esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Possible Ways to Improve WUE Remote Sensing  
### Data Fusion Improves GPP and ET Products  
Developing a higher spatial and temporal resolution of GPP and ET products is the indirect way to WUE remote sensing. Primarily, studies can be attempted to improve the performance of MODIS GPP and ET products at site and regional scales before developing new algorithms and products. Alternatively, using multi-source satellites, spectral data for the estimation of WUE via empirical models is also a choice at the site scale. A comparison of estimated WUE with vegetation indices from multi-platform (i.e., near ground, unmanned aerial vehicle (UAV), and satellites) or multi-sensors (i.e., multispectral, hyperspectral, and SIF) through an empirical relationship will be a valuable work for examining and improving the remote sensing retrieval of WUE. Research into WUE estimation using UAV remote sensing and Landsat measurements in recent years have rendered an option for comparative studies [117; 118]. Moreover, EC tower-based WUE has also shown a stable linear relationship with MODIS land surface temperature (LST) products for specific ecosystem types, and can offer another way for WUE remote sensing [97]. Thermal remote sensing for LST detection has also been provided to use several products available from satellite platforms such as Landsat, Sentinel, and GOES [119; 120; 121]. Space-borne ECOSTRESS, mounted on the International Space Station produces thermally derived LST at very high temporal and spatial resolutions, which can be applied to capture diurnal variability of WUE [122]. UAV-borne thermal cameras can also portably measure the temperatures of different ecosystem components at the regional scale [123].  
Through remote sensing techniques, a better detection of the characteristics of leaf internal biochemistry characteristics to interact with the canopy structure is also an important point for carbon uptake and for the water use of vegetation. For example, the canopy-reflected radiation measured by a satellite-borne sensor should be corrected based on a scattering spectrum of the leaf radiation measured on the ground. This bias is affected by the 3D canopy structure, which cannot be directly measured from space due to specular reflectance at the leaf surface, which is partly polarized and contains no information about its interior [124]. In addition, Hyperspectral Light Detection and Ranging (LiDAR) is an emerging approach to remote sensing techniques, acquiring both the spectral data and the structural parameters of vegetation and can scale the WUE remote sensing to three-dimensional ecological process research.
## 2 Methods  
### Problem Definition  
Assume that the observed mixed speech: \(X(t)=[X_{1}(t),\,X_{2}(t),\,\ldots,\,X_{n}(t)]^{\mathrm{T}}\), which is composed of \(n\) source signals \(S(t)=[S_{1}(t),\,S_{2}(t),\,\ldots,\,S_{n}(t)]^{\mathrm{T}}\), \(\mathrm{T}\) stands for transpose of the matrix, the speech separation task attempts to find \(W\) to recover the source signal, as shown in Formula (1).  
\[X(t)=WS(t), \tag{1}\]  
where \(W\) can be regarded as a filter like a weight matrix between the mixed signal and the real label.  
This paper proposes a separation framework of neural network to construct the prediction signal \(S(t)\) of each source signal \(S(t)\) through a given \(X(t)\), \(S(t)\) is the output value obtained by separating the network, and then there is Formula (2):  
\[\hat{S}(t)=VX(t), \tag{2}\]  
Among them, the parameter in the network model is matrix \(V\). The more the matrix \(V\) converges to \(W^{-1}\), the more accurate the predicted value of the model output, the predicted speech \(\hat{S}(t)\) will be obtained from \(X(t)\), the closer it is to the real speech \(S(t)\).
