esa-sceva/satcom-chunk-collection huggingface dataset:
# Reproducibility and Data Storage for Active Learning-Aided Systematic Reviews
## 3 Data in AL-Aided Screening
### Data in the Labeling Phase  
The data produced during the labeling phase can be divided into data generated by the annotator and data generated by the model, where the data generated by the annotator includes the labeled records, with the corresponding label, the time of labeling, any notes made by the user, any changed decisions, and skipped records. This needs to be saved once for each labeled record; therefore, the data size will be in the same order  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Type of Data** & \begin{tabular}{l} **Stored Every** \\ **Iteration?** \\ \end{tabular} & \
\begin{tabular}{l} **Size Depends** \\ **on Dataset?** \\ \end{tabular} &
\begin{tabular}{l} **Total Size S/M/L** \\ **(Small/Medium/Large)** \\ \end{tabular} & **Essential to Store?** \\ \hline Model parameters per iteration & Y & Y & M/L (depends on the size of the feature space) & N \\ \hline Training set & Y & Y & M/L (M if only size of the training set) & Y \\ \hline Relevance scores & Y & Y & L & N \\ \hline Ranking & Y & Y & L & N (only the ranking of the last iteration) \\ \hline Model training time & Y & N & M & N \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._of magnitude as the dataset itself. The data generated by the model includes the type of model (users might switch to a different model during screening, or multiple models are trained simultaneously), any settings used specifically for the current iteration of the model, including training times, the parameters of the model, relevance scores assigned by the model to each record, and the ranking of the records in order of relevance. Assuming one model is trained each time, the number of trained models will be closely related to the number of labeled records, and thus, it will be linearly related to the size of the dataset. Although the data size of the model typically does not depend on the size of the dataset, it can still be very large. The size of modern neural networks for natural language processing can easily be several hundreds of megabytes or even gigabytes. However, for each model, the data size also depends on the size of the dataset; there will be one relevance score for each record in the dataset for each iteration in the model. Together, this means that the data size of all the generated relevance scores will depend quadratically on the size of the dataset.
## 3 Results
### Power Conversion Efficiency  
Figure 8 shows the efficiency of the LSC PV devices as a function of the dye concentration for the optimal cases with a higher performance. As illustrated in Figure 8, the efficiency of the device without the bifacial cells on the back was increased, whereas the efficiency of the device with the bifacial cells on the edge and back was decreased. The increase in the dye concentration led to an increase in the incident irradiance for the solar cells attached on the sides, and also a decrease in the incident irradiance for the bifacial cells placed on the back. Table 2 summarizes the efficiencies of the LSC PV devices with optimum configuration and performance. These results were obtained based on the minimum and maximum range of the dye concentration (i.e., Lumogen red 305 and orange 240) and also the position of the bifacial solar cells attached on the lightguide.  
According to the results, the highest power conversion efficiency was 16.9% under STC, with 70% direct and 30% diffuse irradiance obtained for the LSC PV device with the bifacial cells on the back and sides with the three by three rectangular cuboids (\(5\times 5\times 1\) cm\({}^{3}\)) at 5 ppm orange 240 dye concentration. The highest efficiency, considering only the bifacial cells on the side, was up to 2.9% under STC for the LSC PV device with the five by five unit cubes (\(1\times 1\times 1\) cm\({}^{3}\)) and the red 305 dye at 110 ppm.
