esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Experimental Set-Up  
We chose the NS-3 simulation platform based on C++ implementation for the experiment. NS-3 is an easy-to-use discrete event network simulator. This simulation experiment mainly includes three types of nodes: the server node that sends data, the user node that receives data, and the router node that forwards data. These nodes form the simulation experimental network topology shown in Figure 4.  
In the simulation experimental network shown in Figure 4, \(RouterA\) and \(RouterB\) are routing nodes with rate control and packet processing capabilities, and their connections form the bottleneck link required for the experiment. \(RouterA\) connects to the server nodes, which includes the data transmission access links necessary for the experiment. All transmission access links have the same bandwidth and latency attributes. The \(RouterB\) is connected to the user nodes, forming the data-receiving end access link required for the experiment. All receiving end access links have the same bandwidth and delay attributes.  
We chose the FIFO + DropTail queue management method on the router with a queue length of 100 packets. In terms of selecting the rate control strategy for the sending node, we chose the NewReno scheme optimized based on the classical Reno scheme, the  
Figure 4: Experimental topology.  
Cubic scheme widely used as the default scheme for Linux systems, and the Bbr scheme representing the new control concept for performance comparison with the proposed scheme (represented as Nodctl) in this paper. To maintain consistency with the comparison scheme and avoid other factors interfering and affecting the results, the Nodctl scheme proposed in this paper, like the three comparison schemes, selected the server for rate control during the experimental process. We conducted simulation experiments on the three aspects proposed in Section 3: the start-up speed, throughput capacity, and multipath transmission. The main parameter settings are shown in Table 2. We set the bandwidth of all server and user nodes to 200 Mbps, and the latency of the sending and receiving access links was 5 ms. The bandwidth and latency of the bottleneck links varied with the experimental requirements, and the maximum bandwidth was smaller than the bandwidth of the access links at both ends. The purpose of this experiment was to compare the performance of different rate control schemes under network resource constraints, and thus it was necessary to avoid performance limitations caused by the sending nodes. We tested the throughput performance of various schemes under different transmission data volumes. When testing other scenarios, the data size was set to zero, which means there is no limit on the data size sent. The sending end would continue to send data until the end of the experiment.  
During the simulation experiment, the NewReno, Cubic, and BBR schemes we used were all built-in versions of NS-3. We implemented the algorithms introduced in Sections 4 and 5 in NS-3. We implemented the Nodctl scheme based on the TCP transmission protocol to maintain consistency with the three comparison schemes.
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 2 Surface Modification of HNTs and the Relevant Properties
### Surfactant Modification  
Surfactant modification refers to the presence of non-polar lipophilic groups and polar hydrophilic groups in the surfactant molecules. HNTs can be successfully modified via electrostatic interactions [18]. The surfactants are able to be adsorbed selectively at the internal or external surface to maintain different hydrophilic/hydrophobic balances due to the charge characteristics of HNTs [19] and prepared into the amphipathic nanoparticles to obtain nanomaterials, such as the oil recovery/solubilization of hydrophobic molecules. The negatively charged surfactants were adsorbed mostly into the internal lumen on account of the positively charged internal surface [20; 21; 22]. [PERSON] et al. [23] prepared high-impact polystyrene nanospheres by emulsion polymerization. In this system, sodium dodecyl sulfate (SDS) was added to aqueous solution containing HNTs. SDS was regarded as an emulsifier to form a molecular layer on the surface of HNTs, so that the surface of HNTs has a strong hydrophilicity to enhance the dispersion in aqueous solution. In addition, [PERSON] et al. [24] used the surfactant of hexadecyltrimethylammonium bromide (HDTMA) to modify the HNTs and prepared a new adsorbent for the removal of Cr (VI) from the aqueous solution. The composite had the maximum adsorption rate for Cr (VI), which reached to 90% in 5 min.
## Appendix D
### Phase 1: Feasibility of Predictions in Edge-Nodes  
The existing SBN-implementation [23] was utilised for predicting energy consumption in buildings. The initial approach was to save the SBN-model into a file, which could be loaded into memory at the edge-node(s). However, due to the way the earlier implementation was constructed, the model couldn't be saved into a file. Thus, an alternative approach was chosen for transferring the model to the edge node(s). Weights of the trained NN model were saved, and transferred to the edge-node(s), where the NN-based model was constructed based on the weight file(s). The goal of the experiment was to ensure that the loaded model provides valid predictions in the edge node(s). In the design (Figure A1), ellipsis is used for describing data stores, rectangles illustrates data processing, and arrows describe data flows between architectural elements (a similar notation was used in the RA [17]).  
A demonstration was performed based on the following steps:  
1. SBN model(s) were trained in the private cloud based on several existing datasets [23]. After each training phase, weights of the trained model were saved into a file. Additionally, predictions were produced based on validation data (energy consumption prediction for 3 weeks) and saved.
