esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection
#### 2.1.3 Inception  
The Inception model, a ground-breaking advancement in the fields of deep learning and computer vision, has emerged as a vital component in SFSS's intrusion detection capabilities. Google Research originally unveiled this model, known as GoogLeNet, in 2014, signaling a paradigm change in the architecture of convolutional neural networks (CNNs).  
The Inception model was created in response to the increasing need for extremely efficient and resilient deep learning models and is known for its revolutionary architectural design. Inception deviates from the practice of regular CNNs, which primarily use one-layered convolutional layers. It has a new design that integrates several filter sizes within the same layer, promoting a more holistic approach to feature extraction. This architectural innovation is showcased by the development of the Inception module, which serves as our model's backbone.  
The use of the Inception model inside the SFSS helps to improve its intrusion detection capabilities. Its diverse characteristics make it ideal for this purpose. Notable characteristics of the Inception model include great object detection, high picture classification accuracy, and a superior capacity to handle a broad variety of object sizes.  
The Inception model is used in the context of the agricultural Guard System to recognize animals and people in agricultural settings. Its ability to recognize a wide range of objects, along with its processing efficiency, enables it to handle real-time video feeds from farms. Its cloud-compatible design offers even smoother system interactions.  
The Inception model makes use of transfer learning, a method that makes use of previously taught information from big-picture datasets. This provides it with a comprehensive grasp of objects and characteristics, which is useful in the context of intrusion detection.  
The Inception model is critical to the SFSS because of its innovative design, varied feature extraction capabilities, and efficiency. It contributes to the system's capacity to safeguard agricultural land, reduce damage, and react quickly to invasions.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Glacier Surface Velocity Maps  
#### 3.1.1 Peruvian Andes  
Figure 1A presents the first complete high-resolution mapping of the surface speed of the glaciers of the Cordillera Blanca, located in the Peruvian Andes, from more than 5300 pairs of Sentinel-2 images. This region is characterized by glaciers of a typical length of 1 to 3 km, reduced ablation zones surrounded by steep mountain walls [30]. The variance or standard deviation in surface displacement (Figure 1B) varies from 1 to 3 m/year over ice-free areas and can reach more than 10 m/year over glaciers, which probably represents not only sensor noise but also significant changes in surface speed that can occur during the year due to seasonal changes and local climatic conditions. We note that a much smaller number of image pairs were processed on the eastern side of the mountain range, as shown in Figure S4A, with only 150 to 200 successful pairs compared to more than 500 on the western side of the cordillera. We attribute this difference to the higher probability of cloud cover from the Amazon basin to the east. As a result, the time average map value on the eastern side of the cordillera is less precise than in the western part.  
The southern part of the Cordillera Blanca shows small glaciers and glacier surface velocity generally ranging from 30 to 150 m/year. The maximum glacier surface velocity derived from Sentinel-2 data is measured at the ice fall of an unnamed glacier west of Pucaranra with a speed of  
Figure 1: **(A)** Glacier surface velocity mosaic of the Cordillera Blanca (Peru) in meters per year averaged over 2017-2018 quantified from Sentinel-2 images, color-bar is coded on a logarithmic scale going from brown (slow) to red (fast) and overlaid on a shaded relief version of the SRTM DEM; **(B)** Weighted standard deviation color coded from white (low variance) to dark red (high variance). Insets show a zoom on the tongue of Copa glacier for each map.  
305 m/year (Figure 1A). For the northern part of the Cordillera Blanca, in the vicinity of glaciers Huillca and Artesonraju, glacier surface velocity shows values typically ranging between 30-80 m/year, except for Huaytapallana, with a speed of 290 m/year in the ice fall. The fastest glaciers of the Cordillera are located in the central part with the Raymond glaciers (12 km\({}^{2}\)), Copa (11 km\({}^{2}\)) and 4989949-39 (5 km\({}^{2}\)), that all have glacier surface velocity above 100 m/year, homogeneously spread all along the ice tongue up the steep mountain peaks surrounding the accumulation region.
## 3 Results  
### Relationship between NCC and NEBF  
We examined the relation between the two pixel similarity measures, the normalized cross correlation (NCC) and the number of equal binary features (NEBF), that is, what NEBF can be expected for a given NCC.  
We used a sequence of 10 image pairs and the original BICOS algorithm, which has 55 binary features per pixel for this sequence length. We compared each pixel of the left camera with every candidate pixel of the right camera and stored the NCC and NEBF values for each comparison. From this data, we created a 2D histogram by dividing the data set into bins with a certain NEBF and a certain range of NCC (Figure 11).  
The two measures show a quasi-linear relation for a large part of the value ranges. In the important range, where the two compared pixels are very similar (i.e., a NCC of close to 1.0), the \"curve\" becomes steeper.
