esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This novel approach has great potential for estimating GPP regionally and globally solely based on continuous satellite SIF observations and can bring revolutionary changes to the mapping of terrestrial photosynthesis globally.
## 2 Distributed Optical Fibre Sensors
### Raman-Based Distributed Sensing  
Spontaneous Raman scattering is an inelastic process caused by molecular vibrations. The incident light interacting with the electrons of vibrating molecules (optical phonons) is scattered, and its frequency is shifted by an amount equivalent to the resonance frequency of the lattice oscillation [48]. Optical fibres made from doped SiO\({}_{2}\) quartz glass show an amorphous solid structure that undergoes molecular oscillations: the molecular bonds in glasses are not uniform and corresponding vibrational modes slightly differ along the fibre. Therefore, Raman scattering of quartz glass is the result of one or more aggregated bands corresponding to the main vibration modes of the molecules. When light is launched into a fibre to probe the Raman scattering, three spectral components are generated: the Rayleigh scattered signal at the wavelength of input light, the Stokes component at a higher wavelength, and the anti-Stokes component at a lower wavelength. Corresponding frequency shifts are approximately 13 THz for silica glass fibre, and due to the aforementioned molecular bonds' inhomogeneity, the bandwidth is very wide (up to approx. 6 THz). The intensity of the anti-Stokes signal is temperature-dependent (sensitivity of approx. 0.8%/K at room temperature), while the Stokes signal is temperature insensitive. Therefore, the ratio between the anti-Stokes and the Stokes light intensity is a direct measurement of the temperature at which backscattered photons have been generated [49; 50]. It is worth mentioning that Raman scattering is a nonlinear process, but the reader should not be confused about this: in all DOFSs based on Raman scattering, the relationship between the scattered power and the input power of the probe is linear. The first proposal of Raman-based DOFSs can be dated to the first years of the 1980s [49; 51; 52]. Raman distributed temperature sensor (DTS) systems are mostly based on Raman optical time domain reflectometry implemented with a pulse laser and analogue receivers. Its working principle is relatively simple, and consists of sending a pulse in the fibre to measure Stokes and anti-Stokes band responses over roundtrip propagation. The corresponding implementation scheme (Figure 3) is simple as well: it includes a laser light source, a directional coupler to separate the forward signal to the backward one and to separate the Stokes from the anti-Stokes band. Photodetectors and devoted electronics to control the devices and to process and store recorded data complete the setup. Between the coupler and the sensing fibre is often inserted a section of fibre at a known temperature, used for reference and system calibration.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 4 Experimental Analysis
### Error Corrected Positioning Results and Evaluations
#### 4.4.2 Dynamic corrected results  
The corrected dynamic positioning results of proposed method are shown in Figure 15, while the corrected dynamic positioning errors are presented in Table 6. It can be seen from the experimental error results that it is obvious that the positioning error corrected by the proposed method in a dynamic scene is smaller on the X-axis, Y-axis and Z-axis than the two methods in a static scene, which illustrates that the error correction method may achieve better results in a dynamic scene.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Axis** & **X** & **Y** & **Z** & **3D** \\ \hline Positioning Error (m) & 0.4949 & 0.4289 & 0.3641 & 0.7493 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Error corrected dynamic positioning errors.  
Figure 14: The position error of four different methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Error Correction Methods**} & \multicolumn{3}{c}{**Root Mean Square Error (m)**} & \multirow{2}{*}{**3D Position Error (m)**} \\ \cline{2-2} \cline{4-5}  & **X** & **Y** & **Z** & \\ \hline WLS–LSTM & 0.880 & 1.345 & 0.770 & 1.782 \\ \hline Kalman–LSTM & 0.523 & 0.705 & 0.554 & 1.038 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Positioning errors of error correction methods.
# Phishing Attacks Survey: Types, Vectors, and Technical Approaches
## 3 Phishing Methods and Techniques
### Phishing Technical Approaches
#### 3.3.2 Whaling  
Whaling is a targeted method of phishing similar to spear phishing, although it differs in the fact that the sole targets are senior level executives (or other high-ranking employees) whose position provides them with privileged access to data within their company [15; 39; 40]. Because this is a highly targeted attack, phishers take time to ensure their scam is as indistinguishable from legitimate mail as possible. The most likely vector for this attack is either eFax or email. As in the case of spear phishing, the attacker's goal is to induce the target to install malware to provide access to the target's system. The malware is distributed in the usual manner of an infected attachment or as a link to download the malware. The malware's purpose is to monitor keystrokes and/or grant the attacker access to the infected system from which they can continue their attack, thus utilizing the high-level privileges they have obtained. Whaling itself may only be the preliminary stage of the overall attack, with a rise in what is referred to as business email compromise (BEC), which is further discussed in Section 3.3.3.
