esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.3 k-Nearest Neighbors (kNN) Classification  
The kNN classification algorithm is a simple yet powerful method used for classification and regression. Introduced by [PERSON] and [PERSON] [61], kNN operates on the principle that similar samples tend to be close to each other in feature space, as highlighted by [PERSON] et al. [62]. This algorithm identifies the k nearest neighbors of an unknown sample within the training set, assigning the sample to the most common class (or the average of the responses) among these neighbors.  
The distance between samples, fundamental to the operation of kNN, can be calculated in several ways. The most common is the Euclidean distance, given by Equation (5):  
\[\text{d}(\text{p},\text{q})=\sqrt{\sum_{i=1}^{m}(\text{p}_{\text{i}}-\text{q} _{\text{i}})^{2}} \tag{5}\]  
Equation (5) calculates the distance between two samples, p and q, each with m features, illustrating how the algorithm navigates the multidimensional space. The choice of k is a critical aspect that directly influences the algorithm's performance. A very small k may make the model overly sensitive to data noise, while a very large k may cause it to overlook class distribution nuances. It is recommended to experiment with various k values and potentially use validation methods, such as cross-validation, to determine the optimal k.  
Cross-validation, as described by [PERSON] (2018), is a technique used to assess a statistical model's generalization ability and to tune hyperparameters, such as k in kNN. The most common cross-validation method is k-fold, which divides the dataset into k subsets. The model is trained k times, each time using k-1 subsets for training and the remaining subset for testing. The model's performance is then evaluated by averaging the results obtained in each of the k iterations.  
kNN, along with cross-validation, offers a robust approach to data classification, leveraging the algorithm's simplicity and the efficacy of cross-validation to adjust hyperparameters and assessing the model's generalization ability for new data.  
The choice of the kNN algorithm over the previously mentioned models (ANN, DT, RF, and SVM) was based on its simplicity of implementation, high accuracy in scenarios with balanced data, and results previously reported in the literature (Kal
## 2 Actual Non-Uniform Bulk Material Distribution Measurement Methodology
### Measurement Facility  
An experimental facility for bulk material distribution measurement is designed and constructed, as shown in Figure 2. It is designed to experimentally measure bulk material cross-sections in real-time and can be used to study the mechanical properties of a belt under actual material distribution conditions. The facility conforms to DIN 22101 (1982) \"Type and Basic Parameters of Belt conveyors\", which can simulate a real process of continuous transportation of bulk material in laboratory conditions. It consists of a variable frequency controller which can adjust the belt speeds varying from 0.2 to 4 m/s. Due to the limited size of the laboratory space, the longitudinal length, horizontal width, and height of the facility are designed as 3500 mm, 760 mm, and 400 mm, respectively. It is a trough-type belt conveyor with three idlers, and the idler roll set spacing is 700 mm. The height of the facility is adjustable from 400 mm to 1338 mm to change the inclination angle of the belt conveyor system. The parameters of the experimental facility are listed in Table 1. A (7000 \(\times\) 500 \(\times\) 11 mm) rubber canvas belt is used to test the non-shrinkage of bulk materials. The electrical control components of the facility include a squirrel cage asynchronous motor, power switch box and frequency converter. The parameters of the frequency converter can be modified to realize open-loop control functions (starting, positive or negative rotating, etc.) or closed-loop control functions.  
In the bulk material distribution measurement system, a laser scanner (Sick LMS291-S05, made in Germany) is installed on the adjustable bracket and located above the conveyor belt to scan the profile of the bulk material cross-section orthographically. The parameters of angular resolution, scanning angle, resolution/typical measurement accuracy and maximum range are configured respectively by using the supplied LMSIBS configuration software. Then the laser scanning points are transmitted to the computer via Ethernet by using a MOXA Nport 6250. In addition, a belt speed monitor (SEW ESIT (OG 72 DN 1024 TTL)) is mounted on the central axis of the driving wheel to collect the belt speed synchronously. After the belt speed is detected by the speed monitor, the microcontroller STM32 records it and sends it to the computer in time through a GSM & GPRS module GTM900C. Accordingly, a three-dimensional point cloud of the material cross-section moving on the belt conveyor can be acquired in real-time. Using the software developed in our laboratory, the profiles and areas of the bulk materials cross section are obtained in real-time.
