esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction to SPRG Materials and Screw Mixing
### SPRG Degradation Mechanism Caused by Excessive Screw Mixing
#### 1.4.1 Physical Degradation Mechanism  
SPRG physical property degradation mechanism can be classified into two types: (1) polymeric crosslink breakage due to shear stress; and (2) particle layer segregation due to emulsion breaking during storage after initial usage. Hooke's Law explains that the deformation of lightly cross-linked polymeric materials with viscoelastic properties depends on the strain and temperature. Stress (\(\sigma\)) is proportional to strain (\(\epsilon\)), which can be expressed with the following equation of Hooke's Law:  
\[\sigma=E\epsilon\text{ or }\frac{\text{Stress}}{\text{Strain}}=\text{Constant} \tag{1}\]  
As mentioned in Section 1.2, SPRGs have a naturally weaker polymeric crosslink, and a lower elastic limit as opposed to other types of polymeric rubber type waterproofing materials. Throughout the screw-mixing process, the temperature of SPRG can rise up to 60-70 \({}^{\circ}\)C, which causes the viscosity to be reduced to less than a tenth of the original viscosity of the SPRG (Table 2), in accordance with the Arrhenius temperature-viscosity model. In this state, polymer crosslink breakage is induced relatively easily if screw mixing occurs for too long. Figure 4 gives an illustration of this concept.  
When set to rest after the screw-mixing process, emulsion breaking and sediment layer hardening can occur more easily. Under normal circumstances, bitumen globules form larger particles when the small aggregate particles coalesce with the emulsifier and the binders, making the emulsion structure stable for a predetermined period [12]. When SPRG is screw mixed for too long, however, the emulsifier is dispersed more thoroughly. In this state, a larger amount of emulsifier leaves the emulsion through the supernatant, mostly comprising process oil and a minimal amount of filler ash content [12]. Supernatants have a higher flow velocity with very low viscosity, and are more easily able to flow through concrete cracks and leaks into the concrete interior. With a higher concentration of solvent chemicals that were not able to bind to the aggregates, this type of supernatant leakage can cause economic and environmental hazards [12]. Figure 5 below provides an illustration of this process.  
Figure 4: Polymeric degradation due to continuous shear strain of screw-mixing process.
## 5 Case Study of Landslide Prone Regions
### India
Inevitably, spontaneous drainage was obstructed or changed on the majority of collapsed slopes via retaining walls without enabling water runoff of surplus rain water during strong storms [74]. The southern Indian state of Kerala has suffered considerable losses due to landslides in the 2018 and 2019 monsoon seasons, and is in danger of debris flows in the foreseeable future [75]. Because of its geographic and geographical qualities, the state is particularly vulnerable to natural disasters. The topography all along the Arabian Sea coast has a severe elevation due to the hills of the Western Ghats. Darjeeling Himalayan landslides are acontinual source of concern in Himalayan mountain areas, inflicting catastrophic loss of property and lives each year [76; 77]. Landslides in Darjeeling Himalaya have happened mostly on cutting slopes, either by the side of roadways, or in places next to developed settlements [78; 79]. The Darjeeling Himalaya is made up of lithology from the tertiary, proterozoic, and upper palaeozoic-mesozoic eras [80; 81]. The delicate condition of the soil in the Darjeeling Himalaya aids in the inception of slope movement processes. June, July, and August are the months with the highest average precipitation [82; 83]. The latest monsoon tragedies in the Western Ghats have prompted officials to take notice of recurring landslides in the area. Landslides have become a serious hazard to people and property in Wayanad, a district in Kerala, India, as shown in Figure 16.
