esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The significant increase of visible LEO satellites not only improves positioning accuracy, but also poses a challenge to low-cost receivers. In this study, we derived the optimal configuration of positioning satellites and transformed it into an optimization problem, which we solved with a genetic algorithm. We verified the performance advantages of our algorithm by simulating data from Starlink satellites in orbit.  
The following conclusions can be drawn from the static and low-dynamic single-epoch positioning results of the Quasi-Optimal algorithm, the recursive algorithm, and the proposed algorithm:  
1. The recursive algorithm cannot be well applied when selecting a small number of satellites out of a large number for localization. Therefore, its application is more appropriate in scenarios where the number of observable satellites is close to that of the number to be selected.
2. Obstruction has a significant impact on the performance of current classical geometric satellite selection algorithms, such as the Quasi-Optimal algorithm, which limits engineering applications.
3. The proposed algorithm can overcome the limitation that some of the satellites cannot be observed due to obstructions.
4. It is easier to obtain a uniform distribution for multiple small-angle obstructions compared with a single large-angle obstruction, which makes the performance difference between the Quasi-Optimal algorithm and the proposed algorithm smaller than when there is a single large-angle obstruction, but the proposed algorithm still has a higher stability.  
In the future, the addition of visible satellites in commercial large-scale LEO satellite constellations will increase the accuracy of localization and enhance the positioning ability under obstructions. This will render the proposed algorithm even more advantageous. Key areas for further research include optimizing the parameters of the genetic algorithm to avoid falling into local optima and improving the algorithm's computational speed.  
Conceptualization, [PERSON] and Y.W.; Methodology, [PERSON]; Supervision, [PERSON] and [PERSON]; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON], [PERSON] and C.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Science and Technology Project of Shenzhen under Grant JCYJ20200109113424990 and in part by the Marine Economy Development Project of Guangdong Province under Grant GDNRC [2020]014.  
The data presented in this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# A New Zenith Tropospheric Delay Grid Product for Real-Time PPP Applications over China
## 3 Real-Time ZTD Grid Product Generation  
In this section, the vertical variations of ZTD derived from ERA-Interim will be first analyzed and the inverse scale height model is determined and then applied to real-time ZTD solutions at GPS stations at a national scale over China for the generation of the real-time ZTD grid product.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Measurement Model** & \\ \hline Basic observables & Ionosphere-free combinations of code and phase measurements on \\  & L1/L2 for GPS \\ \hline Sampling rate & 30 s \\ \hline Elevation cutoff angle & 7\({}^{\circ}\) \\ \hline \multirow{3}{*}{Weighting} & A priori precision of 0.03 cycles and 3.0 m for raw phase and code, \\  & respectively. \\  & Elevation-dependent, 1 for E \textgreater{} 30\({}^{\circ}\), otherwise 2 sin(E) \\ \hline Phase center corrections & igs08.atx \\ \hline Phase wind up & Corrected \\ \hline Tropospheric a-priori model & GPT2w \\ \hline Station displacement & IERS 2003 conventions \\ \hline DCBs & Corrected with products provided by CODE \\ \hline Relativistic effects & Corrected \\ \hline
**Stochastic Model** & \\ \hline \multirow{3}{*}{Estimation method} & Real-time mode: Square Root Information Filter (SRIF) \\  & Post-processing mode: Least Square Adjustments \\ \hline Station coordinates & Fixed \\ \hline Satellite orbit and clocks & Real-time mode: IGS archived real-time products (IGS01) \\  & Post-processing mode: IGS final post-processing products \\ \hline Receiver clocks & White noise with a unit weight variance of 9000 m \\ \hline ZWD corrections & Real-time mode: Random walk process with a constraint of 20 mm/\(\sqrt{\text{h}}\) \\  & Post-processing mode: Piece-wise constants with 5 min interval with \\  & a constraint of 20 mm/\(\sqrt{\text{h}}\) \\ \hline Tropospheric gradients & Piece-wise constants with 12 h interval with a constraint of 5 mm/\(\sqrt{\text{h}}\) \\ \hline Ambiguity & Float \\ \hline \hline \end{tabular}
\end{table}
Table 2: GPS data processing strategy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Jan.** & **Apr.** & **Jul.** & **Oct.** & **Mean** \\ \hline Bias & 0.05 & 0.26 & 0.28 & 0.41 & 0.25 \\ RMS & 1.07 & 1.22 & 1.21 & 1.23 & 1.18 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bias and RMS of real-time ZTD errors (unit: cm).
# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
###### Abstract  
Segmenting brain tumors accurately and reliably is an essential part of cancer diagnosis and treatment planning. Brain tumor segmentation of glioma patients is a challenging task because of the wide variety of tumor sizes, shapes, positions, scanning modalities, and scanner's acquisition protocols. Many convolutional neural network (CNN) based methods have been proposed to solve the problem of brain tumor segmentation and achieved great success. However, most previous studies do not fully take into account multiscale tumors and often fail to segment small tumors, which may have a significant impact on finding early-stage cancers. This paper deals with the brain tumor segmentation of any sizes, but specially focuses on accurately identifying small tumors, thereby increasing the performance of the brain tumor segmentation of overall sizes. Instead of using heavyweight networks with multi-resolution or multiple kernel sizes, we propose a novel approach for better segmentation of small tumors by dilated convolution and multi-task learning. Dilated convolution is used for multiscale feature extraction, however it does not work well with very small tumor segmentation. For dealing with small-sized tumors, we try multi-task learning, where an auxiliary task of feature reconstruction is used to retain the features of small tumors. The experiment shows the effectiveness of segmenting small tumors with the proposed method. This paper contributes to the detection and segmentation of small tumors, which have seldom been considered before and the new development of hierarchical analysis using multi-task learning.  
brain tumor segmentation; gliomas; small tumor segmentation; MRI; deep learning; multi-task learning; feature reconstruction Article
# Deployment Protection for Interference of 5G Base Stations with Aeronautical Radio Altimeters
## 3 Simulation Parameters  
The top and front views of the interference scenario are shown in Figures 9 and 10. The aircraft's position coordinates are \((0,0,AGL)\), where \(AGL\) refers to altitude above ground level. The base station's position coordinates are \((x,y,h)\). The angle between the aircraft and the base station in the top view is denoted by \(\varphi_{h}\in(0,360^{\circ})\). Similarly, in the  
Figure 11: Angle shutoff. (**a**) Angle shutoff in Cartesian coordinate system. (**b**) Angle shutoff in polar coordinate system.  
Figure 10: Front view of interference scenario.  
Figure 9: Top view of interference scenario.  
front view, the angle between the aircraft and the base station is defined as \(\varphi_{v}\in(0,90^{\circ})\). For computational simplicity, the base station's coordinates in the front view can be expressed as \((x,0,h)\). Additionally, the horizontal distance (\(d\)) between the base station and the aircraft is defined for mitigation measure calculations.  
The base station parameters are shown in Table 3 based on the 3 GPP standards [33; 34]. There are three types of base stations, namely RMa, UMa, and UMi stations. Among them, an RMa station has the highest transmission power and the highest base station height. In terms of antenna types, all three types of base stations can use a 15 dBi omnidirectional antenna and a 16 \(\times\) 16 beamforming antenna with a maximum gain of 29.5 dBi. In the RMa scenario, we assume a building height of 7 m and a street width of 25 m. For the calculation of path loss, shadow fading values are assumed for all three types of base station. Additionally, the maximum vertical scan angle for all base stations is 10\({}^{\circ}\).
# Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN
## 3 Theoretical Method and Simplified Calculation
### Evaluation Metrics  
Inception Score (IS) [44] uses an image class classifier to evaluate the quality of the generated images. The image class classifier used is Inception Net-V3. IS is a measure of the clarity and diversity of the generated images, and the bigger the IS value, the better. The specific formula is as Equation (16).  
\[\mathrm{IS}(G)=\exp\Bigl{(}\mathbb{E}_{x\sim p_{g}}D_{KL}(p(y\mid x)\|p(y)) \Bigr{)} \tag{16}\]  
In Equation (16), \(D_{KL}\) denotes KL divergence (Kullback-Leibler divergence). For a picture \(x\), the probability distribution that belongs to all classes is \(p(y\mid x)\). \(p(y)\) denotes marginal probability.  