## 4 Optical Methods
### Preliminary Operations  
#### 4.1.1 Average Background Computation and Removal, Equalization  
Firstly, the background noise was characterized. Such noise was variable with time (Figure 6a), such that an average was necessary. The \"average background\" frame (\(\text{BCK}_{\text{avg}}\)) was generated by taking into account the gray level of the corresponding pixels of 20 frames before the discharge. Then, \(\text{BCK}_{\text{avg}}\) was subtracted from each discharge/combustion frame. As we can see from Figure 6b, the distribution of gray levels in the raw image belonged to the range 60-80 (black curve), while, after the background removal, such a distribution was much closer to zero (blue curve).  
In order to improve the frame comprehensibility, an equalization was also performed. Gray levels were no longer displayed from 0-255 (maximum gray level with 8-bit resolution), but only up to 20. This choice was found to be effective at showing both dark and bright frames (e.g., the first discharge frames and a stoichiometric flame, respectively).
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 2 Model and Data
### In Situ Observations  
The Soil Moisture Observing System-Meteorological Automatic Network Integrated Application (SMOSMANIA) is a network of weather stations with frequency domain reflectometry soil moisture sensors installed along a 400 km Mediterranean-Atlantic transect in Southern France, providing continuous measurements of soil moisture at different soil depths every 12 min from 2006 to the present [52]. In the two regions of France where S1 SSM products are available, there are three SMOSMANIA stations for the Toulouse region and three for the Montpellier region, as shown in Figure 2 (red dots). SSM is measured at a 5 cm soil depth.  
In addition to the SMOSMANIA stations, SSM data are available from the Meteopole-Flux site within the Toulouse domain, as shown in Figure 2. This observatory is located on the Meteo-France campus in the southwest of Toulouse, France (43.57\({}^{\circ}\)N, 1.37\({}^{\circ}\)E; 158 m above sea level), and is designed to monitor atmospheric variables, energy, water, and CO\({}_{2}\) fluxes in real time, on a sub-hourly basis. Since June 2012, the Meteopole-Flux observatory has provided several meteorological variables, including in situ observations of soil moisture at different depths [53].  
The REMEDHUS network is located in the Duero Basin in Spain, covering an area of 35 km \(\times\) 35 km between the cities of Salamanca and Valladolid, and provides hourly observations of SSM at a 5 cm soil depth. The stations are located in the center of the Salamanca domain where S1 SSM products are available (blue dots in Figure 2). These instruments are located in a gently sloping hilly region at an altitude of 750-900 m above sea level and experience a semi-arid continental Mediterranean climate, where the land is mainly used for agricultural activities. Data from this network have been used in many studies to validate different satellite-derived observations [54; 55; 56]. In the context of this study, data from 19 stations were used for the period 2017-2019.  
The SMOSMANIA [52] and REMEDHUS [57] data are available from the International Soil Moisture network (ISMN) [58].  
It must be noticed that there is a mismatch between the in situ SSM observations at a 5 cm soil depth and the smaller C-band penetration depth of 1 to 2 cm [59].
## 2 Power Quality Disturbance Simulations and Dataset Acquisition  
The definition of Power Quality by the IEEE is: Power Quality is the concept of powering and grounding sensitive equipment in a matter that is suitable to the operation of that equipment [17]. The definition given by the International Electrotechnical Commission is: The characteristics of the electricity at a given point on an electrical system, evaluated against a set of reference technical parameters. These parameters might, in some cases, relate to the compatibility between electricity supplied on a network and the loads connected to that network. From these definitions it can be said that PQ always includes voltage quality and supply reliability. The Power Quality Disturbances (PQD) are broadly classified into three categories: magnitude variations, sudden transients and steady-state harmonics, as said in [18].  
As a preliminary stage of this work, the voltage and current time signals were simulated using MATLAB Simulink. By doing this, it has been possible to recreate the disturbances on the line and see how they interact with the other devices connected to the grid. Thanks to the simulation results, it has been possible to acquire a current and voltage dataset with which the identification algorithms were trained and validated. This dataset includes simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. After that, the deep learning algorithms were tested with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above and it has been possible to perform a performance comparison.  
For the generation of the PQD dataset a MATLAB/Simulink model of a micro grid has been implemented. The model is shown in Figure 11 and it includes several different industrial loads. It is possible to identify a three-phase dynamic load which could be associated to an electrical motor with variable load, a linear load and a nonlinear load which injects disturbances on the net [18]. These disturbances include: Sag, Swell, Harmonics, Transient, Notch and Interruption.  
