esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-For-Mobility--A New Research Platform for AI-Based Control Methods
###### Abstract  
AI-For-Mobility (AFM) is the new research platform to investigate and implement novel control methods based on Artificial Intelligence (AI) within the Department of Vehicle System Dynamics at the German Aerospace Center (DLR). A production hybrid vehicle serves as a base platform. Since AI-based methods are data-driven, the vehicle is equipped with manifold sensors to provide the required data. They measure the vehicle's state holistically and perceive the surrounding environment, while high performance on-board CPUs and GPUs handle the sensor data. A full by-wire control system enables the vehicle to be used for applications in the field of automated driving. Despite all modifications, it is approved for public road use and meets the driving dynamics properties of a standard road vehicle. This makes it an attractive research and test platform, both for automotive applications and technology demonstrations in other scientific fields (e.g., robotics, aviation, etc.). This paper presents the vehicle's design and architecture in a detailed manner and shows a promising application potential of AFM in the context of AI-based control methods.  
automated test vehicle; AI-based control methods; test vehicle design; vehicle dynamics; holistic vehicle instrumentation +
Footnote †: journal: Applied  
operation, a drive-by-wire (DBW) kit was designed for the vehicle. It allows to drive defined and reproducible maneuvers, and thus to systematically analyze the implemented AI-based control algorithms. Since AI-based methods are data-driven, the vehicle is equipped with a variety of sensors to measure the states and internal values, as well as environmental quantities as, e.g., the road condition and the surrounding scenery. The vehicle sensors are synchronously logged on a rapid control prototyping system [3].  
The development of algorithms for vehicle dynamics functions is a broad field. A classification can be made using the pyramidal control architecture presented in Figure 2. It comprises three layers related to the corresponding fields of application as follows.
# Biological and Technical Challenges Related to Cultured Meat Production
## 4 Technical Challenges in Cultured Meat Production
### Alternatives to Scaffold Fabrication  
#### 4.2.1 Tissue Decellularization  
An alternative approach to engineered scaffold is plant or tissue/organ decellularization. This approach is based on the removal of resident cells and a large proportion of the major histocompatibility complex from a tissue in order to obtain a natural scaffold to be seeded. In this way, the ECM structure is preserved [87]. There are several successful examples in tissue engineering regeneration that follow the so-called \"like-to-like\" strategy, in which donor and regenerated tissues are of the same type [86]. Nevertheless, in the case of tissue/organ decellularization, the tissue procurement would require the use of animal-derived tissues, completely in contrast with the fundamental prerequisite of cultured meat.
# Numerical Simulation of the Influence of the Baihetan Reservoir Impoundment on Regional Seismicity
## 1 Introduction
XJF1 and XJF2 represent the northern segment and southern segment of the Xiaojiang Fault in Table 1.  
Baihetan is located in the southern Daliangshan sub-block, near the junction of the Zemuhe and Lianfeng faults [22]. Most of the nearby faults are high-angle strike-slip. There is also the thrust Longmenshan fault in the east-north direction (Table 1). [PERSON] et al. (2021) [23] suggested that the seismicity level around the reservoir was quite low before the impoundment of the Baihetan Reservoir but it significantly increased after the impoundment, and \(M\geq 3.0\) earthquakes occurred at the same time (Figure 1c). Therefore, it is very important to carry out further research on the relationship between reservoir impoundment and regional seismicity in the Baihetan Reservoir. In general, seismicity results from the change of reservoir water level or impoundment, which is called a reservoir-induced earthquake. These earthquakes are different from normal tectonic earthquakes, and their occurrence times are strongly correlated with reservoir impoundment. Usually, microquakes occurred immediately or several months after the reservoir began to store water, while larger shocks occurred one or more years later. Different