esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Technologies for Nanocellulose Production
### Ionic Liquid Pretreatment
#### 4.1.3 Physicochemical Pretreatment of Lignocellulosic Biomass  
Physicochemical pretreatment approaches, such as steam explosion, hydrothermal treatment, etc., are more effective and environmentally friendly than traditional physical and chemical pretreatment strategies. Steam explosion (SE), also known as auto-hydrolysis, is the most extensively explored and studied physicochemical pretreatment technique for biomass disintegration. A sudden pressure change during the steam explosion pretreatment generates an explosive disintegration in the biomass and may physically damage the structure of fibers. During this pretreatment, LB is exposed to steam of very high temperature for a residence time of a few seconds to a few minutes, followed by an abrupt release of pressure, which results in the hydrolysis of the acetyl group present in hemicellulose, and ultimately the breakdown of components of the cell wall [86]. Another widely used physicochemical technique is ammonia fiber explosion, which involves the use of liquid ammonia at a temperature and pressure between 60 and 100 \({}^{\circ}\)C and 1.7 and 2.1 MPa, respectively, for a small time period (5-60 min). Ammonia can expand suddenly in conditions of high pressure and temperature, which contributes to the swelling of biomass, increase in surface area, hemicellulose solubilization and physical breakdown of the biomass structure [87; 88].  
Steam explosion with dilute sulfuric acid was used for the pretreatment of _Miscanthus_\(\times\)_giganteus_, poplar and wheat straw. The results revealed that, irrespective of the biomass, approximately 90% of the hemicellulose fraction removed from the pretreated substrate was achieved [89]. Ammonia fiber expansion pretreatment of oil palm empty fruit bunch resulted in a reduced lignin content, 16.84 \(\pm\) 0.95% \(w/w\), as compared to an untreated biomass (23.38 \(\pm\) 0.88% \(w/w\)) [90]. In another work by [PERSON] et al., the effects of hydrothermal pretreatment and depressurization (DP) cycles were investigated on cellulose nanofiber from garlic skin waste biomass that improved the defibrillation, and subsequently affected the thermal stability, yield and crystallinity of nanofibers [13].
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 3 Proposal Architecture
### Big Data Cluster
The query's results are subsequently used to obtain individual records. Figure 7: (**a**) Traffic event detection algorithm part 1; (**b**) Traffic event detection algorithm part 2. The algorithm then consults the database to discover the coverage point each record belongs to. This process consists of calculating the distance, using Equation (1), between the point contained in each vehicle's record and the entire group of the monitoring points. A vehicle's record corresponds to a monitoring point when the distance between the points is equal to or less to than the radius of the monitoring point. \[d=2r\;\text{arcsin}\left(\sqrt{sin^{2}\left(\frac{\Phi_{1}-\Phi_{2}}{2}\right)+ cos\left(\Phi_{1}\right)\;cos\left(\Phi_{2}\right)\;sin^{2}\left(\frac{ \lambda_{2}-\lambda_{1}}{2}\right)}\right) \tag{1}\]  
Equation (1) shows the haversine formula used to calculate the distance between two points, where \(\Phi_{1}\), \(\Phi_{2}\) are the latitudes of the two given points and \(\lambda_{1}\), \(\lambda_{2}\) are their corresponding longitudes. After discovering the monitoring point for the record, the algorithm summarizes its speed with the rest of the record's speeds belonging to the same vehicle. Once all of the registers are processed, the average speed of each vehicle is calculated at each monitoring point. Following this, the algorithm searches for the median speed of each monitoring point, which requires the average speed of a minimum of three vehicles to provide reliable alerts for a real-time scenario. Next, the algorithm orders the monitoring points using the Road_ID and Sequence numbers to produce a list that is used as the cycle input, in which the monitoring points are marked as \"Speed congested\" only when the median speed is slower than the minimum speed threshold. Once all of the monitoring points are processed, they pass to a second cycle where each is evaluated to find the sequences of monitoring points that are marked as speed congested, which are considered queues. The queue length is given by the sum of the diameters of the monitoring points that comprise the queue. If the queue length is longer than the given threshold for the road where the queue is in, a traffic congestion alert is created, including the traffic line length and the central point of the traffic congestion. The algorithm then saves the traffic congestion alerts into the Traffic_congestion_Alerts model. Finally, the algorithm returns to the timer.
