esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Explanation with Example  
Node A is selected as a master from \(N=16\) nodes as shown in Figure 3, and it further selects five nodes for validation and sends data to 5 nodes to generate a block. All the nodes create the selection of master by a voting process. In this process, every single node can vote for one node at a time. Once a master is selected, all the blockchain nodes can send data to the master. Next, the master selects five nodes (but never selects more than one node with a rating less than 0) and sends data to them for validation. Validating nodes generate a block and, after verification, sends it back to master, which is broadcasted to all IoT blockchain nodes to store in their public ledger. Why does the master select odd (e.g., 5) nodes for validation?  
* If it selects all the nodes, then validation will be slowed down as discussed in PoW.
* In case one node for validation could be a better option and blockchain performs efficiently, the attacker can insert data and validate them, if this node is compromised.
* In the case of 3 validating nodes, if one node is compromised, then the generated block will be accurate but less reliable due to one verification.
* If two nodes create two blocks simultaneously, then one block will win due to a greater number of verifications, and it is well performed in an odd number of nodes.  
Figure 3: BCIoT working.  
So, an odd number of validating nodes could be a better option, and reliability will be increased for a more significant number of verifications. In the case of validation, nodes greater than five will make the block more authentic, but the validation rate will be decreased. So, selection of validating nodes should be according to the required scenario.
## 5 Simulation Experiments
### Comparison of Results of Skyline and Top-\(k\) Queries for Irrigation Decision Support  
In this section, we compare the results of the skyline query obtained in the previous section to those of a top-\(k\) query. We selected \(k\) to match the number of solutions found by the skyline query, as shown in Table 2. That is, underwater supply rates of 0-25%, 25-50%, and 50-75%, \(k\) was set to 10, 171, and 484, respectively. For the weight of each top-\(k\) target value, we adopted the most intuitive method and set the weights of the five objective values uniformly (i.e., all are weighted 20%).  
As for the comparison results between the two, we list them on the box-and-whisker plot in Figure 10. Figure 10a-c represent the two queries at 0-25%, 25-50%, and 50-75%, respectively, and each subgraph presents the distribution range of all solutions found by the query on the five objective values. In these figures, we can see that no matter the water supply rate or objective value, the distribution range of the top-\(k\) query results is smaller than that of the skyline query, and they are usually concentrated on one side of its range. This verifies the arguments put forward by previous scholars [12; 13] that in multi-criteria decision-making, the top-\(k\) query is biased towards one side when the objective values conflict. The skyline query overcomes this shortcoming. This experiment verified that this is also the case for the field of irrigation decision-making.
# Solar Concentration for Wastewaters Remediation: A Review of Materials and Technologies
## 4 Wastewaters
### Model Pollutants
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Reference** & **Pollutant Tested** & **Solar Collector** & **Catalyst Material** & **Catalysis Type** \\ \hline
**1** & \begin{tabular}{c} [PERSON] et al., \\ 2002 [133] \\ \end{tabular} & \
\begin{tabular}{c} Incomycin \\ (antibiotic) \\ \end{tabular} & CPC &
