esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Performance Wearable Bi\({}_{2}\)Te\({}_{3}\)-Based Thermoelectric Generator
## 3 Results and Discussion
### Power Generation Performance Optimization of TEGs under Fixed Temperature Difference Conditions  
According to the results in Section 3.1, the \(\Delta T_{\text{eff}}\) between the two ends of the TEG ranges from 0.1 \({}^{\circ}\)C to 3.05 \({}^{\circ}\)C in a typical wearable scenario. Therefore, it is necessary to investigate how to optimize device power generation performance under fixed temperature difference conditions.  
Figure 4 shows the \(P_{\text{out}}\) and \(U_{\text{oc}}\) of TEGs with different TE legs under a temperature difference \(\Delta T\) of 1 \({}^{\circ}\)C and 3 \({}^{\circ}\)C. It is observed that the \(P_{\text{out}}\) increases and then decreases with the increase in current under these two different temperature differences, and the maximum \(P_{\text{out}}\) and \(U_{\text{oc}}\) increase gradually with temperature difference. It should be noted, however, that the maximum \(P_{\text{out}}\) decreases with increasing TE leg length.  
Figure 4: Optimization of the power generation performance of TEG under fixed temperature difference conditions. Open-circuit voltage and output power: (**A**) at \(\Delta T=1\)\({}^{\circ}\)C, (**B**) at \(\Delta T=3\)\({}^{\circ}\)C; (**C**) temperature field of TE; (**D**) relationship between \(P_{\text{out}}\) and \(U_{\text{oc}}\) and TE leg lengths and \(\Delta T\).  
At \(\Delta T=1\)\({}^{\circ}\)C, the maximum \(P_{\text{out}}\) of devices with TE leg length \(L\) of 0.48 mm, 0.88 mm, 1.38 mm, and 1.88 mm is 49.9 \(\upmu\)W, 32.3 \(\upmu\)W, 22.2 \(\upmu\)W, and 16.8 \(\upmu\)W, and the \(U_{\text{oc}}\) is 16.2 mV, 16.7 mV, 17.0 mV, and 17.1 mV, respectively (Figure 4A). The maximum \(P_{\text{out}}\) of a device with a TE leg length of 1.88 mm is 66.3% lower than that of an \(L\) = 0.48 mm device. Meanwhile, the \(U_{\text{oc}}\) increases by 5.6%.  
As \(\Delta T\) increases to 3 \({}^{\circ}\)C, the maximum \(P_{\text{out}}\) of four TEGs with different TE leg lengths is 0.451 mW, 0.292 mW, 0.200 mW, and 0.152 mW; the \(U_{\text{oc}}\) is 48.5 mV, 50.2 mV, 50.9 mV, and 51.3 mV, respectively (Figure 4B). When the hot end temperature \(T_{\text{h}}\) is 33 \({}^{\circ}\)C and cold end temperature \(T_{\text{c}}\) is 32 \({}^{\circ}\)C and 30 \({}^{\circ}\)C, the temperature field of the TEG can be displayed in Figure 4C.  
According to Figure 4D, the TEG resistance gradually increases with the increase in TE leg length under a fixed temperature difference between two ends (Figure S3); the maximum \(P_{\text{out}}\) gradually decreases, but the \(U_{\text{oc}}\) slightly increases. In addition, the Seebeck effect of material is enhanced as the temperature difference between two ends increases, thus improving the power generation performance of a device.
## 5 Case Study of Developing Liver Cancer Diagnosis
### Customizing the Platform for HCC Diagnosis
#### 5.3. Training ML Models for HCC Diagnosis  
5.3.1. Training Set of Labeled CECT Scans  
We acquired the training set, i.e., a set of 130 CECT scans, from two sources: Loma Linda University Medical Center [36] and Radiopaedia. The CECT scans from the university medical center were initially unlabeled and therefore required manual labeling. The labels include image masks of lesions and tumors on the liver regions on CT slices and all the necessary features specified in LI-RADS.  
An attending physician and medical assistants in Vascular and Interventional Radiology manually labeled the CT slices in the following two steps:  
* Step 1. Identifying the Region of Interest (ROI), i.e., target elements such as organs, lesions, and tumors. We used touch-screen monitors and stylus pens to delineate the border areas.
* Step 2. Creating the Mask: Once the ROI is identified, the mask is created by a software library for converting borderlines into masks. Masks are then represented as a set of pixel values corresponding to the ROI, such as [0, 0, 255]. An example of an original CT slice and its masked image of the liver is shown in Figure 11.
