esa-sceva/satcom-chunk-collection huggingface dataset:
# The Influence of Processing on the Bioactive Compounds of Small Berries
## 5 Health Implications and Allergic Reactions to Small Berries
### Anticancer Activity  
Many studies have reported the anticancer activity of berries and extracts of their leaves through different mechanisms such as inducing cancer cell apoptosis with no cytotoxic effect on normal cells [121; 122; 123]. The compounds usually stated to be active are polyphenols, together with lectins, ascorbic acid, and chlorogenic acid [121; 122; 123; 124]. Cyanidin-3-glucoside (C3G) is the most abundant anthocyanin found in blue and red fruits, among over two hundred other phenolic compounds. It was identified in the highest concentration in plasma and urine samples of healthy individuals after eating berries [125]. In particular, C3G from blueberries (_Rubus frticosus_ L.) was reported to have chemo-preventive and chemotherapeutic activity against the human lung carcinoma A549 cell line [122]. Moreover, pure C3G had cytotoxic effects on the MCF-7 breast cancer cell line [123]. [PERSON] et al. [121] stated that bioactive compounds from black mulberry (_Morus nigra_) extract could reduce mitochondrial membrane potential, which is linked to cell autophagy [126]. Also, they may increase the activity of the aspartate-specific 3-, 6- and 7-caspases, which are cysteine-dependent proteases involved in cleaving key cellular proteins at the beginning of apoptosis processes [121; 127]. _Morus nigra_ extracts were also reported to be efficient in inducing apoptosis in human breast adenocarcinoma (MCF-7) [128].  
Despite the fact that polyphenic compounds are usually known for their antioxidant activities, with regard to cancer cells, they are believed to exhibit a pro-oxidative effect as a mechanism for inducing apoptosis. This phenomenon is linked to the redox status of cancer cells, which is higher in comparison to normal cells [127]. [PERSON] et al. [127] reported that _Morus fructus_ extracts inhibited the migration of human glioma U87 MG cells and led to cell death through ROS-dependent mitochondrial pathways. Induction of DNA damage is another mechanism through which polyphenic compounds are believed to act on cancer cells. In this respect, [PERSON] et al. [129] reported that the exposure of HT-29 cells to comelian cherry (_Cornus mas_ L.) extracts induced DNA fragmentation through the fragmentation of cell nuclei and chromatin breakdown. Also, the fermented non-digestible fraction of Andean berry juice (_Vaccinium meridionale_ Swartz) was declared to cause apoptosis of human colon adenocarcinoma HT29 cells by inducing DNA damage, without exerting secondary inflammatory effects, as it does not affect membrane integrity [130].  
Many studies indicated a better bioactive profile for the berry leaf extracts compared to fruits [131; 132; 124]. For instance, mild anticancer activity on a human colorectal cancer cell line, HCT-116. L, was reported for wild raspberry leaf extracts (_Rubusidae_ L.), but not for fruits [124].
## 2 Clouds Properties from Infrared and Microwave Satellite Observations  
As already mentioned in the previous section, clouds influence many aspects of atmospheric physics, and their study can reveal a lot about meteorological phenomena and climate change. Clouds are essential to understanding the global hydrological cycle, assessing the Earth's radiation budgets, monitoring high-impact events, and forecasting the spatial and temporal distribution of precipitation. As for climatological aspects, clouds play a key role in the water cycle and the global energy balance, constituting one of the most complex protagonists of atmospheric physics and the climate machine. Clouds are, therefore, a very complex field of investigation, and each aspect constitutes a research field in itself with specific techniques and algorithms. For this reason, the study of clouds covers a very wide field, and there are many aspects related to clouds that are explored in depth, both from an operational point of view and from a more strictly scientific one, with the implementation of ad hoc techniques that allow for measuring the properties of interest. Among these, we mention methodologies for cloud detection and classification [4], precipitation estimation [5,6], and techniques for NWP assimilation [7,8]. For all these topics, we refer to the specific literature and the references therein. In this review, we limit our investigation to methodologies and algorithms for the study of the microphysical properties of clouds. These can be explored by exploiting some key quantities that can be retrieved from observations made via instruments operating in the MW, VIS, and IR spectra. Among the cloud microphysics characteristics, the main ones we refer to are the vertical profiles of cloud liquid water content (CLWC), cloud ice water content (CIWC) and their column-integrated values, the cloud liquid water path (CLWP), the cloud ice water path (CIWP), and, finally, the cloud drop and ice particle effective diameter (\(\mathrm{D_{e}}\); in this review, we also refer to the effective radius, \(\mathrm{D_{e}}/2\)).
