esa-sceva/satcom-chunk-collection huggingface dataset:
# Icing Mitigation by MEMS-Fabricated Surface Dielectric Barrier Discharge
## 4 Icing Experiment  
### Setup  
The icing measurements were performed at the Icing and Contamination Research facility (iCORE), a laboratory at Airbus Central Research & Technology in Munich [47]. Figure 9 shows the closed-circuit icing wind tunnel with walls consisting of three layers of spruce wood and isolation material. A radial fan accelerates the ambient air to a maximum achievable Mach number of 0.45. Through a heat exchanger cooling system the air can be cooled down to a temperature of \(T_{min}=$-40\,\mathrm{\SIUnitSymbolCelsius}$\). The icing cloud can be set with three individual atomization nozzles to liquid water contents (LWC) between 0.1 g m\({}^{-3}\) to 1 g m\({}^{-3}\) at a median volumetric diameter (MVD) of around 20 \(\mathrm{\SIUnitSymbolMicro m}\) per droplet. A Blackfly GigE machine vision camera (frame rate = 30 fps) and a normal single lens reflex (SLR) camera (Canon EOS 5D) were used for recording the experiments.  
In the experiments the airfoils with the implemented SDBDs were installed with an angle of attack of \(\alpha=$30\,\mathrm{\SIUnitSymbolCelsius}$\) against the direction of the flow, as it can be seen on the right side of Figure 9. This inclination allowed to expose the top surface with the active SDBD to the icing cloud. The electrical contacting of the SDBDs was carried out with a feed-through on the rotary axis. Styrofoam was used as a spacer to keep the high-voltage and the ground wire at a certain distance. The iCORE was started and a wind speed of \(v=$50\,\mathrm{m}\,\mathrm{s}^{-1}$\) was applied for the FR-4 measurements. The intention was to operate all experiments at the same iCORE parameters. Due to an increased aerodynamic load during the acceleration of the flow, cracks appeared in the MEMS-fabricated glass substrate. As a preventive action the wind speed was lowered for the MEMS-samples to \(v\approx$30\,\mathrm{m}\,\mathrm{s}^{-1}$\). Nevertheless, one of the cracks continued to develop and limited the time of the experiment to the moment when the crack finally resulted in an electric shortcut. The zirconia SDBDs were additionally embedded in glass fiber reinforced plastic (GFRP) as described in [32] for stabilization. After the initialization of the cooling system, a temperature of \(T=$-20\,\mathrm{\SIUnitSymbolCelsius}$\) was set. Subsequently, the water spray was started and raised to a rate of 1 mL min\({}^{-1}\) for each nozzle with 3 nozzles in total. Demineralized water was used to inhibit a nucleation of the droplets during free flight through the cold air. The supercooled droplets thus nucleate and freeze only upon impact on a surface, which represents the real aircraft icing process.
# Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions
## 2 Materials and Methods
### LKAS Control Strategy
#### 2.2.2 Feedforward Controller Based on LQR  
To eliminate the steady-state errors that occur when optimizing the front wheel steering angle using the LQR controller [21], Equation (11) is combined with Equations (15) and (17) to obtain the steady-state errors of the system:  
\[\dot{e}_{r}=Ae_{r}+C\dot{\varphi}_{r}+B\left(\delta_{f}-Ke_{r}\right). \tag{18}\]Observing Equation (18), it can be noticed that even when \(\dot{e}_{r}=e_{r}=0\), steady-state errors still exist. Therefore, an appropriate front wheel steering angle needs to be determined to set \(e_{r}=0\). By solving Equation (18), the following expression is obtained:  
\[\delta_{f}=\frac{\dot{\varphi}_{r}}{v_{x}}\Bigg{[}a+b-bk_{3}-\frac{mv_{x}^{2}}{ a+b}\Bigg{(}\frac{b}{C_{f}}+\frac{a}{C_{r}}k_{3}-\frac{a}{C_{r}}\Bigg{)} \Bigg{]}, \tag{19}\]  
where \(k_{3}\) is the gain coefficient in the first row and third column of gain matrix \(K\).