# A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model
## 3 Preliminaries
### Svd
#### 3.1.2 Biased SVD Model  
Obviously, there are many subjective factors in the scoring process. Different people have different scoring habits. For example, some people like to give high scores, while others always give low scores. In addition, the quality of items varies from each other. Naturally, high-quality items always get higher scores than low-quality items. Under this circumstance, it is necessary to introduce bias into the SVD model. In the biased SVD model, the characteristics of the user and item are considered by adding an item bias and a user bias. Compared with the traditional SVD algorithm, the performance of the biased SVD algorithm is better, because it takes individuation into account. As mentioned above, the prediction score formula of the biased SVD model is shown in (7) where \(b_{u}\) and \(b_{i}\) indicate the user bias and item bias, representing the eigenvalues of users and items, respectively, and \(\mu\) expresses the average score of all users.  
\[\hat{r}_{u,i}=\sum_{k=1}^{K}p_{u,k}q_{i,k}+\mu+b_{i}+b_{u}. \tag{7}\]  
After gaining the predicted score, the objective function can be calculated as follows (8). Since \(b_{i}\) and \(b_{u}\) are also variables and need to be updated during iteration, the regularization terms of \(b_{i}\) and \(b_{u}\) need to be added as well.  
\[\min_{Q^{*},P^{*}}\sum_{(u,i)\in R}(r_{ui}-\sum_{k=1}^{K}p_{u,k}q_{i,k}-\mu-b _{i}-b_{u})^{2}+\lambda(\|Q_{i}\|^{2}+\|P_{u}\|^{2}+\|b_{u}\|^{2}+\|b_{i}\|^{2 }). \tag{8}\]  
In the biased SVD model, since \(b_{i}\) and \(b_{u}\) have no product relationship with P and Q, the iterative method of P and Q is the same as that of the traditional SVD method. By calculating the derivative of the objective function, the iterative formulas of \(b_{i}\) and \(b_{u}\) are obtained as follows (9), where \(\alpha\) indicates the learning rate.  
\[\left\{\begin{array}{l}b_{u}^{t+1}=b_{u}^{t+1}+\alpha((r_{ui}-\hat{r}_{u,i}- \lambda b_{u}^{t}),\\ \\ b_{l}^{t+1}=b_{l}^{t+1}+\alpha((r_{ui}-\hat{r}_{u,i}-\lambda b_{l}^{t}).\end{array}\right. \tag{9}\]
# Fast 3D Semantic Mapping in Road Scenes +
## 5 Experiments and Results
### Qualitative Results  
First, we present some qualitative results of the KITTI semantic dataset in Figure 5. Then, we use the trained model to make prediction on the KITTI odometry dataset, and the results are exemplified as shown in Figure 6.  
Take the sequence _odometry_03_ as an example of our semantic mapping approach. The sequence consists of 801 RGB frames on a urban road of about 560m. Figure 7 shows the semantic reconstruction with a close-up view including large-scale annotations such as _road_, _building_, and even small-scale objects like _traffic signs_. Note we discard some keyframes at the beginning, due to random initialization of LSD-SLAM. A close-up view is exemplified to illustrate the offline CRF processing as shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Encoder** & **Learning Rate** & **Learning Power** & **Momentum** & **Weight Decay** & **Batch** & **Steps** \\ \hline \multirow{6}{*}{Cityscapes} & ResNet\_50 & 0.003 & 0.9 & 0.9 & 0.0001 & 8 & 20,000 \\  & ResNet\_101 & 0.003 & 0.9 & 0.9 & 0.0001 & 8 & 20,000 \\  & Xception\_41 & 0.01 & 0.9 & 0.9 & 0.0004 & 8 & 10,000 \\  & Xception\_65 & 0.01 & 0.9 & 0.9 & 0.0004 & 8 & 10,000 \\  & Xception\_71 & 0.01 & 0.9 & 0.9 & 0.00004 & 8 & 10,000 \\  & MobileNet\_v2 & 0.001 & 0.9 & 0.9 & 0.00004 & 64 & 10,000 \\ \hline \multirow{6}{*}{ KITTI} & ResNet\_50 & 0.003 & 0.9 & 0.9 & 0.0001 & 8 & 20,000 \\  & ResNet\_101 & 0.003 & 0.9 & 0.9 & 0.0001 & 8 & 20,000 \\ \cline{1-1}  & Xception\_41 & 0.01 & 0.9 & 0.9 & 0.00004 & 8 & 10,000 \\ \cline{1-1}  & Xception\_65 & 0.01 & 0.9 & 0.9 & 0.00004 & 8 & 10,000 \\ \cline{1-1}  & Xception\_71 & 0.01 & 0.9 & 0.9 & 0.00004 & 8 & 10,000 \\ \cline{1-1}  & MobileNet\_v2 & 0.001 & 0.9 & 0.9 & 0.00004 & 64 & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyper-parameters used in the training step.  
