esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Operating Conditions  
The main operating conditions are different combinations of screw outer diameter, residence time, and screw speed, as detailed in Table 3. This paper focuses on the effect of residence time on conveying performance without considering the load factor, screw pitch, etc. The effect of these factors will be considered in future work. A total of 24 groups are identified using the DOE approach, and details of the experimental design experience and handling can be found in the literature by [PERSON] et al. [27]. The ranges of the three influence factors are shown below:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Specifications Parameters** & **Data** \\ \hline Hopper volume (V) & 0.4 m\({}^{3}\) \\ Screw outer diameter (Do) & 45–60 mm \\ Helix angle of screw shaft (\(\varphi\)) & 20\({}^{\circ}\) \\ Rotating speed (Rev) & 15–35 r/min \\ Residence time (T) & 0–30 min \\ Number of screw shaft (n) & 4 \\ Length of screw shaft (L) & 155 mm \\ Screw pitch (P) & 40 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the specifications parameters of the screw conveyor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Concte DEM Model Parameters** & **Data** \\ \hline Coefficient of restitution (Steel-mortar/Steel- & 0.01, 0.1, 0.05, 0.04, 0.01 \\ stone/Stone-stone/Stone-mortar/Mortar-mortar & 0.018, 0.43, 0.47, 0.52, 0.25 \\ Rolling friction coefficient (Steel-mortar/Steel- & 0.06, 0.08, 0.1, 0.13, 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of concrete DEM model [25].  
Figure 2: The screw conveyor experimental platform and 3D model with dimensions shown (mm). It consists of nine parts: screw conveyor (A); controller (B and C); screw (D); motors (E); power (F); polywhare mixer (G); scale (H); and guide plate (I).  
The screw's outer diameter is reduced from 60 mm to 45 mm in 5 mm increments, primarily to accommodate the change in screw volume.
2. The residence time varies from 0 to 30 min in 10 min increments, primarily to account for the residence time affecting the workability of concrete.
3. The screw speed is increased from 15 r/min to 30 r/min in 5 r/min increments, primarily to account for the variation in material handling capacity.  
The description of the screw conveying mechanism also uses the same key structure nomenclature as [PERSON] et al. (2017) and [PERSON] et al. (2017): The particles are filled with screw shaft cavities called screw fill volume. Particles' movement is usually determined by the shear force of the screw shaft. When the particles are transported from the first pitch, the starting point is the \"root\" of the screw. When the particles move to the last pitch, the destination is the \"end\" of the screw. The mass flow rate is the mass of particles transported by the screw per unit time. The conveying capacity of the screw conveyor provides important information about the operating conditions and can be used to control stable operation. These indicators vary with the different operation conditions. The mathematical model between the indicators and operating parameters can be fitted based on the corresponding results.
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 2 Materials and Methods
### System Structure and Setup
Figure 1: Architecture of a 4 \(\times\) 100 Gbps Apol-CRZ-FSK signal transmission system for optical networks. The generated four 100 Gbps Apol-CRZ-FSK signals are coupled to a single optical fiber through a multiplexer, resulting in a 4 \(\times\) 100 Gbps Apol-CRZ-FSK signal at the location shown in Figure 1d. Its spectral diagram is shown in Figure 2a. The single-span transmission line is shown in Figure 1e. Firstly, a 4 \(\times\) 100 Gbps DWDM signal is power amplified through an EDFA1 amplifier with a noise figure of 4.5 dB. The dispersion-compensating fiber (DCF) on the line side, with a dispersion coefficient of \(-90\) ps/nm/km, is used to compensate for signal distortion caused by the single-mode fiber (SMF) with a dispersion coefficient of 16 ps/nm/km. An EDFA2 amplifier on the receiving side of the signal is used for signal power amplification to compensate for signal attenuation during long-distance transmission before sending it to the receiver, as shown in Figure 1f. A demultiplexer divides the 4 \(\times\) 100 Gbps signal into four 100 Gbps signals, which are then detected by an optical bandpass filter for frequency discrimination and directly received by the receiver.
