esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Unmanned aerial photogrammetric surveys are increasingly being used for mapping and studying natural hazards, such as rockfalls. Surveys using unmanned aerial vehicles (UAVs) can be performed in remote, hardly accessible, and dangerous areas, while the photogrammetric-derived products, with high spatial and temporal accuracy, can provide us with detailed information about phenomena under consideration. However, as photogrammetry commonly uses indirect georeferencing through bundle block adjustment (BBA) with ground control points (GCPs), data acquisition in the field is not only time-consuming and labor-intensive, but also extremely dangerous. Therefore, the main goal of this study was to investigate how accurate photogrammetric products can be produced by using BBA without GCPs and auxiliary data, namely using the coordinates X\({}_{0}\), Y\({}_{0}\) and Z\({}_{0}\) of the camera perspective centers computed with PPK (Post-Processing Kinematic). To this end, orthomosaics and digital surface models (DSMs) were produced for three rockfall sites by using images acquired with a DJI Phantom 4 RTK and the two different BBA methods mentioned above (hereafter referred to as BBA\({}_{\text{-}}\)traditional and BBA\({}_{\text{-}}\)PFK). The accuracy of the products, in terms of the Root Mean Square Error (RMSE), was computed by using verification points (VPs). The accuracy of both BBA methods was also assessed. To test the differences between the georeferencing methods, two statistical test were used, namely a paired Student's _t_-test, and a non-parametric Wilcoxon signed-rank. The results show that the accuracy of the BBA\({}_{\text{-}}\)PFK is inferior to that of BBA\({}_{\text{-}}\)traditional, with the total RMSE values for the three sites being 0.056, 0.066, and 0.305 m, respectively, compared to 0.019, 0.036 and 0.014 m obtained with BBA\({}_{\text{-}}\)traditional. The accuracies of the BBA methods are reflected in the accuracy of the orthomosaics, whose values for the BBA\({}_{\text{-}}\)PFK are 0.039, 0.043 and 0.157 m, respectively, against 0.029, 0.036 and 0.020 m obtained with the BBA\({}_{\text{-}}\)traditional. Concerning the DSM, those produced with the BBA\({}_{\text{-}}\)PPK method present accuracy values of 0.065, 0.072 and 0.261 m, respectively, against 0.038, 0.060 and 0.030 m obtained with the BBA\({}_{\text{-}}\)traditional. Even though that there are statistically significant differences between the georeferencing methods, one can state that the BBA\({}_{\text{-}}\)PPK presents a viable solution to map dangerous and exposed areas, such as rockfall transit and deposit areas, especially for applications at a regional level.  
Keywords:georeferencing; UAV; photogrammetry; GNSS; PPK; accuracy; bundle block adjustment; orthomosaic; digital surface model; rockfalls +
Footnote †: journal: remote sensing
# Development Cycle Modeling: Resource Estimation
## 5 Discussion  
The estimate correlations are important; but other factors should also be considered. On one hand, COCOMO is a well thought out curve fit to a well-known, long-used data set. Using COCOMO is a matter of gathering information about a project in its very earliest stages, casting that data in terms of the COCOMO regression parameters, and taking into account any differences between the COCOMO data set projects and the project being estimated. Once this is done, the actual calculation takes seconds to complete. The results assume that the estimate is made at the beginning of the project. Also, the results are designed to apply to projects that use the Waterfall or the Spiral development  
Figure 8: Statistical Agent-based Model of Development and Evaluation (SAbMDE)-to-COCOMO effort estimation comparison for various KLOC values.  
methodology [38] (p. 44). On the other hand, using SAbMDE requires an agent to select the number of compositions to represent the current project at its current maturity level, to enumerate the composable elements associated with those compositions, and then to assign prices to the composable elements. Once this is done, the actual calculation takes seconds to complete. The calculation can adjusted easily as the current project evolves. An estimate can be performed as frequently as needed. The results are methodology-agnostic. SAbMDE is a work in progress. Further mathematical and software development is necessary. Although the model reproduces COCOMO results quite well and has been shown to match certain accepted characteristics of other design theories, additional validation is necessary and underway. Because the modeling concept is new and because user interface requirements are challenging, practical deployment of the model could be problematic; however, these issues are being given due consideration.
## 5 System Implementation and Field Testing
### Testing Results
#### 5.2.2 Personalized Dynamic Scheduling
The above experiments are not exhaustive because they only cover case \(T_{i}^{Arr}\leq T_{i}^{Ideal}\). We further verified whether the personalized dynamic scheduling function correctly computes the estimated personalized waiting time and recommended session time under case \(T_{i}^{Arr}>T_{i}^{Ideal}\). We added Racing Cars and Mountain Adventures into My Play List, and then activated the personalized dynamic scheduling function with the Hottest First strategy at 12:24. We determined the next session time as 12:40, the personalized waiting time as 6 min, and the recommended session time as 12:50. Figure 10 shows the result derived by the proposed TPTS system. Results validate that the personalized dynamic scheduling function correctly calculated the personalized waiting time and recommended session time.
