esa-sceva/satcom-chunk-collection huggingface dataset:
# Automation and Robotics Pilot Lines in the Context of Industry 5.0
## 4 Case Studies
### Digital Twin-Based Optimization for Ultraprecision Motion Systems with Backlash and Friction (2019)  
The study focuses on developing a Digital Twin for modeling and optimizing an ultraprecision motion system with backlash and friction, commonly found in CNC machining centers. The primary objective is to minimize the maximum positioning error without significantly increasing control effort. The Digital Twin (DT) includes a virtual representation of mechanical and electrical components, models of nonlinearities such as backlash and friction, and integration of a cascaded P-PI control system. Four methods were used to optimize the controller parameters: a standard industrial procedure (FT) and three gradient-free heuristics (SA, GA, and CE). The study demonstrates the DT's effectiveness in improving control system behavior and highlights the superiority of gradient-free heuristics over the FT method, with the cross-entropy (CE) method standing out for its speed and efficiency.  
In particular, improvements in the maximum absolute errors of 28% (GA), 26% (CE), and 20% (SA) with respect to the FT method were demonstrated. Considering the integral time absolute error the improvements were 21% (GA), 23% (CE) and 19% (SA). The parameters obtained through the optimization heuristics did not result in an increase of more than 3% in the control effort, as measured by the Integral of the Absolute Control Signal. These results validate the feasibility of DT-based optimization for improving ultraprecision motion systems, paving the way for industrial implementation [51].  
Cloud-Based Industrial Cyber-Physical System for Data-Driven Reasoning: A Review and Use Case on an Industry 4.0 Pilot Line (2020)  
The paper describes a cloud-based Industrial Cyber-Physical System (ICPS) designed for smart manufacturing, validated on an Industry 4.0 pilot line. Its main objective was to present a cloud-based data-driven reasoning process for reparametrizing predictive models in edge modules. Specific goals included improving surface roughness quality prediction, implementing a monitoring and prediction system in smart manufacturing scenarios, and surpassing the performance of standalone predictive models. The methodology involved describing the pilot line, generating and training data models, configuring the cloud-based ICPS, and validating the system. The study advances the state of the art by proposing a cloud-based ICPS, demonstrating the effectiveness of reinforcement learning through the Q-Learning method, and validating the system on a pilot line, showing improved prediction accuracy and reduced computational load. For example, in the case of computational load, the latency for the best model selection with the reasoning module was 0.085 s, compared to 1.10 s for the brute-force search. Limitations include the need for adaptation to other processes and the representativeness of the dataset used [17].
## 1 Background
In this regard, cascading failures have been studied intensively with the aim to investigate the changes in the structure connectivity caused by node dependence between networks [20; 21; 22]. In particular, Ref. [22] considered the role of interdependences between the networks in relation with recursive processes of cascading failures. In this paper, the original concept of resilience by [23] considered the functionality of one system by proposing a single variable definition. The concept of resilience has been applied in [24] by considering several infrastructures that belong to the same typology (in this case road networks) and in the case of seismic events. Therefore, the novelty of this paper consists of proposing a multidimensional definition of resilience that overcomes the previous one by [23] for 1D systems. The proposed formulation herein aims to include the various dimensions (or variables) that may describe the interconnections between the various lifelines that affect the functionality of the global system. The calculation of resilience needs to account for the complex of infrastructures that may modify the functionality of the community. In this regard, the main novelties of the paper consist of: (1) developing a multi-dimensional definition of seismic resilience that may consider the role of several systems instead of a single infrastructure; (2) proposing a formulation that include interdependencies inside the resilience formulation; and (3) presenting a case study that applies the proposed definition and may be considered as a first attempt to implement the framework. The structure of the paper consists of seven sections. In Section 2, interdependencies are defined to consider their significance in the assessment of resilience for infrastructures. Section 3 shows the original definition of resilience by explaining the limitations of considering a 1D definition, since it cannot consider the various interdependencies that occurs between the several systems that concur to generate the final resilience of the system (and then of the community). Section 4 proposes the novel formulation that may include the presence of interdependencies and overcome the previous ones, allowing more realistic estimations of resilience of systems. Section 5 discusses how the general formulation may be applied if linear hypotheses are assumed to simplify and deduce important considerations in implementing the proposed formulation in several realistic applications. Section 6 shows a case study where two infrastructures have been considered and the resilience has been calculated with the proposed formulation.
