esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 2 Related Works
### Feature Importance Calculation Methods  
Feature importance, also named feature category discrimination ability, refers to the contribution of features to classification. Adapting an appropriate feature importance method to accurately calculatethe contribution of each feature to the classification and selecting features with higher importance to construct the feature space are very important to classification performance. The commonly used methods for calculating the importance of features are document frequency (DF) [35], mutual information (MI) [36], and information gain (IG) [35].  
In order to describe these methods conveniently, we define the following variables: \(F_{j}\) denotes a feature, \(N\) denotes the total number of samples in the training set, \(A\) denotes the number of samples with \(F_{j}\) in category \(k\), \(B\) denotes the number of samples with \(F_{j}\) in categories except category \(k\), \(C\) denotes the number of samples without \(F_{j}\) in category \(k\), \(D\) denotes the number of samples without \(F_{j}\) in categories except category \(k\) and \(q\) is the number of categories.
###### Abstract
With the proliferation of mobile devices, expansive bandwidths, diverse system configurations, and varied propagation environments, there is a growing inclination toward integrating machine learning techniques into UAV communication systems. In [26], an A2G channel model was presented by introducing a generative adversarial network (GAN) into a traditional geometry-based model, where the GAN was used to generate the angle offsets within the clusters. A generative neural network for A2G channel generation was proposed in [27]. Specifically, the LoS/NLoS state was first predicted and then fed into a conditional variational autoencoder (VAE) to generate the channel parameters, including the path loss, delay, and angles of MPCs. Similarly, in [28], an A2G channel generation method was proposed using a conditional GAN. The channel parameters, including the gain, delay, and angles of MPCs, were randomly generated based on the UAV's location and speed. A radio environment map (REM) provides a comprehensive representation of radio frequency information across various propagation environments. In [29], a scenario-dependent channel dictionary that incorporates specific channel properties was initially constructed. Subsequently, an REM was developed using a sparse Bayesian learning algorithm. In [30], a UAV-based federated learning scheme was introduced. In addition to covert model parameter uploading, the UAV also transmits artificial noise to unintended users, thereby reducing the likelihood of information eavesdropping. Furthermore, a distributed proximal policy optimization was employed to achieve a tradeoff between security and training costs. Similarly, in [31], a secure federated learning framework was presented, which utilizes a blockchain-based architecture for securing data training and validating contributions among UAVs.
## 4 Conclusions  
This paper has presented the basic principles of GNSS reflectometry and how these observables can be eventually used to correct for the sea state induced changes in the brightness temperature at L-band. These corrections [2] are needed to improve the quality of the sea surface salinity retrievals using L-band microwave radiometry.  
Figure 16: Estimated brightness temperature sensitivity to changes in the normalized DDM volume at vertical (red) and horizontal (blue) polarizations respectively [9].  
The suite of _PAU_ instruments that has been (is being) developed at the Remote Sensing Lab of the Universitat Politecnica de Catalunya has been presented. These include: (1) _PAU-Real Aperture_ with and array of 4 \(\times\) 4 dual-polarization elements with digital beamforming and polarization synthesis, (2) _PAU-Synthetic Aperture_ with a Y-shaped array of eight elements per arm to test digital technologies and new calibration techniques that can be eventually be used in future SMOS follow-on missions, (3) _PAU-One Receiver_ and _griPAU_, one receiver only versions to demostrate the novel _PAU_ radiometer topology concept (Figure 5) and perform round-based field experiments, (4) _PAU-One Receiver Airborne_, one receiver only version to demostrate the concept from remote controlled aircraft, and (5) _MERITXELL_ a version with multi-frequency Dicke radiometers, multi-spectral and thermal infrared cameras.  
These instruments have been developed for two purposes: (1) to analyze the nature of reflectometric observables and their relationship with the brightness temperature at L-band, and (2) to be technological demonstrators of improvements to be applied in future space-borne missions (SMOS follow-on missions), or secondary payloads that can help in the sea state correction.  
The promising results of two field experiments carried out in Gran Canaria, during 2008 and 2009 have been presented. However, these techniques are not restricted to the observation of the ocean, but they can also be applied over land to infer soil moisture and vegetation height [25, 26].