2. The weight files were transferred to the edge-node.
3. A prediction model was created based on the loaded weight file(s) in the edge-node. Additionally, training data was needed in the edge-node to perform similar scaling of features, which was performed during the training phase.
4. Predictions were performed in the edge node (based on validation data) and saved.
5. The predictions after training (step 1) and after reloading of the model in the edge-node (step 4) were compared to ensure feasibility of the approach.  
Feasibility evaluation:  
* In most cases, the loaded model in edge node produced the same predictions as the original model in the private cloud. However, in \(\sim\)2.7% of the predictions, there was a rounding error (\(10^{-5}\)) which is negligible. Thus, the chosen architectural approach for model training, transfer, and model loading was feasible for further design of the architecture.
# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 2 Related Works
### Semantic SLAM  
With the development of deep learning, many works have introduced semantic segmentation into visual-based or lidar-based SLAM systems. DS-SLAM [13] combines a semantic segmentation network with a moving consistency check method to reduce the impact of dynamic objects; thus, the localization accuracy is improved in dynamic environments. [PERSON] et al. present a visual SLAM system (DynaSLAM) [14] that is built on ORB-SLAM2 [15]. It adds the capabilities of dynamic object detection and background inpainting, and DynaSLAM is robust in dynamic scenarios for monocular, stereo, and RGB-D configurations. SuMa++ [16] is an extension of SuMa [9] that consists of integrating semantic information to facilitate the mapping process. SA-LOAM [17] is a semantic-aided LiDAR SLAM with loop closure based on LOAM, which leverages semantics in odometry as well as loop closure detection. [PERSON] et al. propose lidar-based SLAM under semantic constraints in dynamic environments [18]. They used a spatial attention network (SANet) to achieve the semantic segmentation of point clouds.
## 4 Simulation Results and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Control Algorithm**} & \multicolumn{3}{c}{**Peak Current**} \\ \cline{2-4}  & **Without** & **With** & \\  & **CFFC and DRC** & **CFFC and DRC** & **Reduction Ratio** \\ \hline Proposed method & 157.67 [\%]/4.73 [A] & 147 [\%]/4.41 [A] & 10.67 [\%]/0.32 [A] \\ \hline SRFPI & 155.67 [\%]/4.67 [A] & 146 [\%]/4.38 [A] & 9.67 [\%]/0.29 [A] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation results of GCI system phase current without and with the CFFC and DRC. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Control Algorithm**} & \multicolumn{3}{c}{**Steady State Error [\%]**} & \multicolumn{3}{c}{**Phase Lag [Degree]**} \\ \cline{2-5}  & **Without** & **With** & **Without** & **With** \\  & **CFFC and DRC** & **CFFC and DRC** & **CFFC and DRC** & **CFFC and DRC** \\ \hline Proposed method & 4.9 & \(-\)0.003 & 23.67 & 3.2 \\ \hline SRFPI & 0.8 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results of GCI system phase current without and with the CFFC and disturbance rejection control (DRC). Figure 8: Simulation results of GCI system phase current (**a**) when line-to-ground grid fault occurs, (**b**) without and (**c**) with the CFFC and DRC. Figure 9: Simulation results of the output currents (**a**) without and (**b**) with the CFFC and DRC. Figure 10 shows simulated results of estimated disturbance and grid voltage. The accuracy of grid voltage estimation determines control system performance. If grid voltage and disturbance are decoupled, or grid voltage is rejected, then effect of disturbances to the transient current is expected to be decreased. Figure 10 compares actual and estimated grid voltage and error between the two signals. As shown in the simulation results, an estimation error of disturbance of below 5% is considered expected at steady state. The steady state and transient response of various control methods in case of a grid fault were simulated and compared. The results confirm that command tracking performance is improved by applying CFFC and DRC to the conventional control method. Their application also satisfied the overcurrent trip condition by reducing overcurrent during grid fault. In addition, the disturbance rejection is verified by applying the proposed DRC, which is presented for a backup solution when a sensor or a sensor interface board fails.