# Deceptive Jamming Detection for SAR Based on Cross-Track Interferometry
## 2 Principle of Deceptive Jamming Detection
### Geometric Configuration  
A schematic illustration for deception detection considered in our work is shown in Figure 1. The jammer \(J\) is located at the origin of the Cartesian coordinate system. Points \(A_{1}\) and \(A_{2}\) represent the two cross-track antennas of the side-looking SAR system. \((x,y,z)\) is the location of an arbitrary jammed target \(P\). Since the SAR platform flies along the \(y\)-axis at a fixed altitude with a speed of \(v_{a}\), we assume that the instantaneous locations of the master antenna \(A_{1}\) and the slave antenna \(A_{2}\) are \((X_{s},v_{a}t_{a},Z_{s})\) and \((X_{s}+B\cos(\alpha),v_{a}t_{a},Z_{s}+B\sin(\alpha))\) at slow time \(t_{a}\), respectively. The distances from the master antenna and the slave antenna to the jammer are, respectively, denoted by \(R_{mj}(t_{a})\) and \(R_{sj}(t_{a})\). \(R_{m}(t_{a})\) and \(R_{s}(t_{a})\) are the instantaneous slant ranges between the two antennas and the jammed target. \(\alpha\) denotes the inclination of the baseline, while \(B\) is the length of the baseline.  
Assume the jammer is a stationary point target within one synthetic aperture time, \(R_{mj}(t_{a})\) and \(R_{sj}(t_{a})\) are calculated by  
\[R_{mj}(t_{a})=\sqrt{{X_{s}}^{2}+{(v_{a}t_{a})}^{2}+{Z_{s}}^{2}} \tag{1}\]\[R_{sj}(t_{a})=\sqrt{\left(X_{s}+B\cos(\alpha)\right)^{2}+\left(v_{a}t_{a}\right)^ {2}+\left(Z_{s}+B\sin(\alpha)\right)^{2}} \tag{2}\]  
Similarly, \(R_{m}(t_{a})\) and \(R_{s}(t_{a})\), are given by  
\[R_{m}(t_{a})=\sqrt{\left(X_{s}-x\right)^{2}+\left(v_{a}t_{a}-y\right)^{2}+\left( Z_{s}-z\right)^{2}} \tag{3}\]  
\[R_{s}(t_{a})=\sqrt{\left(X_{s}+B\cos(\alpha)-x\right)^{2}+\left(v_{a}t_{a}-y \right)^{2}+\left(Z_{s}+B\sin(\alpha)-z\right)^{2}} \tag{4}\]
## 3 Iterative Scheduling Algorithm for Throughput Optimization  
To address the multi-objective optimization in the evacuation scenario, two primary stages require deliberation, after the establishment of the communication model outlined in the preceding section. The initial stage involves predicting unknown environmental information, specifically the trajectories of moving ground users. Subsequently, the second stage revolves around formulating an optimization algorithm for UAV to enhance total communication efficiency based on these predictions.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 4 Automatic Navigation System of a ship in a Fairway
### Ship Route Generator  
Before beginning a voyage, deck officers must establish an optimal route for the whole voyage. Nowadays, with the development of science and technology, route planning for ship navigation can be conducted automatically when the navigators enter the departure and destination points in a personal computer. The role of this function is to create the waypoints (WPs) for of route. From these known waypoints, the desired course and speed are easily calculated. Then, in combination with track-keeping algorithms, the necessary rudder angles and ship speeds are captured in each segment of the planned route. In this research, this automatic navigation process for a ship in a fairway was performed by numerical simulations on a computer. Specifically, a fairway in the Mokpo area of South Korea was  
Figure 3: The operational area of the automatic navigation system.  
applied. For this purpose, a route with ten waypoints was created (waypoint No 1 (WP1) to waypoint No 10 (WP10)), as shown in Figure 4.  
To be suitable for use in berthing, the ship's speed must be reduced gradually to avoid damaging the main engine. In this study, the initial speed of the ship chosen in the numerical simulation was 8 knots, with a corresponding propeller index of 2.75 (round per second). From WP1 to WP4, the ship speed is maintained at 8 knots. From WP5 to WP8, the propeller index is decreased to 1.0 rps (as was of the actual aboard the T/S SAE NURI). Then, the ship's propeller is stopped to affect the approach toward the WP10 area for berthing.
