esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Advanced Development of ANNs
### SVMs
In a simple case, a hyperplane can be defined as F(x) = w\({}^{n}\)x + w\({}_{0}\) where w is the adaptable weight vector and w\({}_{0}\) is the bias term, and T is the vector transpose operator. In training, the data points xi are projected into the higher-dimensional space, and the classification is conducted through sign[f(x)] where f(x) is the estimate of F(x). The SVM algorithm searches for a hyperplane f(x) that maximizes the margin between the two sets of data points in class 1 and -1. Figure 9: Margin between support vectors in two-dimension. Further, the margin maximum problem can be solved in any high-dimensional space by introducing a kernel function [95, 96]. With a nonlinear kernel function, the low-dimensional input space is nonlinearly transformed into a high-dimensional feature space such that the probability that the feature space is linear separable becomes higher. Theoretically, the kernel function is able to implicitly map the input space into an arbitrary high-dimensional feature space that can be linearly separable even if the input space may not be linearly separable. Some commonly used kernel functions are polynomial, Gaussian, Sigmoid, and RBF. In general, SVMs's training is to solve the following optimization problem:  
\[ \begin{split}\underset{\mathrm{w},\,w_{\mathrm{o},\delta}}{ \mathrm{MIN}}\big{(}\frac{1}{2}\,\mathrm{w}^{\top}\mathrm{w}+\lambda\sum_{i=1} ^{N}\xi_{i}\big{)}\\ \mathrm{s.t.}\,\,\,\mathrm{c}_{i}\big{[}\mathrm{w}^{\top}\phi( \mathrm{x}_{i})+\mathrm{w}_{\mathrm{o}}\big{]}\,{\geq}\,1-\xi_{i}\end{split}  \tag{22}\]  
where \(\lambda\) is a user defined positive constant, a penalty parameter of the error term, \(\xi_{i}\) (\(\,{\geq}\,0\)) is the slack variable to measure the degree of misclassification of \(\mathrm{x}_{i}\), and \(\phi(\mathrm{x})\) is the function to map data points \(\mathrm{x}_{i}\) from the input space to a higher-dimensional feature space. With equation (22), data points \(\mathrm{x}_{i}\) are mapped into a higher-dimensional space by the function of \(\phi(\mathrm{x})\). Then a linearly separable hyperplane (\(\,\mathrm{w}^{\top}\phi(\mathrm{x}_{i})+\mathrm{w}_{\mathrm{o}}\,\)) is found with the maximal margin in the higher-dimensional space. Further, the mapping function \(\phi(\mathrm{x})\) is determined with a specified kernel function \(\mathrm{K}(\mathrm{x}_{i},\mathrm{x}_{j})\):  
\[\mathrm{K}(\mathrm{x}_{i},\mathrm{x}_{j})=\phi(\mathrm{x}_{i})\phi(\mathrm{x} _{j}) \tag{23}\]  
If RBF is chosen as the kernel function, then \(\mathrm{K}(\mathrm{x}_{i},\,\mathrm{x}_{j})=\exp(\cdot\gamma\|\mathrm{x}_{i}- \mathrm{x}_{j}\|^{2})\) (\(\gamma>0\)). The RBF kernel is good at handling nonlinear classification for SVMs.
# The Application of Intelligent Data Models for Dementia Classification
## 3 Results
### Experimental Settings and Evaluation Metrics  
The experiments are conducted in the Waikato Environment for Knowledge Analysis (WEKA 3.8) data analytic platform. This is freely available and designed to allow easy  
\begin{table}
\begin{tabular}{l l} \hline
**Variable** & **Description** \\ \hline \hline SITE & ID of the site where the visit occurred \\ EXAMDATE & Date of the clinical examination \\ AGE & Age at baseline visit \\ PTGENDER & Sex \\ PTEDUCAT & Total years of education \\ PTETHCAT & Ethnicity \\ PTRACCAT & Race \\ PTMARRY & Marital status at baseline \\ CDRSB & Clinical Dementia Rating Scale Sum of Boxes \\ DX & Class variable \\ \hline \end{tabular}
\end{table}
Table 1: Features selected.  
integration of Java code for reusability purposes within the platform. WEKA contains many supervised and unsupervised learning algorithms, dimensionality reduction, and other data pre-processing approaches and visualization methods.  
