esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Hence, residual shear stress emerges as a significant factor influencing the load transfer behavior of the anchor interface [17]. In summary, existing load transfer models for the anchorage section each have their limitations. The tri-linear bond-slip model effectively captures the different stages of anchor behavior with increasing pullout force, but falls short in reflecting the nonlinear characteristics of shear stress and shear displacement [18; 19; 20; 21; 22]. On the other hand, the hyperbolic model accurately represents the nonlinearities of shear stress and shear displacement but neglects the softening of the anchorage body [11; 12; 23]. Meanwhile, the double exponential curve model provides a comprehensive response to various stages in the anchor pullout process, but its complexity and lack of an easily derived analytical solution hinder its practical applications in engineering [15; 16; 24]. To address these shortcomings, this paper  
Figure 1: Schematic of interface characterization using element anchor specimens. proposes a novel model based on the elasto-plastic relationship. The model combines linear and nonlinear components to describe shear stress and displacement at the anchorage interface. By deriving the distribution of axial and shear stresses at different stages of anchor pullout using the anchor's load transfer equation, the paper aims to offer insights into the patterns of load transfer and the characteristics of the anchor pullout process as the pullout load increases.
# Machine Learning-Based Prediction and Optimisation System for Laser Shock Peening
## 2 Laser Peening Residual Stress Data
### Experimental Laser Peening Data
#### 2.1.2 Multiple-Shot Laser Peening with Overlap (Dataset 2)  
In this dataset, laser spots (circular shape) were offset from and overlapped with proceeding spots to generate a patch of peened material. The experimental laser peening facilities available at Universidad Politecnica de Madrid (UPM), Madrid, Spain, were used. The pulse energy was approximately 2.4 J, with a time duration of about 9.4 ns full width at half maximum (FWHM). The spatial profile of the final pulse incident on the treated surface was nearly Gaussian. The samples were a size of 60 mm \(\times\) 60 mm and thickness of 12 mm. A total of 16 LSP processing parameters covering a range of laser spot diameter and pulse overlapping distance were used as shown in Table 2. The LSP patch, sized 30 mm \(\times\) 30 mm, was located centrally on the surface of the samples and the laser tracking direction was perpendicular to the material rolling direction as shown in Figure 2a.
* Laser offset distance was measured from centre-to-centre of each laser spot. The number of laser spots per cm\({}^{2}\) is a function of offset distance (\(\Delta X\), \(\Delta Y\)) along \(X\) and \(Y\) directions as shown in Figure 2b. It can be expressed as: \[Number\ of\ spots/cm^{2}=\frac{1}{\Delta X\times\Delta Y}\] (1)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Laser Shock Peening Process Parameters**} \\ \hline
**Dataset 1** & **Spot Size/mm** & **Layers** & **Power Density/GW cm\({}^{-2}\)** \\ \hline
8.5 & 2 & 1 \\
8.5 & 4 & 1 \\
8.5 & 7 & 1 \\
5 & 1 & 3 \\
5 & 2 & 3 \\
5 & 4 & 3 \\
5 & 7 & 3 \\
3.5 & 1 & 6 \\
3.5 & 2 & 6 \\
3.5 & 4 & 6 \\
3.5 & 7 & 6 \\ \hline
**Dataset 2** & **Spot Size/mm** & **Layers** & **Offset Distance/mm** \\ \hline
1.5 & 3.6 & 0.7 \\
1.5 & 4.9 & 0.6 \\
1.5 & 7 & 0.5 \\
1.5 & 11 & 0.4 \\
2.0 & 6.4 & 0.7 \\
2.0 & 8.7 & 0.6 \\
2.0 & 12.5 & 0.5 \\
2.0 & 19.6 & 0.4 \\
2.5 & 10 & 0.7 \\
2.5 & 13.6 & 0.6 \\
2.5 & 19.6 & 0.5 \\
2.5 & 30.7 & 0.4 \\
3.0 & 14.4 & 0.7 \\
3.0 & 19.6 & 0.6 \\
3.0 & 28.3 & 0.5 \\
3.0 & 44.2 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Residual stress datasets using the incremental hole drilling method for training and testing the artificial neural network (ANN). Dataset 1 and 2 represent single-shot laser peening with square spots and multiple-shot laser peening with circular spots.  
