esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparations for Galileo PRS in Poland
## 5 Polish Contribution for the PRS
### GNSS Jamming Tests in a Controlled Laboratory Environment
INT2 (Three Continuous Waves), also one of the simplest interfering signals, had a moderate impact on all of the tested smartphones in both the static and dynamic tests. In the static tests, the degradation of the decoded signal quality in the receiver (denoted as the SNR level) could be observed as the interference level was increased. The degradation rate was equal for GPS and Galileo. Further, the DOPs values were adversely affected. The most significant impact was noticed for the Sony smartphone in the Galileo mode, where the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{2}{c}{**Google**} & \multicolumn{2}{c}{**Huawei**} & \multicolumn{2}{c}{**Samsung**} & \multicolumn{2}{c}{**Sony**} \\  & **GAL** & **GPS** & **GAL** & **GPS** & **GAL** & **GPS** & **GAL** & **GPS** \\ \hline \multicolumn{8}{c}{INT1-Continuous Wave} \\ \hline Impact Level & Low & High & Low & Low & Medium & High & Low & Medium \\ C/1 Cut-off Level [dB] & N/D & \(-\)34 & N/D & N/D & \(-\)29 & \(-\)36 & N/D & \(-\)**36** \\ C/1 Degradation Level [dB] & \(-\)45 & \(-\)28 & N/D & N/D & \(-\)23 & \(-\)20 & N/D & \(-\)**30** \\ \multicolumn{8}{c}{INT2-Three Continuous Waves} \\ \hline Impact Level & High & High & Low & High & Medium & High & High & Medium \\ C/1 Cut-off Level [dB] & \(-\)37 & \(-\)38 & N/D & N/D & \(-\)36 & \(-\)36 & \(-\)3 & \(-\)**40** \\ C/1 Degradation Level [dB] & \(-\)12 & \(-\)32 & N/D & \(-\)46 & \(-\)17 & \(-\)19 & **4** & \(-\)**33** \\ \multicolumn{8}{c}{INT3-Wideband AWGN Noise L1/E1 60 MHz} \\ \hline Impact Level & High & High & Low & High & Medium & High & High & High \\ C/1 Cut-off Level [dB] & \(-\)35 & \(-\)37 & N/D & \(-\)39 & \(-\)32 & \(-\)37 & \(-\)31 & \(-\)**36** \\ C/1 Degradation Level [dB] & \(-\)22 & 3 & \(-\)7 & \(-\)22 & \(-\)20 & \(-\)24 & 2 & 3 \\ \multicolumn{8}{c}{INT4-Narrowband Modulated Carrier 4 MHz} \\ \hline Impact Level & High & High & Medium & High & Medium & Medium & High & High \\ C/1 Cut-off Level [dB] & \(-\)28 & \(-\)29 & \(-\)28 & \(-\)37 & \(-\)35 & \(-\)35 & \(-\)28 & \(-\)30 \\ C/1 Degradation Level [dB] & 8 & \(-\)9 & \(-\)13 & \(-\)32 & \(-\)20 & \(-\)23 & \(-\)14 & 0 \\ \multicolumn{8}{c}{INT5-Pulse} \\ \hline PULSE 1 μs/20 ms & Low & Low & Low & Low & Low & Low & Medium & Medium \\ PULSE 1 μs/2 ms & Low & Low & High & Low & Low & High & Low \\ PULSE 1 μs/20 μs & Low & High & Low & Low & Low & High & High \\ PULSE 1 μs/2 μs & Low & High & Low & Low & Low & Medium & High \\ PULSE 1 μs/2 μs & High & High & Low & Low & Low & Medium & High & High \\ PULSE 1 μs/2 μs & High & High & Low & Low & Low & Medium & High & High \\ \multicolumn{8}{c}{INT6-Sweep} \\ \hline Sweep 5 kHz/100 μs & High & High & Low & High & Medium & High & High & High \\ Sweep 10 kHz/100 μs & High & High & Low & High & Medium & High & High & High \\ Sweep 20 kHz/100 μs & High & High & Low & Low & Medium & High & High & High \\ \hline \end{tabular}
\end{table} 
Table 2: Impact level of various interferences for static jamming tests.