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
###### Abstract  
In this work, we propose a deep learning-based model for mapping between the data of the flow field of the propeller generated by the Reynolds-averaged Navier-Stokes (RANS) and those generated by Large Eddy Simulation (LES). The goal of establishing the mapping model is to generate LES data, which needs higher computing power requirements, with the help of RANS data. The model utilizes a deep learning method for computer vision to handle three-dimensional data generated by RANS and those by LES. Firstly, the data samples of the RANS flow field and those of the LES flow field are processed to obtain their corresponding three-dimensional image data, respectively. Secondly, the two kinds of field flow images are used as the training data for constructing a mapping model between RANS flow field images and the corresponding LES flow field images. The obtained mapping model thus can be used to predict the LES flow field images. Thirdly, the regression module is employed to regress the three-dimensional LES image point-by-point to the velocities at the points of the LES flow field, thereby ultimately achieving the generation of LES flow field data from RANS data. The experimental results show that by applying this method to RANS data and LES data of propeller flow fields, the overall error rate of LES flow field prediction by this method is 17.68% compared to actual flow field data, which verifies the effectiveness and accuracy of the proposed model in LES flow field prediction.  
turbulence model; deep learning; turbulent flow simulation; mapping; regression model +
Footnote †: journal: Applied sciences
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 1 Introduction
### Receiver Concept  
The receiver described below belongs to the second group of receivers and uses the equal gain combining technique. In this receiver, all photodetector signal currents are summed and processed as a whole instead of switching to the strongest signal photodetector.  
The aim of this study was to highlight the merits of the equal gain combining technique and describe the same technique in receiver design. Many papers have already discussed multiple photodetector combining techniques, but all of them mainly focused on a mathematical description of optical channels. We focused solely on receiver design. The described receiver also differs in the degree of FOVs overlap. Whereas most papers describe multiple photodetector receivers with only slight or no FOVs overlap, the present receiver works with a large FOVs overlap. In this case, highly overlapping FOVs are valuable for additive inherent noise suppression. The set of photodetectors thus formed forms a parallel amplifier. More information can be found in previously published reports [20; 21; 22; 23; 24; 25].  
For the physical realization of the receiver, the omnidirectional receiver was designed using six photodetectors, PD\({}_{1}\) to PD\({}_{6}\), which are attached to the walls of the truncated pyramid (Figure 1). The pyramid ground plan is hexagon and its walls and base are at a 45\({}^{\circ}\) angle. The photodetectors are mounted on small printed circuit boards (PCBs) and are connected with the following amplifiers, which are located on a separate board below the base of the pyramid. The connections between photodetectors and the following amplifiers had to be as short as possible. For this reason, the photodetectors were finally moved from the centers of the segments to their edges.
# Frequency Modulation and Erosion Performance of a Self-Resonating Jet
###### Abstract  
The self-resonating water jet offers the advantages of both a cavitation jet and a pulsed jet, and thus has been widely used for many practical applications. In the present study, the 120\({}^{\circ}\) -impinging edge Helmholtz nozzle was investigated for better erosion performance. The oscillating mechanism was analyzed from both numerical and experimental perspectives. The results showed that the cavitation clouds in the chamber dominate the oscillating frequency. The frequency resulting from the non-linear interaction was also observed in the simulation. The dominant frequency increases linearly as pressure decreases without entrained air. The frequency modulation was achieved through various inspiratory methods, and the modulation range was dependent on the pressure drop. The erosion performance was improved with entrained air, and the improvement was effected by the inspiratory method. The oscillating frequency was determined by the forced frequency of entrained air, and the best erosion performance was achieved at the frequency closest to the fundamental frequency. A feasible method to improve the erosion performance was investigated in this preliminary study, which could provide a guide for practical applications.  
self-resonating jet; frequency modulation; erosion performance; forced excitation Article
# Theoretical Study of Large Uncoupling Coefficient Loading for Surface Blasting
## 5 Calculation Examples and Engineering Applications  
### Physical and Mechanical Testing of Rocks  
To ensure the practical application of the proposed smooth blasting method, it is applied to the earth work project of Qianxi to Dafang Expressway, Guizhou, China. The volume of excavation reaches 2.88 million m\({}^{3}\). The rocky section accounts for more than 80% of the entire excavation in the roadbed excavation. The rock composition mainly consists of calcite and dolomite, and the overall structure of the rock is good. Smooth blasting excavation is essential for rock slope.  