## 2 Basics of the Cosserat Continuum Theory  
Micropolar or Cosserat theory assumes that micromoments exist at each point of the continuum. In the Cosserat theory, the equilibrium of forces and equilibrium of moments are expressed in the following form [11]:  
\[\sigma_{ij,i}+b_{j}=0 \tag{1}\]  
\[m_{kj,j}+e_{kij}\sigma_{ij}+\
u_{k}=0 \tag{2}\]  
where \(\sigma\) is the Cosserat stress, \(\mathbf{b}\) is the body force, \(\mathbf{e}\) is the permutation operator, \(\mathbf{m}\) is the Cosserat couple stress, and \(\mathbf{\
u}\) is the body couple moment. The stress component \(\sigma_{ij}\) is analogous with stress of the classical continuum theory, which means the projection of the intensity of force on the \(j\)-axis on the cross-section is normal to the \(i\)-axis. Figure 2 shows the representation of the stress and couple stress components in the 3D Cosserat continuum theory. For the stresses, the first subscript of the letter represents the direction perpendicular to which the stress is loaded, and the second subscript indicates the direction in which the stress is actually loaded. For the bending stress, the first subscript of the letter means the axis around which the bending stress is loaded, and the second subscript also indicates the direction in which the bending stress is loaded. The symbols of the components of the stress tensor are similar to the standard symbols used in the classical continuum theory. However, it differs from the notation used in some of the previous papers on the Cosserat theory, for example, References [2; 3; 4; 5; 6; 7; 8; 9; 10].  
In the absence of a couple moment, Equation (2) reduces to the following from [28]:  
\[e_{kij}\sigma_{ij}=0\]Equation (3) demonstrates that the symmetry between stress and its work-conjugate strain measure is maintained. Additionally, the Cosserat continuum simplifies to the classical continuum under these conditions. The symmetry of the stress and strain tensors also introduces a minor symmetry in the elasticity tensor. The definitions of stress and moment traction are provided as follows:  
\[\left\{\begin{array}{l}\mathbf{t}_{r}=\sigma\mathbf{n}\\ \mathbf{t}_{m}=\mathbf{\mu}\mathbf{n}\end{array}\right. \tag{4}\]  
where \(\mathbf{n}\) is the normal vector of the surface in the current coordinates.
## 3 Results and Discussion
### Reactive Oxygen Species (ROS)  
The inhibitory effects of okra ethanol extracts, prepared using different cooking methods, on ROS expression in H\({}_{2}\)O\({}_{2}\)-induced RAW264.7 cells were examined. The results are presented in Table 3 and Figure 5. At the times tested, H\({}_{2}\)O\({}_{2}\) strongly induced high ROS generation in RAW264.7 cells--H\({}_{2}\)O\({}_{2}\) was able to induce ROS generation even at 30 min. H\({}_{2}\)O\({}_{2}\)-treated cells showed increased ROS levels in 0 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL ROE (531.387%), BOE (514.25%), SOE (605.85%), and SVOE (628.35%), which indicated that H\({}_{2}\)O\({}_{2}\) had a pronounced induction of oxidative stress. In addition, the H\({}_{2}\)O\({}_{2}\) and okra-treated groups showed a significantly decreased ROS expression in a concentration-dependent manner--ROE (\(p<0.001\)), BOE (\(p<0.05\)), SOE (\(p<0.001\)), and SVOE (\(p<0.001\)). All four cooking methods showed an inhibitory effect on ROS at 200 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL, compared to the group treated with H\({}_{2}\)O\({}_{2}\) only: ROE--20.40%, BOE--35.18%, SOE--23.45%, and SVOE--47.60%. Notably, the sous-vide cooking led to the highest rate of ROS reduction, indicating the presence of an antioxidant.  
Figure 4: Effect of cooking method and okra extract on inducible nitric oxide synthase (iNOS), tumor necrosis factor alpha (TNF-\(\alpha\)), interleukin 6 (IL-6), and interleukin 1-beta (IL-1\(\beta\)) mRNA levels in LPS-induced RAW264.7 cells. The cells were pretreated with ROE, BOE, SOE, and SVOE and then cotreated with LPS (1 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro}\)/mL) for an additional 24 h. Total RNA was isolated, and Reverse-Transcription-Polymerase Chain Reaction (RT-PCR) was performed for iNOS, TNF-\(\alpha\), IL-6 and IL-1\(\beta\). Actin was used as an internal control for RT-PCR (the relative band intensities are indicated above each band). iNOS: inducible nitric oxide synthase, TNF-\(\alpha\): tumor necrosis factor alpha, IL-6: interleukin 6, IL-1\(\beta\): interleukin 1-beta, LPS: lipopolysaccharide, ROE: raw okra ethanol extract, BOE: blanched okra ethanol extract, SOE: steamed okra ethanol extract, and SVOE: sous-vide cooked okra ethanol extract.
# Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals
## 2 Materials and Methods
### Dual-Output Two-Stream Convolutional Neural Network
When two streams are fused by concatenation, the attention mechanism (CBAM) [36] module will automatically select the most descriptive features learned by the two stream networks. Then, batch normalization (BN) is used to prevent overfitting. The ensemble prediction was the final output, as shown in the bottom layer of Figure 4. The two cross-entropy losses were combined to optimize the learning process. The dual-output and two cross-entropy losses in this structure mainly borrowed the ideas of GoogleNet architecture. The additional classification network mainly provided gradient training for the previous convolution. When the network deepens, the gradient cannot be effectively transmitted from back to front, and network parameters cannot be updated. Such a branch can alleviate the gradient propagation problem. Most CNNs provide the pre-trained models based on the ImageNet dataset. For the pre-trained models, the CSI data is unknown. The transfer learning allowed us to use a small amount of newly  
Figure 4: The framework of our dual-output two-stream network. Figure 3: Architecture of the proposed sign language recognition method. labeled data to build a high-quality classification model for the new data. Therefore, we used the transfer learning to fine-tune the pre-trained CNN models to speed up the training and improve the accuracy. In transfer learning, we freeze the first five layers of the pre-trained model and train the remaining layers. In this way, we retain the generic features learned from the ImageNet data set, and also learn domain knowledge from the CSI data.
## 4 Results and Discussion
### Test of the Numerical Model with Various Thermal Solicitations
In our model, the liquid fraction (Figure 17) during solidification and melting, both complete and partial, is calculated based on Equations (12) and (14), yielding the enthalpy variations \(\Delta h_{m}\) and \(\Delta h_{s}\) (Figure 18) by means of integrating the derivative of enthalpy during melting \(dh_{m}/dT\) and solidification \(dh_{s}/dT\). Figure 17: Evolution of the liquid fraction during partial melting/solidification, as used in our model. We have:  
\[\Delta h_{m}(T)=\int_{T_{i}}^{T}\frac{dh_{m}\left(T\right)}{dT}\ dT-C_{L}(T-T_{i}) \tag{16}\]  
where \(T_{i}\) denotes the initial temperature at which the PCM is assumed to be completely liquid. It is also assumed that the liquid fraction of the hybrid model, during either the melting or partial melting phase, is as follows:  
\[f_{liq\_m}(T)=\frac{\Delta h_{m}(T)}{\Delta h_{m}(T_{M\ _m})} \tag{17}\]  
Similarly, as for the solidification phase, we can determine:  
\[\Delta h_{s}(T)=\int_{T_{i}}^{T}\frac{dh_{s}\left(T\right)}{dT}\ dT-C_{L}(T-T_{i}) \tag{18}\]  
The liquid fraction of the hybrid model, during solidification or partial solidification, is then:  
\[f_{liq\_s}(T)=\frac{\Delta h_{s}(T)}{\Delta h_{s}(T_{M\ _s})} \tag{19}\]  
In Equations (16) and (18), it was assumed that only \(C_{L}\) is used given its proximity to \(C_{s}\). The partial melting tests (Figure 19) were performed over a temperature range extending from T\({}_{1}\) (10 \({}^{\circ}\)C) to T\({}_{2}\) (24 \({}^{\circ}\)C) through an intermediate temperature T\({}_{3}\) (15 \({}^{\circ}\)C). The sample was also subjected to temperature variations ranging from T\({}_{4}\) (30 \({}^{\circ}\)C) to T\({}_{5}\) (18 \({}^{\circ}\)C) through T\({}_{6}\) (23 \({}^{\circ}\)C), causing partial solidification of the PCM (Figure 20). The evolution of the liquid fraction during these partial state changes is obtained from Equations (17) and (19). Figures 19 and 20 present the results obtained in the case of temperature stress leading to partial melting and solidification, respectively. It can be noted that the melting temperatures obtained for the coating containing the Inertek 23 PCM (Figure 18) are very close to those obtained by DSC on PCM (Figure 2). Figure 18: Calculation of the liquid fraction from enthalpy. A good level of agreement could be observed between the experimental flux and the numerical flux obtained with the hybrid model integrating a Gumbel function. These experiments confirm that the model developed herein can correctly simulate the behavior of the ETMV 8 coating even in the presence of various thermal loadings, which generate partial changes to the state of the PCM contained in the mixture.
