esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings and Discussion
### Environmental Impact Produced
#### 4.4.2 Disease Transmission  
Our science mapping analysis revealed many studies on the human-to-human transmissions of the COVID-19 virus, identifying terms such as human, viral disease, disease transmission, virus transmission, and epidemic (Figures 13b and 14). Studies have revealed that the disease is transmitted via exposure to respiratory fluids carrying infectious virus. The epidemiological evidence for the dominance of the airborne spread of COVID-19 via droplets and aerosol is increasing [73]. The two principal ways of exposure are the (1) inhalation of droplets and aerosol particles or (2) the deposition of droplets and particles on exposed mucous membranes in the mouth, nose, or eye. Nevertheless, studies have revealed the persistence of COVID-19 on inanimate surfaces; for example, the virus can survive for 6.8 and 5.6 h on plastics and stainless steel, respectively [74]. Therefore, it is possible for people to be infected by touching their mucous membranes with their hands if they have touched surfaces or objects (fomites) with virus on them; however, the risk is generally considered to be low. In any case, cleaning by using soap or detergent and disinfection, using a product or process designed to inactivate the virus, can reduce the risk of fomite transmission.  
Consequently, efforts to prevent disease transmission have been centered on reducing exposure to droplets and aerosol particles by effective ventilation and air disinfection systems [75], as well as the use of PPE, especially face masks, which reduce the emission and spread of respiratory viruses through airborne droplets and aerosols and reduce the inhalation of airborne respiratory viruses [76]. In this sense, terms such as face masks, personal protective equipment, or health care personnel are represented in our science mapping analysis (Figure 13b). However, after their use, face masks can present contamination. One study reported a virus survival time of 7 days on the surface of PPE waste, such as face masks [50,77], resulting in considerable health risks associated with the possibility of COVID-19 virus transmission by handling PPE litter [16] or uncontrolled dumping in land-fills [78]. Consequently, we must cautiously manage the enormous amount of PPE waste generated during and after the pandemic to avoid the risk of the secondary transmission of the virus.  
In the case of waste produced in sanitary facilities, their classification as biomedical waste promotes their being handled in accordance with safety measures. However, public participation in the separate and timely collection of face masks is one of the key factors in the effective management of this type of waste [50].
## 3 Methodology
### ARIMA Models  
ARIMA is a popular forecasting tool used in time series forecasting. In this approach, the forecast is based on a linear combination of past values and random errors. The general formula for an ARIMA (p,d,q) model is given in Equation (1). Two primary components of ARIMA are autoregressive (AR) and moving average (MA) functions. The p and q parameters in Equation (1) represent the order of AR and MA functions, respectively. The third parameter, d, gives the number of differences performed on the series to make it stationary.  
\[\Big{(}1-\phi_{1}B-\phi_{2}B^{2}-\ldots-\phi_{p}B^{p}\Big{)}(1-B)^{d}Y_{t}= \Big{(}1-\theta_{1}B-\theta_{2}B^{2}-\ldots-\theta_{q}B^{q}\Big{)}e_{t} \tag{1}\]  
where \(Y_{t}\) and \(e_{t}\) represent the actual value and the error (offset between the actual and the fitted value). Parameter sets \(\phi_{i}\) (\(i=1,\,2,\,\ldots,\,p\)) and \(\theta_{j}\) (\(j=0,\,1,\,2,\,\ldots,\,q\)) represent AR and MA sections of the ARIMA. The term \((1-\mathrm{B})^{\mathrm{d}}\) serves as a difference operator.
## Results  
### Evaluation Metrics  
To assess the performance of both the baseline model and the EAND-LPRM model, we introduced three evaluation metrics: test accuracy, loss, and time. Firstly, we used test accuracy to determine the model's performance on the test dataset, which represents the proportion of license plate samples correctly predicted by the model. It is calculated as  
\[Accuracy=\frac{Number\ of\ Correct\ Predictions}{Total\ Number\ of\ Predictions}, \tag{8}\]  
Secondly, we considered the experimental loss metric, which measures the errors occurring during the training process, indicating the degree of difference between the model predictions and the true labels of the license plates. It is typically measured using the cross-entropy loss function, defined as  
\[L=-\frac{1}{N}{\sum_{i=1}^{N}{(y_{i}log(p_{i})+(1-y_{i})log(1-p_{i}))}}, \tag{9}\]  
Figure 3: Self-made dataset of Chinese license plate images with multiple complex conditions.  
Lastly, we focused on the time metric, representing the time required for the model to complete a license plate character prediction task, which is particularly crucial for applications requiring real-time processing.
# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 3 Challenges in Recommendation Systems
### Scalability  
Scalability problems have been significantly raised due to the fast growth of e-commerce sites. Modern RS methodologies are required to generate quick results for large scale applications. RSs can search for many potential neighbors in real-time, but the demands of modern e-commerce sites require them to search for a larger number of neighbors. Algorithms also experience performance issues for consumers with large amounts of information [32]. For example, if a site has tens of thousands of data points for one user, it can be difficult and tedious to find a relevant neighbor for a given neighbor. Filtering algorithms that utilize nearest-neighbor techniques need an increase in computation power due to the massive increase in products or users. For a platform that has millions of users and products, scalability is a serious issue. A common technique to reduce scalability issues is by using one-dimensionality reduction [33; 34]. Clustering techniques can be utilized to mitigate scalability issues. Their primary function is to segment the users using a clustering algorithm and use each segment as a neighborhood. Next, for any active user, its neighborhood is selected by looking into the partition, and the partition is used as the user's neighborhood. Upon completing the neighborhood selection, classical filtering algorithms can be implemented to generate a prediction [35]. There are two significant benefits of implementing clustering techniques. Firstly, it alleviates the sparsity of the data set. Secondly, it divides the data into smaller partitions, which significantly reduces prediction generation speeds. Singular value decomposition (SVD) has also been used to reduce the scalability issue [34]. SVD is used for dimensionality reduction. SVD produces a set of uncorrelated eigenvectors. Customers and products are each represented by a unique eigenvector. This process allows customers who have rated similar (but not the same) products to be mapped by the same eigenvectors. Once the n x m rating matrix is decomposed into SVD component matrices, predictions can be generated by calculating the cosine similarities (dot product) between \(n\)-pseudo customers and \(n\)-pseudo products.
# Physical Layer Latency Management Mechanisms: A Study for Millimeter-Wave Wi-Fi
## 3 Latency-Inducing Receiver Digital Baseband  
The TX and RX both need to comply with the IEEE 802.11 ad PHY standard, which is especially important for the TX as it should correctly prepare packets for transmission, use the appropriate waveform, and stay within transmission power limitations. Its main contribution to latency is the 1.76 Gsps finite transmission data rate. This study assumes that data are prepared for transmission using high-throughput components [31], avoiding any potential bottlenecks. Moreover, any processing delay is compensated for by formatting the data during preamble and header transmission, filling the transmission buffer with payload symbols in real-time.  
The RX, on the other hand, is only responsible for correct data reception; the path it takes to achieve this goal is left to the system designers to determine. Several common RX DBB design solutions exist, depending on how the RX DBB blocks are organized, which signal domains are used, and how timely or accurate data reception is. The RX DBB used in this work operates in single carrier (SC) mode and employs frequency domain equalization (FDE). It is roughly based on the work presented in [32], while Figure 3 outlines its design.
## 4 Optimal Control Modeling for the UAV-UGV Formation-Maintenance Controller
### Linearization of the Relative Equations of Motion  
During the UAV-UGV formation-maintenance process, assuming that \(\theta_{f}\), \(\theta_{l}\), and \(\psi_{e}=\psi_{vl}-\psi_{vf}\) can be considered as small quantities, while considering the state of the UGV as an input quantity, Equation (11) is converted to  
\[\left\{\begin{array}{l}\dot{x}=V_{f}+V_{f}\theta_{f}\theta_{l}-V_{l}+y\dot{ \theta}_{l}-z\dot{y}_{vl}\\ \dot{y}=-V_{f}\theta_{l}+V_{f}\theta_{f}-x\dot{\theta}_{l}+z\dot{y}_{vl}\theta _{l}\\ \dot{z}=V_{f}\psi_{e}-y\dot{\psi}_{vl}\theta_{l}+x\dot{y}_{vl}\\ \psi_{e}=\psi_{vl}-\psi_{vf}\end{array}\right. \tag{12}\]  
Using the small-perturbation linearization method, Equation (12) can be linearized as  
\[\left\{\begin{array}{l}\dot{x}=\dot{\theta}_{l}y-\dot{\psi}_{vl}z+\left(1+ \theta_{f0}\theta_{l}\right)V_{f}+V_{f0}\theta_{l}\theta_{f}-V_{l}\\ \dot{y}=-\dot{\theta}_{l}x+\dot{\psi}_{vl}\theta_{l}z+\left(\theta_{f0}-\theta _{l}\right)V_{f}+V_{f0}\theta_{f}\\ \dot{z}=\dot{\psi}_{vl}x-\dot{\psi}_{vl}\theta_{l}y+\left(\psi_{vl}-\psi_{vf0 }\right)V_{f}-V_{f0}\psi_{vf}\end{array}\right. \tag{13}\]  
where \(v_{f0}\), \(\theta_{f0}\), and \(\psi_{vf0}\) are the state equilibrium points selected during linearization.  
