esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrical Resistance Response to Strain in 3D-Printed Conductive Thermoplastic Polyurethane (TPU)
###### Abstract  
Additive manufacturing (AM) offers new possibilities in soft robotics as materials can easily be combined in multi-material designs. Proper sensing is essential for the soft actuators to interact with the surroundings successfully. By fabricating sensors through AM, sensors can be embedded directly into the components during manufacturing. This paper investigates NinjaTek Eels electrical resistance response to strain and the feasibility of using the material to create strain sensors. Strain sensors were 3D-printed out of NinjaTek Eel, a soft conductive TPU, and was tested during cyclic loading. A custom resistance-strain test rig was developed for measuring sensor behavior. The rig was calibrated for electric resistance, able to measure electric resistance as a function of strain. A parabolic response curve was observed during cyclic loading, which led to ambiguous readings. A 10-specimen validation test was conducted, evaluating the statistical variation for the first 100 loading cycles. The validation test showed that the sensor is capable of accurate and predictable readings during single load cases and cyclic loading, with the overall root mean square error being 66.9 \(\Omega\). Combining two sensors of different cross-sections gave promising results in terms of calibrating. By monitoring load cycles and strain rates, calibration can also be achieved by machine learning models by the microcontroller used to extract data. The presented work in this article explores the potential of using conductive TPUs as sensors embedded in products such as soft robotics, life monitoring of products with structural, and digital twins for live product to user feedback.  
flexible and stretchable sensors; printable sensors; additive manufacturing; soft robotics sensors +
Footnote †: journal: Applied Sciences
## 5 Numerical Simulation Calculation
### Distribution of Abutment Pressure and Plastic Zone When the 20 m Pillar Affected by Mining  
When the coal pillar width is 20 m, affected by mining on one side of the 113 working faces, the distribution of stress zone and plastic zone in the coal pillar is shown in Figure 6, counting from 0 m in the 113 transportation roadway, 0-2 m is the low-stress zone and plastic zone, 2-5 m is the high-stress zone and elasticity zone, 5-7 m is the stress-decreasing zone and elasticity zone, 7-18 m is the low-stress zone and elasticity zone, and 18-20 m is the low-stress zone and plasticity zone. After more than a 7 m area in the coal pillar reaches the low-stress zone, the coal pillar can be shortened appropriately and will not affect the stability of the 111 air return roadway.  
When the coal pillar width is 20 m, affected by the mining on both sides of the 113 working face and 111 working face, the distribution of abutment pressure and plastic zone in the coal pillar is shown in Figure 7. Counting from 0 m in the 113 transportation lane, 0-2 m and 18-20 m are the low-stress zone and plastic zone, 2-4 m and 16-18 m are the high-stress zone and elasticity zone, 4-6 m and 14-16 m are the stress decreasing zone and elasticity zone, and 6-14 m are the low-stress zone and elasticity zone. There exists an 8 m low-stress zone in the coal pillar, and the shortening of the 20 m coal pillar has almost no effect on the stability of the roadway.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Rock Formation** & **Thickness** & **Compressive** & **Tensile** & **Elastic** & **Poisson’s** & **Cohesive** & **Unit Weight** \\
**(m)** & **Strength** & **Strength** & **Strength** & **Ratio** & **Force (MPa)** & **(kN/m\({}^{3}\))** \\ \hline Silstone & 10.11 & 69.65 & 6.97 & 14.62 & 0.22 & 4.27 & 2.50 \\ Fine sandstone & 7.70 & 65.55 & 6.58 & 13.64 & 0.23 & 4.66 & 2.35 \\ Mudstone & 4.28 & 64.81 & 6.50 & 12.35 & 0.23 & 3.97 & 2.50 \\ Fine sandstone & 4.31 & 56.26 & 5.62 & 14.62 & 0.22 & 4.83 & 2.35 \\ Silstone & 0.25 & 42.65 & 5.67 & 14.62 & 0.22 & 4.27 & 2.35 \\ Carbonaceous mudstone & 0.20 & 36.12 & 2.35 & 8.67 & 0.21 & 2.04 & 2.25 \\ Coal beam & 11.07 & 12.10 & 1.10 & 1.86 & 0.28 & 0.75 & 1.50 \\ Sandy mudstone & 0.51 & & 2.35 & & & & 2.25 \\ \hline \end{tabular}
\end{table}
Table 2: Physical and mechanical parameters of surrounding rock.  
