esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Geo-Localization for Land Vehicles Using LIV-SLAM and Referenced Satellite Imagery
## 2 Related Work
### Matching Based Global Localization for Land Vehicles  
Matching-based global localization typically matches a real-time image with a referenced image or referenced database, which determines geographic position without relying on GPS.  
Street-view image matching (SVIM) is a commonly used visual geo-localization technique for land vehicles. It estimates the location of a query image by matching it to a geo-referenced street-view database [17; 18; 19; 20; 21; 22; 23; 24]. Most existing SVIM algorithms adopt a retrieval approach, in which the distance between feature embeddings represents the likelihood that two images originate from the same or nearby locations. During inference, nearest neighbor search is performed within the database to find the best match for a given query image. However, this approach often results in limited localization accuracy because the referenced street-view image databases are not dense enough. Furthermore, the SVIM-based applications are often constrained by the incomplete coverage of street-view image databases [25].  
Aerial and satellite images offer more extensive and higher-density coverage of outdoor areas than street-view databases, while also enabling efficient acquisition and frequent updates. Cross-view matching methods enable global localization by aligning ground-level images to aerial or satellite referenced images. Deep learning-based approaches, such as CVM-Net [26], utilize Siamese networks to learn viewpoint-invariant feature embeddings, while L2 LTR [27] and TransGeo [28] further enhance the matching performance though attention model and positional encoding. [PERSON] et al. introduced Sample4 Geo [29], a contrastive learning-based method leveraging symmetric InfoNCE loss [30; 31], which incorporates hard negatives during training to enhance overall accuracy. More recently, [PERSON] et al. proposed a novel search region layout with consistent cell resolutions, enabling statewide visual geo-localization [25].  
[PERSON] and [PERSON] [32] have explored the integration of cross-view matching into autonomous navigation systems using a probabilistic state estimation framework, which improves ground-to-aerial geo-localization through temporal consistency. This work has successfully realized long-distance, drift-free autonomous navigation for land vehicles, and the experiment results show that an average localization accuracy of approximately 20 m can be achieved.  
However, cross-view matching methods are typically based on image retrieval techniques, which struggle with precise alignment between query images and reference maps, limiting the localization accuracy [25].
## 4 Methodology
### Case Study 2: Live Outdoor Site  
The outdoors comparison data was collected in the Glasgow CBD with use of a Multitech mDot operating in AT command mode and controlled by a Python script. The mDot requested ACKs with each transmission and only transmitted using SF9 and SF10. The gateways were Kerlink [45] devices mounted on the rooftops of two buildings 1.9 km and 2.1 km away from the transmitting more. Logged data from the mode and the NS was combined for the analysis. Data was available from different live sensor devices, but these devices did not request ACKs and two-way performance could not be measured with this data.  
Several LoRa and LoRaWAN network performance measures were used in this analysis, including the total number of test-site packets handled by the network and a per-mote per-gateway basis. For every transmission, the channel frequency, RSSI, and SNR were  
Figure 3: Approximate floor plan for the ground-floor. The middle and top-floors were almost completely open plan. The ground-floor gateway (marked) was in a cupboard. ‘Near’ motes were located near the bottom of the plan, ‘far’ motes located near the top of the plan, except for the ground-floor far mote which was near the light well. The shower mote is marked on the plan.  
captured by the NS and the uplink and downlink counters, the server time, gateway time, and other parameters such as network deduplication of packets.
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 4 Experimental Results
### LoRa PHY Intercepting and Decoding  
We started by investigating how the LoRa-encoded data transfer can potentially be intercepted by a GNU Radio. The experimentally found configuration (blocks and the respective settings) allowing reception of the LoRa-modulated signals using the Gr-lora library is depicted in Figure 8.  