Figure 4: Instances in the _odometry_03_ sequence. IC: Illumination Change, MO: Moving Objects, T: Turns.  
Figure 5: Qualitative results of 2D semantic segmentation.  
Figure 6: Instances of 2D semantic segmentation in the KITTI odometry set.  
Figure 7: Qualitative results of 3D semantic mapping from the sequence KITTI _odometry_03_. Our approach not only reconstructs and labels entire outdoor scenes that include roads, sidewalks, and buildings, but also accurately recovers thin objects such as traffic signs and trees. The close-up views show the details of the map.  
Another possible qualitative comparison on the KITTI _odometry_05_ as used in [PERSON] et al.'s work [28] is illustrated in Figure 9. Whereas monocular LSD-SLAM is not resistant to strong rotation in the sequence, we present the qualitative result based on the subset (500 frames) of this data.
## 4 Potential Decolorizing Enzymes and Their Characteristics
### Lignin Peroxidases (LiPs)  
LiP (E.C. 1.11.1.14) is one of the most significant ligninolytic enzymes that contains heme (protoporphyrin IX) as a prosthetic group. This enzyme was firstly discovered in nitrogen- and carbon-limited cultures of the white rot fungi _Phanerohaete chrysosporium_[137]. The presence of this enzyme was later detected in other white rot fungi such as _Trametes versicolor_[138]), _Bjerkandera_ sp., _Trametes trogii_, and _Pthebia tremelosa_[139,140]. LiP has been purified in bacteria _Acinetobacter calcoacticus_[140] and also has shown activity in _Streptomyces viridosporus_[141]. This enzyme is relatively non-specific to its substrate and its main function is the oxidation of different phenolic aromatic compounds as well as a broad range of non-phenolic lignin model compounds [124]. The aforementioned microorganisms secrete LiPs as a family of isozymes with relative composition and isoelectric points (pI), depending on the growth medium and nutrient conditions [142].
## 5 Discussion  
### Percentage of Clipping Entities  
To have a general understanding of what methods are used to represent BIM models, a script has been written to calculate the number of entities that are represented by each representation method. Table 6 presents the number of entities for each building element that are represented by each representation method.  
It can be noticed that clipping is, in most cases, used to represent \(IfcWallStandardCase\) and sometimes \(IfcColumn\). It is not a frequently used representation type, as the percentage of clipping  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **IFC Class** & **B-Rep** & **Swept Solid** & **Clipping** & **Surface** & **Total** \\ \hline \multirow{8}{*}{House-1} & IfcBeam & 3 & 1 & 0 & 0 & 4 \\  & IfcDoor & 5 & 0 & 0 & 0 & 5 \\  & IfcMember & 42 & 0 & 0 & 0 & 42 \\  & IfcRailing & 2 & 0 & 0 & 0 & 2 \\  & IfcSlab & 0 & 4 & 0 & 0 & 4 \\  & IfcStair & 1 & 0 & 0 & 0 & 1 \\  & IfcWallStandardCase & 0 & 9 & 4 & 0 & 13 \\  & IfcWindow & 11 & 0 & 0 & 0 & 11 \\  & Total & 64 & 14 & 4 & 0 & 82 \\ \hline \multirow{8}{*}{Institute} & IfcColumn & 0 & 2 & 0 & 0 & 2 \\  & IfcDoor & 77 & 0 & 0 & 0 & 77 \\  & IfcRailing & 12 & 0 & 0 & 0 & 12 \\  & IfcSlab & 21 & 5 & 0 & 0 & 26 \\  & IfcStair & 0 & 0 & 0 & 4 & 4 \\  & IfcWallStandardCase & 0 & 111 & 10 & 0 & 121 \\  & IfcWindow & 206 & 0 & 0 & 0 & 206 \\  & Total & 316 & 118 & 10 & 4 & 448 \\ \hline \multirow{8}{*}{House-2} & IfcBeam & 0 & 39 & 0 & 0 & 39 \\  & IfcBuildingElementProxy & 0 & 0 & 0 & 8 & 8 \\  & IfcColumn & 0 & 8 & 2 & 0 & 10 \\  & IfcDoor & 14 & 0 & 0 & 0 & 14 \\  & IfcRailing & 6 & 0 & 0 & 0 & 6 \\  & IfcRoof & 1 & 0 & 0 & 0 & 1 \\  & IfcSlab & 0 & 8 & 0 & 0 & 8 \\  & IfcStair & 4 & 0 & 0 & 0 & 4 \\  & IfcWall & 11 & 36 & 10 & 0 & 57 \\  & IfcWallStandardCase & 0 & 36 & 10 & 0 & 46 \\  & IfcWindow & 25 & 0 & 0 & 0 & 25 \\  & Total & 61 & 91 & 12 & 8 & 172 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantity of entities for each building element that are represented by B-Rep, swept solid, clipping, and surface.  
entities is only 4.9%, 2.2%, and 7.0% for the House-1, Institute, and House-2 model, respectively. The most frequently used representation types are B-Rep and swept solid.