On the one hand, smooth blasting can ensure that the slope surface and slope rate meet the design requirements. On the other hand, a well-designed rock slope can save construction costs to a certain extent, improve work efficiency, and avoid construction work caused by over-ex excavation or under-ex excavation. Typical rocks are collected from the site, and the basic physical and mechanical tests of rock are carried out, shown in Figure 5. The measured physical and mechanical properties are shown in Table 1.  
In the test, 2 # rock emulsion explosives with density \(\rho_{0}=1.24\) g/cm\({}^{3}\) and explosive burst speed \(D=4200\) m/s are used. The charge has a diameter of \(d_{c}=32\) mm, length of 300 mm, mass of 300 g, and blast hole diameter of \(d_{b}=90\) mm. The other parameters are calculated as follows: \(p_{k}=200\) MPa, and the resulting axial uncoupling coefficient \(K_{1}\) is listed in Table 2 for different radial uncoupling coefficients for bare face blasting of the blast hole charge.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Density/(g/cm\({}^{3}\))** & **Compressive Strength/MPa** & **Tensile Strength/MPa** & **Elastic Modulus/MPa** & **Poisson Ratio** \\ \hline Dolomite & 200 & 97 & 3.2 & 39.55 & 0.191 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material characteristics of sandstone specimens.  
Figure 5: Rock compression and splitting test. (**a**) Uniaxial compression test damage pattern (**b**) Splitting test damage pattern.
## 2 Experimental
### Apparatus and Operating Conditions  
SO-free PS was heated at a fixed temperature for a given period in a flask. The reaction mixture was recovered with 10 mL benzene and was transferred to a 50 mL separating funnel and washed three times with 10 mL purified water to remove PEG. After drying with 5 g anhydrous sodium sulfate overnight, the solution was filtered and transferred to 10 mL methanol to precipitate unreacted PS. The solvent was completely removed from the solution by evaporation at 25 \({}^{\circ}\)C and the residue was collected with benzene in a 10-mL volumetric flask. Following the addition of phenanthrene, 1 mL of the solution was injected into GC/MS with a micro-syringe and analyzed. The details are shown in Figure 1b. The GC used was HP6890 (Agilent Technologies, Inc., Santa Clara, CA, USA). The mass spectrometer (MS) was JMS-AMII manufactured by JEOL, Ltd. (Tokyo, Japan) and the separation column was DB-1 manufactured by Agilent J & W. The operating conditions are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} Apparatus & JEOL JMS-AM II with GC/MS (HP6890) \\ Selected Ion (SI, \(m\)/z) & 78, 104, 105, 152, 178, 193, 196, 207, 208, 312 \\ Injection & 1 mL \\ Column & DB-1, L 30 m, ID 0.32 mm, Thick 0.25 \(\upmu\)m \\ Injection method & Spitless \\ Injection temperature & 250 \({}^{\circ}\)C \\ Column temperature & Initial temp. 40 \({}^{\circ}\)C (holding 5 min), max temp. 290 \({}^{\circ}\)C (holding 5 min), \\  & program rate 15 \({}^{\circ}\)C/min \\ Interface temperature & 250 \({}^{\circ}\)C \\ Ion source temperature & 200 \({}^{\circ}\)C \\ Ion acceleration current & 70 eV \\ Current & 300 \(\upmu\)A \\ PM voltage & 600 V \\ Carrier gas & He, 1.4 mL/min \\ \end{tabular}
\end{table}
Table 1: Apparatus and operation conditions of GC/MS analysis.  
Figure 2: Schematic diagram of a decomposition equipment.