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 1 Introduction
In recent years approaches based on correlation filter stand out among a series of discriminative approaches in many competitions such as VOT, rank top and surpass the other branches with a great superiority not only in accuracy but also on FPS. Correlation is used to measure the similarity between two signals in signal processing subject, involved in visual tracking by [PERSON] et al. [20] and achieved great success. After a filter with extracted features constructed at the beginning of tracking, the commonly used classifier is replaced by a cross-correlation score which will be computed between the filter and a subsequent frame. The target's position can be predicted locating on the maximum of the response score as the similar characteristic in signal processing. [PERSON] [21] introduced the circulant matrices which implement a great simplification for the matrices operation in complex filed due to its diagonalizable characteristics by Fourier matrices. The kernel trick commonly used in SVM was also introduced in [21], enriched the extracted features with great diversification, and improved performance further. [PERSON] et al. [22] and [PERSON] et al. [23] focused on the variation of target's scale in tracking, greatly ameliorated the drifting issue caused by the variation of scale. [PERSON] et al. [24] and [PERSON] et al. [25] expanded the ratio of detecting area to filter area and punished the filter coefficient around the border of the tracking box which makes effort to alleviate the boundary effects. [PERSON] et al. [26] and [PERSON] [27] deeply studied on long-term tracking direction through a confidence level evaluated on correlation. They constructed a third filter besides translation and scale used to assess the confidence degree which will be activated to correct the filter reloading when the confidence degree descends below a threshold. In this work, the main contribution is described as follows. We develop a prototype of CHSM for complicated tasks such as salvage or rescue, which is controlled based on a visual servo framework and consists of a separating structure between the power subsystem and motion subsystem. A simplified forward and inverse kinematics model under the visual servo control is derived under some assumptions to avoid the time-consumingcomputation of matrix operation. A visual tracking algorithm based on dual correlation filter (DCF) is presented and customized for visual tracking control of the CHSM. The remainder of this paper is organized as follows. The implementation of the tracking component is proposed in Section 2. In Section 3, the overall structure of the CHSM and kinematics model is introduced, meanwhile, the control method is proposed and analyzed. In Section 4, an experiment is carried out and the experimental results validate the availability of the proposed compound control method with the derived kinematics model. Finally, a conclusion is presented in Section 5.
## 4 Case Study and Results
### Operating Costs  
Comparison results of the three configurations (base case, P2P, and MV) are provided in Table 4. The application of VSCs increased the total losses of the system, since the switching action in the VSCs introduced additional losses. Nevertheless, the use of VSCs, either in P2P or MVDC configurations, improved performance, reduced the shifted demand and DG curtailment, in comparison to the base case. When using the proposed MVDC interface, the curtailed energy was dramatically reduced by almost 3.5 in comparison to the base case and twice in comparison to the P2P configuration. Overall, results of Table 4 proves that, using the MVDC configuration, the DSO could save over 80% of operational costs in comparison to the base case while improving the continuity of the supply condition.
## 2 Materials and Methods
### Data Processing
#### 2.5.4 Acceleration  
To quantify the acceleration input \((a_{\text{in}})\) on the LVP, two indicators are used as follows: peak value \((a_{\text{in}}^{\text{peak}})\) and root mean square (RMS) value \((a_{\text{in}}^{\text{RMS}})\)[13; 15]. The \(a_{\text{in}}^{\text{peak}}\) was used for the statistical analysis of the LVP performance, whereas the \(a_{\text{in}}^{\text{RMS}}\) was used for \(T\) calculation. However, for the vibration acceleration outputs, \((a_{\text{out}})\), only the RMS value \((a_{\text{out}}^{\text{RMS}})\) was used because the signals at body sites were not purely sinusoidal and the peak value of these waveforms could lead to inaccurate results (as shown, for example, in Figure 3). On the one hand, the peak value is calculated as the mean of the peak accelerations from the triaxial acceleration signal \(\left(a_{\text{in}}^{x}+a_{\text{in}}^{y}+a_{\text{in}}^{z}\right)/3\). On the other hand, the RMS value (3) is calculated using the three components of the acceleration as follows [16; 22; 24; 25]:  
\[a^{\text{RMS}}=\sqrt{\frac{1}{M}\sum_{j=1}^{M}\left(a_{xj}^{2}+a_{yj}^{2}+a_{ zj}^{2}\right)} \tag{3}\]where \(j\) is the index that moves along the sampling in time; and \(M\) is the total amount of sampling values in the analysed signal. Both peak and RMS values are expressed in g, and they have been calculated using the WIMU raw data in the time domain.