Frechet Inception Distance (FID) [30] does not rely on a classifier, but directly considers the distance between the generated data and the real data at the feature level. FID uses the 2048-dimensional vector before the full connection of Inception Net-V3 as the feature of the  
Figure 1: Structure of the network model.  
picture. FID is the distance between the generated image and the real image; the smaller the FID value, the better. The specific formula is as Equation (17).  
\[\text{FID}=\left\|\mu_{r}-\mu_{g}\right\|^{2}+\text{Tr}\Big{(}\Sigma_{r}+\Sigma_ {g}-2\big{(}\Sigma_{r}\Sigma_{g}\big{)}^{1/2}\Big{)} \tag{17}\]  
In Equation (17), \(\mu_{r}\) denotes the feature mean of the real images. \(\mu_{g}\) denotes the feature means of the generated images. \(\Sigma_{r}\) denotes the covariance matrix of the real images. \(\Sigma_{g}\) denotes the covariance matrix of the generated images. Tr denotes the trace of the matrix.  
Perceptual Path Length (PPL) [1] subdivides the interpolation path of two noise points into multiple segments, finds the length of each segment, and then averages it. PPL evaluates the distance at which the generator changes from one image to another. The smaller the PPL value, the better.The specific formula is as Equation (18).  
\[\text{PPL}=\mathbb{E}\bigg{[}\frac{1}{\epsilon^{2}}d(G(\text{slerp}(z_{1},z_{2 };t)),G(\text{slerp}(z_{1},z_{2};t+\epsilon)))\bigg{]} \tag{18}\]  
In Equation (18), \(\epsilon\) denotes subdivision, replaced with \(1\times 10^{-4}\). \(d\) denotes perceptual distance, measured using pre-trained VGG. \(G\) denotes the image generator. Function slerp denotes spherical linear interpolation, an interpolation method. \(t\) denotes the interpolation parameters, subject to the uniform distribution.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 4 Data Processing  
All data acquired in the geosensor network is immediately transferred to an off-site central server (Inform@Risk cloud), where it is processed and analyzed in near real time.
# Online Estimation of Short-Circuit Fault Level in Active Distribution Network
## 1 Introduction
Some similar methods are proposed for the distribution networks with DGs [14; 15; 16], but only the conventional models are used, which cannot distinguish the different DG forms. Moreover, the methods above are all passive and dependent on the inherent events or disturbances. The active methods, on the contrary, create the artificial disturbances on the operating condition or inject the harmonic currents with a band around the fundamental frequency [17; 18]. There are also the third kind of method, namely the hybrid technique, which incorporates both the artificial (or controlled) and the natural disturbances to measure the fault current [19]. No matter which technique is applied, the existing real-time estimation methods lack the effective detection and judgement on the disturbance types. Sometimes the data from different disturbance, even though the grid configuration has already changed, are compromised with each other and disposed together. It might undermine the accuracy of real-time estimation. With the development of the distribution level phasor measurement units (PMU), both the protection and the fault location scheme in the distribution networks considering DGs can be designed based on the synchronous phasor data [20; 21; 22]. The PMUs in distribution networks offer high accuracy, which is helpful to detect and measure the load fluctuations. Moreover, the proper placement of PMUs can enhance the location identification of the disturbances [23; 24]. In this paper, an innovative method is proposed to estimate the short-circuit fault level in active distribution networks. The contributions of both rotating DGs and IBDGs to potential short-circuit current are taken into consideration. The proposed method is based on the online evaluation of parameters in the equivalent circuit, so that the time-varying factors are captured and the potential currents are estimated in advance of the fault occurrence. The natural disturbances are monitored to reckon the equivalent circuit. A fuzzy logic identifier is applied to effectively distinguish the useful disturbances upstream from those downstream, and meanwhile evaluate their confidence levels. The upstream and downstream disturbances are disposed in different ways. The short-circuitcurrents calculated on the basis of several continuous disturbances are weighted by the predefined confidence levels. The rest of the paper is organized as follows. Section 2 describes the principle of the proposed method for short-circuit fault level estimation in ADN. In Section 3, the implementation of the proposed method is demonstrated. Meanwhile, it is validated on a typical distribution network, under different conditions of load changes. Conclusions are drawn in Section 4.