## 2 System Model
### UFMC Systems  
UFMC is one of the 5G candidate waveforms. Its block diagram is shown in Figure 2. First, the input data are QAM modulated, then inserted using pilot tones prior to S/P conversion. Then, the signal is divided into \(B\) sub-bands, each with \(M\) QAM symbols. Moreover, each sub-band passes through zero-padding, IFFT processing, and filtering in sequence before performing the vector addition operation to obtain the UFMC signal. It must be noted that the zero-padding step should make up the sub-band length to the subcarrier length \(N\). The UFMC signals can be expressed as [33]  
\[x_{UFMC}[n]=\sum_{b=1}^{B}\sum_{l=0}^{L-1}\sum_{m=0}^{N-1}X[b,m]\cdot e^{\frac {2-\text{min}}{N}}f_{b}(l),0\leq n\leq(N+L-1), \tag{2}\]  
Figure 1: Block diagram of the OFDM.  
where \(X[b,m]\) is the QAM symbol after padding with zeros and \(f_{b}(l)\) is the finite impulse response (FIR) filter. Usually, we select the filter type for \(f_{b}(l)\) based on the Dolph-Chebyshev filter, which can be expressed as [37; 45]  
\[f_{b}(l)=h_{b}(l)e^{\frac{\ln\pi}{N}(\frac{N-N_{ZG}}{2}+(b-\frac{1}{2})n+\frac{ N}{2})J},\ 0\leq l\leq(L-1), \tag{3}\]  
where \(N_{ZG}\) is the zero-padded part of the sub-band and \(h_{b}(l)\) is the Dolph-Chebyshev prototype filter. This filter has the mathematical model described below [37; 45]:  
\[h_{b}(l)=(-1)^{l}\frac{\cos\left[N\text{cos}^{-1}\left[\mu\cos\left(\frac{\pi l }{N}\right)\right]\right]}{\cosh\left[N\text{cosh}^{-1}(\mu)\right]} \tag{4}\]  
where \(\mu=\cos[\frac{1}{N}\text{cosh}^{-1}(10^{\alpha})]\) and \(\alpha\in\mathbb{R}^{+}\) is defined as the attenuation parameter determining the filter side lobe attenuation level. Before transmitting, the UFMC signal must be up-converted into an RF signal.  
At the receiving end, the received signal is down-converted from the channel output into a baseband signal and rearranged into a parallel signal through the S/P procedure. Next, the signal is padded with zeros to double the subcarrier length and the \(2N\)-point FFT is applied. After discarding the even-indexed elements and extracting the remaining odd-indexed ones [33; 45; 46], the signal is separated into pilot parts to provide channel estimation and data parts for detection. Finally, the detected signal passes through a P/S process and QAM demodulation to recover the transmitted data.
## 3 Methodology
### Step 4 Monte Carlo Simulation  
Monte Carlo simulation is employed to estimate the availability. Figure 11 shows the flowchart of the Monte Carlo simulation [21]. First of all, components, their states, and their configuration are defined. Moreover, the next transition time for each component is estimated by the random number generation. The transition time is the time when the phase of a component in the system is changed from normal to failure. In this step, the generated random number is converted into a value of time using a conversion method at a cumulative distribution function. Figure 12 shows how the generated random number is transferred to the value of time by the conversion method. The cumulative distribution function for the exponential distribution is indicated in Equation (1).  
\[F(x)=1-e^{-\lambda x} \tag{1}\]  
where \(\lambda\) is the failure rate, and \(x\) is a value of time.  
\[R=F(x)=1-e^{-\lambda x} \tag{2}\]  
where \(R\) is the random number between 0 and 1. \(R^{*}\) is a generated random number between 0 and 1.  
The shortest transition time is found among all of the predicted times, and then the system time is changed to the shortest transition time. If the time is shorter than the mission time, the transition times for all component are estimated again. The mission time is total operation time required to the system like lifespan. When the time is longer than the mission time, the system's availability is calculated. This process is just one simulation. If the number of simulations is lower than the desired number of simulations, the next simulation is repeatedly performed. The desired number of simulations is determined as referring the convergence of results. When a result converges sufficiently, the number of simulations is selected as the desired number of simulations. The desired number of simulations is determined as setting a sufficiently high number of simu  
Figure 11: Procedure for availability estimation using Monte Carlo simulation [21].  
number of simulations after the initial simulation. When the number of simulations is the same as the desired number of simulations, the average system availability is calculated finally. The average system availability is the result after the last simulation, while the system availability is the result of each simulation.  
