esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### DL-Based Applications for SAR Imagery
Recognizing that the attention mechanisms commonly used in optical image processing may not enhance performance in SAR tasks, due to complex sea clutter and often indistinct wake features, SWNet utilizes a ConvNeXt-T [95] backbone and introduces a specifically developed feature pyramid network called HR-FPN*. Their method achieved a mean average precision (mAP) of 49.0%, outperforming existing approaches on the OpenSARWake dataset. Their work served to highlight the importance of designing specialized network architectures that consider the unique characteristics of SAR images in ship wake detection.
# Numerical Quantification of Controllability in the Null Space for Redundant Manipulators
## 3 Simulation and Experiment  
To begin with, simulation and experiment were conducted with the 7 DoF robot (PANDA, Franka Emika GmbH, Munchen, Germany ) consisting of the Denavit-Hartenberg parameters shown in Table 1 to evaluate the singularities. Then, the simulation was conducted to verify the proposed method by using 6 DoF for the primary task and excess 1 DoF for the secondary task. We compared the general input and the proposed input introduced in Equation (14) to determine the capacity of the projected force input in the proposedmethod. The magnitude of both force inputs were equal, but the direction differed for each simulation and experiment.
## 4 Results and Discussions
### System Identification Analysis
#### 4.2.1 Training Setting Evaluation
Finally, a comparison between model 8-NARX and model 3-ANFIS shows that the two models can be used to estimate the behavior of controlled structure but model 8-NARX gives higher accuracy and generally performs better than the traditional models [1,11] and developed model 3-ANFIS for the ANFIS method, as shown in Tables 2 and 4. In addition, the correlation between observation and predicted signals are higher with model 8-NARX than model 3-ANFIS, as shown in Figures 7 and 9.
# Optimizing CNT Loading in Antimicrobial Composites for Urinary Tract Application
## 2 Materials and Methods
### Bacterial Strain and Culture Conditions  
_E. coli_ CECT 434 expressing the green fluorescent protein (GFP) was used as a model pathogen to perform the antibiofilm assays. For the construction of _E. coli_ CECT 434 GFP, the previously described pCM11 plasmid (Pmid19610092) carrying the gene encoding superfolder GFP (sGFP) and conferring ampicillin resistance was introduced in the CECT 434 strain by the heat shock method [34]. Besides this strain having a growth rate and biofilm formation capacity similar to the reference strain (_E. coli_ CECT 434), it allows biofilms to be analysed by confocal microscopy without invasive sample preparation. The bacterial strain was preserved at \(-80\)\({}^{\circ}\)C in Luria-Bertani Broth (Thermo Fisher Scientific, Waltham, MA, USA) containing 30% (_v/v_) glycerol, streaked on Plate Count Agar (PCA; Merck KGaA, Darmstadt, Germany) plates, and incubated for 24 h at 37 \({}^{\circ}\)C. Single colonies were collected from PCA plates, inoculated in 250 mL of artificial urine medium (AUM) [35] and incubated at 37 \({}^{\circ}\)C and 120 rpm for 16 \(\pm\) 2 h to prepare a starting culture. Ampicillin at 0.1 g L\({}^{-1}\) final concentration was used to maintain pCM11 in _E. coli_ CECT 434. The overnight culture was centrifuged (Eppendorf Centrifuge 5810R, Hamburg, Germany) at 18 \({}^{\circ}\)C, 3772\(\times\) g for 10 min, and resuspended in fresh AUM in order to obtain a final suspension with an optical density at 610 nm (OD\({}_{610\,\mathrm{nm}}\)) of 0.15. This OD\({}_{610\,\mathrm{nm}}\) corresponds to approximately 1 \(\times\) 10\({}^{8}\) colony-forming units per mL (CFU mL\({}^{-1}\)), as confirmed by the retrospective count of CFUs.  
