esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Figure 1: Examples of agricultural product transportation methods in the Republic of Korea. LiDAR sensing technology can be broadly categorized into two-dimensional (2D) and three-dimensional (3D) systems, enabling rapid information acquisition through distance and angular resolution. While single-channel 2D LiDAR scanners offer a cost-effective solution, their lower specifications can limit perception performance without the implementation of sophisticated algorithms and data processing methods. On the other hand, high-performance LiDAR sensors are often used for perception in autonomous vehicles, providing superior specifications but coming at a relatively high cost [17]. Table 1 compares the specifications of the YDI:DAR G6 sensor used in this study with those of other leading LiDAR sensors. As shown, LiDAR sensors from SICK and Velodyne provide a detection range of over 80 m with high resolutions of 0.167\({}^{\circ}\) and 0.11\({}^{\circ}\), respectively, but their size and cost pose challenges for small mobile robot applications. In contrast, the YDI:DAR G6 sensor offers a cost-effective solution with sufficient key functionalities suitable for such applications. This research was conducted to develop a near-distance object-following system using a relatively low-spec 2D LiDAR sensor for a crop transport platform suitable for the unique outdoor agricultural conditions in the Republic of Korea, and to analyze its performance. Through this study, we have observed the potential feasibility of the system in practical applications. We plan to conduct tests in actual field environments within the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **YDI:DAR G6** & **SICK-LMS** & \begin{tabular}{c} **Velodyne** \\ **Alpha Prime** \\ \end{tabular} \\ \hline \begin{tabular}{c} Number of \\ channels \\ Field of view \\ (horizontal) \\ \end{tabular} & Single & Single & 128 channels \\ \begin{tabular}{c} 360\({}^{\circ}\) \\ 16 m \\ \end{tabular} & \begin{tabular}{c} 190\({}^{\circ}\) \\ 80 m \\ \end{tabular} & \begin{tabular}{c} 360\({}^{\circ}\) \\ 245 m \\ \end{tabular} \\ \begin{tabular}{c} Angular \\ resolution \\ \end{tabular} & \begin{tabular}{c} 0.1\({}^{\circ}\), 0.14\({}^{\circ}\), 0.24\({}^{\circ}\) \\ 0.333\({}^{\circ}\), 0.5\({}^{\circ}\), 0.667\({}^{\circ}\), 1\({}^{\circ}\) \\ \end{tabular} & \begin{tabular}{c} Minimum 0.11\({}^{\circ}\) \\ \end{tabular} \\ \begin{tabular}{c} Protocol \\ Price \\ \end{tabular} & \begin{tabular}{c} Serial \\ About \\ 300 dollars \\ \end{tabular} & \
\begin{tabular}{c} TCP/IP, UDP/IP \\ About \\ 10,000 dollars \\ \end{tabular} &
\begin{tabular}{c} TCP/IP, UDP/IP \\ About \\ 70,000 dollars \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of LiDAR sensor specifications. Figure 2: Applications of LiDAR sensors: positioning aid for container handling, navigation aid and collision prevention for cranes, vehicle classification, and monitoring open spaces for building security. Republic of Korea to further explore its usability and effectiveness for real-environment agricultural applications.
# Secure Utilization of Beacons and UAVs in Emergency Response Systems for Building Fire Hazard
## 3 UAV-Assisted Emergency Monitoring and Response System
### Overview of UAV-EMOR System  
There are four novel functionalities of our UAV-EMOR system: (1) provide secure communications between UAVs, smart sensors, a control server and the security manager's smartphone app, which is based on the AllJoyn security platform; (2) support real-time monitoring using smart sensors; (3) support active response using indoor/outdoor UAVs; and (4) support beacon-aided rescue. Figure 1 provides an overview of our UAV-EMOR system. The system consists of six entities: smart sensors, indoor/outdoor monitoring UAVs (Unmanned Aerial Vehicles), a security manager, a control server, users and CCTV. Indoor and outdoor UAVs periodically follow designated routes, monitor conditions inside and outside of the building while interfacing with smart sensors that have been placed in each room. Each smart sensor checks the surrounding temperature and notes potentially abnormal situations inside the room. Each UAV transmits its monitoring data and images directly to the control server, where a building security manager can monitor the images at any time. CCTV is still used to monitor the space for potential dangers, like fire, and stores those recordings on the server. However, the indoor UAVs on each floor can focus on blind spots that CCTV cannot see.  
