esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimum Variance Distortionless Response--Hanbury Brown and Twiss Sound Source Localization
## 3 Simulation Analysis  
To verify the feasibility of the algorithm, a simulation analysis was carried out using Matlab software, and the positioning performance of the algorithm under a low SNR was evaluated and compared with the positioning performance of HBT. In addition, the effects of different sound source positions and frequencies on the positioning performance were also investigated.  
Figure 3 shows the microphone array structure. Two linear microphone arrays were used for sound source localization. The initial parameters of the simulation were as follows: the microphone coordinates of array I were (\((\text{i}-1)\times\text{d},0\)), and the microphone coordinates of array II were (\(10+(\text{i}-1)\times\text{d},0\)), where i = 1, 2, 3,..., 16; D = 10 m was the distance between the two arrays, and d = 0.043 m was the distance between adjacent microphones; and the sound source signal was a 600 Hz single-frequency signal.
## 3 Results and Discussion  
### Structural Analysis  
Figure 2 shows the main peaks in X-ray diffraction patterns for the Mg-doped GaN films (Figure 2a), and Zn-doped GaN films (Figure 2b), as well as the ICDD card 00-050-0792 for hexagonal GaN (Figure 2c), which was used to compare the different X-ray diffraction patterns. The peaks observed in Figure 2 were indexed in the ICDD card 00-050-0792 for GaN. The \(a\) peak was located in the plane orientation (100), \(b\) had a localization at (002), \(c\) had the highest intensity at (101), \(d\) peak was located at (102), while \(e\) peak was located at (110). The lattice constants for the hexagonal structure were a = 3.18 A and c = 5.18 A, with a ratio c/a = 1.62. The FWHM average measurements for the c peak (101) of the X-ray diffraction patterns of Figure 2, had a value of 1.15\({}^{\circ}\). The \(c\) peak broadening of the X-ray diffraction patterns could be produced for two reasons. First, this deposition technique does not produce good crystalline quality in the layers at room temperature; however, when the substrate is heated, the crystalline quality in the films could get better [22]. Second, the presence of nano-crystallites hinders the crystalline quality [16]. On the other  
Figure 1: (**a**) Process diagram to obtain the Mg- or Zn-doped GaN films; (**b**) Laboratory-prepared targets using Mg- or Zn-doped powders.  
hand, Figure 2a,b does not show a significant difference between their diffraction angles. This similarity might show that the incorporation of the magnesium or zinc atoms into the GaN lattice did not affect its crystalline structure, due to the approximate atomic radius of the dopant elements [23]. The percentage similarity of Mg and Zn compared to gallium is 88% and 92%, respectively. Using the ICDD PDF-4+ 2018 software and the Debye-Scherrer equation, the crystal size was computed, finding an average of 7 nm for all GaN films. Figure 2a,b, showed small peaks in GaN in the (102), and (110) planes, which could be related to growth temperature, a poor nitrogen incorporation, and the introduction of probable oxygen non-intentional impurities into the crystalline lattice during the deposition by sputtering.
## 3 Methodology
### Evaluate Alternatives with WASPAS  
WASPAS integrates the Weighted Sum Model (WSM) and Weighted Product Model (WPM) as a hybrid MCDM method. Its design allows us to evaluate and rank alternatives for balanced decision-making [68]. On one side, WSM is based on additive aggregation, where the score of alternatives is calculated as a weighted sum of their performance across all criteria; on the other hand, WPM relies on multiplicative aggregation, multiplying the weighted criteria values to capture interactions between them.  
Steps:  
1. Normalize values based on benefit or cost criteria: In this case, a simplified min-max normalization is used, where the maximum or minimum value is used for optimization considering benefit or cost criteria. \(\overline{\xi_{ij}}_{WASPAS}\) is the normalized value of \(\xi_{ij}\) using the WASPAS method. \[\overline{\xi_{ij}}_{WASPAS}=\frac{\xi_{ij}}{\underset{j}{\max}\xi_{ij}}\text { for beneficial criteria}\] (7) \[\overline{\xi_{ij}}_{WASPAS}=\frac{\underset{j}{\min}\xi_{ij}}{\xi_{ij}}\text { for cost criteria},\] (8) where \(\overline{\xi_{ij}}_{WASPAS}\) is the normalised value of \(\xi_{ij}\).
