esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 High-Voltage Direct Current (HVdc) Grid Protection
### Overhead Lines and Underground Cables  
Principles of protection for overhead lines (OHL) are similar to underground cables (UGC). However, the behavior of protection methods for overhead lines is different than the underground cables because of the following technical dissimilarities:  
* OHL impedance is greater than UGC impedance [40].
* X/R ratio of OHL is much higher than UGC [40].
* Series inductance of OHL is 30-50% higher than UGC [40].
* Shunt capacitance of UGC is 30-40 times higher than OHL [40].
* Steady-state charging current is lower in OHL compared to UGC [40].
* Transient charging and discharging currents caused by the process of energization and de-energization of the grid when faults occur is lower in OHL than UGC [40].
* UGCs are more vulnerable to permanent faults than OHL [41].
* Voltage and current transients due to dc fault are different for the OHL and UGC because of impedance. OHL have large impedance as compared to UGC [42].  
Protection of Line Commutated Converter (LCC) and Voltage Source Converter (VSC) Based High-Voltage Direct Current (Systems)  
Protection methods for VSC based HVdc systems and LCC based HVdc systems are not the same. Protection methods are entirely different because of the fact that VSC-HVdc systems require rapid interruption and limitation of dc fault current, thereby reducing operating time of protection  
Figure 3: Variations of direct current (dc) voltage and currents in the event of malfunction of breaker.  
techniques as compared to LCC-based HVdc systems. Moreover, provisions of bidirectional power flow without reversal of polarity and extension to multi-terminal girds in VSC-HVdc systems change the configuration for dc grid protection [43; 44; 45].  
It is unacceptable for an electric equipment to allow flow of large short circuit currents. High rate of change of current results in failure of IGBTs because of localized heating losses. Therefore, in real life applications, if the technique of fault detection is not quick enough to block the converter switches, these converter switches would be blocked immediately as the dc fault current or rate of change of current surpassing the threshold values of the self-protection of the IGBTs, results in transients. Therefore, technique of fault detection must be fast enough to issue a block command to converter switches before self-protection of IGBTs [46; 47; 48].
# The Presence of Marine Filamentous Fungi on a Copper-Based Antifouling Paint
## 2 Materials and Methods
### Copper Resistance of Fungal Isolates  
In order to prove that fungal isolates are able to grow on antifouling paints, we tested their sensitivity to copper by an agar diffusion technique. Because copper oxide is not soluble in water, copper sulfate (CuSO4, Sigma Aldrich, Ronkonkoma, NY, USA) was used. Firstly, different concentrations (500-0.01 g L\({}^{-1}\)) of copper sulfate in autoclaved seawater were prepared. Secondly, fungal isolates were grown onto 2.5% potato dextrose agar (PDA, Merck, Kenilworth, NJ, USA) for 3 days. PDA was made using autoclaved seawater from the marina. A four mm disc of each fungal isolate was cut and individually placed onto the PDA Petri dish. Thirdly, 10 um of copper sulfate solution was added to a sterile paper disk (diameter 6 mm). As a control, disks with 10 um of seawater were used. The disks were air-dried at room temperature and placed in the middle of a PDA Petri dish two cm away from the isolate. The dishes were incubated at 25 \({}^{\circ}\)C for 5 days. The experiment was made in triplicate. The presence or absence of an inhibition zone was detected. Finally, the minimal inhibitory concentration of copper (II) sulfate (\(\mu\)g cm\({}^{-2}\)) for each fungal isolate was calculated.
# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 3 Results and Discussion
### Detailed Description
#### 3.2.3 Category 3: Considering Land Use and Spatial Heterogeneity/Dependence
First, a clustering analysis is performed to handle the spatial heterogeneity of PM\({}_{2.5}\) concentrations. Several clusters are defined, based on homogeneous subareas of the city of Beijing (China). Second, input spatial features that address the spatial dependence are calculated using a Gauss vector weight function. And third, spatial dependence variables and meteorological variables are used as input features of an SVR-based model of prediction. The performance of the proposed Space-Time Support Vector Regression (STSVR) algorithm is compared to the ARIMA, traditional SVR, and NN. The best model depends on the forecasting time span. STSVR outperforms the prediction of the other models from 1 h to 12 h ahead, whereas the global SVR outperforms STSVR from 13 h to 24 h ahead. This study demonstrates that (i) the relationship between air pollutant concentrations and other relevant variables changes over spatial areas, and (ii) as the forecasting time increases, the prediction accuracy decreases more with STSVR than SVR.
