esa-sceva/satcom-chunk-collection huggingface dataset:
# Fluoride Risk Assessment of Different Brands of Coffee Commercialized in the Canary Islands
## 3 Results and Discussion
### Fluoride in Coffee Samples Prepared with Tap Water  
As indicated by surveys, more than half of the population may prepare coffee with tap water. Considering the historical background of the Canary Islands, especially those of the island of Tenerife, which has a high concentration of fluoride in this kind of water [3; 32; 33], it would be interesting to know the fluoride concentrations under these conditions in order to estimate the fluoride exposure.  
The use of tap water for the preparation of coffee markedly increases the concentration of fluoride in these beverages regardless of the area of origin. The highest mean concentration was recorded in coffee made with water from the southern part of the island with 2.73 mg/L, followed by the northern part with 1.35 mg/L. Finally, in the metropolitan area, where part of the supply comes from desalination water, an average of 0.39 mg/L was recorded (Table 5).  
These results show that although coffee has been identified in some studies as a possible dietary source of fluoride [17; 29], unlike tea [30; 34; 35], the main source of fluoride in these beverages is the water from which they are made. The variations in concentrations by area are based on the different water supplies on the island of Tenerife; in some cases, the water comes directly from underground galleries (providing high concentrations of fluoride, especially in times of drought) and in other cases. The water comes from the mixing of desalinated water with groundwater. This is why, as can be seen in the table above, there are large differences even between neighborhoods in the same municipality.  
These results are similar to those found by [PERSON] et al. in India, where they recorded concentrations from 1.45 mg/L in coffee brewed in areas without endemic fluorosis to 2.70 mg/L and 3.85 mg/L in regions with higher fluorosis [35].
## 2 Simulation Methodology  
The calculations in this paper employed a standard passenger tire size (185/60R15). Figure 1 depicts the size and two-dimensional cross-section of the tire.  
Some fair assumptions and simplifications were made for the simulations in order to simplify the situation:  
* It was supposed that the tire was made of steel bead wires, rubber, cord plies, and rim.
* The rubber was considered homogenous, isotropic, and hyperelastic over the whole temperature range.
* It was assumed that the steel bead wire, rim, and cord ply were homogenous, isotropic, and elastic materials.
* It was assumed that the road and the rim were rigid.
* It was assumed that the friction coefficient between the road and the tire was constant at 0.1, while neglecting the fluctuation in the amount of friction between the road and tire. Then, the friction coefficient was changed to investigate the effect of this variable on the distribution of temperature within the rubber.  
Table 1 provides a summary of the material attributes [22]. These values had been previously obtained by experiments in the literature, and were provided by Kenda Rubber Industrial Corporation. Bead wires were considered to have stainless steel qualities in terms of their mechanical and thermal properties. The inner volume of the tire was applied with the equivalent inflated pressure and heat transfer properties in order to represent the filled air response. Moreover, the reinforcements were also modeled using 3D elements with structural steel properties to achieve a sense of their real behavior in terms of their structural and heating responses. The mesh for the simulation was constructed using structural mesh and using ANSYS ICEM. The overall simulation mesh is shown in Figure 2. The software package employed for the simulation work was provided by ANSYS, Inc, which includes ANSYS Transient Mechanical and ANSYS Steady-state thermal version 19.1 [28].  
Figure 1: The cross-section of an inflated tire (185/60 R15): (**a**) symmetric components of the tire; (**b**) a half tire size, unit (mm).
## 3 The Proposed Scheme  
In this section a novel adaptive feature weighting scheme is proposed called CDS-C (Category Discriminative Strength with Chi-square model), which properly measures the inter-category and intra-category significance of a feature. A new mathematical model called Category Discriminative Strength (CDS) is presented to measure the inter-category discriminative strength of the features, while a modified Chi-square model is introduced to measure the dependency of the features in intra-category. Also, a fine-grained clustering strategy is proposed to precisely define the margin of discriminative features and aggregate the features of similar distributions for efficient feature weighting. Furthermore, an adaptive weighting strategy is proposed to properly decide the weight for each feature.