## 3 Results
The Gluteus Maximus activity (ranging from 19.2 \(\pm\) 14.6% for PRE-1 to 19.4 \(\pm\) 11% for POST-6) for STOOP was altered over the familiarization protocol (\(p\) = 0.043, \(W\) = 0.03), without revealing further statistical effect after the post-hoc comparisons. Mean neuromuscular activities over the protocol for ES (40.8 \(\pm\) 1.2% and 43 \(\pm\) 2.2% for STOOP and SQUAT, respectively), OE (7.5 \(\pm\) 1.6% and 6.4 \(\pm\) 0.6%), RA (3 \(\pm\) 0.4% and 2 \(\pm\) 0.2%), and TFL (5.9 \(\pm\) 1.3% and 13 \(\pm\) 10.8%) were not influenced by the familiarization, either for STOOP or SQUAT. NASA-TLX global scores were not influenced by the familiarization protocol, whatever the technique used, with mean scores ranging from 119 to 133 for STOOP and from 121 to 152 for SQUAT. Nevertheless, the statistical analysis demonstrated a main effect on mental demand in SQUAT (\(p\) = 0.03, \(W\) = 0.11). Pairwise comparisons showed that POST-6 (2 \(\pm\) 2) was lower than PRE-1 (3.5 \(\pm\) 2.75), suggesting that stabilization could occur after POST-6. [PERSON]'s tests showed that PRE-CON (2 \(\pm\) 2.75) was not different from PRE-1 (\(p\) = 0.07) \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **PRE-CON** & **PRE-1** & **POST-1** & **POST-2** & **POST-3** & **POST-4** & **POST-5** & **POST-6** \\ \hline \multirow{3}{*}{Ellipse Area (mm\({}^{2}\))} & STOOP * & 1808 & 1820 & 1880 & 1895 & 1849 & 1825 & 1815 & 1835 \\  & & (234) & (343) & (371) & (441) & (266) & (346) & (197) & (239) \\  & SQUAT * & 2790 & 2983 & 3506 & 3360 & 3444 & 3327 & 2977 & 2805 \\  & & (1694) & (1288) & (1733) & (988) & (1126) & (1612) & (1535) & (1296) \\ \hline \multirow{3}{*}{Antero posterior Amplitude (mm)} & STOOP & 90 & 85 & 88 & 99 & 82 & 92 & 86 & 91 \\  & & (26) & (25) & (34) & (30) & (28) & (29) & (20) & (40) \\ \cline{1-1}  & & 75 & 79 & 80 & 88 & 76 & 78 & 73 & 75 \\ \cline{1-1}  & & (24) & (26) & (19) & (25) & (21) & (16) & (30) & (16) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Median (interquartile range) of confidence ellipse area and anteroposterior amplitude of postural oscillations in STOOP and SQUAT in PRE-CON and over the familiarization protocol from PRE-1 to POST-6. * Statistical main effect of the familiarization protocol.
## 3 Fundamentals of GLI-Split
### GLI-Addressing
#### 3.2.2 Address Assignment  
The remaining 16 bits are used for checksum compensation so that checksums calculated, e.g., by TCP, are still valid after locator changes. TCP uses a 16-bit checksum in its header and includes the source and destination address of the IP header in the computation.  
Horizontal and vertical address translation in GLI-Split change source and destination addresses. When two GLI-nodes communicate with each other, checksum problems do not occur because GLI-nodes see only identifier addresses on the transport layer. However, checksum problems possibly occur for communication with non-upgraded nodes because they use locator-dependent GLI-addresses for checksum calculation which are subject to changes. GLI-Split solves this problem by compensating these bit changes with an additional checksum inside the GLI-address (see Figure 3). The 16-bits are computed like in the TCP header as the one's complement sum of the preceding three 16-bit words. Therefore, changing a GLI-address does not modify the TCP checksum. This makes translation of GLI-addresses invisible to TCP checksum operations.  
Global locators are IPv6 prefixes that are globally assigned to GLI-gateways from ISPs in a hierarchical way, just like regular IPv6 prefixes are assigned today in the Internet. IDs are also hierarchically assigned in a similar way, but they are independent of any routing information. The hierarchy here is important to improve the scalability of the mapping system, which can then work with ID-prefixes instead of individual IDs.  
HIP-like IDs may be also supported using the concept in [23]. Local locators are locally allocated according to a network's topology and management needs. They can be dynamically changed and re-assigned to IDs when nodes move within a GLI-domain.  