# A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion
## 3 Deep Learning-Based TKGC Methods
### GCN-Based Methods
Given a fact \((s,r,o,t)\), THOR obtains the temporal embeddings of \(s\) and \(o\) relative to \(t\) (via TempGCN), the atemporal embeddings of \(s\) and \(o\) (via iTempGCN), and the embedding of \(r\) (via RelGCN). Finally, THOR learns embeddings of entities and relations with the following two objectives: (1) maximize the likelihood of facts in TKG; (2) make the temporal embedding of each entity more accurate. For (1), they adopt the negative log-likelihood loss, and for (2), they propose a time-aware self-supervised loss that can learn auxiliary supervision signals for each entity. Methods in this category have similar characteristics as the GNN-based methods. Since GCN is a special version of GNN, which uses the convolution-like operation to aggregate information, to some extent, methods in this category can be regarded as a subset of the GNN-based methods.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 5 Future Research Issues
### Security  
FANET is easy to be interfered by external signals because of its completely autonomous characteristics. For example, when the UAV group is deployed in the military area to detect the war situation, the UAV may be attacked by the enemy and fail to perform tasks. The secret data we transmit by UAVs will also be stolen by the enemy. Therefore, security [61] is a important problem that cannot be ignored. However, there is a lack of research on the network security of FANET. In future, we can consider the issue of node privacy protection. Second, some security measures are also needed to strengthen the anti-interference performance of the network.
## 6 Experimental Verification
### Verification of the IPSO-RBFNN Model at Different Rotational Velocities  
Based on the trained neural network model, the verification experiment is carried out. The experimental data at 4000 rpm and 8000 rpm are used to analyze the prediction performance of the thermal error of the spindle in Z-direction based on IPSO-RBFNN. The thermal errors in Z-direction at 4000 rpm and 8000 rpm are shown in Figures 11 and 12, respectively.  
Figure 10: Prediction performance of each model. (**a**) Residual error; (**b**) Mean residual error.  
Figures 11 and 12 show that the predicted values at 4000 rpm and 8000 rpm based on the IPOS-RBFNN model are both closer to the actual values than the RBFNN model. Furthermore, two metrics, \(MSE\) and \(RMSE\), are used to evaluate the predictive performance of IPOS-RBFNN and RBFNN models. The results are displayed in Table 6. It is visible from the table that the \(RMSE\) and \(MSE\) values of IPOS-RBFNN model are both much smaller than those of RBFNN model at 4000 rpm and 8000 rpm, which proves that IPOS-RBFNN model has higher predictive accuracy than RBFNN model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Velocity (rpm)** & **Model** & **MSE (\(\mathbf{\mu}\)m\({}^{2}\))** & **RMSE (\(\mathbf{\mu}\)m)** \\ \hline
4000 & RBFNN & 1.946 & 43.26 \\
4000 & IPSO-RBFNN & 1.499 & 3.927 \\
8000 & RBFNN & 0.495 & 22.26 \\
8000 & IPSO-RBFNN & 0.026 & 5.162 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation results of predictive performance of two models.  
Figure 11: The thermal errors in Z-direction of different models at 4000 rpm.  
Figure 12: The thermal errors in \(Z\)-direction of different models at 8000 rpm.