The Confusion Matrix, shown in Figure 2, is adopted to evaluate the performance of the models applied in our experimentation. Performance measurements are detailed below.  
1. Sensitivity \(=\frac{TP}{TP+FP}\) (1)
2. Specificity \(=\frac{TN}{TN+FP}\) (2)
3. F-measure \(=2^{*Precision+Sensitivity}\) (3)
4. Precision \(=\frac{TP}{TP+FP}\) (4)
5. ROC: Receiver Operator Characteristic Curve, which is generated by plotting the TPR (\(y\)-axis) against the FPR (\(x\)-axis).  
For our experiments, we are focusing on the four main performance metrics to evaluate the performance of our chosen models: sensitivity, specificity, F-measure, and ROC.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results
### Teamwork  
Teamwork is the product of human-human interactions between operators, supervisors, and the situation team inside the SCCs. It includes the integration between attitudes, knowledge, and certain skills that leads to members adapting and optimizing their performance [33]. According to [PERSON] et al. [29], human interactions between operators affect the whole performance of a remote control system in both a positive and a negative way. A study of UAV accidents highlights the role of crew/team leadership and supervision. Supervisors are responsible for guidance, leadership, motivation, steering team members toward training opportunities, and serving as effective role models. Researchers argue that UAV operators make more mistakes if supervisors fail to obtain these goals [16]. It seems likely that teamwork and supervision play an important role in SCCs.  
Various factors, such as the organizational culture, may also affect the performance of teams. Team performance has a direct relationship with the process of decision-making, in that SCCs are organized such that operators monitor ship operations and engineers assist them, when required. Furthermore, in remote control mode, the situation team take direct remote control over the vessel. Hence, teamwork is another significant human factor issue in the field of remote ship operations.
# Autonomous Vehicles: Vehicle Parameter Estimation Using Variational Bayes and Kinematics
## 2 State of the Art
### Bayesian State Estimation Approaches
Since classic approaches such as Kalman Filters use strong assumptions such as Gaussian noise or linear movement models, the work [8] introduces a Gaussian-process (GP) [26] extended Kalman filter and a particle filter for highly nonlinear and accurate Bayes filters using machine learning. Our approach is based on this implementation. Since both the movement and measurement models are used in each timestep in the Bayes filter, the matrix multiplication with \(\mathbf{K}\in\mathbb{R}^{n\times n}\) must be performed. Since each GP prediction is based on the whole dataset, GPs are typically not useful for real-time applications. Thus, the GP models are \"sparsed\" or approximated for real time applications.
# Experimental Study on Dynamic Effects of a Long-span Railway Continuous Beam Bridge
###### Abstract  
Studies on impact effects of trains on the railway bridge are important for ensuring the reliability of bridge and the safety of train operation. This paper presents an experimental study on the dynamic effects of moving trains on a long-span railway continuous beam bridge. The dynamic responses of the bridge under the moving trains were measured through in-situ testing and finite element analysis. The influences of the moving trains and track irregularity are considered. The investigated influencing factors include the weight and speed of the train and the irregularity of the track on the bridge. The results indicate that the train's speed does not have obvious influence on the impact factor, while train's weight and track irregularity have notable effects on the impact factor. But from the overall development law, with the increase of train speed, the impact factor increases. The impact factors obtained in this study are larger than the values provided by the China bridge design codes, which indicates that the bridge code underestimates the impact effect of the train on the bridge. The design value of the impact factor should be properly improved in the bridge design.  
