0.8601** & **0.7988** & 0.9914 & **2.168** & **0.3749** & **0.0625** \\ \hline \multirow{4}{*}{**IOSTAR**} & Res U-Net & 0.7977 & 0.7448 & 0.8048 & 0.7737 & 0.9849 & 8.225 & 0.3841 & 0.0549 \\ \cline{2-9}  & Attention U-Net & 0.7987 & 0.7287 & **0.8265** & 0.7746 & **0.9873** & 10.085 & 0.4333 & 0.0619 \\ \cline{2-9}  & DeepLabv3+ & 0.7274 & 0.6724 & 0.6745 & 0.6734 & 0.9729 & 11.249 & 0.6793 & 0.0970 \\ \cline{2-9}  & Proposed & **0.8069** & **0.7802** & 0.7935 & **0.7868** & 0.9831 & **2.168** & **0.3767** & **0.0538** \\ \hline \multirow{4}{*}{**STARE**} & Res U-Net & 0.8194 & 0.8252 & 0.7830 & 0.8036 & **0.9811** & 8.225 & 0.3970 & 0.0794 \\ \cline{2-9}  & Attention U-Net & 0.8249 & 0.8395 & 0.7841 & 0.8109 & 0.9809 & 10.085 & 0.4217 & 0.0843 \\ \cline{1-1} \cline{2-9}  & DeepLabv3+ & 0.7398 & 0.7293 & 0.6621 & 0.6941 & 0.9693 & 11.249 & 0.6626 & 0.1325 \\ \cline{1-1} \cline{2-9}  & Proposed & **0.8313** & **0.8528** & **0.7877** & **0.8189** & 0.9810 & **2.168** & **0.3665** & **0.0733** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison with Existing Segmentation Baseline Models.
## 3 Results and Discussion
### Predictions
#### 3.4.2 Predictions by Separating Stages  
When predictions were made by separating the stages and including SAR variables (DPSVI, RVI, \(V\!H\), and entropy) along with EVI, better R\({}^{2}\) values (Figure 8) and RMSE values (Figure 9) were achieved compared to using all three stages together with both SAR and optical data. Moreover, separating the stages showed that the performance of predictions including SAR variables alongside EVI surpassed that of using EVI alone, resulting in increases ranging from 6% to 18% in R\({}^{2}\) values between the stages.  
Figure 8: Percentage difference in R\({}^{2}\) of predictions with EVI and adding SAR variables in models using each stage individually compared to all stages combined.  
Figure 7: DPSVI index map for distinct growth stages and soybean harvests. The highlighted area in black shows the difference in cultivar in harvest 3.  
Improved performance was observed when incorporating SAR data and separating the analysis by development stages, resulting in higher coefficients of determination (R\({}^{2}\)) and lower RMSE compared to using optical data alone (Figures 10 and 11; Table 9). In Stage 1, the plants had not reached their full growth stage, with greater influence from soil moisture or crop residue [53]. Similar effects of soil on backscattering response in the initial stages of vegetative development are also reported by [PERSON] et al. [54] and [PERSON] et al. [41]. In Stage 2, when the crop reaches peak vegetative growth, backscatter is primarily determined by the scattering from the upper leaf layer [55], resulting in volumetric backscatter due to the dense leaf cover. At this stage, the crop retains most moisture in the leaves, which increases backscatter and interferes more with the radar signal [56]. Conversely, signal saturation can occur due to the high canopy density of SAR and optical images [57]. Therefore, at this stage, the radar signal is influenced by moisture and interaction with canopy biomass, which does not relate directly to grain yield. In Stage 3, with plants in the maturation phase and reduced leaf area, adding information from SAR data during this phase considers the radar wave's greater ability to penetrate moderately dry canopies, as variations in vegetation water content can reduce SAR signal attenuation [58]. Thus, the inclusion of SAR data in Stage 3 showed the most significant improvement compared to the other stages when using only optical data, making it the best option for including SAR data to enhance soybean yield predictions. This result highlights the ability of SAR data to provide complementary information to that obtained from optical sensors [12], especially when the availability of optical images before and during peak vegetative growth is more challenging due to high cloud cover.  
Figure 10: R\({}^{2}\) values for predictions using all growth stages with only optical data and using optical data in conjunction with all SAR variables.  
Figure 9: Percentage difference in RMSE of predictions with EVI and adding SAR variables in models using each stage individually compared to all stages combined.
## 4 Results Evaluation and Discussion  
In this section, the simulation results are presented, analyzed, and discussed. Each parameter is discussed in detail, with reference to tabular and graphical representations. For the proposed CR-NBER protocol, four evaluation parameters were taken for analysis and evaluation. All of these parameters were tested under diverse settings, and three existing protocols were considered for comparison. The results are portrayed in the following subsections. The end-to-end delay, energy consumption, packet delivery ratio, and pathloss parameters were taken for evaluation purposes. Each protocol was compared the others based on the mentioned parameters.  
To give each protocol a fair chance, all protocols started the simulation from the same point, corresponding to zero seconds in the simulation. Each protocol started its simulation at zero seconds, corresponding to the essential system initialization time. After that, each protocol followed its own method, and continued until the last second of the simulation time.