The Simulink schematic for the PQD simulation consist of a three-phase voltage source connected to a fault block, then to a line impedance, to a transformer and finally ending in a linear load. The voltages and currents are measured after the transformer which are going to be used for the classification. Inside the fault block, there are different types of disturbance generation blocks which were listed above. The sag block reduces the voltage from its nominal value. The swell block rises the voltage from its nominal value, and it is modeled with a switch that connects the grid to a capacitor bank. The harmonic distortion is modeled as a resistor in parallel with a capacitor in parallel with a free willing diode. The transient is modeled with an impulse generator. The notch block is modeled by a thrysistor in parallel with a resistance and an inductance. Finally the interruption block is simply a switch. The simulink schematic for the simulation of the PQD along with each disturbance block are shown in Appendix A.
# A Closer Look at Few-Shot Classification with Many Novel Classes
## 6 Conclusions and Future Work  
We introduced a new problem named few-shot learning with many novel classes (FSL-MNC) which encourages the exploration of few-shot learning frameworks in real-world scenarios characterized by a large diversity of classes. This scenario presents significant computational and generalization challenges. In addressing FSL-MNC, we demonstrated that meta-training with a fixed number of ways provides a practical compromise between computational overhead and predictive performance. Notably, when handling a large number of classes, effectively extracting and utilizing the class hierarchy structure was shown to substantially enhance performance. Additionally, we developed a lightweight distributed framework specifically tailored for FSL-MNC, enabling a robust comparison against established baselines. Our evaluations confirm that the proposed SHA-Pipeline delivers exceptionally competitive performance in FSL-MNC contexts.  
**Limitations and Future Work.** Our investigation predominantly centered on image data, without integrating textual information, which could provide additional context and improve model robustness and applicability in FSL-MNC. Future research could explore multimodal approaches that combine both visual and textual data, potentially enhancing performance in complex real-life applications such as visual tracking [54; 55]. Furthermore, our current implementation of SHA-Pipeline, while effective, is somewhat basic in its integration with meta-training techniques. Investigating more advanced meta-training strategies could lead to further enhancements in performance. Additionally, the computational demands of SHA-Pipeline, particularly during the meta-testing phase, pose a challenge. Future efforts should aim to optimize the computational efficiency of the pipeline during the fine-tuning stages, potentially reducing runtime and resource consumption without sacrificing accuracy.  
[PERSON] and [PERSON] carried out the experiments and wrote the first draft of the manuscript. [PERSON] conceived and supervised the study and edited the manuscript. [PERSON] and [PERSON] contributed to the data analysis. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China (Key Program Grant No. 62032024 and General Program Grant No. 62376282, 62372459).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Geometric Similarity Measurement Method for Micro Scene Generalization
###### Abstract  
Geometric similarity plays an important role in geographic information retrieval, map matching, and data updating. Many approaches have been developed to calculate the similarity between simple features. However, complex group objects are common in map and spatial database systems. With a micro scene that contains different types of geographic features, calculating similarity is difficult. In addition, few studies have paid attention to the changes in a scene's geometric similarity in the process of generalization. In this study, we developed a method for measuring the geometric similarity of micro scene generalization based on shape, direction, and position. We calculated shape similarity using the hybrid feature description, and we constructed a direction Voronoi diagram and a position graph to measure the direction similarity and position similarity. The experiments involved similarity calculation and quality evaluation to verify the usability and effectiveness of the proposed method. The experiments showed that this approach can be used to effectively measure the geometric similarity between micro scenes. Moreover, the proposed method accounts for the relationships amongst the geometrical shape, direction, and position of micro scenes during cartographic generalization. The simplification operation leads to obvious changes in position similarity, whereas delete and merge operations lead to changes in direction and position similarity. In the process of generalization, the river + islands scene changed mainly in shape and position, the similarity change in river + lakes occurred due to the direction and location, and the direction similarity of rivers + buildings and roads + buildings changed little.  