# Human Activity Recognition Using CSI Information with Nexmon
## 4 The Experimental Setup and CSI Data Collection
### Hardware Detail  
CSI is the collection of data that explains how wireless signals propagate from a transmitter to a receiver, which means it is a mode of communicating amplitude and phase-specific data of individual subcarriers over a transmitting and receiving antenna pairing in the IEEE 802.11N standard [26]. There are exclusively some manufacturers who make this CSI data available to practitioners. The design of standardized CSI implementation was to control the connection quality. As commercial CSI solutions have been becoming more convenient, numerous comprehensive nonstandard CSI applications have become relevant. For the experimentation, an NIC Broadcom BCM43455c0 and Asus RT-AC86U router were used. Both NICs support the IEEE802.11n/ac standard with MU-MIMO, making it suitable for the frequency band of 20 MHz, 40 MHz, and 80 MHz. On 40 MHz, there are 128 subcarriers; 108 for data, 6 for the pilot, and 14 as null subcarriers. For the duration of data collection, each packet contained the data of all the 128 subcarriers. However, in reality only 114 subcarriers are useful for HAR on Pi NIC. Raspberry Pi official operating system (OS) version Raspbian Buster Kernel v4.19.97 was used on Pi 3B+ and Pi 4B. The default kernel version loaded with the Wi-Fi BCM firmware version does not support the monitor mode to capture the CSI data. The default drivers of NIC by the manufacturer do not enable the monitoring mode functionalities for the end-users.  
Another two experiments based on Asus RT-AC86U frequency band of 80 MHz are used in entirely different surroundings with 256 subcarriers. In these experiments, the frequency of 5 GHz was chosen since it has less interference than 2.4 GHz [27; 28]. One of the Asus RT-AC86U routers is configured to be an access point (AP), and the second one a CSI extractor that has patched with Nexmon firmware which enables the extraction of the CSI data. Additionally, Intel NUC was used that acts as a sender to generate the traffic by sending ping packets to the AP, then AP replies with a pong packet back to the sender. The CSI extractor then captures and extracts CSI data for each pong (see Figure 1).
# Real-Time Image Detection for Edge Devices: A Peak Fruit Detection Application
## 2 Materials and Methods
### SSD: Single Shot Detector
#### 2.3.3 MobileNet Edge TPU  
MobileNetV1 and MobileNetV2 were designed manually, entirely by hand. In contrast, the MobileNet edge TPU was developed using the accelerator-aware auto-machine learning (AutoML) [41] approach, which significantly reduces the manual process of designing and optimizing neural networks for hardware accelerators [42]. MobileNet Edge TPU is a version of MobileNet that has been adapted to run optimally on edge TPU devices (and take advantage of their features). In this study, this model was expected to perform significantly better in terms of accuracy and latency than MobileNetV1 and MobileNetV2 when running on a TPU device.
# A Review of Hydrogen Sensors for ECLSS: Fundamentals, Recent Advances, and Challenges
## 1 Introduction  
The manned space station environmental control and life support system (ECLSS) is the infrastructure required to guarantee the survival of astronauts in the complex and changing space environment. Since astronauts in manned space stations are mostly in orbit for medium-to-long periods, the space station is equipped with a semi-regenerative environmental control and life support system, i.e., a physical-chemical regeneration system. The physical-chemical regenerative life technology remains the primary life support technology used on the space station today and will be so for a long time to come. This technology produces oxygen via electrolysis of water to meet the breathing needs of astronauts during their life on the space station and as a means of controlling the partial pressure of oxygen in the sealed module atmosphere. The hydrogen, as a product of the electrolytic oxygen production, is eventually discharged to the bunker or supplied to the CO\({}_{2}\) reduction system as a feedstock for the CO\({}_{2}\) reduction reaction. However, hydrogen is very permeable and is highly susceptible to leakage during system operation, and will explode when the hydrogen content in the air reaches 4-74.4% if it encounters an open flame.  
The lack of timely and effective means of hydrogen monitoring will seriously threaten both astronauts' lives and the safety of manned spacecraft cabin equipment. The hydrogen sensor is a sensing device that detects the concentration of hydrogen and generates a response signal, which has the advantages of small size, low cost, online measurement, and short response time compared to traditional detection instruments [1; 2; 3; 4; 5; 6]. Therefore, the development of hydrogen sensors with a high detection accuracy, a fast response time, a long calibration period, and good stability has become the focus of regenerative life support technology design.  