# Automatic Tooth Detection and Numbering Using a Combination of a CNN and Heuristic Algorithm
## 2 Prior Research  
### Object Detection and CNN Algorithm  
The first technology used to detect and recognize objects and people involved object detection using a convolutional neural network (CNN). However, the simple use of a CNN generally requires a substantial amount of training data and a significantly long training time. However, owing to the development of object detection techniques for classifying objects through a bounding-box, the accuracy of the algorithms used for object detection has increased significantly [14]. With the R-CNN technique, which is the basis of object detection, the potential regions of interest (ROIs) are first extracted, and detection is conducted according to the given algorithm. The main advantage of an R-CNN is that it can rapidly extract the location of regions with a relatively high accuracy even if there are few datasets. After the development of an R-CNN algorithm in 2013, various algorithms, including the fast R-CNN, Faster R-CNN, YOLO, and RefineDet, have been developed as object detection techniques [15].  
A study on object detection conducted in 2014 used the Sports-1M dataset, which is composed of YouTube video data, to classify the labels into 487 classes. In this study, each video class was classified using a fusion CNN, which analyzes the fusion of two or more spatial and temporal dimensions in a single frame for video classification. In another study proposed in 2017, the object regions of the image data were divided, and training on the regions was conducted using a CNN to send the feature extraction results to the LSTM model for object detection [16]. In this study, as the analysis data, the Chinese University of Hong Kong square dataset, related to walking, and MIT traffic data, related to vehicle detection, were used. Using the CNN model specialized for image analysis and the LSTM model specialized for long-term memory, the feature results of images extracted through a CNN were trained using LSTM. The subsequent memorization of the patterns on a long-term basis enabled object detection through the extraction of the object labels.  
In a study conducted in 2018, the Detection with Enriched Semantics (DES) model, which detects objects using a single image, was used to verify the detection data of PASCAL VOC and MS COCO. The DES model was trained using six consecutive activation maps, where each activation map provides values for multi-box detection. The integration of the six activation maps enables detecting objects in a single image when using the trained model [17]. Object detection techniques have also been widely used in various areas including self-driving cars, CCTV, surveillance, and sporting events. In this study, object detection was implemented using faster R-CNN frameworks. Although a faster R-CNN is known to be slightly slower than the recently developed RefineDet and YOLO, it is known to perform at a similar or even higher level of accuracy [14].
## 3 Kinematics Modeling of Self-Insulating Mechanical Arm Joints
### Inverse Kinematics  
Given that the self-insulated mechanical arm designed in the manuscript has seven degrees of freedom, a hierarchical control strategy is adopted to avoid the multi-solution problem of joint angles. Specifically, the control process first determines the position of joint point \(C\) and then calculates the pose of the end-effector. This approach decomposes the 7-DOF mechanical arm into upper and lower parts, solving the inverse kinematics sequentially. Such a solution method not only eliminates redundant joint configurations but also prevents collisions between the arm's components.  
Therefore, the inverse kinematics of the lower arm segment is first solved starting from joint \(C\). Specifically, given the coordinates of \(C\) as \([x_{C}\,y_{C}\,z_{C}]^{T}\), the pose angles \(\theta_{m2}\), \(\theta_{r2}\), and \(\theta_{b2}\) are derived. The results are as follows:  
\[\begin{split}&\theta_{m_{1}}=\arctan\Bigl{(}\frac{y_{C}}{x_{C}} \Bigr{)}\\ &\theta_{r_{1}}=\varphi\\ &\theta_{b_{1}}=\arctan\Bigl{(}\frac{\sqrt{\frac{x_{C}^{2}+y_{C }^{2}}{z_{C}-h}}}{z_{C}-h}\Bigr{)}\end{split} \tag{13}\]  
The specification of the angle \(\varphi\) does not affect the coordinate position of joint \(C\), and the values of the elbow joint rotation angle \(\alpha\) and angle \(\varphi\) are designated in the first control step. Therefore, in the inverse kinematics solution of the lower self-insulated joint segment in the second step, the coordinate position of the self-insulated joint is already determined, requiring only the same inverse kinematic solution process. The results are as follows:  
\[\begin{split}&\theta_{m_{2}}=\arctan\Bigl{(}\frac{y_{P}^{\prime} \,z_{P}}{x_{P}^{\prime}\,z_{P}}\Bigr{)}\\ &\theta_{r_{2}}=\psi\\ &\theta_{b_{2}}=\arctan\Bigl{[}\frac{\sqrt{\bigl{(}x_{P}^{ \prime}\,z_{P}^{\prime}\,\bigr{)}^{2}+\bigl{(}y_{P}^{\prime}\,\bigr{)}^{2}}}{z_ {P}^{\prime}\,-h}\Bigr{]}\end{split} \tag{14}\]  
Figure 6: Key dimensioning diagram.  
Here, \([x_{pr}\ y_{pr}\ z_{pr}]^{\mathrm{T}}\) is known as the end-coordinate of the sensor probe in the world coordinate system. The angle \(\psi\) is arbitrarily specified, influencing neither the mechanical arm's state and pose nor its kinematic configuration, but only the orientation of the sensor probe. The coordinates \([x^{\prime}{}_{pr}\ y^{\prime}{}_{pr}\ z^{\prime}{}_{pr}]^{\mathrm{T}}\), representing the probe's position in the elbow joint coordinate system, are calculated as follows:  
\[R_{O_{1}O_{1}}^{-1}R_{C}^{-1}\begin{bmatrix}x_{pr}\\ y_{pr}\\ z_{pr}\end{bmatrix}=\begin{bmatrix}x^{\prime}_{pr}\\ y^{\prime}_{pr}\\ z^{\prime}_{pr}\end{bmatrix} \tag{15}\]