impoundment processes for reservoirs with different geological conditions affect the occurrence time and locations of larger shocks; [PERSON] et al. (2022) [24] studied and predicted the potential seismicity changes caused by the Baihetan Reservoir before its impoundment, using the poroelastic equation and an assumed water storage model. However, after the impoundment of the Baihetan Reservoir, there have been few studies on seismicity using its actual impoundment model. Furthermore, the long-term effect of reservoirs on seismicity has not been studied. There are still many problems to be discussed and studied, such as what will be caused by the continuous downward infiltration of reservoir fluid and whether long-term loading will affect seismicity. This study proposed a new pore-viscoelasticity coupled mechanism, derived physical equations, and developed a finite element program to study the influence of the long-term effect of a reservoir on seismicity. We established a three-dimensional finite element model for the Baihetan area, calculated Coulomb failure stress on the major faults, analyzed seismicity changes using water level data as the boundary condition, and discussed the long-term influence of the Baihetan Reservoir on seismicity. This study may provide a theoretical basis and valuable reference for future reservoir-earthquake hazard mitigation.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 5 Performance Evaluation
### Average Channel Utilization Per Data Frame Transmission  
In order to have a successful data frame transmission, a tagged WE spends \(1/(\gamma_{tr}\gamma_{s})\) average number of slot times on the communication channel. Of those average slot times spent, \((1-\gamma_{tr})\) is the time when channel is observed as idle and each idle slot time lasts for \(\sigma\). Figure 7a plots the average number of idle slot times per data frame transmission for two different \(W_{min}\) values with varying network density. The results show that for \(W_{min}=32\) and \(W_{min}=64\), the idle slot time per data frame transmission is very low for both COSB and BEB. This idle channel utilization is more significant when the initial contention window is greater, as shown for \(W_{min}=64\) in Figure 7a. More specifically, for BEB mechanisms, the idle slot time per data frame transmission drastically decreases for a dense network environment even for a large number of initial contention windows that is \(W_{min}=64\). COSB does not reset its initial contention window to \(W_{min}\) after a successful transmission and it recursively remains near the adaptive values of the contention window size according to the channel observation-based \(p_{obs}\).  
Another important measure to discuss is the average number of transmissions that a tagged WE must perform for a successful transmission of a data frame which is given by \(1/(1-p_{obs})\). Figure 7b shows that the number of transmission attempts per data frame transmission considerably increases as the size of the initial contention window decreases, that is \(W_{min}=32\), especially for BEB when the network size is \(n=50\). The figure shows that for \(W_{min}=32\), BEB WEs suffer an average 2.1 retransmissions (collisions), and COSB WEs suffer an average 1.5 retransmissions (collisions) when network density is \(n=50\). The increased idle slot times (Figure 7a) and reduced number of retransmissions (Figure 7b) for dense networks are the reason for efficient saturation throughput achievement of COSB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**WEs (n)** & **Max. Approx. Throughput** & **COSB** & **BEB** \\ \hline
5 & 0.510 (\(\gamma_{opt}=0.0573\)) & 0.493 (\(\gamma=0.034\)) & 0.468 (\(\gamma=0.048\)) \\
10 & 0.502 (\(\gamma_{opt}=0.0287\)) & 0.501 (\(\gamma=0.024\)) & 0.452 (\(\gamma=0.037\)) \\
20 & 0.498 (\(\gamma_{opt}=0.0143\)) & 0.498 (\(\gamma=0.016\)) & 0.421 (\(\gamma=0.026\)) \\
30 & 0.497 (\(\gamma_{opt}=0.0095\)) & 0.493 (\(\gamma=0.012\)) & 0.401 (\(\gamma=0.020\)) \\
40 & 0.497 (\(\gamma_{opt}=0.0072\)) & 0.488 (\(\gamma=0.010\)) & 0.381 (\(\gamma=0.017\)) \\
50 & 0.497 (\(\gamma_{opt}=0.0057\)) & 0.484 (\(\gamma=0.008\)) & 0.365 (\(\gamma=0.015\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of maximum approximate saturation throughput achieved from \(\gamma_{opt}\) with throughputs of COSB and BEB.  
Figure 7: (a) Average number of idle slot times per successful data frame transmission; (b) average number of transmissions per data frame.