Keywords: railway; continuous beam bridge; dynamic test; impact factor; finite element analysis +
Footnote †: journal: Applied Sciences
###### Abstract  
To address the geological hazard posed by unstable loess slopes prone to collapse and landslides, a high-strength geopolymer cementing material was developed utilizing green steel slag-fly ash as its primary constituent and activated through the application of sodium silicate alkalinity. The mechanical properties and microstructure changes of loess under varying dosages of steel slag-fly ash geopolymers and curing age were investigated through a series of tests, including unconfined compressive strength, direct shear, disintegration, electron microscope scanning, and X-ray diffraction. The findings indicate that the incorporation of geopolymers can significantly enhance the internal friction angle, cohesion, and unconfined compressive strength of loess, while mitigating the disintegration quantity and rate of stabilized soil. When 20% geopolymer is mixed into the solidified soil and cured for 28 days, the resulting solidified soil exhibits an internal friction angle of 31.12\({}^{\circ}\), a cohesion of 81.09 kPa, and an unconfined compressive strength of 570.86 kPa. These values are 1.62 times, 1.76 times, and 3.36 times higher than those of loess, respectively. Moreover, the solidified soil shows minimal disintegration within 1800 s, with only 1.97% disintegration. The curing age of solidified soil has a significant impact on its curing effect. Enhancing the curing time can considerably enhance the mechanical properties of solidified soil. When the geopolymer content is 20% and the curing time is extended to 28 days, the internal friction angle, cohesion, and unconfined compressive strength increase by approximately 0.23 times, 0.48 times, and 1.61 times, respectively, compared to a curing time of 7 days. By analyzing SEM and XRD, it was found that the hydration of steel slag-fly ash geopolymer produces C-S-H and C-A-S-H cementing materials, which effectively fill the gaps between soil particles and enhance the mechanical properties of solidified soil. The research findings can serve as a theoretical foundation for the consolidation of loess subgrade utilizing steel slag-fly ash geopolymer.  
experimental research; geopolymers; solidified loess; steel slag powder; subgrade engineering Article
# Disentangled Feature Learning for Noise-Invariant Speech Enhancement
## 2 Related Work
### Domain Adversarial Training  
Domain adaption [PERSON] et al. (2018) addresses the problem of mismatch between the training and test datasets by transferring the knowledge learned from the source domain to a robust model in the target domain. DAT is one of the approaches that attempts to match the data distributions across different domains. In [PERSON] et al. (2018), DAT exploits an adversarial training method in order to learn intermediate features which are invariant to the shifts in data from different domains. Here, the neural network learns two different classifiers: (i) a classifier for the main classification task, and (ii) the domain classifier. The training objective of the domain classifier, in particular, is to learn whether the input sample is from the source or target domain, given features extracted using labeled data from the source domain and unlabeled data from the target domain. The feature extractor is shared by both the main task and the domain classifiers. In implementation, a gradient reversal layer (GRL) is employed to act as an identity transformer in the forward-propagation and to reverse the gradient by multiplying a negative scalar during the back-propagation [PERSON] et al. (2018). Consequently, the GRL encourages the latent features to act discriminatively when solving the main classification task, yet act indiscriminately towards the shifts across different domains. In other words, the feature extractor is trained so that the model maps data from different domains to the latent features with similar distributions via adversarial learning.  
Many speech processing frameworks have adopted the idea of DAT in order to extract domain-invariant features. Under the noise robust speech recognition scheme, the clean speech was regarded as the source domain data and was used to train the senone label classifier, while the noisy speech played the role of the target domain data to be adjusted by the feature extractor [PERSON] et al. (2018); [PERSON] et al. (2018). DAT was also used to learn speaker-invariant senone labels, as shown in [PERSON] et al. (2018) where the adversarial training successfully aligned the feature representation of different speakers. In [PERSON] et al. (2018), the authors demonstratedthat accent-invariant features could be learned for the ASR system. For speaker recognition tasks, DAT was adopted to tackle the channel mismatch problem. In particular, the latent features were extracted in order to learn channel-invariant, yet speaker-discriminative representations [39]. In [40], the authors showed that DAT was able to adapt multiple forms of mismatches (e.g., speaker, acoustic conditions, and emotional content) when solving the acoustic emotion recognition task. As for the speech enhancement problems, a noise adaptive method exploiting DAT was proposed in [41]. In their work, however, DAT was only used to classify stationary and non-stationary noises, and the authors did not make use of various noise components for domain-invariant regression.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation  
The surface shape of the mirror component serves as a key factor to affect the imaging quality of the space remote sensing payload, and the surface shape substantially depends on the stiffness of the component with the number of materials as well as the thermal matching ability between various materials. The material data on each part of the mirror component are provided in Table 3. The numerical value will be given to each component to facilitate the simulation model after mechanical and thermal coupling analysis, leveraging the analysis result to assess the rationality of the design.  