## 7 Performance Analysis
### Simulation Results
#### 7.2.3 Comparison of Link Load Balancing Capabilities  
This section demonstrates the load-balancing capabilities of the algorithm at low and high average link loads. Under different algorithms, we calculated the load and proportion of all inter-satellite links in the limited area. As shown in Figure 13, no matter if in the low or high average link load scenario, there are no inter-satellite links belonging to the highest level of load under the LPRA algorithm, and there are more inter-satellite links at intermediate levels of load than SPF and IADR. This shows that the LPRA algorithm has the strongest load balancing capability, and also verifies that the LPRA algorithm maintains a low packet loss rate without causing excessive degradation in delay performance.  
Figure 11: (**a**) Packet loss rate under different duration of sun outage; (**b**) average end-to-end delay under different duration of sun outage.  
Figure 12: (**a**) Packet loss rate under different average link load; (**b**) average end-to-end delay under different average link load.
# Acoustic Parametric Signal Generation for Underwater Communication
## 2 Theoretical Considerations  
The level of the secondary beam signal has been obtained following the model by [PERSON] and [PERSON] [3]. This model takes into account that the shock wave may limit the control of the secondary beam modulation. It is usually the base for the design of AUV's communication systems, allowing using simple calculations for circular piston arrays, and other geometries such as square or rectangular shapes [10].  
The input parameters of this model are: sound speed \(c\), carrier frequency \(f_{p}\), secondary beam frequency \(f_{s}\), absorption of the carrier frequency \(\alpha_{p}\), absorption of the secondary frequency \(\alpha_{s}\), diameter of the transducer \(d\), and the power of the source \(W_{o}\). Thus, the design is done according to the following equations (see ref. [7] for more information on the notation and units of the quantities used):  
\[S=(\pi d^{2}/4)\ \ \text{Vibrating surface} \tag{2}\]  
\[\lambda_{p}=c/f_{p}\ \ \text{Wavelength of carrier carrier} \tag{3}\]  
\[SL_{p}=10\log W_{o}+171+10\log(4\pi S/\lambda_{p}^{2})\ \ \text{Primary beam pressure level} \tag{4}\]  
\[SL_{s}=2 SL_{p}+20\log(f_{s})kHz+20\log(\Delta)-287\ \ \text{Secondary beam pressure level} \tag{5}\]\(\Delta=\int_{0}^{\infty}\frac{\sigma^{-x}}{x+Z}\ dx\), where \(Z=\alpha_{T}R_{p}(\frac{f_{p}}{f_{s}})\) and \(R_{p}=(\frac{S}{\lambda_{p}})\) Effective length of the parametric array \(\alpha_{T}=2\alpha_{p}-\alpha_{s}\) Total Absorption  
\[SL_{c}=20\log L-20\log\bigl{(}f_{p}\bigr{)}+292\ \text{Critical source level (saturation)} \tag{7}\]  
\[TL=60+20\log R_{km}+\alpha_{s}R\ \text{Transmission losses} \tag{8}\]  
\[NL=60-17\log f_{s}\ \text{Noise level} \tag{9}\]  
\[DI=10(4\pi S/\lambda_{s}^{2})\ \text{Directivity and $\lambda_{s}$ wavelenght of secondary beam} \tag{10}\]  
\[SNR=SL_{s}-TL-NL+DI-10\log B\ \text{Signal-to-noise-ratio} \tag{11}\]  
Transmitter Acoustic Response  
As an example, these equations are applied to the emitter Airmar P19 transducer used in this paper, whose active element is a cylindrical piezoelectric ceramic with a diameter of 0.033 m working at the thickness resonant frequency of 200 kHz. The results for a secondary beam of 40 kHz with 1 kHz signal bandwidth at a distance of 10 km are presented in Table 1.  
It can be observed that, for our transducer, the value for \(SNR\) is very high. This is because of the assumptions on the noise level (it may be considerably higher in some real situations) or on the transmission loss [10]. Even so, with this example we can show the potential of the parametric array concept, serving as the basis for the design of the application in underwater acoustic communication.
## 5 Evaluation and Performance
### Results and Discussion
#### 5.2.1 Network Throughput  
The comparison of the proposed RHCER against considered state-of-the-art techniques in terms of network throughput under varied simulation rounds has been performed (refer Figure 2). The throughput is calculated as number of bits of data transmitted over network per sec. The simulation results show that the proposed scheme improves network throughput performance by 13%, 17.5%, 21%, and 26.8% for EEMAC, EACRP, M-LEACH, and LEACH, respectively. The improved network throughput is due to better network management in terms of regions and sub-regions, which leads to better load distribution. Moreover, unlike other considered solutions where signal strength has not been taken into account, the proposed scheme utilizes SNR for optimal selection of cluster heads, thereby improving the proportion of packets delivered in agricultural land due to their robust actions. In this regard, the suggested framework has a higher network throughput than previous alternatives.