Figure 5: The grid diagram of the 3D numerical model for the working face.  
Combined with the above analyses, the existing 20 m coal pillar is large, and there is sufficient space for shortening. When the coal pillar width is reasonable, the coal pillar can play the bearing capacity and is hard to deform and destabilize when digging into the roadway, and it can directly improve coal production.
## 1 Introduction
[PERSON] [30] installed eight temperature sensors on a U-shaped girder bridge, located at the top surface of the flange, the middle of the web, and the bottom slab, and analyzed the data from these sensors to determine the temperature distribution of the U-shaped girder bridge. However, due to the limited number of sensors, these data could not capture the temperature distribution of any sections of the U-shaped girder bridge. [PERSON] et al. [31] established a finite element model of a U-shaped girder bridge considering factors such as climatic conditions, bridge location, structural dimensions, and thermal properties of materials. They selected the six days with the strongest radiation throughout the year for temperature field simulation to compare the temperature difference distribution of the bridge under different radiation intensity parameters. However, the model only simulates specific dates and fails to cover temperature changes throughout the year. [PERSON] et al. [32] established a refined finite element model of a 25-m U-shaped girder bridge and verified the accuracy of the model through field tests. Then, they analyzed the performance changes of the U-shaped girder bridge under different temperatures and loads, where the deflection caused by temperature changes could reach up to 2.91 mm. While the refinement of the finite element model improved the precision of the simulation, there are limitations in the robustness of the model due to the complexity of engineering applications. In summary, although existing studies have analyzed the temperature distribution in U-shaped girder bridges through field data and numerical simulations, there is still room for improvement in data comprehensiveness, model accuracy, and practical applicability. Therefore, it is urgent to study the temperature distribution of U-shaped girder bridges, and propose a simulation method to describe the temperature variation under solar radiation throughout the year. At the same time, developing a simple and effective prediction model of the temperature gradient is essential for ensuring the long-term stability and durability of the structure. This study proposes a method for analyzing the temperature distribution of U-shaped girder bridges and develops a prediction model to estimate temperature gradients. An improved ASHRAE clear sky model is first used to simulate the shadowed areas of a U-shaped girder bridge under solar radiation. Then, a finite element model of the U-shaped girder bridge is established to simulate the temperature difference changes under different solar radiation effects. Finally, a prediction model of the temperature gradient is developed using nonlinear fitting approaches.
# Urban Flood Mapping Using SAR Intensity and Interferometric Coherence via Bayesian Network Fusion
###### Abstract  
Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks.  
urban flood mapping; synthetic aperture radar (SAR); InSAR coherence; Bayesian network 27 August 2019; Accepted: 23 September 2019; Published: 25 September 2019
## 1 Introduction
### Contributions and Organization  
We summarize the contributions of this work as follows:* We propose an analytical model that, given the network topology, a budget in terms of storage capacity and characteristics of the video content catalog, identifies the optimal number of caches to deploy in the network and the optimal distribution of the storage capacity among caches at different network levels, with the aim of minimizing the overall amount of network resources (i.e., capacity) occupied due to VoD content request provisioning.
* To verify the correctness of the analytical model, we perform extensive simulations using a discrete, event-based dynamic simulator for VoD-content caching and distribution (presented in our previous work [9]).