# A Review on the Importance of Microbial Biopolymers Such as Xanthan Gum to Improve Soil Properties
## 2 Biological Processes in Geotechnical Engineering  
Over the years, most engineers and researchers have not given adequate attention to biological processes in the soil, but the demand to satisfy society's concerns about environmental issues has forced researchers to focus on new procedures that combine the use of microorganisms in innovative soil improvement techniques [7]. As a result, the amount of research involving microbial activities to improve the soil properties required by engineering has increased and, simultaneously, so has the intention to lower rates of pollutant gas emissions when compared to the usual geotechnical practices that use Portland cement [5].  
The part of Geotechnical Engineering that uses biological processes to solve geotechnical difficulties is called Biogeotechnology [2]. In other words, this is a field related to the chemical reactions achieved and measured within the soil by means of biological action [2,3]. Important factors such as the selection and proof of appropriate microorganisms for specific applications have to be considered, as do the cost-effectiveness and biosafety of the application [5]. Several studies on biotechnological engineering applications already exist, such as the use of vegetation, algae, bacteria, enzymes, and biopolymers. However, two of them, namely bioccmentation and bioclogging, have received special attention due to the favorable results presented by applications that modify soil's properties, in a way that is favorable to engineering, by increasing stiffness and strength and even reducing permeability [5,8]. Bioccmentation, usually based on calcium carbonate precipitation, can be promoted by microorganisms (MICP--microbial induced calcium carbonate precipitation) or by enzymes extracted from bacteria or plants (EICP--enzyme-induced calcium carbonate precipitation). These two methodologies rely on the same chemical reactions that decompose urea into carbonate and ammonium ions. The carbonate ions combine with calcium ions to form precipitated calcium carbonate (CaCO\({}_{3}\)) [9]. Bioclogging relies on the use of biopolymers. These bio-mediated soil improvement techniques are low-cost and require minimal extra energy, which guarantees a reduction in the carbon dioxide released and thus also contributes to reducing greenhouse gases [10].
# Lightweight, Secure, Similar-Document Retrieval over Encrypted Data
## 4 Scheme Overview
### Initialization Stage  
The data owner _[PERSON]_ initiates the process of the privacy-preserving DSD mechanism by generating the secret keys \(K_{1}=(b,M),kpub,kpriv\), and _Kcoll_, where \(b\) is the base parameter and \(M\) is a constant. Then the _DO_ shares the following information with other authorized data users to launch a secure DSD.  
* _Kcoll_: secret key of document collection encryption/decryption.
* \(K_{1}\): secret keys of index construction.
* _kpub_: the public key of the Paillier encryption function.  
Figure 1: The proposed privacy-preserving DSD.  
* _kpriv_: the private key of the Paillier decryption function.
## 5 Experiments
### Experiment 2--Reconstruction of Human Kidney  
In Experiment 2, we reconstructed a human kidney from non-parallel contours re-sampled from an original surface model of the kidney. The original surface model of the kidney was created from its CT counterpart as follows: First, CT images of a volunteer were acquired in our cooperative hospital. Second, the kidney was segmented slice by slice from the CT image data, using the semi-automatic segmentation function in Amira software (version 3.1, FEI Corporate, Hillsboro, OR, USA, 2005) [49], which was based on Active Contour Model. Third, the Amira software was used to reconstruct the surface of the segmented kidney to produce the original surface model of this experiment.  
The differences between the four reconstructed surfaces and the original surface are illustrated in Figures 6 and 7 and Table 2.  
The results of this experiment conformed to those of Experiment 1. When the input data were dense enough (_i.e._, 14 or 12 contours), all four methods obtained satisfactory surfaces. All the quantitative differences between the reconstructed surfaces and original surface were small for all four methods. However, even with a dense point cloud, the VSR method still remained the best whether evaluated visually or quantitatively. Although the Hausdorff distance of the Power Crust method was slightly less than that of the VSR method when the number of used contours was greater than 10, it behaved poorly when the number of contours used decreased.  
For the BPA, Power Crust, and Poisson methods, the quality of the reconstructed surface dropped dramatically when the number of contours used decreased. However, the performance of the VSR method was still excellent, both visually and quantitatively. Even when six, four, and three contours were used, the VSR reconstruction was a close approximation to the original surface, while the other three methods failed to create complete or closed surfaces.  