According to the principle, hydrogen sensors can be divided into catalyst combustion type [7; 8; 9], heat conduction type [10; 11; 12], semiconductor type [13; 14; 15], fiber-optic type [16; 17; 18], etc. Currently, hydrogen sensors of the heat conduction principle are used for hydrogen concentration detection in the electrolytic oxygen generation system of the space station, and hydrogen sensors of the catalyst combustion principle are used for hydrogen concentration detection in the space station environment. The purpose of this paper is to describe and review the working principles and characteristics of the catalyst combustion and heat conduction type hydrogen sensors based on the practical application requirements of hydrogen sensors for space stations' physical and chemical regeneration systems. The article also provides a preliminary summary of the operating principles and characteristics of semiconductor-type and fiber-optic-type hydrogen sensors. Finally, recent technological developments and performance improvements in hydrogen sensors are reviewed, and the future of hydrogen sensors is discussed.
# Graph Representation-Based Deep Multi-View Semantic Similarity Learning Model for Recommendation
## 4 Collaborative Filtering Algorithm Based on Graph Embedding Model
### Collaborative Filtering Algorithm Based on Graph Embedding Model  
An embedded representation of user nodes learned by the GraphSAGE model captures both the local and global structure of the social network. Based on the embedded representation of the user nodes, the trust between users is defined as:  
\[SIM_{uv}=\frac{h^{T}_{u}\cdot h_{v}}{\|h_{u}\|_{2}\|h_{v}\|_{2}}. \tag{6}\]  
Compared to the original trust relationships in social networks, the user trust level obtained by Equation (6) is not only fine-grained and dense, but also captures both explicit first-order trust relationships and implicit second-order trust relationships between users of social networks. In other words, if there is no explicit trust relationship between the users, but the users have similar neighborhood structure; the user trust Suv calculated according to Equation (6) captures the second-order trust relationship between the users.  
Likewise to the memory-based collaborative filtering algorithm, after computing the trust relationship between users according to Equation (6), Equation (7) is used to calculate the prediction score of user u for item \(j\).  
\[\gamma^{T}_{uj}=\tilde{\gamma_{u}}+\frac{\sum_{v\in L}SIM_{uv}\times(\gamma_{ vj}-\tilde{\gamma_{v}})}{|\sum_{v\in L}SIM_{uv}|}, \tag{7}\]  
where \(\tilde{\gamma_{u}}\) denotes the average rating of user \(u\) on all rated items, \(\tilde{\gamma_{v}}\) denotes the average rating of user \(u\) on all rated items, \(\gamma_{vj}\) denotes the rating of user \(v\) on item \(j\), and \(H\) denotes the set of users that user \(u\) trusts the most. Note that, as shown in Equation (2), in the classical user-based collaborative filtering algorithm,
## 6 Conclusions  
In this paper, a hybrid framework capable of modeling and evaluating data quality in real-time telemedicine systems is presented. Alongside a system specification, we have provided a use case that demonstrates how our simulation and emulation techniques can be applied to various system components. Using simulations, we are able to observe how data quality changes under specific conditions. Our simulations are capable of closely approximating the operations of the algorithms, allowing correct results to be obtained even with a comparatively limited database. After analyzing our results and multiple classification algorithms, it was determined that latency can have a substantial impact on data quality, to the point where the reduction can exceed 15%. Future plans include benchmarking our hybrid approach and applying it to other types of real-time systems. Furthermore, we plan to integrate machine learning into this framework so that data quality can be predicted without the need for simulations. Large Language Models (LLM) make it simple to comprehend processes and algorithms, and they can extend our framework with an asymptotic analysis based on the implementation of the algorithms and the data structures used.  
**Author Contributions:** Conceptualization, V.B.; methodology, Z.R.J.; software, Z.R.J.; validation, Z.R.J. and V.B.; formal analysis, Z.R.J.; investigation, Z.R.J.; resources, Z.R.J.; data curation, Z.R.J.; writing--original draft preparation, Z.R.J.; writing--review and editing, Z.R.J. and V.B.; visualization, Z.R.J.; supervision, V.B.; project administration, V.B.; funding acquisition, V.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the EU-funded Hungarian grant GINOP-2.2.1-15-2017-00073, project no. TKP2021-NVA-09 has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme; project no. II-NKFIH-1528-1/2021 has been implemented with the support provided by the Ministry of Innovation and Hungary from the National Research, Development and Innovation Fund, financed under the II-NKFIH-1528-1 funding scheme. This study was also supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program (RRF-2.3.1-21-2022-00004).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The framework is available from the corresponding author upon request, and the data used to support the conclusions of this study are accessible in Physionet.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Cavity of Length \(L=16\lambda\)
#### 3.2.4 Several Source Frequencies  
A study similar to Sections 3.2.2 and 3.2.3 is carried out by keeping \(p_{s}\) constant at \(p_{s}=20\) kPa and \(v_{v}\) constant at \(v_{v}=3.2725\times 10^{-5}\) for different source frequencies. Figure 8 represents the maximum amplitude of the 2 nd, 3 rd, and 4 th harmonics vs. ratio \(f_{0}/f\) for \(f=200\) kHz, \(f=300\) kHz, and \(f=400\) kHz. The lowest \(f\) value generates the higher harmonic amplitudes, in a way similar to Section 3.1.4. For the three harmonics, the maximum is shifted slightly toward a lower ratio \(f_{0}/f\) for higher source frequencies.  