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Predominant Algorithms
However, naive solutions assume that features are independent, and more complex Bayesian methods require the modeling of such dependencies. In this regard, network traffic features are prone to show high correlation [127], a fact that in principle advises against Bayes-based approaches. * Clustering: NTA unsupervised methods consist mainly of clustering. Unsupervised methods are more often applied as parts of the analysis frameworks than supervised methods. Clustering is frequently used for data reduction or space simplifications, also after other space transformations (e.g., PCA, graph representation, SOM). Therefore, their suitability must be assessed within the corresponding framework. Two main trends are observed here: prioritizing fastness (K-means and K-means variants) or accuracy (e.g., hierarchical clustering, DBSCAN). K-means is a simple, fast algorithm, but unstable and prone to generate suboptimal results. Hence, internal validation is almost mandatory, though it is actually not often incorporated in detection frameworks (Figure 6(a)). On the other hand, hierarchical clustering and other popular clustering options like DBSCAN or OPTICS are more accurate and robust, but computationally costly and less flexible for stream data and evolving scenarios. Their incorporation into real, stand-alone detection systems is therefore difficult. Figure 6: Algorithm families used by research authors as the main options for NTA analysis and the contributions claimed by authors in the conclusions.
# Single Image Reflection Removal Based on Residual Attention Mechanism
## 4 Experiments
### Ablation Study  
We ran an ablation study by altering the model components, removing or replacing the loss function, and retraining the network to better assess our network design and evaluate the loss function. In particular, we used the full Equation (9) for the ablation analysis of the network structure. We analyzed the loss function using the complete network structure as in Figure 1. We conducted three comparative experiments, respectively, deleting ResCbamBlock, using CBAM to replace ResCbamBlock, and replacing the mixed loss with pixel loss and the same network structure. PSNR and SSIM were obtained by evaluating the retrained models in these experiments, and the experimental results of the ablation study are shown in Table 2. Compared with using CBAM alone, using ResCbamBlock improved the PSNR score by 1.406 dB and the SSIM score by 0.052. Removing ResCbamBlock reduced PSNR by 2.930 dB and SSIM by 0.087. Compared with using pixel loss alone, the hybrid function of pixel loss and structural similarity loss improved PSNR by 0.587 dB and SSIM by 0.064 without changing the network structure.  
Figure 8: Qualitative comparison between the proposed method and four state-of-the-art techniques. The images are obtained from ‘SIR2’ (Rows 1–3). In particular, the first column represents the input image, the last column represents the ground truth image without reflections, and the other columns represent the out-put corresponding to the comparison method. Column 2 from RmNet [25], column 3 from [PERSON] [1], column 4 from ERRNet [28], column 5 from IBLCN [26] and column 6 from ours work.  
In addition, we analyzed the parameters of different models, as shown in Table 3. The RABRRN achieved better removal results with parameters of only 18.524M, which was one third of the RmNet parameters.
## 5 Challenges of Wearable Textile Antennas
Recently, RF measurements have demonstrated the feasibility of using Hook and Loop as an RF connection mechanism at the low GHz frequency range [115]. This has the advantage that the antenna can be disconnected from the electronics and/or clothing to allow washing or for the electronics to be placed in a different item of clothing. However, considerable work is needed to find the optimal solution to this challenge. Finally, manufacturing costs are still a challenge for wearable antennas. The specialized threads for embroidery typically cost $1.50 per meter and although prices will decrease, the silver content means that costs may not decrease until other alternative materials can be found. As a rough guidelines embroidered antennas operating at 2.4 GHz (~6 cm \(\times\) 4.5 cm) can contain 5 m of thread. The cost of Nora Dell and inkjet inks are also dependent on the cost of the silver raw materials.
## 4 Experiments and Discussion
### Systematic Experiments  
After self-assembly, the composite entity had a better capacity to respond to the environments. Five SMMRobs formed a snake-like robot and combined caterpillar tracks that were invented by humans and joints that mimicked animals to lift itself to cross a 10 cm high obstacle that was higher than the module, as shown in Figure 19. The time of crossing was 161 s. To avoid rolling, the joint target positions in control periods were interpolated.  