geometric similarity; micro scene; cartographic generalization; direction Voronoi diagram; position graph; quality assessment; Hausdorff distance; curvature +
Footnote †: journal: Applied  
1
## 3 Data Processing and Parameter Determination
### Determination of SSA-LSTM Model Parameters
Figure 4 illustrates the fitness values under different hidden layers of LSTM. The results indicate that under the given input data dimension and varying numbers of hidden layers, the SSA algorithm could converge to a stable state within 25 iterations. Thus, the maximum number of iterations for the SSA algorithm was set to 25. As for the alert value (ST), its range typically lies between 0.5 and 1.0. Figure 5 indicates that when the alert value was 0.7, the fitness achieved its optimum. The number of producers (PDs) ranged from [0, 1]. Figure 6 illustrates the fitness values at different PD levels, and the results indicate that the fitness was optimal when the PD was set to 0.4. Figure 4: Determination of the number of iterations. Figure 3: Determination of the sparrow population. In the LSTM neural network architecture, the number of nodes in the hidden layer is a crucial parameter. Increasing the number of nodes in the hidden layer can enhance the predictive capability of the model and reduce errors. However, too many nodes in the hidden layer may lead to the problem of \"overfitting\" during training. Although there are many studies discussing how to determine the count of nodes within the hidden layer, there is currently no universally effective method. Some proposed calculation methods might only be suitable for large sample data. In real applications, specific projects might necessitate distinct hidden layer configurations. To ensure the network's performance and generalization capacity, the number of nodes selected should be as minimal as possible, while still meeting the accuracy criteria. As illustrated in Figure 7, when the number of nodes in the hidden layer ranged from 1 to 10, the prediction accuracy of the LSTM network fluctuated, with the mean absolute error first increasing and then decreasing. When the number ranged from 10 to 20, the mean absolute error began to rise, which could be attributed to overfitting of the network. Although the mean absolute error decreased in the 20 to 30 range, to avoid overfitting, the chosen number of nodes in the hidden layer was set to 10. Figure 5: Determination of the number of alert values. Figure 6: Determination of the number of PDs. After a series of numerical experiments, the following parameter configuration was determined for the sparrow search algorithm in the ground subsidence prediction model: a population size of 30, a maximum iteration number of 25, an alert threshold of 0.7, a producer ratio of 0.4, and a vigilance threshold (SD) typically set at 0.2. Simultaneously, the number of nodes in the hidden layer of the LSTM neural network was set to 10.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
## 3 Results
### Phenotypic Screening of Wheat Genotypes  
The key objective in plant breeding research is to screen genotypes to select better-performing lines with higher growth and yield potential. Non-destructively collected crop height and biomass estimates are crucial contributors in effectively mapping the growth profile. The crop height, FW, and DW that were estimated across the two time points showed growth trends for the wheat genotypes (Figure 10). The growth height was maximum for Aus27919 between the two time points i.e., 110 and 140 DAS. The genotypes Aus428 and Aus79 achieved the maximum height of 102 cm amongst all other genotypes on second time point, 140 DAS (Figure 10a). In terms of biomass, the genotype Aus7992 had the highest FW and DW of 4.5 Kg and 2.1 Kg, respectively, amongst all other genotypes on 140 DAS. The genotype Cara was shortest and produced minimum FW and DW on 140 DAS (Figure 10b,c).  
Figure 9: Regression analysis between the CBM sensor readings and the manual measurements (**a**) crop height, (**b**) fresh weight (FW), and (**c**) dry weight (DW). Frequency domain analysis of the absolute residual error for the CBM sensor (**d**) crop height, (**e**) FW, and (**f**) DW.  
Figure 10: The phenotypic growth profile of the wheat genotypes that were measured between two time points—100 and 140 DAS. (**a**) plant height, (**b**) fresh weight, and (**c**) dry weight. The phenotypic growth profiles that were measured using the CBM sensor aid in the genotypic screening of wheat varieties.
# A Unified Framework for Automatic Detection of Wound Infection with Artificial Intelligence
###### Abstract  
Background: The surgical wound is a unique problem requiring continuous postoperative care, and mobile health technology is implemented to bridge the care gap. Our study aim was to design an integrated framework to support the diagnosis of wound infection. Methods: We used a computer-vision approach based on supervised learning techniques and machine learning algorithms, to help detect the wound region of interest (ROI) and classify wound infection features. The intersection-union test (IUT) was used to evaluate the accuracy of the detection of color card and wound ROI. The area under the receiver operating characteristic curve (AUC) of our model was adopted in comparison with different machine learning approaches. Results: 480 wound photographs were taken from 100 patients for analysis. The average value of IUT on the validation set with fivefold stratification to detect wound ROI was 0.775. For prediction of wound infection, our model achieved a significantly higher AUC score (83.3%) than the other three methods (kernel support vector machines, 44.4%; random forest, 67.1%; gradient boosting classifier, 66.9%). Conclusions: Our evaluation of a prospectively collected wound database demonstrates the effectiveness and reliability of the proposed system, which has been developed for automatic detection of wound infections in patients undergoing surgical procedures.  
artificial intelligence; wound infection; telecare +
Footnote †: journal: MedicalTraditionally, the surgical staff would make a call to inquire the patients of the recovery of the general condition and the wound, whereas the condition of the wound is difficult to be remotely evaluated by verbal communications only. To reduce the gap between home and hospital, mobile health (mHealth) has been created to help patients upload wound images and obtain immediate responses regarding their wound condition and has proved feasible in several studies [6; 7; 8]. Furthermore, the patients receive feelings of reassurance because the medical staff continuously and seamlessly monitor their recovery. However, these novel programs depend on professional staff to review the wound pictures and to provide responses, which may add extra workload to already burdened staff [5].