* The effective number of layers can then be calculated as a function of offset distance and spot diameter (\(\O\)) using: \[Number\ of\ layers=\frac{\pi O^{2}}{4(\Delta X\times\Delta Y)}\] (2)
## 4 Case Study--Analysis of Disruptions
### Logistics Process of Container Circulation in the Cold Supply Chain
Some warehouses may have a limited division of zones, for example:  
* The Receiving/Dispatch Zone may also serve as the Loading/Unloading Zone;
* The Storage Zone may also serve as the Picking Zone. Additionally, there are service zones in the warehouse, such as restrooms, offices, dressing rooms, customer service points, and battery charging areas. Special zones, such as the cleaning/drying zones, may be integrated into the typical receiving zone, while cooling zones may not exist, and containers are cooled in the storage zone. The processes carried out in the zones are presented in Table 1. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Process**} & \multirow{2}{*}{**Obligatory**} & \multirow{2}{*}{**Zone**} & \multirow{2}{*}{**Scanning**} & \multirow{2}{*}{**Frequency per Cycle**} & \multicolumn{2}{c}{**The**} & \multirow{2}{*}{**Notes**} \\  & & & & & & **Environmental Temperature of the Coolbox** & \\ \hline Arrival at DC & Yes & Receiving/Dispatch & Yes & 1 & 2 °C + 20 °C & \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Container \\ eashing \\ \end{tabular} } & \multirow{3}{*}{Yes} & \multirow{3}{*}{Receiving/Dispatch} & \multirow{3}{*}{No} & In every cycle or every few cycles & 60 °C & \multirow{3}{*}{60 °C} \\  & & & & & & high temperature \textgreater{} \\ \hline \multirow{3}{*}{Drying} & \multirow{3}{*}{No} & \multicolumn{3}{c}{Cleaning Zone/Storage Zone CB} & \multirow{3}{*}{Recommended} & As often as washing & \multirow{3}{*}{2 °C + 50 °C} \\  & & & & & & \\ \cline{1-1} \cline{6-6} \cline{8-8}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Container circulation cycle.
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 3 Algorithm Framework and Modules  
### Overall Framework and Process  
The overall framework of ICSF consists of an encoder, decoder, feature filtering network (FFN), image fusion module (IFM), and registration module (RM), as shown in Figure 2. Both the encoder and FFN form the feature decomposition network.  
First, the encoder decomposes the source image into convolutional sparse features, which the FFN then processes to extract homogeneous and isolated features. If registration is not necessary, the various features of the source image are combined through the fusion unit and then decoded to produce the fused image. However, if registration is required, the registration module utilizes the homogeneous features of the source image to determine the appropriate registration parameters. Once the original image is registered, the encoder is reapplied to extract the homogeneous and distinct features of the registered image, which are then utilized for image fusion.
# A Review of MEMS Scale Piezoelectric Energy Harvester
## 2 Research Progress of PEH
### Optimization of Energy Harvesters' Structure
#### 2.4.2 Frequency Up-Converting
Hence, it could harvest energy from the natural vibration of piezoelectric internal cantilever and the relative motion between a magnet and coil simultaneously. The maximum power was measured to be 5.76 mW. The frequency of human-body-induced motions is mostly between 2.5 Hz and 6 Hz, and the acceleration is between 15 and 20 m/s [70]. To collect the vibrational energy generated during the movement of human-limb, [PERSON] et al. [71] proposed the PEH using two mass-loaded unimorph piezoelectric beams clamped on two flexible sidewalls in 2017. The energy harvester had a size of 40 mm \(\times\) 32 mm \(\times\) 18 mm. The metal ball was stimulated by the movement of the human-limb,  
Figure 8: Schematic of the PEH using frequency up-conversion technology. Reprinted by permission from [PERSON] et al. Microsyst. Technol. 23, 2459 (2017). Copyright 2017 Springer Nature. Figure 9: The simulated mass displacements of the LFPB and HFPB. Reprinted by permission from [PERSON] et al. Microsyst. Technol. 23, 2459 (2017). Copyright 2017 Springer Nature. and the sidewalls were impacted back and forth, then the sidewalls transferred the impact force to the cantilever beams so that they vibrated at a higher frequency. The experiments showed that when an external excitation of 4.96 Hz and 2 g acceleration was applied, each piezoelectric single crystal could produce 96 \(\upmu\)W of peak output power. In addition, the system could produce 175 \(\upmu\)W of peak output power while two piezoelectric generators connected in series.