As shown in Figure 20, the head module was blocked by a gap, detected by the camera and lidar. It called two other modules to connect, and they moved over the gap in a chained train. After that, the head continued moving in the next environment and others moved backward.  
Figure 19: A snake-like robot using combined caterpillar tracks that were invented by humans and joints that mimicked animals to lift itself to cross a tall obstacle.  
Figure 17: Experiments on the self-assembly of a snake-like composite entity consisting of one SMMRob head module and four body modules and collective motion in a chain.  
Figure 18: Experiments on the self-assembly of a quadruped structure.  
An experiment in which a composite entity consisting five modules climbed up a stair was performed. The maximum angle of inclination was 20\({}^{\circ}\), as shown in Figure 21. Because of height limit of the track's beam, it was difficult for the composite entity to climb up steeper stairs.  
In these experiments, it was found that in order for the assembled structure to conform well to irregular surfaces, it is necessary for the SMMRob modules to actively actuate the joints to match the surface. In this regard, a more complicated motion for a tree-like topology has to be designed and more advanced actuators such as compliant mechanisms need to be considered. In robot docking and connected structures, friction and dynamics are neglected because the robots move at low speed, but when dealing with high-speed docking, friction and dynamics need to be considered further. In addition, when multiple connected robots of multiple branches use tracks to move, skid steering simultaneously could make locomotion difficult and the kinematics of the connected structure has to be designed in this regard.
# A Combined Dynamic-Kinematic Extended Kalman Filter for Estimating Vehicle Sideslip Angle
## 2 Combined Extended Kalman Filter
### Kinematic Model  
The kinematic approach considers longitudinal and lateral velocities as state variables and longitudinal and lateral accelerations to be inputs to the system. The state model is composed by the kinematic relations between accelerations and vehicle velocities:  
\[\dot{v}_{x} =v_{y}r+a_{x} \tag{10}\] \[\dot{v}_{y} =-v_{x}r+a_{y} \tag{11}\]  
The kinematic model can be hence written as a continuous-time state-space model  
\[\dot{\mathbf{x}}^{kin} =\mathrm{F}\mathbf{x}^{kin}+\mathrm{G}\mathbf{u}^{kin} \tag{12}\] \[\mathbf{x}^{kin} =\begin{bmatrix}v_{x}\\ v_{y}\end{bmatrix}\text{, }\mathbf{u}^{kin}=\begin{bmatrix}a_{x}\\ a_{y}\end{bmatrix}\] \[\mathrm{F} =\begin{bmatrix}0&r\\ -r&0\end{bmatrix}\text{, }\mathrm{G}=\begin{bmatrix}1&0\\ 0&1\end{bmatrix}\]  
and it may be linearised using the forward Euler method:  
\[\mathbf{x}_{k}^{kin}=(\mathrm{F}\Delta t+1)\mathbf{x}_{k-1}^{kin}+\mathrm{G} \Delta t\mathbf{u}_{k-1}^{kin}=\mathrm{F}_{k-1}\mathbf{x}_{k-1}^{kin}+\mathrm{ G}_{k-1}\mathbf{x}_{k-1}^{kin} \tag{13}\]  
The longitudinal velocity is the only measurement, obtained from Equation (5).  
The measurement equation is straightforwardly obtained and the discrete-time state-space model is hence completely defined:  
\[\mathbf{x}_{k}^{kin} =\mathrm{F}_{k-1}\mathbf{x}_{k-1}^{kin}+\mathrm{G}_{k-1}\mathbf{ u}_{k-1}^{kin} \tag{14}\] \[\mathbf{y}_{k}^{kin} =v_{x,meas}=\mathrm{H}_{k}\mathbf{x}_{k}^{kin}=\begin{bmatrix}1 &0\end{bmatrix}\mathbf{x}_{k}^{kin}\]  
Since the model is linear, it is possible to resort to a canonical Kalman filter approach. Again, the estimate of \(\beta\) is computed with Equation (4), but in this case also the longitudinal velocity is estimated by the Kalman filter.