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### Mobility Metrics
#### 3.2.2 Distance Calculation  
In geographic protocols, the position of the destination vehicle is a crucial information. It is assumed that it is given by a service which is out of the scope of this paper. In the relay selection phase, the sender uses the predicted position of its neighbours to calculate the distance between neighbours and the destination using Equation (2):  
\[D=\sqrt{(x_{p}-x_{d})^{2}+(y_{p}-y_{d})^{2}} \tag{2}\]  
where \((x_{p},y_{p})\) is the predicted position of one of the neighbours and \((x_{d},y_{d})\) is the position of the destination vehicle.
# Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention
## 2 Related Work
### Research Related to Small-Sample Object Detection  
In recent years, the field of object detection has witnessed a significant surge in research efforts dedicated to the application of few-shot learning (see Table 2). [PERSON] et al. (2020) made groundbreaking strides by employing a transfer-learning-based approach for addressing few-shot object detection in synthetic aperture radar (SAR) images using convolutional neural networks (CNNs). Initially, they pre-trained a CNN using a substantial number of samples from the source domain. Subsequently, fine-tuning was performed using a limited set of samples from the target domain of SAR images, resulting in the successful creation of a novel model. On a similar note, [PERSON] et al. (2020) proposed a transferability-based transfer learning path and transfer method selection strategy to train deep learning models, thereby enhancing the feature extraction capabilities of CNN models, especially under multiple reboots scenarios. Conversely, [PERSON] et al. (2020) introduced the multi-scale positive sample refinement (MPSR) technique, which leveraged modular components to enhance samples across different scales. While this strategy augmented the dataset and generated a richer training set, the results indicated that relying solely on these auxiliary methods could not fundamentally address the dual challenges of sample quantity and quality, ultimately limiting significant improvements in model performance. Apart from the aforementioned methods, few-shot object detection based on meta-learning has also emerged prominently. Diverging from conventional training strategies, meta-learning builds upon the foundation of transfer learning with the aim of enabling models to adapt more effectively to new tasks or categories. Meta-learning focuses on task-level learning, aiming to identify initial parameters that excel across a spectrum of tasks, thereby facilitating the rapid adaptation to new learning tasks. The adaptive nature of meta-learning enables swift parameter adjustments in few-shot scenarios, allowing models to adapt to new situations or task requirements. However, due to the complexity in the design of meta-learning algorithms and their high demands for computational resources, they still face certain challenges in practical applications, resulting in relatively limited practical usage ([PERSON] et al., 2014).
## 3 Results and Discussion
### Electrochemical Performance
Figure 2: CV curves recorded in 1 M V\({}^{3+}\) +3 M H\({}_{2}\)SO\({}_{4}\) solution at a scan rate of 3 mV/s. \[2H^{+}+2e^{-}\to H_{2}\uparrow \tag{4}\]  
\[xH^{+}+Bi^{0}+xe^{-}\to BiH_{x} \tag{5}\]  
\[BiH_{x}+V^{3+}\to Bi^{0}+xH^{+}+V^{2+} \tag{6}\]  
The hydrogen evolution reaction (HER) is an unavoidable side reaction. Excessive hydrogen evolution will not only cause irreversible attenuation of cell capacity but also bring serious safety risks. Therefore, HER is a serious problem that must be explored in acidic electrochemical power sources. As shown in Figure 4a, the onset potential of the HER for TGF is the highest, which is found to be \(-0.72\) V (vs. SCE) in the H\({}_{2}\)SO\({}_{4}\) solution. The HER onset potentials of the Bi/TGFs shifted to a more positive position probably due to some additional reactions. Although bismuth has been known to inhibit HER ineffectively [30; 43], the onset potentials of Bi/TGF-1.6V and Bi/TGF-1.2V are indeed much closer to the TGF. The electrodeposited Bi on the graphite f  