* We perform a sensitivity analysis of the effects of the popularity distribution, the location of metro cache with respect to end-users and the total available storage capacity, on the optimal cache deployment strategy. We summarize the takeaways of the analysis and provide guidelines to identify the optimal cache deployment among the hierarchical levels of a metro network.  
The rest of the paper is organized as follows. Section 2 discusses some relevant related works. Section 3 describes the network and VoD-content catalog models. Section 4 states the problem at hand and presents the analytical model proposed to solve it. In Section 5 we describe the event-based simulator developed to perform dynamic VoD-content caching and distribution. Section 6 reports analytical and simulation results. Section 7 concludes the paper.
# Influence of Aging Conditions on the Dynamic Stiffness of EPDM and EVA Rail Pads
## 3 Methods
### Mechanical Characterization: Dynamic Stiffness Tests  
Stiffness tests were conducted using a universal servo-hydraulic testing machine equipped with a \(\pm\)100 kN capacity load cell, as can be shown in Figure 3. The deformation of the rail pads was recorded using four Linear Variable Differential Transformers (LVDTs) mounted on a metallic base replicating the sleeper's geometry. The loads were applied to the rail pads through a UIC60 rail sample, which was connected to the testing machine via a ball joint to ensure the vertical alignment of the applied load. Throughout the tests, both the vertical load and the rail pad deformation were measured, with the latter calculated as the average of the four LVDTs.  
According to standards UNE-EN 13481-2 [19] and UNE-EN 13146-9 [18], dynamic stiffness was determined by subjecting the rail pad to 1000 sinusoidal load cycles. An example of a force-displacement curve required to evaluate the dynamic stiffness of the pads is presented in Figure 4.  
The dynamic stiffness of the rail pads was calculated as the ratio of the average load range to the average displacement range over the final 100 cycles of each test, as expressed in Equation (1):  
\[k_{dyn}=\frac{\overline{F_{max}}-\overline{F_{min}}}{\overline{D_{max}}- \overline{D_{min}}} \tag{1}\]  
The standard establishes a reference frequency of 5 Hz but allows testing at 10 and 20 Hz for specific cases, so tests were conducted at these three frequencies. Regarding load amplitude, the standard provides different values based on the track category; therefore, all specified values (15.5, 21.0, and 31.5 kN) were considered. For the minimum test load, the standard specifies a value of 18 kN, representing the fastening's tightening. However, since fastening tightening varies significantly, four different minimum load values were selected: 1 kN (simulating a broken fastening), 9 kN (loosened fastening), 18 kN (reference condition), and 25 kN (overtightened fastening).  
Figure 3: Image of the stiffness test conducted on a testing machine.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 3 Methodology
### HTTP Header Enrichment  
The HTTP header collection occurred as an optional post-processing step, executed through a HEAD request following TLS connection establishment. Unlike [PERSON] et al. [27], who captured only the Server header--commonly overwritten in CDNs--our methodology preserved all header keys in their original sequence, while discarding values except where explicitly specified. In the base implementation, only the Server value was retained. Header ordering was maintained as a critical metric for application similarity mapping. The collected headers were then hashed, using MurmurHash3. The Python aiohttp module was used during the implementation of the enrichment process, to asynchronously make each HEAD request. During the header collection, the session keys were written to key.log file, to enable future decryption of the session if required. An example of a full HEAD request taking place within Wireshark can be seen in Figure 3, with the corresponding HTTP header response being seen in Figure 4.  
