esa-sceva/satcom-chunk-collection huggingface dataset:
# FM-UWB: Towards a Robust, Low-Power Radio for Body Area Networks
## 6 Conclusions  
The FM-UWB has been recognized as a technique that finds use in applications such as WBANs due its very low power consumption, potential for miniaturization and low complexity. Although FM-UWB receivers can never achieve as low power as the narrow-band wake-up receivers, they compensate with small size, robustness in multi-path channels and resilience against interferers. The FM-UWBs became part of the IEEE 802.15.6 standard; however, the standard specifications limit the use of FM-UWB to the very basic case of the 2-FSK sub-carrier modulation. If a small increase in the complexity of the receiver can be afforded, FM-UWB can achieve higher data rates, either by simply increasing the speed and the sub-carrier frequency or by using higher order modulations such as 4-FSK and 8-FSK. The FM-UWB also provides support for communication on multiple sub-carrier frequencies through SC-FDMA and, with a minor modification of standard FM-UWB, multi-channel transmission.  
All of the aforementioned techniques could prove to be useful in future applications. The SC-FDMA allows multiple transmitters to transmit either to a single receiver or to different receivers at the same time, without a significant performance loss. It can also be combined with the multi-channel transmission to speed up communication in both directions. Finally, a relatively simple configurable transceiver could be implemented that would allow for trading a number of channels for the data rate per channel in order to conform to the specific needs of the application. The methods described here could eventually provide the basis for implementation of more advanced techniques at higher levels of abstraction, such as, for example, channel hopping [30], and generally lead to more optimal system performance.  
This work is partly funded by the Nano-Tera national research program under Swiss National Science Foundation (SNF) Grant No. 20 NA21-143070.  
[PERSON] carried out the analysis and the experimental work. [PERSON], [PERSON] and [PERSON] contributed to the writing of this paper.  
The authors declare no conflict of interest.
## 3 Results  
### SSR Marker Statistics  
A total of 21 pairs of SSR markers were selected for screening the polymorphisms among 10 broccoli resources with multiple CR genes. In total, 90 polymorphic bands were obtained (Table 3). The percentage of polymorphic bands (PPB) of each marker pair was 100%. High polymorphism proved that the tested materials belonged to two cultivated resources: diploid (2\(n\) = 18, CC) and allotetraploid (4\(n\) = 48, AACC). The number of fragments simplified by each marker varied from 1 to 10, with an average of 4.29. The average PIC, which is a measure of heterozygosity, was as low as 0.16 (LTSSR180) and as high as 0.95 (LTSSR712), with an average of 0.41. There were 6 SSRs, LTSSR584, LTSSR712, LTSSR785, BoSF2421, BoSF2435, and BoSF2860, with high PIC values greater than 0.50, sharing 28.57% of the total polymorphic SSR markers. Six pairs of markers (LTSSR13, LTSSR81, LTSSR94, LTSSR180, LTSSR340, LTSSR688) with more amplification bands were selected, and the amplification results are shown in Figure 1.  
Figure 1: The PCR results of some SSR markers in 10 broccoli genotypes. M, D500 Marker, letters (**a–f**) represent markers LTSSR13, LTSSR81, LTSSR94, LTSSR180, LTSSR340, and LTSSR688, and numbers 1 to 10 represent B368, B578, B606, B611, B831, B910, B936, B1019, B1024, and B1027, respectively.
## 4 Discussion  
In this work, a performance analysis in terms of ABER was first presented for SISO and SIMO MMR TOW links with spatial diversity and OC technique, under the presence of NZB stochastic pointing errors. Additionally, considering both transdermal pathloss together with the strict power requirements of many biomedical applications with IMDs, the average  
Figure 4: Average SNR evolution over a wide range of power spectral density of the transmitted signal for various generalized pointing errors strength and skin thicknesses.  
