esa-sceva/satcom-chunk-collection huggingface dataset:
# Gait Energy Response Functions for Gait Recognition against Various Clothing and Carrying Status
## 4 Experiments
### Discussion
#### 4.6.2 Consideration for Real-World Applications  
Although we have achieved promising results under various covariate conditions, there still exists some limitations of the proposed method to real-world applications (e.g., surveillance), that is, the proposed method is based on the transformation of appearance-based features (i.e., GEI), whose quality is largely subject to the extracted silhouettes of a gait period. In case of real scenes, however, more difficult variations, such as complex background and occlusions, will greatly affect the extraction of subjects' silhouettes and may further drop the performance of gait recognition. Fortunately, recent deep learning-based techniques (e.g., Mask R-CNN [59]) have brought great improvement in the performance of human detection and segmentation in complex real scenes, which benefits for the silhouette extraction to generate good appearance-based features. This implies a combination of these methods for real scene applications.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Methods** & **Small Speed** & **Large Speed** & **Average (All Speed)** \\ \hline STM [56] & 90 & 58 & - \\ DCM [57] & **98** & 82 & 92.44 \\ RSM [58] & **100** & 95 & 98.07 \\ SSGEI [55] & **100** & **98** & **99.33** \\ Gabor+Global GERF [20] & **100** & 92 & 96.89 \\ Gabor+SD-GERF (proposed) & **100** & **96** & **98.11** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Rank-1 identification rates [%] compared with other state-of-the-arts in case of small speed change (3 vs. 4 km/h), large speed change (2 vs. 6 km/h) and average of all speed changes in OU-TD-A.  
Figure 8: The Global GERF under speed variation: **(a)** feature examples of GEI and Global GERF under different speed (from 2 km/h to 7 km/h); **(b)** profile of the Global GERF.
## 4 Results and Discussions
### Influence of Different Laser Pulse Energy on Ablation Depth
Firstly, the water jet serves as a multi-mode fiber to deliver the laser to the surface of the material in the LWJ technique; only the compact section of the water jet can realize a perfect laser transmission [26]. At the initial stage of LWJ machining, the material of the top surface could be removed smoothly. As the processing continues, a pit feature is generated instead of a flat surface. Particularly, the anisotropic fiber braided method will create a more complex interior structure, which could go against the formation of a compact section of the water jet; moreover, turbulence or a broken jet might have emerged. Secondly, the procedure of LWJ machining could be expressed as follows: (1) The water jet of LWJ contacts the material, (2) the temperature of the top surface material rises, (3) the material exceeds the melting point and is peeled from the substrate by the scouring effect of the water jet, (4) slags are ejected with the water flow. So far, the entire LWJ machining process has been accomplished. The vital factor of this procedure is step (4), which means that adequate drainage space is essential for LWJ machining, or the stability of the water jet will be strongly disturbed by the reversed spurring slags and spray. As a result, the CMCs material removal is severely weakened by this negative phenomenon [27]. Therefore, the numerical simulation can hardly reveal actual machining abidingly as time goes on. However, the numerical removal depth of the initial laser pulse could be defined as a judgment criterion to measure the processing capacity of different laser pulse energies. It can be seen from Figure 9 that the proportion of removal depths with 0.4 mJ, 0.6 mJ, 0.8 mJ, and 1 mJ are 1:1.389:2.094:3.195, respectively. To verify the numerical simulation results, a scribing experiment was adopted.
# Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions
## 4 Case Study: Histological Image Analysis  
### Use Case Scenarios  
We demonstrate the feasibility of the proposed pipeline on a medical image dataset of histological whole slide images (WSIs). These types of data come from the digitization of histological tissue slides into high-resolution images containing several levels of magnification, similar to those of a microscope. More precisely, the WSI dataset used in this study consists of a spitzoid melanocytic lesions dataset of 84 biopsies provided, labeled, and annotated by dermatopathologists from the Pathology Laboratory of the University Clinic Hospital of Valencia. This type of uncommon neoplasm that originates from the melanocytes is associated with ambiguous histological features and clinical behavior [23], thus representing a formidable challenge for dermatopathologists.  