# Data-Driven Redundant Transform Based on Parseval Frames
## 3 Data-Driven Redundant Transform Model Based on Tight Frame
### Transform Learning for the Drtbf Model
#### 3.2.2 Transform Pair Update Phase
```
Input and Initialization:  Training data \(\mathbf{X}\), frame bound \((A,B)\), iteration \(num\). Build frames \(\mathbf{\Phi}\in\mathbb{R}^{M\times N}\) and \(\mathbf{\Psi}\in\mathbb{R}^{M\times N}\), either by using random entries or using N randomly chosen data. Output:  Frames \(\mathbf{\Phi}\), \(\mathbf{\Psi}\), Sparse coefficients \(\mathbf{Y}\), and thresholding values \(\mathbf{\lambda}\) Process: For l=1:\(num\) Sparse Coding Step:
1: Compute the sparse coefficients \(\mathbf{Y}\) and the thresholding values \(\mathbf{\lambda}\) via Algorithm(1). Frame Update Step:
2: Update \(\mathbf{\Psi}\) columnwise. Compute \(\mathbf{W}=\mathcal{S}_{\mathbf{\lambda}}(\mathbf{\Psi}^{T}\mathbf{X})\). For \(i=1:M\) Denote \(\mathcal{J}_{i}\) as the indices of zeros in the \(i^{\text{th}}\) column of \(\mathbf{W}\). Set \(\mathbf{\psi}_{i}^{T}\mathbf{X}^{\mathcal{J}_{i}}=0\). Compute \(\mathbf{\psi}_{i}\) via (21) and (22). End For
3: Update \(\mathbf{\Phi}\) via Gradient Descent, which is given as (25) and the step length is usually set to 0.01. End for
```  
**Algorithm 2**Transform pair learning algorithm.
## 3 Method
### Post-Processing of Cochlea Model Output
Figure 2: Temporal development of ISI histogram, summing all ISIs in all Bark bands and transferring them into frequencies f = 1/ ISI for three example sounds. Top: A single-tone saxophone melody; middle: a string pad sound consisting of two notes with fundamental frequencies \(f_{1}=93\) Hz and \(f_{2}=1762\) Hz; bottom: a large buckle gong with three lowest frequencies \(f_{1}=126\) Hz, \(f_{2}=199\) Hz, and \(f_{3}=252\) Hz. The lines at the upper part of the middle and bottom plots indicate these frequencies. The saxophone melody is perfectly represented by the strongest peak in all ISI histograms, as shown in Figure 4 in detail. The string pad’s sounds’ fundamentals are also clearly represented by the ISI histogram. The gong’s lowest partial is clearly there, still a residual ISI below this \(f_{1}\) is there, too. Figure 3: Same as Figure 2 for three two-note intervals played on a classical guitar. All have the same fundamental pitch g at \(f_{1}=193\) Hz. Top: octave above \(f_{1}\) at \(f_{2}=393\) Hz; middle: fifth above \(f_{1}\) at \(f_{2}=294\) Hz; bottom: major seventh above \(f_{1}\) at \(f_{2}=382\) Hz. In all cases, \(f_{1}\) is clearly represented in the ISI histogram. With the octave the \(f_{2}\) partial is nearly not present, with the fifth \(f_{2}\) is again only slightly there, still a residual periodicity below \(f_{1}\) appears. With the major seventh, again \(f_{2}\) is nearly not present; still, many periodicities above and below \(f_{1}\) are slightly present. In a third step, the ISI histogram is fused into a single number to make it comparable to the judgements of separateness and roughness of the listening tests, which are also single numbers for each sound. Three methods are used: the number of peaks N, a sum of the peaks, and each weighted with its amplitude like  
\[\text{W}=\sum_{\text{i=1}}^{\text{N}}\text{ISI}_{\text{i}}\, \tag{1}\]  
and a Shannon entropy like  
\[\text{S}=1/\log\text{N}\sum_{\text{i=1}}^{\text{N}}\text{ISI}_{\text{i}}\log \text{ISI}_{\text{i}}\, \tag{2}\]  
where N is the number of peaks and ISI\({}_{i}\) is the amplitude of the ith peak. The three methods decide which perception process is more likely to occur in listeners when correlating the results with the separateness and roughness perceptions.