The BPA method was the worst when reconstructions were produced with more than six contours, as seen in Table 2. When the input data were extremely sparse (_i.e._, six, four, and three contours), the Poisson method performed worst.  
Figure 6: _Cont._  
Figure 6: Surface reconstruction of a human kidney by four methods. For each row, from left to right: the contours used, the original surface, the surface reconstructed by BPA, the surface reconstructed by Power Crust, the surface reconstructed by Poisson, and the surface reconstructed by VSR.
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 3 Results
### Determination of Peaks in Chl-a and Turbidity Concentrations
Additional cases supporting the importance of lake depth can be found in three other lakes located in the South American continent, with comparable burned areas in their catchment, with similar SPI values and different lake depths, Lake Vera (Paraguay) being a shallow lake (average depth = 2.2 m), Lake Guarico (Venezuela) medium (average depth = 9.8 m), and Lake da Brisas (Brazil) deep (average depth = 51.2 m). Their Chl-a response to fire was quite diverse and more evident in the shallowest Lake Vera, which experienced a steep progressive increase after two successive important fires in 2020 (increased from 15 to over 50 mg m\({}^{-3}\), annual mean from 16 to 25 mg m\({}^{-3}\) in 2020). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Chl-a Peak (Log)**} & \multirow{2}{*}{**R\({}^{2}\) = 19.5\%**} & \multicolumn{3}{c}{**Chl-a Peak (Log) (Cluster IV)**} & \multirow{2}{*}{**R\({}^{2}\) = 45.8\%**} \\ \cline{3-3} \cline{5-6}  & & & & **IV)** & \\ \hline
**Variable** & **Coefficient** & \(p\) & **Variable** & **Coefficient** & \(p\)** \\ Depth\_mean & \(-\)0.0067 & 0.0001 & Depth\_mean & \(-\)0.4107 & 0.0001 \\ Latitude & 0.0046 & 0.0001 & Residence time (Log) & 0.2341 & 0.0001 \\ BA/A\_mean & 0.2652 & 0.0218 & BA level 0–4 (Log) & \(-\)0.1310 & 0.0001 \\ SPI\_mean & \(-\)0.0676 & 0.0057 & SPI (90 th percentile) & 0.6872 & 0.0279 \\ \hline
**Turbidity (NTU)** & R\({}^{2}\) = 1.2\% & \multicolumn{3}{c}{**Turbidity (NTU) (Cluster IV)**} & \multirow{2}{*}{R\({}^{2}\) = 42.3\%} \\ \hline
**Variable** & **Coefficient** & \(p\) & **Variable** & **Coefficient** & \(p\)** \\ SPI\_mean & 0.5341 & 0.0275 & Elevation & \(-\)0.0004 & 0.0001 \\  & & & Log Catchment/lake area & 0.3511 & 0.0001 \\  & & & SPI\_mean & 1.9097 & 0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of Stepwise Multiple Regression Analysis results for chlorophyll-a (Chl-a) and turbidity maximum peak concentrations as dependent variables. Left column = all lakes. Right column = lakes having a significant regular annual burning, located mainly in the African continent.