The predicted time from the generated random number is shown in Equation (3).  
\[x=F^{-1}(R)=-\frac{1}{\lambda}\ln(1-R) \tag{3}\]
###### Abstract  
In this paper, we present a method to characterize a complete optical Holographic Data Storage System (HDSS), where we identify the elements that limit the capacity to register and restore the information introduced by means of a Liquid Cristal on Silicon (LCoS) microdisplay as the data paper. In the literature, it has been shown that LCoS exhibits an anamorphic and frequency dependent effect when periodic optical elements are addressed to LCoS microdisplays in diffractive optics applications. We tested whether this effect is still relevant in the application to HDSS, where non-periodic binary elements are applied, as it is the case in binary data pages codified by Binary Intensity Modulation (BIM). To test the limits in storage data density and in spatial bandwidth of the HDSS, we used anamorphic patterns with different resolutions. We analyzed the performance of the microdisplay in situ using figures of merit adapted to HDSS. A local characterization across the aperture of the system was also demonstrated with our proposed methodology, which results in an estimation of the illumination uniformity and the contrast generated by the LCoS. We show the extent of the increase in the Bit Error Rate (BER) when introducing a photopolymer as the recording material, thus all the important elements in a HDSS are considered in the characterization methodology demonstrated in this paper.  
**Keywords:** holographic data storage; holographic and volume memories; parallel-aligned; liquid-crystal on silicon; liquid crystals; spatial light modulator; photopolymer
## 4 Conclusions  
* There is a positive correlation between the forming pressure and the mechanical properties of aluminum dress non-fired bricks, with a critical value of 18 MPa. When the forming pressure is greater than 18 MPa, the forming pressure continues to increase, which slightly improves the performance of non-fired bricks. The content of the main phases of non-fired bricks under different forming pressures does not change significantly.
* The mixing-water amount has a positive correlation with the mechanical properties of aluminum dress non-fired bricks before 14%, and has a negative correlation after 14%. When the mixing-water amount is 14%, the two hydration products CAH and CSH on the surface of the non-fired bricks are most abundant and intertwine, thereby increasing the strength of the non-fired bricks.
* When the particle size of aluminum drops is 100-150 mesh, the mechanical properties of non-fired bricks reach the maximum value and then decrease rapidly. This is because the active alumina is mainly distributed in the range larger than 200 mesh, which reacts with Ca(OH)\({}_{2}\) and CaSO\({}_{4}\) to generate excess AFT. Since there is no connection between the particles, the performance decreases overall.
* Through the multi-factor test based on the response surface method, the influence of different factors on the compressive strength of aluminum dress non-fired bricks is as  
Figure 14: (**a**) Interaction response surface between forming pressure and mixing-water amount; (**b**) the interaction response surface between forming pressure and aluminum dress particle size; (**c**) response surface of interaction between mixing-water amount and aluminum dress particle size.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Value** & **Item** & **Value** \\ \hline Std. Dev & 0.3802 & R\({}^{2}\) & 0.9891 \\ Mean & 20.70 & Adjusted R\({}^{2}\) & 0.9696 \\ C.V.\% & 1.84 & Predicted R\({}^{2}\) & 0.8544 \\  & & Adeq Precision & 22.3974 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Correlation analysis result of experimental model.  
follows: forming pressure > aluminum drops particle size > mixing-water amount. The optimal process parameters are as follows: forming pressure is 18 MPa, mixing-water amount is 15% and aluminum drops particle size range is 80-130 mesh. Under this condition, the compressive strength of non-fired bricks is 24.66 MPa, which is only 0.36% different from the model's predicted value.  
Conceptualization, H.N. and W.W.; methodology, W.W.; validation, W.W.; formal analysis, C.L. and H.N.; investigation, X.W. and S.L.; resources, H.N. and S.L.; writing--original draft, H.N.; writing--review and editing, S.L. and W.W.; visualization, Y.Z. and W.W.; supervision, S.L. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 3 System Overview and Problem Statement
### Challenges and Design Goals  
Based on this backhaul system architecture, several challenges as the consequence of adopting mm-wave links and corresponding design goals for our proposed mm-wave backhaul framework are summarized as follows.