A finite element simulation analysis discretizes physical problems into mesh elements for the computer to conduct a numerical calculation and simulation. Tetrahedral mesh and hexahedral mesh are two commonly used mesh types when creating finite element simulation models, each targeting different physical shapes. The hexahedral mesh is composed of hexahedral elements, with each hexahedron consisting of six rectangular faces. Hexahedral meshes are more effective in handling models with regular geometric shapes, as they can better approach the shapes of cubes and rectangles. A tetrahedral mesh is composed of tetrahedral elements, with each tetrahedron surrounded by four triangular faces. Tetrahedral meshes have flexibility in handling complex shapes and boundary conditions, making them suitable for modeling complex geometric shapes. They can be well applied to surfaces and irregular shapes in three-dimensional space.  
The reflector component model in this article belongs to an irregular and complex model. Based on the advantages and disadvantages of the tetrahedral and hexahedral meshes mentioned above, we chose tetrahedral meshes when building up the finite element simulation model as illustrated in Figure 7, which can greatly save the time required for mesh partitioning.  
Figure 6: Schematic diagram of flexible hinge model.
## 1 Introduction
### Advances and Limitations with Estimating GPP  
Ecosystem GPP has been estimated by field observations, remote sensing, and/or modelling, each of which has advances and limitations. A major limitation of field observations is that sites with eddy covariance (EC) measurements are scarce, representing only small areas of the landscape that cannot be easily scaled up spatially [12; 13], while the major limitations of remote sensing are the coarse temporal resolutions of GPP and their general inability to provide a detailed explanation of the processes that determine GPP [14].  
To overcome the above limitations, modelling has been used in conjunction with field observations and remote sensing for the estimation, explanation, and prediction of GPP [15], and particularly GPP in peatlands [10]. However, many detailed models are usually data-intensive and parameter-expensive due to their detailed schemes for dynamic modelling of photosynthesis, leaf area index (LAI), and GPP, driven by sub-daily multivariable meteorological forcing, such as incoming short-wave radiation, I\({}_{\text{swr}}\), ambient air temperature, T\({}_{\text{air}}\), atmospheric relative humidity, RH, wind speed, v\({}_{\text{w}}\), and rain/show precipitation [10; 16; 17; 18; 19].  
Simplistic models use empirical hydrological modifiers [20; 21], and hydrologically sophisticated models explicitly simulate plant water relations [10; 22], as their accurate representation is a key feature for modelling the GPP of peatlands [23; 24; 25]. Although empirical modifiers may work well, they generally lack the depth of process explanation under different ecological conditions. However, hydrologically sophisticated models are not only parameter-expensive and data-intensive but also bring additional uncertainty with their coupled simulations for soil water fluxes driven by precipitation, vertical and lateral drainage, and evapotranspiration via water potential gradients, as well as highly variable and, in many cases, unknown hydraulic conductivities [26; 27].
## 2 Cost Model of Predictive Maintenance
### Optimization Process  
#### 2.4.1 Objective Function  
Risk and cost minimization can take different forms. We distinguish two main types (Figure 1):  
* _Comparison of the evaluated risks with risk acceptance criteria_: in this case, the decision variable is the risk which is evaluated and compared with risk acceptance criteria in order to determine the optimal time for maintenance [7].