# Effect of Plasma Surface Modification on Print Quality of Biodegradable PLA Films
## 1 Introduction
On the other hand, in the case of oxygen activation, a longer activation time even has a negative effect on the wetting angle value [22; 29; 30]. Moreover, the type of gas used for plasma modification has been found to have a much greater effect on the wettability of modified substrates using solvent-based inks than in the case of water-based inks [22]; however, this study was limited only to the investigation of the contact angle of a modified substrate using three different inks without taking into account the printability of the substrate. The research carried out in this paper, on the other hand, will include an extensive study of the effect of PLA film activation and the kind of flexographic ink used for printing on print quality and the parameters used to evaluate it, such as ink adhesion to the substrate, gloss, CIELab color coordinates, optical density and relative contrast. In this study, we attempt to determine the effect of the gas used during plasma modification of PLA film and the type of substrate and printing ink on the printability of the film and the quality of the prints obtained. The relevance of the plasma process before flexographic printing with various printing inks has been demonstrated. To the author's knowledge, there are no available studies presenting the influence of plasma activation on the printability of PLA films with a thorough analysis of parameters determining the quality of prints, which was realized in this study. Available publications addressing the issue of the effect of substrate activation on printability deal with other materials, other printing technologies and other activation processes [50; 51; 52; 53; 54] or are only an introduction to the issue [22]. On the other hand, publications dealing with printability of PLA films do not consider plasma modification of the substrate [55; 56; 57; 58].
# A Multi-Dimensional Matrix Product--A Natural Tool for Parameterized Graph Algorithms
## 1 Introduction
### Finding Lowest Common Ancestors  
Our third application of the multi-dimensional matrix product provides a simple generalization of known results on finding the lowest common ancestors for all pairs of vertices to include all \(k\)-tuples of vertices for fixed \(k\geq 2\).  
The problem of finding a _lowest common ancestor_ (LCA) for sets of vertices in a tree, or more generally, in a _directed acyclic graph_ (DAG) is a basic problem in algorithmic graph theory. It has several applications ranging from algorithm design through object-oriented programming languages [28] to phylogenetic networks [29]. An LCA of a set \(S\) of vertices in a DAG is an ancestor of all vertices in \(S\) that has no proper descendant which is an ancestor of all vertices in \(S\)[30]. The problem of preprocessing a DAG such that LCA queries can be answered quickly for any pair of vertices has been studied extensively in the literature [9; 31; 32; 33]. The all-pairs LCA problem is to compute LCA for all pairs of vertices in the input tree or DAG. For trees, it can be solved in linear time [34]. For DAGs, [PERSON] et al. were the first to provide a substantially subcubic algorithm for this problem [35]. [PERSON] et al. improved their upper bound to \(O(n^{2.575})\) by a reduction to the problem of finding maximum witnesses for the Boolean matrix product of two \(n\times n\) Boolean matrices [9]. Taking into account the recent progress in fast rectangular matrix multiplication, the latter bound reduces to \(O(n^{2.529})\). Very recently, [PERSON] et al. have presented an \(\tilde{O}(n^{2.447})\)-time algorithm for the all pairs LCA problem [33].  
The problem of finding LCAs for vertex sets of size greater than two is natural, e.g., in a genealogy search in phylogenetic networks [29]. [PERSON] generalized the all-pairs LCA problem in DAGs to include finding lowest common ancestors for all \(k\)-tuples of vertices [36] (cf. [37]). We term the generalized problem as the all \(k\)-tuples LCA problem.  
We provide a simple reduction of the all \(k\)-tuples LCA problem to the maximum witness problem for the \(k\)-dimensional Boolean matrix product of \(k\) copies of the transitive closure matrix of the input DAG. As a result, we obtain upper time bounds for the all \(k\)-tuples LCA problem matching those established by [PERSON] in [36] and [PERSON] et al. in [37].
