esa-sceva/satcom-chunk-collection huggingface dataset:
# Extraction of Humic Acid from Lignite by KOH-Hydrothermal Method
## 2 Materials and Methods
### Experimental Setup and Procedures  
Hydrothermal tests were performed in a stainless steel reactor [46]. The detailed process is shown in Figure 1. The alkali-to-carbon mass ratio (mass ratio of potassium hydroxide to coal sample), water-to-coal mass ratio (mass ratio of water-to-coal sample), reaction time (3-11 h), and reaction temperature (130-190 \({}^{\circ}\)C) were studied to determine their effects on the HA yield.  
Figure 1: Flow chart of humic acid (HA) extracted by the hydrothermal method.  
The process was as follows.  
Step 1: Coal samples were dried in a vacuum oven at 105 \({}^{\circ}\)C to constant weight [47]. An air-dried sample (20 g) was treated with potassium hydroxide (KOH) and deionized water in the reactor. Then, the reactor was placed into oven (130-190 \({}^{\circ}\)C). After the reaction was finished, the kettle was naturally cooled to room temperature [48].  
Step 2: The supernatant was separated from residues by centrifugation. After centrifugation, the residuals were washed with distilled water until almost reaching neutral pH. Then, the residuals were filtered and dried in a vacuum oven at 105 \({}^{\circ}\)C.  
Step 3: The supernatant and cleansing solution were acidized to pH < 2 with HCl, further separated by centrifugation, and dried in a vacuum oven at 60 \({}^{\circ}\)C. The obtained dried materials were HA.  
The HA yield was calculated on an air-dried basis according to the following formula.  
\[\varepsilon=\frac{\text{M}_{\text{YM}}(1-M_{\text{ad}})-M_{\text{CY}}}{\text{ M}_{\text{YM}}(1-M_{\text{ad}})} \tag{2}\]  
where \(\varepsilon\) is the yield of HA, %; \(M_{\text{YM}}\) is the mass of lignite, g; \(M_{\text{CY}}\) is the mass of the residual coal, g; and \(M_{\text{ad}}\) is the water content in raw coal, %.  
According to the GB/T212-2008 standard [49], the moisture content (M\({}_{\text{ad}}\)), ash content (A\({}_{\text{d}}\)), volatile content (V\({}_{\text{daf}}\)), and fixed carbon content (FC\({}_{\text{daf}}\)) were detected based on the air-dried samples. The elements C, H, O, N, and S were analyzed based on the air-dried samples by a TE-CHN700B Elemental Analyzer (Thermo Fisher Scientific, USA) in accordance with the GB/T476-2001 standard. The total HA content in raw coal was determined by the GB/T11957-2001 volumetric method, and the HA content of raw coal was 14.6%.
# Improved Recommendations Based on Trust Relationships in Social Networks
## 4 A Recommendation Algorithm Based on Trust Relationships  
Based on formalized trust relationships, a target user's acceptances of items can be predicted, and some recommendation can then be generated. In order to ensure the accuracy of prediction, we first define an index to evaluate the comprehensive trust to a trusted user:  
\[C_{ab}=\frac{D_{ab}(1+USim_{ab})}{\underset{u_{a}\in U_{a}}{\max}D_{an}\underset {u_{m}\in U_{a}}{\max}(1+USim_{am})} \tag{5}\]  
where \(C_{ab}\) is the comprehensive trust of \(u_{a}\) to \(u_{b}\), and \(U_{a}\) is the set of users in the trust relationship of \(u_{a}\).  
Then, the predicted ratings can be calculated by Equation (6).  
\[t_{aj}=C_{m}\frac{r_{\text{max}}r_{j}^{b}}{\underset{i_{b}\in I_{b}}{\max}r_{ k}^{b}}\;\;,\;\;\;C_{m}=\underset{u_{a}\in U_{a}}{\max}C_{an} \tag{6}\]  
where \(t_{aj}\) is the predicted rating value of item \(i_{j}\) by target user \(u_{a}\), and \(I_{b}\) denotes the set of items rated by \(u_{b}\), who has the maximum comprehensive trust of \(u_{a}\), namely, \(C_{ab}=C_{m}\).  