## 4 Protocol for Source Location Protection Based on Limited Flooding
### Network Initialization  
As the foundation of the source location protection protocol, network initialization is responsible for distributing the keys, discovering neighbors, and helping each sensor to acquire its \(D_{bs}\). After the network initialization, the base station stores a public/private key pair (\(K_{pub}\), \(K_{pri}\)), and each sensor obtains the public key \(K_{pub}\) distributed from the base station. To defend against the active attack, each packet is encrypted by the public key before being transmitted by the source. After the sensors are deployed, base station broadcasts a beacon packet \(BM=\{BRO\_BASE,\ ID,\ hop\_b\}\), where \(BRO\_BASE\) denotes the packet type, \(ID\) field is the unique identifier of the packet sender, \(hop\_bs\) records the transmission times of the packet and is initialized as 0. For any sensor \(u\), if \(u\) receives \(BM\) for the first time, it first increases the \(hop\_bs\) by 1 and updates \(Hop_{u,bs}\) as equal to \(hop\_bs\). After that, \(u\) will broadcast this packet.
## 4 Results  
We constructed an apparatus similar to the one simulated, and measured a complex 3D object in order to demonstrate the validity and applicability of our proposed method. The system included a color camera (Model: GS3-U3-41C6C-C, FLIR, Wilsonville, OR, USA) with a 25 mm focal length lens and two digital projectors (Model: LightCrafter 4500, Texas Instruments DLP(r) Technology, Dallas, TX, USA), as shown in Figure 10.  
We used an [PERSON] statue as the measured object due to its complex surface. The left projector was uploaded with red vertical stripes, and the right projector was uploaded with blue horizontal stripes. The color camera captured the superimposed gratings in the middle of the two projectors. We adopted the dual-frequency four-step phase-shifted method to obtain the unwrapped phase map. The frequency of the low-frequency gratings was 1, and that of the high-frequency gratings was 6. Figure 11 shows the pictures of the superimposed gratings.  
In Figure 11, the upper pictures are low-frequency, and the bottom ones are high-frequency. By separating different color channels, we obtained 16 pictures of high and low frequency fringes using the double projectors (Figures 12 and 13). According to Equations (2) and (3), we unwrapped the wrapped phase. The unwrapped phases are shown in Figures 14 and 15.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Coordinate of the Sphere Center/(mm, mm, mm)** & **Radius/mm** & **Error/mm** \\ \hline Truth value & (456, 570, 0) & 300 & N/A \\ Left projection & (4,569,983, 570.0014, 0.0650) & 299.9507 & \(-\)0.0493 \\ Right projection & (456.0021, 569.9983, \(-\)0.0191) & 300.0199 & 0.0199 \\ \hline \end{tabular}
\end{table}
Table 1: Fitting results for the hemisphere.  
Figure 10: The experimental device diagram.  
Figure 11: The superimposed gratings.  
Figure 12: Pictures of the red channel projected by the left projector.  
Figure 13: Pictures of the blue channel projected by the right projector.  
As shown in Figure 14a and Figure 15a, the red and blue channel pictures were used to unwrap the phase directly, with good-quality results. The blue lines in Figure 14a and Figure 15a, are the background parts, and we made a linear fit to their cross profiles, as shown in Figure 14b and Figure 15b. R-square represents the goodness of fit. The R-square in Figure 14b was 0.9992, and in Figure 15b it was 1, demonstrating excellent linearity. The red ones are cross profiles of the measured object that have fluctuations, and the disorder areas are shadows. The background and the measured object both had a fine unwrapped phase. In the double-projector structured-light three-dimensional measurement system, the two projectors projected red and blue stripes, respectively, and the superimposed grating was separated simply without a complicated coding mode or separation algorithm. The separation results can be directly used in phase unwrapping and subsequent processing without other filtering operations.
