esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.8. Sagitario Project--Magallanes Region  
The Sagitario on-shore wind farm is a facility that will be located in the north of Punta Arenas, Magallanes region. It will have 417 wind turbines, the owner of which will be Consorcio Austral. Furthermore, the height of the towers from the ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 2500 MW, with an estimated capacity factor of 0.57. Figure 17 shows a panoramic image of the landscape of the project site, and Table 10 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Porvenir Commune, North of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & Consorcio Austral & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 417 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 2500 & MW \\ \hline Capacity Factor & 0.57 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 2,437,500 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 1875 & MW \\ \hline GH2 Production & 215,625 & Tons/year \\ \hline NH\({}_{3}\) Production & 1,209,375 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 2,156,250 & Tons/year \\ \hline \hline \end{tabular}
\end{table}
Table 10: Sagitario GH2 plant main specifications.  
Figure 17: Panoramic view of the landscape in the Sagitario project.  
Considering Figure 17, it is evident that the Sagitario on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 10, this GH2 production plant will use seawater from a desalination plant, providing a water supply of 2,437,500 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 1875 MW of installed capacity. In this sense, the GH2 production plant will generate 215,625 tons/year of GH2, and 1,209,375 tons/year of NH\({}_{3}\). Finally, this facility will save 2,156,250 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
## 3 A New Approach for Bias Detection Combining Rule Mining and Regression Analysis
### Systematic Hypothesis Generation  
Given a decisional process \(D\), we apply class association rule mining to derive the set of relevant rules \(R_{rel}\). To measure the relevance of rules, we use _support_ and _confidence_, as defined in Equations (1) and (2). Specifically, we say that a rule is relevant if its _support_ and _confidence_ levels are above some given thresholds \(\rho_{supp}\) and \(\rho_{conf}\), respectively. Formally, \(R_{rel}=\{r_{i}\mid supp(r_{i})\geq\rho_{supp}\wedge conf(r_{i})\geq\rho_{conf}\}\). Each rule in \(R_{rel}\) is considered a candidate hypothesis of biases in the decisional process.  
Figure 1: Depiction of the phases of the proposed methodology.  
**Example 1**.: _Below, we show some example (relevant) rules that can be extracted by the application of association rule mining to the decisional process of Table 1:_  
\[r_{1}:\quad\texttt{Income} =5000,\texttt{EthnicGroup}=\textit{White}\rightarrow\texttt{HighRisk}=0\] \[r_{2}:\quad\texttt{Gender} =M\rightarrow\texttt{HighRisk}=1\] \[r_{3}:\quad\texttt{Gender} =M,\texttt{Employed}=N\rightarrow\texttt{HighRisk}=1\] \[r_{4}:\quad\texttt{Income} =2000,\texttt{EthnicGroup}=\textit{White}\rightarrow\texttt{ HighRisk}=0\]  
An analyst should investigate all the rules in \(R_{rel}\) to check whether they correspond to actual biases. However, as discussed earlier, association rule mining provides very little support for this. For instance, \(R_{rel}\) might contain rules that are not 'independent' from each other. In particular, many rules can be \"subrules\" of others, i.e., they extend other rules with additional itemsets as in the case of \(r_{2}\) and \(r_{3}\) above. Formally, a rule \(r_{i}:X_{i}\to Y\) is a _subrule_ of a rule \(r_{j}:X_{j}\to Y\) if \(X_{i}\subset X_{j}\). Thus, the analyst might not know whether the (possible) bias concerns the population characterized by a given rule (e.g., employed males) or whether it affects a larger population as characterized by a subrule (e.g., all males).  
To find more reliable evidence of biases, in this work, we employ regression analysis to determine the statistical validity of the evidence found.
## 2 TLS Measurement and Data Compression  
### 3D Laser Scanning  
Terrestrial laser scanning is a technology that helps users easily collect data and generates a 3D virtual model of the physical asset using point clouds as a set of points in 3D space. Laser scanning refers to deflection of the laser beam due to movement of the reflector (mirror), reflection of the laser beam on the object surface, and reception of the reflected laser beam. The laser scanning method includes a triangulation method, a phase difference method, and a time-of-flight method [43]. Laser scanning consists of on-site installation, positioning scan, surface scan, and post-processing of point group coordinate data. The hardware required to operate the laser scanner in the field includes a scanner, a laptop computer, a power supply, and a conveying and a mounting device. When the hardware installation is finished, the scan angle is adjusted to set the scan range on a target object [44].  
Since the entire model is achieved by merging the point group coordinate values obtained by each scan, a reference point (reflection target) should be provided to the target point so that at least four reference points are overlapped for each adjacent scanning point. The reference point installed at this time may be absolute coordinates, or relative coordinates around the installation position of the laser scanner. Point coordinate values are accumulated in database (DB) form and can be restored as a 3D image using a post-processing program or numerical analysis of the degree of deformation compared with the original design drawing.