Antibiotic film assays were performed in 12-well microtiter plates (VWR International, Carnaxide, Portugal). Sterilized surfaces of PDMS, 0.1, 1, 2, 3, 4 and 5 wt% CNT-O/PDMS, and 3 wt% CNT/PDMS containing 1, 2, 3 and 4 h ball-milled CNTs were placed on the microplate wells and inoculated with 3 mL of _E. coli_ suspension at a density of \(1\times 10^{8}\) cells mL\({}^{-1}\). Negative controls with 3 mL of AUM were prepared to evaluate the surface sterility throughout the experiments. Plates were incubated at 37 \({}^{\circ}\)C for 24 h under shaking conditions (100 rpm).  
Biofilm formation assays were performed in three independent assays, each one with three technical replicates.
# Performance of SIMO FSO Links over Mixture Composite Irradiance Channels
## 1 Introduction
The remainder of the paper is organized as follows. In Section 2, we introduce the channel model, composed of the atmospheric turbulence, and the NZB-PE sub-models, respectively. Next, in Section 3, we proceed to the performance analysis of the SIMO FSO system in terms of the OP and the ABER, while the corresponding numerical results are presented in Section 4. Finally, the concluding remarks are presented in Section 5.
## 3 Coded Cooperation with Hard Information Relaying and Relay Selection
### Outage Probability Analyses of the CC-HIR-RS Scheme
#### 3.1.2 CC-HIR-RS Scheme with Optimal PSR (CC-HIR-RS-OPS)  
The fraction \(1-\rho_{r}\) of the energy is used for the channel decoding and channel encoding processes, and the fraction \(\rho_{r}\) of the received energy from the source node is harvested for information relaying during the second time-slot. Therefore, power allocation for each transmission slot will affect the quality of the end-to-end communication. In this section, we present an optimal power-splitting scheme and perform the derivation of the closed-form outage probability expression of the CC-HIR-RS-OPS scheme. As noted above, optimal power allocation aims to maximize the capacity of the end-to-end \(S-R_{r}-D\) channel given in (6). Hence, the optimal PSR at the relay \(R_{r}\) can be obtained as follows:  
\[\rho_{r,\text{opt}}^{\text{HIR}} = \arg\max_{0<\rho_{r}<1}\min\{C_{SR_{r}},C_{R_{r}D}\} \tag{14}\] \[= \arg\max_{0<\rho_{r}<1}\min\bigg{(}(1-\rho_{r}),\frac{R_{R}}{R_{ S}}\frac{1}{d_{R_{r}D}^{\text{a}}}\
u\rho_{r}|h_{R_{r}D}|^{2}\bigg{)}.\]  
The optimal power allocation is attained if the powers in both channels are equal to maximize the capacity of the end-to-end channel [30]. Thus, the optimal PSR is obtained when the values in the the \(\min\{,\}\) operator are equal, and hence  
\[\rho_{r,\text{opt}}^{\text{HIR}} = \frac{1}{1+\frac{R_{R}}{R_{S}}\frac{1}{d_{R_{r}D}^{\text{a}}}\
u|h _{R_{r}D}|^{2}}. \tag{15}\]The capacity of the end-to-end \(S-R_{r}-D\) channel when substituting (15) in (6) is expressed as  
\[C_{SR_{r}D,\text{opt}} = \frac{1}{2}\log_{2}\left(1+\frac{G_{SR_{r}}v|h_{R,D}|^{2}|h_{SR_{r} }|^{2}\ P_{SD}}{\frac{R_{S}}{R_{R}^{2}}d_{R,D}^{2}+v|h_{R,D}|^{2}\ \frac{ \sigma_{n}^{2}}{\sigma_{n}^{2}}}\right). \tag{16}\]  
The best relay selection criterion is expressed as  
