esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Modular Bio-Inspired Autonomous Underwater Vehicle for Close Subsea Asset Inspection
## 3 Robofish Design
### Electronic Design
#### 3.4.3 Hardware Implementation  
Head board: The head board is based around a Trenz electronic TE0720 system on Module. This module incorporates the Zynq 7020 SoC, a 1 GB DDR3 RAM, 32 MB QSPI flash for configuration, an 8 Gbyte E.MMC flash for non-volatile storage, along with the power supply and configuration electronics for the SoC. This module was chosen over creating a custom board to accelerate development and ease upgradability (shown in Figure 9). If additional processing power and FPGA fabric is required in the future, this module can be swapped for a more powerful one without affecting the carrier board.  
Figure 8: RoboFish Control Requirements.  
The carrier PCB, shown in Figure 10, contains all of the necessary hardware for interfacing the Trenz SoM with the rest of Robofish, programming the SoM and regulating the battery power. Camera interfacing can be accomplished using either a MIPI CSI-2 connector or USB. An SD card slot is provided to increase onboard non-volatile storage. For communication with other modules in the system, CAN was used for initial testing, and Ethernet was chosen as the final solution. Power is transferred between modules by using a modified power-over-ethernet (PoE) methodology with the DP83825 PHY chip and HX1198 FNLT transformer IC. It also contains an LSM9 DS1 IMU to provide orientation awareness of the head segment. The head also interfaces with the acoustic modem and SONAR rangefinder via RS-485 bus and breaks out GPIO pins used to drive LEDs, one PWM signal that controls the servo that drives the movement of the segment, and another PWM signal to be used for a buoyancy control unit that is still in development as of this writing. A general SPI and power pin header is provided for future expansion also.  
Figure 10: RoboFish Head Carrier PCB with the TE0720 SoM.  
Figure 9: RoboFish Carrier Head board: a carrier PCB designed to contain all of the necessary hardware for interfacing the TE0720 SoM with the rest of RoboFish, programming the SoM and Regulating DC supplies. Either MIPI CSI-2 connector and USB is used for camera interfacing. SD card slot is provided. Either CAN or Ethernet is used for communication; LSM9 DS1 IMU is used to provide orientation awareness.  
Segment board: The segment board is built around an STM32F417 Microcontroller. This serves as a networked extension to the robot's capabilities in a segment. It communicates with the head board using CAN bus (initial testing) or Ethernet with PoE and contains all of the necessary IO for any servos or sensors that may be required. It also contains an LSM9 DS1 IMU for orientation awareness (shown in Figure 11) and breaks out control pins for driving LEDs and the servo and a buoyancy control unit with PWM and the general SPI and power pin header.
## 2 Analytical Model of Mesh Stiffness of the Internal Gear Pair
### Calculation of Slice Mesh Stiffness
#### 2.2.3 Gear Foundation Stiffness  
(1) Foundation stiffness of the external gear  
The mesh stiffness of the gear is not only the cantilever beam part above the tooth root, but also the gear foundation part that will produce stiffness resistance deformation. The external gear is treated as elastic rings, as illustrated in Figure 4. For external gears, the inner and outer diameters of the ring correspond to the shaft hole and the root circle diameter, respectively.  
The foundation stiffness is calculated as follows [25]:  
\[\frac{1}{k_{f}}=\frac{\cos^{2}\alpha_{1}}{EdB}\left\{L^{*}\bigg{(}\frac{u_{f} }{S_{f}}\bigg{)}^{2}+M^{*}\bigg{(}\frac{u_{f}}{S_{f}}\bigg{)}+P^{*}\Big{(}1+Q^ {*}\tan^{2}\alpha_{1}\Big{)}\right\} \tag{8}\]  
In the formula, the specific calculation formula and the meaning of each parameter can be found in reference [25].  
Foundation stiffness of the internal gear ring  
Because the foundation structure of the internal gear ring and the external gear is quite different and Formula (8) has not been successfully applied to the solution of the internal gear ring foundation deformation, this study decided to utilize the Timoshenko beam theory to obtain the deformation of this part. When this theory is applied to the analysis of internal gear ring foundation deformation, the deformation can be categorized into three components: the radial displacement \(w\), circumferential displacement \(u\), and cross-section rotation \(\phi\) under the action of force \(F\). The specific calculation principle of this parameter can be found in reference [26]. The deformation of the gear ring foundation is shown in Figure 5:  
From Figure 5, it can be observed that the deformation of the gear ring foundation can be expressed as:  
\[\delta_{ring}=w\cos\theta+u\sin\theta+\phi|OA|\sin\theta_{1} \tag{9}\]  
The specific calculation methods of \(w\), \(u\), and \(\phi\) are shown in [26].  