Table 3 demonstrates the correlation between HTTP header fingerprints and their corresponding TLS fingerprints:  
\begin{table}
\begin{tabular}{l c} \hline
**TLS Fingerprint (SHA256)** & **HTTP Headers (MMH3)** \\ \hline
71a72d0a2d5478 cdn7b7c513 fe120129a4 db55 dd21671 eded5314034b0b72124 & 3898065973 \\ ab545 cfcf96261433c3531d7b9d0935d8 db41 ab3b7 faef85f5e4283d66 ad5 ed49d & 3898065973 \\ ab545 cftf96261433c531d7b9d0935d8 db41 ab3b7 faef85f5e4283d66 ad5 ed49d & 3898065973 \\
71a72d0a2d5478 cafb7c513 fe120129a4 db55 dd21671 ed5314034b0b72124 & 2350846486 \\ ab545 cftf96261433c531d7b9d0935d8 db41 ab3b7 faef85f5e4283d66 ad5 ed49d & 1200561793 \\ \hline \end{tabular}
\end{table}
Table 3: TLS fingerprint and HTTP header hash correlation for CDN-hosted domains, demonstrating how incorporating HTTP headers increases fingerprint granularity. While there are only two unique TLS fingerprints shown, there are three unique HTTP header hashes, suggesting better server differentiation capability.  
Figure 2: The raw fingerprint produced from the active scan.  
Figure 3: A screenshot of the HEAD request being made, as seen within Wireshark. The HTTP Protocol is highlighted in green.  
HEAD / HTTP/l.1  
Host: ouyb.cn  
Accept: */*  
Accept-Encoding: gzip, deflate, br  
User-Agent: Python/3.12 aiohttp/3.9.5  
HTTP/1.1 200 OK  
Connection: Keep-Alive  
Keep-Alive: timeout=5, max=100  
x-powered-by: PHP/7.4.33  
content-type: text/html; charset=UTF-8  
cache-control: no-store, max-age=0, no-cache  
date: Sun, 23 Feb 2025 19:04:10 GMT  
server: LiteSpeed  
alt-svc: h3=\":443\"; ma=2592000, h3-29=\":443\"; ma=2592000, h3-Q05@=\":443\"; ma=2592000, h3-Q046=\":443\"; ma=2592000, h3-Q043=\":443\"; ma=2592000, qu  
ic=\":443\"; ma=2592000; v=\"43,46\"
# Phenotype of White Sika Deer Due to SCF Gene Structural Variation
###### Abstract  
Breeding ornamental white sika deer is a new notion that can be used to broaden the sika deer industry However, it is very rare for other coat phenotypes to occur, especially white (apart from albinism), due to the genetic stability and homogeneity of its coat color phenotype, making it difficult to breed white sika deer between species. We found a white sika deer and sequenced its whole genome. Then, the clean data obtained were analyzed on the basis of gene frequency, and a cluster of coat color candidate genes containing 92 coat color genes, one SV (structure variation), and five nonsynonymous SNPs (single nucleotide polymorphisms) was located. We also discovered a lack of melanocytes in the skin tissue of the white sika deer through histological examination, initially proving that the white phenotype of sika deer is caused by a 10.099 kb fragment deletion of the _SCF_ gene(stem cell factor). By designing SCF-specific primers to detect genotypes of family members of the white sika deer, and then combining them with their phenotypes, we found that the genotype of the white sika deer is SCF\({}^{\text{\textregistered}89}\)/SCF\({}^{\text{\textregistered}99}\), whereas that of individuals with white patches on their faces is SCF\({}^{\text{\textregistered}89}\)/SCF\({}^{\text{\textregistered}1-9}\). All these results showed that the _SCF_ gene plays an important role in the development of melanocytes in sika deer and is responsible for the appearance of the white coat color. This study reveals the genetic mechanism of the white coat color in sika deer and supplies data as a reference for breeding white ornamental sika deer.  