The indoor UAV only monitors when it is given a task, but UAVs currently in commercial use have an average of about 30 min-1 h of battery life, which is sufficient for most tasks. So after use, they must be recharged. To solve this battery issue, we assume that an indoor UAV usually stays in a specific landing spot on each floor. The indoor UAV can remain charged at the landing spot until it receives the command from the control server.  
The Bluetooth beacons are attached to or embedded in the indoor UAVs and the users' smart ID cards for indoor location recognition. The indoor UAV's beacon transmits the ID of the indoor UAV to the smart sensor. Only authorized UAVs can get entry into the room. The beacons in the smart ID cards transmit a signal to the smart sensor in an emergency situation so that the smart sensor determines the number of users and the users' information in each room. The smart sensors, UAVs and security manager's smartphone are all connected to the control server via the wireless network. We use the AllJoyn security platform in order to provide secure communications among the various components of our system. Figure 2 shows the main components of UAV-EMOR system.  
Figure 1: The overview of our UAV-assisted emergency monitoring and response system.  
Figure 2: The Main components of the UAV-EMOR system.
## 9 Samples of the Results
### Real-World Autonomous Navigation Using DRL  
[PERSON] et al. [38] proposed a DRL framework for the autonomous navigation of mobile robots in real-world, unstructured indoor environments. The system uses fused data from a 2D laser scanner and an RGB-D camera as input, while the output consists of linear and angular velocity commands generated by an asynchronous advantage actor-critic (GA3C) network. The robot is trained in a custom simulation environment capable of parallel processing, which accelerates learning by simulating multiple environments simultaneously. Key features of the framework include the fusion of 2D and 3D sensor data for robust obstacle detection, the use of a simplified network architecture to optimize for hardware constraints, and a reward function tailored to encourage goal-directed navigation while minimizing collisions. The learned policy is deployed on a real robot (Turtlebot 2), demonstrating its capability to navigate efficiently without requiring pre-built maps or external planners.  
Figure 19: Comparison of average returns for CMBAC and baseline methods on six benchmark tasks. CMBAC demonstrates superior sample efficiency and robustness (Adapted from [86]).  
A particularly illustrative result is presented in Figure 21, which compares training results for 1D and 2D convolutional network architectures over 20,000 episodes. The figure highlights that 1D convolutions achieve comparable navigation performance while reducing training time significantly, making them more practical for real-world applications. Additionally, the red-circled area in the figure indicates a reduction in environmental diversity during later training stages, focusing on challenging scenarios to refine policy performance.  
Figure 20: Comparison of learning curves for agents trained with and without auxiliary tasks across different maze configurations. Agents leveraging auxiliary tasks, such as depth prediction and loop closure detection, achieve faster convergence and higher final performance (adapted from [34]). Star in the label indicates the use of reward clipping.
## 2 Materials and Methods
### Preliminary Data Collection  
A national-scale EPC-based model was developed following the methodology defined by [PERSON] et al. [9]. This model includes a GIS database that combines data from EPCs, Spain's cadastral data, the INSPIRE cadastral data of Spain, climate zones, and the size of the municipality where the building is located. The input data used are those existing and valid as of 1 January 2024.  
This preliminary model contains the geographic and physical data of all buildings in Spain available in Spain's cadastre, excluding the regions of Navarra and the Basque Country, which have their own cadastral systems. It also contains the energy information of all buildings with at least one EPC available in the open databases published by the regions of Spain. EPCs provide information on the energy performance of buildings, specifically the NRPEC (non-renewable primary energy consumption) in kWh/(m\({}^{2}\)y) and the GHG emissions in kgCO\({}_{2}\)eq/(m\({}^{2}\)y).  
As of 1 January 2024, Spain, excluding the regions of Navarra and the Basque Country, has 11 million existing buildings, of which 8.7 million are residential, and 1.2 million of them have open EPC data. Therefore, energy performance data are available for 13.8% of existing residential buildings. Since the objective is to train a model capable of predicting the NRPEC and the GHG emissions of all residential buildings of the country, the energy performance data and physical characteristic of the buildings will be used to train the algorithm. From the open EPCs, only the target variables to be predicted, NRPEC and GHG emissions, are used. To ensure the model's applicability, it uses information from other accessible sources for all buildings in Spain as input data to learn NRPEC and GHG emissions patterns.  