Thus, the foundation stiffness of the flexible gear ring can be expressed as:  
\[K_{f}^{g}=\frac{F_{m}}{\delta_{ring}} \tag{10}\]  
Figure 4: Geometrical parameters of gear foundation.  
Figure 5: Schematic diagram of the gear ring foundation deformation.  
According to slice mesh theory [27], the calculation formula of the comprehensive mesh stiffness of any mesh point on the internal mesh gear slice is as follows:  
\[\frac{1}{k}=\frac{1}{k_{h}}+\frac{1}{k_{t}^{p}(\alpha)}+\frac{1}{\lambda_{1}k_{ f}^{p}}+\frac{1}{k_{t}^{S}(\alpha)}+\frac{1}{\lambda_{2}k_{f}^{S}} \tag{11}\]  
where \(\lambda_{1},\lambda_{2}\) represents the tooth foundation stiffness correction factor of the external gear and the internal gear, respectively [28].
## 1 Introduction  
Topology optimization is still one of the most important issues in the field of optimal design of engineering structures. It has gained enormous popularity, for many years being an effective tool applied to innovative design projects with a wide range of applications in various areas, including mechanical engineering, civil engineering, bioengineering, material science, and many others. Today, the interest of researchers and designers is mainly focused on improving the quality, versatility, and effectiveness of optimization algorithms to be applied to solving demanding engineering design tasks. Apart from studies on theoretical issues, e.g., [1, 2], new numerical algorithms and methods, e.g., [3], and innovative applications, the attention is focused on computational efficiency and accuracy.  
Modern fast algorithms are developed with the aim of getting optimization codes, which will guarantee that a relatively small number of iterations is needed. At the same time, the reduction of the dimensionality of problems, combined with the refined mesh adaptation, has been considered. Since the first implementation of adaptive mesh in topology optimization [4], the concept of reduction of the number of design variables has become popular, because it allows creation of similar topologies, compared to those with a large number of design variables involved, without extensive computational effort. Moreover, mesh refinement allows inexpensive but efficient improvement of the finite element solution, as shown in [5; 6]. A broad discussion of the current state of knowledge can be found in [7; 8; 9]. Most published papers focused on adaptive strategies discuss filtering, e.g., [10], errors indicators [11; 12], or some aspects of a finite element grid, e.g., [13; 14].  
This paper is focused on the development of a Cellular Automata algorithm with the refined mesh adaptation technique, and the implementation of this algorithm in topology optimization problems. The versatility of the method was confirmed by the results of numerical tests performed for structures under one- and two-load cases, and also for the design-dependent self-weight loading. The results were compared with solutions gained for very fine mesh to confirm their correctness. In this paper, the discussion is also expanded to include some results obtained using algorithms proposed by other authors.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 4 Key Distribution
### Key Exchange via an HTTPS Secure Web Service  
The proposed mechanism to conduct the initial registration process is to provide the device with an Secure Hyper-Text Transfer Protocol (HTTPS) URL (Uniform Resource Locator), which addresses a website to be used to securely exchange the data. By using a TLS protected connection to the web server, we secure the data in transit; and since TLS also provides proof of the identity of the web-server, we are able to trust the exchange process. By using such a mechanism, we do not need to solve the the key exchange problem within SRUP. TLS solves the problem of key exchange for the HTTPS connection, by using the standard web-based key signing: ITU X.509 [13].  
The concept of key signing is in essence very simple. Rather than a web server simply generating its own (\"self-signed\") key-pair, it instead has its keys signed by a trusted third-party known as a Certificate Authority (CA); who signs the keys using their own trusted keys. The CAs keys are themselves also signed by a superior intermediate CA, and so on, creating a chain of trust. Ultimately, all such chains have at their source a root CA certificate; these root certificates confer trust on all subordinate intermediate CA certificates. The public keys associated with these root CAs are (typically) distributed as a part of the operating system or web browser application software and, hence, may be assumed as being already present on the target device.  