## 4 Detailed Design
### Social Tie Classifier System
#### 4.1.2 Nodes' Friends Discovering Mechanism
* **Average Contact duration (ACD)--**Represents the average time that two nodes remain within each other's coverage area. Possible values: low, average, and high. * **Average Time Between Contacts (ATBC)--**Represents the average time between the last contact and the new contact. Possible values: low, average, and high. * **Meetings outside the university (MOU)--**Represents the information about the meetings between a pair of nodes outside the university. Possible values: yes (they meet outside the university), no (they do not meet outside the university). Figure 3: Self-reported friendship graph from the nodes participating in the Sassy experiment. * **Common Acquaintances** (CA)--Represents information about the common acquaintances between nodes. Possible values: yes (they have common acquaintances), no (they do not have common acquaintances). * **Friendship strength** (FS)--Represents the self-reported friendship between a pair of nodes participating in the experiment. From a machine learning perspective, it is the class. Possible values: strong (they are friends), and weak (they are not friends). In this database, the values for the attributes NM, ACD, and ATBC correspond to the history of meetings between each pair of nodes from the MIT experiment at the above-mentioned time \(T\). In order to categorize the values for these attributes in the levels described above (low, average, high), we performed two steps. Firstly, we have used algorithms from machine learning to preprocessing the database used as training for the classifier. Then, we have used three percentile groups (25 th, 75 th, 100 th). Then, let NM values from the MIT experiment be represented by numerical values (0 to 50 encounter times). We create three levels of NM, and convert these numerical values into NM levels. For example, from 0 to 4 encounters, the frequency is tagged as low, from 5 to 15, it is considered average, and above 15 it is considered high. In turn, we model the attribute CA by using the aforementioned node's acquaintances discovery mechanism. We choose the attributes NM, ACD, and ATBC based on other works found in the literature [13; 35]. We also included the attributes CA and MOU because we believe that they are representative parameters for classifying the friendship between a pair of nodes. Finally, once again, the \(T\) value depends on the scenario (refer to Table 1). Table 2 shows an example of some tuples used in this work as training data by the Naive Bayes algorithm.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 4 Joint Resource Allocation in Cognitive LEO SatComm System
### Non-Synchronous Uplink  
In the above, the entire analysis is that of a synchronous uplink cognitive LEO satellite spectrum sharing system. In this section, we extend to the non-synchronous case.  
For the non-synchronous uplink cognitive LEO SatCom system, the total received signal at the satellite base station can be written as,  
\[\mathbf{y}=\mathbf{H}_{1}\mathbf{h}\circ\mathbf{c}_{1}+\mathbf{H}_{3}\mathbf{ f}\circ\mathbf{c}_{3}+\mathbf{z}, \tag{28}\]  
where \(\mathbf{H}_{3}=[\mathbf{t}_{L},\mathbf{t}_{L+1},\cdots,\mathbf{t}_{K},\mathbf{ t}_{1},\mathbf{t}_{2},\cdots,\mathbf{t}_{L-1}]\) is \(N\times K\) matrix formulated by the spreading codes of the IoT users, \(\mathbf{c}_{3}=[b_{L},b_{L+1},\cdots,b_{K},b_{1},b_{2},\cdots,b_{L}]^{T}\) is \(K\times 1\) vector composed by the data symbols the IoT users transmit, and \(L\) is the synchronous error between the primary and the secondary systems. Based on the discussions in Section 2, we can obtain the MMSE output for the non-synchronous uplink cognitive LEO SatCom system, which is given by  
\[\mathbf{w}_{u}^{H}\mathbf{y}=\mathbf{w}_{u}^{H}\mathbf{s}_{u}a_{u}+\mathbf{ w}_{u}^{H}(\sum_{i=1,j\
eq u}^{U}\mathbf{s}_{i}a_{i}+\sum_{k=1}^{K}b_{k} \mathbf{t}_{k}+\mathbf{n}). \tag{29}\]  
From (29), we can find that although the primary and secondary systems are not synchronous perfectly, the MMSE output of the non-synchronous uplink system is similar tothat of the synchronous uplink system. Thus, the analysis and scheme of resource allocation of the non-synchronous uplink system are similar to that of the synchronous uplink system and thereby omitted.
## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques  
One of the most crucial elements of current data security methods is complex transformation technologies, such as data encryption. Using algorithms, data encryption converts plaintext data into ciphertext, an unreadable form. Encryption keys are used to ensure that the data are only exposed by those with the right decryption keys. To eliminate the possibility of unauthorized access, encryption is required to secure sensitive data during transfer, retrieval, or analysis [24]. Selecting the right encryption algorithm is a significant challenge when establishing data encryption. DES, XDES, Blowfish and Advanced Encryption Standard (AES512) are common symmetric encryption methods. The form of secured data, calculating speed, and unique security requirements impact the method selection. Because of its outstanding security and productivity, AES512 is commonly used to secure data in transmission or at rest [25].
###### Abstract  
For truss structures, the question of whether to weld hidden welds or not has been controversial. In the actual construction process of truss structures, the members are usually spot welded in place on the assembly platform, and then welded as a whole, while the hidden welds of the truss are not welded, especially for small pipe diameter trusses. Furthermore, in this study, under hidden weld unwelded conditions, two kinds of reinforcing method (adding a half outer sleeve on each joint and filling concrete into the chord members) are adopted to achieve the purpose of strengthening the truss. Therefore, this paper presents an experimental study on the static behavior of four types of Warren tubular trusses made of CHS members. These four types are (1) T-HW: The truss with hidden welds welded; (2) T-HN: The truss with hidden welds unwelded; (3) TS-AS: The truss strengthened on the basis of T-HN by adding a half outer sleeve on each joint; (4) TS-FC: The truss strengthened on the basis of T-HN by filling concrete into the top and bottom chord members. The mechanical behavior, failure mode, bearing capacity, and load-displacement of all specimens were investigated. The surface plasticity of the bottom chord member, the weld fracture around tubular joints at the bottom chord member, and the bending deformation of the bottom chord member were observed in the tests. Compared with the T-HW specimen, the load carrying capacity of the T-HN specimen decreased by 18%. On the other hand, the T-HN specimen has better deformability than the T-HW specimen. The reinforcing method of adding a half outer sleeve on each joint and filling concrete into the chord members can effectively improve the load carrying capacity and stiffness of the truss, thus reducing the overall deformation of the truss, but the reinforcing method of filling concrete into the chord members is more efficient.  
Keywords:reinforced warren circular hollow section (CHS) tubular truss; hidden weld; static performance; finite element analysis +
Footnote †: journal: Applied Physics Letters
## 1 Introduction
However, it was only applicable to scenarios of monitoring from the sky, since there were no characteristics of sun glints when viewing the airborne scenes from underwater [15; 23]. The above methods all attempted to first estimate the slope distribution of the water surface and then recovered the distorted images. However, several current methods of recovery of the water surface are not suitable for harsh application conditions, requiring special illumination settings or relying on special ambient illumination conditions (requiring the sun or uniform brightness in the sky). Furthermore, the accuracy of the estimation is also unable to meet the requirements. In order to overcome the dependence of previous methods on natural illumination conditions and realize the distortion correction of instantaneously distorted images, an image restoration model based on structured light projection is proposed in this paper. Compared to previous approaches, our method does not require special natural illumination [15; 16; 17; 23; 25], multiple viewpoints [24] or a complex experimental setup [17; 23]. It only requires a simple projection setup and an image of the distorted scene. The main contributions of this paper are as follows: (1) we propose a new image restoration model for instant images via structured light projection, (2) we introduce a WAI reconstruction algorithm based on structured light, (3) we analyze some of its limitations.