## 2 Materials and Methods
### ComBat Harmonization  
ComBat method is an empirical Bayes-based method that is used to estimate the effects of different batches on the quantitative readings. For RFs, ComBat formula assumes that a feature value can be approximated by the equation:  
\[\text{Yij}=\alpha+\beta\text{ Xij}+\gamma\text{i}+\delta\text{i}\epsilon\text{ij} \tag{1}\]  
where \(\alpha\) is the average value for HRF Yij for ROI j on scanner i; X is a design matrix of the biologic covariates that are known to affect the value of HRFs; \(\beta\) is the vector of regression coefficients corresponding to each biologic covariate; \(\gamma\)i is the additive effect of scanner i on HRFs, \(\delta\)i is the multiplicative scanner effect, and \(\epsilon\)ij is an error term, presupposed to be normally distributed with zero mean. Following the estimation of effects, ComBat performs feature harmonization as follows:  
\[\text{Yij}\text{ComBat}=((\text{Yij}-\alpha^{\text{-}}-\beta^{\text{-}}\text{ Xij}-\gamma\text{i}^{\text{*}}))/(\delta\text{i}^{\text{*}})+\alpha+\beta^{\text{-}} \text{Xij} \tag{2}\]  
where \(\alpha^{\text{-}}\) and \(\beta^{\text{-}}\) are estimators of the parameters \(\alpha\) and \(\beta\), respectively; and \(\gamma\)i* and \(\delta\)i* are the empirical Bayes estimates for the parameters \(\gamma\)i and \(\delta\)i, respectively [38]. ComBat harmonization was applied in this study using the slice thickness and pixel spacing values as the batch. For the slice thickness, the scans were divided into thin slices (\(\leq\)3 mm) and thick slices (\(>\)3 mm). For the pixel spacing, the scans were divided into three groups: between 0.5 and 0.7 mm, between 0.71 and 0.80 mm, and between 0.81 and 0.98 mm. The scans were acquired using similar convolution kernels in the majority of the scans, making the number of observations for the minority kernels not sufficient for ComBat application. The same applies when the medical center where the scans were acquired is considered as the batch.
## 4 Experimental Results  
In this section, we present two different types of experiments to validate the proposed AMDS Cartographer technique. The first test compares the performance accuracy of the proposed system against the standard Cartographer technique using KITTI Vision Benchmark Suite [27] which is an open public dataset developed by [PERSON] et al. that measures SLAM trajectory performances against ground truth measured with a real-time kinematic global positioning system (RTK-GPS). Meanwhile, for the second test, the system was integrated in an actual vehicle to test its capability in a more exclusive area where the KITTI dataset could not provide. It also validates the consistency of the AMDS Cartographer technique with a different hardware setup as compared with the Vanilla Cartographer techniques, NDT-SLAM and LOAM.
# Path Planning of Multi-Type Robot Systems with Time Windows Based on Timed Colored Petri Nets
## 5 Path Planning for MTRSs Based on TCPNs
### Path Planning of the MTRSs by an MILPP  
Combining the linear transformations for the exclusive, collaborative, and the public task regions, i.e., Equations (4), (8), and (10), Problem 1 can be solved by the following MILPP:  
(15)  
Note that \(\mathbf{w}=[\mathbf{w}(t_{1}),\ldots,\mathbf{w}(t_{m})]\) is a vector that contains the travel distance between each adjacent region. The objective function of (15) represents the total travel distance for all robots from initial to final position. Condition (15) represents all the states of the MTRSs from initial to final position. Condition (15) ensures the logic requirements and time windows for the exclusive and collaborative tasks. Condition (15) ensures the logic requirements and time windows for the public tasks. The optimal solution \(\mathbf{\sigma}_{j}\) (\(j=1,\ldots,|task|\)) of MILPP (15) denotes the firing count vector of the TCPN system that corresponds to the movement strategy of each type of robot. Therefore, the path of each type of robot \(k\) that contains a sequence of regions, denoted by \(\mathbf{path}_{k}\), can be iteratively constructed by using Algorithm 3.  
The computational complexities of the developed algorithms are as follows. For Algorithms 1-3, the complexities are \(O(n^{2})\), \(O(n\cdot m)\) and \(O(|Task|\cdot m^{2})\), respectively, where \(n\) denotes the number of regions (or places), \(m\) denotes the number of transitions and \(|Task|\) denotes the number of tasks. For MILPP (15), it belongs to the NP-hard class. Some efficient tools such as LINGO and CPLEX can be used to solve the MILPPs.  