Since HTTPS over SSL/TLS provides this for us, there is no need to reimplement the process within SRUP.  
To use this approach for the distribution of the server's key, we simply need to add a suitable certificate and a RESTful [21] HTTPS web service to the C2 server or wider system; and arrange for this to provide, for example in a JavaScript Object Notation (JSON) format, the data that the device requires.
# Fatigue Crack Growth from Notches:
## 4 Results
### Unnotched-Cracked Specim in No Contact Simulations  
First, a numerical model containing only the crack was implemented, to make comparisons with numerical data resulting from different notched cracked models. In this first model, the contact of the crack flanks was removed, i.e., the crack closure phenomenon was prevented.  
Figure 4a plots \(\delta_{p}\) versus crack propagation, \(\Delta a\), for the three material models studied and both plane stress and plane strain conditions. A great influence of material is evident. Higher values of \(\delta_{p}\) are achieved for AA6082-T6, which may be attributed to the lower yield stress of this alloy (see Table 1). Regarding the AA7050-T6, the pure kinematic hardening model provides greater resistance to plastic deformation than the isotropic model, since the level of the stress-strain curve (in uniaxial tension) is higher for the pure kinematic hardening model than for the isotropic hardening model [34]. The effect of stress state varies significantly with material. It has a more pronounced effect in the AA6082-T6 model, where the plane stress case offers less resistance to plastic deformation. For the AA7050-T6 models, there is some influence for small crack increments, which disappears as the crack propagates. The lower values of \(\delta_{p}\) observed for plane strain state compared with plane stress state could be expected considering that stress triaxiality due to plane strain state promotes lower levels of plastic deformation. Concerning the effect of crack increment, there is an initial peak of deformation that results from the material not having previous hardening when first loaded. The application of the following loads produces hardening, which explains the progressive reduction of \(\delta_{p}\). The minimum value indicates the end of this initial transient regime. On the other hand, the increase of crack length increases the stress level ahead of crack tip, which explains the progressive increase of \(\delta_{p}\) with \(\Delta a\). The variation is linear with a slope that depends on material, being higher for the AA6082-T6. Figure 4b plots the slope of the linear increase, m, versus \(\delta_{p}\) (measured for a crack increment of 800 \(\upmu\)m). The increase of \(\delta_{p}\) promotes a greater influence of crack length, and a linear relation exists between m and \(\delta_{p}\).  
Figure 3: Typical plots of crack tip opening displacement (CTOD) versus load, F. (**a**) Total CTOD vs. F; (**b**) Plastic CTOD vs. F (contact).
## 3 Experiments
### Experimental Results and Analysis
#### 3.3.2 Single Brain Region Emotion Recognition  
To explore the importance of different brain regions for EEG emotion recognition, this paper explores and analyzes the effect of emotion recognition using signal features from a single brain region based on the Capsule-Transformer method. For each subject, the frontal, temporal, parietal, and occipital lobes were used as inputs to the model using DE feature matrices of shapes (2400, 4, 7), (2400, 4, 10), (2400, 4, 10), and (2400, 4, 5), respectively. The results, as shown in Table 3, showed that the frontal region EEG signals were the best for emotion recognition, followed by the parietal and temporal lobes, and finally the occipital lobe. The frontal EEG signal features achieved 91.88%, 91.25%, and 90.62% classification accuracy on the three dimensional classification tasks, respectively.
# Toward More Efficient Large-Scale Green Hydrogen Systems via Waste Heat Recovery and ORC
### Proton Exchange Membrane Electrolyzer  
Proton exchange membrane electrolyzers are a type of equipment that utilizes a solid polymer electrolyte to produce hydrogen through the electrolysis of water. The main advantages of using PEM electrolysis are its compact design, high efficiency, high rate of hydrogen production, rapid response to power fractionation, and high hydrogen purity. The main challenges associated with PEM are material costs, water purity sensitivity, and the durability of the membrane and catalysts. PEM electrolysis occurs in three key steps:  
1. Water splits at the anode: \[2\mathrm{H_{2}O}\to 4\mathrm{H^{+}}+4\mathrm{e^{-}}+\mathrm{O_{2}},\] (1)
2. Protons migrate through the PEM toward the cathode.
3. The protons (H\({}^{+}\)) recombine with electrons supplied from the external circuit to form molecular hydrogen gas: \[4\mathrm{H^{+}}+4\mathrm{e}\to 2\mathrm{H_{2}},\] (2)  
The overall reaction:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
\[2\text{H}_{2}\text{O}\to 2\text{H}_{2}+\text{O}_{2}, \tag{3}\]  
The schematic of PEM electrolysis process is shown in Figure 2.