## 2 Materials and Experimental Testing
### Materials
#### 2.1.3 Juncus Fibers  
The natural fibers used in this work were from the Juncus acutus plant. Its stems have a length of approximately 1 m, with diameter of 3 mm. It belongs to juncaceae family [42], which grows in humid and wet regions of Africa, South and North America, and Southern and Western Europe [43]. The Juncus acutus plants used in this research (Figure 3a) were harvested from the same region as the soil used in this study. Before their use in the composite, fibers were cleaned with drinking water to remove the presence of any dust and impurities on their surface (Figure 3b). The fibers were dried at 50 \({}^{\circ}\)C for 24 h in an ambient temperature laboratory. Then, after drying, they were cut manually into small lengths of between 3 and 5 cm (Figure 3c). Afterward, the fibers were ground using a grind mixer (Figure 3d). The chemical composition of JF is presented in Table 2 [44]. Table 2 also lists the mechanical and thermal properties of the fibers determined experimentally according to ASTM C1557 [45] and a hot disc device, TPS 1500 (Hot Disk AB, Gothenburg, Sweden), respectively. Figure 4 presents the microstructure of Juncus acutus plants by means of scanning electronic micrographs (SEM). The SEM observations indicate that Juncus stems have a hairless and soft appearance with the presence of many dust particles and impurities (wax, fat, etc.) referred to as stomata and located on the surface (Figure 4a) based on longitudinal stem observation. Also, this plant has a cellular structure with pores (Figure 4b), according to SEM of the fiber cross section. The void network morphology of this kind of vegetal fiber is promising for use to develop CEB composites with better thermal performance and a low unit weight.  
Figure 3: Juncus fibers: (**a**) Juncus plant; (**b**) cleaned fibers with water; (**c**) cut Juncus fibers; (**d**) ground Juncus fibers.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Evaluation Metrics
#### 3.3.4 Recall  
The fraction of accurate positive predictions out of all real positive cases is referred to as recall. It is also called sensitivity or the true positive rate (TPR) of a model. The formula to compute recall is given below in Equation (3).  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{3}\]  
It assesses how effectively a model can accurately detect all positive cases. Recall is a critical measure in a case where false negatives are more costly than false positives. Forexample, in spam email classification, it is important to correctly identify all spam emails, even if some legitimate emails are classified as spam.
# A Vision-Based Approach for Autonomous Motion in Cluttered Environments
## 3 Methods
### Perception System
#### 3.2.1 System Composition  
The architecture diagram of the perception system using two stereo cameras as sensors is shown in Figure 2. The first camera is a stereo fisheye camera (Intel's Realsense T265)  
Figure 1: Schematic of the framework of the autonomous motion system.  
with a built-in IMU chip, which provides an SDK capable of fusing camera and imu data and outputs reliable position and attitude information in real time. The second camera is a stereo depth camera (Intel's D455) that uses active infrared stereo technology to output a high-precision depth map of the environment, which can also provide reliable depth information in areas with sparse feature points, such as white walls. Our perception algorithm mainly works as follows: First, by virtue of the camera projection model and coordinate transformation, the depth map is used to calculate the coordinates of the obstacle in the world coordinate system. Then a Raycast Model is introduced to estimate whether the cell observed by the camera at the current time is occupied. Finally, we use the observation information to update the occupancy grid map in real time through the occupancy-grid-mapping algorithm.  
The use of two binoculars (T265 and D455) rather than a single binocular camera or a monocular camera to achieve perception can be attributed to the following three reasons. First of all, compared with a monocular camera or a binocular camera with a built-in IMU chip (d455), the combination of the T265 fisheye binocular camera plus IMU can provide a larger viewing angle, thereby achieving more reliable positioning data. Secondly, the T265 camera can accelerate the pose estimation through the built-in Tpu chip, so that the system can run on platforms (e.g., Raspberry Pi) with weak computing power and offset the hardware cost. Thirdly, the binocular-depth estimation algorithm does not work well in areas with sparse feature points, such as white walls. Therefore, in addition to T265, a D455 camera equipped with structured light is needed to provide reliable obstacle-depth information.
