esa-sceva/satcom-chunk-collection huggingface dataset:
# Technological Processes for Steel Applications in Nuclear Fusion
## 7 First Wall Mock-Up
### Design  
We designed the mock-up to get a down-sized representative model of a typical first wall, e.g., that of a DEMO reactor, which may be installed in the Helium Loop Karlsruhe (HeLoKa) for high heat flux tests. In principle, an ODS steel-plated first wall could yield advantages for all breeding blanket concepts, e.g., in terms of longer life-times, but the water temperature is limited to a sub-critical operation regime (super-critical water cooling has not been taken seriously into account for a DEMO reactor). Therefore, the benefit of ODS steel plating might be higher for the helium-cooled concepts due to the possibility of increasing the operating temperature. From a fabrication point of view, i.e., regarding a possible ODS steel plating, there are no big differences between the blanket concepts. Thus, we decided to use the Helium-Cooled Pebble Bed (HCPB) breeding blanket concept as a reference for the mock-up design. Detailed outlines of the design are given, for example, in [34; 35; 36] and references therein.  
The mock-up consists of six parts: a mock-up plate with 5 cooling channels (EUROFER97/2), an ODS steel plate (13 Cr-1.1W-0.3 Ti0.3-Y\({}_{2}\)O\({}_{3}\)), 2 helium distributors (P92), and 2 reducers (P92). P92 steel is a conventional 9 Cr-2W-Mo steel, type 1.4901. For the TIG welds, P92 filler wires were used. The layout is illustrated in Figure 30. The helium distributors allow a homogenous mass flow distribution in the 5 channels. Their design was developed based on CFD analyses for a total mass flow rate of 40 g/s in each channel (i.e., 200 g/s in total), an inlet temperature of 300 \({}^{\circ}\)C and an inlet pressure of 8 MPa. The computed maximum deviation of 4% relative to the nominal mass flow rate is fully acceptable, since the effect on the temperature distribution on the mock-up during the experiment is negligible. Moreover, the total pressure-drop in the distributors and in the mock-up have been calculated to be 0.05 MPa only.  
We designed the mock-up with five cooling channels. The geometry and details of the ODS steel plating are depicted in Figure 31. The inner surfaces of the channels were designed flat, i.e., they were not equipped with swirls or other turbulence enhancers. Note: The middle channel was intentionally misaligned, so that the weld seam of the ODS steel plating appeared at the corner, which is the highest stressed location. This generated two lines of preferential crack initiation.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 2 Background and Related Work
### NAND Flash Memory-Based Storage Devices  
Figure 1 describes typical NAND flash-based storage devices. The basic hardware architecture of NAND flash memory-based storage devices consists of a high-performance controller, DRAM main memory, and an array of NAND flash memory chips. NAND flash memory is a nonvolatile semiconductor storage device that is formed by integrating a floating gate-based semiconductor device. The main components include a page, which is a read and write unit, and a block, which is an erase unit. The page sizes are mainly about 4 KB, 8 KB, and 16 KB, and dozens of pages are collected to form a block. There are three internal commands used for NAND flash memory: read,  
program, and erase. The read and program commands transfer data to and from the flash chip, and a data transfer unit is a page. Actually, the erase command does not transfer data and works in block units. A client device can make only two types of requests, namely, a read and a write request. The read request is related to the read command and the write request is related to the program command.  
Currently, triple-level cells (TLC) are the most commonly used NAND flash memory. It stores three bits per cell, where each of the three bits of data in a TLC flash cell are either programmed or erased, which means the cell has a total of eight different states from 000 to 111. In contrast, a single-level cell (SLC) and a multi-level cell (MLC) store one bit and two bits per cell, respectively. With several states, the quantized voltage level of memory cells decides the value of the cell. The growth in the number of states per cell raises interference between states as the quantized decision levels of the cell start moving close between adjacent states, which results in detection errors. Recent advances in 3D lamination technology make it easier to secure cell-to-cell spacing compared to conventional 2D methods, resulting in a slight increase in P/E cycles compared with those of conventional 2D.  
There are two crucial points regarding NAND flash memory. One, if the data are written to a flash memory, write operations should be preceded by erase operations internally. Second, the basic units of erase and write operations are different from each other. To overcome the unit mismatch between write and erase operations and to efficiently use flash memory, special software layers called flash translation layers (FTLs) have been developed [12; 13; 14; 15; 16; 17]. Beyond single internal operational issues, other works have focused on the parallelism issue within embedded storage devices to speed them up [18; 19; 20; 21; 22; 23].