To validate the correctness of the system's operation and these settings, we disabled the encryption on the victim device and configured it to transmit a pre-defined hexadecimal text (\"NESIFROVANY_TEXT\" encoded in ASCII, to be specific) as a PHY layer payload. The decoded by the GNU Radio packet and its structure are shown in Figure 9 (the hexadecimal values are in red font) and Figure 10, respectively. Note that the RSSI of the received radio signal, depicted in Figure 9, may provide some insight into the location of both the ED and, in case of downlink packets, the GW. Similarly, the commercial LoRaWAN networks often reserve one frequency channel (i.e., the g3 band, 869.40-869.65 MHz, allowing for 10% duty cycle) for the second receive window--RX2. The eavesdropping of packets sent in this band may allow an attacker to obtain information about the locations of the GWs even if these data are not publicly available.  
Figure 8: GNU Radio configuration for eavesdropping a LoRa-encoded packet.  
Figure 9: Results of message capture by the GNU-Radio.
## 2 CFD Methodology and Numerical Modeling
### CFD Numerical Model
#### 2.1.2 Numerical Modeling: Porous Media Model  
In real environments, crop (plant) resistance could lead to the decline of wind speed around the crop and an increase of air disturbances. In most CFD studies on crop impact, the complex microstructure is ignored, and the crop model is treated as a porous medium. The porous media model is employed to describe the physical characteristics of fluid flow in the crop cultivation area within a plant factory. In some previous studies, the viscous loss term was disregarded for higher air velocity [37; 38; 39; 40; 41]. However, in this research, the air velocity is relatively low, and the viscous drag is not negligible. An additional source term of momentum is added to the momentum control equations to describe the pressure losses induced by the crop cultivation area to the airflow. The Darcy-Forsheimer equation is used to express the momentum additional source term \(S_{i}\)[42]:  
\[S_{i}\ =\ -\left(\left(\frac{\mu}{K_{\rm P}}\right)u+\left(\frac{C_{\rm F}}{K_{ \rm P}^{0.5}}\right)\rho u^{2}\right) \tag{4}\]  
\[C_{1}\ =\ \frac{1}{K_{\rm P}} \tag{5}\]  
\[C_{2}\ =\ \frac{2C_{\rm F}}{K_{\rm P}^{0.5}} \tag{6}\]  
where \(\mu\) is the aerodynamic viscosity and \(\rho\) denotes the air density. \(K_{p}\) and \(u\) represent the porous medium permeability and air velocity, respectively. \(C_{F}\) denotes the nonlinear momentum loss coefficient. \(C_{1}\) and \(C_{2}\) are the viscous resistance factor and inertial resistance factor, respectively.  
For simple geophysical models, \(K_{p}\) is calculated using the crop geometry parameters with [PERSON]'s equation [43], as follows:  
\[K_{\rm P}\ =\ \frac{d_{p}^{2}\phi^{3}}{180(1-\phi)^{2}} \tag{7}\]  
where \(d_{p}\) and \(\phi\) are the average particle diameter and the porosity of the porous medium, respectively. In a relevant study, [PERSON] et al. (2012) found an average particle diameter of 0.2 m and a porosity of 0.9 for a continuous tomato crop with stems, fruits, and leaves [37]. [PERSON] et al. (2019) measured grape canopy porosity values by image analysis and found it to be 13.3% for porosity \(\phi\)[44].
###### Abstract  
A method of estimating tropical cyclone (TC) intensity based on Haiyang-2A (HY-2A) scatterometer, and Special Sensor Microwave Imager and Sounder (SSMIS) observations over the northwestern Pacific Ocean is presented in this paper. Totally, 119 TCs from the 2012 to 2017 typhoon seasons were selected, based on satellite-observed data and China Meteorological Administration (CMA) TC best track data. We investigated the relationship among the TC maximum-sustained wind (MSW), the microwave brightness temperature (TB), and the sea surface wind speed (SSW). Then, a TC intensity estimation model was developed, based on a multivariate linear regression using the training data of 96 TCs. Finally, the proposed method was validated using testing data from 23 other TCs, and its root mean square error (RMSE), mean absolute error (MAE), and bias were 5.94 m/s, 4.62 m/s, and \(-\)0.43 m/s, respectively.  