## 3 Results
### Approaches for Pasture Quantification
#### 3.4.2 Botanical Composition
\begin{table}
\begin{tabular}{c c c c c} \hline
**Vegetation Indices** & **Model** & **Studies Focus** & **Sensor** & **Reference** \\ \hline Ratio vegetation index (RVI), enhanced vegetation index (EVI), NDVI & Logarithmic regression & Aboveground biomass & MODIS & [64; 189; 194; 196] \\ EVI, LAI, & Linear regression model & Aboveground biomass & Worldiew, Sentinel-1, Sentinel-2, Landsat & [45; 196] \\ Vegetation indices & Sparse partial least-square regression & Aboveground biomass & Sentinel-2, HySpiri, & [179; 197] \\  & & Pasture quality & UAS, & [120; 142] \\ NDVI & Power regression & Pasture biomass, forage dry biomass & MODIS, & [91] \\ LAI derived from satellite & Radiative transfer model & Distance biomass prediction at the padded level & Sentinel-2 & [32] \\  & & Aboveground net primary production (i.e., carbon stock) & \\ NDVI derived from fused satellite sensors & Linear regression model & (ANPP) estimated from Absorbed photosynthetically active radiation (APAR) at paddock level & Fusion of Landsat/MODIS & [34] \\  & & Aboveground net primary production (ANPP) estimated from Absorbed photosynthetically active radiation (APAR) at paddock level & Fusion of Landsat/PlanetScope & [114] \\ NDVI derived from fused satellite sensors + UAS & Linear regression + Light use efficiency model & Distance production (ANPP) estimated from Absorbed photosynthetically active radiation (APAR) & \\ To compare NDVI and FVC derived from UVA (multispectral image) & Exponential function, linear function, logarithmic function, polynomial function and power function & Estimate carbon yield canopy cover for individual plant and across & Multispectral camera (i.e., SpecTerra) & [130] \\ NDVI + cellulose absorption index derived from satellites & Linear unmixing approach and multiple linear regression & FVC, non-photosynthetic vegetation cover and bare soil & Hyperion and MODIS & [91; 141] \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of the main vegetation indices and associated regression algorithms commonly used in the studies.
## 1 Introduction
### Discussion
#### 3.2.3 Comparison with Previous Studies  
Based on the existing research on road landscapes, the role of road landscapes in guiding road alignment has been established, which can alleviate drivers' visual perceptual issues on roads with a combination of horizontal and vertical curves. Researchers such as [PERSON] and [PERSON] [13; 14; 15; 16] have all concluded that inappropriate plant landscapes can lead to accidents and have negative physiological effects on drivers. However, previous studies on plant landscapes often focused solely on a single factor, neglecting the interrelationships among these factors.  
Specifically, scholars like [PERSON] and [PERSON] [9; 10] have found that the color of road landscapes affects both the driving stability and psychology of drivers. Moderate increases in landscape color brightness reduce driver fatigue, but excessively vivid colors can decrease driving stability. These findings align with the results of this study. Building upon these studies, we have determined that drivers feel most comfortable when the brightness of the road landscape falls within the range of 4.2-4.8. Researcher [PERSON] [11; 12] investigated the influence of the distance of roadside objects on driving behavior. The study indicated that drivers experience less pressure in open areas; that is, the greater the distance between objects and the road, the less pressure experienced. However, this study did not specify the exact roadside distance. Therefore, based on this research, we have predicted and verified the optimal configuration of road landscapes, determining that a roadside distance of 1 m and a height of 4 m result in the most stable driving conditions.  
This study utilized the Box-Behnken Design response surface methodology to experimentally design and predict the optimal configuration of road landscapes, expanding the existing research on road landscapes and safety on mountain roads with combined  
Figure 11: The surface and contour maps of the influence of factors B and D on the standard deviation of the mean velocity.  
Figure 12: Surface and contour maps of the influence of factors C and D on the standard deviation of the mean value of lateral displacement.  
horizontal and vertical curve sections. This has significant implications for the design of road landscapes and the improvement of driving safety on mountain roads.
## 2 Methods and Process Procedures
### Cloud and Cloud Shadow Identification
#### 2.3.2 Cloudy Pixel Identification
In addition, the threshold \(T_{c}\) can be derived automatically from the image data and once it is determined, a cloud mask can be produced simply by comparison of the \(dDN_{c}\) of a pixel to \(T_{c}\), while the parameter settings and mask computation of other methods are much more complicated.