Due to the nature of WSIs, histological images are particularly vast (e.g., often more than \(100,000\times 100,000\) pixels) and require a lot of storage space, as a single WSI often exceeds 1 GB, thus leading to heavy computational operations when training models. For these reasons, a typical method used in WSI analysis involves cutting down these images into small patches.  
In this case study, we perform a patch-level region of interest (ROI) classification, that is to say, differentiate tumorous patches from non-tumorous ones. To do so, we divide the WSIs into small patches of \(224\times 224\) pixels to make the computational needs lighter and allow the use of well-known model architectures with images of the same size. Figure 2 depicts a few patch examples of the type of histological data fed into the convolutional neural network in this use case.  
Hundreds to thousands of patches can be extracted from a single WSI. Table 1 details the number of extracted patches per class, i.e., tumorous vs. non-tumorous, for the WSIs available at each of the three institutions simulated in this case study. To demonstrate the potential of our method, we use twenty of the available biopsies, four of which are used for testing purposes to allow a stable comparison of results across institutions. It is important to highlight the fact that the number of patches is not correlated with the number of WSIs used, as the size of ROIs might vary depending on the WSI under study.
## 2 System Model
### Connectivity Model  
To ensure data collection, processing, and transmission, nodes in a wireless sensor network need the ability to communicate with surrounding nodes. Therefore, mutual connectivity between nodes must be considered during sensor deployment. The concept of connectivity is defined as follows:  
Connectivity: If the distance between node \(S_{i}\) and node \(S_{j}\) does not exceed the communication radius \(R_{e}\) of node \(S_{i}\), then the two nodes \(S_{i}\) and \(S_{j}\) are considered connected. For homogeneous nodes, this connection is bidirectional, while for heterogeneous nodes, it is unidirectional.  
C-Connectivity: C-connectivity means that each sensor node deployed in the monitoring area is connected to at least some other \(C\) sensor nodes within the monitoring area.
## 2 Results
### Specificity of PM-477 for the Genus Gardherella  
We characterized the activity and specificity of the engineered endolysin PM-477 (the combination of the H2 and B10 domain) against panels of 13 _Gardherella_ strains of the 4 species described above, 12 strains of vaginal lactobacilli (including _L. crispatus_, _L. jensenii_, and _L. gasseri_, selected as representative of the healthy vaginal microbiome), and 9 other species typically found in BV, by using the quantitative assay for bacterial viability described above. The engineered endolysin caused a 4-5 log\({}_{10}\)-fold loss of viability in three _Gardherella_ strains (Gs_GS10234, Gs_GS9838-17, Gp_UGent 18.011), a 2-3 log\({}_{10}\)-fold loss of viability in five strains (Gv_ATCC 140187, Gv_BV50.1, GL_UGent 09.48, Gp_P80275, Gs_BV7.1), and a 1-2 log\({}_{10}\)-fold loss of viability in a further five strains (Gv_UGent 09.07, GV_UGent 09.01, GL_BV13.2, GL_BV86.5, GP_UGent 21.28) (Figure 3a). The same treatment of lactobacilli, by contrast, had no statistically significant effect on the viability of any of the 12 strains tested (Figure 3b). It also had no effect on strains of _Atopobium vaginate_, _Mobiluncus mulieris_, _Prerotella bivia_, and _Streptococcus agalaciae_, and had only a minor effect on one of the three strains tested of _Mobiluncus curtisii_ (Figure 3c). (In the cases of _Atopobium vaginate_ and _Prerotella bivia_, the cells either grew poorly or died rapidly upon transfer from agar plates to suspension, even without treatment.) Thus, the engineered endolysin PM-477 is highly selective for _Gardherella_: it kills strains of each of the four main species [23], without harming beneficial lactobacilli or other species typical of the vaginal microbiome.  
Figure 3: \(Cont.\)The genus-specific bacteriolytic effect of PM-477 was confirmed by microscopy in mixed cultures of _Gardureella_ and lactobacillus. These two genera can be distinguished by their distinct morphologies: _Gardureella_ cells are small coccoidal rods, whereas lactobacillus form mostly long and thick rods. PM-477 (at 460 \(\mathrm{\SIUnitSymbolMicro g}\)/mL for 5 h) lysed _G. vaginalis_ and _G. swidsinskii_ cells in monoculture and selectively lysed them in mixed cultures of _G. vaginalis_ and _L. crispatus_ and of _G. swidsinskii_ and _L. gasseri_, respectively, while the lactobacillus in these mixed cultures were unaffected (Figure 4a).  