## 5 Conclusions  
We have studied an approach to mitigate the jammer attack on an \(N\times 1\) FSO system in this work via employing an aperture selection technique. The system suffers from jamming and Gaussian noise simultaneously, while the transmission channel models saturated AT accompanied with PE. An exhaustive expression of ABER encompassing both types of noise has been derived. The simulation results are plotted with analytical results to confirm the veracity of the derived expression. It has been discerned that as the number of user transmitters increases, the bit error performance of the system improves extensively. This model saves the power demand of allowing \(N\) transmitters to operate en masse. Moreover, the system achieves the same diversity order as an MISO-FSO system with a jammer. We also notice that as the degree of the jammer's activity increases, the ABER reduces. However, raising the SJNR (dB) insulates the system from reacting to variation in \(\rho\). Furthermore, variation in PE conditions in the jamming link is also examined. We observe that as the PE increases for the jammer, the system's performance improves. Moreover, expanding the number of user transmitters lowers the bit error of the system for any value of the PE parameter.  
Conceptualization, I.C. and M.R.B.; methodology, I.C. and M.R.B.; software, I.C.; validation, M.R.B.; formal analysis, I.C.; investigation, I.C. and M.R.B.; resources, M.R.B.; data curation, M.R.B.; writing--original draft preparation, I.C.; writing--review and editing, M.R.B.; visualization, M.R.B.; supervision, M.R.B.; funding acquisition, M.R.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Brigadier [PERSON], Indian Institute of Technology-Delhi, New Delhi, India.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.  
Figure 9: ABER versus SJNR (dB) curves for FSO systems having \(N=2\) and \(3\) with varying AT conditions in presence of jammer (\(\rho=1\)).
## 5 Interplanetary Laser Communications
The dots along the lines indicate the average distance of the various planets from Earth, as listed in Table 1. The graph allows the reader to quickly assess the laser power and telescope diameters needed to support a given OOK bandwidth between 1 MHz and 1 GHz. As a simple example, let us assume that the diameter product for the transmitting and receiving Hubble Space Telescope equals 1 m\({}^{2}\) or \(D_{tP}\) = 1 m\({}^{2}\)/\(D_{rE}\) = 1 m\({}^{2}\)/2.4 m = 0.42 m. Then a 10 W laser at 532 nm combined with a 42 cm diameter planetary telescope would allow roughly 900 MHz communications from Mercury or Venus, 700 MHz from Mars, 70 MHz from Jupiter, 10 MHz from Saturn, 6 MHz from Uranus, 1 MHz from Neptune, and 900 kHz from Pluto. Using Equation (8), the required power and telescope diameters for other laser wavelengths can easily be determined from Figure 5 by multiplying the \(y\)-axis result by the square of the wavelength ratio, (\(\lambda\)/532 nm)\({}^{2}\), which equals 4 for a wavelength of 1064 nm (Nd:YAG) and 8.5 at 1550 nm.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 5 Results and Discussion of the Power System Design  
### Battery Characteristics  
The battery type chosen is a solid-state lithium battery, and more details on the battery selection can be found in [2]. Since the Wigeon is designed to be released in 2030, future-oriented battery specifications can be used. The assumed batteries have a specific energy of 500 Wh/kg, a volumetric energy density of 1000 Wh/L, and a power density of 6500 W/kg. These values were based on [18] and were adapted herein to fit a commercial product.  
The depth of discharge (DoD) chosen was 80% or 0.8. There are cells in development, designed to perform at close to 100% DoD whilst also having an operational life of over 800 cycles [19]. For further calculations, a DoD of 80% was chosen as a precaution, since the batteries mentioned in [19] are still in the experimental phase, and the final DoD could turn out to be less. This 80% DoD means that the battery has a deep discharge, as the DoD is higher than 50%. From [18], it follows that the battery can withstand these discharges over the design life. At the end of life, 85% of the original capacity should remain. Based on three flights a day, this end-of-life capacity (EOLC) will be reached after 7.5 years. Since the operational lifetime for the Wigeon eVTOL is estimated to be 15 years [3], this means that the batteries will only require one change over its lifetime. The final characteristic of the batteries is that they charge quickly, with an 80% state of charge reached in 15 min [19] and the estimated time for a full charge being 25 min.  
Each individual battery cell has a nominal voltage of 3.7 V and an internal capacitance of 5 Ah, based on current battery cells [20].