## 4 Discussion
PIPS is a novel laser irrigation technique used in combination with an Er: YAG laser, which is based on photoacoustic and photomechanical effects (with the tip only in the chamber, without needing to reach the root apex), making it different from other irrigation techniques [29]. PIPS improves clinical effects for the following reasons: (1) in immature permanent teeth, bacteria may penetrate more dental tubules and advance deeper [30]. The area of Er: YAG laser-activated penetration is significantly larger than that of the other methods [29]. (2) The cumulative fluid movement generated by PIPS appears to be sufficient for the irrigation of lateral canals [19]. (3) The existence of a smear layer on the apical part of root canal walls may potentially lead to the failure of regenerative endodontic treatments [31]; meanwhile, PIPS is significantly more effective in removing dentine debris from this part of the root canal [8]. (4) PIPS exhibits lower extrusion [9], which suggests the safety of the Er: YAG laser in root canal disinfection. For immature teeth with open apices, irrigation may have risks for apical irrigant extrusion, thus damaging the apical papilla. A recent laboratory study suggested that an Er: YAG laser could be used in teeth with open apices for improving disinfection and cleaning of the root canal without risk of apical extrusion when used cautiously [7]. In immature permanent teeth undergoing revascularization, there is a 50% chance that the pulp will regain its vitality [32]. In our case, after 53 months, the patient responded positively to both pulp electrical testing and cold testing. [PERSON] et al. [33] reported the resolution of apical radiolucency within an average of 8 months and \"nearly normal\"root development within an average of 16 months after revascularization. In our case, the apical radiolucency had disappeared at the 3-month follow-up, and apical closure was approximately completed at the 11-month follow-up. In the last follow-up visit, we observed X-ray evidence of continued root development including the obvious increase in root thickness and length. At the same time, the teeth continued to erupt to the occlusal plane. The patient felt sensitivity when we prepared the cavity, which might indicate that there was vital tissue in the root canal space [34].
# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Data Annotation  
After the preprocessing, dataset annotation is another important stage. In the annotation stage, the necessary labeled information is defined to enhance the understanding and usability of the data, which is particularly important for supervised learning. For image data, annotation involves labeling objects, regions, or attributes within images, enabling algorithms to recognize and interpret visual elements accurately for tasks such as object detection, segmentation, and classification. For instance, in brain tumor detection, bounding boxes or masks are generated, allowing the machine to learn the features associated with tumors and identify them during the testing phase. Similarly, annotation of text data involves labeling text segments with semantic or syntactic information, facilitating tasks such as sentiment analysis, named entity recognition, and document classification. For instance, the authors of [107] used labels such as the category-specific number of documents, number of documents that do not belong to any category, etc., to categorize text documents. Named entity recognition (NER) labeling identifies and categorizes entities within text, such as the names of persons, organizations, and locations. Annotation of the data enables more effective AI model training and evaluation, enhancing the accuracy, robustness, and generalization of the resulting model in real-world applications Several popular data annotation methods and the related software are described in Table 8.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Annotation Technique** & **Tools** \\ \hline Line Labeling & LabelMe [108], VGG Image Annotator (VIA) [109], CVAT [110] \\ Landmark Labeling & LabelImg [111], DeepLabCut [112], CVAT [110], VIA [109] \\ Key Point Labeling & COCO Annotator [113], LabelMe [108], CVAT [110], Supervised [114] \\ Class Labeling & Labelbox [115], RectLabel [116], Supervised [114], CVAT [110] \\ Semantic Labeling & LabelMe [108], VIA [109], CVAT [110] \\ Bounding Box & LabelImg [111], Labelbox [115], YOLO Mark [117], CVAT [110] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Different image annotation techniques and related software.
## 5 Results and Discussion
### Slide Scoring  
On the HER2 SC test set, this method achieved an F1-score of 86.7% and a weighted accuracy of 83.3% (see Table 2). Despite the small size of this test set, the proposed method was able to correctly classify all positive WSI and only misclassify one negative sample as positive. In this context, one might consider this a desirable behaviour, as false positives are less impactful than false negatives.  
When tested on the BRCA test set, this method achieved an F1-score of 21.5% and a weighted accuracy of 53.8% (see Table 2). The method retains the behaviour presented in HER2 SC, preferring to err on the side of false positives than the alternative. On the other hand, the performance metrics on BRCA differ considerably from those obtained on HER2 SC. While the method was trained on HER2 SC data, which is expected similar to the test data, the WSI of the BRCA dataset presents some notable differences. These slides have a greater extent of tissue, which generates more tiles per image and impacts the distribution of the scores, which may influence the method's behaviour.  