This database is generated with open data provided by public sources, and the tool for generating the database is available on the GitHub of the article [9]. The steps related to pre-processing the data, feature extraction, and outlier detection have already been developed in the input database as detailed in [9]. By means of the cadastral reference, it is possible to link the information available on a building from different information sources. This data will be used to train the model and relate its physical characteristics to its energy performance (Figure 2).  
Figure 2: Data obtained from different data sources.
## 4 Discussion
The clustered components are then matched with the corrected MR spectra in the frequency domain. The comparison of TARQUIN results with the phantom data ground truth shows, our approach outperforms TARQUIN in quantifying the metabolite concentration. Several important limitations must be considered. First, the metabolites relaxation times, T1 and T2, were not measured due to limited scan time; therefore, the values from other studies were used to quantify the metabolite's concentrations [42; 43], which provides a potential source of error. Second, generally, some uncertainty exists in the parameters that were used to calculate the correction factor from the internal reference, which might cause a systematic error that must be addressed in an independent study. Third, the number of subjects and their age range were too small to investigate age-related variances in the metabolite quantification process. Fourth, the study was conducted with a 3T scanner, and using MR scanner machines with higher field strength provides signals with better quality and SNR.
## 5 Results and Discussion
To determine the effect of variations in training power sequence, NCMSE is plotted in Figure 5 for the proposed SiT-StOMP and SiT-GP for several values of TIR i.e., \(\sigma_{c_{n_{t}}}^{2}/\sigma_{b_{n_{t}}}^{2}=0.2,0.5\) and \(1\). For this purpose, the channel sparsity parameter \(Q/L\) is set equal to \(3/14\). It can be observed from Figure 5 that, for a fixed information sequence power, \(\sigma_{b_{n_{t}}}^{2}\), increasing the training sequence power \(\sigma_{c_{n_{t}}}^{2}\) results in an improved NCMSE performance for both SiT-StOMP and SiT-GP. However, increasing the TIR leads to a poor BER performance, as this increased training power could have been utilized for the information sequence. Therefore, a suitable value of TIR must be chosen in order to relieve the NCMSE and BER trade-off. Figure 3: Effect of channel sparsity variation on superimposed training stagewise orthogonal matching pursuit (SiT-StOMP), \(M=1500\) bits, and \(\sigma_{c_{n_{t}}}^{2}/\sigma_{b_{n_{t}}}^{2}=0.2\), (**a**) NCMSE based performance; (**b**) BER based performance. A detailed performance comparison (in terms of NCMSE versus channel sparsity and TIR) of SiT-StOMP, SiT-GP, and SiT-LS algorithms is presented in Figure 6a,b, respectively. In Figure 6, it can be observed that, for all levels of TIR and channel sparsity, both SiT-StOMP and SiT-GP outperform SiT-LS. Moreover, it is evident from Figure 6a that for a TIR of 0.3, SiT-StOMP outperforms SiT-GP for high levels of channel sparsity; however, for a less sparse channel, a converse behaviour is observed. This performance gain (for highly sparse channels) of SiT-StOMP compared to SiT-GP is also influenced by the TIR level (which is also depicted in Figure 5). Therefore, the effect of TIR levels on NCMSE performance of the proposed algorithms is demonstrated in Figure 6b. It can be observed that SiT-StOMP performs equally well both for lower as well as higher values of TIR in comparison with SiT-GP and SiT-LS (plotted for a highly sparse channel). However, there is a performance trade-off (as discussed earlier) between the accuracy of channel estimates and retrieved information sequence for different values of TIR. Therefore, it is desirable to keep TIR at a certain low value (e.g., TIR = 0.2) that promises an adequate BER performance. With such setting of TIR for an environment exhibiting highly sparse CIR, it is realized that SiT-StOMP is a favorable choice for obtaining channel estimates. Figure 5: Effect of training-to-information ratio (TIR) variation on NCMSE performance of proposed methods, \(M=1500\) bits, and \(Q/L=3/14\), (**a**) SiT-StOMP; (**b**) SiT-GP. Figure 6: Performance comparison of SiT-StOMP, SiT-GP, and SiT least squares (SiT-LS) algorithms. (**a**) effect of channel’s sparsity; (**b**) effect of TIR.