## 4 Evaluation  
### Set-Up  
For the evaluation of our concept, we designed a simple prototype based on a Raspberry Pi 4 (RPI4) to interface with development kits for the sensors and the NM500. This allows for easy implementation of data logging and processing while still being mobile and compact. The LWIR sensor used had a resolution of 160 \(\times\) 120 pixels for a field of view (FOV) of 57\({}^{\circ}\) and a frame rate of 9 frames per second. Our protorype set-up is shown in Figure 1b,c. We performed a series of indoor and outdoor tests. The outdoor tests were performed in different locations in a public park. In the park, we tested the performance with different surfaces, including gravel, asphalt, and grass. Due to privacy concerns, the performance in these outdoor settings was only judged using the live image, and no data including persons were stored and processed further. To acquire data without privacy concerns for further testing, we performed an indoor test with a small group of participants. During the indoor test, we took images of bikes, e-bikes, scooters, e-scooters, and cargo bikes. The sensors were mounted at a height of 3.5 m and an inclination of 20\({}^{\circ}\) and rotated 45\({}^{\circ}\) toward the road.  
During the indoor test, we acquired more than 40,000 images. This set of images was then filtered. First, all images without objects of interest were excluded, and then the labeling was performed. During labeling, the classes of bike (Bike), pedestrian (Pede), scooter (Scoo), and cargo bike (Carg) were distinguished. In addition, objects overlapping each other were given an additional label. After labeling, the images were downsampled to simulate a frame rate of two frames per second to match our concept and reduce similarities inside the dataset. Lastly, it was decided to exclude overlapping objects from the scope of this work and evaluate these cases in a future work. After filtering, 4400 objects of interest remained in the dataset. All further development of the algorithms was performed using this dataset.
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
###### Abstract  
Radar is one of the primary means of monitoring maritime targets. Compared to electro-optical systems, radar offers the advantage of all-weather, day-and-night operation. However, existing radar target detection algorithms predominantly achieve binary detection (i.e., determining the presence or absence of a target) and are unable to accurately classify target types. This limitation is particularly significant for coastal-deployed maritime surveillance radars, which must contend with not only maritime vessels but also various land-based and island targets within their monitoring range. This paper aims to enhance the informational breadth of existing binary detection methods by proposing a land-sea classification method of radar targets based on dynamic dense connections. The core idea behind this method is to merge the interlayer output features of the network and to augment and weigh them through dynamic convolutional combinations to improve the feature extraction capability of the network. The experimental results demonstrate that the proposed attribute recognition method outperforms current deep network architectures.  
radar target attribute identification; dynamic dense connection; deep learning 2025 15 8703
# A Transformer-Based Approach for Efficient Geometric Feature Extraction from Vector Shape Data
## 2 Methods  
The method proposed in this study is organized into four main steps: serialization of the vector shape data, construction of the PSBT model, pre-training of the PSBT model, and application to downstream tasks.  
In the first stage, the serialization of vector shape data involves converting raw two-dimensional vector data into a serialized format that can be efficiently processed using deep neural networks. This conversion is achieved by calculating the centroid of each shape and establishing suitable control points. The main goal was to normalize and standardize the data format, thereby providing a solid basis for feature extraction. This transformation ensures that the vector data are structured in a manner conducive to neural network processing.  
The core of this process is the construction of the PSBT model. This model comprises two key components: a self-supervised pre-training task and the Shape Representation Transformer (SRT). In this study, a 'pre-training task' refers to a self-supervised learning objective designed to train the PSBT model on a large dataset of unlabeled vector shape data before it is applied to specific downstream tasks. These tasks are designed to enable the model to learn generalizable features from the data without requiring manual annotations. This task prepares the raw vector shape data for input into the SRT model. The SRT model employs a transformer-based encoding network, leveraging a multi-head self-attention mechanism to derive feature representations directly from sequences of coordinate points.  
This supports an end-to-end learning process that eliminates the requirement for manually engineered features.  
To further refine its feature extraction capabilities, the PSRT model underwent pre-training through multiple self-supervised tasks. These pre-training tasks include coordinate masking prediction (CMP), coordinate offset correction (COC), and coordinate sequence rearrangements (CSRs). Through such tasks, the model learns to capture various contextual dependencies, thereby enhancing its ability to generalize diverse and complex input data.  
Once pre-trained, the PSRT model is prepared for a variety of downstream tasks. As an illustration, its application to single-sample and sample pair classification is presented. These examples showcase the model's ability to effectively translate and utilize vector data in practical applications, highlighting its practicality in real-world contexts.