## 2 State of the Art  
### Traditional Systems for Document Verification  
In recent years, governments, companies, and institutions have tried to achieve a high degree of trust in their certificates and fight counterfeits [28]. In fact, many universities around the world make use of their own systems to verify their diplomas and certificates. However, these systems are based on centralized models, which have well-known security issues:  
* They introduce Single Points of Failure (SPoFs), so they are prone to DoS attacks, which impact data availability.
* They usually do not provide secure traceability, so, if an attacker manages to impersonate someone, he/she will be able to perform unauthorized modifications without leaving a trace.
* They imply the existence of central authorities, which are not necessarily trustworthy regarding the way they manage user private data.
* They are prone to data leaks, which is a critical concern in industries like healthcare, insurance or education.  
Until recently, most stakeholders had to accept the previously mentioned risks as a trade-off for the easier and faster operation of centralized systems. However, consequences are becoming increasingly dangerous as companies gather more data [29].
## 4 Experimental Results and Performance Analysis
### Complexity Analysis  
According to the CD process parameters in Table 2, there are 42,142 candidate points in the execution of the \"CD/All_Sat/All_RS\" algorithm. The CD algorithm takes 2880 ms to execute the acquisition process of 20 code periods to process all satellites in view (nine satellites) and combining the RS1 and RS2 measurements whereas 1860 ms to process all satellites in view with only RS1 measurement (best RS). For \"CD/Best_Sat/All_RS\" algorithm, the execution time takes 1920 ms whereas 1240 ms if RS1 only is used. We can see that the execution of \"CD/Best_Sat/All_RS\"  
Figure 8: Ratio of maximum peak/average of remaining peaks of all 4 CD algorithms.  
algorithm is 1.5 times faster than \"CD/All_Sat/All_RS\". Table 3 shows some overall conclusion of the performance of algorithms based on the criteria used.  
To better see the tradeoff between these two important performance metrics, Figure 9 shows the 4 CD algorithms compared in this study. The choice to use all visible satellites or to select the best ones as well as all the reference stations or the best one depends on the desired application. Here we can choose to use the best satellites while combining all reference stations that meets the criterion \"not very complex but allows to have a good sensitivity\".
# Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique
###### Abstract  
Yield maps provide essential information to guide precision agriculture (PA) practices. Yet, on-board yield monitoring for sugarcane can be challenging. At the same time, orbital images have been widely used for indirect crop yield estimation for many crops like wheat, corn, and rice, but not for sugarcane. Due to this, the objective of this study is to explore the potential of multi-temporal imagery data as an alternative for sugarcane yield mapping. The study was based on developing predictive sugarcane yield models integrating time-series orbital imaging and a machine learning technique. A commercial sugarcane site was selected, and Sentinel-2 images were acquired from the beginning of the ratoon sprouting until harvesting of two consecutive cropping seasons. The predictive yield models RF (Random forest) and MLR (Multiple Linear Regression) were developed using orbital images and yield maps generated by a commercial sensor-system on harvesting. Original yield data were filtered and interpolated with the same spatial resolution of the orbital images. The entire dataset was divided into training and testing datasets. Spectral bands, especially the near-infrared at filtering crop stage showed greater contribution to predicting sugarcane yield than the use of derived spectral vegetation indices. The Root Mean Squared Error (RMSE) obtained for the RF regression based on multiple spectral bands was 4.63 Mg ha\({}^{-1}\) with an R\({}^{2}\) of 0.70 for the testing dataset. Overall, the RF regression had better performance than the MLR to predict sugarcane yield.  
orbital images; precision agriculture; remote sensing; vegetation index 2021 13 232 10.3390/rs13020232
## 3 Test Results and Analysis
### Mechanical Properties
#### 3.4.4 Energy Dissipation  
Energy dissipation coefficients (\(\eta=S_{OUTYC}/S_{OABC}\)) are calculated according to the load-displacement curve. It can be observed from Figure 17 that \(S_{OUTYC}\) is the area of the shaded portion surrounded by the load-displacement curve when the load is reduced to 85% of the peak load (when the minimum value of the load in the descending section is greater than 85% of the peak load, the full curve is taken), and \(S_{OABC}\) is the area surrounded by the rectangle passing through the peak point (\(U\)) and the limit displacement point (\(\gamma\)).  