\begin{tabular}{c} TiO\({}_{2}\) (P-25) suspended \\ and support \\ \end{tabular} & Heterogeneous \\ \hline
**2** &
\begin{tabular}{c} [PERSON] et \\ al., 2002 [134] \\ \end{tabular} & Remazol red B (azo-dye) & IPC & ZnO supported & Heterogeneous \\ \hline
**3** & \begin{tabular}{c} [PERSON], 1994 \\ [108] \\ \end{tabular} & \
\begin{tabular}{c} Nitroglycerine \\ Rhodamine dye \\ \end{tabular} & IPC &
\begin{tabular}{c} TiO\({}_{2}\) (P-25) \\ Pt-TiO\({}_{2}\) \\ supported \\ \end{tabular} & Heterogeneous \\ \hline
**4** & \begin{tabular}{c} [PERSON] et al., 2005 \\ [135] \\ \end{tabular} & \
\begin{tabular}{c} Formetanate \\ Indigo carmine \\ (indicator dye) \\ \end{tabular} & IPC/CPC &
\begin{tabular}{c} TiO\({}_{2}\) (PC 500) supported \\ and suspended \\ \end{tabular} & Heterogeneous \\ \hline
**6** & \begin{tabular}{c} [PERSON] \\ et al., 2014 [2] \\ \end{tabular} & \
\begin{tabular}{c} Synthetic wool dying in \\ different colors (yellow, \\ blue and red) \\ \end{tabular} & CPC &
\begin{tabular}{c} TiO\({}_{2}\) \\ \end{tabular} & Heterogeneous \\ \hline
**7** & \
\begin{tabular}{c} [PERSON] et al., \\ 2017 [68] \\ \end{tabular} & Methylene blue dye & IPC &
\begin{tabular}{c} TiO\({}_{2}\) \\ \end{tabular} & Heterogeneous \\ \hline
**8** & \
\begin{tabular}{c} [PERSON] et \\ al., 2019 [136] \\ \end{tabular} &
\begin{tabular}{c} Acid Red 1, Acid Yellow \\ 17, Reactive Black 5 and \\ Reactive Orange 1 \\ \end{tabular} & CPC/IPC & Fe (II) & Homogeneous \\ \hline
**9** &
\begin{tabular}{c} [PERSON] et \\ al., 2009 [137] \\ \end{tabular} & Orange II dye & CPC & Ferrioxalate in solution & Homogeneous \\ \hline
**10** & \begin{tabular}{c} [PERSON] \\ et al., 2008 [138] \\ \end{tabular} & \
\begin{tabular}{c} Procion Red H-E7B and \\ Cibacron Red FN-R \\ (azo-dyes) \\ \end{tabular} & CPC &
\begin{tabular}{c} Fe(II)/H\({}_{2}\)O\({}_{2}\) \\ photo-Fenton process \\ \end{tabular} & Homogeneous \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BROCs model wastewaters (Dyes). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Reference** & **Pollutant Tested** & **Solar Collector** & **Catalyst Material** & **Catalysis Type** \\ \hline
**1** & \begin{tabular}{c} [PERSON] et al., \\ 2005 [140] \\ \end{tabular} & \
\begin{tabular}{c} Incomycin \\ (antibiotic) \\ \end{tabular} & CPC &
\begin{tabular}{c} TiO\({}_{2}\) (P-25) \\ suspended \\ and support \\ \end{tabular} & Heterogeneous \\ \hline \hline \end{tabular}
\end{table} 
Table 5: BROCs model wastewaters (Pesticides).
## 2 PFC Converters Power Components Design Analysis and Selection
### Output Capacitor Selection  
For both topologies, the design techniques of the output capacitor are the same. The output capacitor value usually calculated to meet the specified output voltage ripple using (14), also, this value should be enough to deliver the output minimum voltage hold up with the specific time (t\({}_{\text{hold}}\)), as given in (15)  
\[C_{b}\geq\ \frac{P_{o}}{2\times\pi\times F\times\ V_{rpp}\times\ V_{o}} \tag{16}\]  
\[C_{b}\geq\ \frac{2\times P_{o}\times t_{hold}}{V_{o}^{2}-V_{o\ min}^{2}} \tag{17}\]  
where V\({}_{rpp}\) is the peak to peak output voltage ripple. The larger value among the two equations was selected to design the output capacitor.  
The capacitor rms current across the 60 Hz line cycle is given as [40]  
\[I_{\text{Cb\_rms}}=\sqrt{\left(\frac{8\sqrt{2}\times P_{o}^{2}}{3\pi\times V_{ in\_min}\times\ V_{o}}-\frac{P_{o}^{2}}{V_{o}^{2}}\right)} \tag{18}\]  
The capacitor equivalent series resistance (ESR) is calculated as  
\[ESR=\frac{DF}{2\times\pi\times F\times C_{b}} \tag{19}\]  
where DF is the chosen capacitor dissipation factor and can be obtained from the capacitor datasheet.  