# HLFSRNN-MIL: A Hybrid Multi-Instance Learning Model for 3D CT Image Classification. _Applied_
## 4 Experiments and Results
### Datasets and Experimental Settings  
The TCIA lung cancer dataset comprises comprehensive lung cancer images updated in 2020, encompassing adenocarcinoma (A), small-cell carcinoma (B), and squamous-cell carcinoma (G). It provides DICOM format medical data files, facilitating the adjustment of imaging parameters as per specific requirements. On the other hand, the CC-CCII novel coronavirus pneumonia dataset, released in 2020, consists of 3D CT images of novel coronavirus pneumonia (NCP) and common pneumonia (CP). This dataset offers processed PNG format data files suitable for direct training.  
In the experiment of this project, the learning ability and generalization ability of the proposed model will be assessed on these two datasets separately. Detailed information on the two datasets is provided in Table 2, with the number of slices ranging from 30 to 300. The TCIA dataset comprises 36,031 slices, while the CC-CCII dataset contains 36,950 slices. To evaluate the model's performance, a 5-fold cross-validation is employed during the training and testing phases. All packages are approximately split into training and test sets in a 4:1 ratio. During each batch's training process, four-fifths of the training set is utilized to learn the weight parameters in the model, while the remaining one-fifth is used to assess the model's generalization ability. The experimental results will be averaged across five experiments.  
When transforming the data content into the tensor format required for the experiment, each 512 \(\times\) 512 resolution CT image is scaled down to 256 \(\times\) 256, representing only one-quarter of the initial CT image size. This resizing reduces the file size and training time of the training data overall, while also alleviating the demand on video memory to some extent. Importantly, there is minimal disparity between the data after model testing and the data before modification.  
Figure 4: Overview of the proposed SRNN method. (Here different colors represent the feature representations of different examples).  
All experiments of this evaluation model will be conducted on a local workstation equipped with an Intel(r) Core(tm) i9-12900H CPU and an NVIDIA GeForce RTX 3070 Ti Laptop GPU. The manufacturers of CPU and GPU are Intel and NVIDIA, both of which are purchased in China. Given the large dataset, 32 GB of memory is utilized. The initial learning rate of the model is configured to 0.0005, and subsequently, the learning rate of the Adam optimizer is decayed using the cosine annealing method. The number of training epochs for each model is set to 50.
## 2 Model of Quasi-Zero Stiffness Pneumatic Vibration Isolator
### Description
in which the pressure (\(P_{qp1}\)) of the cylinder 1 at the equilibrium position is determined as follows:  
\[P_{ep1}=P_{so1}\left(\frac{A_{1}h_{1}+V_{t}}{A_{1}h_{1}+V_{t}-2A_{1}H_{o}\tan \alpha}\right)^{n} \tag{5}\]  
To ensure the load-bearing capacity in the expected working region \(|u|\leq H_{o}\), the initial pressure \(P_{so1}\) of the LBM must be larger than one. This indicates that the working parameters of the cylinder 1 must satisfy the following condition:  
\[\frac{2A_{1}H_{o}\tan\alpha}{A_{1}h_{1}+V_{t}}\ <1-\ \frac{1}{\left(P_{ep1} \right)^{1/n}} \tag{6}\]  
For instance, \(H_{o}\) = 73.5 mm and \(A_{1}\) = 0.0095 m\({}^{2}\), through Equations (4)-(6), the relationship between the weight of the isolated load and the initial volume for which the pressure in the cylinder 1 is always higher than one as shown in Figure 2. It can be seen that either increasing the initial pressure or reducing the tank volume will result in an increase in the isolated load. However, the effect of these parameters on the isolated load is different. Specifically, the isolated load is almost increased gradually according to the growth of the initial pressure as observed in Figure 2b, created by a cutting section \(V_{t}\) = 0.01 m\({}^{3}\). Meanwhile, the Figure 2c cut by a section at \(P_{so1}\) = 2 bar indicated that the isolated mass can be remarkably varied, as there is a change of the tank volume in the region of less than 0.05 m\({}^{3}\). Similarly, the pressure in cylinder of the SCM must be always larger than one for the remaining function of correcting the dynamic stiffness of the SCM. The working region of the SCM is predicted as following:  
\[|u|\leq u_{L\_SCM}=\sqrt{\left(R+r\right)^{2}-\left(\frac{A_{2}h_{2}+2A_{2} \sqrt{\left(R+r\right)^{2}-H_{o}^{2}}-V_{ep2}\left(\frac{P_{ep2}}{P_{dem}} \right)^{1/n}}{2A_{2}}\right)^{2}} \tag{7}\]  
Figure 2: (**a**) The relationship between the initial volume and pressure in cylinder 1 for \(H_{o}\) = 73.5 mm and \(A_{1}\) = 0.0095 m\({}^{2}\). (**b**) Effect of the initial pressure \(P_{so1}\) on the isolated mass and \(V_{t}\) = 0.01 m\({}^{3}\), (**c**) Effect of tank volume \(V_{t}\) of the isolated mass and \(P_{so1}\) = 2 bar. herein \(P_{\sigma\rho 2}\) and \(V_{\sigma\rho 2}\) are denoted as the pressure and volume in the cylinder 2 at the equilibrium position.