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 6 Conclusions
This work was supported and funded by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union--NextGeneration EU within the framework of the Recovery Plan, Transformation and Resilience (PRTR) (Call UNICO I+D 5G 2021, ref. number TSI-063000-2021-5-6 GSatNet-SS) and by the Government of Catalonia within the scope of the NewSpace Strategy for Catalonia. Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DSS & Distributed Space Systems \\ ISL & Inter-Satellite Link \\ LEO & Low-Earth Orbit \\ NTN & Non-Terrestrial Network \\ TLE & Two-Line Elements \\ SL & Supervised Learning \\ SGP4 & Simplified General Perturbation 4 \\ \(T\) & Distance threshold \\ SSO & Sun-synchronous orbit \\ OE & Orbital Elements \\ MAE & Mean Absolute Error \\ \(P\) & Precision \\ \(R\) & Recall \\ \(F1\) & F1 score \\ \(TP\) & True Positive \\ \(TP\) & False Positive \\ \(FN\) & False Negative \\ \(BA\) & Balance Accuracy \\ \(TNR\) & True Negative Rate \\ AP & Average Precision \\ PRC & Precision-Recall Cruve \\ \end{tabular}
# An Overview of Infrared Remote Sensing of Volcanic Activity
## 5 Infrared Remote Sensing of Volcanoes: A History
(2010), for example, later demonstrated the utility of such hyper-spectral imagery by using Hyperion data to distinguish various expressions of thermal activity, such as lava lakes, flows and fountains and, indeed, even different lava types, at various volcanoes globally. As time progressed, long datasets of remotely-sensed infrared imagery had accumulated, extending back to early AVHRR and Landsat observations and coming forward to the NASA-EOS sensors. This provided the possibility of determining the \"average\", or baseline radiant emissions, for a particular volcano, and of identifying potential periodicity in these signals and/or chronologies of particular eruptive events. [PERSON] et al. (2010) harnessed the long AVHRR time series for the purpose determining an average infrared signal, both spatially and temporally in volcanic regions, against which volcanic thermal anomalies could be compared and/or identified. The algorithm used to calculate this average was called the Robust AVHRR Technique, and while this technique has been criticised [110], it was later harnessed by [111], but this time with MODIS data, to develop an even more sensitive hot-spot detection algorithm. [PERSON] et al. (2010) analysed the entire MODVOLC database, identifying different eruption types and cyclical activity at various volcanoes and calculating the overall heat-flux from all volcanoes active during the operational period of the system. Chronologiesof specific eruptive episodes have also been presented, from [112], which used various sensors to derive a 2003 eruption chronology of Anatahan, Indonesia, to [11; 113; 114], each of which document eruption chronologies using infrared data for volcanoes in the North Pacific. Since the end of the last decade, the number of new publications in the discipline has been declining, although the discipline has certainly not stopped making progress. [PERSON] et al. [15; 115], for example, document and validate a new and more sensitive volcanic activity and real-time detection algorithm called MIROVA which uses MODIS MIR data. Additionally, [12] used MODIS TIR data to identify distinct phases of lava dome growth and collapse at Merapi Volcano, Indonesia, and to determine the varied volcanic causes of thermal anomalies detected. Now is perhaps a period of reflection as scientists review data from the past (e.g., [116; 117]) and offer suggestions for the design of future infrared imaging satellites (e.g., [118]).
## 7 Discussion  
With the emergence of a large number of intelligent road applications and the increasing competition in the intelligent transportation industry, more and more intelligent transportation application enterprises (task publishers) tend to dispatch intelligent VUs to assist in completing the training of application models. Currently, a large number of studies have proposed applying the FL framework to IoV scenarios, utilizing VUs' resources for local training, thereby greatly reducing communication latency and improving training efficiency. However, the dynamicity, low participation, and limited resources of VUs are all challenges faced by FL in the IoV.  
The proposed UAV-assisted FL framework utilizes UAVs to enhance connectivity. The contract incentive mechanism proposed in the system aims to stimulate VUs to participate and determine VUs' data quantity used for training. The experiments show that VUs of different types should contribute different amounts of data and achieve corresponding optimal rewards. In addition, VUs can obtain their maximum utility and ensure non-negative utility only when they choose appropriate contract items, which verifies the IC and IR principles of the contract mechanism.  
The energy-efficient resource allocation algorithm is designed to manage VUs' computing and communication resources during the training process. This algorithm determines the local training iterations and training willingness of different VUs based on the results of the contract mechanism, and then constructs and solves the energy consumption minimization problem. Experiments show that the energy consumption of the proposed algorithm is lower than that of the baseline algorithms under the conditions of different transmission parameters, different sojourn times, and different numbers of participating VUs.  
Utilizing the data quantity determined by the contract for FL, it is observed that the accuracy and convergence speed of the proposed system surpass those of the comparative algorithms. This further validates the efficacy of the proposed system, demonstrating its ability to effectively incentivize VUs to participate in federated training using appropriate resources.  
The UAV-assisted FL framework leverages the role of UAVs as edge servers, enhancing connectivity in dynamic vehicular networks and assisting in the training process of federated models. Future research needs to focus more on the augmenting role of UAVs in system communication, addressing issues such as reliability and stability. More attention should be given to the UAVs' contribution to reducing the risk of communication interruptions and minimizing data transmission latency.