Based on the above, we present an Improved Recommendation Algorithm based on Trust Relationships (IRATR) in a social network, and its specific steps can be described as follows:  
```
0: target user \(u_{a}\), the set of users \(U\), the set of items \(I\) and the rating matrix \(R\)
0: predicted ratings \(E_{a}\) for \(u_{a}\)
1. Initialize the trust relationship \(T_{a}\)
2. set\(E_{a}=\emptyset\) and \(C_{m}=\emptyset\)
3. for each user \(u_{m}\in U_{a}\)do
4. calculate \(USim_{\text{am}}\)
5. calculate \(C_{am}\)
6. end for
7. for each item \(i_{j}\in I\)do
8. select\(C_{m}=\text{max}\;C_{mt}\) where \(u_{n}\in U_{a}\) and \(r_{nj}\
eq\emptyset\)
9. if\(C_{m}\
eq\emptyset\)
10. \(E_{a}\)[\(j\)] = \(t_{nj}\)
11. else
12. select\(C_{m}=\text{max}\;C_{am}\) where \(u_{n}\
otin U_{a}\) and \(u_{n}\) in \(P_{a}\) and \(r_{nj}\
eq\emptyset\)
13. if\(C_{m}\
eq\emptyset\)
14. \(E_{a}\)[\(j\)] = \(t_{nj}\)
15. else
16. \(E_{a}\)[\(j\)] = \(\mathbf{avg}(r_{nj})\) where \(r_{nj}\in\mathbf{R}\)
17. end if
18. end if
19. end for
20. return \(E_{a}\)
```  
Due to the sparsity of the rating matrix, a user may not be recommended a certain item in traditional recommendation methods, but he/she can obtain a predicted rating on the item through an indirect trust path in IRATR. Moreover, based on the trust relationship, most of the final recommended items have only been rated by trusted neighbors of the target user, and they are well evaluated in these neighbors' user experiences.
# Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach
## 4 Analysis and Findings from Experiments
### Result Analysis

Table 7: Performance comparison between original and improved YOLOv11. We further investigate the performance of Bangla character recognition using a variety of advanced CNN models. In this study, we employed models such as BanglaNet, ResNet-50, VGG-16, CNN, DenseNet, DenseNet121, Vision Transformer, Inception V3, and our  
Figure 3: Performance metrics and visualizations for the improved model. method on a custom dataset. The performance results for classification are presented in Figure 4.  
As anticipated, DenseNet121 and ResNet-50 exhibit lower accuracy performance compared to other models, especially during testing for DenseNet121 and training for ResNet-50. However, YOLOv11 outperforms all other models, providing the best accuracy, as reflected in both training and testing performance. These results highlight the efficiency and robustness of the YOLOv11 model for Bangla character recognition tasks.
## 4 Discussion
### Different Purposes and Practices
#### 4.2.2 Mangrove Removal Project  
In Taiwan, due to topographic factors, sediments from the upper watershed were deposited by rivers in the estuaries of the west coast [46]. Thus, this deposition forms a series of shoals, and the swamps are suitable for mangrove growth and expansion. Therefore, mangroves have become one of the main habitat types of coastal wetlands in Western Taiwan [47]. Since 2006, ecotourism and conservation education have been carried out in wetlands. The delineation of \"National Important Wetlands\" has been completed by the Construction and Planning Department of the Ministry of the Interior. In 2011, there were 100 important wetlands, including 51 \"international wetlands\" and \"national wetlands\", 40 \"local wetlands\", and 7 undetermined wetlands [48; 54]. Moreover, the Wetland Conservation Act was passed in July of 2013, and the implementation of the Act began on 2 February 2015. Therefore, Taiwan has become an area of wetland protection and specific legislation [54].  