\[R_{\text{opt}}^{\text{CC-HIR-RS-OPS}} = \arg\max_{R_{r},r=1,\cdots,n_{R}}\left(\frac{G_{SR_{r}}|h_{R,D}|^{ 2}|h_{SR_{r}}|^{2}}{\frac{R_{S}}{R_{R}}d_{R,D}^{2}+v|h_{R,D}|^{2}}\right). \tag{17}\]  
Thus, the outage probability of the CC-HIR-RS-OPS scheme can be expressed as  
\[P_{\text{out}}^{\text{HIR-CC-RS-OPS}} = P\bigg{(}\max_{r=1,\cdots,n_{R}}C_{SR_{r}D,\text{opt}}<R\bigg{)} \tag{18}\] \[= \prod_{r=1}^{n_{R}}P\bigg{(}|h_{SR_{r}}|^{2}<\frac{\[PERSON]_{r}d_{R,D }^{a}R_{S}/R_{R}}{|h_{R,D}|^{2}}+\[PERSON]_{r}v\bigg{)}\] \[= \prod_{r=1}^{n_{R}}\left[1-\int_{0}^{\infty}P\bigg{(}\gamma_{SR_ {r}}>\frac{\[PERSON]_{r}d_{R,D}^{a}R_{S}/R_{R}}{x}+\[PERSON]_{r}v\bigg{)}f_{\gamma_{R,D} }(x)dx\right]\] \[= \prod_{r=1}^{n_{R}}\left[1-\frac{1}{\gamma_{R_{r}D}}\exp^{\frac{ \[PERSON]_{r}v}{\gamma_{SR_{r}}}}\int_{0}^{\infty}\exp^{-\frac{\[PERSON]_{r}d_{R,D}^{a} R_{S}/R_{R}}{\gamma_{SR_{r}}x}-\frac{x}{\gamma_{R,D}}}dx\right]\] \[= \prod_{r=1}^{n_{R}}\left[1-2\exp^{-\frac{\[PERSON]_{r}v}{\gamma_{SR_{ r}}}}\sqrt{\frac{\[PERSON]_{r}d_{R,D}^{a}R_{S}/R_{R}}{\gamma_{SR_{r}}\gamma_{R,D}}}K_{1}(2 \sqrt{\frac{\Phi_{r}d_{R,D}^{a}R_{S}/R_{R}}{\gamma_{SR_{r}}\gamma_{R,D}}}) \right],\]  
where \(\Phi_{r}=(2^{2R}-1)/(\
u G_{SR_{r}}P_{SD}/\sigma_{n}^{2})\).
## 5 Materials and Methods
### Feature Extraction  
Feature extraction is desirable in pattern recognition that focuses on distinguishing between the normal and the cancerous breast. We used a CNN as a powerful feature extraction ability to extract an efficient feature set of segmented grayscale images. We constructed a stacking of a series layer as follows:  
* Image input layer: The image input layer is the connection layer to the network, as it contains the channel size and the input image size. Based on the database containing the grayscale images, the channel size was 1, and the size of the post-processing images was 56 \(\times\) 56, so the input layer was 56 \(\times\) 56 \(\times\) 1.
* Convolution layer: A set of filters in the CNN processed the input image to generate activation maps. By stacking the activation maps along the depth dimension, the layer output was produced. Three parameters determined the convolution layer structure; the first parameter determined the filter size, and the second parameter determined the feature mappings. Then, via a Stride, we slid the filter over the following receptive area of the same input image and repeated the procedure. The same procedure was repeated until we completed the entire image. The input for the following layer will be the output.
* Max pooling layer: After convolution, the spatial volume of the input image was reduced using a pooling layer. It was employed between two convolution layers. Max pooling is a reduced feature map that retains the majority of the dominant features from the previous feature map after redundant data have been eliminated.
* Dropout layer: Overfitting may be avoided by using the dropout technique, which also offers a way of roughly combining many distinct neural network architectures efficiently and exponentially. Dropout refers to units in a neural network that are dropping, both hidden and visible. Dropping a unit out entails removing it temporarily from the network.