# Indistinguishability Operators Applied to Task Allocation Problems in Multi-Agent Systems
## 3 Preliminaries on Indistinguishability Operators
### Examples  
We begin by applying the aforesaid construction to the Luckasievicz \(t\)-norm \(T_{L}\), which is continuous and Archimedean (see Section 3).  
**A \(T_{L}\)-indistinguishability operator**  
Let \(d\) be a distance on a nonempty set \(X\) and consider the Luckasiewicz \(t\)-norm \(T_{L}\). It is clear that the function \(f_{T_{L}}:[0,1]\to[0,\infty]\), given by \(f_{T_{L}}(x)=1-x\) for all \(x\in[0,1]\), is an additive generator of \(T_{L}\). Applying (4), an easy computation shows that the pseudo-inverse \(f_{T_{L}}^{(-1)}\) of the additive generator \(f_{T_{L}}\) is given by  
\[f_{T_{L}}^{(-1)}(y)=\max\{0,1-y\}\]  
for all \(y\in[0,\infty]\). Then, using the construction of Theorem 3, i.e., \(E_{T_{L}}(x,y)=f_{T_{L}}^{(-1)}(d(x,y))\) for all \(x,y\in X\), we obtain a \(T_{L}\)-indistinguishability operator on \(X\) that separates points, which has the following expression:  
\[E_{T_{L}}(x,y)=\left\{\begin{array}{ll}1-d(x,y),&\mbox{ if }0\leq d(x,y)<1; \\ 0,&\mbox{ elsewhere,}\end{array}\right.\]  
for all \(x,y\in X\).  
**A \(T_{P}\)-indistinguishability operator**  
Let \(d\) be a distance on a nonempty set \(X\) and consider the product \(t\)-norm \(T_{P}\). It is clear that the function \(f_{T_{P}}:[0,1]\to[0,\infty]\), given by \(f_{T_{P}}(x)=-\log(x)\) for all \(x\in[0,1]\), is an additive generator of \(T_{P}\). The pseudo-inverse \(f_{T_{P}}^{(-1)}\) of \(f_{T_{P}}\) is given by  
\[f_{T_{P}}^{(-1)}(y)=\mathrm{e}^{-y}\]  
for all \(y\in[0,\infty]\). Then, using the construction of Theorem 3, i.e., \(E_{T_{P}}(x,y)=f_{T_{P}}^{(-1)}(d(x,y))\) for all \(x,y\in[0,1]\), we obtain the following expression:  
\[E_{T_{P}}(x,y)=\mathrm{e}^{-d(x,y)}\]  
for all \(x,y\in X\). As in the above example, Theorem 3 ensures that \(E_{T_{P}}\) is a \(T_{P}\)-indistinguishability operator on \(X\) that separates points.  
After presenting these two easy yet illustrative preceding examples, which are constructed by means of the most commonly continuous Archimedean \(t\)-norms used in fuzzy logic, we will continue showing that the response function \(P\), given by (3), is an indistinguishability operator that opens a wide range of potential applications from a mixed framework based on indistinguishability operators and distances to task allocation problems in multi-agent systems.