# Comparative Study of the Presence of Heavy Metals in Edible Vegetable Oils
## 2 Analysis of Publications and Trend of Heavy Metals in Edible Vegetable Oils
### Comparison between the Oils and the Most Used Techniques
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Oils** & **Heavy Metal** & \
\begin{tabular}{c} **Analytical** \\ **Techniques** \\ \end{tabular} &
\begin{tabular}{c} **Limit of** \\ **Detection (LD)** \\ \end{tabular} & **Reference** \\ \hline Rapeseed oil & Cd & ICP-OES & 6.00 \(\times\) 10\({}^{-5}\) & [53] \\ Olive oil & Cd & GF-AAS & 2.00 \(\times\) 10\({}^{-6}\) & [52] \\ Sunflower oil & Cd & GF-AAS & 2.00 \(\times\) 10\({}^{-6}\) & [52] \\ Corn oil & Cd & GF-AAS & 2.00 \(\times\) 10\({}^{-6}\) & [52] \\ Rapeseed oil & Pb & FAAS & 3.06 \(\times\) 10\({}^{-4}\) & [25] \\ Olive oil & Pb & GF-AAS & 2.00 \(\times\) 10\({}^{-5}\) & [53] \\ Sunflower oil & Pb & GF-AAS & 2.00 \(\times\) 10\({}^{-5}\) & [25] \\ Corn oil & Pb & GF-AAS & 2.00 \(\times\) 10\({}^{-5}\) & [52] \\ Rapeseed oil & Cu & ICP-OES & 1.20 \(\times\) 10\({}^{-4}\) & [53] \\ Sunflower oil & Cu & FAAS & 3.50 \(\times\) 10\({}^{-4}\) & [55] \\ Corn oil & Cu & GF-AAS & 4.34 \(\times\) 10\({}^{-4}\) & [56] \\ Olive oil & Cu & GF-AAS & 5.00 \(\times\) 10\({}^{-4}\) & [54] \\ Olive oil & Fe & ICP-OES & 1.00 \(\times\) 10\({}^{-3}\) & [52] \\ Sunflower oil & Fe & ICP-OES & 1.00 \(\times\) 10\({}^{-3}\) & [52] \\ Corn oil & Fe & ICP-OES & 1.00 \(\times\) 10\({}^{-3}\) & [52] \\ Rapeseed oil & Fe & ICP-OES & 9.00 \(\times\) 10\({}^{-4}\) & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detection limits (LD) of the most widely used techniques for the determination of heavy metals and most studied vegetable oils, expressed in mg/kg. Figure 3: Frequency of use of the predominant techniques. tification of metals due to its low sensitivity; however, the quantification of some metal is a challenge due to its low concentration in this type of matrices [57]. In this sense, the FAAS technique is one of the most widely used for the detection of heavy metals due to its low cost and easy handling, although it presents low nebulization efficiency and short residence time of the excited atoms in the region of the flame [58]. Another drawback of the use of AAS techniques is its expensive calibration due to the high price of organometallic standards [59]. On the other hand, the use of EM in combination with another chromatographic technique, such as ICP-OES, presents a high performance with a low flow rate of solvent in mobile phase and with the ability to suppress the problem that AAS has for matrices dense matrices and their difficulty in extracting analytes from such matrices [59]; also, ICP-MS is the most powerful technique for the determination of trace metals in different matrices, including olive oils, with the advantages of its high sensitivity, its good linearity, and precision, and as disadvantages its high cost and reduced availability in laboratories [60].
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 4 Network Model CNN-ST
### Swin Transformer Block  
To improve the feature representation performance and decrease the loss of detailed information of dual-component radar signals under different SNRs, this paper integrates the swin transformer into the designed CNN model, which possesses powerful global modeling capability [48]. Figure 4 illustrates the swin transformer structure in detail.  
Figure 4 demonstrates that the swin transformer structure mainly includes LN, W-MSA, MLP, MSA, and SW-MSA, and a residual connection is used after each module, wherein the LN layer is added before each W-MSA and SW-MSA. Therefore, the output \(s^{l}\) of the \(l\)-th layer in the swin transformer is written as  
\[s^{l}=W-MSA\left(LN(s^{l-1})\right)+s^{l-1} \tag{5}\]  
\[s^{l}=MLP\left(LN(s^{l})\right)+s^{l} \tag{6}\]  
where \(s^{l}\) and \(s^{l}\) represent the outputs of W-MSA and MLP. In W-MSA, the input is a patches sequence, \(s^{l-1}\in R^{L\times D}\).  
The outputs of SW-MSA and MLP modules are expressed as  
\[s^{l+1}=SW-MSA\left(LN(s^{l})\right)+s^{l} \tag{7}\]  
\[s^{l+1}=MLP\left(LN(s^{l+1})\right)+s^{l+1} \tag{8}\]  
Figure 4: Structure of swin transformer block.  
where \(s^{l+1}\) and \(s^{l+1}\) refer to the outputs of SW-MSA and MLP, respectively. The self-attention computation in W-MSA and SW-MSA is defined as  
\[\text{Attention}(z_{l})=SoftMax\Big{(}QK^{T}/\sqrt{d}+B\Big{)}V \tag{9}\]  
\[Q=s^{l}W_{Q},\ K=s^{l}W_{K},\ V=s^{l}W_{V} \tag{10}\]  
where \(W_{Q}\), \(W_{K}\), and \(W_{V}\in R^{D\times d}\) represent the three projection matrices' parameters; \(Q\), \(K\), and \(V\in R^{L\times d}\) represent the query, key, and value matrices, respectively; \(d\) denotes the dimension of query or key; and \(B\in R^{L\times L}\) represents the relative position bias.