We compared the bactericidal and bacteriolytic effects of PM-477 on _G. vaginalis_ and _G. swidsinskii_ by treating suspensions of the bacteria with 25-400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL PM-477 for 5 h and then counting the cell numbers by microscopy and determining their viability by quantitative plating (Figure 4b-e). The highest concentration of PM-477 (400 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) decreased the number of cell-like structures by only 80-90% (Figure 4b,c), whereas the same treatment resulted in a \(\geq\) 5.6 \(\mathrm{log}_{10}\) reduction in viable cells. This indicates that PM-477 may kill before fully dissolving bacterial cells, resulting in visible but dead cell-like structures seen under the microscope (Figure 4d,e).
# A Presentation of Ionic Liquids as Lubricants: Some Critical Comments
## 3 Mechanism of Action of ILs as Lubricants
### Hydrodynamic lubrication (HL)  
Hydrodynamic lubrication takes place when two surfaces move perpendicularly toward each other, and a thicker layer of lubricants is added to penetrate the interaction zones to prevent physical or direct contact between them and to reduce rubbing, friction, and wear phenomena. In fact, if the fluid is convergent to the direction of motion, it adheres to the sliding surface as a viscous film that builds up pressure sufficient to carry the load (Figure 5a) [60].  
This lubrication process is the core of efficient functioning of the whole modern industry. It is clear that the high viscosity of ILs becomes the dominant property with regard to their lubricant action. However, if we take the viscosity index as the only parameter to compare ILs and mineral (or synthetic) oils, high variations are not observed. In Table 2, the viscosity index of some ionic liquids and base oils are reported.  
The interest in ILs as lubricants, instead, is to be traced back to other factors, which will, step by step, be commented on in what follows. For example, we can already introduce, here, that ILs are particularly fit for tuning the overall viscosity by simply mixing two of them. This is not trivial, since usually the mixing of two fluids can lead to nonideal mixing and nonlinear behaviour, with the possible overall reduction of the performances due to the interactions of the two constituents. In our opinion, in the case of a mix of two ionic liquids with the same cation, the interactions of the two constituents reduce to the interactions between the anions only, which are inherently reduced since they are separated by the positively charged cations. Our speculative consideration is actually supported by some experimental works: a work of [PERSON] et al. proved that a mix of two ionic liquids with the same cation ([BMIM][TFSI] and [BMIM][PF\({}_{6}\)]) varied its viscosity as a function of the mixing ratio [61; 62; 63]. This experimental observation has opened up a new scenario in the field of ILs as lubricants because it stands to reason that the adjusting of the viscosity can be accomplished by mixing several types of ionic liquids, also recalling their low vapour pressure and high thermal stability.  
Figure 5: Representation of the main mechanisms of lubrication: (**a**) boundary lubrication, (**b**) mixed lubrication, and (**c**) hydrodynamic lubrication. P: pressure; V: velocity.  
One development of hydrodynamic lubrication is the _elastohydrodynamic lubrication_ (EHL) that is prevalently utilised for friction pairs with elastic contact, such as ball bearings and gears. In this case, the thickness of the lubricant is smaller than that of hydrodynamic lubrication, and the elastic deflection of the surfaces in contact shall be taken into to account. Under these circumstances, the predominant physical property for lubrication is the viscosity under high pressure that is measured for some methylimidazole-substituted ionic liquids, which is similar to liquid crystals under high pressure [64].
# Secure Beamforming in 5G-Based Cognitive Radio Network
## 6 Discussion  
This article shows the highlighted secrecy rate and effectively reduces the outage probability compared with the previous results [12]. The proposed technique in the article was used in securing the beamforming in the 5G technology without causing interference during a data transmission between a cluster from one cell to another cell. The findings of the proposed method are oriented to the MMSE technique, which belongs to one of the beamforming techniques in the MIMO. Here, the proposed MMSE iterative technique was used to enhance the secrecy rate by cancelling the interference as possible.  