## 2 Data and Methods
### GNSS Integrated Water Vapour (IWV) Processing  
The Global Navigation Satellite System (GNSS) data are organized in units of 24 h periods and were processed using the earth parameter and orbit system software (EPOS) at the German research centre for geosciences (GFZ) [40]. To estimate the zenith path delay (ZPD), we used the Vienna Mapping Functions (VMF) [41]. The elevation cutoff angle is 7 degrees. The data processing was done based on the following steps: First, the two stations were processed in a Precise Point Positioning (PPP) model using the GFZ's own second reprocessed Global Positioning System (GPS) satellite clock and orbit products [42]. In the PPP processing, the low quality observations and outliers were removed.  
In a second step, the remaining data were used for network processing. Several well distributed IGS (International GNSS Service) core stations are included in this network. The ZPD changes mainly with the temperature and water vapour content. In the last step, the estimated ZPD were converted to integrated water vapour (IWV) [17]. The retrieved IWV has an accuracy of 1-2 kg m\({}^{-2}\) and a precision of 1 kg m\({}^{-2}\)[43]. The humidity-induced part of ZPD provides a valuable source of vertically integrated water vapour, e.g., [16; 17]. To estimate the IWV, meteorological information (ground pressure and the mean temperature above the station) is needed. In this study, the ECMWF ground pressure and mean temperature data were used to convert the ZPD to IWV [44].
## 5 Results and Discussion
A natural next step in this line of work is to devise algorithms that can select the configuration decisions (splitting and compressing) in a dynamic fashion. Another future research direction associated with our subject of work could be about addressing the challenge of finding more effective image compression methods like autoencoders aiming to get better results in transmission delay and also to study ways to improve the accuracy metric with other Deep Learning techniques like distillation. Therefore, it would be useful to study autoencoders as image compressors and the distillation method and see how these affect the accuracy and execution time in an edge-assisted object detection system. The limitation we faced in this work was, as said before, that the proposed system cannot make the splitting and compression decisions in a dynamic fashion which means that the user has to take into account all the feasible solutions and choose manually the optimal decision variables the get the minimized execution time or the maximized accuracy.
## 4 Experimental Validations
### Compliant Path Generator
#### 4.1.3 The Effect of Permissible Coupling Indices [\(\delta\)] and [\(\gamma\)]  
In this work, there are only two parameters [\(\delta\)] and [\(\gamma\)] that need to be set manually. Their influences on the design results are investigated here. Figure 11 plots four optimized designs obtained using the optimization model (13) with different values of [\(\delta\)] and [\(\gamma\)], and the design result shown in Figure 11b is just the one drawn in Figure 8. It can be seen that all of the optimized results have reasonable and similar topologies even if [\(\delta\)] and [\(\gamma\)] vary greatly.  
The performance data of the resulting mechanisms is given in Tables 3 and 4. For different decoupling levels (i.e., different values of [\(\delta\)] and [\(\gamma\)]), the desired displacements \(u_{O1}\) and \(u_{O2}\) have little difference, whereas the undesired displacements change significantly, so that the values of all the coupling indices could be strictly limited to [\(\delta\)] and [\(\gamma\)]. These results indicate that the values of [\(\delta\)] and [\(\gamma\)] have an evident impact on the decoupling degree of resulting mechanisms. Both the input and output couplings of optimized mechanisms can be controlled very well and the completely decoupled mechanisms could be obtained by using the optimization model (13) with very small [\(\delta\)] and [\(\gamma\)].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(\delta_{2,1}\) & \(\delta_{1,2}\) & \(\gamma_{2,1}\) & \(\gamma_{1,2}\) \\ \hline Figure 7 & 0.29195 & 0.34736 & 0.14032 & 0.08440 \\ Figure 8 & 0.00956 & 0.00999 & 0.00003 & 0.00002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Output and input coupling indices of design results shown in Figures 7 and 8.  
Figure 10: Convergence histories of the optimization using Equation (13).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(u_{O1}\) & \(\widetilde{u}_{O2,1}\) & \(\widetilde{u}_{I2,1}\) & \(u_{O2}\) & \(\widetilde{u}_{O1,2}\) & \(\widetilde{u}_{I1,2}\) \\ \hline Figure 7 & 11.1167 & 6.0066 & 4.1642 & 14.3340 & 8.4481 & 4.1642 \\ Figure 8 & 11.7840 & \(-\)1.15
# Dynamic Similitude Design Method of the Distorted Model on Variable Thickness Cantilever Plates
###### Abstract  
In the present study, a new method of predicting the dynamic behavior of a variable thickness (VT) cantilever plate by using a thin plate scaled model is proposed. The thin plate model, defined as the model thin (MT) plate, is designed by using the newly proposed similitude design method. The method is derived based on the transfer matrix of both the stepped thickness (ST) plate that is simplified by the VT plate and the thin plate. The thickness of the MT plate is calculated by introducing the equivalent thickness corresponding to each VT plate's vibration modals, such that a series of accurate distorted scaling laws are provided to predict each corresponding property. Moreover, an algorithm of designing the MT plate is proposed and a design process is summarized in steps. Finally, an example, where the prototype VT plate is made of 42 CrMo and the MT plate is made of NO. 45 steel, is discussed to validate the proposed design method, showing that the MT plate, which is designed by using the proposed method, can accurately predict the dynamic properties of the prototype VT plate, and showing its significance in engineering practice.  