attainable electrical SNR was evaluated. In this context, novel analytical average SNR and ABER expressions were derived. By utilizing them, proper representative analytical results were presented and verified further through simulations over a wide average electrical SNR range, for several MRR TOW link configurations with multiple receivers, typical skin thicknesses along with varying stochastic zero boresight and NZB pointing errors. Our findings demonstrated encouraging attainable ABER values, especially when NZB boresight pointing errors were getting weaker and multiple photodetectors were employed. Additionally, apart from increasing the number of effective TOW backward links through spatial diversity, it is also crucial to bear in mind that skin thickness influences not only transdermal pathloss, but also zero-boresight and/or nonzero-boresight pointing-error effects. Furthermore, it is wise to select the operational optical wavelength within the medical optical window, more precisely around 1100 nm, in order to minimize skin-induced attenuation detrimental effects. The latter becomes even more critical for the design of MRR TOW systems, since in such cases, the light beam traverses the skin channel twice to reach the receiver's side. Consequently, it is noteworthy and also encouraging in the design of a TOW MRR system that even in the worst-case scenario investigated--including the stronger generalized stochastic pointing errors, the larger transdermal propagation distance, and the lowest PSD for the optical signal--having selected 1100 nm as the operational wavelength for each transdermal linkand having selected also appropriate skin thicknesses to place the retromodulator, we achieved adequate average electrical SNR, which can offer, as demonstrated, significant ABER enhancements, especially when spatial diversity is properly utilized.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 3 Estimation Methods for DRR, CDC Error, and Phase Inconsistency Errors  
Precise DRR, CDC, and well-calibrated phase inconsistency errors are important for focusing of multi-channel BS-SAR data. Compared with single-channel SAR, multi-channel SAR systems can achieve higher resolution or/and wider swath. As a result, the effects of DRR error on imaging are more serious and the required precision of the CDC is higher. At the same time, the spectrum aliasing brought by non-uniform sampling must be solved or circumvented before the estimation of DRR error ad CDC error, which do not exist in single-channel SAR system. Furthermore, the signal-noise-rate loss resulted from non-uniform sampling and the phase inconsistency errors between different channels increase the difficulties in estimation of DRR error and CDC error.  
There have been numerous studies of the effects of the CDC error [30] and phase inconsistency errors [21; 22; 23; 24; 25; 26; 27; 28; 29] on imaging, but little analysis of the derotation rate error. In this section, the impact of DRR error on the pre-processing is first presented, and estimation methods for the DRR error, CDC error and phase inconsistency errors are then introduced. A novel weighting strategy is followed to improve the effectiveness and robustness of these estimation methods. Once these errors are estimated, they can be corrected in the imaging procedure.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 3 Data-Partitioning Strategy Based on Grid Segmentation
### Grid Topology Reconstruction
#### 3.2.1 Coordinate-Mapping Algorithm
Each process maintains a mapping table, T\({}_{\text{realPoint}}^{\text{i}}\), to record the relationship between the coordinate point ID used by process i in the geometric model file and the ID in process i. A temporary array, T\({}_{\text{temp}}^{\text{i}}\), is required for interim mapping, as the number of points used by the current process can only be determined after all grids have been constructed. Specifically, the coordinate ID in nodeIdList\({}_{\text{i}}\) is obtained and stored as the value of T\({}_{\text{temp}}^{\text{i}}\) at the index position of pointsCnt \(-\) 1. Upon completion of grid construction,  
Figure 5: Coordinate-mapping algorithm. During the first mapping process, each mesh element is taken and its topology is mapped as an index to the process. This mapping is used only to construct the sub-mesh. During the second mapping process, the number of point sets that the current process needs to maintain is obtained first. Then, a memory copy is performed to complete the actual mapping. the temporary mapping table finalizes the mapping and copies the memory space of length pointsCnt to \(\mathrm{T}_{\mathrm{realPoint}}^{\mathrm{i}}\). The first mapping stage is utilized for sub-grid reconstruction. During the acquisition of points comprising the cell topology, duplication may occur, where a single coordinate point is shared among multiple cells. To establish a new grid topology, it is necessary to eliminate duplicate points. The second mapping stage facilitates subsequent attribute data extraction. As attribute data files store grid center and grid point data from points or units with an ID of 0 by default, it is imperative to remap the global and local coordinates. Hence, in the coordinate-mapping algorithm proposed in this paper, performing two mappings of the coordinate points is essential.
## 4 Experiment
### Effects of Static Noise  
In high-precision measurements, the external environmental effect is crucial for the measurement data. In dynamic measurements, the influence of external vibrations or the environment leads to oscillation of the sensor return data. Consequently, the data reliability of the equipment in dynamic measurement is reduced, resulting in poor repeatability. Therefore, the static noise data of the measurement system are collected by the probe and used to analyze the effect of static noise on the measurement accuracy.  