Figure 3: Three types of GLI-addresses are encoded in an IPv6 address.  
The assignment of local locators to nodes inside a GLI-domain may be done by enhanced DHCP. This DHCP also communicates the information on how to reach the mapping service. An upgraded GLI-node knows its ID, informs the DHCP, which then returns a local locator, as well as a set of global locators. The upgraded GLI-node registers the ID-to-LL and ID-to-GL mappings with the local and global mapping system, including the information that the associated node has upgraded GLI-functionality. When an upgraded node changes its attachment point, it performs this procedure again. For non-upgraded nodes in GLI-domains, the assignment process works differently. The DHCP server knows by configuration the MAC address and the ID for every non-upgraded node in its area, and assigns a local GLI-address to this node that reflects the ID of the node. Due to missing capabilities of non-upgraded nodes, the DHCP server is in charge of registering the appropriate ID-to-LL and ID-to-GL mapping with the local and global mapping system. As a consequence, stateless address auto configuration [24] cannot be used in this case.
## Abbreviations  
The following abbreviations are used in this manuscript:  
UAV unmanned aerial vehicle  
MUCS multi-UAV cooperative search  
DTSC dynamic two-stage closed search  
ST search tracking  
RC range constraint  
OC orientation constraint  
\(N_{p}\)SAP \(N_{p}\)-step-ahead prediction  
TEP target existence probability  
EU environmental uncertainty  
PIO pigeon-inspired optimisation  
CPIO coevolution pigeon-inspired optimization  
3D three-dimensional  
Here, \(d_{ij}(k)\) is described as (A1)  
\[d_{ij}(k)=\begin{bmatrix}d_{11}(k)&d_{12}(k)&...&d_{1M}(k)\\ d_{21}(k)&d_{22}(k)&...&d_{2M}(k)\\...&...&...&...\\ d_{M1}(k)&d_{M2}(k)&...&d_{MM}(k)\end{bmatrix}\] (A1)  
where \(d_{ij}(k)=\sqrt{(x_{i}(k)-x_{j}(k))^{2}}+\sqrt{(y_{i}(k)-y_{j}(k))^{2}}\) denotes the Euclidean distance of \(UAV_{i}\) and \(UAV_{j}\). \(M\) is the number of UAVs. \(d_{ii}(k)=\infty\). (\(x_{i}(k),y_{i}(k)\)) and (\(x_{j}(k),y_{j}(k)\)) are the positions between \(UAV_{i}\) and \(UAV_{j}\) in time step \(k\).  
\(d_{ii}(k)\) and \(d_{ij}(k)\) are similar:  
\[d_{ij}(k)=\begin{bmatrix}d_{11}(k)&d_{12}(k)&...&d_{1H}(k)\\ d_{21}(k)&d_{22}(k)&...&d_{2H}(k)\\...&...&...&...\\ d_{M1}(k)&d_{M2}(k)&...&d_{MH}(k)\end{bmatrix}\] (A2)  
where \(d_{ih}(k)=(k)\sqrt{(x_{i}(k)-x_{h}(k))^{2}}+\sqrt{(y_{i}(k)-y_{h}(k))^{2}}\) is the Euclidean distance between \(UAV_{i}\) and airport\({}_{h}\) (\(h=1,2,...,H\)), \(H\) is the number of airport.
# Recurrent Deep Learning for Beam Pattern Synthesis in Optimized Antenna Arrays
## 2 Problem Formulation
### Deep Neural Network Modeling
It is worth noting that meticulous optimization of these architectures was conducted through the systematic tuning of hyperparameters, including but not limited to the number of layers, neurons, activation functions, and learning rate, among others. Algorithm 1 shows the general procedure used to train the different proposed architectures. This algorithm outlines the process of training a neural network using gradient descent optimization to minimize the mean squared error (MSE) loss function. It involves iterating through the training data multiple times (epochs) and updating the model parameters (weights and biases) after each mini-batch based on the calculated gradients. The goal is to fine-tune the model's parameters in order to reduce the prediction error and enhance the performance of the neural network when analyzing the training data. These architectures were implemented using the Keras framework [28]. Two metrics were used to assess the performance of the deep neural network architectures. The first metric was the mean absolute error (MAE), which measures the absolute error between the desired label and the label predicted by the neural network:  
\[\text{MAE}=\frac{\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|}{n}. \tag{3}\]  
The second metric was the mean square error (MSE), which evaluates the quadratic error between the desired and predicted labels:  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}, \tag{4}\]  
where \(y_{i}\) is the desired label, \(\hat{y}_{i}\) is the predicted label, and \(n\) is the number of patterns.