Figure 8: Channel capacity of zero forcing beamforming (ZFBF) and the proposed technique.  
Figure 9: Throughput of the ZFBF and the proposed technique.  
The proposed method normally uses the technique that is oriented to MMSE will enhance the total secrecy rate of the system than the preceding method of the ZFBF [12]. The SOP and BER were also reduced. The capacity and throughput were significantly increased in the proposed method, which is shown in Simulation results. Therefore, this new beamforming technique shows the performance of their robustness, which may be used in another part of technology, like Internet of Things (IoT)-based CRN, to secure the communication as possible.
# Automated Design of CubeSats using Evolutionary Algorithm for Trade Space Selection
## 4 CubeSat Design
### Communication  
For communication, first the ground station-to-satellite line-of-sight, \(LOS_{c}\), is computed based on the dot product between the Earth-to-ground station unit vector and the ground station-to-body vector in the inertial frame, as shown in Figure 9. The discontinuous function is smoothed using a cubic function. Next, the resulting data download rate, \(B_{r}\), is calculated using Equation (18) [32; 33].  
\[B_{r}=\frac{c^{2}G_{r}\eta_{p}P_{comm}L_{l}G_{t}}{16\pi^{2}f^{2}kT_{s}(SNR)S^{2 }}LOS_{c} \tag{18}\]  
where \(c\) is the speed of light, \(k\) is the Boltzmann constant, \(f\) is the transmission frequency, \(T_{s}\) is the system noise temperature, \(G_{r}\) is the receiver (ground station) gain, \(SNR\) is the maximum acceptable signal to noise ratio, \(\eta_{p}\) is the communication efficiency, \(L_{l}\) is the line loss factor, \(P_{comm}\) is the communication power, \(G_{t}\) is the transmitter (satellite) gain, and \(S\) is the distance from the satellite to the ground station.
## 3 Methodology
### Data Acquisition and Preprocessing  
The approach started with the loading of raw seismic data recorded from three orthogonal components, namely East (E), North (N), and Vertical (Z), for each station on a given day. Then, we read data using the obspy library, which is well known for its efficiency in handling seismic time-series data. Data were then arranged properly for each station in chronological order, supported by a strict naming convention, enabling us to find every seismic event and process them coherently.
# DAWE: A Double Attention-Based Word Embedding Model with Sememe Structure Information
## 3 Related Works
### Word Embeddings with Internal Semantic Information  
In addition to the word co-occurrence, the internal features of words have also been shown to contribute to word embedding. Related works can be roughly divided into three categories: models based on morphological information, models based on character information and models based on subword information. Examples of \"morphological information\", \"character information\" and \"subword information\" are illustrated in Figure 4, where morphological information mainly refers to the features from components (i.e., prefix, root and suffix) of the word. [PERSON], [PERSON] and [PERSON] [1] utilized morphological (prefix, root and suffix), syntactic and semantic knowledge to achieve high-quality word embeddings. [PERSON] et al. [13] and [PERSON] et al. [14] performed character-level embedding and word embeddings obtained by fusing character features and word features. [PERSON] et al. [15] also used a character-level embedding, and the weight information of different characters was taken into account in the fusion process. [PERSON] and [PERSON] [16] combined both the morphological information of the word and the information of the character-level and captured the structure information of the context by adding the subword information (character n-gram, root/affix and inflections). To better discover the laws of language for word embedding, [PERSON] et al. [17] tried to discover the relationship between morphology and semantics in language expression and summarized 68 implicit morphological relationships and 28 display semantics relationships.  
Actually, in Chinese, characters are not the smallest granularity units, but strokes. On top of this, there are structures such as radicals and components. [PERSON] et al. [18] and [PERSON] et al. [19] added the features of radicals of the characters inside target words to CBOW model. [PERSON] et al.'s [20] method, regarded as a more refined version of those of [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [18] and [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [19], captured not only the radical information but also other components inside the character. To better exploit the structural information inside the character, [PERSON] et al. [21] proposed to use the set of Stroke n-gram information of characters to supplement the semantics of characters.  