The noise observed in the measured data originates from the system's operation factors, including temperature, humidity, pressure gradient, and proportion integration differentiation control. Owing to experimental conditions, the respective frequencies of these noises remain determined; therefore, only qualitative descriptions are available, with pending quantitative analysis. Figure 9a shows the static noise data collected by the measuring head at a sampling frequency of 100 HZ for 10 s, showing its stability within 88 nm. Figure 9b shows that the amplitude of the laser interferometer's inherent noise is within 10 nm and tends to stabilize. Overall, the level of readout noise during system operation was stable and very little, both on the sub-micron scale.
# Towards 6G Satellite-Terrestrial Networks: Analysis of Air Mobility Operations
## 5 Results  
Using the methods discussed in the previous section, the link budget analysis and mobility-and-handover analysis were carried out by simulating the provided problem statement in MATLAB R2024a The results are discussed below:
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 4 Results
### Measured Accuracy  
Figure 6 shows a histogram of the fix error for a known location. This shows that the relative codephase least-squares minimisation produces accurate fixes most of the time. The median error is 27.7 m, and all results are based on 4 ms of sampled data from a GPS front end (Maxim MAX2769B Universal GPS Receiver).  
There are outliers that are caused by convergence on to a local, but not global minimum. Work is continuing on improving the performance of this; however, the results are already good enough to produce a device based on this technique. Section 4.4 outlines some techniques that may improve the robustness and accuracy of the results. This positioning error is similar to other snapshot positioning algorithms [21].  
Figure 5: Least squares intensity map of \(L_{\delta\phi}\), centered around the receiver location. Many local minima can be seen separated by approximately 300 km.
## 3 Results and Discussions
### Online Monitoring the ROUR for Industrial Wastewater Containing Potential Toxic Elements (PTEs)  
The performance of the online early-warning system based on the ROUR index was illustrated in Figure 7. Different PTEs showed distinct effects on the ROUR and the predicted ROUR by the developed toxicity model matched the experimental data. All the ROUR decreased with the increasing concentrations of PTEs but to different extent. According to the model calculation, the EC\({}_{50}\) value of Zn\({}^{2+}\), Ni\({}^{2+}\), Cr(VI), Cu\({}^{2+}\), and Cd\({}^{2+}\) on the activated sludge activity was 13.40, 15.54, 97.56, 12.01, and  
Figure 6: Impacts of pH on the ROUR.  
14.65 mg/L, respectively (Figure 7f). It is worth mentioning that the metal toxicity followed the order: Cu\({}^{2+}\) > Zn\({}^{2+}\)\(\approx\) Cd\({}^{2+}\)\(\approx\) Ni\({}^{2+}\) > Cr(VI), consistent with the results from previous studies as Cu\({}^{2+}\) > Zn\({}^{2+}\)\(\approx\) Cd\({}^{2+}\) > Ni\({}^{2+}\)[59] and Ni\({}^{2+}\) > Cr(VI) [49].  
Zn\({}^{2+}\) and Ni\({}^{2+}\) in industrial wastewater are mainly originated from electroplating in various industries including automobiles, construction, shipbuilding, light, etc. [48; 76; 77]. The predominant sources of Cr(VI) are industrial wastewater derived from the processing of chrome ore, metal surface treatment, leather tanning, printing, and dyeing [78]. High concentrations of Cu\({}^{2+}\) are often found in the discharge from electrical and electronic industries, light industry, machinery manufacturing, construction industry, and national defense industry [79; 80; 81]. The majority of Cd\({}^{2+}\) in industrial wastewater is from the activities of electroplating, pigments, plastic stabilizers, alloys, and battery manufacturing [82; 83]. Data from eight typical WWTPs of five cities in China showed that the metal concentrations in industrial wastewater are averagely 1755 mg/L for Zn\({}^{2+}\), 68 mg/L for Ni\({}^{2+}\), 317 mg/L for Cr(VI), 524 mg/L for Cu\({}^{2+}\), and 17 mg/L for Cd\({}^{2+}\), respectively [84]. Comparing to the EC\({}_{50}\) value obtained in this study (Figure 7f), municipal wastewater with 0.76% (volume) of the shock load from industrial wastewater containing Zn\({}^{2+}\) will lose 50% of the activated sludge activities and might cause the failure of WWTPs. The volume percentage of the shock load from industrial wastewater potentially resulting in the WWTP paralysis was 22.85% (Ni\({}^{2+}\)), 30.78% (Cr(VI)), 2.29% (Cu\({}^{2+}\)), and 86.18% (Cd\({}^{2+}\)). Accordingly, WWTPs are more sensitive to the shock load of industrial wastewater containing Zn\({}^{2+}\), followed by Cu\({}^{2+}\), Ni\({}^{2+}\), Cr(VI), and Cd\({}^{2+}\). Zn\({}^{2+}\) and Cu\({}^{2+}\) in industrial wastewater are very sensitive toxic substances affecting the performance of WWTPs and should be defined as prior pollutants for WWTP management. Ni\({}^{2+}\) and Cr(VI) are considerably sensitive toxins in industrial wastewater, whereas Cd\({}^{2+}\) is the least sensitive metal on the activated sludge activities in WWTPs although its EC\({}_{50}\) value is similarly as other PTEs.