Figure 4: LSVs performed in (**a**) 3 M H\({}_{2}\)SO\({}_{4}\) and (**b**) 1 M V\({}^{3+}\) + 3 M H\({}_{2}\)SO\({}_{4}\) solution at a scan rate of 1 mV/s. Figure 3: The EIS spectrograms recorded in 1 M V\({}^{3+}\) +3 M H\({}_{2}\)SO\({}_{4}\) solution at DC voltage \(-0.4\) V vs. SCE. BiH\({}_{\rm x}\) by combining with H\({}^{+}\) ions in the electrolyte. Therefore, the abrupt potential from the reduction curves of Bi/TGFs is assumed to be the reactions (5). The ability for the absorption of H\({}^{+}\) ion is in the order of TGF \(<\) Bi/TGF-1.2V \(<\) Bi/TGF-1.6V \(<\) Bi/TGF-0.8V by decreasing current density [49]. The nonlinear relationship between the reaction activities of each sample to reactions (4,5) and the deposition voltage should be attributed to the morphology of Bi particles [43], which was further analyzed by the SEM results. As an intermediate product, BiH\({}_{\rm x}\) acts as an effective catalyst for the V\({}^{3+}\)/V\({}^{2+}\) reaction, which makes the charge transfer of the reduction reaction faster in the reaction (6). As shown in Figure 4b, the reduction peak in 1 M V\({}^{3+}\) + 3 M H\({}_{2}\)SO\({}_{4}\) is more pronounced. It is verified that the V\({}^{3+}\)/V\({}^{2+}\) reduction potential is around \(-\)0.55 V (vs. SCE). The values of reduction current and reduction potential corresponding to Bi/TGFs are significantly higher than those of TGF. Considering the HER, the electrocatalytic performance of Bi/TGF-1.6V and Bi/TGF-1.2V for the V\({}^{3+}\)/V\({}^{2+}\) redox couple is better than that of Bi/TGF-0.8V.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 1 Introduction
As a consequence, GCMR:  
* Provides a scalable routing architecture because each router stores in its routing table only the (direct) neighbour-related entries, rather than tree structures or network graph information;
* Can work with any addressing space structure like IPv4/IPv6, with identifiers like geometric coordinates or with data/information centric content, as it does not rely on the unicast routing information;
* Can support a variety of metrics to decide the optimal branching path from the multicast source to leaf nodes;
* Has a scope that is not limited to routers and thus can be initiated by any host in such a way that a real end-to-end multicast distribution tree is provided and the additional host-router subscription protocol is not needed anymore. In summary, GCMR is a name-independent, distributed, dynamic, leaf-initiated, and end-to-end multicast routing algorithm. It is worth mentioning that, although GCMR has been designed to improve scalability of inter-domain multicast routing, it can perform in other environments where only limited topology/routing information is available and routing scalability is a main issue. In this paper we present the details of GCMR and evaluate its performance. In particular, we are interested in finding the optimal trade-offs between the stretch of the routing scheme, the memory space required to locally store the routing information, the communication cost needed to setup and properly operate the MDT, and the transmission cost to reach all leaf nodes of the multicast group. The comparative performance analysis against state of the art multicast schemes is carried out using both simulation and emulation platform. The simulation platform allows us to evaluate the performance considering large-scale internet topology maps with up to 46k Autonomous Systems (ASes) and up to 4k leaf nodes in the MDT. On the contrary, the GCMR prototype has been developed and tested to experimentally validate its behaviour and performance in a large-scale realistic network environment with more than 200 ASes. The rest of the paper is organised as follows. Section 2 presents the related work on multicast routing scheme and the contribution of this paper. Section 3 describes the GCMR scheme. In Section 4 we present the prototype of a GCMR routing engine. Section 5 details the performance and comparative analysis using the simulation platform, while in Section 6 we provide and discuss the results of the experimental tests. Finally, Section 7 concludes this paper.
## 3 Results  
### Simulation of Sensing Structure  
The change in the effective RI of the suspension core is determined by MF, and eventually leads to the movement of the interference spectrum. Therefore, the sensitivity of the RI change of the suspension core waveguide of this structure is simulated based on Rosoft software. The optical path diagram of the simulation model is shown in Figure 3a, and the simulated optical path diagram is consistent with the optical path structure shown in Figure 1. The RI of the core in the air hole of the simulation model is changed in the input light wavelength range of 1520\(\sim\)1600 nm, and the wavelength shift of the simulated transmission spectrum as RI rises of the suspension core is shown in Figure 3b. There are obvious interference valleys (peaks) at 1525, 1540, 1565, and 1570 nm of the transmission spectrum, and it is found that the sensitivity is 1620 nm/RIU at about 1565 nm after wavelength demodulation. The linear fitting of wavelength demodulation is shown in Figure 3c, and the value of R\({}^{2}\) is 0.9881.  