# _Bacillus subtilis_ Simultaneously Detoxified Aflatoxin B1 and Zeralenone
## 3 Results
### Identification of Active Components of B. subtilis ZJ-2019-1
(**A**) Effects of _B. subtilis_ fractions on AFB1 degradation. (**B**) Effects of _B. subtilis_ fractions on ZEN degradation. The supernatants of _B. subtilis_ ZJ-2019-1 were autoclaved or boiled. Additionally, the supernatants were treated with proteinase K, SDS, or both at 58 \({}^{\circ}\)C for 3 h. Different letters indicate significant differences among the means according to [PERSON]’s test (\(p<0.05\)). ZEN increased significantly with longer incubation time, reaching maximum degradation rates of 89.74% for AFB1 and 61.25% for ZEN at 144 h (Figures 5E and 6E). Figure 5: Degradation of AFB1 by the crude enzymes of _B. subtilis_ ZJ-2019-1 under different conditions. (**A**) Effect of different temperatures on AFB1 degradation by the crude enzymes of ZJ-2019-1. (**B**) Effect of pH on AFB1 degradation by the crude enzymes of ZJ-2019-1. (**C**) Effect of EDTA concentration on AFB1 degradation by the crude enzymes of ZJ-2019-1. (**D**) Effect of toxin concentration on AFB1 degradation by the crude enzymes of ZJ-2019-1. (**E**) Effect of incubation time on AFB1 degradation by the crude enzymes of ZJ-2019-1. (**F**) Effect of metal ions on AFB1 degradation by the crude enzymes of ZJ-2019-1. Different letters indicate significant differences among the means according to [PERSON]’s test (_p_ < 0.05). Figure 6: Degradation of ZEN by the crude enzymes of _B. subtilis_ ZJ-2019-1 under different conditions. (**A**) Effect of different temperatures on ZEN degradation by the crude enzymes of ZJ-2019-1. (**B**) Effect of pH on ZEN degradation by the crude enzymes of ZJ-2019-1. (**C**) Effect of EDTA concentration on ZEN degradation by the crude enzymes of ZJ-2019-1. (**D**) Effect of mycotoxin concentration on ZEN degradation by the crude enzymes of ZJ-2019-1. (**F**) Effect of incubation time on ZEN degradation by the crude enzymes of ZJ-2019-1. (**F**) Effect of metal ions on ZEN degradation by the crude enzymes of ZJ-2019-1. Different letters indicate significant differences among the means according to [PERSON]’s test (_p_ < 0.05). The effect of divalent metal ions on mycotoxin degradation by the crude enzymes of _B. subtilis_ ZJ-2019-1 was also assessed. As shown in Figure 5F, Co\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{2+}\), Mn\({}^{2+}\), and Ni\({}^{2+}\) exhibited significant enhancement in the activity of crude enzymes from _B. subtilis_ ZJ-2019-1 for AFB1 degradation, resulting in a maximum degradation increase of 31.07%. Similarly, Co\({}^{2+}\), Fe\({}^{2+}\), Mn\({}^{2+}\), and Ni\({}^{2+}\) augmented the capability of the crude enzymes to degrade ZEN, whereas Fe\({}^{3+}\) and Mg\({}^{2+}\) inhibited ZEN degradation, with reduced degradation rates of 9.40% and 7.29%, respectively (Figure 6F).