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
## 2 Preliminary
### Expected Rank Functions
As the independent packet loss pattern is a Bernoulli process, which is stationary, \(E(r,t)\) has the following properties according to [73]:  
* \(E(r,t)\) is concave and monotonically increasing with respect to \(t\);
* \(0=E(r,0)\leq E(r,t)\leq r\) for any non-negative integer \(t\). We can understand these properties by the following intuitions:
* A newly received recoded packet of a batch at the next node is either linearly independent or not of the already received recoded packets of the same batch. The chance of being linearly independent is smaller when there are more received recoded packets at the node, i.e., \(E(r,t)\) is concave with respect to \(t\). * Receiving a new recoded packet will not decrease the rank of the batch, as spanning a vector space with one more vector will not decrease the dimension of the vector space--that is, \(E(r,t)\) is monotonically increasing with respect to \(t\). * The recoded packets are random vectors in an \(r\)-dimensional vector space, and the dimension of their span cannot exceed \(r\), i.e., \(E(r,t)\) is upper-bounded by \(r\). * When no recoded packet is sent, the rank of the batch at the next node must be 0, i.e., \(E(r,0)=0\). As in [73], we extend the domain of \(t\) from the set of non-negative integers to the set of non-negative real numbers by linear interpolation as  
\[E(r,t)=(1-\epsilon)E(r,\lfloor t\rfloor)+\epsilon E(r,\lfloor t\rfloor+1),\]  
where \(\epsilon=t-\lfloor t\rfloor\). That is, we first send \(\lfloor t\rfloor\) packets; then, the fractional part of \(t\) represents the probability that we send one more packet. The concavity of \(E(r,t)\) with respect to \(t\) is preserved. The reasoning behind this extension is that if we want to maximize the expected rank with a probability distribution for transmitting different numbers of recoded packets, the optimal distribution is either deterministic (Dirac distribution) or has a consecutive support of size 2, which is due to applying [PERSON]'s inequality on the concave expected rank function [73].
# Numerical Study on the Bending Resistance of Lightweight Built-Up Steel-Concrete Composite Beams
## 3 Parametric Numerical Analysis Results
### Influence of Steel Beam Height  
The influence of the steel beam height is analyzed for three different values--400 mm, 500 mm, and 600 mm. In Figure 12, the influence of the steel beam height on models with a full shear connection and when steel elements are tied is shown, (TIE_TIE). The analyzed models have a CW thickness of 1.5 mm. In accordance with the presented results of the numerical model, it is concluded that the influence of the steel beam height is significant. Increasing the steel beam height from 400 mm to 500 mm increases the bending capacity to approximately 60 kNm. The same difference is observed between models with a steel beam height of 500 mm and 600 mm. This leads to the conclusion that increasing the steel beam height to 200 mm increases the bending resistance to approximately 100 kNm.
# Mapping with Pleiades--End-to-End Workflow
## 4 Methods
### Digital Surface Model Generation
#### 4.2.4 DSM Resampling  
Since in remote sensing a classical data representation is still the 2.5D DSM the 3D point cloud has to be mapped to a 2D image with equidistant spacing (i.e., a regular grid). This process is also called resampling or regridding. We present two simple approaches to doing so. First, the dimension and spacing of the output DSM are defined. Then, we use either nearest neighbor interpolation or areal interpolation in the regridding step. In the former case, each 3D point is projected into the DSM and put into the nearest integer position. If this value was already assigned then the maximum between the previous and the current value is chosen. In this way, the height value closest to the sensor is reconstructed. In areal interpolation, four adjacent 3D points are projected into the DSM. Then, all integer locations that are covered by this quadrangle are linearly weighted interpolated. Double assigned pixels are treated as described above. Overall, this procedure is a direct resampling that potentially is not hitting all pixels of the DSM grid. However, as multiple DSMs get fused in further processing, this simple strategy is sufficient and in reality the nearest neighbor approach is usually used due to performance benefit. The DSM resampling is depicted in Figure 12.  
Figure 11: Subpixel distribution (**a**) based on the parabola interpolation [61] and (**b**) after our proposed normalization.