t 18 February 2019; Accepted: 11 March 2019
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 5 Integration WLAN with 5G TSN
### WLAN Access within the 5G System  
The 5 GS simultaneously supports different types of access networks: not only 5G NR via gNB but also WiFi via non-3 GPP networking function (N3 IWF). Therefore, WLAN can be used as well as 5G NR in 5 GS as an access network. Figure 10 shows the integration model that simultaneously supports both 5G NR and WiFi access when the UE is equipped with 5G NR and WiFi interfaces. In this model, WLAN should support deterministic communication to provide deterministic QoS within the 5 GS for the TSN service. However, WLAN does not guarantee deterministic transmission, so this integration model may be selectively used depending on service requirements.  
Figure 9: WLAN access for TSN node.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 4 Cellular Networks  
In addition to DSRC, cellular networks have been recommended as a communication framework for vehicular communications, especially for V2I communications, considering wide cellular coverage, low latency, high capacity, and infrastructure readiness due to the current massive cellular network deployments worldwide. In fact, the European Telecommunications Standards Institute (ETSI) addressed vehicular communications using a set of standards for intelligent transportation systems (ITS) to identify the different layers of the V2X and I2V communication systems on top of the cellular physical layer [65]. These standards mainly depend on the LTE network infrastructure with certain modifications to enable effective V2X communications, and address the utilization of these vehicular communications in traffic management, emergency management, driving assistance, etc.
# Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area
## 2 Experimental Approach  
### Measurements Sites Conditions  
The results presented, as mentioned, are part of an extensive experimental campaign carried out in the metropolitan area of Porto. Given its extension and variability of traffic conditions, 15 measurement sites were considered to cover various types of tracks that run through the urban mesh. The chosen locations allow the acquisition to be the most reliable and realistic of the noise emitted directly from the source without obstacles or other constraints that might interfere with the measurements.  
As mentioned, the Porto surface metro network presents, in its extension, different types of circulation conditions, so it is important to define its main characteristics. It should be highlighted that four different types of track make up the rail network. The type of track influences noise emission, due to the way it is built or to the capacity of its materials to absorb the sound energy generated by passing trains. The types of tracks are listed below and a photograph of each is shown. For the sake of clarity and readability, each type of track has been given a designation, which is explained and will be used in the comparative analyses in the following sections. Finally, a particular type of noise will also be compared, the noise generated by rail traffic on bridges, since the light traffic network includes a metallic bridge.  
* Track whose rails are embedded in the grass is the most absorbent (Type I), Figure 2a;
* Ballast track (Type II), Figure 2b;
* Slab track (Type III), Figure 2c;
* Track where the rails are embedded in the pavement (Type IV), Figure 2d;
* Track whose rails are connected to wooden sleepers, with the presence of large metal plates for pedestrian crossings, Figure 2e.  
In addition to the characteristics of the infrastructure of each measurement site and the circulation speeds, in the Porto metro network two different types of vehicles circulating, namely _Eurotram_ and _Flexity Swift_, whose photographs can be found in Figure 3a,b. Both vehicles are supplied by Bombardier. Due to its nature, the _Eurotram_ is a vehicle with lightweight characteristics and essentially for urban use, while the _Flexity Swift_ is intended for longer journeys, applying to suburban connections [25]. Both vehicles are about 35 m long and two units can be coupled together. The _Eurotram_ vehicle reaches a speed of 80 km/h, while the _Flexity Swift_ unit has a top speed of 100 km/h. As already mentioned, in the case of the Porto network the vehicles reach maximum speeds of about 80 km/h.  
In terms of the mechanical properties of the vehicles, _Eurotram_ is composed of 5 modules, 4 bogies and 16 wheels per tram, whose loads are shown in Figure 3c. In the case of the _Flexity  
Figure 2: Photographs of the type of tracks: (**a**) Type I; (**b**) Type II; (**c**) Type III; (**d**) Type IV; (**e**) Bridge.  