Describing Equation (13) as a state-space form (SSF) yields  
\[\left\{\begin{array}{l}\dot{\mathbf{X}}=\mathbf{A}\mathbf{X}+\mathbf{B}\mathbf{U}+\tilde{\mathbf{B}} \mathbf{W}\\ \mathbf{Y}=\mathbf{C}\mathbf{X}\end{array}\right. \tag{14}\]  
where \(\mathbf{X}=\left[\begin{array}{ccc}x&y&z\end{array}\right]^{T}\) denotes the state variable; \(\mathbf{U}=\left[\begin{array}{ccc}V_{f}&\theta_{f}&\psi_{vf}\end{array}\right]^ {T}\) denotes the control variable of the formation controller, which is the motion state of the UAV; \(\mathbf{Y}=\left[\begin{array}{ccc}x&y&z\end{array}\right]^{T}\) denotes the output variable; the perturbation variable is the velocity \(\mathbf{W}=\mathbf{V}_{l}\) of the UGV.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 4 Industrial-Grade Circular Pipe Conveying Test of Gangue Slurry
### Design of Long-Distance Pipeline Transportation Test
#### 4.2.2 Process of Long-Distance Pipeline Transportation Test  
The long-distance pipeline transportation test process was divided into six primary functions: pre-filling lubrication, mortar and water separation, grouting filling, mortar and gangue separation, post-filling pipe washing, and air pressure cleaning, as shown in Figure 5. The detailed processes were as follows.  
Pre-filling lubrication pipe: a a grouting pump was used to pump 30 m\({}^{3}\) of clean water into the pipeline, achieving the goal of wetting the pipeline and removing debris inside the pipeline. Mortar waterproofing: before the pumping of the 30 m\({}^{3}\) of water was completed, 40 m\({}^{3}\) of prepared mortar was pumped into the pipeline through a grouting pump to isolate the water from the gangue slurry. Grouting filling: Before the 40 m\({}^{3}\) of slurry was pumped, the prepared gangue slurry was pumped into the pipeline using a grouting pump. When the slurry was seen at the end of the pipeline, the pressure at different positions of the pipeline was monitored by changing the pumping flow rate of the grouting pump. Mortar separation: before the pumping of the gangue slurry was completed, the prepared 20 m\({}^{3}\) of mortar was pumped into the pipeline again using a grouting pump to isolate the gangue slurry from the water. After filling and washing the pipeline: Before the 20 m\({}^{3}\) of mortar was pumped, the injection pump was used again to pump clean water into the pipeline. When the clean water was seen at the end of the pipeline, the pump was stopped from achieving the purpose of cleaning the pipeline. Pressure air cleaning: after the clean water was pumped, the pressurized air system was opened, and high-pressure airflow was used to blow out the remaining large particles of gangue from the pipeline, achieving the goal of removing sewage from the pipeline and thoroughly cleaning the pipeline.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### Error Analysis
\begin{pmatrix}\frac{\partial\xi_{1}}{\partial x}&\frac{\partial\xi_{1}}{ \partial y}\\ \frac{\partial\xi_{2}}{\partial x}&\frac{\partial\xi_{2}}{\partial y}\end{pmatrix}  \tag{32}\]The Fisher Information Matrix \(I\) is computed:  
\[I=J^{T}\cdot\beta^{-1}\cdot J \tag{33}\]  
Here, \(\beta\) denotes the covariance matrix of the observation noise. The observation errors are assumed to be independent and follow a Gaussian distribution. As a result, \(\beta\) is a diagonal matrix, with its diagonal elements representing the variances of the observation errors:  
\[\beta= \begin{pmatrix}\rho^{2}&0\\ 0&\rho^{2}\end{pmatrix}  \tag{34}\]  
Finally, the Cramer-Rao lower bound is given by the diagonal elements of the inverse of the Fisher Information Matrix \(I\):  
\[CRLB=\sqrt{trace(I^{-1})} \tag{35}\]  
The CRLB is compared with the RMSE to verify the positioning performance. After completing the positioning, the terminal needs to estimate the TA and perform Doppler frequency offset pre-compensation using satellite ephemeris and its own position to complete the entire uplink synchronization process. \(U_{h-sat}\) represents the distance from the computed position \(\hat{\mathbf{q}}\) to the satellite; \(U_{n-sat}\) represents the distance from the true position \(\mathbf{q}\) to the satellite; and \(\Delta U\) denotes the difference between \(U_{\hat{n}-sat}\) and \(U_{n-sat}\). To ensure that the uplink signal from each terminal received by the satellite falls within the range of one CP, \(\Delta U\) should satisfy:  
\[\Delta U=\left|U_{n-sat}-U_{h-sat}\right|\leq\left(cp/2\right)\cdot c \tag{36}\]  
In this paper, the length of the cyclic prefix is taken as one-quarter of the OFDM symbol length, and the calculated target value is approximately 2500 m:  
\[\Delta U\leq 2500\text{ m} \tag{37}\]  
When the distance between the position obtained after localization and the satellite is less than 2500 m compared to the true distance from the position to the satellite, the NB-IoT terminal can correctly complete the uplink synchronization based on the computed TA value and pre-compensated Doppler frequency offset.
# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 3 Cases for Investigation  
This section describes the detection and selection of the jamming incidents used for positioning analyses.
## 1 Introduction
However, there are still some deficiencies in the dynamic response analysis of underwater towed systems. Most of the research on the control of vehicles concentrates on control algorithms, but there is virtually no research on the dynamic response, especially the three-dimensional hydrodynamic characteristics of towed systems in complex flow fields, especially wake and waves. The way in which energy is transferred from the cable to the fluid, the transfer rate, the smoothness of the transfer, and the mutual transfer are all key issues. Due to the effects of momentum exchange and wake non-uniformity, the pulling cable undergoes alternating changes of relaxation and tension, and the mechanisms behind the effects of cable vibration and cable shape instability need to be studied. Therefore, considering the interactions between the towed cable and the fluid, a new method is needed to address the three-dimensional dynamic response of underwater towed systems. Building on the extensive work of previous researchers, this study proposes a new coupled three-dimensional hydrodynamic model for the dynamic analysis of underwater drag systems in complex flows. The hydrodynamic characteristics of the towed system are numerically simulated using the CFD method, discretizing the towed cable into a series of elastic segments using the lumped mass method. Velocity is transferred from the flow field to the cable dynamic model in an innovative way to imitate the motion of the cable. Several iterations are conducted to enhance the dynamic reactivity of the cable system. The phenomenon of mutual influence between the cable and the wake around the spacecraft was discovered, and the energy method was constructed. The region division criterion was derived, and the region boundary was defined to distinguish between the free towed zone and the attached towed zone. The wake of an underwater vehicle is divided into a free towed zone, a transition zone, and an attached towed zone. The coupling mechanism of the unsteady non-uniformity of the wake caused by the manipulation of the underwater vehicle on cable vibration instability is studied.
## 4 Deployment Scenario and Prototype  
Based on the proposed architecture, a prototype has been developed and deployed. The physical location corresponds to a closed community of neighbors, covering approximately 6000 m\({}^{2}\) in the town of Merida, Badajoz in Spain. The complex consists of three buildings with a total of 148 homes and is equipped with two swimming pools, green areas, a playground, and a paddle tennis court.
## 3 Proposed Solution
### Forwarding Strategy
#### 3.2.2 Consumer Interest and Producer Data Packet Forwarding
By doing so, the router establishes a path toward the producer for future consumer Interest forwarding without relying on the involvement of the Anchor, particularly, in the worst-case scenarios, where the path between the MP and consumer is stretched to the Anchor. The forwarding process of the Data packet is detailed in Algorithm 2. Figure 3: Example of the forwarding of Interest and Data packets. ```
1:Regular CS and PIT lookup
2:if this.IsAnchor then
3:Entry \(\leftarrow\) FIB.GetMatchedPrefix (I.name)
4:if Entry \(\
eq\) e and Entry. HasFace then
5: SendToNeighbor(I, \(f_{i}\))
6:else
7: AddecoordinateToInterest (I)
8:for\(N_{i}\) in Neighbors do
9:\(Dist_{i}=\sqrt{\left(x_{N_{i}}-x_{Inter}\right)^{2}+\left(y_{N_{i}}-y_{Inter} \right)^{2}+\left(z_{N_{i}}-z_{Inter}\right)^{2}}\)
10:endfor
11:\(CloseNei=min(Dist_{i})\)
12: SendToNeighbor (\(I,CloseNei.face\))
13:else
14:if I.HaspositifCoordinates and I.IsNoteCreate_Nor_RemovePath then
15:for\(N_{i}\) in Neighbors do
16:if Entry \(\
eq\) e and I. HasNegatifCoordinates then
17:\(Dist_{i}=\sqrt{\left(x_{N_{i}}-x_{Inter}\right)^{2}+\left(y_{N_{i}}-y_{Inter} \right)^{2}+\left(z_{N_{i}}-z_{Inter}\right)^{2}}\)
18:endfor
19:\(CloseNei=min(Dist_{i})\)
20: SendToNeighbor (\(I,CloseNei.face\))
21:else
22:Entry \(\leftarrow\) FIB.LongestPrefixMatch(I.name)
23:for\(f_{i}\) in Entry.NextHop do
24: SendToNeighbor(I, \(f_{i}\))
25:endfor
26:return
```  
**Algorithm 1** Consumer_InterestForward(Interest \(I\), incomingface \(F\))  
Figure 4: Pipeline of the prediction–update procedure of the KF.