The evaluation results in single-database (HER2 SC) and cross-database (BRCA) settings show the potential of the proposed method in standard and more challenging situations. However, the method appears to be dataset-dependent: it performed much better in conditions similar to the training. This should be addressed with additional efforts regarding domain adaptation.  
The other shortcomings of the method appear to be related to the invasive tumour tissue segmentation and the individual tile scoring network, which could be improved with additional data and more accurate ground truth information. With these additional efforts, the proposed method could offer robust weakly-supervised WSI HER2 classification without IHC information.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**True Class**} \\ \hline \multirow{3}{*}{**Prediction**} & \multicolumn{2}{c}{_0_} & \multicolumn{2}{c}{_1_} & \multicolumn{2}{c}{_2_} & \multicolumn{1}{c}{_3_} \\ \cline{2-5}  & 0 & 490 & 132 & 2 & 0 \\ \cline{2-5}  & 1 & 176 & 384 & 64 & 0 \\ \cline{2-5}  & 2 & 45 & 159 & 419 & 1 \\ \cline{2-5}  & 3 & 0 & 0 & 1 & 623 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix of the CNN for HER2 scoring in IHC tiles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **F1-Score** & **Precision** & **Recall** \\ \hline HER2 SC & 83.3\% & 86.7\% & 89.6\% & 87.5\% \\ BRCA & 53.8\% & 21.5\% & 81.2\% & 31.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: H&E HER2 status classification results of the proposed method on the HER2 SC and BRCA test sets.
# Latent Structure Matching for Knowledge Transfer in Reinforcement Learning
## 3 Method
### Latent Structure Matching (LSM)
Thus, we have  
\[argmin_{\Phi,\Phi^{T}\Phi=\mathbb{I}}S=argmin_{\Phi,\Phi^{T}\Phi=\mathbb{I}}(1 -\mu)tr(\Phi^{T}A\Phi)+2\mu tr(\Phi^{T}L\Phi), \tag{5}\]  
where \(A=(\mathbb{I}-B)^{T}(\mathbb{I}-B)\), and \(L\) is the combinatorial graph Laplacian \(L=D-C\), where \(D\) is the diagonal matrix of row sums \(D_{i,i}=\sum_{j}C_{i,j}\). To construct a loss function from (5), a Lagrange multiplier \(\delta\) is introduced to the right side of (5):  
\[\mathcal{L}(\Phi,\delta)=(1-\mu)tr(\Phi^{T}A\Phi)+2\mu tr(\Phi^{T}L\Phi)+ \langle\delta,\Phi^{T}\Phi-\mathbb{I}\rangle. \tag{6}\]  
To minimize the loss function (6), the roots for the partial derivatives of \(\mathcal{L}\) for \(\Phi\) and \(\delta\) are calculated as:  
\[ \begin{cases}\frac{\partial\mathcal{L}}{\partial\Phi}=(1-\mu)A\Phi+2\mu L\Phi -\delta\Phi=0\\ \frac{\partial\mathcal{L}}{\partial\delta}=\Phi^{T}\Phi-\mathbb{I}=0.\end{cases}  \tag{7}\]  
Therefore, to optimize (5) to obtain embedding \(\Phi\), we need to calculate the \(d\) smallest non-zero eigenvectors of matrix \(((1-\mu)A+2\mu L)\). After the embedding \(\Phi\) is obtained, the distance between \(E\) and \(\mathcal{E}\), that represents the similarity between source and target task set, can be estimated. Finally, the highly-matched source task for a target task can be found. The whole procedure of LSM is fully described in pseudocode Algorithm 1. ```
0: source action value function \(Q\); target action value function \(\mathcal{Q}\); embedding dimension \(d\); weight parameter \(\mu\)