To summarize, in this open-field case, larger source amplitudes, smaller source frequencies, and a ratio \(f_{0}/f\) of approximately 5, 7, and 9 are more convenient and efficient for maximizing the 2 nd, 3 rd, and 4 th harmonic, respectively. The generation of harmonics in this configuration is independent of void fraction.  
Figure 6: Maximum amplitude of \(2f\) (**a**), \(3f\) (**b**), and \(4f\) (**c**) in the cavity of length \(L=16\AA\) vs. \(f_{0}/f\), for three different source amplitudes \(p_{s}=20\) kPa (blue line), \(p_{s}=22.5\) kPa (green line), and \(p_{s}=25\) kPa (red line).
# Impedance Modeling Based Method for Sub/Supsynchronous Oscillation Analysis of D-PMSG Wind Farm
###### Abstract  
Subsynchronous oscillation (SSO) is a critical issue for the direct-drive permanent magnet synchronous generator (D-PMSG) based wind farm integrated to a weak onshore AC grid. To analyze the mechanism of the SSO phenomenon of D-PMSG based wind farm, widely used impedance-based stability analysis method is utilized in this paper. First, the impedance model based on the harmonic linearization theory of grid-connected D-PMSG is proposed, and the mechanism of sub/supsynchronous currents coupling is analyzed quantitatively for the first time. Then, based on the impedance model and relative stability criterion, the influence of wind farm operating parameters and grid impedance on stability is discussed. Simulations are carried out to verify the correctness of theoretical analysis.  
direct-drive permanent magnet synchronous generator (D-PMSG); frequency coupling; harmonic linearization; impedance modeling; sub/supsynchronous oscillation Article
## 4 Verification of Pipeline's Dynamic Response by Numerical Simulation
### Verification of Numerical Model  
The monitoring data were selected for comparison to verify the application of the numerical calculation model shown in Figure 8. The subsequent studies on the dynamic responses of the existing pipelines were based on the reliability of the numerical model. Table 3 shows the numerical results of the resultant peak particle velocity (PPV) and the monitored data of each monitored point.  
Figure 8: Relative location between pipelines and subway station.  
Figure 7: Numerical model of Shi Kui Subway Station.  
Figure 9 shows the monitored vibration velocity waveform and the numerical vibration velocity waveform of the ground surface monitored points 3 and 2 (directly above the pipelines DN300 and DN800). As shown in Table 3, the PPVs of the monitored points were slightly smaller than those of the numerical simulation. The relative differences of the resultant PPVs were 4.4% and 2.3%, which are less than 10%.  
The numerical waveforms of the vibration velocity were consistent with the monitored ones. The waveform consisted of a large peak in PPV at the beginning and numerous peaks that decayed with time. The vibration period time of the numerical and monitored results  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Monitored** \\ **Point Location** \\ \end{tabular} & \begin{tabular}{c} **Pilot Tunnel** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Monitored PPV** \\ **(cm/s)** \\ \end{tabular} & \
\begin{tabular}{c} **Numerical PPV** \\ **(cm/s)** \\ \end{tabular} &
\begin{tabular}{c} **Relative** \\ **Difference (\%)** \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{3} & 2 nd & 0.90 & 0.94 & 4.4 \\  & 4 th & 2.58 & 2.64 & 2.3 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison of monitored data and numerical results.  
Figure 9: Comparison of vibration velocity waveforms: (**a**) PPV of monitored point 4 induced by the 2 nd pilot tunnel; (**b**) PPV of monitored point 2 induced by the 4 th pilot tunnel.  
was close. The PPV of the numerical model lagged behind that of the monitored points by approximately 0.1 s. It can be attributed to the homogenous simplification of the rock and soil during the numerical simulation. Indeed, soil pores, rock mass joints, and discontinuity in the field delay the propagation of the blast-induced stress waves [36; 37; 38].  
By comparing the numerical calculation results with the monitored data, the numerical model is applicable, and the model parameters are selected reasonably. The PPVs at each node in the numerical simulation and the field are close. The numerical simulation is used to study the dynamic responses of the existing pipelines because it is impossible to directly monitor the existing pipelines themselves in the actual project.