## 2 Related Work
### Transfer Learning in RSSC  
The purpose of transfer learning is to leverage knowledge from the source domain to boost the learning ability in the target domain [32; 33; 34]. Pretrained models on large-scale datasets have been reported to have better generality than randomly initialized models. Fine-tuning [35; 36] is one powerful method for transfer learning. For RSSC tasks, the majority of state-of-the-art approaches rely on a paradigm consisting of pretraining on ImageNet and then fine-tuning on the target dataset. The marginal center loss [37] was proposed to overcome the challenges presented by large intraclass variations. [PERSON] et al. [38] proposed a Siamese CNN that combines identification and verification models based on CNNs. A multisource compensation network (MSCN) [39] has been proposed to address the problems of distribution discrepancy and category incompleteness by using pretrained CNNs. Based on bilinear pooling [40] and hierarchical attention [41], [PERSON] et al. [5] presented a feature fusion framework for RSSC. Most of these advanced approaches do not use the CNN backbone directly but build on it with a lot of careful design.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 4 Isara
### Operations  
After deployment of the CubeSat from the International Space Station, a five-month mission was used to test the Ka-band antenna. As mentioned before, the spacecraft was also equipped with a UHF telecommunication system which was used to communicate with the UHF ground station owned and operated by Aerospace Corporation [12].  
The Ka-band operations were instead performed through a customized Ka-band receiver station developed and operated by the Jet Propulsion Laboratory. The Ka-band station was very simple and designed with mostly off-the-shelf components. It included a 70 cm parabolic dish, low noise amplifier, filter, downconverter to intermediate frequency, filter, amplifier and then an open-loop recorder [12]. The JPL recorder was used to acquire and then measure the Signal-to-Noise-Ratio (SNR) and the gain of the antenna. The HGA gain was measured by rapid switching (\(\sim\)1 Hz) between the HGA and the SGA in a way that was not influenced by atmospheric attenuation, space loss, receiver stability [12]. The CubeSat was also slewed in orbit to allow for measuring of the antenna pattern. The pointing system on board was designed to achieve 0.2 deg precision. The pointing accuracy on the ground was achieved by retrofitting the Ka-Band station with a positioner previously used for the Deep Space Network (total measured was uncertainty 0.8 dB, [12]).  
During each measurement session (telecommunication pass), the ground receiver recorded data including HGA and SGA signal level as well as a record of ground station pointing. The UHF telemetry data, transmitted through the UHF channel provided information on the spacecraft antenna pointing which was then used to reconstruct the antenna pattern. These measurements confirmed the antenna gain results obtained by ground testing through a series of conical scans that were used to search for the main beam [12].
## 3 Experimental Results and Analysis
### A Comparison with the Current State-of-the-Art Works
The security analysis confirms that our scheme produces a secure image encryption result to become the current state-of-the-art work.
## 5 Satellite Remote Sensing Techniques for Infrastructure Monitoring
### Stand-Alone Applications of Satellite Remote Sensing for Infrastructure Monitoring
#### 5.3.4 Railways
In general, the integration of satellite remote sensing and ground-based techniques into data-integration and data-fusion algorithms stands as a challenging topic for further investigation in the systematic monitoring of infrastructure assets.
# Temperature Estimation Adaptive to Variables over Distance Using Infrared-LiDAR
## 1 Introduction
As mentioned above, conventional approaches to temperature estimation have attempted to estimate accurate temperature by means of geometric compensation, compensation for atmospheric transmittance, and compensation for the surrounding environment such as atmosphere, sun, and dust. However, there is a limit to estimating accurate temperature by considering various variables with only the measured digital count. In this paper, we propose a new method for accurate temperature estimation using long-wave infrared cameras and LiDAR. Unlike previous studies, distance information using LiDAR is additionally required to use the proposed method. The proposed method first obtains a digital count for a specific distance and builds a Radiation-Digital count mapping function for each distance through this. The mapping function constructed by distance reflects the effects of various variables (atmospheric and solar Radiation, emissivity, atmospheric transmittance, etc.) by distance. It is used to estimate the exact temperature by obtaining the distance to the object and the digital count for the object. If an object exists at a specific distance where the mapping function is not established, it is solved through linear interpolation. However, the proposed method also has limitations. In fact, the effects of CO\({}_{2}\) absorption and water vapor are large in measurements made by infrared cameras. Therefore, the digital count is affected a lot by the air temperature and water vapor between the object and the camera. Therefore, if these conditions change, there is a limitation that recalibration is required. The contribution of this paper can be summarized as follows:  
1. We analyze Radiation-Digital count mapping and blackbody radiation in the long-wave infrared band. 2. We propose a method to estimate accurate temperature through Temperature-Radiation and Radiation-Digital mapping by distance. 3. We verify that the proposed method accurately estimates the temperature of a blackbody over distance, and through body temperature experiments, we establish the possibility of accurate body temperature measurement with future LiDAR-Infrared camera fusion. The remainder of this paper is organized as follows. Section 2 explains the [PERSON]'s law and radiation-digital counts mapping function as background knowledge. Section 3 explains the proposed method, a mapping function for each distance, and a mapping function using interpolation. Section 4 evaluates the accuracy of the proposed method for each distance through the temperature of the blackbody, and shows that it is possible to accurately estimate the temperature by fusion of LiDAR-Infrared cameras in the future through the experiment of measuring body temperature according to the distance. The paper concludes in Section 5.