# Hyperspectral Data: Efficient and Secure Transmission
## 2 An Efficient and Secure Transmission Framework  
Figure 1 shows the architecture of the proposed transmission framework. First, the digest \(h(HI)\) of the input hyperspectral image \(HI\) is computed, by invoking a cryptographic hash function \(h(.)\) (e.g., SHA-3 Keccak [12], etc.). Subsequently, the obtained digest \(h(HI)\) is used as a watermark string and is embedded into \(HI\), by using a reversible invisible watermarking scheme. As it is observable from Figure 1, the secret key \(K\), is used for the computation of the digest as well as for the embedding of the obtained digest \(h(HI)\) into \(HI\).  
After that, the watermarked hyperspectral image \(HI^{W}\) (i.e., the output of the reversible invisible watermarking scheme) is compressed by using an efficient lossless compression algorithm. The output of this compression stage, is denoted as \(HI^{CW}\) and it can be efficiently transmitted.  
In the following, we denote as \(HI^{\prime}\) the hyperspectral image which is reconstructed from \(HI^{W}\) (where \(HI^{W}\) is obtained as the output of the decompression of \(HI^{CW}\)), and as \(w^{\prime}\) the watermark string which is extracted from \(HI^{W}\).  
The receiver can verify the integrity of \(HI\), in the following manner: if there are no alterations of \(HI^{W}\), then the value of \(h(HI^{\prime})\) will be equal to the value of \(h(HI)\). Consequently, it is satisfied that \(w=h(HI)=h(HI^{\prime})=w^{\prime}\) and \(HI^{\prime}\) is exactly equal to \(HI\).
## 5 Conclusions  
Instead of relying solely on statistical cuts, this work employed a deep learning approach using a deep multi-layer perceptron for signal and background classification. The model achieved a high accuracy of about 0.9876 on the validation set used for selecting the best-fitting neural network.  
Integrated into the KF Particle Finder package for a final test, the neural network effectively reduced reconstructed background, particularly eliminating ghosts (combinatorial background) that lack physical meaning. Importantly, the \(\Lambda\) particle signal was minimally affected. The model achieved an accuracy of 0.9859, a precision of 0.9852, a recall of 0.8651, an F1-score of 0.9212, and a specificity of 0.9986, indicating strong overall performance in classifying signal and background. However, reducing the physical background and rejecting these particles has implications for other particles and should be carefully investigated in future works.  
Overall, this deep learning method significantly improved the S/B ratio by a factor of 10.97 (from 3.49 to 38.28) and also increased the significance by a factor of 1.14 (from 11.38 to 12.95). These advancements highlight the potential of machine learning to improve particle reconstruction and signal clarity, paving the way for more refined analyses in heavy-ion physics. Future work on extending the model to other particle types, integrating inter-particle interactions, and assessing performance under diverse collision conditions will further enhance its utility and impact.  
Conceptualization, I.K.; methodology, R.L. and G.Z.; software, R.L. and G.Z.; validation, R.L. and G.Z.; formal analysis, G.Z.; investigation, R.L. and G.Z.; resources, R.L. and G.Z.; data curation, R.L. and G.Z.; writing--review and editing, I.K., R.L. and G.Z.; visualization, G.Z.; supervision, I.K.; project administration, I.K.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the Federal Ministry of Education and Research [grant numbers 01 IS21092, 05P24 RF3, 05P24 RF7], Germany, and Helmholtz Research Academy Hesse for FAIR (project ID 2.1.4.2.5), Germany.  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from the Facility of Antiproton and Ion Research (FAIR), Germany, and are available from the authors with the permission of FAIR.  