## 2 Preliminaries
### Notation  
We denote the set of integers modulo \(q\) by \(\mathbb{Z}_{q}\). Column vectors are represented by lower-case bold letters and matrices by upper-case bold letters. For a matrix \(S\in\mathbb{R}^{m_{1}\times m_{2}}\), we say the norm of \(S\) is \(\parallel S\parallel=\max\limits_{1\leq i\leq m_{2}}\parallel s_{i}\parallel\), where \(\parallel s_{i}\parallel\) denotes the \(\ell_{2}\)-norm (Euclidean norm) of the column vector \(s_{i}\). We let \(\widetilde{S}\in\mathbb{R}^{m_{1}\times m_{2}}\) denote the matrix whose columns \(\widetilde{s}_{1}\),..., \(\widetilde{s}_{m_{2}}\) represent the Gram-Schmidt orthogonalization of the vectors \(s_{1},\ldots,s_{m_{2}}\) taken in the same order. Let \(\parallel\widetilde{S}\parallel\) denote the Gram-Schmidt norm of \(S\). Let \(f(n)\) and \(g(n)\) denote two positive real-valued functions. We say that \(f=O(g)\) if there exist two constants \(c_{1}\), \(c_{2}\) such that \(f(n)<c_{1}\cdot g(n)\) for all \(n\geq c_{2}\); \(f=\Omega(g)\) if \(g=O(f)\); and \(f=\Theta(g)\) if \(f=\Omega(g)\) and \(g=O(f)\). We say \(f=\widetilde{O}(g)\) if \(f=O(g\cdot poly(\log g))\) and \(f=\widetilde{\Theta}(g)\) if \(f=\Theta(g\cdot poly(\log g))\).
###### Abstract  
This study presents an experimental comparison of three characterization methods for phase change materials (PCM). Two methods were carried out with a calorimeter, the first with direct scanning (DSC) and the second with step scanning (STEP). The third method is a fluxmetric (FM) characterization performed using a fluxmeter bench. For the three methods, paraffin RT58 and polymer PEG6000, two PCM suitable for domestic hot water (DHW) storage, were characterized. For each PCM, no significant difference was observed on the latent heat and the total energy exchanged between the three characterization methods. However, DSC and STEP methods did not enable the accurate characterization of the supercooling process observed with the FM method for polymer PEG6000. For PEG6000, the shape of the enthalpy curve of melting also differed between the experiments on the calorimeter--DSC and STEP--methods, and the FM method. Concerning the PCM comparison, RT58 and PEG6000 appeared to have an equivalent energy density but, as the mass density of PEG6000 is greater, more energy is stored inside the same volume for PEG6000. However, as PEG6000 experienced supercooling, the discharging temperature was lower than for RT58 and the material is therefore less adapted to DHW storage operating with partial phase change cycles where the PCM temperature does not decrease below 52 \({}^{\circ}\)C.  
phase change material; latent heat storage; domestic hot water; experimental characterization; thermal performances +
Footnote †: journal: Applied Sciences
## 5 Existing Cybersecurity Solutions for Smart Manufacturing
### Blockchain Security in Smart Manufacturing  
Blockchain works on the principle of the linked list structure combined with cryptographic techniques. Each data block holds the timestamp and a link to the previous block which is actually the cryptographic hash of the data in the previous block, forming a chain represented as a Merkle Tree. Since each block in the blockchain contains a fingerprint of data in the previous block, it is impossible to make any changes in the data without being traced unless the modification is carried out in all the subsequent blocks. The benefits of implementing blockchain in securing the smart manufacturing environment include resistance against data tampering, enhanced system resilience, and immutable manufacturing [22].  