The methods mentioned above only use the semantic information of the word itself, such as from word-level embeddings to character-level embeddings or other more fine-grained embeddings. However, the semantic information obtained from the word is limited. Besides, the models are influenced by the formation of language, the characteristics of language, etc., thus it is difficult to generalize to other languages.
# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 1 Introduction
### Style-Based Study  
This study determines if the intention of news is whether to deceive the public or not. The style is a characteristic that represents and differentiates fake news from veracity. Deception analysis investigates how the style of misleading content is written across various kinds of information. Features or characteristics based on attributes require some supplementary level of calibration or computing, which is time-consuming, but connects to the greater significance of the evaluation of characteristics based on attributes and filtering for the detection of misleading content.
# Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes
## 3 Gaussian Processes  
Gaussian Processes are a class of supervised machine learning algorithms, which describe the functional relation between input and output data as a multivariate Gaussian distribution. They are a powerful nonlinear regression and classification method [24]. An application of GPs is the modelling and prediction of trajectories, such as vehicle and pedestrian trajectories [26; 27]. They have also been used to model the driving intention of human drivers on intersection scenarios [28; 29].  
Using GPs to build a surrogate model of a scenario-based testing framework for AVs has been relatively unexplored. A comparable study for autonomous, unmanned underwater vehicles was conducted by [PERSON] [30], where the state space of the vehicle was adaptively searched using a Gaussian Process Regression (GPR) model. In this paper, we adopt the notion of the performance boundary, which separates the different modes of the system under test, and adapts the concept to automated ground vehicles.  
GPR was used in an automotive context in Reference [31] to model the probability distribution of a logical scenario, which was then used to conduct importance sampling. The logical scenario considered was a lane change scenario, where they could show an improvement over crude MC sampling.  
In our application, we use Gaussian Process Classification (GPC) and Regression (GPR) to optimise the parameter space of a given logical scenario. Once trained, these GP models allow for a quick prediction of untested concrete scenarios. We apply the optimisation method on a complex logical scenario, which is further described in Section 6.1. The scenario data is obtained by evaluating concrete scenarios in the vehicle simulation software CarMaker [32], and for the purpose of this study, the simulation is regarded as ground truth. For this to be valid in the overall scope of AV validation, the simulation must be validated by physical tests.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 2 Background and Related Work
### UAV Aerial Base Station  
Due to the good flexibility and adaptability of UAV-BSs, they can be strategically deployed to any location to provide communication services. After completing their service, UAV-BSs can quickly be repositioned to the next mission point. Moreover, UAV formations can be dynamically expanded to improve coverage efficiency, making UAV-BSs an essential component of modern communication networks. Most scholarly research on UAV-BSs focuses on three aspects: UAV-BS flight path planning, UAV-BS autonomous control, and UAV-BS network topology.
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 7 Results and Discussion
### Casing Parameters  
The elastic modulus of the casing varies with temperature, and different types of pipes experience varying changes in elastic modulus due to temperature fluctuations. These changes have an impact on the radial deformation of the casing and the volume change of the annulus, thereby affecting the annulus pressure. Table 4 shows the elastic modulus of three common types of pipes at different temperatures. Similarly, Poisson's ratio and elastic modulus can also affect casing deformation and annulus pressure.  
As shown in Figure 18, annulus pressure increased linearly and rapidly with the increase in casing elastic modulus. On the contrary, the higher the Poisson's ratio, the lower the annular pressure, but the smaller the variation of annular pressure.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Type of Casing** & **20 \({}^{\circ}\)C** & **200 \({}^{\circ}\)C** & **250 \({}^{\circ}\)C** & **300 \({}^{\circ}\)C** & **350 \({}^{\circ}\)C** \\ \hline N80 (MPa) & 205.94 & 165.73 & 149.06 & 136.31 & 117.68 \\ P110 (MPa) & 205.94 & 184.37 & 177.50 & 172.60 & 166.71 \\ TP90H (MPa) & — & 170.75 & 163.25 & 155.75 & 148.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The elastic modulus of the casing varies with temperature.  
Figure 17: The influence of fluid expansion coefficient and compression coefficient on annular pressure.  
Figure 18: Relationship between elastic modulus of casing and pressure.