## 1 Introduction
To prove the concept, a layer of high Q ceramic powders of BaNd\({}_{2}\)Ti\({}_{5}\)O\({}_{14}\) (BNT) was deposited by EPD on the top of the carbon coated alumina. One-hundred-micrometre-thick BNT films on Al\({}_{2}\)O\({}_{3}\) substrates exhibited \(\varepsilon_{\rm r}\) and Q of 146 and 1161 at about 10 GHz when sintered at 1250 \({}^{\circ}\)C/1 h. Besides high Q values BNT films on alumina possess high thermal stability and, therefore, are potentially useful for high frequency applications [13]. To date this is the only report on EPD to produce high-Q dielectric thick films on alumina substrates. A limitation of the BaO-Nd\({}_{2}\)O\({}_{3}\)-TiO\({}_{2}\) system is the high sintering temperature (>1300 \({}^{\circ}\)C) required to densify the ceramic bulk or thick layers. Looking for low sintering temperature dielectrics with low dielectric losses and high capacitance stability tellurium-based systems stand out [14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29]. Ceramics of Te-based compounds sinter at temperatures under 900 \({}^{\circ}\)C [28; 29] and exhibit low dielectric losses (Q \(\times\) f up to 66,000 GHz for Zn\({}_{2}\)Te\({}_{3}\)O\({}_{8}\) ceramics [23]) and dielectric permittivities ranging from 10.5 for MgTe\({}_{2}\)O\({}_{5}\)[25] to 56 for Bi\({}_{2}\)TeO\({}_{6}\)[16]. The temperature coefficient of resonant frequency varies from \(-\)119 ppm/\({}^{\circ}\)C for TeO\({}_{2}\)[16] to +372 ppm/ \({}^{\circ}\)C for BaTiTe\({}_{3}\)O\({}_{9}\)[18]. For well sintered TiTe\({}_{3}\)O\({}_{8}\) ceramics, \(\varepsilon_{\rm r}\) is of 50 and Q \(\times\) f is of 30,600 GHz at a measurement frequency of 5 GHz [15]. However, there is only one report on the preparation of tellurium-based thick films by EPD [30]. In this paper we reported the low-frequency dielectric properties of the TiTe\({}_{3}\)O\({}_{8}\) films on platinized silicon [30]. Moreover, so far there is no report on the microwave characterization of Te-based thick films neither on their deposition on alumina. In the present work, we have extended our previous approach of using sacrificial carbon layers for EPD on fully non-conducting substrates, to fabricate for the first time low sintering temperature high Q dielectric Te based thick films on insulator Al\({}_{2}\)O\({}_{3}\) substrates. The dielectric characterization at high frequency suggests that these films are suitable for microlayer microstrip arrays.
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
## 2 Topological Modeling
### Regional Energy Consumption  
The process of exchanging information between distant nodes consumes a lot of energy in belt-type sensor networks. In a certain region, the life cycle of the sensor node can be approximately defined as the total energy of the region divided by the rate of energy consumption. To facilitate the analysis of energy consumption in the network area, we selected a small area which named as \"A\". In order to maximize the extension of the life cycle of the sensor networks, the period of each sensor node should be set in a relatively close range. Therefore, the ratio of the energy consumption to the total energy in each region is set to a constant. According to the above analysis, the network horizontal range is set in [\(-\)L/2, \(+\)L/2], and the gateway nodes are arranged in the symmetrical position. The network area used to analyze energy consumption is shown in Figure 3.  
Figure 3: The network area diagram for analyzing energy consumption.  
Figure 2: Node communication radius.  