# Towards Blockchain-Based Federated Machine Learning: Smart Contract for Model Inference
## 2 Related Work
The comparison of overviewed blockchain-based FL solutions with respect to various technological aspects is summarized in Table 1. We can notice that the fields of application are quite broad and implementation details (DLT type, consensus used, support for smart contracts) are diverse. As most existing blockchain frameworks and their application to FL are in the early stages of development, no common trends and state-of-the-art can be distinguished. Our approach differs from the aforementioned research in a combination of several important aspects: (a) Our solution could be extended to include several different model types, and could be customized for various model or data acceptance strategies by tweaking smart contracts; (b) we measure the runtime of model inference entirely implemented as a Hyperledger Fabric chaincode solution; (c) we measure the runtime of blockchain oracle \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & \
\begin{tabular}{c} **Consensus** \\ **Algorithm** \\ \end{tabular} & **Incentive** & **DLT Type** &
\begin{tabular}{c} **Smart** \\ **Contracts** \\ \end{tabular} & **Field of Application** & **Access Type** \\ \hline
[24] & PoQ & No & Custom & No & Industrial IoT & Permissioned \\ \hline
[6] & RR & No & MultiChain & No & Cyber security & Permissioned \\ \hline
[23] & PoW & Yes & Custom & No & Universal & Public \\ \hline
[25] & PoI & No & MultiChain & Undefined & Healthcare & Permissioned \\ \hline
[8] & Algorand 1 & Yes & Corda & Yes & Deep learning & Permissioned \\ \hline
[28] & PoW, PoS, PoA & Yes 2 & Ethereum & Yes & Universal & Public \\ \hline
[7] & BFT 3 & No & Hyperledger Fabric & Yes & Disease modelling & Permissioned \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of overviewed federated learning (FL) solutions employing blockchain. Abbreviations: PoQ: Proof of Training Quality; RR: Round-robin-based; PoW: Proof-of-Work; PoI: Proof-of-Information; PoS: Proof-of-Stake; PoA: Proof-of-Authority; BFT: Byzantine Fault Tolerance. service implementation, to determine whether it can be used to include the wide range of ML approaches on blockchain technology; (d) we compare the runtime differences between chaincode and oracle service implementations for the ML model inference. Additionally, the proposed solution is specified using UML diagrams, whereas other authors mostly use non-formalized notations, such as flowcharts.
## 3 Monitoring Methods
### Calculation of Slope Displacement
The red line represents the shooting Angle before the digital camera and the landmark are moved. The blue line represents the shooting Angle after the digital camera and the mark point are moved. Figure 7: Calculation principle of \(\Delta x_{1}\), \(\Delta z_{1}\). The red line represents the shooting Angle before the digital camera and the landmark are moved. The blue line represents the shooting Angle after the digital camera and the mark point are moved. vertical coordinates \(M^{\prime}E^{\prime}\) of the monitoring landmarks \(E^{\prime}\) can be calculated from the photos taken from point \(O^{\prime}\), and the unit is pixels. The actual displacement of the digital camera and the landmarks is in millimeters. Therefore, the following formula is used to express the relationship between the above vertical directions. \[\Delta z_{1}=(M^{\prime}E^{\prime}-ME)\times\rho+OO^{\prime} \tag{18}\]  
where \(M^{\prime}E^{\prime}\) is the vertical coordinate after the landmark moves and \(ME\) is the vertical coordinate before the landmark moves. \(\rho\) is the proportional coefficient and \(OO^{\prime}\) is the vertical displacement of the digital camera, which can be obtained by actual measurement. Similarly, the relationship between the horizontal directions can be expressed by the following formula:  
\[\Delta x_{1}=(P^{\prime}E^{\prime}-PE)\times\rho+OO^{\prime} \tag{19}\]  
where \(P^{\prime}E^{\prime}\) is the horizontal coordinate after the landmark moves and \(PE\) is the horizontal coordinate before the landmark moves. \(\rho\) is the proportional coefficient and \(OO^{\prime}\) is the horizontal displacement of the digital camera that is obtained by actual measurement. 4. Calculating \(\Delta y_{1}\)  
At time \(t_{0}\), the obtained proportional coefficient is recorded as \(\rho_{1}\), and at time \(t_{1}\), the obtained proportional coefficient is recorded as \(\rho_{2}\). The positions of the monitoring landmarks and the digital camera have been changing over time, and therefore \(\rho_{1}\
eq\rho_{2}\). Using Equation (16), the distance \(d\) from the digital camera to the landmark can be calculated from the proportional coefficient \(\rho\). Therefore,  
\[\Delta y_{1}=\frac{(\rho_{1}-\rho_{2})\times f\times p}{q} \tag{20}\]  
where \(\rho_{1}\) is the proportional coefficient at time \(t_{0}\), \(\rho_{2}\) is the proportional coefficient at time \(t_{1}\), \(q\) is the size of the digital camera CCD, \(f\) is the focal length, and \(p\) is the resolution of the digital camera.
# Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things
## 5 Numerical Simulations  
In this section, we will simulate the proposed dynamic game to get the optimal allocated resource level for the UAV. MATLAB software is used to construct the simulation environment. The simulated IoT system includes two kinds of wireless nodes, which have different energy consumption requirements. Based on the Nash equilibrium given in Section 4, we can get the optimal power level of wireless energy transfer for each kind of wireless node. Figure 3 shows the optimal allocated resources of wireless energy transfer for each kind of wireless node. The UAV should transmit the energy to the wireless nodes based on the nodes' requirements and maximize the profit during the energy transfer. As shown in the Figure 3, the wireless nodes in the HEC group need more energy compared to the wireless nodes in the LEC group, because the energy consumption in the HEC group is higher than that in the LEC group. As the game continues, the UAV needs to increase its power level for wireless energy transfer to satisfy more requirements of the wireless nodes, and to increase its profit.  
The discount factor also affects the optimal policies for wireless energy transfer, which is shown in Figure 4. As shown in Figure 4, the optimal power level for wireless energy transfer is decreased with the incensement of the discount rate. The optimal unit price for the transferred energy is given in Figure 5. With the time incensement, the UAV will increase the unit price for the transferred energy, because the larger the energy transfer time, the higher the transfer cost for the UAV. As the energy conversion efficiency is low for wireless energy transfer, and it is a long distance for wireless energy transfer, the UAV should carry more energy for wireless power transfer. All these factors will cause an exponential increase in the energy transfer cost. Then, the UAV should increase the unit price for the transferred energy when the time required for the wireless power transfer is large. In Figure 6, the optimal solutions for the wireless nodes are given. In order to simplify the simulations for the users, we assume that all wireless nodes in the same category are uniform and standard. From Figure 6, we can find that the wireless sensors will increase the transmitted power for information transmission to earn more profit, although the unit price for the transferred energy is increased over time. Based on Figure 3, we can see that the wireless nodes will have more energy over time, because the UAV increases its power level for wireless energy transfer to satisfy more requirements of the wireless nodes. Then the wireless nodes can harvest more energy and will have more energy for information transmission.  
Figure 3: Power level for wireless energy transfer.  
Figure 4: Power level for wireless energy transfer with different discount rates.  
Figure 5: Optimal unit price of the transferred energy.
## 4 Experimental Results
### Application
#### 4.7.2 Experiment Dataset  
In this application scenario, we use face image as the input biometric image and alice29.txt from Canterbury corpus as plaintext to test the performance on the user computer. For biometric images, we adopt the USB camera to collect face images of different users in a real environment, which are composed of 10 different face images with 640 \(\times\) 480 resolution of each of the 40 face subjects. These datasets, including different illuminations, expressions, and poses, are aligned to obtain the cropped face images with the resolution of 112 \(\times\) 96 by using MTCNN [63]. In addition, we randomly select five face images of each subject for training our key generation model, and other face images are used for testing. For alice29.txt of plaintext file, it consists of various characters, and the size of the file is 152,089 bytes.