# A Comparative Performance Analysis of Four Wind Turbines with Counter-Rotating Electric Generators
## 2 Problem Formulation
In order to facilitate the kinematic modeling, it is considered that the clutch disengaging, afferent to a rotor \(R\), is accompanied by fixing the rotor to the base, i.e., the rotational speed \(\omega_{R}=0\);
* Case C, Figure 1c: \(C1=C3=0\), \(C2=1\), \(2\)-DOF transmission with two inputs and two outputs (\(L=4\)); analogous to Case B, for \(C3=0\), the rotor \(R3\) becomes inactive and blocked, i.e., \(T_{R3}=0\) and \(\omega_{R3}=0\);
* Case D, Figure 1d: \(C1=1\), \(C2=C3=0\), \(1\)-DOF transmission with one input and two outputs (\(L=3\)); analogous with Cases B and C, the rotors \(R2\) and \(R3\) become inactive and blocked for C2 = C3 = 0, i.e., \(T_{R2}=0\), \(\omega_{R2}=0\) and \(T_{R3}=0\), \(\omega_{R3}=0\), respectively. The four variants of _WSCGs_ (Figure 1) use the same speed increase under the following premises:
* homologous gears of the four _WSCGs_ have the same number of teeth \(z_{i}\), \(i=1^{\prime}\), \(1^{\prime\prime}\),..., 6;
* homologous gear pairs with fixed axes have the same efficiency;
* the three wind rotors have the possibility to modify the pitch angle of their blades to adjust the input powers, the angular speeds and the input torques implicitly while considering a constant wind speed (i.e., a steady-state regime);
* in order to facilitate the kinematic modeling, it is considered that the clutch disengaging for a wind rotor is accompanied by the rotor locking at the base, which means that both the rotor torque and speed become null. Under these premises, the problem addressed in the paper refers to the analytical modeling of the parameters considered as the main comparison criteria: the kinematic amplification ratio of the speed from the primary rotor \(R1\) to the generator \(G\) (\(i_{a}\)), the transmission efficiency (\(\eta\)) and the mechanical power transmitted to the electric generator (\(P_{G}\)), as well as the power flow. For simplicity, the meaning of the symbols used in the equations is detailed in the Nomenclature section, without repeating it in the text.
###### Abstract  
In the early stage of product development, reliability evaluation is an indispensable step before launching a product onto the market. It is not realistic to evaluate the reliability of a new product by a host of reliability tests due to the limiting factors of time and test costs. Evaluating the reliability of products in a short time is a challenging problem. In this paper, an approach is proposed that combines a group of experts' judgments and limited failure data. Novel features of this approach are that it can reflect various kinds of information without considering the individual weight and reduces aggregation error in the uncertainty quantification of multiple inconsistent pieces of information. First, an expert system is established by the Bayesian best-worst method and fuzzy logic inference, which collects and aggregates a group of expert opinions to estimate the reliability improvement factor. Then, an adaptive Bayesian melding method is investigated to generate a posterior by inaccurate prior knowledge and limited test data; this method is made more computationally efficient by implementing an improved sampling importance resampling algorithm. Finally, an application for the reliability evaluation of a subsystem of a CNC lathe is discussed to illustrate the framework, which is shown to validate the reasonability and robustness of our proposal.  
reliability evaluation; product development; bayesian inference; reliability improvement; servo turret Vol. 1, 2020, 10, 7591; doi:10.3390/appl0217591
## 3 Results and Discussion
### Light Spectrum  
The circadian rhythm generator implemented in Section 2.2.5 was tested. The intensity for white LED channel, \(I_{\rm max1}\), was set to linearly vary from 100% to 80% over a period of three days with no offset time. \(I_{\rm max2}\) and \(I_{\rm max3}\) were fixed at 100% for red and blue LED channel with +ve and \(-\)ve offsets, respectively. The intensity for green LED channel \(I_{\rm max4}\) was fixed at 20% without offset. These amplitudes and offsets were selected such that the morning light was enriched with blue and evening light was enriched with red wavelength components. The resulting control signals \(u_{\rm N}\)\({}_{\rm N}\)\({}_{\rm W}\), \(u_{R}\), \(u_{G}\), and \(u_{B}\) corresponding to neutral white, red, green, and blue channels, respectively, are compared against the outside solar irradiation (\(I_{out}\)) in Figure 11d.  
The controller was activated after Day 1 indicating the use of offsets to adjust the day and night times within the chamber. The shape and time periods of the control signal generated for \(u_{\rm N}\)\({}_{\rm N}\)\({}_{\rm W}\) could reproduce that of the external light. Spectrum of the incident light could be enhanced with specific wavelengths temporally using the control logic and hardware configuration. Lack of sensor feedback in the current setup limits the control of the generated spectrum. Thus, any drift in spectral composition due to heating and LED degradation cannot be compensated online, therefore requiring offline compensation.