# Thermal Stability of Ionic Liquids: Effect of Metals
## 3 Results and Discussion
A similar mechanism has been suggested to rationalize the extraction of Cu\({}^{2+}\) ions from aqueous solutions using pr  
Figure 6: Intensity of H2,1 (black squares) and H2,2 (red circles) signals as a function of the heating time, as obtained from the \({}^{1}\)H HRMS NMR spectra of B, 4B4, 4B24 and 4B168 (filled symbols) and of B, 1B4, 1B24 and 1B168 (empty symbols). The intensity value is relative to that of H2 protons of the non-degraded ionic liquid, which was set to 100 in all spectra. ionic liquids [24]. However, due to the large linewidth of the signals, it was not possible to confirm this hypothesis. For the samples degraded in the presence of steel, the kinetics of formation of compounds 1 and 2 were monitored. Figure 6 shows the trends of the intensity of the H2,1 and  
Figure 8: Expansions of \({}^{1}\)H HRMAS NMR spectra of 4B168 (**a**), 1B168 (**b**), 2B168 (**c**), and 3B168 (**d**). Expansion factors are reported for each region. Selected signals of the degradation compounds are labeled using the same notation as that applied in Figure 5. Water (3.3–3.6 ppm) and residual DMSO-d\({}_{5}\) (2.50 ppm) signals are marked with asterisks. Figure 7: \({}^{1}\)H HRMAS NMR spectra of 4B168 (**a**), 1B168 (**b**), 2B168 (**c**), and 3B168 (**d**). H2,2 signals as a function of heating time in the samples degraded in the presence and absence of metals. The kinetics of formation of compound 2 from compound 1 seems to be accelerated by steel. HS-GCMS analysis was performed on the 168 h aged samples, with the aim of observing possible volatile degradation compounds in the most extreme condition. Figure 9 shows the peak area of nine main compounds identified in samples B, 4B168, 1B168, 2B168 and 3B168; these are cyclohexane, butanal, ethyl acetate, tert-butanol, methyl-vinyl ketone, N,-N-dimethylformamide, 2-ethyl-1-hexanol and benzothiazole. However, 1-butene was not detected, likely because it was lost in the pre-analytical phase, considering its high volatility, or because of its oxidation. The relevant result is, indeed, the release of butanal in the 3B168 sample, i.e., in the IL treated with copper. We can hypothesize that copper and copper particles may act as catalyzers for the further reaction of butene. Recently, several authors have reported the remarkable long-term stability and high selectivity towards alkenes of Cu nanoparticles as a promising alternative to replace precious-metal-based catalysts in selective hydrogenation [25]. It must be pointed out that even the concentration of butanal was too low to be detected by FTIR and NMR. These volatiles probably result from minor side reactions occurring during the degradation process.
## 3 Compressive Sensing Framework  
Let a signal \(\mathbf{x}\in\mathbb{R}^{N}\) (a column vector) be sparse in some domain and \(\mathbf{s}\) be its sparse representation (that is with \(S<<N\) non-null coefficients)  
\[\mathbf{s}=\boldsymbol{\
abla}\mathbf{x}, \tag{1}\]  
in which \(\boldsymbol{\
abla}\in\mathbb{R}^{N\times N}\) is a transform that provides a sparse \(\mathbf{s}\), for example, DCT or Wavelet. Consider that  
\[\mathbf{y}=\boldsymbol{\Phi}\mathbf{x}, \tag{2}\]  
in which \(\mathbf{y}\in\mathbb{R}^{M}\) (\(M<N\)) is the vector that contains the coefficients (linear measurements) and \(\boldsymbol{\Phi}\in\mathbb{R}^{M\times N}\) is called _measurement (or sensing) matrix_[29]. The \(m\)-th coefficient in \(\mathbf{y}\) is obtained by the inner product between \(\mathbf{x}\) and the measurement function \(\boldsymbol{\phi}_{\boldsymbol{m}}\) (the \(m\)-th row of _sensing matrix_). Given \(\mathbf{y}\), a reconstruction procedure should aim at finding the solution for \(\mathbf{y}=\boldsymbol{\Phi}\boldsymbol{\
abla}^{*}\mathbf{s}\), with the smallest \(l_{0}\) norm for \(\mathbf{s}\) (the number of its non-zero entries). (\(\boldsymbol{\
abla}^{*}\) is the complex conjugate transpose of \(\boldsymbol{\
abla}\).) Unfortunately, this is an ill-posed problem requiring a combinatorial approach [3].  
Alternatively, in order to circumvent the combinatorial problem, one minimizes the \(l_{1}\) norm of the reconstructed signal [4]. In this case, the optimization problem is given by  
\[\hat{\mathbf{s}}=\arg\min_{\mathbf{s}}\|\mathbf{s}\|_{1}\quad\text{ s.t. }\quad\mathbf{y}=\boldsymbol{\Phi}\hat{\mathbf{x}},\quad\hat{\mathbf{x}}= \boldsymbol{\
abla}^{*}\hat{\mathbf{s}}, \tag{3}\]  
where \(\|\mathbf{s}\|_{1}=\sum_{i=1}^{n}|s_{i}|\) is the \(l_{1}\) norm of the signal \(\mathbf{s}\), being \(s_{i}\) the components of \(\mathbf{s}\).