The authors declare no conflicts of interest.
# Deep Activation Pooling for Blind Image Quality Assessment
## 2 Approach
### Convolutional Summing Map  
In the convolutional layer of a CNN, the filters traverse the image in a sliding-window manner to generate convolutional maps. The convolutional maps can be regarded as a tensor with the size of \(W\times H\times N\), which possesses \(N\) convolutional maps with width \(W\) and height \(H\). Typically, the top-left (bottom-right) activation response in a convolutional map is generated by the top-left (bottom-right) part of the input image. Each activation response in a convolutional map describes a local part of the input image, and the high responses indicate the salient parts. Hence, we utilize the convolutional maps for high-contrast patch selection and pooling weight learning.  
In this paper, we choose the widely-used VGG-19 [(20)] as the CNN model, and its architecture is listed in Table 1. The VGG-19 network contains five convolutional building blocks. The first and second convolutional building blocks both contain two convolutional layers. There are four convolutional layers in the third, fourth and fifth convolutional building blocks. All the receptive fields are the size of 3 \(\times\) 3. Both the convolution stride and the spatial padding are set to 1 pixel. Max-pooling is implemented by a 2 \(\times\) 2 pixel window, with stride 2 after convolutional building blocks. The last convolutional building block is followed by three fully-connected (FC) layers: the first two have 4096 channels each, and the third has 1000 channels.  
The convolutional maps in the convolutional layer could describe the important features and spatial structural information [(21; 22)]. To further capture the complete spatial response information, we add all the convolutional maps of one convolutional layer to obtain the convolutional summing map  
Figure 1: The flowchart of the proposed method for BIQA. It is composed of (**a**) images and CSM, (**b**) selecting high-contrast patches, (**c**) building feature vectors, (**d**) learning MVG models, and (**e**) DAP is used to obtain the overall image quality score.  
(CSM), which could build the relationship between the spatial structure and the activation responses. Let \(C_{l}(i,j)\) denote the activation response of CSM at position \((i,j)\) in the \(l\)-th convolutional layer:  
\[C_{l}(i,j)=\sum_{n=1}^{N}c_{l}^{n}(i,j), \tag{1}\]  
where \(c_{l}^{n}(i,j)\) denotes the activation response of the \(n\)-th convolutional map at position \((i,j)\) in the \(l\)-th convolutional layer and \(N\) is the number of the convolutional maps in the \(l\)-th convolutional layer. The shallow convolutional layers usually contain structural and textural local features, which are very important for BIQA. In the experiment, we present a detailed description of how to select CSM in Section 3.2. In this paper, we utilize the CSM in the 4 th convolutional layer for selecting high-contrast patches, and the CSM in the 7 th convolutional layer for learning pooling weights.
# Adaptive Noise Exploration for Neural Contextual Multi-Armed Bandits
## 4 Proposed Method
### Real-World Application Analysis  
Both EAD and EAP are well-suited for \"online learning\" scenarios, where only a small amount of data (context and reward) are acquired with each interaction, and the model is updated immediately based on these data. EAD introduces noise into the rewards to increase the model's perceived uncertainty during training, encouraging better exploration. The key idea behind EAD is that when the external environment provides rewards with significant random fluctuations (e.g., advertising click-through rates varying over time or across demographic groups), adding noise amplifies this inherent randomness. This enables the model to continue learning and adapting to the dynamic environment without frequent updates to its internal parameters. Therefore, EAD is better suited for applications where rewards are influenced by random factors, such as advertising click rates or recommendation systems, enabling the model to self-adapt and generalize under time-varying uncertainty.  
In contrast, EAP adds noise to the model's internal parameters without modifying the reward data. The core concept of EAP is that when reward data are particularly valuable (e.g., high-cost experimental data, medical data, or financial transactions), adding noise directly to the model's parameters is more appropriate. This approach maintains exploration during training while preserving the authenticity and traceability of the original reward data. As a result, EAP is better suited for applications where the stability and accuracy of reward data are crucial, allowing the model to explore sufficiently without altering the valuable data.