###### Abstract  
This study aimed to produce low-energy strawberries of the Joly cultivar by breeding them under different growing conditions. The efficacy of such a process was screened by the means of common physico-chemical parameters underpinned with the findings of elementary chemical analysis. The preferred strawberry breed line (treated with microbiological fertiliser of choice) showed both a decreased energy value (130 \(\pm\) 6 kJ/100 g or 31.07 kcal) and an increased water content (91.0 \(\pm\) 7.3 g/100 g). Due to a rich content of phenolic compounds (Total Phenolic Content /TPC/ 4184 \(\pm\) 39 mg GAE/kg; Total Anthocyanin Content /TAC/ 394 \(\pm\) 4 mg C3 GE/kg), it also exhibited potent antioxidant activity (0.110 \(\pm\) 0.008 %/uL, HPMC/Hydrox-Perhydroxyl Mercury(II) Complex/) assay; EC\({}_{50}\) = 2.01 \(\pm\) 0.10 ug and ARP = 0.50 \(\pm\) 0.05 ug\({}^{-1}\), DPPH assay). Finally, the polyphenic extract of the same breed line showed high anti-\(\alpha\) glucosidase activity (IC\({}_{50}\) = 19 \(\pm\) 1 ug/mL), being more than four-fold active compared to acarbose (IC\({}_{50}\) = 78 \(\pm\) 4 ug/mL), the anti-diabetic drug used as a positive control. Taken all together, the utilisation of microbiological fertiliser (_Bacillus_ sp.) in tunnels might be recommended for the breeding of low-energy strawberries of the Joly cultivar (the starting point for developing of novel functional foods targeting the obese population) in the same or similar agricultural conditions, if further studies confirm its likely potential and credibility.  
Keywords:_Fragaria \(\times\) ananassa L. Duch; microbiological fertiliser (_Bacillus_ sp.); low-energy foods; obesity +
Footnote †: journal: Applied Soft. 2019, 9, 5140; doi:10.3390/app9235140
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 1 Introduction
Therefore, compensation methods for the fingerprinting-based localization technology to reduce such the RF signal's distortion and instability problems must be devised. To solve this problem, there have been researches integrating the Pedestrian Dead-Reckoning (PDR) with RF-based localization technology. The PDR can accurately estimate a location using a foot-mounted Inertial Measurement Unit (IMU) for a short period of time. By integrating a map matching algorithm, it is possible to operate accurately for a longer period of time [19; 20]. However, in the case of a smartphone, it is difficult to expect a high-performance of the PDR due to the various activities of a pedestrian. [PERSON] et al. [21] developed a particle filter based integration method of inertial measurements, RSS of Wi-Fi, and map information using a smartphone. It is highly reliable and accurate to estimate the location of a pedestrian in an indoor environment. But it is still difficult to guarantee stable performance in the outdoor where the density of LTE BS and signal strength are very low due to the use of only instantaneous signal strength data. In this paper, we propose a new fingerprinting method based on the concept of Surface Correlation (SC) in the LTE environment of urban areas. The SC estimates a location by comparing the spatial RSS pattern data provided by the PDR algorithm with a fingerprinting database, rather than relying just on instantaneous RSS measurements as in the conventional fingerprinting method. This spatial RSS pattern not only improves the accuracy and stability of localization performance by reducing ambiguity but also increases the availability of the LTE-based localization technology. The contributions of this study are following:  
* We develop an SC-based fingerprinting technology that computes a location through comparison of spatial RSS patterns, rather than instantaneous RSS data as applied in the conventional fingerprinting method, between the database and measurements. * For comparison between the measurements and database, we develop methods for generating spatial RSS patterns from them according to the movement of a pedestrian. * We obtain accurate and stable location information by using few LTE base stations even in error-prone urban areas. The paper is organized as follows: Section 2 provides an overview of the state-of-the-art surface correlation approach to the proposed fingerprinting method. Section 3 describes the details of its application on the fingerprinting method. The performance of the proposed fingerprinting method is discussed in Section 4. Finally, Section 5 presents conclusions drawn in this work.
## 2 Theoretical Formulation
### Displacement Field  
As already mentioned in the introduction, we follow a higher order shear deformation theory (HSDT) to define the kinematic field of the structure, i.e., [37].  
\[\begin{array}{ll}u(x,y,z,t)=u_{0}(x,y,t)+z\,u_{1}(x,y,t)+z^{2}u_{2}(x,y,t)+z ^{3}u_{3}(x,y,t)\\ v(x,y,z,t)=v_{0}(x,y,t)+z\,v_{1}(x,y,t)+z^{2}v_{2}(x,y,t)+z^{3}v_{3}(x,y,t)\\ w(x,y,z,t)=w_{0}(x,y,t)\end{array} \tag{6}\]  
where \((u,v,w)\) refer to the axial displacement components of an arbitrary point \((x,y,z)\) within the domain; \((u_{0},v_{0},w_{0})\) stand for the related components at the reference mid-plane; \((u_{1},v_{1},w_{1})\) are the rotations of the normal about the \(y\)-, \(x\)-, and \(z\)-axis respectively;  
Figure 2: Distribution patterns of GPLs: (**a**) GPL-UD distribution, (**b**) GPL-X distribution, (**c**) GPL-O distribution.  