0: embedding matrix \(E\) of source task; embedding matrix \(\mathcal{E}\) of target task. 1: Compute the reconstruction coefficient matrices as described in Appendix A:
2: Build the block matrices \(\Phi\), \(B\) and \(C\). 3: Construct the error function (3), namely \(S(\Phi)=(1-\mu)\|\Phi-B\Phi\|_{F}^{2}+\mu\sum_{i,j=1}^{m}\|\Phi_{i}-\Phi_{j}\| ^{2}C_{i,j}\)
4: Minimize the loss function (6): \(\mathcal{L}=(1-\mu)tr(\Phi^{T}A\Phi)+2\mu tr(\Phi^{T}L\Phi)+\langle\delta, \Phi^{T}\Phi-\mathbb{I}\rangle\)
5: The embedding matrices \(E\) and \(\mathcal{E}\) can be obtained by calculating the \(d\) smallest non-zero eigenvectors of matrix \((1-\mu)A+2\mu L\). ```  
**Algorithm 1** Latent structure matching
# Design, Experiment, and Improvement of a Quasi-Zero-Stiffness Vibration Isolation System
## 2 The QZS Vibration Isolation System
### Force-Displacement Characteristic of the Two Coupled Magnet Springs
Therefore, the distance between point \(P\) and point \(Q\) can be given as:  
\[r=\sqrt{x_{1}^{2}+\left(r_{3}\cos\beta-r_{2}\cos\alpha\right)^{2}+\left(r_{3} \sin\beta-r_{2}\sin\alpha\right)^{2}}. \tag{12}\]  
After substituting Equations (11) and (12) into Equation (7), the axial force produced by face 2 and face 3 is given as:  
\[F_{32}=C\int_{R_{1}}^{R_{2}}\int_{R_{3}}^{R_{4}}\int_{0}^{2\pi}\int_{0}^{2\pi} \frac{\left(r_{3}\sin\gamma-r_{2}\sin\varphi-x_{1}\right)r_{2}r_{3}dr_{2}dr_{ 3}d\alpha d\beta}{\left[x_{1}^{2}+\left(r_{3}\cos\beta-r_{2}\cos\alpha\right) ^{2}+\left(r_{3}\sin\beta-r_{2}\sin\alpha\right)^{2}\right]^{\frac{3}{2}}}. \tag{13}\]  
Then, the axial force produced by the other faces can be found in the same way:  
\[F_{31}=C\int_{R_{1}}^{R_{2}}\int_{R_{3}}^{R_{4}}\int_{0}^{2\pi}\int_{0}^{2\pi} \frac{\left(r_{3}\sin\gamma-r_{1}\sin\varphi-x_{1}\right)r_{1}r_{3}dr_{1}dr_{ 3}d\alpha d\beta}{\left[\left(H_{1}+x_{1}\right)^{2}+\left(r_{3}\cos\beta-r_{ 1}\cos\alpha\right)^{2}+\left(r_{3}\sin\beta-r_{1}\sin\alpha\right)^{2}\right] ^{\frac{3}{2}}}, \tag{14}\]  
\[F_{41}=C\int_{R_{1}}^{R_{2}}\int_{R_{3}}^{R_{4}}\int_{0}^{2\pi}\int_{0}^{2\pi} \frac{\left(r_{4}\sin\gamma-r_{1}\sin\varphi-x_{1}\right)r_{1}r_{4}dr_{1}dr_{ 4}d\alpha d\beta}{\left[x_{1}^{2}+\left(r_{4}\cos\beta-r_{1}\cos\alpha\right) ^{2}+\left(r_{4}\sin\beta-r_{1}\sin\alpha\right)^{2}\right]^{\frac{3}{2}}}, \tag{15}\]  
\[F_{42}=C\int_{R_{1}}^{R_{2}}\int_{R_{3}}^{R_{4}}\int_{0}^{2\pi}\int_{0}^{2\pi} \frac{\left(r_{4}\sin\gamma-r_{2}\sin\varphi-x_{1}\right)r_{2}r_{4}dr_{2}dr_{ 4}d\alpha d\beta}{\left[\left(H_{2}-x_{1}\right)^{2}+\left(r_{4}\cos\beta-r_{ 2}\cos\alpha\right)^{2}+\left(r_{4}\sin\beta-r_{2}\sin\alpha\right)^{2}\right] ^{\frac{3}{2}}}, \tag{16}\]  
where \(H_{1}\) and \(H_{2}\) are the heights of the inner and outer ring magnets; \(R_{1}\) and \(R_{2}\) are the inner and outer radii of the inner ring magnet; \(R_{3}\) and \(R_{4}\) are the inner and outer radii of the outer ring magnet. The axial resultant force is a sum of the four forces and can be expressed as:  
\[F=F_{41}+F_{32}-F_{31}-F_{42}. \tag{17}\]  