2. Calculate WSM and WPM scores using \(w_{j}\) obtained by CRITIC: In this case, WSM multiplies the criterion values by their respective weights and then sums them to obtain a total score for each alternative, thereby achieving a linear relationship between the criterion and the alternative. In contrast, WPM raises the criterion values to the power of their respective weights and then multiplies them to obtain a total score for each alternative, resulting in a non-linear relationship between the criterion and the alternative. The combination of both metrics gives greater robustness to the result delivered for a decision. \(S_{WSM}\) denotes the WSM score, and \(S_{WPM}\) is the WPM score: \[S_{WSM}=\sum_{j=1}^{n}\left(w_{j}\times\overline{\xi_{ij}}_{WASPAS}\right)\] (9) \[S_{WPM}=\prod_{j=1}^{n}\left(\overline{\xi_{ij}}_{WASPAS}\right)^{w_{j}}\] (10)
3. Combine the WSM and WPM scores: \[S_{i}=\lambda\times S_{WSM}+(1-\lambda)\times S_{WPM}\] (11)The coefficient \(\lambda\) expressing the weighting of WSM and WPM scores could be further analyzed and optimized [69], but it is frequently set to 0.5 [70].
## 5 Experiment
### Ablation Experiment  
To evaluate the effectiveness and importance of each component of SGPVEFA, this paper conducts ablation experiments by removing various steps of the algorithm to assess their impact on the overall algorithm. The test subjects include the basic algorithm (Basic), which only performs SGP search without optimization; the obstacle vertex extraction algorithm (OVEA), which removes the obstacle extraction module from the overall algorithm; the path-smoothing method (PSM), which removes the path-smoothing module from the overall algorithm; and the SGP vertex extraction filtering algorithm (SGPVEFA).  
The experiment randomly generates 100 maps, with the map side length N increasing from 100 to 1000, with each increment of 100. For each N, 10 maps are randomly generated, and for each map, a random obstacle grid and start/finish points are created, with an obstacle rate of 0.3. The changes in path length for each sub-algorithm as the map side length N increases are shown in Figure 15, and the changes in search time as the map side length N increases are shown in Figure 16. Since the search time approaches zero when the map side length is too small, Figure 16 only shows part of the search time for N \(\geq\) 400.  
Figure 16: Comparison of search time for each part of ablation experiment.  
Figure 15: Comparison of path lengths in each part of the ablation experiment.  
From Figure 15, it is evident that if obstacle vertices are not extracted and path smoothing is performed directly, the total path length can be reduced to a certain extent, but it cannot be optimized. If only obstacle vertices are extracted without path smoothing, the total path length might even slightly increase. This is because the set of obstacle vertices includes irrelevant points, which may cause unnecessary turns. Only by running both parts simultaneously can the path length be optimized. As shown in Figure 16, the time consumed by SGPVEFA is minimal, and almost negligible compared to the search time for SGPs.  
The ablation experiment shows that SGPVEFA is very efficient and can further shorten SGPs in a short space of time, and the optimization effect is more significant with the increase in map size.