variable thickness; thin plate; transfer matrix; distorted model; scaling laws +
Footnote †: journal: Applied
supervised multiclass methodologies have been implemented. These methodologies employed several algorithms, frequently including random forest models [5; 6; 7] and light gradient-boosting machine (Light GBM) [8], to distinguish between different types of vessels. In the latter case, a classification model was effectively implemented using 60-dimensional feature vectors as input, although its application was confined to just three distinct categories of vessels. These feature vectors encapsulated various metrics derived from AIS trajectory data, such as the mean, first quartile, median, third quartile, standard deviation, and coefficient of dispersion associated with changes in speed, course, longitude, latitude, and displacement.  
From the studies examined so far, it emerges that current approaches mainly rely on the creation of a single, albeit complex, neural network to which the entire task of recognition is delegated. From an innovative perspective, this paper proposes an alternative strategy rooted in the idea of building an ecosystem of neural networks, diverse in both topological and mathematical terms, and not based exclusively on gradient descent or decision trees. In this regard, each network is purposefully designed to focus on a particular statistical subset of ship trajectories, resulting in an enhancement in performance.  
More specifically, this study has focused on the need to provide the Italian Navy with an accurate and efficient solution for monitoring and classifying ship trajectories, employing AIS signals in the context of the Mediterranean Sea. A significant problem arises when ships refuse to respond to AIS signals or provide potentially misleading responses, making it difficult for the Navy to correctly identify the type of ship and its intention. In this context, a solution was needed that allowed the Navy to filter and prioritize its interventions, enabling it to focus resources on cases that presented higher levels of risk or suspicion. This involved the development of a system capable of distinguishing, for example, between a fishing boat and a cruise ship based solely on trajectories, thereby improving the efficiency and effectiveness of the Navy's monitoring and response operations.  
The approach presented here aims to recognize the type of vessel among N possible classes using adaptive algorithms specifically trained for this purpose. The data considered for the feature vector include the identifier, position, instantaneous velocity, and heading of each vessel. The adaptive algorithms have been trained using data acquired from radars on multiple vessels (after excluding the identifiers). Positional data are not included as vessels may follow new routes that are not present in the database, making the algorithm less reliable. Additionally, changes in vessel positions have been eliminated due to variations in sampling frequency, which could result in unreliable acceleration and deceleration data.
# Microbial Depolymerization of Epoxy Resins: A Novel Approach to a Complex Challenge
## 3 Results
### Microbiological Water
#### 3.2.2 Isolation and Identification of Microbes  
Isolations were carried out by incubating water and epoxy samples from the tank containing the microbiological water in selective media containing the minerals necessary for microbial growth and epoxy powder, without the addition of an alternative carbon source. Based on color and colony morphologies, two fungi and three bacteria have been isolated. The presence of two distinct fungi was detected after four days of incubation. The first one (ER-F1) appeared white and woolly after four days of incubation, then after six days it turned to green, becoming more powdery and dense (Figure 8a,b). The second one (ER-F2) was characterized by black color, far less dense mycelium and the presence of visible conidiophores (Figure 8c). In addition, three bacteria were isolated from the water of the tank. Two of them (ER-BacA and ER-BacB) were growing close to each other. They had an opaque yellowish aspect, but since they had different dimensions, they were considered as distinct bacterial colonies. Another bacterial colony (ER-BacD) growing in a different zone of the Petri dish with more transparent appearance were also detected (Figure 8d).  
All isolated fungi have been successfully identified through sequencing of the ITS rDNA region and comparison with known sequences using NCBIs' Basic Local Alignment Tool (BLASTn) on the Genbank database. Sequencing data from ER-F1 showed 99.65% homology with _Trichoderma harzianum_ ITEM908 (accession number OL831174). Sequencing data from ER-F2 showed 99.44% homology with _Aspergillus calidoustus_ Kw18-1 (accession number OL831175). Identification of bacterial isolates was performed by 16S ribosomal RNA (rRNA) sequencing. Data allow to identify ER-BacA and ER-BacB both as _Variovorax_ sp. (99.61% and 100% homology with _Variovorax_ sp. 3 LII(+)_OTU11, respectively) (accession numbers OL831168 and OL831169). Finally, sequencing data from ER-BacD showed 99.62% homology with _Methylloversatilis discipulorum_ FAM1 (accession number OL831170).  