```
0: TCPN system \(G=\langle N,\mathbf{\theta},\mathbf{M}_{0}\rangle\), \(N=(P,T,Co,\mathbf{Pre},\mathbf{Post})\), incidence matrix \(\mathbf{C}=\mathbf{Post}-\mathbf{Pre}\), firing count vector \(\mathbf{\sigma}_{j}\) (\(j=1,\ldots,|task|\))
0: Path of each type of robot \(\mathbf{path}_{k}\), \(k=1,2,\ldots,K\)
1: Let \(\mathbf{M}=\mathbf{M}_{0},j=0\), \(\mathbf{path}_{k}=\epsilon\), \(k=1,\ldots,K\)
2:while\(j\leq|task|\)do
3:while\(\mathbf{1}^{T}\cdot\mathbf{\sigma}_{j}\
eq 0\)do
4:for each\(t\in T\)do
5:while\(\mathbf{\sigma}_{j}[t](k)>0\) and \(M[\cdot^{t}](k)>0\)do
6: Pick the corresponding type of robot \(\mathcal{C}_{k}\) from the input places '\(t\) of \(t\);
7: Assign movement corresponding to \(t\) to a \(\mathcal{C}_{k}\) robot, i.e., a \(\mathcal{C}_{k}\) robot moves from the input places '\(t\) of \(t\) to the output places \(t\)' of \(t\) (i.e., from region \(r_{t}\) to region \(r_{t}\));
8: Let \(\mathbf{path}_{k}:=\mathbf{path}_{k}\cdot r_{t}\cdot r_{t}\), %update the path of \(\mathcal{C}_{k}\) type of robot
9: Let \(\mathbf{M}:=\mathbf{M}+\mathbf{C}[\cdot,t]\); %update the current state
10: Let \(\sigma_{j}[t](k):=\sigma_{j}[t](k)-1\);
11:\(\mathbf{\sigma}_{j}:=\mathbf{\sigma}_{j}\), %update the firing count vector
12:endwhile
13:endfor
14:endwhile
15:\(j:=j+1\);
16:endwhile
```  
**Algorithm 3**Iterative construction of the paths of each type of robot
## 3 Results and Discussion
### Corner joints Studies
#### 3.2.2 The Influence of Different Factors on Load-Bearing Capacity and Failure Points
response to loading types, whereas there was no significant difference in moment resistance between compression and tension loadings for joints connected with 2 mm to 5 mm diameter screws. Particleboard joints with 5 mm diameter screws with a length of 60 mm showed significantly greater moment resistance to compression loads than to tension loads. The failure modes for these corner joints typically involve the crushing of screw heads into the face member, followed by screw withdrawal from the butt members along with base material and edge splitting around the screws. According to [PERSON] [1], the load-bearing capacity of particleboard corner joints is significantly influenced by the presence and thickness of edge banding. Edge banding enhances the bending strength under compression tests, with a thickness of 0.4 mm being sufficient for Minifix and Rafix connectors to achieve 15-25% higher bending strength. The type of edging material, specifically the ABS band, and its application process also play a crucial role in the joint strength. Furthermore, the ultimate bending moment, which is a critical measure of the load-bearing capacity, was positively affected by edge banding, as evidenced by the statistical analysis of different joint types. Notably, edge banding prevents the splitting of the particleboard edge, which is a common failure point in unedged joints, thereby enhancing the overall structural integrity of the furniture. Table 6 presents a comprehensive overview of the factors that influence the load-bearing capacity and potential failure points in the design of panel corner joints.
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 3 Attention-Gated Residual U-Net and ECA-ResNet
### Palm Vein Segmentation
#### 3.2.5 Loss Function  
The primary function of segmentation is to classify each pixel in terms of the specified output. Therefore, cross-entropy loss, which is the popular loss function among several classification problems, is often used to classify pixels. However, unlike character/text recognition in which the text usually occupies a relatively larger portion of the image, vein pixels only relate to the narrower region of the image than the background and occupy a smaller portion of the image compared to the background, which can lead to a class imbalance problem. This imbalance can cause the traditional loss function such as cross-entropy loss to be biased towards the majority class (background), affecting the model's performance. Hence, dice loss, which is more suitable for handling such imbalances and not plagued by the ratio of foreground pixels to background pixels, is utilized in this research. Calculating the dice coefficient function solves the imbalances between foreground and background, but it ignores another imbalance between easy and difficult instances. Dice loss (DL) is formalized as Equation (10):  
\[DL(p,g)=1-\frac{2 pg+1}{p+g+1} \tag{10}\]  
where \(p\) and \(g\) represent pairs of corresponding pixel values of prediction and ground truth, respectively.