## 2 TLC Description and Mathematical Modeling
### Algae Cell Modeling  
The productivity of microalgae is strongly affected by hydrodynamic mixing, which ensures single cell exposure to light, even the distribution of nutrients, the prevention of sedimentation, and an enhanced utilization of CO\({}_{2}\). The mixing phenomenon is well explained by the cell distribution inside the reactor. The criterion that determines whether the algae cells follow the fluid stream is called the Stokes number, which is defined as follows:  
\[S_{t}=\frac{U}{D_{h}} \tag{14}\]  
\[\tau_{p}=\frac{\rho_{p}d_{p}{}^{2}}{18\mu} \tag{15}\]  
where \(\tau_{p}\) denotes the relaxation time of algae cells (s), \(d_{p}\) represents the algae cell diameter (m), and \(\rho_{p}\) is the density of algae cells (kg/m\({}^{3}\)). A single-cell spherical shaped Chlorella specie, with an algae cell diameter \(d_{p}=7\)\(\mu\)m and density \(\rho=864\) (kg/m\({}^{3}\)) was used in this study [27]. The calculated Stokes number is less than 1, which means that chlorella cells may follow the fluid stream without affecting the fluid flow in the mixing process [28]. The motion of the algae particles in the reactor can be traced by [PERSON]'s second law of motion. The fluid exerts drag force on the particles, and this drag force can be calculated by using [PERSON] law, provided that the cell Reynolds number is less than 1(Re \(<<\) 1). The governing equations for the algae cell tracing are as follows:  
\[\frac{d\big{(}u_{p}\big{)}}{dt}=F_{d}\big{(}\textbf{U}-u_{p}\big{)} \tag{16}\]  
\[\frac{dx_{p}}{dt}=u_{p} \tag{17}\]  
\[Re_{p}=\frac{\big{(}\textbf{U}-\textbf{u}_{p}\big{)}d_{p}\rho_{p}}{\mu} \tag{18}\]  
\[F_{d}=\frac{18\mu}{d_{p}{}^{2}\rho_{p}} \tag{19}\]  
where \(u_{p}\) denotes the chlorella cell velocity (m/s), \(x_{p}\) is the position vector of algae cells (m), and \(F_{d}\) represents the drag force (N). The density of the released particles is normalized to the magnitude of the fluid velocity at the inlet. This means that more particles are released where the inlet velocity is highest, and fewer particles are released where the velocity field is low. A user-defined auxiliary dependent variable R with a set value of 1 was defined to evaluate the mixing and residence time of particles. This variable solves first-order differential equations by using the Euler method with defined initial values to calculate the mixing and residence time of algae particles. The following are the governing equations for residence time and mixing length:  
\[R_{t}(t)=\int_{t1}^{t2}Rdt \tag{20}\]  
\[M_{l}(s)=\int_{s1}^{s2}Rds \tag{21}\]where \(R_{t}\) represents the residence time (s), \(M_{l}\) is the mixing length (m), \(t_{1}\) and \(t_{2}\) show the time steps of algae particles at the inlet and outlet of the reactor, and \(s_{1}\) and \(s_{2}\) represent the direction of algae particle motion at the inlet and outlet, respectively.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 4 Projects, Activities, and Standards Related to Open RAN
### Projects and Activities
The members of the project group are mostly companies who have become members of TIP. They include Vodafone and Telefonica, along with Intel as TIP co-chair [31]. The fourth project is ONAP. ONAP is an open source project started in 2017 that provides a common platform for telecommunication companies, providers, and MNOs to design, implement, and manage differentiated services [49]. ONAP project is one of LF projects [4] which is linked to Open RAN, primarily through the SMO project in OSC [50]. The project automates 5G by using SDN and NFV technologies. ONAP includes all the Management and Orchestration (MANO) layer functionality that compliant with NFV architecture from European Telecommunication Standards Institute (ETSI). ONAP includes network service design framework and fault, configuration, accounting, performance, and security (FCAPS) functionality. The fifth Open RAN related project is SD-RAN. SD-RAN is one of ONF's projects. ONF is a community of MNOs that contribute to their own open source codes and frameworks for their networks' deployments [4]. ONF always works closely with O-RAN Alliance, TIP, Broadband Forum, LF, and Open Compute Project in every network deployments [24; 51]. Started in 2020, SD-RAN's purpose is to build open source RAN components. In particular, SD-RAN is an ONF's component project that focuses on building the near Real-Time (RT) RAN Intelligent Controller (RIC) and xApps [4; 6; 52]. The xApps-SDK code made by SD-RAN is shared with OSC. SD-RAN has made its initial release in January 2021, called SD-RAN v1.0. In addition to the projects above, there are also testbeds that can instantiate software 5G networks by leveraging some of the open-source components above. POWDER-RENEW, COSMOS, and Colosseum are a few examples [4]. The three testbeds are part of the Platforms for Advanced Wireless Research (PAWR) program [4; 53]. POWDER-RENEW and COSMOS have worked with O-RAN and can support indoor and city-scale outdoor scenarios [4]. On the other hand, Colosseum is advertised as the world's most powerful wireless network emulator. Colosseum can support a large-scale network emulator scenario with 256 programmable software radios [4; 53].