## 2 Background Theory
### Aspect Ratio and Crack Density
The crack porosity may be re-expressed in terms of crack density and aspect ratio, as follows:  
\[\varnothing=\varnothing_{\text{ep}}+\varnothing_{\text{cp}}=\varnothing_{\text{ ep}}\ +\frac{4}{3}\pi\rho\varepsilon \tag{3}\]  
Crack density is mathematically defined by the representative elementary volume (V), the length of a spheroid semi-axis (c), and the number of cracks (n) in a material, as shown in Equation (4):  
\[\varepsilon=\frac{1}{V}\sum_{i}^{n}\text{C}_{i}^{3} \tag{4}\]  
The total porosity can be determined by using elastic moduli and the theory of wave propagation suggested by [PERSON] [22] with such a relationship having already been applied to various materials, including rock, sand, silty sand, silty clay, silt, and clay-sand mixtures [28]. This Equation for total porosity is given as follows:  
\[\phi=\text{f}(\text{K}_{\text{g}},\text{K}_{\text{sk}},\text{K}_{\text{f}}, \text{V}_{\text{p}},\text{V}_{\text{S}},\text{\gamma}) \tag{5}\]  
where Kg, Ksk, and Kf are the bulk moduli of a grain, skeleton, and fluid, respectively; VP and VS are compressional and shear wave velocities, respectively; and \(\gamma\) is the mass density. As such, the parameters for shear moduli based on both Biot-consistent (GBiot-consistent) and self-consistent (Gself-consistent) theories may be evaluated through Equations (3) and (5), to produce the following:  
\[\text{GBiot-consistent}=\text{f}(\text{G}_{\text{S}},\,\phi_{\text{ep}},\, \varepsilon,\,\
u)=\text{f}(\text{G}_{\text{S}},\,\phi,\,\varepsilon,\,\rho, \,\
u)=\text{f}(\text{G}_{\text{S}},\,\text{K}_{\text{g}},\,\text{K}_{\text{sk }},\,\text{K}_{\text{f}},\,\text{V}_{\text{p}},\,\text{V}_{\text{S}},\,\text{ \gamma},\,\varepsilon,\,\rho,\,\
u) \tag{6}\]  
\[\text{G}_{\text{self-consistent}}=\text{f}(\phi_{\text{ep}},\,\
u,\,\rho,\, \text{E},\,\varepsilon,\,\text{K}_{\text{f}},\,\text{K})=\text{f}(\phi,\,\
u,\, \rho,\,\text{E},\,\varepsilon,\,\text{K}_{\text{f}},\,\text{K})=\text{f}( \text{K}_{\text{g}},\,\text{K}_{\text{sk}},\,\text{K}_{\text{f}},\,\text{V}_{ \text{p}},\,\text{V}_{\text{S}},\,\text{\gamma},\,\
u,\,\rho,\,\text{E},\, \varepsilon,\,\text{K}) \tag{7}\]  
Equations (6) and (7) thus define crack density as a function of compressional (Vp) and shear (Vs) wave velocities, which has prompted various researchers to estimate \(\varepsilon\) in a material by measuring its elastic wave velocity, mass density, and assumed bulk moduli. However, the exact crack density is difficult to determine, as the aspect ratio remains unknown; an independent technique is therefore necessary to define its value. Although Equations (1) and (2) were developed using different theoretical backgrounds, both consider elastic interactions, and so here we establish a new relationship between the unknown parameters of crack density and aspect ratio by combining the two Equations.
## 3 Materials and Methods
### Baselines
#### 3.3.1 Customized 3D-CNN  
The architecture defined for the 3D-CNN is summarized in Figure 5. It is composed of 5 3D convolution layers, each followed by a max-pooling and a batch normalization layer. Despite not being clear in the literature whether to use dropout and/or batch normalization to optimize the model focusing on generalization, our experiments pointed out batch normalization layers as a better option. It is worth mentioning that similar results were observed in [36]. Their results indicate that batch normalization improved model accuracy without considerably increasing the training time. The opposite was observed when using dropout layers, since these incurred a reduction in the model accuracy of evaluating anomalies. These authors concluded that batch normalization layers allowed the model to increase its performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Input** & **Output** \\ \hline Conv3D & (224, 224, 55, 3) & (112, 112, 55, 64) \\ \hline LeakyReLU & - & - \\ \hline Dropout & - & - \\ \hline Conv3D & (112, 112, 55, 64) & (56, 56, 55, 128) \\ \hline LeakyReLU & - & - \\ \hline Dropout & - & - \\ \hline LeakyReLU & - & - \\ \hline Dropout & - & - \\ \hline GlobalMaxPooling3D & (28, 28, 55, 128) & (128) \\ \hline Dense & (128) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Architecture of the customized discriminator component of the 3D-GAN designed to detect anomalies in video data.  
Figure 4: Two illustrative examples of anomalous frames obtained by the proposed generator using Gaussian noise applied to the normal frame in (**a**). The noise severity is higher at the beginning of the discriminator learning process, as shown in (**c**). It becomes smoother and more realistic as the discriminator loss decreases, as observed in (**b**).  