## 4 Experimental Verification
### Comparing Location Results with and without Outliers
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**PCFWE**} & \multicolumn{6}{c}{**TSWLS**} & \multicolumn{6}{c}{**LLS**} \\ \cline{3-14}
**AE** & **Coultiens** & **x** & **y** & **z** & **Absolute** & **x** & **y** & **z** & **Absolute** & **x** & **y** & **z** & **Absolute** \\
**Source** & & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **Error (mm)** \\ \hline \hline \multirow{2}{*}{P} & No & 80.46 & 119.12 & 84.00 & 0.99 & 79.96 & 122.18 & 84.00 & 2.18 & 77.98 & 124.97 & 84.00 & 5.37 \\  & Yes & 80.60 & 118.86 & 84.00 & 1.29 & 98.24 & 164.94 & 84.00 & 48.50 & 93.94 & 106.20 & 76.60 & 20.96 \\ \hline \multirow{2}{*}{Q} & No & 158.08 & 61.40 & 84.00 & 2.38 & 162.65 & 95.87 & 84.00 & 2.69 & 167.06 & 54.11 & 84.00 & 9.20 \\  & Yes & 157.80 & 61.88 & 84.00 & 2.89 & 114.45 & 80.80 & 68.23 & 52.50 & 121.71 & 71.02 & 73.45 & 41.21 \\ \hline \multirow{2}{*}{R} & No & 0.00 & 121.08 & 44.75 & 2.96 & 5.78 & 119.36 & 44.05 & 6.17 & 10.11 & 117.79 & 47.93 & 11.93 \\  & Yes & 0.00 & 121.24 & 44.81 & 3.07 & 55.81 & 110.82 & 44.08 & 56.60 & 53.29 & 117.88 & 63.78 & 57.61 \\ \hline \multirow{2}{*}{T} & No & 114.43 & 0.00 & 0.53 & 5.76 & 113.24 & 0.00 & 42.30 & 6.76 & 112.17 & 0.00 & 39.88 & 8.12 \\  & Yes & 114.28 & 0.00 & 40.55 & 5.90 & 101.38 & 14.82 & 53.56 & 26.46 & 95.25 & 38.87 & 33.73 & 46.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coordinates of AE sources solved by three methods. Figure 7: Location results from three methods: (**a**) three-dimensional (3D) schematic diagram of location results and sensor layout; (**b**) projections of AE events on the _x-z_, _y-z_, and _x-y_ planes.
# A Single Image Enhancement Technique Using Dark Channel Prior
## 3 Improved Image Defogging Method Based on Dark Channel Prior
### Treatment of Dark Channel Prior Failure Areas
#### 3.2.1 Sky Region Segmentation Based on Edge Detection and Maximum  
In this section, we propose a method based on edge detection and maximum inter-class variance to optimize the sky region segmentation algorithm. The specific steps of sky segmentation in this paper are as follows: (1) convert RGB images into gray-scale images with the goal of retaining more edge information; (2) use Sobel to calculate the gradient information of the gray-scale image; (3) the gradient information is distinguished according to the gradient threshold and brightness threshold. The gradient threshold in this paper is set to 0.83. The brightness threshold is obtained by maximum inter-class variance. The segmentation image is shown in Figure 4.
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 6 Blind Tests
### Results with 4-Axle Railway Carriage Model
The whiskers extend to the most extreme data points not considered outliers, and the outliers are plotted individually using the '+' symbol. The box plot shows that the mid-span MR-IL values provide good repeatability, with obvious differences between damage states. The mean of the 50 mid-span MR-IL values and the area under the mean calculated MR-IL is used to calculate the bridge damage indicator defined in Equation (24). The corresponding true levels of damage, reported by NTNU, are defined as the percentage losses in flexural rigidity. The numerical results are presented in Table 5. All damage states were identified, and the level of damage was accurately found, without prior knowledge of the true states. There is little sensitivity to the profile used in the simulations. Furthermore, there is little difference in the effectiveness of _Damage Indicator_ 1 and _Damage Indicator_ 2. Figure 10: (**a**). Calculated vehicle masses for each of the 50 runs in the batch, running on Profile 1, State 7. (**b**). Calculated vehicle moments of inertia for each of the 50 runs in the batch, running on Profile 1, State 7. (**c**) Calculated vehicle speeds for each of the 50 runs in the batch, running on Profile 1, State 7. Figure 11: The true and calculated AP of the bridge from one run over Profile 1, State 7. Figure 12: Box plot of mid-span fundamental MR-IL values of carriage model for Profile 1, organised by (**a**) bridge state number and (**b**) damage level.