## 2 Machine Translation Approaches
### Rule-Based Machine Translation  
RBMT is the earliest method of machine translation. It is a system that relies on linguistic information about the target and source languages that are retrieved from traditional language sources such as dictionaries, grammar rules, etc. This information includes lexical, syntactic, semantic, and morphological congruities of both languages. RBMT then performs a morphological analysis on the grammatical structure of both the source and target languages using a parser to construct an output [6]. Due to the number of language rules and variations of forms for linguistic structures, building a complete rule-based system is a prohibitively long and rigid process.  
Moreover, RBMT requires manual placement of some linguistic information, and significant post-amendments made by human experts are needed to obtain a sufficient output. As discussed earlier, many types of ambiguities also make it infeasible when working with large systems, and improvements depend on more hard-coded rules and human involvement [6]. With the emergence of SMT and NMT, RBMT has become the least common method of translating text, though there are still many online translation systems existing that use a rule-based approach. Some depend on less complex and ambiguous sentence structures such as inputs to perform well and thus are not reliable for more general use cases.
## 4 Results  
### Total Magnetic Field and Reduction to Pole  
The data interpretation process starts with the calculation of the Total Magnetic Field (TMF) from the filtered records (Figure 4A), followed by the application of the Reduction to Pole (RP). By combining the magnetic inclination and declination data in the sampling area, together with the use of the International Geomagnetic Model Reference Magnetic Field (IGRF), it is possible to obtain a direct representation of the primary magnetic sources responsible for the generating bodies (Figure 4B) [26; 27].  
By examining both representations, a variation in the magnetic field that delimits the study area into two distinct formations stands out. Formation 1 is composed of metagranites, quartz schists and/or ultramylonites (target formation), while Formation 2 corresponds to orthogneisses (very impermeable formation with low hydrogeological possibilities).  
The results reveal a disparity of up to 20 nT in the magnetic field between these two geological structures. Interpretation of these data suggests that the lower magnetic field values are linked to the metagranites, quartz schists and/or ultramylonites that make up Formation 1 due to their lower magnetic propensity, compared to the orthogneisses of Formation 2. [28; 29] This conclusion is strengthened by the location of nearby drill holes, which indicate a higher permeability in Formation 1.  
A critical aspect is the precise identification of the contact point between these two geological structures. The proximity to this transition zone is crucial, as a decrease in flow is anticipated due to the limited aquifer capacity of Formation 2.  
This analysis provides valuable information to guide decisions in planning the exploitation of groundwater resources. This enables a more informed choice in the selection of sites for well drilling and a deeper understanding of the local hydrogeology, thus enriching the knowledge base for sustainable groundwater resources management.  
Figure 4: Total magnetic field (**A**) and pole reduction (**B**).