# Secure Quantum Communication Technologies and Systems: From Labs to Markets
## 3 Devices for QKD Systems  
The development of devices has a key role in the industrialization of QKD, due to the high costs involved and integrability of the solution. Many of the current devices for QKD are still quite bulky. This is the reason why the path of photonic integration, especially using complementary metal oxide semiconductor (CMOS)-compatible silicon photonics, is being pursued to provide enhanced functionalities and miniaturization in platforms that are suitable for mass manufacturing and easy to integrate with existing telecommunication equipment.  
The main devices characterizing a QKD systems, as discussed in the following section, are the photon sources and detectors with which to send and receive the quantum states, and the quantum random number generators (QRNG) with which to achieve pure random bit streams. Further, to overcome the distance limitations already mentioned, quantum repeaters represent a fundamental milestone that could take QKD to a higher level, enabling arbitrary long-distance communication.
## 4 The Linear Machine Dataset
### Definition of Physics-Based Channels  
The channels are defined based on two criteria: (1) Each channel should have a clear and unambiguous physical meaning, and they should consist of the component in each axis or direction if the property is a vector, such as two channels for a 45-degree magnetization vector M = (1, 1) in the 2-D problem and three channels for a z-direction current density J = (0, 0, 100 A/mm\({}^{2}\)) in the 3-D problem. (2) Different channels should have significant variance in geometric structure or property. These four types of channels are identified in the dataset, as listed below. Figure 1 shows examples of our annotations.  
1. Current density (\(J\)--The current excitation applied to the material on the mesh of the linear motor will be converted to the current density. The number of channels of  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Winding type & Concentrate Winding & - \\ Number of turns & 380 & - \\ Residual flux density of the PMs & 1.23 & T \\ Number of slots & 12 & - \\ Airgap length & 0.8 & mm \\ Edge Height (h1) & 17.4 & mm \\ Edge Height (h2) & 2.0 & mm \\ Edge Width (w1) & 4.5 & mm \\ Edge Width (w2) & 5.2 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of fixed parameters of the PMLSM.  
Figure 3: The B-H curve of the steel DW310 in the iron.  
Figure 2: The cutting view and key dimensions of the PMLSM.  
current density is three in the 3-D problem. In the particular 2-D model, the number of channels of current density can be simplified to one (containing only the vertical direction, such as \((0,0,J_{z})\) to \((J_{z})\)) or two (containing any two components, such as \(\left(J_{x},J_{y},0\right)\) to \(\left(J_{x},J_{y}\right)\)) to reduce the storage space occupation.
2. Magnetization of magnetic materials (\(M\))--The relative permeability of the magnetic material is close to one, and this material involves channels for the magnetization direction vector. The magnetization will have two channels in the 2-D problem and three in the 3-D problem. If the magnetization has only one direction in the 2-D problem, one channel will be added to reduce the space complexity.
3. Motion band (_Motion_)--The moving part will be marked as 1, and the materials and approximated fields inside the moving band will be extracted after approximation. The number of channels of the motion band is 1.
4. Permeability of permeable materials (\(\mu_{r}\))--The relative permeability of the permeable material is larger than one. The value of channels of the permeable material is one when the material is isotropic. The anisotropy material will have two channels in 2-D problems and three in 3-D problems.  
The dataset contains two parts: the training dataset and the test dataset. The training dataset is used for training the neural network, and the test dataset is used for testing whether the model is well trained for magnetic field approximation.
## 5 Experimental Analysis
### Coverage Optimization  
Jamming coverage measures the degree to which key areas are covered by jamming beams. The proportion of ground users covered by jamming beams in a certain area was used as an indicator of jamming coverage. In the experiment testing jamming coverage optimization, the NSGA-III algorithm was tested first, followed by comparison with the c-DPEA, MTCMO, MSCEA, and URCMO algorithms. The changes in jamming coverage rates for the different optimization algorithms are plotted in Figure 9.  