_Swift_ vehicle, it is composed of 3 modules, 4 bogies and and 16 wheels per tram whose the maximum axle loads are illustrated in Figure 3d.
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 2 Popular Pooling Methods
### Spatial Pyramid Pooling Method  
Spatial pyramid matching (SPM) is another term used for pyramid pooling. This pooling method has the unique ability to eliminate the requirement of fixed size image [44; 45], which is the essential requirement in standard CNNs [46]. Fully connected layers apply fixed-size constraints instead of convolutional layers. Prior to the innovation of pyramid pooling for fitting images to CNNs, cropping and warping were the only techniques for resizing images. However, image cropping [38] and warping [47] each have different effects, leading to content loss and geometric distortion, respectively. The SPP layer is positioned on top of the final convolution layer to prevent the engagement of cropping and warping, which creates a new network named as SPP-net [48]. Spatial pyramid pooling (SPP) is considered to be a cutting-edge methodology for assessing pooling layer efficiency [49]. The SPP layer is designed to match the size of the feature maps in the SPP network, and the number of spatial bins remains constant at each pyramid level. Container sizes may vary. Using an image of size 128 \(\times\) 128 and the four container numbers at the bottom of the pyramid can create a patch of size 32 \(\times\) 32. Since there are 16 bins in total, the highest value within each bin is chosen as the activation value for the second level of the pyramid. The SPP layer generates a vector with a fixed dimension and fixed length as an outcome of pooling the activations for each bin, which is comparable to the product of the number of filters in the last convolutional layer and the number of bins in the SPP layer. As a result, the SPP technique can generate an output with a fixed length without taking the input's size into  
Figure 4: Stochastic pooling: a random sample is selected from a multinomial distribution of activations based on probabilities estimated by normalizing the activations. The chance of being selected increases with the size of the activation. In this figure, there are two activations, 1.6 and 2.4, with probabilities of 40% and 60%, respectively. However, 1.6 was chosen as the sampling activation despite the high probability of 2.4. Stochastic pooling can lose essential features because there is no way to predict which part of the input will be selected. Enhanced and regenerated from [43].  
account. Moreover, the SPP's testing and training phases allow for adaptation to the input image scales, which strengthens the scale-invariance property and eliminates the overfitting problem in the network [50]. However, instead of going into various pooling functions or incorporating learning, spatial pyramid pooling is primarily designed to deal with images of variable sizes and can result in a more complicated learning procedure, resulting in less efficient output, such as a 16:89 percentage error rate on unaugment CIFAR10 [51].
## 2 Materials and Methods
### Chromatographic Analysis of Extracts  
To identify and quantify kaempfer and formononetin, methanolic solutions of propolis extracts (10 mg:mL\({}^{-1}\)) obtained under different processing conditions were prepared. Before injection, samples were filtered on a 0.45 \(\upmu\)m cellulose membrane filter (Millipore). The chromatograph used was an HPLC LC-20 AT interfaced with an automatic injector and diode array detector (DAD) SPD-M20, both from Shimadzu (Kyoto, Japan). The chromatographic separation was adapted as proposed by [PERSON] [50] and [PERSON] et al. [51]. The column used was a NUCLEODUR(r) 100-5 C18 EC (150 \(\times\) 4 mm \(\times\) 5 \(\upmu\)m) coupled with ZORBAX Eclipse Plus C18 pre-column (4.6 \(\times\) 12.5 mm) (Agilent, Santa Clara, CA, USA).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Names** & **Temperature (\({}^{\circ}\)C)** & **Ultrasound Time (min)** \\ \hline E:UT (control this study) \({}^{\ast}\) & Ambient (\(\pm\)25 \({}^{\circ}\)C) & Not applied \\ E:25\(-\)10 & 25 & 10 \\ E:25\(-\)20 & 25 & 20 \\ E:25\(-\)30 & 25 & 30 \\ E:50\(-\)10 & 50 & 10 \\ E:50\(-\)20 & 50 & 20 \\ E:50\(-\)30 & 50 & 30 \\ E:75\(-\)10 & 75 & 10 \\ E:75\(-\)20 & 75 & 20 \\ E:75\(-\)30 & 75 & 30 \\ \hline \hline \end{tabular} \({}^{\ast}\)Conventional extraction as it is currently employed in the industry.  