# A Computational Framework for 2D Crack Growth Based on the Adaptive Finite Element Method
## 2 Procedure of the Developed Program
### Generation of the Background Mesh  
The dichotomy approach is used to construct the background mesh, which builds the mesh in a triangle form using all of the initial exterior boundary nodes of the figure. The computational space in this approach is represented as a polygon by continually splitting the computational domain into two subsets until the complete polygon subsets were created. Consequently, in order to connect any internal boundaries, including holes, to the external boundary, connecting lines must be constructed. The shortest path connecting all internal and external boundary points is used to construct the connection line [43]. Figure 2 displays the proposed method, which starts by separating each of the initial selected boundary sites with a large-faced angle and creating an angle set for selecting the nearest nonadjacent point to be linked with a new connected line. The face angle size is identified by the following precedence: \(\pi\leq\theta_{1}<2\pi,\ \pi/2<\theta_{2}<\pi,\ 0<\theta_{3}\leq\pi/2\).  
As a consequence, singular elements must be built to precisely characterize the singularity field at the crack front. Since the advancing front method [39] generates triangular elements from the faces of the boundaries, the area surrounding the crack front should be separated before generation of the singular elements. First, nodes are created surround the crack tip in a rosette manner, and then the node at the crack front and the related edge segments are extracted. Rosette elements are then constructed by forming triangles as seen in Figure 3.  
Figure 2: Polygon division using the proposed dichotomy approach.
## 3 Results and Discussion
### Kinetics
#### 3.5.1 Kinetics in the Simulated Digestion Process
These equations can also be used to express the time evolution of \(c_{t}-c_{\infty}\), the difference between the current and final concentrations (the part of the compounds that needs to be liberated or degraded). The terminology of first and second order for Equations (10) and (11), respectively, arises because the time derivative of this concentration difference is proportional to its current value (first order) or to the square of its current value (second order), respectively. The expression for the half-life of reactants, in this case, is the time at which the concentration reaches a value halfway between the initial \(c_{0}\) and the final \(c_{\infty}\). It can be obtained by replacing \(c_{t}\) with \((c_{0}-c_{\infty})/2+c_{\infty}\) in Equations (10) and (11). By expressing \(t\) from these equations, we can solve for \(t_{1/2}\). The expression for the first-order reaction half-life is again:  
\[t_{1/2}=\frac{0.693}{k} \tag{12}\]  
and the expression for the second-order reaction half-life becomes:  
\[t_{1/2}=\frac{1}{k|c_{0}-c_{\infty}|} \tag{13}\]  
For the analysis of the experimental results of phenolic compounds liberated in the stomach and then liberated or degraded in the small intestine, to express the concentration as a function of time, we used Equations (10) and (11), and to express the half-life we used Equations (12) and (13), which enabled us to determine \(k\), \(c_{\infty}\) and \(t_{1/2}\). It is specified that \(c_{t}\) (mg kg\({}^{-1}\)) is the concentration of phenolic compounds at time \(t\), \(c_{0}\) (mg kg\({}^{-1}\)) is the concentration of the phenolic compounds at \(t\) = 0, \(c_{\infty}\) (mg kg\({}^{-1}\)) is the concentration of phenolic compounds at the endpoint of the reaction, \(k\) is the reaction rate constant (min\({}^{-1}\) for the first order kinetics, kg mg\({}^{-1}\) min\({}^{-1}\) for the second order kinetics), \(t\) is time (min), and \(t_{1/2}\) is the time when the concentration reaches 50% of the way from \(c_{0}\) to \(c_{\infty}\).