The weights were initialized using Glorot Normal Initialization [37] and the convolution layers employed the rectified linear unit (ReLU) as the activation function. After the last convolutional layer, the global average polling 3D process for temporal data is carried out. In this process, the final normalization of the feature maps is performed to allow each feature map to be converted into a single value. Then, a dense layer is added after the Global Average Polling 3D layer. Finally, dropout is used to focus on reducing overfitting. In addition, since this is a binary problem, the last layer employs sigmoid as the activation function. Therefore, the prediction reaches values close to 1 when an anomaly is detected and close to 0 otherwise. Finally, the remaining hyperparameters applied are: cross entropy and Adam--using a learning rate of \(10^{-4}\)--as the loss function and optimization algorithm, respectively. All hyperparameters were experimentally tuned.
# Seamless 5G Multi-Hop Connectivity Architecture and Trials for Maritime Applications
## 4 Trials in the Baltic Sea Corridor (Tallinn-Helsinki)
### Vision of Multi-Hop and Trial Possibilities  
The overall vision presented in this paper for the maritime scenario will be validated through demonstrations that include multi-hop (see Figure 13). These demonstrations will show how to ensure the selection of several possible connections based on the best QoS. The trials will be performed in two phases:  
1. A vessel connected with only one route from the vessel where the 5G network is deployed;
2. A vessel connected with redundant routes from several vessels with 5G networks deployed.  
The planned trials have similarities with the IAB concept [38; 39], but considering that the 5G IAB functionality will not be available during the project's lifetime, the implementation and use of NPN networks on two ships were chosen as the primary solutions. To prepare for the first phase of the connectivity trials, the 5G private network provided by Ericsson has been deployed in a test lab located in Latvia, as it can be seen in Figure 14. The solution provides all the necessary 5G functionality, and it is very compact and quick to deploy, and it consists of the following main components:  
* Two DELL VEP4600 network controllers;
* A 6651 baseband for RAN;
* A 6250 power unit for the baseband;
* An AIR 6449 outdoor antenna operating in the 3.5 GHz frequency;
* A GNSS receiver and GPS antenna;
* Cloud management;
* Centralized extended network management.  
Figure 13: Vision of multi-hop trials.  
In multi-connectivity, redundant routes can either be used as a backup or for load-balancing scenarios. Multi-hop backhauling for range extension is mainly considered with mmWave frequencies, but in the planned trials, a spectrum below 6 GHz will be used as a primary alternative because higher frequencies in the millimeter domain are expected to have several disadvantages in providing seamless cross-border connectivity.  
In a multi-hop network, the increased number of hops, the distance between vessels, the used frequency for backhaul, the limited nodes resources due to congestion and other aspects have a clear impact on the quality of service (QoS). Accordingly, in the planned trials, measurements of the latency, throughput and signal-to-noise ratio will be carried out to identify and validate the assumptions about the factors affecting the provided QoS.  
The time frame of the execution of the 5G-ROUTES project will not allow for sufficient studying of the future standard implementations of Release 17 [39], but it will give a benchmark for future extended 5G coverage trials in measurements of hybrid backhaul solutions with current cellular and satellite technologies. Nevertheless, the need for improved coverage and cellular 5G service will be verified by the piloting ferry operator and the 5G-ROUTES use cases.
## 1 Introduction
At the same time, a certain amount of active power support is also provided to the grid during the violent oscillation period to ensure the fault ride-through capability of the power system, and the ESS cost is reduced as much as possible under the premise of achieving the expected suppression effect. In addition, by real-time optimization of the ESS controller's parameters, online regulation of the equivalent inertia and damping of the power system is realized, and the dynamic performance of oscillation suppression is improved, ensuring that the grid oscillation is restrained at the fastest speed under the premise of satisfying the constraints of smaller frequency and rate of change of frequency (ROCOF) deviation below their threshold values. This paper is organized as follows. The mechanism of using the ESS to suppress electromechanical oscillation is first introduced in Section 2. In Section 3, the electromechanical oscillation process of SG dominated power systems is analyzed in detail and the basic principle of electromechanical oscillation control is also investigated. In Section 4, the modified control strategy is proposed to improve the dynamic performance of oscillation suppression and the controller parameter design method of the modified ESS control strategy is also proposed. In Section 5, a large number of comparative experiments are carried out and the experimental results verify the effectiveness of the proposedcontrol strategy and its controller parameter design method for ESSs. Finally, the main conclusions of this paper are summarized in Section 6.