The ESR losses of the capacitor can be calculated as  
\[P_{ESR\_Loss}=ESR\times I_{\text{Cb\_rms}}^{2} \tag{20}\]  
An important issue when choosing the output bulk capacitor for the conventional CCM PFC topology is that the ESR should be as small as possible, as it affects power efficiency and voltage regulations. A larger ESR presents more ripple, affecting the boost converter control loops stability [41]. Usually, aluminum electrolytic capacitors are used where high-power density is required, because of their small volume, these types of capacitors are also preferred in PFC applications since they provide high capacitance value with low equivalent series resistance (ESR), but these capacitor types are expensive when compared with the higher ESR types. On the other hand, the interleaving technique in PFC circuits causes the inductor current ripple reduction and then produces a reduced rms output capacitor current, so capacitors with higher ESR (lower cost) can be used.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 6 Future Directions and Real-World Challenges
### Data Privacy
* **Future directions**: Data privacy is an increasingly important issue in FRSs. In order to ensure that all users' data are secure, FRSs must have security protocols in place for exchanging data between different organizations. These protocols should be designed to protect user privacy by preventing unauthorized access to user data and by allowing users to control with whom their data are shared. In addition, the FRS must take steps to ensure that user data are not misused or abused. This could include implementing policies and procedures for data security and monitoring, as well as developing technologies to detect any unauthorized access or misuse of user data.
* **Real-world challenges**: A major challenge for FRSs is ensuring that user data are secure, both in terms of the data exchanged between different organizations and the data stored on each organization's servers. To address this challenge, FRSs must ensure that all data exchanged between different organizations are encrypted, secure, and adequately protected. Besides, the system must ensure that each organization's servers are secure and that all user data are stored in a secure manner that prevents unauthorized access or misuse.
###### Abstract  
This study aims to investigate the effect of a constant magnetic field on heat transfer, flow of fluid, and entropy generation of mixed convection in a lid-driven porous medium enclosure filled with nanofluids (TiO\({}_{2}\)-water). Uniform constant heat fluxes are partially applied to the bottom wall of the enclosure, while the remaining parts of the bottom wall are considered to be adiabatic. The vertical walls are maintained at a constant cold temperature and move with a fixed velocity. A sinusoidal wall is assumed to be fixed and kept adiabatic at the top enclosure. Three scenarios are considered corresponding to different directions of the moving isothermal vertical wall (\(\pm 1\)). The influence of pertinent parameters on the heat transfer, flow of fluid, and entropy generation in an enclosure are deliberated. The parameters are the Richardson number (\(\widetilde{R_{i}}\) = 1, 10, and 100), the Hartmann number (\(0\leq\widetilde{H}\)a \(\leq\) 75 with a 25 step), and the solid volume fraction of nanoparticles (\(0\leq\widetilde{\Phi}\leq 0.15\) with a 0.05 step). The Grashof and Darcy numbers are assumed to be constant at \(10^{4}\) and \(10^{-3}\), respectively. The finite element method, utilizing the variational formulation/weak form, is applied to discretize the main governor equations. Triangular elements have been employed within the studied envelope, with the elements adapting as needed. The results showed that the streamfunction and fluid temperature decreased as the solid volume fraction increased. The local \(\widetilde{\text{Nu}}\) number increased by more than 50% at low values of \(\widetilde{\Phi}\) (up to 0.1). This percentage decreases between 25% and 40% when \(\widetilde{\Phi}\) is in the range of 0.1 to 0.15. As \(\widetilde{\text{Ha}}\) increases from 0 to 75, these percentages increase at low values of the value of \(\widetilde{\text{Ri}}\) = 1 and 10. These variations are primarily dependent on the value of the Richardson number.  