The data security categories, confidentiality, integrity, and availability could be strengthened by using blockchain. In the case of confidentiality, the distributed and cloud-based environment of smart manufacturing makes access control more complex. However, with blockchain, the public-key system variant and smart contracts provide privacy and dynamic access control policies for data modification. The confidentiality for transactions is provided by Zero-knowledge proof and blockchain, based on homomorphic encryption which enables calculations on the encrypted data without revealing the actual information. In the case of integrity, the chain structure of the blockchain along with its signature verification technique safeguards the data and transactions against any integrity loss. In order to achieve data accountability blockchain is used to assemble the provenance records across  
Figure 6: NNO Classification algorithm framework.  
the smart manufacturing distributed environment. In terms of availability, it is hard to detect fault quickly in a distributed manufacturing environment because it can cause by a single node and due to the decentralized architecture, it may remain undetected. However, with blockchain, there exists a replica node for each block of data that holds a copy of it and thus provides availability. Being a secure approach to smart manufacturing, blockchain implementation encounters many technical challenges which include a practical consensus algorithm, privacy protection mechanisms, and mutability of data. It is also difficult to balance the cost of implementation, the complexity of the system, and managing security. Moreover, due to the increasing size and number of data blocks, the analysis capability of blockchain is inefficient.
## 3 J-C Constitutive Model and Damage Failure Model
### J-C Constitutive Model  
The Johnson-Cook constitutive model focuses on describing the nonlinear stress-strain relationship of materials entering hyper elasticity, and the specific expression is as follows [2]  
\[\sigma=(A+B\epsilon^{n})\left(1+C\ln\dot{\epsilon}^{*}\right)\left(1-\left(T^{ *}\right)^{m}\right) \tag{1}\]  
In the formula, \(\sigma\) is the stress; \(\dot{\epsilon}\) is the plastic strain; \(\dot{\epsilon}\) represents the strain rate; and \(T^{*}\) represents the relative dimensionless temperature parameter at the current temperature (\(T^{*}=(T-T_{r})/(T_{m}-T_{r})\)), where \(T_{r}\) is the room temperature and \(T_{m}\) is the reference temperature. The physical meaning of each parameter is: \(A\) represents the yield strength of the material; \(B\) and \(n\) represent material strain-strengthening parameters; \(C\) represents  
Figure 1: Simplified diagram of deep-sea impact model.  
the empirical strain-rate sensitivity coefficient; and \(m\) represents the temperature-softening effect. It is the reference strain rate of the constitutive model, usually taken as the strain rate of \(10^{-3}\) s\({}^{-1}\) in the quasi-static state.  
When the temperature effect is not considered, the temperature remains at room temperature; that is, at this point, Equation (1) of the J-C model will become  
\[\sigma=(A+B\epsilon^{n})\Big{(}1+C\ln\hat{\epsilon}^{*}\Big{)} \tag{2}\]  
Based on the tensile/compressive test data of materials under different strains at room temperature, the least squares method can be used to determine the parameters of the J-C constitutive model of materials at room temperature. Under room temperature conditions, the values of \(A\), \(B\), and \(n\) are determined based on the curves obtained from material compression/tensile tests at strain rates. At this point, Equation (2) becomes  
\[\sigma=A+B\epsilon^{n} \tag{3}\]  
Take the logarithm of both ends of Equation (3), with  
\[\ln(\sigma-A)=\ln B+n\ln\varepsilon \tag{4}\]  
And let \(\ln B+n\ln\varepsilon=y\), \(\ln B=x\), by substituting it into Equation (3), we obtain  
\[y=x+n\ln\varepsilon \tag{5}\]  
Similarly, for the initial plastic strain moment (\(\varepsilon=0\)) at different strain rates, i.e., Equation (2) becomes  
\[\sigma=A\Big{(}1+C\ln\hat{\epsilon}^{*}\Big{)} \tag{6}\]  
Immediately available  
\[\sigma/A-1=C\ln\hat{\epsilon}^{*} \tag{7}\]  
By substituting \(z\)= \(\varepsilon/A-1\) into Equation (7), it can be obtained that  
\[z=C\ln\hat{\epsilon}^{*} \tag{8}\]  
Therefore, the least squares method (LSM) for data processing of Equations (5) and (8) can ultimately determine parameters \(A\), \(B\), \(n\), and \(C\).