From Figure 18 it can be found that, the energy dissipation of each member after exposure to different high temperature. Figure 18a-h shows the variation law of the energy dissipation coefficient of SRRAC members is similar to that of ductility. It tends to decrease with increasing temperature when exposure temperatures are lower than 400 \({}^{\circ}\)C. For the maximum temperature of 400 \({}^{\circ}\)C, the decreases of specimens, columns, bending beams, and shear beams are 13%, 11%, 7%, and 10% respectively; and for maximum temperatures of 400 \({}^{\circ}\)C-600 \({}^{\circ}\)C, the energy dissipation coefficient decreases slightly. When exposure temperatures are larger than 600 \({}^{\circ}\)C, the energy dissipation coefficient is gradually increased. With the increase of temperature, the energy dissipation coefficient of RRAC members differs from that of SRRAC members. The trend of the energy dissipation coefficient of columns is increasing-decreasing-increasing, the variation range is -12%-23%, and the energy dissipation coefficient of beams has a tendency to fluctuate.  
From Figure 18i can be found that the replacement percentage has different effects on the displacement ductility coefficients of different members. The energy dissipation coefficient of prism specimens and SRRAC members is not obviously affected by the replacement percentage. With the increase of replacement percentage, the energy dissipation coefficient fluctuates within the range of -5.5%-6.5%. The energy dissipation coefficient of RRAC members is also not obviously affected by the replacement percentage. With the increase of replacement percentage, the energy dissipation coefficient fluctuates within the range of -11%-4%, and the energy dissipation coefficient of bending beams and shear beams is -4%-4% and -6.5%-0%, respectively.  
In general, the influence of temperature on energy dissipation coefficient of RRAC members is greater than that of SRRAC members, the effect of replacement percentage on the energy dissipation coefficient of RAC members is not obvious.  
Figure 17: Schematic diagram of energy dissipation coefficient.
###### Abstract  
Ubiquitous sensing allows smart cities to take control of many parameters (e.g., road traffic, air or noise pollution levels, etc.). An inexpensive Wireless Mesh Network can be used as an efficient way to transport sensed data. When that mesh is autonomously powered (e.g., solar powered), it constitutes an ideal portable network system which can be deployed when needed. Nevertheless, its power consumption must be restrained to extend its operational cycle and for preserving the environment. To this end, our strategy fosters wireless interface deactivation among nodes which do not participate in any route. As we show, this contributes to a significant power saving for the mesh. Furthermore, our strategy is wireless-friendly, meaning that it gives priority to deactivation of nodes receiving (and also causing) interferences from (to) the rest of the smart city. We also show that a routing protocol can adapt to this strategy in which certain nodes deactivate their own wireless interfaces.  
ubiquitous sensing; wireless-friendly; interference; power saving; portable mesh network; smart city +
Footnote †: journal: Sensors
## 4 Experimental Analysis
### Parameter Settings
#### 4.3.3 Algorithmic Efficiency
In summary, although the PPPCF increases the time overhead caused by the similarity calculation on the server side, it does not affect the efficiency of the rating prediction, and the recommendation error of PPPCF is superior to other algorithms for comparison on datasets of different sparsity. This shows that under the interaction of the privacy \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Runtime/s** & **DPLCF** & **LDP Item-Base CF** & **Truncated PPPCE** & **PPPCF** \\ \hline \multirow{3}{*}{**Yahoo Music**} & Local Data Processing & 2.36 & 2.55 & 1.78 & 1.83 \\ \cline{2-6}  & Similarity Calculation & 32.79 & 41.25 & 22.71 & 382.54 \\ \cline{2-6}  & Rating Prediction & 53.47 & 52.80 & 47.59 & 57.39 \\ \hline \multirow{3}{*}{**MovieLens 1M**} & Local Data Processing & 8.22 & 8.62 & 7.79 & 7.71 \\ \cline{2-6}  & Similarity Calculation & 652.23 & 833.37 & 507.95 & 5497.68 \\ \cline{1-1} \cline{2-6}  & Rating Prediction & 464.94 & 488.14 & 467.07 & 475.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the time overhead. sensitivity classification module and the Bayesian data reconstruction module, the similarity calculation is less affected by the perturbed data to achieve higher data availability and recommendation accuracy.