# CoAR: Congestion-Aware Routing Protocol for Low Power and Lossy Networks for IoT Applications
## 3 The RPL and Problem Description
### DODAG Construction  
In this phase, a root node initiates the DODAG construction by broadcasting a DIO message periodically. The neighboring nodes that reside within the communication range receive the message.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & **Congestion** & \multirow{2}{*}{**Congestion Detection**} & **Congestion** & \multirow{2}{*}{**Traffic Type**} \\  & **Alleviation** & & **Notification** & \\ \hline QU-RPL [5] & Resource control & Buffer overflow & Implicit & Continuous \\ \hline GTCCF [20] & Traffic control & \begin{tabular}{c} Ratio between total receiving \\ rate and total forwarding rate \\ \end{tabular} & Implicit & Continuous \\ \hline CA-RPL [21] & Resource control & No & Explicit & Continuous \\ \hline ECRM [25] & Resource control & Buffer overflow & Implicit & Continuous \\ \hline CoAR & Resource control & \
\begin{tabular}{c} Buffer occupancy, ratio of \\ incoming traffic rate over \\ outgoing traffic rate \\ \end{tabular} & Implicit &
\begin{tabular}{c} Continuous, \\ Event-based \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Control message format in routing protocol for low power and lossy networks (RPL).  
Upon receiving the message, based on their OF, a neighboring node decides whether to join the DODAG or not. Each of the nodes that select the root node as a parent, re-broadcast the message. This process repeats at each node and continues until all the nodes in the network join DODAG to form a tree-structured topology. In RPL, the frequency of DIO message transmission is governed by the trickle algorithm in [26]. When a node decides to join the DODAG, it adds the address of the DIO sender to its candidate parent list and computes the rank. Along with this, the node ensures that its rank is greater than each of the parents which are listed. Once the node joins the DODAG, it forwards the message with updated information including the rank. A node receives the message, which is already associated with the DODAG, and it can either proceed with the message to maintain or improve the rank. Otherwise, it can discard the message. To avoid the routing loop, when a node changes its rank, it must discard all the listed parent nodes whose ranks are smaller than the newly computed node's rank. If a node receives the message from multiple neighbors, it selects its parent from the candidate parent list based on the metrics or constraints defined by the objective function. In the case, when a node does not receive the message within a specific time or a new node wants to join the network, it first asks for the message from the surrounding nodes by sending a DIS message. Once a requesting node receives the message, it processes the message as explained before and constructs the route by sending a DAO message. For more detail refer to [4].
## 4 Results
### Drought Risk Estimate  
#### 4.2.1 Estimation of Future Hazard  
To identify key risk areas in vulnerable eco-regions, a model-MRI-CGCM3 (Meteorological Research Institute atmosphere-ocean coupled global climate model version 3) with high spatial resolution that was more suitable for Asia was used to analyze precipitation, temperature and evaporation. MRI-CGCM3 can capture regional differences over subtropical China [45] and can better simulate the spatial distribution of Chinese meteorological factors. The uncertainty of the model is given in Appendix A. The uncertainty is calculated based on the statistics of the model and observations and the error between them.  
\[\text{Error}=Y-X \tag{3}\]  
where \(Y\) is the model value and \(X\) is the observations value.  
The projected future hazards for the vulnerable eco-regions in China as a whole under two emissions scenarios are shown in Figure 3a,b,d,e. High hazard risks around the Tarim Basin are exhibited for both RCP 8.5 and RCP 4.5. We also calculated the hazard differences over the next three decades (Figure 3e,f) to determine where the changes will be the greatest relative to the first half of the 21 st century. As the low-emission mitigation scenario, the RCP 4.5 scenario projects that the hazards will increase throughout most of the research area (Figure 3e). However, the high hazard risks under the RCP 8.5 scenario will occur in the traditionally vulnerable eco-regions (Figure 3f), while a drastic countrywide risk is projected for RCP 4.5. It is worth noting that when emissions increase, the changes in hazard risks are less than those under the low emissions scenario in the Cainozoic vulnerable eco-regions, which may mean that the increase in emissions will result in hazards occurring earlier.  
Figure 3: Risk estimates and changes in hazards in the future under different emissions scenarios. (**a**,**b**) and (**c**,**d**) describes the distribution of hazards in 2020 and 2050, respectively. In (**e**,**f**), the red grids indicate an increased hazard risk, and the blue grids indicate the opposite.