porous media; nanofluids; mixed convection; magnetic fields; entropy generation 2020
# Developing Machine Learning-Based Models for Railway Inspection
## 4 Deep Learning-Based Methods
### Representation Method of Raw Data
#### 4.3.1 Data Segmentation  
Each data point, indexed by the distance measure from the start point, is a vector of two acceleration values of left and right sides of the rail. The whole dataset is ordered by the distance measure in increasing order. Three class labels (\"None-joint\", \"Left-joint\", \"Right-joint\") are considered. To train a deep learning neural network model, the data has to be formulated as \(D=\{(x_{1},y_{1}),(x_{2},y_{2})\ldots(x_{n},y_{n})\}\). Each pair \((x_{i},y_{i})\) (\(1\leq i\leq n\)) is one instance, with n instances in the data set. Each \(x_{i}\) is an interval of time series with fixed length, i.e., each \(x_{i}\) has the same number of data points and each point has the same number of elements; \(y_{i}\) is a categorical value representing the class label. To manipulate the acceleration dataset into the required format, the bivariate time series data is segmented by sliding windows with each segmentation as a sample. The sliding window uses a fixed distance interval. With the raw data (in the time domain), the number of data points in a fixed distance interval varies at different locations due to the varied speeds of the inspection vehicle. To resolve this issue, a data interpolation method is adopted. The interpolation method is discussed in the next subsection. Associated with \(x_{i}\), \(y_{i}\) is the class label taking a value from {\"None-joint\", \"Left-joint\", \"Right-joint\"}.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements
### Requirements of Low Frequency Accelerometers
#### 2.2.5 Earthquake Monitoring  
Advances in seismology have relied closely on the development of instruments. From a scientific point of view, accuracy is crucial: the more accurate the detection of seismic properties, the more reliable the understanding of earthquake hazards, which in turn is of guiding importance for the study of seismic prevention techniques. In addition to seismic research, monitoring of earthquakes is also used in resource exploration, gravity-assisted navigation, and monitoring of volcanic activity. According to previous studies, the frequency range of the Earth's near-source strong ground motions is 0.3-3.0 Hz [19]; therefore, earthquake monitoring accelerometers need to have good low-frequency response characteristics.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 5 Results
### User Traffic Prediction Module Results
#### 5.1.3 Ablations  
Ablation studies were conducted for the loss function components of our proposed hybrid Transformer-TCN and LSTM models. We evaluated our models with four different loss configurations, as reported in Table 4, using (i) only an MSE loss, (ii) a trend direction MSE loss, (iii) a volatility MSE loss, and (iv) a MSE loss incorporating trend direction and volatility. These ablation studies were performed on a short and a long horizon of the test data. Error metrics are reported when the model processes data at the beginning of the test dataset, and similarly at the middle and at the end of the test set. Our findings in terms of RMSE are illustrated in Table 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Loss Configuration** & **Trend Direction** & **Volatile** \\ \hline MSE\_Only & - & - \\ MSE\_Direction & ✓ & - \\ MSE\_Volatile & - & ✓ \\ MSE\_Dir\_Vol & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Loss configurations.  
Figure 5: User traffic in active users predicted for the next 60 time steps of an instance in the dataset with the Transformer-TCN model.  
From Table 5, we observe that training our model with an MSE loss with trend direction and volatility (MSE_Dir_Vol) produces slightly worse results, compared with the loss configuration without volatility (MSE_Direction). However, when evaluating the figures qualitatively, as in Figure 5 for example, we deduce that the graph better approximates the ground truth but features sparse deviations that impact the loss in a significant way. Overall, the results of the ablation study should be compared only in relation to themselves since the training of the Transformer-TCN was conducted in fewer epochs.
# Recognition Algorithm Based on Improved FCM and Rough Sets for Meibomian Gland Morphology
## 3. MGD Identification Based on Improved FCM and Rough Sets  
Different morphologies of the meibomian gland show different texture features in images. Figure 4 shows four typical kinds of meibomian glands. Figure 4a shows a normal type, the distribution of the gland ducts is uniform, and there is no expansion or deletion of the gland ducts; Figure 4b shows a shortened type, and ductal arrangement is neat, shortened, and the loss area of the gland ducts is less than one third of the total area; Figure 4c shows a deletion type, the loss of the gland ducts is obvious, and the loss area is one thirds to two thirds of the total area; Figure 4d shows a serious deletion type, the gland ducts are not obvious, and basically all of them are missing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm Category** & **Clustering Center** & **Number of Iterations** & **Objective Function** \\ \hline traditional FCM & (16.32,70.00), & & \\  & (60.96,23.88), & 31 & \(3.49\times 10^{4}\) \\  & (39.98,86.77) & & \\ \hline improved FCM & (16.07,63.25), & & \\  & (42.24,86.78), & 16 & \(1.95\times 10^{4}\) \\  & (20.02,82.18) & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Clustering effect comparison between the traditional FCM algorithm and the improved FCM algorithm.  
Figure 4: Various kinds of meibomian gland images, they are listed as: (**a**) normal type; (**b**) shortened type; (**c**) deletion type; (**d**) serious deletion type.  
Figure 3: Comparison of clustering effects of two methods, they are listed as: (**a**) datasets; (**b**) clustering effect using the traditional FCM algorithm; (**c**) clustering effect using the improved FCM algorithm.  
The methods used for meibomian gland image recognition usually include image preprocessing, feature extraction, classification, and decision-making. In this paper, we applied the improved FCM and rough sets theory to meibomian gland image recognition. The workflow chart is shown in Figure 5.