## 3 Results
### Comparison with Other Cell Imaging Tools and Segmentation Methods
These results were achieved by using the same settings in the pipeline, further showing that ACDC is a reliable method that can also be easily tuned to obtain satisfactory segmentation and cell counting outcomes on different datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Dataset** & **Pearson Coeff. (\(p\)-Value)** & **DSC (\%)** & **IoU (\%)** \\ \hline ACDC & VU & \(\rho=0.99\)\((p=6.6\times 10^{-74})\) & 76.84 \(\pm\) 6.71 & 62.84 \(\pm\) 8.58 \\ ImageJ (with Gaussian filter) & VU & \(\rho=0.99\)\((p=9.5\times 10^{-75})\) & 74.97 \(\pm\) 6.17 & 60.32 \(\pm\) 7.62 \\ ImageJ (without Gaussian filter) & VU & \(\rho=0.99\)\((p=5.3\times 10^{-44})\) & 74.43 \(\pm\) 6.20 & 59.62 \(\pm\) 7.61 \\ \hline ACDC & DSB & \(\rho=0.96\)\((p=2.6\times 10^{-169})\) & 88.64 \(\pm\) 7.41 & 80.37 \(\pm\) 10.58 \\ ImageJ (with Gaussian filter) & DSB & \(\rho=0.97\)\((p=5.7\times 10^{-205})\) & 86.50 \(\pm\) 6.86 & 76.78 \(\pm\) 9.66 \\ ImageJ (without Gaussian filter) & DSB & \(\rho=0.97\)\((p=3.4\times 10^{-207})\) & 86.68 \(\pm\) 7.75 & 77.23 \(\pm\) 10.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ACDC and the tested ImageJ pipelines in terms of cell nuclei counting and segmentation. The results are expressed as mean value \(\pm\) standard deviation. Figure 11: Regplots showing the scatter plots obtained by considering the number of manually detected cells (y-axis) and the number of cells automatically detected (x-axis) with ACDC (left), ImageJ with Gaussian filter (center), and ImageJ without Gaussian filter (right), along with the fitted regression model (regression line and the 95% confidence interval for that regression). Plots (**a–c**) report the results obtained on the VU dataset, while plots (**d–f**) are obtained from the DSB dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **DSC (\%)** & **IoU (\%)** \\ \hline ACDC & 80.92 & 67.96 \\ CellProfiler & 78.12 & 64.10 \\ MC-Watershed & 86.54 & 76.27 \\ SM-Watershed & 83.10 & 71.09 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of ACDC, CellProfiler, MC-Watershed, and SM-Watershed in terms of cell nuclei segmentation on the SNPHEP-2 dataset. Figure 12: Examples of cell nuclei segmented by using ACDC and the implemented ImageJ pipelines with and without Gaussian filtering. (**a,b**) present images taken from the VU dataset, with different visual characteristics.
# Improving Short Term Clock Prediction for BDS-2 Real-Time Precise Point Positioning
## 3 Realization of the Improved Model
### Determination of Periodic Terms  
[PERSON] indicates that significant periodic characteristics are found in BDS-2 satellite clock residuals after quadratic polynomial fitting and periodic terms vary from satellite to satellite due to different geometric configurations [12]. In order to find out the potential periods for each BDS-2 satellite clock, FFT is applied to analyze the clock residuals after quadratic polynomial fitting using more than 100 days data from day 001 to day 105 in 2018. The relationships between amplitude and frequency are identified for GEOs, IGSOs, and MEOs, respectively in Figure 4, and the outstanding periods of each BDS-2 satellite clock are given in Table 1. Results show 24 h and 12 h are two significant periodic terms for most of GEOs and IGSOs with different amplitudes. As for MEOs, a 12 h harmonic with a smaller  
Figure 3: Preprocessing for satellite clock offsets. The raw data series are presented in black, while a cleaned time series getting rid of abnormal data are in red.  
amplitude than for GEOs and IGSOs is noticeable. In general, the most noticeable periods of BDS-2 satellite clocks are related to their corresponding orbit periods. Interestingly enough, a special periodic term of about 1.3 h has been detected with a pronounced amplitude for the C11 satellite, and it is different from the normal period of the MEO orbital geometric configuration. This obvious period will have a large impact on prediction accuracy and must be considered. This noticeable period may be caused by the hardware of the atomic clock itself. Additionally, due to the uneven distribution of ground tracking stations and the relatively stable geometry between the GEO satellites and stations, the amount of data of C04 is less than other satellites, which is probably the reason that the residual noise level is large, and it leads to a saw-tooth pattern when doing FFT.  
Figure 4: Frequency analysis for BeiDou satellite navigation system (BDS)-2 clocks.