* _Optimization of an objective function_: in this case, we aim at minimizing a risk/cost under constraints. The decision variables to determine are usually the maintenance/inspection plan [40]. It is possible to combine both types by adopting multi-criteria optimization [41]. In this paper, we chose an economic approach through minimizing a cost function. In fact, data on risk acceptance criteria are not always available and usually very specific to the adopted case study. The objective function that we want to reduce is the total cost \(C_{tot}\) of maintenance during the time cycle \(D_{T}\). This objective function is given by the equation below: \[C_{tot}=C_{p}+C_{c}+C_{in}+C_{d}+R_{h}+R_{f}+R_{e}\] (9)
## 2 Results
### Transmission Electron Microscopy (TEM)  
TEM helped us better understand the mode of action of the AMP in bacterial membranes. In S. aureus, membrane damage, intralamellar structures resembling mesosomes, and errors in the division septum were observed in most cells (Figure 6). Division septum errors also occurred for the positive control, but at a frequency of 21 \(\pm\) 10%, whereas cells treated with 1 \(\times\) MIC and 4 \(\times\) MIC presented 61 \(\pm\) 20% and 54 \(\pm\) 12%, respectively. This difference in frequency is significantly different from the positive control (\(p<\) 10\({}^{-4}\)). Mesosomes can occur in response to membrane damage (due to phospholipid reorganization) and cell wall damage (similar patterns seen in vancomycin and tetracycline studies [24]. Therefore, this peptide may also affect cell wall biosynthesis. As the machinery responsible for cell wall biosynthesis is in the division septum [25], it is common to observe these mesosomes near this structure.  
Figure 6: TEM for _S. aureus_ ATCC 25923 treated with Pln149-PEP20 (MIC = 8 mg/L). (**a**) non-treated cells. Scale bar = 1 \(\upmu\)m (**b**) non-treated cell in detail, with intact cell wall and membrane. Scale bar = 200 nm (**c**) 1 \(\times\) MIC treated cells, showing cell debris and deficient cells. Scale bar = 1 \(\upmu\)m (**d**) 1 \(\times\) MIC treated cells in detail, showing cell debris, cell with defective division septum, and intralamellar structures. Scale bar = 200 nm (**e**) 4 \(\times\) MIC treated cells, showing cell debris and deficient cells. Scale bar = 1 \(\upmu\)m (**f**) 4 \(\times\) MIC treated cells in detail, showing cell with defective division septum, intralamellar structures, and diffuse membrane. Scale bar = 200 nm.  
In _A. baumannii_-treated cells (Figure 7), damage to both the outer and inner membranes was evident at higher peptide concentrations, with wrinkled surfaces and bleb formation. In lower concentrations, cytoplasmic material condensation can be observed in specific positions in the cell. These positions coincide with those of specific proteins, such as PIsX, FloT, and MurG [26]. These proteins are related to the cell membrane or cell wall, suggesting Pln149-PEP20 targets. Although it is impossible to determine from the TEM experiment in which protein the interaction occurs, the carpet mode of action explains the rapid membrane depolarization. The binding of Pln149-PEP20 to MurG would explain the other forms of damage observed. Such damage was usually associated with the cell wall. Thus, MurG is a strong candidate for investigation.
## 6 Performance Evaluation
### SECOM Implementation and Validation
#### 6.1.2 Experimental Results  
After spinning all the containers using Docker-compose commands, SECOM can be used as a secure infrastructure by the users (from the ship and ashore). Both end users (the bridge team and the seafarers) bootstrap the application and are redirected to the Keycloak login page for authentication. Assuming each user has been authenticated successfully, they are provided with an ID and an access token so that they can use every microservice. After that, users are redirected to the home page of the application. From this point on,each user can establish a shared key when they want to send a message to the other side. In our case, the bridge team wants to send a sample vessel routing plan to the seafarers, as can be seen in Figures 8 and 9.  
The same shared key is established in both parties using the Diffie-Hellman key exchange protocol. The bridge team uses this key to encrypt the message, generate a tag, and finally send the message to the seafarers. The seafarers authenticate the tag, decrypt  
Figure 8: Example message of the routing plan of the vessel entering the Port of Livorno.  
Figure 9: Message encrypted with the shared key and received by the vessel.  
the message, and can view the message in clear text. During the data exchange process, data integrity and confidentiality, as well as service authentication, are guaranteed by the SECOM standard. The implemented system is also fault-tolerant and scalable, since each microservice is independent and can be easily scaled by running multiple instances. On top of this, all the requests can be load-balanced by the API Gateway in case of high naval traffic and traced by Zipkin.