# DAB Signal Preprocessing for Passive Coherent Location
## 2 Materials and Methods  
### Signal Acquisition and PCL Processing  
For the experiments described in this paper, DAB signals acquired during APART-GAS 2019 campaign [11; 12] have been employed. The illuminating transmitter was localized near Koszalin, Poland, and operated at a VHF frequency of 216.84 MHz with an Effective Radiated Power (ERP) level of 3.2 kW and vertical polarization. The PCL signal acquisition platform was moving [13] because of the installation on a ship sailing on the Baltic Sea, near the Polish coastline. The PCL receiver was based on a lightweight, four-channel configuration with RSP duo devices described in [32]. Further details of the system setup, antenna array, and supplementing hardware have been described in [12]. Two baseband DAB signals acquired coherently from directional antennas (the reference and the surveillance) have been digitized with 14-bit ADCs (sample rate \(F_{8}\) = 2 MHz) and stored. The remaining two signals were reserved for multi-channel processing planned in the further research.  
The signal processing pipeline used for obtaining the bistatic velocity/range plots with PCL detection results consists of the clutter-removal filter (based on an adaptive lattice filter) to suppress the direct path interference in the surveillance channel and the cross-ambiguity function calculator, as described in [12].
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 3 Results  
The Android Raw GNSS Measurements API provides measurements enabling the reconstruction of the traditional GNSS observables. Different apps are already available in the Google Play Store allowing the GNSS measurements to be converted to RTCM (Radio Technical Commission for Maritime Services) or RINEX (Receiver Independent Exchange Format) format. RINEX files from a Xiaomi Mi 8 were obtained in this work using _rinex ON_[21], which is the only RINEX logger able to decode GNSS measurements and navigation messages (RINEX 3.03 format): the only two types of information required to perform analyses in real-time scenarios with VADASE and VARION. The analysis in the velocity/positioning domain, carried out with VADASE, was based on L1/E1 GPS and Galileo measurements with a cut-off angle equal to 15\({}^{\circ}\). As discussed in Section 1, due to its poor quality, the GNSS antenna used within the smartphone is less resistant to cycle slips and multipath effects. In order to improve the final accuracy, a cycle-slip detection algorithm [13] and a LOOCV (leave one-out cross validation) [15] algorithm were used to remove faulty measurements from the final solution.  
In addition to the analyses with VADASE, the VARION algorithm was applied to observations coming from the Xiaomi Mi8. In detail, GPS and Galileo L1/E1 and L5/E5a signals were used. These tests represent a preliminary analysis of using smartphones to monitor the ionosphere. In particular,only the rate of change of sTEC as a function of time, that is, the sTEC time derivative (\(\Delta sTEC/\Delta t\)), was considered. Hence, no filtering and no integration were applied. In the end, only satellites with high SNR were chosen, in order to remove noisier observations.  
Although the Xiaomi Mi 8 is able to track L1/E1 and L5/E5a signals for GPS and Galileo, only L1/E1 measurements were considered in this paper during the analyses with VADASE. In fact, the redundancy within the estimation process is reduced using satellites with L1/E1 and L5/E5a in combination with ionosphere-free combination: only block IIF from GPS satellites currently transmits L5 signal. Furthermore, the development of a single-frequency methodology allows a larger number of users to be reached, considering the number of single-frequency smartphones now available on the market.
## 2 Ge Growth Techniques
### Poly Ge Films  
For ease of integration of near-infrared detectors with standard Silicon process lines for signal acquisition, amplification and processing, low temperature growth of Ge layers is much desired. In 2000, a Ge deposition approach based on the thermal evaporation with process temperatures as low as \(\sim\)300 \({}^{\circ}\)C was first proposed in the pioneering work conducted by [PERSON] _et al_.[6]. It was found that polycrystalline Ge deposition can be possible at substrate temperatures as low as 300 \({}^{\circ}\)C, as confirmed by the Raman spectra results (Figure 1). This method allows simple and low cost integration with Si processes. Monolithic integration of an array of eight polycrystalline Ge pixels with CMOS readout electronics was demonstrated based on this method [7], shortly after which [PERSON] _et al_.[8] reported the realization of a digital camera, further confirming the process compatibility of the low-temperature approach.  
Figure 1: **(a)** Raman spectra of the Ge on Si samples grown at different temperatures by thermal evaporation method. From [6]. **(b)** Photograph of one pixel of the digital camera (top) and a sketch of its cross section. From [8].  
Moreover, although the low temperature deposition introduces a relatively high density of defects and dislocations into the poly-Ge layer and worsens the electrical properties compared to crystalline Ge films, it was shown recently that by a careful design, acceptable performance of the polycrystalline Ge photodetector for Si photonics integration can be obtained, with responsivities between 0.1 A/W and 0.3 A/W [9].