## 3 Experiment Results and Discussion  
### Experiment Setup  
To demonstrate the proposed FPGA-based DDL-TDC, our proposed DDL-TDC architecture was implemented on a Xilinx XC5 VLA110T FPGA chip by using a Xilinx ISE 14.7 tool to synthesize the RTL Verilog code and do placement and routing to evaluate its performance in terms of time linearity. Figure 6 shows the experimental setup of the proposed DDL-TDC measured system. For this, _Trigger 1_ and _Trigger 2_ 166-MHz clock signals were generated using Agilent (Agilent Technologies, Santa Clara, CA, USA) 81130A signal generators with two asynchronous sources. Calibrations _cal_start_ and _cal_stop_ were respectively generated using two on-board oscillators from 100-MHz and 200-MHz clocks that employed discrete on-chip phase-locked loops (PLL) to generate two 166-MHz clock signals. Based on a clock signal of 166 MHz, the time conversion range was 6 ns. The resource utilization of the proposed DDL-TDC is shown in Table 2. The DDL-TDC required an additional 2% of the slice registers, 3% slice LUTs, 5% occupied slices, two PLLs, and two block RAM units, which caused a light resource overhead relative to the TDL-TDC, and the power consumption of the DDL-TDC was increased by only 7% relative to the TDL-TDC. The 6-ns interval was chosen in this work because we measured the average delay time for a vertical CARRY4 delay chain. The common technique to set the (average or total) delay of the cells is to lock the total delay of the line to a reference. For this circuit, 6 ns was a suitable delay time for 512 delay cells. A longer time would not have been suitable because the delay cells would not have worked in a single vertical chain. A smaller time would not have been suitable because the conversion range would have been too small. Thus, we chose 6 ns (166 MHz) as the clock cycle in this work. This was only suitable for the particular FPGA in this work. If a different FPGA were used, a different frequency would be expected.
## 2 Route Generation Methodology
### Feasible Path Generation: Turn Radius Consideration
#### 2.4.2 Reeds-Shepp Method  
The Reeds-Shepp path was developed to find the shortest path between two points that considers a minimum turn radius and allows forward and backward motion [40]. The Reeds-Shepp curve also requires starting and ending trajectories so that the turn radius can be properly incorporated.  
The Reeds-Shepp curve is used when the portion of the desired path being removed by the corner-cutting method is larger than twice the turn radius. This is the only condition where the Reeds-Shepp curve is used since it is a comparatively complex maneuver involving forward and backward motions that add additional travel to the path.  
To use the Reeds-Shepp path to resolve sharp turns, the start/end point must be where the two path segments meet each other, the start trajectory is the trajectory of the path before the turn, and the end trajectory is the trajectory of the path after the turn. Using these values, the Reeds-Shepp algorithm can be used to calculate the shortest forward/backward curved path that can be added to resolve the sharp turn. The Reeds-Shepp algorithm used in this research was borrowed from [PERSON] [41]. The process of using the Reeds-Shepp algorithm is visualized in Figure 10. As seen in Figure 10, the Reeds-Shepp path makes sharp turns feasible by adding some additional distance to perform the forward/backward turns such that the desired trajectories can be achieved while considering the vehicle turn radius. Like the corner-cutting method, the Reeds-Shepp method may not be ideal for vehicles with a large turn radius. In these cases, the added maneuver will be large, increasing the travel distance, and potentially colliding with obstacles. To limit the possibility of collisions, the distance buffer between the outer boundary of the area and obstacles should be increased in the graph generation stage. However, this reduces the coverage ratio since the vehicle cannot get close enough to the border, and it may restrict movement in tight spaces.  
In the study, the corner-cutting method was applied where applicable, and the Reeds-Shepp method was used when the corner-cutting method removed too much of the desired path. This means that the resulting path consists of smooth turns (from the corner-cutting method) and three-point turns (from the Reeds-Shepp method).  
Figure 9: A visual representation of when the corner-cutting method cannot be applied. In this example, the portion of the desired path \((x+y)\) being removed is greater than twice the turn radius \((2R)\).