In Figure 9, it can be observed that the initial coverage rate of the NSGA-III algorithm was the highest (at 84%), while the URCMO algorithm had the lowest initial coverage rate (at 60%). With an increase in the number of iterations, the NSGA-III, MSCEA, c-DPEA, MTCMO, and URCMO algorithms reached jamming coverage rates of over 90% at the 21 st, 32 nd, 49 th, 40 th, and 60 th iterations, respectively, satisfying the set jamming coverage rate constraint. At the end of the iterations, the coverage rate of the NSGA-III algorithm reached 100%; the coverage rates under the MSCEA, c-DPEA, and MTCMO algorithms were 96%; and the coverage rate of the URCMO algorithm was 92%. This demonstrates the efficiency of the NSGA-III algorithm in optimizing the jamming coverage rate in the context of LEO satellite downlink distributed jamming.
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 5 Performance Evaluations
Figure 27 shows the PER of the IEEE 802.15.4 network protocols under the interference of the WLAN with a 2 MHz center frequency offset. The distance between the IEEE 802.15.4 device and WLAN device varied from 0 m to 10 m. As the distance increased, the PER of the IEEE 802.15.4 standard and IEEE 802.15.4 mesh protocols decreased. This was because the power of the WLAN was concentrated at the center frequency. However, the proposed scheme maintained a constant PER regardless of this distance because it could prevent interference by the WLAN device.
# Mine Fire Behavior under Different Ventilation Conditions: Real-Scale Tests and CFD Modeling  
[PERSON]  
1  
[PERSON]  
2 Department of Mining, Topography and Structures, University of Leon (ESTIM), Campus de Vegazana, s.n, 24071 Leon, Spain; [EMAIL_ADDRESS] (A.M.C.); [EMAIL_ADDRESS] (F.G.-F)  
[PERSON]  
2  
and [PERSON]  
3 Department of Mining, Industrial and ICT Engineering, Polytechnic University of Catalonia (UPC), Manresa, Av. Bases de Manresa, 61-73, 08242 Barcelona, Spain  
34 Correspondence: [EMAIL_ADDRESS]; Tel.: +34-93-877-72-4224  
with inert gas [11], or analyzing the rock characteristics and its relationship with coal spontaneous combustion [12], with a special approach in the protection of workers, firefighting equipment, and facilities such as rescue chambers to guarantee safe conditions for a certain number of workers trapped by fire or high concentrations of pollutants [13] and know the best escape route in different scenarios [14]. However, there is still a long way ahead to increase the safety conditions and knowledge of full-scale fires in underground mines. In this regard, knowing the behavior of the fire and the environmental conditions that can generate becomes relevant.  
Fires may be subjected to diverse conditions within a mine, either due to random location of the potential fire [12] or because the ventilation of the mine can have a dynamic behavior [15], and it is necessary to have proper tools for its study. Computational fluid dynamics (CFD) modeling is an option widely used to determine the ventilation conditions in underground spaces. The study done by [PERSON] et al. [16] is good guidance for CFD analysis and details the order of magnitude between modeled and traditional calculations. [PERSON] and [PERSON] [17] studied the fire behavior in a physical tunnel and compared the analytical data with the CFD, but in a very narrow tunnel. On the other hand, [PERSON] et al. [18] determined the backlayering in a full-scale tunnel, achieving good results with considerable dimensions of the grid cell. Despite the specialized code (FDS) developed by the National Institute of Standards and Technology (NIST) being commonly used, there are still some uncertainties to solve in large-scale fires [19]. Moreover, it is very difficult to find real-scale CFD analysis compared to experimental data, where studies mostly focused only on simulating the fire [20]. In recent years, small-scale tunnels were used to combine experimental data and simulations. [PERSON] et al. [21] exposed a detailed layout of the experiment that can be extrapolated to full-scale tests, while results from [PERSON] et al. [22] indicated that FDS simulations agreed well with small-scale experiments. However, there is a lack of research in real-scale underground mines applying and comparing experimental data and simulations [23].  
The aim of this study is to determine the ventilation behavior due to the development of a fire in an underground mine, varying the parameters that characterize the fire in a set of several monitored scenarios in a full-scale laboratory drift, as well as to obtain a model adjusted to the reality with FDS.