## 6 Experimental Results
### Comparative study of the mean values:
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**BenFun** & **VLE-PSO** & **VLE** & **VLE-GSA** & **VLE** & **VLE-DE** & **VLE** & **VLE-WOA** & **VLE** \\ \hline \(f_{1}\) & \(-1.3555\times 10^{-4}\) & \(1\) & \(4.4989\times 10^{-7}\) & & & \(4.4989\times 10^{-7}\) & & \(4.4989\times 10^{-7}\) & \\ \(f_{2}\) & \(-4.2141\times 10^{-2}\) & \(1\) & \(-5.5652\times 10^{-2}\) & \(1\) & \(3.0825\times 10^{-6}\) & & \(3.0840\times 10^{-6}\) & \\ \(f_{3}\) & \(-649.24\) & \(1\) & \(-8.9133\times 10^{2}\) & \(1\) & \(5.2020\) & & \(5.2020\) & \\ \(f_{4}\) & \(-175.19\) & \(1\) & \(11.656\) & & \(79.199\) & & \(51.333\) & \\ \(f_{5}\) & \(-7.7247\times 10^{3}\) & \(1\) & \(-9.7449\times 10^{3}\) & \(1\) & \(-1.4859\times 10^{3}\) & \(1\) & \(-7.4852\times 10^{3}\) & \(1\) \\ \(f_{6}\) & \(-12.1210\) & \(1\) & \(8.6146\) & & \(-34.6170\) & \(1\) & \(34.5830\) & \\ \(f_{7}\) & \(2.8944\) & & \(3.1083\) & & \(3.1704\) & & \(-4.2339\) & \(1\) \\ \(f_{8}\) & \(0.49816\) & & \(-27.194\) & \(1\) & \(0.50737\) & & \(0.50708\) & \\ \(f_{9}\) & \(0.2301\) & & \(-1.5627\) & \(1\) & \(0.2369\) & & \(-0.1027\) & \(1\) \\ \(f_{10}\) & \(-2.6292\) & \(1\) & \(-4.8618\) & \(1\) & \(-4.0000\times 10^{-6}\) & \(1\) & \(-1.1140\) & \(1\) \\ \(f_{11}\) & \(1.3000\times 10^{-4}\) & & \(1.3000\times 10^{-4}\) & & \(1.3000\times 10^{-4}\) & & \(1.3000\times 10^{-4}\) & \\ \(f_{12}\) & \(2.6300\times 10^{-4}\) & & \(2.6300\times 10^{-4}\) & & \(2.6300\times 10^{-4}\) & & \(2.3600\times 10^{-4}\) & \\ \(f_{13}\) & \(9.7000\times 10^{-3}\) & & \(9.7000\times 10^{-3}\) & & \(9.7000\times 10^{-3}\) & & \(9.7000\times 10^{-3}\) & \\ \(f_{14}\) & \(-2.0000\times 10^{-5}\) & \(1\) & \(-2.0000\times 10^{-5}\) & \(1\) & N/A & & \(-6.6400\times 10^{-3}\) & \(1\) \\ \(f_{15}\) & \(-5.1560\times 10^{-2}\) & \(1\) & \(-1.2000\times 10^{-4}\) & \(1\) & N/A & & \(-0.33685\) & \(1\) \\  & VLE & \(9\) & VLE & \(8\) & VLE & \(3\) & VLE & \(6\) \\  & PSO & \(6\) & GSA & \(7\) & DE & \(10\) & WOA & \(9\) \\  & VLE & \(60.00\%\) & VLE & \(53.33\%\) & VLE & \(23.08\%\) & VLE & \(40.00\%\) \\ \hline \end{tabular}
\end{table} 
Table 10: Averages comparison by function and metaheuristic (PSO, GSA, DE or WOA), using the classical benchmark functions; \(n=30\).
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Radio Stripes
#### 2.3.3 Radio-Stripe Network Propagation  
In a cellular or cell-free wireless network, waves must travel hundreds of meters (from the base station/AP to the user and vice versa), which may experience little attenuation in the sub-6 GHz range but substantial attenuation in the mmWave spectrum over the equal space. However, as the radio stripe is distributed in every nook and cranny, the distance between the AP and users is only a few meters. As a result, propagation concerns such as air absorption, rain, and penetration through walls, become null or minimal [14].
## 3 The Proposed Method and Scenarios
### Handover Phase  
The schematic representation illustrating the handover signaling process of the suggested approach can be observed in Figure 7. Upon recognition by a preceding Road Side Unit (RSU) that a vehicle or a cluster of vehicles has exited its designated coverage zone, an initiation of the handover procedure takes place.  
* Step 1: The vehicle send the VID-targetRSU that verifies the target RSU has authenticity. The source RSU will determine the authenticity of the destination RSU by checking the neighbor table.
* Step 2: The vehicle advances by transmitting a request message to the destination RSU. This RSU will send the request for the AuthenticationMessageUploading() transaction to the blockchain ledger.
* Step 3: The blockchain ledger will check the RSU with the smart contract agreements, especially for the entity name. After that, it will give the \(AU_{s}m\) to the destination RSU.
* Step 4: That RSU will equalize the authentication message sent by the vehicle and the authentication message in the blockchain ledger. Following successful validation, the destination RSU sends a message confirming the completion of the handover to the vehicle, thereby finalizing the transfer. Subsequently, the vehicle updates its ledger and disseminates the information.