Most of energy consumption in sensor networks mainly occurs during the data transmission. According to the classical signal propagation theory [9], the energy consumption of the transmitted information \(E_{T}(l,d)\) can be expressed as Equation (1):  
\[E_{T}(l,d)=E_{e}(l)+E(l,d)_{\text{amp}} \tag{1}\]  
where \(l\) is the length of the packet, \(d\) is the transmission distance, \(E_{e}(l)\) is the energy consumption of the circuit, \(E(l,d)_{\text{amp}}\) is the energy consumption parameter for the emitter. The energy consumption of the received information \(E_{R}(l)\) can be expressed as Equation (2):  
\[E_{R}(l)=lE_{e} \tag{2}\]  
From the Equations (1) and (2), we can achieve the total energy loss rate of the whole region. As shown in Equation (3).  
\[(E_{R}+E_{T})\int_{r+\frac{l}{2}}^{\frac{1}{2}}\lambda dx+E_{T}\int_{r-\frac{l} {2}}^{r+\frac{l}{2}}\lambda dx \tag{3}\]  
The energy consumed by sending and receiving unit data are represented by \(E_{T}\) and \(E_{R}\) respectively. \(t\) is the communication distance of a single node. \(r\) stands for the distance between the reference node and the base station. When \(r\) is not equal to \(t\) and \(r>>t\), the node density of the region (\(\rho(r)\)) is approximately \(2n(\text{L}-2r)/L^{2}\). In summary, if the sensor node is closer to the gateway node, its distribution density should be larger. Because the node which is closer to the gateway node not only needs to send the collected perceptual data, but also to forward the information collected from others. Theoretically, more nodes need to join the network to share the communication consumption, achieve balanced load and extend network survival time.
## 2 Related Works
### Research on Intelligent Data-Driven Fault Diagnosis
[43] proposed a reconstruction domain adaptation transfer network, which contributes to identify health conditions and extract domain-invariant features. The recent advances in domain adaptation approaches have been well reviewed [44; 45], where the adversarial transfer network has achieved high test accuracy on fault transfer tasks across domains. Although the domain adaptation technology in the existing fault diagnosis methods have developed rapidly, most methods generally assume that all labels of source domain training data are available during training process. However, it is almost impossible to satisfy this assumption in the real industry. Currently, little research can be found in diagnosis studies of artificial label generation in source domain data. This paper aims at bridging the distribution gap and addressing the cross-domain problem of insufficient source domain labels under diverse operating conditions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Technologies** & **References** & **Methodologies** \\ \hline Deep learning & [PERSON] et al. [24] & A transfer approach with the TrAdaBoost algorithm for bearing fault diagnosis. \\  & [PERSON] et al. [28] & A deep transfer diagnosis framework for dealing with limited sparse target data. \\  & [PERSON] et al. [29] & A deep learning-based diagnosis approach for ball screw across domain. \\  & [PERSON] et al. [30] & The DDTLN model for intelligent cross-machine fault transfer diagnosis. \\  & [PERSON] et al. [31] & A deep multisource transfer learning model for bearing fault diagnosis. \\ Maximum mean discrepancy & [PERSON] et al. [33] & A feature-adaptive motor fault diagnosis method applying MMD as \\  & [PERSON] et al. [34] & A diagnosis model based on polynomial kernel induced MMD reused for detection knowledge across machines. \\  & [PERSON] et al. [35] & A DNN model for domain adaptation in fault detection minimizes the distribution discrepancy between data by maximizing MMD. \\  & [PERSON] et al. [36] & A sparse filtering domain adaption approach that incorporates l1-norm and l2-norm for maximizing MMD. \\  & [PERSON] et al. [37] & An indirect sensing fault diagnosis method for domain alignment by minimizing MMD marginal distribution. \\ Domain adversarial learning & [PERSON] et al. [38] & A machinery diagnosis method for partial domain adaptation using conditional data alignment and unsupervised prediction consistency. \\  & [PERSON] et al. [41] & A hybrid fault diagnosis model based on DAGAN and DANN utilizing a few data to accomplish domain adaptation. \\  & [PERSON] et al. [42] & A spatial subdomain adaption graph convolution neural network applying adversarial domain adaptation and local MMD to reduce structural discrepancy between domains. \\  & [PERSON] et al. [43] & A reconstruction domain adaptation transfer network for cross-domain invariant features extraction and equipment health condition detection. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of relevant literature on fault diagnosis applications.