# Traffic Flow Prediction Method Based on Seasonal Characteristics and SARIMA-NAR Model
## 3 Case Analysis
### The Prediction of the SARIMA Model
#### 3.1.2 The Determination of Model Parameters  
The autocorrelation (ACF) and partial autocorrelation (PACF) are shown in Figure 4. It can be seen from Figure 4b that, when \(d=0\) and \(D=1\), the autocorrelation coefficient exceeds the confidence range for four periods, and the coefficient of the sixth period is still larger than the upper confidence limit, showing a tailing phenomenon. It can also be found that, when \(d=0\) and \(D=1\), the sequence is still in a non-stationary state; when \(d=1\) and \(D=1\), only one coefficient in the autocorrelation and non-autocorrelation plots exceeds the confidence limit, showing tail censoring, suggesting that the sequence is stationary when \(d=1\) and \(D=1\). Referring to the empirical method, the value of \(P\) and \(Q\) is generally less than or equal to 2; therefore, the multiple SARIMA models shown in Table 3 were selected to calculate the Bayesian Information Metric (BIC), and the smaller the BIC value, the better the fitting effect of the model. It can be seen that the BIC value of the SARIMA (0,1,2) (0,1,2) \(\mathrm{l}_{12}\) model was 22.119, indicating the highest fitting accuracy and that this model is the optimal model.  
Figure 3: The time series of traffic flow and its breakdown from January 2014 to December 2018. (**a**) Original, (**b**) Trend, (**c**) Seasonal, (**d**) Residual.
## 5 Computational Analysis
### Computational Mesh Generation  
The geometry domain is sliced into three sections; upper, middle and lower. The lower part is associated with the distributor plate section, where unstructured mesh was generated. However, the structured mesh was generated in the middle and upper sections to capture the fluidization phenomena more accurately. The structured mesh is renowned for being more efficient for the quality and quantity of computational results. A hybrid mesh which is a combination of unstructured and structured mesh, is shown in Figure 14.  
Figure 13: Geometry of fluidization column along with air distributor plates.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Properties of Gas and Particle Flow** & **Parameter Values** \\ \hline Mean Particle diameter (\(\mu\)m) & 250, 590, 850 \\ \hline Density of sand (kg/m\({}^{3}\)) & 2590, 2510, 2420 \\ \hline Density of air (kg/m\({}^{3}\)) & 1.225 \\ \hline Multiphase model & Eulerian-Eulerian \\ \hline Superficial gas velocity (m/s) & 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4.0, 4.5 \\ \hline Restitution coefficient & 0.90 \\ \hline Packing limit of solids & 0.63 \\ \hline Drag model for gas-solid flows & Syamlal-Obrien \\ \hline Solution methodology & Pressure velocity coupling \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fluid properties and simulation parameters of gas-solid flows hydrodynamics.
## 1 Introduction
[30] proposed an enhanced encoder-decoder framework, which inputs the time series feature data into the encoder-decoder network model based on LSTM and calculates the RUL value at the end of the acquired signal combined with the linear regression algorithm of the output layer. [PERSON] et al. [31] combined kernel principal component analysis and long short-term memory network method to predict the remaining useful life of rotating machinery in view of the difficulty in extracting degraded information caused by redundant data from multiple sensors. The above RUL prediction model has been applied in the field of intelligent fault diagnosis and remains useful for life prediction to a certain extent. However, traditional remaining life prediction models, such as the convolutional neural network model (CNN), recurrent neural network model (RNN) and, long short-term memory network model (LSTM), Transformer model are difficult to capture long-term temporal dependence effectively and have insufficient feature extraction ability for signal key information. The accuracy of model prediction still needs to be further improved. In order to overcome the limitations of the above-mentioned method for predicting the remaining useful life of rolling bearings, the main contributions of this paper are as follows: (1) Dung Beetle algorithm optimized VMD combined with correlation coefficient method is proposed to reduce the noise of the original signal. It realizes automatic optimization of VMD initial parameters, reduces manual intervention, and captures degradation information in noisy signals more accurately. (2) A feature dimension reduction method based on a multi-domain mixed feature and isometric feature mapping (ISOMAP) algorithm is proposed. To capture the characteristics of vibration signals in different fields, obtain comprehensive information on signals, and combine the ISOMAP algorithm to better solve the problem of nonlinear vibration signal feature dimensionality reduction. (3) A remaining useful life prediction model of TCNMABG is proposed. Enhance the feature extraction ability of the model in the process of rotary machinery equipment degradation and improve the prediction accuracy of the remaining useful life prediction model. The structure of this article is as follows. Section 2 introduces the general framework of the remaining useful life prediction method for TCNMABG rolling bearing. Section 3 introduces the implementation process of the proposed model in detail based on the XJTU-SY dataset, and the results of the comparison experiment and ablation experiment of the model are analyzed and discussed. Section 4, the FEMTO-ST bearing dataset, is used to verify the performance of the model further. Finally, the whole research content is summarized, and the prospect is put forward.