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 1 Introduction
excavating in order to penetrate the material, and the boom of the LHD is lifted on demand to prevent or correct wheel skidding. Wheel skidding is detected with a patented method that uses LIDAR-based odometry and internal measurements of the LHD's actuators [18]. The excavation algorithm was validated in an underground mine (a sublevel stoping copper mine located in Chile), using full-scale excavation experiments with a real LHD and a typical production rock pile. The validation process in a productive area of a real mine and the lessons learned are fully disclosed here. The 2D-pile modeling was validated afterwards with data from these tests. It is also important to mention that commercial solutions to the problems of autonomous loading need to be compatible with the 24 \(\times\) 7 operation of mines, where production throughput is one of the main requirements. That means that the LHD needs to be able to load without stopping during the transition from navigation to loading. The proposed automation framework considers this requirement, and it is able to characterize the rock pile, without stopping the LHD, while it approaches the pile. The main contributions of this paper are:  
* A system that models and implements the whole loading process required in underground mining operations, from rock pile identification to payload weighing. The system is based on the shared autonomy paradigm, which allows the system to obtain assistance on demand from a human operator. * An excavation algorithm that is based on the way that human operators excavate, and that uses a patented method for detecting wheel skidding, which is required for successful excavation of fragmented material in draw points. * Full-scale rock excavation experiments using a commercial LHD in a production level of a sublevel stoping mine. This paper is organized as follows: Section 2 presents the background and related work on loading automation for LHDs. Section 3 describes the proposed autonomous loading system. In Section 4, results of the full-scale experiments are shown and discussed. Finally, in Section 5, the main conclusions of this work are drawn.
## 3 Results and Discussion  
### Droplet-Particle Collision Regimes  
The analysis of the experimental findings revealed two regimes of the interaction of droplets with solid particles (Figure 3): agglomeration (A) and stretching separation (SS). Sometimes, stretching separation with the formation of several child droplets [23] is additionally identified. In the current study, the transition boundaries between the regimes with a formation of several child droplets were determined. In agglomeration, the droplet-particle collision does not result in child droplet formation. The droplet spreads over the particle surface. At a higher velocity of the initial droplet, it is disrupted and child droplets are produced. In this study, this collision regime was recognized as stretching separation, as in [23]. To explore the way liquid properties affect agglomeration, the time of the droplet spreading over the particle was examined. This was the interval from the time of the droplet-particle contact until the thickness of liquid on the particle surface in the contact point stopped changing.  
Figure 2: Scheme for calculating the characteristics of droplet-particle (**a**) and droplet-substrate (**b**) collisions.  
The duration of agglomeration indicates the efficiency of the solid particle capturing in dust removal (e.g., in venturi scrubbers). The shorter the agglomeration, the more effective the solid particle capturing [53; 54]. The duration of the droplet spreading over a solid particle surface also determines the efficiency of the slurry fuel droplet generation in combustion chambers when coal particles, water, oil, biomass, peat, slime, heavy coal-tar products, resins and other components are injected separately [55]. For example, for the 50 vol% and 10 vol% glycerol solutions, there were 8 and 14 child droplets, respectively. The results are consistent with the findings [45].
## 2 Related Work
### Graph-Based Models  
Graph-based models have emerged as a powerful approach to better capture complex relationships and dependencies in POI recommendation. The Geographical-Temporal influences Aware Graph (GTAG) model [21] utilizes geographical and temporal influences within a graph structure to recommend POIs. The Hierarchical Multi-Task Graph Recurrent Network (HMT-GRN) [22] addresses data sparsity by learning user-region matrices, thereby improving recommendation accuracy. The GETNext model [23] incorporates a user-agnostic global trajectory flow map and a graph-enhanced transformer to exploit collaborative signals, achieve better next POI recommendation and addressing cold-start problems. DisenPOI [30] disentangles sequential and geographical influences using contrastive learning, providing more interpretable and effective POI recommendations. The Adaptive Graph Representation-enhanced Attention Network (AGRAN) [24] replaces pre-defined static graphs with learned graph structures, improving representation learning and capturing dynamic user preferences. The Spatio-Temporal HyperGraph Convolutional Network (STHGCN) [25] leverages a hypergraph to capture trajectory-level information and combines it with spatio-temporal data, outperforming existing methods and alleviating cold-start issues. The MMPOI framework [26] incorporates multi-modal content information into POI recommendations using a Multi-Modal Trajectory Flow Graph and an adaptive multi-task transformer, outperforming state-of-the-art methods. GSA-CTM [31] utilizes GraphSAGE to identify real user preferences and incorporates continuous-time modeling to enhance POI recommendation accuracy. BGSL [32] constructs a bi-level graph structure to capture global spatio-temporal POI correlations and utilizes auxiliary next-region prediction tasks to alleviate data sparsity issues.  
Our proposed HGL explicitly integrates a three-level hierarchical structure with rule-based spatio-temporal clustering, achieving both multi-scale semantic representation and enhanced interpretability.