# IoTDS: A One-Class Classification Approach to Detect Botnets in Internet of Things Devices
## 5 Results and Discussion
### Discussion  
Overall, all algorithms presented high predictive performance, detecting the seven botnets in all profiles. The different legitimate actions the device performed in the proposed profiles seemed to be distinguishable from malicious behaviours by most of the algorithms. All these good results were reached using only 1080 data instances to train the models and a 1-s time-window. It means that the system does not require long periods of training and can perform a persistent analysis. OSVM and LOF had the best performance out of the four tested classifiers, with LOF holding a slight advantage in most of the datasets.  
The used host-based features showed to be accurate indicators of malware presence in the device. It is noteworthy to say that the adoption of host-based data does not rule out the possibility of using other types of data. Using host-based data in conjunction with network-based data, for example, can provide even more security for the entire system, including hosts and network. The impact of the IoTDS in the performance of the device was also considered. Our tests showed that although there was some increase in resource consumption when the IoTDS was running, this was far from hindering the capabilities of the device, regardless of its profile.  
This work also has two limitations that are outlined as follows. Firstly, our approach does not handle concept drifts automatically. The normal behaviour of IoT devices can change as time passes, which may have multiple reasons, such as firmware updates or changes in user behaviour. To cope with this kind of situation, the proposed approach has to induce new models to the IoT devices, which cannot be done automatically for the time being. Secondly, the possibility of attacks against machine learning algorithms was not considered. This way, adversaries can try to poison the ML algorithms to make them classify malicious behaviours as legitimate.  
Lastly, considering the memory and CPU utilisation of the Raspberry Pi during the tests, it is possible to say that the IoTDS could be a solution for multiple IoT devices found in today's households.  
Figure 12: Behaviour of the ST profile infected with botnets regarding resource usage with and without the IoTDS.  
Examples of these IoT devices are: Odroid-XU4 ([[https://www.hardkernel.com/](https://www.hardkernel.com/)]([https://www.hardkernel.com/](https://www.hardkernel.com/))), which has a 2 GB RAM and a Octa-Core processor, Roku TV ([[https://www.roku.com/en-gb/](https://www.roku.com/en-gb/)]([https://www.roku.com/en-gb/](https://www.roku.com/en-gb/))), which has a 512 MB-1 GB RAM in the 4k version with a ARM Quad-Core processor, and smart TVs such as the TCL L40S4900 FS, which has a Quad-Core processor and a 1 GB RAM.
## 1 Introduction
It is also expected to improve load sharing imbalances and problems with circulating currents that may arise in systems that require parallel operation [12; 13; 14]. Also, the voltage at the communication line is generated by transmission data from the transmitter and transmission data signals from the receiver. Thus, high security is expected as signal information from communication lines can only be analyzed with two devices that communicate with each other. To verify the validity of the proposed communication hardware, load sharing simulation was performed using PSIM, Powersim's simulation tool. In addition, the load-sharing experiment of the power converter with the proposed communication hardware confirmed that a stable parallel operation was performed.
# Reliability and Maintenance Analysis of Unmanned Aerial Vehicles +
## 6 The UAV as a Complex Maintenance System  
Now we consider an UAV as a complex aerial system composed by \(m\) subsystems (or subparts) defined as J = {1, 2,..., \(m\)}, and consisting of \(l_{j}\) components. At the component level, we can continuously control and check the degradation of a defined collection of physical parameters. The physical conditions degrade monotonically during use, and are restored by maintenance actions. For each component or subpart \(i\in I\), \(X_{i}(t)\) indicates the degradation trajectory in a fixed time interval \(t\in[0,\,\infty)\). Soft failure can be defined as the ability of a component, part, subsystem or system to continue its work even if with degraded performance, i.e., up to the point when its reduced performance exceeds a specific fixed threshold \(H_{i}\) (with \(X_{i}(t)>H_{i}\)), called _soft failure_ one. Typically, components subjected to thermal stress or mechanical degradation are hit by soft failures.  
When \(X_{i}(t)\) exceeds \(H_{i}\), a soft failure happens between two maintenance points \((n-1)\tau\) and \(n\tau\). This implies an action of corrective maintenance (CM), which has a specific cost (\(c_{i}{}^{CM}\)) on the critical component. This action is executed in a fixed time called maintenance point \(n\tau\), as shown in Figure 3.  
Figure 3: Degradation threshold of a system with a cycle of corrective maintenance only.  
The period between the occurrence of the soft failure point and the maintenance point \(n\tau\) is defined as \"soft failure period\". This period defines loss of quality in production or poorer performance with a cost rate indicated with \(c_{i}{}^{P}\)[61].