## 2 Related Works  
### Life Narratives  
Life narratives involve revisiting one's life experiences through communication with others, serving as an intervention to break social barriers and alleviate the sense of loneliness among older adults [9]. Traditionally, the life experiences of older adults were often documented in physical formats, like storybooks, collages, or memory boxes [10]. Organizing life narratives typically combines textual descriptions with visual elements, such as significant individuals or event photographs. These life narratives are organized around different topics, such as birth, educational pursuits, family life, career endeavors, and travel experiences [11].  
With the evolution of technology, digital storytelling aided by computational means has been widely embraced. The life narrative approach, as a form of human intervention, has had applications within eldercare services [12]. Nursing homes routinely engage in extensive communication with older adults and their families upon admission. This communication aims to gather a substantial amount of information about the older adults' past life experiences, habits, and interests, among other aspects, forming the basis of a \"personal biography\". These biographies serve as the foundation for crafting electronic health records and facilitating tailored caregiving services for older adults [3]. In the American eldercare market, a business model merging \"memoirs\" with loneliness intervention has emerged [13]. This model not only revitalizes traditional memoir products but also renders depression therapy more approachable and readily accepted by older adults.  
However, the current application of the life narrative approach heavily relies on manual collection, organization, and information mining, lacking automated means of processing life stories. Life narratives gathered through daily interactions and face-to-face interviews suffer from fragmentation, disorder, and redundancy, imposing burdens on caregivers for story structuring and information extraction.
## 4 Experimental Datasets and Evaluation
### Results
The first two rows are aerial images and ground truth, respectively. Rows 3–7 are building extraction results of SegNet, AGs-UNet, C3 Net, CSA-UNet, and our proposed AGSC-Net, respectively. The green and black pixels of the maps represent the predictions of true positive and true negative, respectively. The red, purple, yellow, and blue rectangles represent the parts of the extraction results that need to be focused on in the cases of sparse building, moderately dense building, dense building, and dense vegetation, respectively. by 0.3-3 percentage points and the IoU metric by 1-2.8 percentage points. This indicates that AGSC-Net can identify building pixels more accurately and provide finer building segmentation results. On the second dataset, as delineated in Table 3, AGSC-Net improved the OA metric by 0.2-0.5 percentage points and the IoU metric by 0.6-1 percentage points compared to other models, maintaining significant performance advantages. Although C3 Net has the highest precision P on the first dataset, AGSC-Net has a higher F1 score by balancing precision and recall, evaluating the overall effect of the model more comprehensively. In addition, the IoU metric combines accuracy and recall metrics, and the IoU metric of AGSC-Net is significantly higher than that of the other models, indirectly verifying that its extraction results are more accurate and complete. AGSC-Net has stronger adaptability to different types of rural buildings. It can be seen from the comparison of the two datasets that the metrics of all models on the second dataset are generally slightly lower than those on the first dataset. This is because the second dataset covers samples from the main terrain types in China, including masonry, stone, brick and other building types, but has fewer data samples than the first dataset, and the building styles are more complex, increasing the extraction difficulty. However, the performance decline in the AGSC-Net model is smaller than that of the other models, proving its stronger adaptability to different types of rural buildings. Specifically, although the P, R, and F1 metrics of all models decreased slightly on the second dataset compared to the first dataset, the decline in AGSC-Net is the smallest, and the R metric even increased slightly, indicating that its extraction results are more comprehensive and complete. At the same time, the IoU metric of AGSC-Net only dropped from 0.717 to 0.747, a decrease of less than 0.5%, significantly better than the 1-2% decrease in other models. Compared with other models using similar improvements, e.g., attention mechanisms such as CSA-UNet, the AGSC-Net model goes further by simultaneously utilizing the AG and context collaboration network (CC-Net), improving the representation capability and modeling capability of building features, thus achieving optimal performance. In summary, by achieving the best extraction results on two representative rural building datasets, the AGSC-Net model has proven its stronger generalization adaptability to extracting different building types in complex rural scenes.