## 5 Results
### Distribution of Studies (RQ1)  
The selected papers were analyzed to find their number and distribution, as belonging to the period 2015-2022, as shown in Figure 11. It is clear that the number of studies that deal with this topic increased each year, since there is an incremental usage of smart city  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
applications around the world. A total of 35 of the selected papers were published in the IEEE journals and conferences, while 11 papers were published in Springer, 9 in Elsevier, and 13 in other Scopus journals. The ratio of these numbers to the total number of the chosen papers is shown in Figure 12.
# Monocular Real Time Full Resolution Depth Estimation Arrangement with a Tunable Lens
## 1 Introduction  
Standard cameras can extract 2D light information from a scene, which includes intensity and wavelength. However, 3D details are lost during this process due to lack of information regarding the direction of each ray of light. The 3D data is essential to a better understanding of the scene as it could be used to find the placement of objects more accurately in the scene [1, 2], or to construct 3D mesh [3, 4, 5, 6], or for artistic purposes in disciplines such as cinematography or computer game development [7].  
Several methods exist that obtain 3D volumes. They can be classified in two main groups: active or passive. While active methods require additional hardware that emits light information from the device to sense the 3D information of the scene [7, 8, 9, 10, 11], passive methods use only the received light information. Classical passive methods consist of utilizing stereo [12, 13, 14], structure from motion (SFM) [15, 16, 17], depth from focus (DFF) [18, 19] or approaches with a monocular camera and single images using deep learning techniques [20, 21]. Passive methods usually do not allow obtaining the 3D information in real time, as they require a high level of computational processing, and the single capture approach cannot obtain real distances.  
The vision system presented in this paper extracts 3D information in real time using a sensor coupled with a variable focus lens, obtaining a full pipeline for passive real time 3D extraction. A comparison with other widespread methods yields that our algorithm is faster and more accurate.
## 2 Methodology
### Windowing of Data Points in Distorted Sinusoidal Signal  
Data samples extracted from various signals are formed into data windows of a specific length. The best window size (i.e., number of data points for each data window) should be preferred for better accuracy of the classification model. The number of data points in a particular window for calculating features represents the window size. Different window sizes of 5, 10, 12 and 15 data points are used to train the model. For a 50 Hz signal with distinct levels of harmonic distortion and noise, the greatest accuracy is obtained for the data window with 15 data points in the classification of zero-crossing and non-zero-crossing points. Therefore, the window with 15 data points is used for all the tests that are carried out further. Each data window is classified into two classes, as ZCP class or non-zero-crossing point (NZCP) class based on the availability of ZCP in that window.  
Figure 1: Mechanism of data preparation.  
ZCP class is labelled as 1 and NZCP class is labelled as 0. Date sampling based on window size 15 along with its class is shown in Figure 5.
## 6 Conclusions  
This study aims to build an automatic recognition and quantitative detection system for surface damage on historical gray brick buildings in Macau so as to achieve a more efficient, standardized, and sustainable detection mechanism for architectural heritage protection. Based on the YOLOv8 deep learning target detection framework, the study finally selected the best-performing Model 3 (297 th epoch) as the deployment model after systematic data collection, image processing, fine annotation, and multiple rounds of model training. The image data source includes 162 gray brick historical buildings in Macau, with a total of 375 annotated training images, covering eight categories of labels, including \"crack, damage, missing, vandalism, moss, stain, plant, and intact\". Multi-dimensional performance evaluations were carried out through training sets, test sets, and external new samples and included the mAP, F1 score, recall, precision, LAMR, confusion matrix, heatmap response, and quantitative statistics, laying a solid foundation for subsequent deployment.  
In terms of performance evaluation, Model 3 shows a stronger generalization ability and spatial response stability. It is significantly better than Model 1 and Model 2 in some categories (such as a moss recall = 0.89, missing recall = 0.85, and crack F1 = 0.69), especially in complex backgrounds, where the thermal map response range is wider and the intensity is higher. In the quantitative detection test, Model 3 not only has excellent frame selection consistency for damage and cracking but also maintains recognition accuracy under different lighting, material repair, and natural weathering conditions. In the detection of the new dataset, Model 3 accurately identifies key damage types, such as cracks and damage in close-range samples, showing excellent perception of high-density image details. Additionally, by using portable detection terminals, Model 3 was built into a portable device that can take 4K images, create thermal maps, label quantities, and export data. The hardware design also fully considers the needs of field inspections, ensuring stability, portability, and scalability while also having the potential for large-scale production and application.