Figure 3: (**a**) Optical path diagram of the simulation model; (**b**) the wavelength shift of the simulated transmission spectrum as RI rises of the suspension core (core 2); (**c**) the linear fitting curve of RI sensitivity.
# Behaviour of PPCP Substances in a Fluvial Aquifer after Infiltration of Treated Wastewater
## 3 Experimental Setup and Performance  
The area of interest was equipped with observation wells PZ1, PZ5, and CP1 (Figure 2). A well VSK was used for infiltration of treated wastewater from the local WWTP. All wells penetrated the entire thickness of the aquifer. The depth of the wells was between 8 and 10 m, depending on the thickness of the fluvial Quaternary. The casing diameter of pumping/infiltration wells CP1, CP5 and VSK was 425 mm, in the case of monitoring wells PZ1 and PZ13 the diameter was 273 mm. Length of casing again depended on local lithological conditions and was most often in the range of 2-7 m.  
Special attention was paid to monitoring the expected clogging of the VSK infiltration well, which was equipped with a casing observation probe Solinst for this purpose. The probe worked on the principle of measuring barometric pressure and was placed in a pipe in the filter material of the well.  
The level in the Morava River is maintained by manipulation at the weir at 190.9 m a.s.l. The levels in the wells VSK, CP1, PZ1 in the original artesian regime are at the level of 190.6 m a.s.l.  
Czech legislation does not allow the discharge of wastewater into groundwater, but always through the unsaturated zone. Therefore, a container with a volume of 1 m\({}^{3}\) filled with fine-grained sand (its granulometry was very similar to the unsaturated zone) was placed between the outlet of the WWTP and the infiltration VSK well.  
A slug test was performed before the major infiltration experiment conducted with clean water. The goal was to specify the infiltration rate into the VSK well. The water level measurement took 5.5 h. At one time, the borehole was filled with 212 L of water  
Figure 2: Location of pumping (CP1), monitoring (PZ1 and PZ5), and infiltration wells (VSK) at the Kojetin experimental site (blue points—wells, yellow point—ground filter)  
and the decrease in its level over time was measured. The test was evaluated using the Jacob-Lohman method [20].  
The initial amount of infiltrated wastewater was set at 2 L/s and then adjusted so that the level in the VSK well was constant. Due to clogging, the infiltration rate in VSK well gradually decreased from 1.8 L/s to 0.7 L/s.  
The infiltration was intensified by pumping from adjacent well CP1. The withdrawal from well CP1 was therefore maintained systematic greater than the infiltration rate. The pumped volume of groundwater from CP1 well ranged from 2.9 L/s to 1.6 L/s. During the experiment, 2366 m\({}^{3}\) of treated wastewater was infiltrated into VSK well, and 4332 m\({}^{3}\) of groundwater was pumped from CP1 well.  
Water samples were taken at scheduled intervals from five monitoring points--the inlet into VSK well with treated wastewater, CP1 well, PZ5 and PZ1 wells, and Morava River for a period of 36 days. A total of 113 PPCP substances were analyzed.
# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 3 Case Study
### Results and Discussion
#### 3.2.4 GC2--Wind+ PSH + Grid  
The second configuration for a grid-connected solution, **GC2**, involves wind energy with pumped-storage hydropower instead of the solar energy in the previous solution (GC1). The optimization yielded a requisite wind power installation of 65 kW, with nominal pump and turbine powers of 20 and 40 kW, respectively.  
The obtained installed power demonstrates a grid independence of 91.7% with a positive grid balance wherein exports are twice the imported energy required. To be fully independent from the grid, it would require a wind power installation of 205 kW and a pump power of 65 kW, representing a doubling of the initial investment. Figure 14 illustrates the yearly energy and water balance for the second grid-connected configuration, constituted by wind energy and pumped hydropower storage. This microgrid solution is the optimal choice regarding installed power versus energy needs. The annual wind energy production is 123 MWh, with a grid energy requirement of only 9.5 MWh to satisfy the load demand.  