\end{table}
Table 1: Process conditions for obtaining red propolis extracts with or without ultrasound.  
The injection volume was 20 \(\mu\)L with a flow of 1 mL\(\cdot\)min\({}^{-1}\) at room temperature (25 \(\pm\) 2 \({}^{\circ}\)C). The elution gradient was composed of mobile phase of acetic acid (5%) and methanol (solvent B). The total run time was 42 min, being 0 to 35 min with 0\(-\)92% solvent B, 35 to 40 min with 92\(-\)0% solvent B; and 40 to 42 min (0% B). The DAD detection was set with a range of 190 to 800 nm, and the chromatographic acquisition between 300 and 320 nm. For the identification of the compounds, the ultraviolet spectrum and the retention time of the samples and the standards were compared (Table 2). In order to ensure reliable results, the validation followed the methodologies of the National Institute of Metrology, Quality and Technology (INMETRO) [52] and the National Health Surveillance Agency (ANVISA) [53]. This analysis was performed according to the parameters of selectivity, linearity, accuracy, detection and quantification limits. The chromatographic run and method validation were performed with other phenolic compounds (standards) already reported or not in propolisis samples (results not shown--total of eleven compounds). However, only commonettin and kaempfer met the limit of detection and limit of quantification.
## 3 Results
### Dataset Augmentation  
This study employed a unique and proprietary dataset consisting of images of clean, dirty, and broken solar panels. However, a significant imbalance was observed in the dataset, as the number of images depicting broken panels was relatively lower compared to the clean and dirty panel images. A data augmentation pipeline was developed using Python 3.12 and the Keras library to address this issue and mitigate the risk of overfitting. This augmentation process aimed to enhance the representation of broken panel images while improving the overall generalization capability of the deep learning models.  
The effectiveness of data augmentation techniques depends on the diversity of the dataset, which should ideally include images captured under various real-world conditions. In this study, augmentation was performed by incorporating images taken at different distances, from various angles, under varying lighting conditions, and with different camera movements (e.g., panning and tilting). This approach transformed a single original image into six distinct variations, enriching the dataset and providing the model with a broader range of training examples.  
The augmentation techniques applied in this study are as follows:  
* Rotation Augmentation: To improve model robustness by exposing it to different orientations, the images were rotated from 0\({}^{\circ}\) to 30\({}^{\circ}\). The degree of rotation was carefully limited to prevent the loss of critical features related to broken panels.
* Width and Height Shift Augmentation: To simulate slight displacements in real-world images, the original images were shifted horizontally and vertically by 10% of their size.  
This technique helps the model recognize solar panel defects even when positioned at different locations in the frame.
* Shear Augmentation: Image distortion was introduced by tilting the images with a shear intensity of 0.2. This transformation ensured that key structural features remained identifiable while improving the model's ability to detect deformations and cracks in solar panels.
* Zoom Augmentation: Images were either enlarged or shrunk within a zoom range of 0.2, effectively creating variations that simulate differences in camera distance and focal length.
* Flip Augmentation: Images were flipped along the horizontal axis, allowing the model to recognize panel conditions from different perspectives. Vertical flipping was avoided to maintain the realistic orientation of the solar panels.
* Fill Mode: At the end of the augmentation process, fill mode was applied to ensure newly generated pixels were assigned values from their nearest neighboring pixels, preventing artifacts and inconsistencies in the transformed images.  
This augmentation strategy significantly improved the diversity and robustness of the dataset, allowing the deep learning models to generalize more effectively to unseen images. A sample visualization of the data augmentation outcomes is provided in Figure 3, illustrating the variations generated from an input image.