## 5 Study on the Limit of the Fundamental Lateral Frequency
### Discussion on the Limit of the Fundamental Frequency  
It is necessary to analyze their acceleration response to evaluate whether structures with different stiffnesses produce excessive vibration under pedestrian load. To study the influence of structural stiffness on the structural response of the MTTS, we used the walking load in Figure 17 as the excitation condition. The German EN03 (2007) code stipulates that the lateral acceleration of the structure is not more than 0.3 m/s\({}^{2}\) from the perspective of pedestrian comfort, and the lateral fundamental frequency limit of the straddle PC structure without pedestrians in the Code for Design of Straddle Monorail Traffic GB50458-2008 is 70/L (\(L\) represents the bridge span). This paper also considered avoiding the sensitive frequency and limiting the dynamic response. Through finite element analysis, it explored the value of the lateral fundamental frequency of the structure when the lateral acceleration was just below 0.3 m/s\({}^{2}\) to ensure that the pedestrian comfort reached the \"comfort\" standard. In combination with the horizontal fundamental frequency value specified in GB50458-2008 Code for Design of Straddle type Monorail Transit, the larger value of the two was taken as the lower limit of the fundamental lateral frequency of the span straddle type monorail tourism transportation system from the perspective of safety. For the span length of 18 m, the time history curve of the lateral acceleration response of the structure in the span with different lateral stiffnesses is shown in Figure 18. The lower limit of the fundamental lateral frequency of different spans is shown in Table 4.  
Figure 17: Time history of walking load.  
It can be seen from Table 4 that, at the same span, with the increase in the lateral stiffness of the structure, that is, the increase in the lateral fundamental frequency, the lateral acceleration response in the middle of the span decreased. For MTTS, this paper suggests that the lower limit of the fundamental frequency for the span length of 15 m is 5.0 Hz, for 18 m it is 3.5 Hz, and for 25 m it is 2.8 Hz.
## 2 Materials and Methods
### Degree of Fragmentation from DLR TanDEM-X  
The TerraSAR-X add-on for Digital Elevation (TanDEM-X) is a German radar satellite from the German Aerospace Center (DLR) and the first bistatic SAR mission. The aim of this mission was to generate a consistent global digital elevation model with high accuracy. TanDEM-X, launched in 2007, and TerraSAR-X, launched in 2010, were the first configurable Synthetic Aperture radars (SAR) in space. Both radar satellites moved around the Earth at an altitude of 500 km and flew in close formation [17; 19].  
For this study, data from the TanDEM-X global binary forest/non-forest Map (FNF), developed by the Microwaves and Radar Institute at the German Aerospace Center (DLR), were used [20]. The available data of TanDEM-X were captured between 2011 and 2016 using the stripmap single polarization (HH) mode of the TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) with a spatial resolution of 50 \(\times\) 50 m and divided into 1\({}^{\circ}\) by 1\({}^{\circ}\) geocells. Pixels flagged as urban areas and invalid pixels were classified as non-forest [17; 20].  
To determine the distance of individual forest areas from the forest edge, the Hoshen-Kopelman algorithm was used [21]. This cluster detection algorithm has already been used in [4; 5; 22] for fragmentation analysis and forest edge detection. The connection between a forest pixel and forest in neighboring pixels was determined assuming a four-pixel neighborhood. An additional feature of this algorithm is that it determines the distance of each forest pixel to the nearest forest edge. This additional feature was already implemented and tested in [5]. These estimated distances make it possible to distinguish forest core areas from forest edge areas. Forest edge forest areas correspond to forest pixels with a distance of less than 100 m to the forest edge. If a non-forested area, e.g., a river or a road, is wider than 50 m, the neighboring forest borders are considered as edges. It should be noted that the algorithm cannot distinguish between anthropogenic and natural edges. The algorithm is implemented as a C++ program and calculates the distances as efficiently as possible. Further details of this forest fragmentation algorithm are explained in [PERSON] et al. 2021 [5].