## 5 Results and Discussion
### Face and Emotion Recognition Performance  
The face recognition component of the proposed methodology was evaluated using the LFW dataset [19; 46], which is a benchmark dataset that was described in Section 4 and is the most widely used for unconstrained face recognition in images and videos. The performance results for face recognition were reported while following the unrestricted images with labeled outside data protocol. We proposed to use ArcFace for face recognition in our integrated approach to robot vision, and it achieved an accuracy of 99% [44] on the LFW dataset [19]. When evaluated using the self-collected HMTA dataset with facial images for 100 people, ArcFace achieved an accuracy of 99.2%. Moreover, for emotion recognition, we employed a residual masking network [45] for the extraction of facial emotion features and their subsequent classification. The residual masking network that we utilized in our study comprises four layers, where each layer consists of a residual layer and a masking block. When tested on the FER2013 dataset [20] for facial emotion  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline Harris3D detector + HOG/HOF descriptors [33] & 78.1 \\ ST-SIFT detector + HOG3D descriptors [36] & 80.5 \\ Dense sampling + HOG/HOF descriptors [33] & 81.6 \\ MBH + dense trajectories [37] & 84.2 \\ Stacked convolutional independent subspace analysis [35] & 86.5 \\ [PERSON] et al. [34] & 87.3 \\
3 DCNN-based fused feature maps + LSTM [38] & 93.9 \\ The proposed method without key frame extraction & 89.3 \\ The proposed method with key frame extraction & 93.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance evaluation of different methods for action recognition on the UCF Sports dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline HRNet + DDNet & 94.4 \\ OpenPose + DDNet & 92.8 \\ The proposed method with key frame extraction & 98.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of different methods for action recognition on the self-collected HNH dataset.  
recognition, the residual masking network resulted in an accuracy of 74.14%. We compared the emotional recognition performance of the residual masking network with a pretrained IR50 backbone network [44; 50] combined with a softmax classifier. The model was trained using stochastic gradient descent (SGD) as the optimizer to optimize a logarithmic loss function at a learning rate of \(1\times 10^{-3}\).The training was performed for 200 epochs with a batch size of 64. The IR50 backbone network was used to extract features from the images for facial emotion recognition. These features were then subsequently used to train the softmax classifier. The IR50 backbone and softmax classifier delivered an accuracy of 73.04% on the FER2013 dataset [20] for facial emotion recognition, as given in Table 4.  
Face and emotion recognition make the proposed framework very well-suited for social robots, which are expected to have a more personal interaction with humans.
###### Abstract  
The energy consumption of current vertical-lifting underwater monitoring devices mainly falls into two categories: one fully supplied by battery packs; and the other partially by battery packs, with the rest from ocean thermal energy. Constrained by battery capacity, their operation time is limited, making long-term remote operations difficult. This study focuses on a device powered entirely by ocean thermal energy, which realizes the absorption and storage of energy through a phase change heat-exchange system, significantly extending its operation cycle and working area. A composite phase change material of n-hexadecane and graphite with a volume ratio of 9:1 is used. The Fluent software 2022 R1, based on the enthalpy-porosity method, simulates the phase change process of the device to analyze the effects of different structures and seawater temperatures. Results show that with the same phase change material volume and inner diameter of the cylindrical heat exchanger, a smaller outer diameter yields better phase change performance. Lower seawater temperature facilitates solidification. Due to natural convection in the liquid phase, the melting time is 520 s and solidification time is 4800 s, with the melting rate far exceeding the solidification rate.  
underwater monitoring device; ocean thermal energy; phase transition; enthalpy-porosity model; numerical simulation Article
# Future Sustainable Internet Energy-Defined Networking
## 4 Future Internet Energy-Defined Networking--Design Methods
### Methods for Network Energy Management and Associated Research Challenges
#### 4.4.3 Computational-Level Energy Design Methods  
Computational operational support functions (\(\sim\)10% of energy consumption); the methods' main goals: to delegate/distribute computational/processing tasks across the network, focusing on energy efficiency.  
The specific critical methods include the following:  
1. Workload orchestration to warrant the efficient use of computing resources.
2. The placement and configuration of virtualized network components to reduce energy consumption.
3. Methods for solving the energy optimization problem following a meta-heuristic technique for allocating the AI algorithms/workloads to physical nodes.
4. Energy-aware scheduling for executing different AI tasks, considering heterogeneous resources and latency boundaries.
5. The adaptive configuration of a target computational platform (a central processing unit (CPU), graphics processing unit (GPU), neural processing unit (NPU), etc.) is abstracted from the offloading workload API.
6. Continuous cloudification for network functions and data spaces for energy efficiency, RAN functions, and edge clouds.