# Ephemeral Node Identifiers for Enhanced Flow Privacy
## 3 Method and Implementation
### Communication Across IPv6 Networks
#### 3.3.1 Lab-Based IPv6 Testbed  
eNIDs are an alternative to SLAAC and TA-SLAAC for managing IID values. For our demonstration, each of these mechanisms was used to defend an identical set of application-layer exchanges against an eavesdropper.  
Figure 4 shows the Testbed topology, and the analogous real world scenario. The traffic was produced by a set of three clients fetching web pages from a server. The server had multiple IP addresses, and served different web pages on each, giving the same behaviour as having three different servers--one with each IP address. Clients were connected to the server by a backbone network of three edge routers, one of which acted as the attacker.  
The client nodes requested web pages using the curl program, and the server node ran the widely-used nginix web server software. These binaries were not modified in any way: ILNP support, including eNIDs, was provided transparently by the modified branch of the FreeBSD 14.0-CURRENT operating system. The routers were unmodified Ubiquitin EdgeRouters running EdgeOS routing software, and were configured to route IPv6 traffic: ILNP packets are routed in exactly the same way as IPv6 packets, so this network provided the same behaviour in each test case.  
In each experiment, each client used curl to request the three pages available on the server every 5 min for a 24-h period. Three cases were tested:  
1. In the first case, each client machine configured a single, fixed address using SLAAC, and used it for all connections.
2. In the second case, each client used TA-SLAAC. The TA-SLAAC addresses were configured to become deprecated after 1 h, and be expired after 1 h more. This provided the same characteristics as the default 24-h period, but with greater privacy, as address reuse was decreased. Decreasing this further was limited on FreeBSD by traffic storm counter-measures.
3. The third case used eNIDs for each client, i.e., each flow had a different NID value.  
In each case, every packet seen by the attacker was recorded. Due to the constrained storage space of the EdgeRouters, this packet capture was performed by the server node using tcpdump, instead of on the router. As both nodes see every packet from every flow, this produces the same view of the exchanges. The packet capture was analysed using Suricata, a widely-used, open-source, network analysis and threat detection platform, to detect TCP flows. The detected TCP flows were then correlated based on the IID bytes (lower 64 bits) of the IPv6 source address field.
## 2 Hybrid VOF-Lagrangian Approach
### Discrete Phase Model  
The so-called VOF-to-DPM method implemented in the industrial CFD solver that is utilized in this study allows for automatic switching from the above VOF approach to a Lagrangian model for approximating the dynamics of the sub-droplets that are stripped from the main droplet [29]. Depending on the local flow conditions, where it is appropriate, the liquid volume fraction of the FV cell is converted into liquid particles, while an equal volume of gas is created to maintain the volume conservation in the same cell. The possible transition is controlled by a couple of user-defined parameters related to both size and shape of the water fragments originating from the breakup process. The first parameter corresponds to the diameter of the volume-equivalent sphere, which needs to be within a specified range. The second parameter corresponds to the so-called asphericity, which stands for a normalized measure of the shape deviation from that spherical one. For the present simulations, the maximum equivalent diameter is chosen corresponding to a spherical domain occupying eight times the volume of an interface cell, while the threshold of 0.5 is prescribed for the maximum asphericity. Practically, only water fragments obeying these two criteria are converted into liquid particles. Note that the VOF-to-DPM transition tool is directly combined with dynamic mesh adaption following the mixture fluid evolution [30].  
Once originated, the discrete phase is simulated by tracking the particles that evolve in the continuous flow field, while imposing the momentum balance on each particle. Namely, given the position \(\mathbf{x}_{p}(t)\) and the velocity \(\mathbf{u}_{p}=d\mathbf{x}_{p}/dt\) of a generic particle, the corresponding trajectory is calculated by integrating the following momentum equation (per unit particle volume):  
\[\rho_{1}\frac{d\mathbf{u}_{p}}{dt}=\rho_{1}f_{\text{d}}\left(\mathbf{u}- \mathbf{u}_{p}\right)+(\rho_{1}-\rho)\mathbf{g}\;. \tag{4}\]  
The two terms at the right-hand-side of (4) represent the aerodynamic drag and the gravity forces, whereas the residual forces exerted on the particle (for instance, the virtual mass force) are considered negligible. The aerodynamic drag force is determined by fixing:  
\[f_{\text{d}}=\frac{18\mu}{\rho_{1}d_{p}^{2}}\frac{C_{d}\text{Re}_{p}}{24}\;, \tag{5}\]  
where \(\text{Re}_{p}=\rho|\mathbf{u}-\mathbf{u}_{p}|d_{p}/\mu\) stands for the particle Reynolds number, and \(C_{d}\) is the drag coefficient. The latter parameter is evaluated as a known function of \(\text{Re}_{p}\), according to the experimental relationship derived in [31] for spherical particles. Due to the Lagrangian approach, micro-droplet properties such as equivalent diameter, position, and velocity can be statistically determined for the ongoing simulation.