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.3 Communication Technologies and Schemes
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Wireless Networks** & **Range of Coverage** & **Frequency** & **The Data Rate** & **Maximum** & & & \\
**Rare** & **Coverage** & **Bands** & **for Downlink** & **Payload** & **Security** & **Battery Life** \\ \hline LoRa & & & for Asia: & & & & \\  & & 433 MHz & & & & \\  & Urban: 1–5 km & for Europe: & & & & \\  & Rural: 10–40 km & 868 MHz & 0.3–50 kbps & 243 bytes & Medium (AES-128) & 10 years \\  & & for America: & & & & \\  & & 915 MHz & & & & \\ \hline NB-IoT & & & EU: 800, 900, & & & & \\  & & 1800 MHz & & & & \\  & & Middle & & & & \\  & & East-North & & & & \\  & & Africa: 800, & & & & \\  & Urban: 1 km & 900 MHz & & & & \\  & Rural: 10 km & North America: & & & & \\  & & 600, 700, 850, & & & & \\  & & 1700 MHz & & & & \\  & & Asian Pacific: 700, & & & & \\  & & 800, 850, 900, & & & & \\  & & 1800, 2000 MHz & & & & \\ \hline Bluetooth & Bluetooth 4.x & & Bluetooth 4.x & Bluetooth 4.x & & \\  & to 100 m, & & 1 MB/s, & 31 bytes, & Low & \\  & Bluetooth 5.x & & Bluetooth 5.x & Bluetooth 5.x & (AES-128) & some weeks \\  & to 200 m & & 2 MB/s & 255 bytes. & & \\ \hline RFID & LF: 1 m & 125–135 kHz (LF) & LF: 100–300 bps & LF: 64 bits HF: & Low & \\  & HF: 10 m & 13.56 MHz (HF) & HF: 1 kbps & 128 bits & (Encryption-Wake & Passive: 3–5 years \\  & UHF: 100 m & & 40–640 kbps & UHF: & 256 bits & Algorithm) & \\ \hline WIFI & & & 802.11/n:600 & & & \\  & max 300 m & & 5 GHz & Mbps & & \\  & & 6 GHz (802.11 ax) & /ac: 1.3 Gbps & no limit & High (AES, \\  & & & /ax: 9.6 Gbp/s & & & WPA2/WPA3) & power supply \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main features of Wireless Network technologies. Figure 6: Data Rate vs. Range. Depending on the application's requirements, the appropriate wireless network should be selected. Particularly in the context of establishing connectivity among IoT devices like sensors, the primary role is the selection of a network with wide coverage and low power consumption. The LoRa network meets these needs, is open, and is used in articles [25; 39; 40; 41; 42; 44]. In cases where the application requires a higher data rate, Bluetooth technology can be used, as mentioned in the article [44], but it has significantly higher power consumption compared to LoRa, and the coverage is 100 m for Bluetooth 4.x and 200 m for Bluetooth 5.x. For applications aimed at object identification and detection, such as products in warehouses and animals in agricultural operations, RFID technology is a suitable choice as mentioned in articles [43; 45]. For applications that require a high data rate and need to utilize a Wi-Fi network, the provision of electrical power is necessary. Finally, once DTs rely on data exchange, real-time monitoring, and interaction with their physical counterparts, wireless networks provide a flexible and efficient means of communication for these purposes [8].
## 2 Materials and Methods  
### Polish Standards for Calculating Usable Floor Area  
The usable area is one of the most important parameters for property valuation, but its calculation is not a straightforward process. Over the years, different measurement standards have been in force in Poland, which has led to inconsistencies in the results. Until 1999, the PN-B-02365:1970 standard [29] was widely used, according to which only usable rooms such as living rooms, kitchens or bathrooms were to be considered as part of the usable area. In the case of storied houses, the type of roof and also its slope were important, as the usable area also depends on the height of the rooms (see [30; 31] for further details). Subsequently, the standard PN-ISO 9836:1997 [32] was introduced, which excluded areas such as basements or garages. Since 2012, the standard PN-ISO 9836:2015-12 [33] has been in force, which only applies to new buildings (see details in [29; 31; 32; 33; 34; 35; 36; 37; 38; 39]). This standard is consistent with European standards. It provides comprehensive regulations on measuring usable area, including the area under sloping roofs. According to this standard, the usable floor area is calculated as follows: the area with a height above 2.2 m is fully included, while the area with a height between 1.4 and 2.2 m is counted at 50%, and areas below 1.4 m are completely excluded from the calculations. This standard does not take partition walls into account when calculating the usable floor area, which was a practice in previous standards. The changing regulations and the variety of standards lead to difficulties in interpreting the data. In the case of older properties, non-compliance with the current standards can result in valuation errors of several or even several percent, depending on the size of the building or the type of roof. Moreover, the REPR frequently lacks information on which standard the usable floor area of the building was calculated, which makes it even more difficult to compare properties. Detailed information on the norms and their discrepancies can be found in available scientific studies and Polish standards [21; 22; 23; 24; 25; 26]. This study focuses on estimating the floor area of single-family homes with multi-pitched roofs using the principles set out in the PN-ISO 9836:2015-12 standard.