# EGMStream Webapp: EGMS Data Downstream Solution
## 2 Materials and Methods  
The upgrading of EGMSteam from the R-based desktop application to a web application involved a complete redesign of the software architecture, aimed at enhancing accessibility and operational efficiency. This transformation also improved scalability, enabling the system to support an increasing number of users and process larger datasets seamlessly by leveraging web-based and cloud resources, ensuring robust performance even under high demand. The EGMSteam webapp has been developed using a client-server architecture, where the backend and frontend collaborate to deliver effective and interactive data management. Specifically, the webapp is structured into two main components: the backend, which handles all functionalities and processes, and the frontend, serving as the interface between the backend and the user, ensuring a smooth and user-friendly experience.  
An additional enhancement has been made through the use of a Docker container. Docker is a software platform that enables developers to build, deploy, and run applications within lightweight, portable containers [52]. These containers encapsulate everything needed for an application to run, including code, runtime, libraries, and dependencies, ensuring consistency across different environments, from local development to cloud servers. By isolating applications within containers, Docker eliminates dependency conflicts and ensures that applications behave consistently, regardless of the host system. Containers are more resource-efficient than virtual machines, starting quickly and consuming fewer resources, making them ideal for scalable deployment. Furthermore, Docker enhances security by isolating applications, thereby reducing the risk of interference or vulnerabilities spreading between them. In essence, Docker streamlines the development and deployment process, providing a reliable, portable, and efficient framework for managing applications.  
The core functionality of the EGMSteam has been enhanced while maintaining its user-friendly nature and minimising the number of user inputs required. Indeed, the webapp structure involves downloading a textual (_txt_) file from the EGMS Explorer and configuring several parameters to initiate the extraction and conversion of EGMS data. Once the process is completed, the user receives an email for downloading the converted data (Figure 1).
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 6 Discussion  
This study aims to analyze the important parameters required for bacteria to produce H\({}_{2}\) through dark fermentation. The analysis showed the influence of pH, temperature, HRT, substrate used, and its concentration on the ability of bacteria to produce H\({}_{2}\) as a product of their metabolism. Table 8 shows the advantages and disadvantages observed during the analysis of each strain for H\({}_{2}\) production.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Strain** & \begin{tabular}{c} **Substrate** \\ **(Concentration, g/L)** \\ \end{tabular} & & & **Conditions** & & \begin{tabular}{c} **Operation Mode** \\ **(Reactor Volume, mL)** \\ \end{tabular} & \begin{tabular}{c} **H\({}_{2}\) Yield as Mol** \\ **H\({}_{2}\)-mol Substrate \({}^{*}\)** \\ \end{tabular} & **Ref.** \\ \hline \hline \multirow{3}{*}{K-12} & Garden waste & \
\begin{tabular}{c} **T** \\ **(e\(\cdot\)O)** \\ \end{tabular} & 33 & & & **Purged with argon** & Batch (100) & 2.73 (cellulose) & [143] \\  & & & & & **Purged with 95\%** & & **Batch (69)** & 0.21 & [144] \\  & & & & & **N\({}_{2}\)** & & **Batch (69)** & 0.21 & [144] \\  & & & & & **N\({}_{2}\)** & & **1.15-1.73 mol H\({}_{2}\)/mol & [145] \\  & & & & & **200 rpm** & & **Batch (10)** &
\begin{tabular}{c} 1.15-1.73 mol H\({}_{2}\)/mol \\ substrate \\ \end{tabular} & **Ref.** \\ \hline \multirow{3}{*}{WDHFL} & Glucose (15) & & & & & **175 rpm** & **Batch (1000)** & 1.02 (boxes) & [146] \\  & & & & & & **1.12** & \\  & & & & & & **1.05** & \\  & & & & & & **1.05** & \\  & & & & & & **1.17 (fructose) & \\  & & & & &  
The treatment of wastewater and simultaneous production of H\({}_{2}\) using a biological method is a very realistic approach, especially when operating costs, energy losses, or fees per volume of water treated are critical factors. _Clostridium_ spp. has been preferred for H\({}_{2}\) production, as shown in several studies, due to its ability to grow under varied conditions. A pH range of 5-7, a temperature of around 30-37 \({}^{\circ}\)C for mesophiles, and around 60 \({}^{\circ}\)C for thermophiles are widely used for the dark fermentation process. The highest H\({}_{2}\) production obtained from different species is shown in Table 9.