Figure 14: GC2—Wind + PSH + Grid yearly balance: (**a**) energy balance, (**b**) water balance, (**c**) energy by sources, (**d**) power data, and (**e**) statistics (between percentile 25 and 75%).
## 4 Discussion
### What Factors Contributed to SOS/EOS Trends  
Though our study detected only few of pixels (<4.0% of total pixels) showing significant SOS/EOS trends during 2000-2016 in Inner Mongolian grasslands. However, by comparing the multi-year mean SOS/EOS of 2000-2004 and that of 2011-2015, we still found a significant advance of SOS in typical steppe and desert steppe, and a significant advance of EOS in desert steppe, which have also been identified by other studies with different data and methods ([PERSON] and [PERSON], 2018; [PERSON] et al., 2018). SOS experienced a larger shift than EOS. This may, again, indicate that SOS is more sensitive to climate changes than EOS in temperate grasslands. To explore what factor likely contributed to SOS/EOS trends in the two steppes, we compared the differences of the multi-year mean pre-SOS temperature and precipitation between 2000-2004 and 2011-2015 with a two-sample \(t\)-test. Results showed that the significant advance of SOS in typical steppe and desert steppe may both result from the pre-SOS significant increase of precipitation (8.6 mm for typical steppe and 6.7 mm for desert steppe), while pre-SOS temperature showed no significant changes from 2000-2004 to 2011-2015 (Figure 9). This is likely due to the negative influence of precipitation on SOS in typical steppe and desert steppe which has been revealed in Section 3.5, namely, more precipitation in preseason would advance the occurrence time of SOS. In comparison, the significant advancement of EOS occurred in desert steppe may stem from the combined effects of the significant elevation of pre-EOS temperature and precipitation. Though the precipitation has increased by 15.4 mm from 2000-2004 to 2011-2015, the significantly boosted temperature (0.77 \({}^{\circ}\)C) may accelerate the evaporation of land surface and lead to more water loss and an earlier EOS.
## 4 Scenarios  
The workflow of this research is depicted in Figure 2. Each of the databases has been split into the training set containing the 80% of the files and the test set containing the rest 20%. Following, the features are extracted with two methods: (A) handcrafted feature extraction and (B) CNN-based extraction.  
For the **handcrafted feature extraction**, we used the feature descriptors LBP and HOG with the images of the databases with their original dimensions and downsized by two. During the scenarios, maximum accuracy has been achieved with different feature sizes. As the feature size is based on the image resolution, we have investigated the existence of an analogy (ratio) between the image and the feature sizes. To this end, we have downsized by two and by four to verify this ratio.  
We apply three cell sizes to the images with their original sizes: \(8\times 8,\ 16\times 16\), and \(32\times 32\). To the images downsized by two, we apply cell sizes that produce features of the same size as the original sizes, i.e., \(4\times 4,\ 8\times 8\), and \(16\times 16\). For each combination of image size, descriptor, and cell size, the extracted features feed an SVM classifier to determine the best combination in terms of classification accuracy for each database. We also apply two types of noise, Gaussian and salt and pepper noises imposed on the test set files, in the best combination to investigate the effect on the classification accuracy.  
In the **CNN-based feature extraction**, we use two techniques: the CNNs (ResNet18, ResNet50, ResNet101, Inception_v3, and EfficientNet-B0) as trained on ImageNet ([[http://www.image-net.org/](http://www.image-net.org/)]([http://www.image-net.org/](http://www.image-net.org/)), accessed on 1 February 2022) and the CNN retrained in the new data. For the originally trained CNN, feature extraction takes place from four different depth levels, i.e., 25%, 50%, 75%, and 100% of its depth, and feeds the SVM classifier. For retrained CNNs, the features are extracted from the last layer (as the intermediate depths did not lead to improved results in terms of classification accuracy and computational time). The extracted features feed the SVM classifier and the CNN's fully connected layer (transfer learning). No downsizing of the images has been used as the images are resized according to the dimensions required by the CNNs. The comparison in terms of classification accuracy gives the best combination. For this combination, we examine the effect of the two types of noise imposed on the test set files.  
The algorithms were implemented with Matlab R2022a and were executed on a desktop PC with 32 GB RAM, with Intel Core i7-10700K processor, eight cores up to 3.8 GHz, with the graphic card NVIDIA GeForce RTX 3060.