## 5 Experimental Results
### Results Analysis  
From the observation of the experimental results it is clear that band ordering based on Pearson's Correlation improves the lossless compression of hyperspectral images.  
Contrariwise, the results achieved with the band ordering based on Bhattacharyya distance are worst with respect to the results achieved without band ordering.  
Therefore, in our experiments Bhattacharyya distance does not work correctly for our purposes.  
In detail, the average improvement introduced with the [PERSON]'s correlation-based band ordering is in terms of 0.04 on C.R. (approximately +1.27%) with respect to the achieved results without band ordering.  
Instead, the results achieved with band ordering based on Bhattacharyya distance are worst with respect to the results without band ordering in terms of \(-0.27\) on C.R. (approximately \(-8.5\%\)).  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline  & **Without** & **Correlation-based** & **Bhattacharyya-based** \\  & **band ordering** & **band ordering** & **band ordering** \\ \hline Lunar Lake & 3.19 & 3.24 & 3.01 \\ Moffett Field & 3.17 & 3.20 & 2.77 \\ Jasper Ridge & 3.19 & 3.22 & 2.85 \\ Cuprite & 3.19 & 3.24 & 3.00 \\ Low Altitude & 3.01 & 3.04 & 2.78 \\
**Average** & **3.15** & **3.19** & **2.88** \\ \hline \end{tabular}
\end{table}
Table 7: Average results in terms of C.R. for each hyperspectral image without ordering, with [PERSON]’s correlation-based band ordering and Bhattacharyya distance-based band ordering.  
Figure 19: Average results in terms of C.R.–Histogram.
## 2 Materials and Methods  
### Experimental Material  
#### 2.1.1 Carbonate Rock  
All rock specimens used in the experiment were Nanling Formation limestone, obtained from core specimens of the same stratum at a depth of 30-50 m in an industrial solid waste landfill. The specimens were selected for their relatively intact condition, with a height exceeding 30 mm and differing only in fracture aperture. The overall color of the rock specimens ranged from grayish-white to grayish-black, and they exhibited a microcrystalline structure. On the surface of the rocks, small areas of yellowish-brown weathering could be observed, likely caused by long-term natural erosion and oxidation, which altered the color of certain parts of the rock surface. Additionally, the rocks contained numerous calcite veins, with apertures ranging from 1-5 mm, which appeared as distinct linear patterns within the rock. Figure 1 illustrates some of the rock specimen: (a) shows the specimen with fracture apertures of 0.6 \(\sim\)0.2 mm, (b) shows the specimen with fracture apertures of 0.2-0.05 mm, and (c) shows the specimen with fracture apertures of 0.05-0.01 mm.  
The rock specimens were analyzed using an X-ray Diffractometer (XRD, DX-2700, Dandong Haoyuan Instrument Co., Ltd., Dandong, China). To minimize the impact of subjective factors on the analysis results of rock specimen mineral composition during XRD analysis, a Scanning Electron Microscopy (SEM, SU8010, Hitachi, Tokyo, Japan) equipped with an Energy Dispersive Spectrometer (EDS) was used to analyze the chemical composition of the rock specimens (see Table 1). The mineral composition of the rocks was then determined by combining the EDS analysis results with the XRD analysis results, as shown in Table 2. After measurement, it was found that the main component of all collected rock specimens is calcite, with the proportion of CaCO\({}_{3}\) ranging from 60% to 69%.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **C** & **O** & **Ca** & **Si** & **Al** & **Mg** & **K** & **S** & **Fe** \\ \hline Content & 42.05\% & 40.99\% & 12.53\% & 1.43\% & 1.06\% & 0.80\% & 0.54\% & 0.38\% & 0.22\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The EDS analysis results.  
Figure 1: Limestone specimens with varying fracture apertures used in dissolution experiments (fracture aperture ranges: 0.2–0.6 mm for Specimen (**a**), 0.05–0.2 mm for (**b**), and 0.01–0.05 mm for (**c**)).