###### Abstract  
DC short-circuit faults are one of the challenges for modular multilevel converter (MMC) based DC grid. It is vital for proper design of protection system to estimate the fault currents and voltages. The existing calculation methods based on RLC equivalent model of MMC have enough accuracy in estimating the branch currents but suffer from poor accuracy in estimating the node voltages. To better reflect the dynamics of MMC control during the fault, MMC is equivalent to a RLC series circuit in parallel with a variable controlled current source. This model not only considers the discharge of sub-module capacitors but also the AC active power and MMC control. Then, based on the discrete adjoint model of the equivalent MMC model and the RL series equivalent model of DC lines, the fault voltages and currents for the pre-fault and faulted DC grids could be easily obtained. From the aspect of power balance, the importance of AC active power on estimating the fault currents and voltages is discussed then. At last, based on the Zhangbei bipolar DC grid, comparisons are conducted between the simulations on PSCAD, the numerical calculation under the proposed method and the existing methods. The results show that the proposed method and the existing methods are both able to accurately estimate the fault currents within a relative error of 1%. However, compared with the error of the existing methods in calculating the fault voltages, the relative error for the proposed method is limited to less than 5% for the whole DC gird.  
modular multilevel converter (MMC); DC grid; discrete adjoint model; fault current and voltage +
Footnote †: journal: Applied Sciences  
1 March 2021  
19 March 2021
# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approaches** & **Techniques** & **Constellation** & **Limitations** & **Citations** \\ \hline Multipath Identification & Code-minus carrier (CMC) & GPS/GLONASS/BeiDou/ & Provides invalid solutions at the start of the data acquisition & [18] \\ and Mitigation & based & Random forest, Convolutional & & \\ Machine Learning & Neural Networks (CNN), & Dual Constellation & Can not considered the effects of the multi-frequency & [19; 20; 21] \\ methods & Support Vector Machine (SVM) & Dual Constellation & No real implementation & [22] \\ Multipath estimation & & & & \\ delay lock loop & Decision Tree & Single Constellation & No real implementation & [23] \\ Deep Learning method & Code-minus carrier & Dual Constellation & Only static scenarios were considered not dynamic sites & [24] \\ Regression method and & Support Vector Machine & Dual Constellation & Only static scenarios were considered not dynamic site & [25] \\ Mitigation & (SVM)-based nonlinear & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Multi-constellation GNSS Data Statistics in each candidate sites. Figure 1: Complete organization of the paper.
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 5 The Tethered Balloon Solution
### The Telecommunication System  
Simulations of radio coverage performed upstream will define the flight height. In this register, software like mobile radio can be handy. By fixing the height of the emission source, the transmitter's power, the receiver's sensitivity, and the losses in the transmission chain, it allows the sector covered to be represented [84]. It uses the Longley Rice model, which considers the geography and the composition of the site's atmosphere to be assessed. Figure 17 shows a radio coverage study on the Schefferville site (Northern Quebec) with a height for the transmitting antenna of 90 m and transmitter power of 1 watt corresponding to XBee Pro modules. Two frequencies were studied, 915 MHz and 2.4 GHz, for each of the two different sensitivities (0.5\(\upmu\)V and 1.58 \(\upmu\)V) for the receiver. We note that the 915 MHz gives broader coverage with the 0.5 \(\upmu\)V sensitivity. The area covered is the complete disc.