\(u_{2}\),\(v_{2}\),\(u_{3}\),\(v_{3}\) define the higher-order terms in the Taylor's series expansion. Also, the non-null strain components are defined in Appendix A.  
The constitutive relations for the elastic problem are expressed as:  
\[\left\{\begin{array}{c}\sigma_{x}\\ \sigma_{y}\\ \tau_{yz}\\ \tau_{xz}\\ \tau_{xy}\end{array}\right\}^{(K)}=\left[\begin{array}{ccccc}Q_{11}&Q_{12}&0 &0&0\\ Q_{21}&Q_{22}&0&0&0\\ 0&0&Q_{44}&0&0\\ 0&0&0&Q_{55}&0\\ 0&0&0&0&Q_{66}\end{array}\right]^{(K)}\left\{\begin{array}{c}\varepsilon_{x} \\ \varepsilon_{y}\\ \gamma_{yz}\\ \gamma_{xz}\\ \gamma_{xy}\end{array}\right\}^{(K)} \tag{7}\]  
where the elastic constants are defined in Appendix A.
# A High-Dynamic-Range Optical Remote Sensing Imaging Method for Digital TDI CMOS
## 4 Image Fusion Based on Gradient Pyramid Decomposition and Entropy Weighting  
In the process of image fusion, the selection of fusion strategy and operator is the key factor. The fusion method based on gradient pyramid decomposition has the inherent advantage in increasing the average gradient of the image, but it is slightly weak in terms of improving the entropy. This is because the method itself has no evaluation and maintenance mechanism for the information content of the source images, and the fusion process is aimless to some extent.  
This paper takes the entropy as the weighting factor to fuse the gradient pyramids, which can effectively improve the entropy of the obtained image and increase its information content. In the process of fusion, the entropy of sub-images in each direction on each decomposing layer of image pyramids is calculated, and used as the weight for weighted average calculation of each of the two corresponding sub-images. With this method, a higher entropy and average gradient of the obtained image can be provided by relatively simple calculation, so as to make it more suitable for personal eye observation in terms of clarity and gray-scale details. The image fusion process can be summarized as follows:  
1. perform Gaussian tower decomposition on the source images \(A\) and \(B\), respectively, and obtain the image pyramids \(\{AG_{l}\}\) and \(\{BG_{l}\}\), where \(l\) represents the number of layers;
2. perform gradient filtering on the images of each decomposing layer (except the highest layer) of \(\{AG_{l}\}\) and \(\{BG_{l}\}\) in horizontal, vertical and two diagonal directions, and, respectively, obtain four images with details and edge information of each layer in each direction. Then, form the gradient pyramids \(\{AG_{lk}\}\) and \(\{BG_{lk}\}\), where \(k\) = 1, 2, 3, 4, which represents four directions. The computational formula is: \[AG_{lk}=d_{k}(AG_{l}+\omega_{0}AG_{l})\] (4) among them, \(d_{1}=[1,\,-1]\), \(d_{2}=\frac{1}{\sqrt{2}}[1,\,-1;\,1,\,0]\), \(d_{3}=[-1,\,1]^{-1}\), \(d_{4}=\frac{1}{\sqrt{2}}[-1,\,0;\,0,\,1]\), \(\omega_{0}=\frac{1}{16}[1,\,2,\,1;\,2,\,4,\,2;\,1,\,2,\,1]\).
3. use the entropy of sub-images in each direction on each decomposing layer to calculate with Equation (1) and name \(HAG_{lk}\) and \(HBG_{lk}\), as the weight in order to obtain the fused gradient pyramid \(\{FG_{lk}\}\). The subimage on the \(l\) layer and in the \(k\) direction of the fused pyramid is calculated as: \[FG_{lk}=\frac{HAG_{lk}AG_{lk}+HBG_{lk}BG_{lk}}{HAG_{lk}+HBG_{lk}}\] (5) transform the gradient pyramid \(\{FG_{lk}\}\) into the Laplace pyramid \(\{FL_{l}\}\) with Formula (6), and then perform reverse pyramid calculation to obtain the fused image. \[FL_{l}=(1+\omega)\Bigg{(}\sum_{k=1}^{4}-\frac{1}{8}d_{k}FG_{lk}\Bigg{)}\] (6)  
Taking the trilaminar pyramid decomposition as an example, the above process can be described in Figure 12.