###### Abstract
It has been found to have high or almost perfect correlations when compared to other methods such as jump mats, mobile applications, and the _Optojump_ infrared barrier [42]. However, the sensor has not been validated against a true gold standard such as a motion capture system or force plates. The _ADR jumping_ infrared barrier sensor (ADR, Toledo, Spain) is an economical and easy-to-use tool for estimating the vertical jump. It uses a photoelectric barrier and wireless technology to send data directly to a smartphone or tablet. The validity of the _ADR jumping_ was determined against a jump mat, with strong correlations (\(r=0.98\)) for the countermovement jump (CMJ) and high ICC (0.89), determining the device to be valid and reliable. However, there are no studies that compare the values obtained by _ADR jumping_ against a MoCAP or FP [43]. The purpose of this study was twofold: (1) to determine the criterion validity of the _ADR jumping_ infrared barrier when compared to a MoCAP system in the measure of CMJ, and (2) to examine the reliability of the instrument understood as its internal consistency in the within-session test-retest for consecutive attempts. It is hypothesized that the _ADR jumping_ infrared barrier is a valid and reliable instrument when compared to a standard instrument based on motion capture.
## 2 Materials and Methods
### Data Collection
#### 2.2.2 Remote Sensing Data  
The model construction (Table 1) uses NDVI and land cover data derived from MODIS datasets. NDVI was calculated based on processed MODIS reflectance data (MOD09A1) obtained from the red and near-infrared bands. The preprocessing of MODIS reflectance involved cloud and snow identification, as well as anomaly detection. Subsequently, spatio-temporal filtering and interpolation techniques were applied to address missing and anomalous pixels in the long time series, resulting in a seamless spatio-temporally continuous dataset of MODIS reflectance [19]. The quality of the reflectance data is relatively high. The land cover data, classified according to the International Geosphere-Biosphere Project (IGBP) system, was sourced from the MCD12Q1 Version 6 product. For land use types such as rivers and lakes, the fill value of GPP estimation was set to NODATA.  
Furthermore, GPP data from MODIS products were used for comparative validation. The MOD17A2H Version 6 GPP product is an aggregate 8-day composite with a pixel size of 500 m, employing the radiation use efficiency concept. The annual cumulative GPP value was obtained using the Google Earth Engine (GEE) platform ([[https://code.earthegine.google.com/](https://code.earthegine.google.com/)]([https://code.earthegine.google.com/](https://code.earthegine.google.com/))) with a 500 m resolution.
## 2 Methodology
### Remotely Sensed Reflectance Data  
The satellite-derived surface reflectance data, R\({}_{\mathrm{rs}}\)(\(\lambda\)), was obtained from Copernicus ([[https://www.copernicus.eu/](https://www.copernicus.eu/)]([https://www.copernicus.eu/](https://www.copernicus.eu/)) accessed on 11 September 2018). The Copernicus program has the objective of offering merged data records from multiple sensors (SeaWiFS, MODIS, MERIS, VIIRS-SNPP&JPSS1, and OCLI-S3A&53B) using the highest quality merging processes to date. Level 3 monthly averages of R\({}_{\mathrm{rs}}\)(\(\lambda\)) at 412 nm, 443 nm, 490 nm, 555 nm, and 670 nm wavebands were retrieved for the area (Figure 1), which ranges from 23\({}^{\circ}\)N to 32\({}^{\circ}\)N in latitude and 120\({}^{\circ}\)W to 112\({}^{\circ}\)W in longitude. The composite images, with a resolution of 4 by 4 km, cover the period from January 1998 to May 2016, aligning with the collection dates of the in situ Chl-\(a\) data.