## 3 Results and Discussion  
### EDS Analysis  
EDS analysis revealed that the elemental composition of Dowex 21K XLT consists of C, O, N, and Cl (Figure 1). Some of the C in the EDS spectra would have come from the carbon tape used to fix the sample onto the stub sample holder. While C, O, and N formed the styrene-DVB matrix of Dowex, Cl is the exchangeable anion adsorbed to the positively charged quarternary methyl amine functional group [23; 24]. The EDS data showed that the percentage of Cl was 11.7 which was approximately the same as 10.1% reported for this resin by [PERSON] and [PERSON] [23].
## 3 Results
### Canopy Deposition
#### 3.2.2 Leaf Surface Level Deposition  
Spray deposition data collected at samplers installed on abaxial and adaxial surface of the leaves revealed that there was no significant difference in spray deposition on either surface of leaves regardless of the SSCDS treatments. Moreover, there was no significant interaction between treatment and leaf surface (Table 4). The highest spray deposition was reported for adaxial leaf surface treated with modified irrigation micro-emitter i.e., T1 (1112.3 \(\pm\) 242.2 ng cm\({}^{-2}\)) followed by hollow cone nozzle SSCDS i.e., T2 (914.5 \(\pm\) 167.1 ng cm\({}^{-2}\)) (Figure 6b). The abaxial deposition in T1 (798.7 \(\pm\) 190.1 ng cm\({}^{-2}\)) was also numerically higher than T2 (577.9 \(\pm\) 123.4 ng cm\({}^{-2}\)). Nevertheless, the differences were not significant with an HSD test. Furthermore, the CV in spray deposition on the leaf surfaces for T1 and T2 were 23.2% and 31.9%, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **df** & **MS** & **\(F\)** & \(p\)** \\ \hline Main plot & & & & \\ Block & 2 & 34.53 & & \\ Treatment & 1 & 7.46 & 0.49 & 0.48 \\ Error (a) & 1 & 97.21 & & \\ Canopy zone & 2 & 3.56 & 0.23 & 0.79 \\ Leaf surface & 1 & 17.34 & 1.14 & 0.29 \\ Treatment \(\times\) Canopy zone & 2 & 23.07 & 1.52 & 0.22 \\ Treatment \(\times\) Leaf surface & 1 & 1.51 & 0.1 & 0.75 \\ Canopy zone \(\times\) Leaf surface & 2 & 20.12 & 1.32 & 0.268 \\ Treatment \(\times\) Canopy zone \(\times\) Leaf & 2 & 4.12 & 0.27 & 0.76 \\ Surface & 2 & 192 & 15.2 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: ANOVA of cube root transformed canopy deposition data.
# Research on Human-Robot Collaboration Method for Parallel Robots Oriented to Segment Docking
## 3 Control Design  
This paper proposes a fractional-order variable damping admittance control (FVDAC) and an inverse dynamics robust control, facilitating highly compliant, real-time, and stable human-parallel robot collaboration specifically tailored to aerospace cabin segment docking. The proposed control schemes are illustrated in Figure 4.  
FVDAC aims to dynamically adjust the robot's movements in real-time during collaboration, thereby averting collisions between cabin segments. This approach ensures high compliance and real-time collaboration in human-parallel robot collaboration. Throughout the collaboration, this controller receives interaction forces and real-time poses from the force sensor and Stewart parallel platform, producing an ideal compliant movement plan \((x_{c},\dot{x}_{c},\ddot{x}_{c})\) in Cartesian coordinates. It contains a variable damping matrix, adapting damping parameters in response to the operator-applied interaction forces. Additionally, fractional-order control is used to enhance the controller's responsiveness.  
To prevent the unknown interaction forces during collaboration from affecting the motion accuracy of the Stewart platform, and consequently the assembly accuracy, this paper proposes an inverse dynamics robust control. This method ensures highly stable human-parallel robot collaboration. This controller formulates a stable control law to align the robot's end-effector motion state \((x,\dot{x},\ddot{x})\) with the motion plan \((\dot{x}_{c},\dot{x}_{c},\ddot{x}_{c})\) devised by FVDAC. Furthermore, to account for unknown interaction forces affecting the end-effector and potential errors in the assembly scenario, it incorporates a nonlinear robust control.