# Mechanical Fault Prognosis through Spectral Analysis of Vibration Signals
## 3 Test Platform and Software Flow
### Software Flow  
Qt/Embedded has the advantages of high configurability, cross-platform, portability, rich interfaces, high reliability and stability, and low system overhead. Thus, the analyzer application is designed using Qt programming. The software flow of the application is shown in Figure 2. The embedded system communicates with the DAU via TCP protocol to collect real-time vibration acceleration data. The application uses FFT (Faster Fourier Transform) analysis to obtain the spectral distribution of vibration acceleration signals. Then, the velocity and displacement are estimated from the acceleration data by means of the first-order and the second-order digital integral in frequency domain. The specific description of the process is given in Section 2.1. The vibration acceleration, velocity, and displacement signals of each sensor in the low and high frequency bands are obtained through digital filters, respectively. Furthermore, after simple calculations, the statistics of the acceleration, velocity, and displacement signals in the whole frequency band, as well as the low and the high frequency bands are obtained, respectively. MySQL service is running on the Raspberry Pi. The application connects to a MySQL database and periodically stores raw vibration acceleration data and statistical information to the database. In order to use more complex algorithms on the server in the future for more accurate vibration assessments, the application uses TCP protocol to upload the sampled data to the server.  
Figure 2: Software flow of the vibration analysis.
## 2 Materials and Methods
### Data Analysis  
The main parameters obtained from the PDD curves were R100, R80, and R50, i.e., the depths in water of the corresponding maximum value of the absorbed dose (following the definition of PDD in Section 2 and in Equation (1)) of the 80%, and of the half of its maximum value, respectively. The practical range Rp (only for the 50 mm diameter beam size) and the mean energy at the phantom surface E\({}_{0}\) were also determined.  
The difference over the entire depth range and the difference in distance (mm) within the region of the curve maximum slope (20--80%) were evaluated to compare PDDs for all beam energies: for the 50 mm diameter tubular applicator, the differences between each solid-state dosimeter and the ionization chamber were evaluated, while for the remaining tubular collimators, the diode E was chosen as the reference detector.  
The profiles measured by the different solid-state detectors were compared in terms of field size, evaluated by the full-width half maximum (FWHM, in mm), and in terms of the penumbra, defined as the distance in mm between the points, corresponding to the 20% and the 80% of the maximum dose along the average beam profile (averaging is performed between the left- and right-side values of the profile).  
Similarly to the PDD case, the difference over the entire off-axis range and the difference in distance (mm) within the penumbra region were evaluated between the profiles measured by the diode E and the other two solid state detectors, the microDiamond and the microSilicon, respectively.  
The maximum uncertainty, related to detector positioning over the PDD curve and dose profiles, was calculated by multiplying the used scanning step size (0.4 mm) for the evaluated dose gradient in different regions of the dose curve.  
For OFs evaluation, the reference detector choice was dissimilar according to the tubular applicator size: for the 50 mm aperture diameter, the ionization chamber was considered as the reference, while for the other beam diameters (20, 30, and 40 mm), the diode E was considered as the reference. Relative differences between OF values, measured by each detector and the reference one, were calculated.  
Four main components of uncertainty were considered for OF determination due to: the position accuracy of the detector, the electrometer reading, the statistical dispersionof repeated measurements, and the reproducibility of the OF values between different measurement sessions.  
Comparisons of dose parameters, measured by the reference detector and other dosimeters, were performed using a two-tailed Wilcoxon signed-rank test, considering a _p_-value of <0.05 as significant.
# The Analysis and Verification of Unbiased Estimator for Multilateral Positioning
###### Abstract  
The ranging error model is generally very complicated in actual ranging technologies. This paper gives an analysis of the biased distance substitution and proposes an unbiased multilateral positioning method to revise the biased substitution, making it an unbiased estimate of the squared distance. An unbiased estimate of the multilateral positioning formula is derived to solve the target node coordinates. Through simulation experiments, it is proved that the algorithm can improve the positioning accuracy, and the improvement is more obvious when the error variance is larger. Experiments using SX1280 also show that the ranging conforms to the biased error model, and the accuracy can be improved by using the unbiased estimator. When the actual experimental error standard deviation is 0.16 m, the accuracy can be improved by 0.15 m.  
multilateral positioning method; unbiased multilateral positioning method +
Footnote †: journal: _signals_