Figure 7: ESEM images of the observed surface modifications potentially induced by microbial colonization, on the older epoxy samples sampled on the microbiological water. (**a**) Epoxy surface presenting microbes and holes, (**b**) closer look at the holes and the different shape of the microbes present close to the surface modifications, (**c**) typical quasi-spherical shape of the observed microbes with depression on the surface below the cells and (**d**) closer look at the depression discovered below the microbial cells.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 2 Compressed Sensing  
Compressed sensing theory can reduce the rate of sampling, which makes it more efficient than the traditional sampling theory [4]. The signal \(\mathbf{x}\in R^{N\times 1}\) is an \(N\)-dimensional vector of sparsity \(K\), which means that it has only \(K\) non-zero coefficients in this sparse signal. We can reconstruct the length \(N\) signal from \(M(M<N)\) measurements when the measurement matrix \(\mathbf{\Phi}\in R^{M\times N}\) satisfies the restricted isometry property (RIP) [17]. RIP is a related condition, which indicates the incoherence between the rows of measurement matrix and the columns of the sparse basis. As there are small numbers of activated users in a given time, the transmitted signals from all users can be considered a sparse signal in a sparse basis with an identity matrix. The measurement vector \(\mathbf{y}\in R^{M\times 1}\) is the compressed sampling vector \(\mathbf{y}=\mathbf{\Phi}\cdot\mathbf{x}\). Based on the small vector of random measurements, the sparse signal \(\mathbf{x}\) can be recovered using a mathematical algorithm. To obtain the optimal solution, \(l_{1}\)-minmization is employed:  
\[\min||\mathbf{x}||_{l_{1}}\text{ subject to }\mathbf{y}=\mathbf{\Phi}\cdot\mathbf{x}. \tag{1}\]
## Questions concerning the Relationship between CLIL and Digital/Computer Technology
### How Have New Digital Technologies Impacted CLIL Learning?  
According to a report [11] for the European Commission based on a systematic analysis of published and unpublished literature on applying CLIL and technology to enhance language acquisition, students have benefited in various ways when digital technologies  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Approach** & **Structural/Restricted CALL** & **Communicative CALL Open CALL** & **Integrative CALL** & **TELL** \\ \hline Technology & From mainframe to mobile & PCs & Multimedia, internet & Mobile devices, tables, multiplayer games, virtual worlds \\ English teaching paradigm & Grammar translation and audio-lingual & Communicative language teaching & Content-based ESP/EAP & Communication, interaction \\ View of language & Structural (a formal structural system) & Cognitive (a mentally constructed system) & Socio-cognitive (developed in social interaction) & Structural, cognitive, socio-cognitive, adaptable \\ Principal use of technology & Drill and practice & Communicative exercises & Authentic discourse & Normalized \\ Principal objective & Accuracy & Fluency & Agency & Autonomy within community \\ View of learning & Behaviourism & Constructivism & Social constructivism/situated learning & Connectivism \\ Role of technology & Tutor & Tutee & Mediational tool & Environment resource \\ \hline \hline \end{tabular}
\end{table}
Table 1: From CALL to TELL (Reprinted with permission from Ref. [2]. Copyright 2013, [PERSON], [PERSON]) [2].  
and CLIL teaching have been combined. The advantages of incorporating technologies and CLIL instruction are divided approximately into four aspects: multiple opportunities for learning, personalized learning pathway, language competence improvement, and motivation and confidence of students.
# Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy
## 4 Discussion
### Common Learning Models  
Three particular Deep Learning models appear most frequently in the research pool in support of autonomous decision making. Firstly, \"VGG-16\" [40] is a CNN image classifier that has been trained on the \"ImageNet\" dataset [41] of over 14 million images matched to thousands of labels. VGG-16 supports wide-ranging image classification or can serve as a base for transfer learning with fine-tuning using images specific to a target drone environment. The majority of research works that adopt it or the object detection model \"YoloV3\" [42] in the research pool use it as a base for collision avoidance or object detection/distinction. The \"ResNet\" architecture [43] originates from a CNN-based paper discussing the optimisation of the \"AlexNet\" architecture [44] through the utilisation of residual layer \"shortcuts\" that can approximate the activity of entire neural layers. Similar to VGG-16, ResNet is trained on the ImageNet dataset. The benefit of ResNet's shortcuts architecture is a considerable reduction of processing overhead, resulting in efficient models with low response times but maintaining comparable accuracy. This is favourable for drone operations that require a low CPU overhead. \"DroNet\" is more specific to the area of autonomous drone navigation and applies manually labelled car and bicycle footage as training data for navigation in an urban environment. Outputs for DroNet from a single image are specific to the purposes of drone navigation, providing a steering angle, to keep the drone navigating while avoiding obstacles, and a collision probability, to let the UAV recognize dangerous situations and promptly react to them. As a purpose-built autonomous drone network, the DroNet work [22] is highly cited and used as a base network for several other papers in the research pool.