## 3 Results
### Crop Production Loss
As far as the fruit trees category is concerned, Calabria and Sicily lost almost 40,000 (0.41%) and 75,000 q (0.58%), Trentino-Alto Adige 39,000 q (0.76%), and Campania and Apulia 30,000 q (0.65% and 0.49%, respectively). On the other hand, the loss was lower (below 10,000 q) in the other regions. Finally, the variation in vineyards due to soil consumption in the period under examination led to a reduction of about 10,000 q in all of the regions, with the exception of Apulia (58,200 q, 0.38%), Sicily (60,160 q, 0.40%), and Veneto (56,000 q, 0.69%). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Crop Typologies**} \\ \cline{2-7}
**Regions** & **Fruit Trees** & **Olive Groves** & **Vineyards** & **Fodder** & **Arable Land** & **Total** \\ \hline Piedmont & 0.19 & 0.00 & 0.17 & 0.38 & 0.32 & 0.31 \\ Aosta Valley & 0.67 & \(\backslash\) & 1.67 & 0.79 & 0.90 & 0.88 \\ Lombardy & 0.30 & 0.75 & 0.29 & 0.76 & 0.50 & 0.59 \\ Trentino-Alto Adige & 0.76 & 0.00 & 0.90 & 0.64 & 0.66 & 0.68 \\ Veneto & 0.95 & 0.68 & 0.69 & 0.94 & 0.72 & 0.76 \\ Friuli-Venezia Giulia & 0.35 & 0.00 & 0.45 & 0.71 & 0.50 & 0.53 \\ Liguria & 0.38 & 0.21 & 0.52 & 0.21 & 0.41 & 0.34 \\ Emilia Romagna & 0.23 & 0.07 & 0.13 & 0.32 & 0.35 & 0.34 \\ Tuscany & 0.23 & 0.18 & 0.17 & 0.26 & 0.28 & 0.26 \\ Umbria & 0.19 & 0.23 & 0.27 & 0.20 & 0.40 & 0.34 \\ Marche & 0.15 & 0.36 & 0.43 & 0.53 & 0.43 & 0.43 \\ Latium & 0.31 & 0.25 & 0.57 & 0.65 & 0.57 & 0.58 \\ Abruzzo & 0.40 & 0.54 & 0.51 & 0.29 & 0.57 & 0.51 \\ Molise & 0.09 & 0.47 & 0.32 & 0.22 & 0.22 & 0.23 \\ Campania & 0.65 & 0.32 & 0.60 & 0.93 & 0.64 & 0.71 \\ Apulia & 0.49 & 0.60 & 0.38 & 0.47 & 0.35 & 0.39 \\ Basilicata & 0.26 & 0.29 & 0.33 & 0.35 & 0.32 & 0.32 \\ Calabria & 0.41 & 0.22 & 0.42 & 0.33 & 0.29 & 0.32 \\ Sicily & 0.58 & 0.44 & 0.40 & 0.39 & 0.38 & 0.42 \\ Sardinia & 0.19 & 0.13 & 0.21 & 0.19 & 0.26 & 0.24 \\ Italy & 0.45 & 0.42 & 0.41 & 0.56 & 0.43 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage loss of crop production due to land consumption for five agricultural classes in the period 2012–2020 at the regional level. Figure 5: Estimation of crop production loss (quintals) due to land consumption for five agricultural classes in the period 2012–2020 at the regional level.