# Small-UAV Radar Imaging System Performance with GPS and CDGPS Based Motion Compensation
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Test 1-Track 1** & **Test 1-Track 2** & **Test 2-Track 1** & **Test 2-Track 2** \\ \hline \multirow{2}{*}{**Percentage of fix/float solution**} & \multirow{2}{*}{36\% fix, 64\% float} & \multirow{2}{*}{70\%fix, 30\%float} & \multirow{2}{*}{100\% fix} & \multirow{2}{*}{51.3\%fix, 48.7\% float} \\  & & & & & 48.7\% float \\ \hline \multirow{2}{*}{**Number of visible satellites**} & \multirow{2}{*}{9} & \multirow{2}{*}{9} & \multirow{2}{*}{10} & \multirow{2}{*}{11} \\ \cline{2-2} \cline{5-5}  & & & & \\ \hline
**Average values of GDOP**, PDOP**, HDOP**; VDOP** & & & & \\ \hline
**Mean of East Standard Deviation (m)** & 0.0429 & 0.0155 & 0.0056 & 0.0101 \\ \hline
**Mean of North Standard Deviation (m)** & 0.0446 & 0.0176 & 0.0072 & 0.0141 \\ \hline
**Mean of Up Standard Deviation (m)** & 0.0927 & 0.0351 & 0.0212 & 0.0228 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: CDGPS operative conditions. The computational time is in the order of few unit seconds for Test 1-Track 1 and for both tracks of Test 2, i.e., when the average altitudes are in the range of [4.5-5.6] m. Conversely, for Test 1-Track 2, the computational time is about 14 s. This is compliant with the average altitude, which increases up to 10 m. As highlighted before, the higher altitude implies that the Shift and Zoom synthetic aperture adopted for Test 1-Track 2 is larger than those used for the other tracks. This analysis corroborates that, thanks to the MoCo, the required computational time is compliant with that expected for quasi real-time imaging. In addition, with respect to other classical data inversion strategies, exploiting the positioning information directly in the focusing stage (see [14; 18; 21]), the MoCo supports the creation of an off line library of scattering operator and their SVD. This feature is useful especially in view of a real-time automatic on board processing for long flight surveys.
# Balloon Design for Mars, Venus, and Titan Atmospheres
## 4 Balloon Design for Different Atmospheres
### Balloons on Titan
The fineness factor used for comparison is 3 for oblate, 0.4 for airship and 0.6 for a prolate shape. Figure 18 presents the ascent speed for all balloon types and shapes. The ascent speed of the ZP and OZP balloons is similar. The prolate-shape balloons for all three types of them have higher velocity, while the oblate shape has the least. The SP balloons for all shapes have velocity lower than the corresponding shapes of the ZP and OZP balloons. Figure 16: Altitude variation of the OZP balloons for different shapes with varying fineness ratios in order to find the stable fineness ratio on Titan (**a**) oblate (_f_: 2–8) (**b**) prolate (0.1–0.8) (**c**) airship (0.1–0.8). Figure 17: Mass variation of different balloon types at varying fineness ratios on Titan (**a**) airship and prolate (**b**) oblate. Note that the lines for ZP gas mass and OZP gas mass are superimposed, as well as the lines for ZP envelope mass and OZP envelope mass. To summarize, for the balloon mission on Titan, the OZP and ZP balloons seem to be more mass efficient than the SP balloons, which means that the latter balloons need almost twice the amount of gas. The envelope masses of ZP and OZP are similar, while the ZP balloons are slightly heavier. In the case of shape, it is preferred to use the prolate, airship, and oblate shapes with low prolateness, and oblateness factor. Balloons with high prolateness and high oblateness are heavier in comparison to a sphere. In terms of the ascent speed, if high ascent speed is desired, then the prolate shape could be a good choice; if low ascent speed is required then the oblate shape can be used.
## 5 Experimental Results and Analysis  
### Parameter Settings and Simulations  
According to the downlink routing topology shown in Figure 2, the topology of downlink communication routing of the LoRaWAN network is established with the Mininet platform. The Ryu manager is used as the network resource analog controller for collecting, sending and receiving traffic information. The downlink data traffic is simulated by the testing software iPerf. The maximum bandwidth of each link is set as 80 Mbit/s. The distance parameters of the link are set as follows: \(v_{data}=2\times 10^{8}~{}m/s\), \(T_{switch}=0.1~{}ms\), \(T_{filter}=0.1~{}ms\). In the experiment, the distribution of downlink data types in the LoRaWAN network is simulated and modified by adjusting the network parameters such as the request bandwidth value of the downlink routing data packets and the data requests of different priorities in the LoRaWAN network. The minimum and maximum requested bandwidth of downlink communication, the average requested bandwidth of downlink communication data, and other communication parameters are all sent out by the host.