In Equation (17), the positive sign means that the two faces have the same magnetic charge, and the negative sign means that the two faces have a different magnetic charge. The negative stiffness can be found by differentiating Equation (17) with respect to the relative axial displacement \(\mathbf{x}_{1}\) between the inner ring magnet and the outer ring magnet:  
\[K_{N}=-\frac{dF}{dx_{1}}. \tag{18}\]  
The negative sign in Equation (18) shows that the restoring force produced by the magnetic spring is opposite to the magnetic force.
## 3 System Model and QoT Estimation
### Network Model  
In the literature, the network topology is denoted using \(G\)(\(V\),\(E\)), where \(V\) = {\(v_{1}\), \(v_{2}\),..., \(v_{end}\)} represents the collection of nodes and \(E\) = {\(e_{1}\), \(e_{2}\),..., \(e_{end}\)} denotes the collection of network links, each of which can satisfy bi-directional transmission requests. \(F_{c}\) = {\(f_{c1}\), \(f_{c2}\),..., \(f_{c~{}end}\)} denotes the collection of FSs in C band, and \(F_{l}\) = {\(f_{l1}\), \(f_{l2}\),..., \(f_{l~{}end}\)} denotes the collection of FSs in L band. Each FS has a bandwidth of 12.5 GHz. The requests are all unknown before arriving network, their arrival and duration obey the Poisson and negative exponential distribution, respectively. Each request \(r\) is defined as \(r\)(\(r_{s}\), \(r_{d}\), \(r_{b}\), \(t_{s}\), \(t_{h}\)), where \(r_{s}\) denotes the source node of the request, \(r_{d}\) denotes the destination node of the request, \(r_{b}\) denotes the bandwidth requirement of the request, \(t_{s}\) represents the request arriving time, and \(t_{h}\) represents the request holding time. For each request, the quantity of necessary frequency slots is dictated by the chosen modulation format and OSNR.
# Opportunistic and On-Demand Network Coding-Based Solutions for LPWAN Forwarding
## 6 Implementation and Evaluation
### Evaluation
#### 6.3.4 Unnecessary Forwarding Issue  
We investigated the performance degradation in the previous experiment. The left-axis in Figure 9b shows the number of packet collisions in the gateway, while the right-axis tells the percentage of unnecessary forwarding resulted by FP and CPR. The results reveal that our work can minimize the number of packet collisions by preventing packet duplication.  
Without a relay, the faraway end-devices have to use slow data rate settings to reach the gateway. Consequently, the transmission takes more time, blocking other end-devices to use the time resource. However, the FP forwarding shows a high amount of packet collision, although it can use a relay to utilize multiple faster data rates. This packet collision is probably caused by unnecessary forwarding. As per the approximate results, 30% of transmissions correspond to unnecessary forwarding. The amount of unnecessary forwarding is decreasing owing to the packet loss. Similarly, CPR achieves high reliability by packet redundancy. However, as the number of end-devices increases, the benefit of packet redundancy is not very effective. If we compare the reliability results in Figure 9a and the number of packet collisions, there is a trade-off between network reliability and efficiency. Although packet duplication by a forwarding mechanism can increase the network reliability, the unnecessary transmission can degrade the network capacity.