SCF; sika deer; whole-genome sequencing; melanin Article
## 3 CE Methods and System Model
### Proposed Method
A fully connected 4-class layer of SoftMax and a classification output layer follow. The peephole LSTM model can be deepened by adding further layers. Figure 3 illustrates the design of the suggested channel estimator. When training the suggested DL peephole LSTM model, three optimization methods are applied for the loss function's minimization: adaptive moment estimation (Adam), root mean square propagation (RMSProp), and stochastic gradient descent with momentum (SGDm). Due to the limited number of pilots, we have been searching for the most accurate and reliable estimator possible. Recent research has led to the developing of a series of CSI channel models that accurately reproduce the actual channel's statistics. These channel models can effectively model training data. In this study, we used the 5G channel model. According to TR38.901 [30, 31], this model describes errors affecting CE performance. Other channel types include narrow-band Rayleigh fading channels and double selective fading channels [11]. The training dataset for one subcarrier is randomly generated during offline training. Transmitters provide OFDM frames to receivers over the adopted channel. After that, online training tracks channel conditions to recover transmitted data. The OFDM signal is recovered using transmitted frames subjected to various channel defects, including channel distortion and noise. Three optimization methods train the suggested estimator in simulated datasets to produce the best model with the fewest pilots. The training dataset for a subcarrier is collected. The transmitter sends data and pilot symbols in each OFDM packet by multiplexing pilot series data symbols. The offline process and training symbol creation shown in Figure 4 is used to create the proposed peephole LSTM-based channel estimator. The flowchart of the proposed model is shown in Figure 5.  
Figure 4: Creating training data and the offline DL procedure for the proposed estimator. \[f_{t}=\delta_{g}\left(W_{f}\ x_{t}+V_{f}h_{t-1}+U_{f}c_{t-1}+b_{f}\right) \tag{1}\]0 The suggested DL peephole LSTM method-based CSI estimator.
## 2 Integer Formulation  
Let  
\[\mathcal{F}=\{S\subseteq V\mid A(S)\
eq\emptyset,A(V\setminus S)\
eq\emptyset\}\]  
Given any tour cover \(TC\) of \(G\), by definition, for any \(S\in\mathcal{F}\) we have \(TC\cap\delta^{+}(S)\geq 1\) and \(TC\cap\delta^{-}(S)\geq 1\). Note that the condition \(TC\cap\delta^{-}(S)\geq 1\) is equivalent to \(TC\cap\delta^{+}(V\setminus S)\ \geq\ 1\), and by definition of \(\mathcal{F}\), a vertex subset \(S\) belongs to \(\mathcal{F}\) if and only if \(V\setminus S\) belongs to \(\mathcal{F}\). This observation motivates our integer formulation for DToCP. For any \(e\in A\), let \(x_{e}\) indicate the number of copies of \(e\) included in the tour cover. We minimize the total weight of arcs included, under the condition that every outgoing cut associated to some \(S\in\mathcal{F}\) should be crossed at least one. In order to ensure that our solution is a tour we also need to specify that for any node \(v\in V\), the number of arcs entering \(v\) is equal to the number of arcs leaving \(v\) in the tour. This integer formulation can be stated as follows:  
\[\min\sum_{e\in A}c_{e}x_{e}\] \[\sum_{e\in\delta^{+}(v)}x_{e} =\sum_{e\in\delta^{-}(v)}x_{e} \text{for all }v\in V\] \[\sum_{e\in\delta^{+}(S)}x_{e} \geq 1 \text{for all }S\in\mathcal{F}\] \[x_{e}\text{ integer} \text{for all }e\in A\]  
If one specific vertex \(u\) should belong to the optimal tour, we just need to set \(\we obtain the linear programming relaxation. We use DToC(\(G\)) to denote the convex hull of all vectors \(x\) satisfying the above constraints (those of the linear programming relaxation). Clearly minimizing the linear cost function \(\sum_{e\in A}c_{e}x_{e}\) over DToC(\(G\)) can be done in polynomial time since the separation problem of the cut constraint can be solved in polynomial time. Indeed, given a candidate solution \(x\) and for every \(e\in A\), let us consider \(x_{e}\) as the capacity on arc \(e\). For each pair of arcs \(e_{1},\;e_{2}\in E\), we compute the minimum capacity cuts \(\delta^{+}(U)\) separating them.