The Siangshan Wetland is located in Hsinchu City. Mangroves have been planted since 1969, causing a change in the original habitat and posing a serious threat to the endemic Taiwanese fiddler crab species (such as _U. formosensis_). Unfortunately, mangroves were defined as invasive species, which altered the structure and function of benthic habitats and caused sediments to accumulate with estuarine flooding during heavy rains, and allowed the invasion of the small black mosquito (_Forcipomyia taiwana_) [47; 48]. The expansion of mangrove lands changed the state of the habitat substrate, and _U. formosensis_, for example, was forced to move outwards or even disappeared. When mangroves are removed, the habitat gradually returns to its original state [36]. At this time, species that disappeared in the past gradually return and increase in abundance. Five indicator species, _M. brevidactylus_, _M. hunzai_, _U. arcuata_, _U. lacteal_, and _U. borealis_, were identified as having high co-occurrence probabilities, which provide direct evidence of the benefits of mangrove removal on wetland restoration.  
Compared with our results, both studies selected similar indicator species. These ISs also have a high probability of co-occurrence and a high contribution to biodiversity. Under the premise of biodiversity, these species can also be set as target species for policy implementation.
# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 1 Introduction
Therefore, this study focused on the accuracy improvement of Wi-Fi fingerprint-based localization, adopting a robust localization model [19] and utilizing the standard particle swarm optimization (SPSO) algorithm [20] to determine the optimal location estimation. The main contributions of this study are as follows:  
* A two-panel fingerprint homogeneity model was adopted to characterize fingerprint similarity. In addition to considering both the real distance and direction difference of two fingerprints, this study proposes another combination, Euclidean metric and cosine distance, which was used in the system for a more robust performance. * An effective application of a standard particle swarm optimization (SPSO) algorithm for Wi-Fi fingerprint-based indoor localization is proposed to improve the localization accuracy. * Experiments on data sets and tests were conducted in a real-world environment and the results were compared with those obtained using other classical localization methods, thereby verifying the effectiveness of the proposed localization method. The remainder of this paper is organized as follows. In Section 2, related work is briefly reviewed. Section 3 describes the proposed localization system in detail. In Section 4, the field experiments conducted to examine the proposed algorithm are described, followed by the conclusions in Section 5.
## 4 Results
### System Accuracy Tests  
The accuracy of the system was tested by calculating the ability of the manipulator to press the lift button chosen by the user. At the same time, to verify the goodness of the whole algorithm to reach the target regardless of the non-idealities introduced by the low cost nature of the prototype, the entire system was simulated using _RViz_ simulator, as shown in Figure 8.  
The simulation ran on a personal computer concurrently with the real prototype tests, by using the same algorithms. In the simulation environment, a _virtualized camera_ was exploited: when an object was recognized by the AI algorithm in the frames captured by the real camera prototype, the coordinates of the found object were used in the simulation. Owing to that, only the camera non-idealities were considered in the RViz simulation, which, instead, did not consider the mechanical shaking effects. Moreover, the tests and the simulation were performed with and without the implementation of the _Target stabilizer_ to test its effectiveness.  
Table 1 sums up the results of this investigation. The accuracy was calculated as the success rate over 50 iterations of the experiment.  
Simulations showed the manipulator can reach the target with an average distance between the object center of gravity and the arrival point of 4.5 mm and an average time of 25 s. Such time was also considered as the maximum time available in the real tests for the manipulator to reach the target for that attempt to be considered successful. Fixing a limit was necessary because in some real cases the manipulator was able to reach the desired button only after a huge quantity of time from the user request, being stuck in the proximity of the target.  
Figure 8: The manipulator structure shown in RViz simulation environment.  
The results in Table 1 show a gap between the simulation and prototype tests result. It is due to the presence of mechanical non-idealities that were not considered in the simulation, which reached 100% in accuracy when the _KCF_ algorithm was used. The advantages offered by the latter were proved by the sensible accuracy drop, which was verified in the prototype tests when _KCF_ was not used, increasing the manipulator sensitivity to mechanical vibrations and other effects.
## 2 Past Research and the Innovations in This Work
### Novel Contributions of This Paper  
We carried out a comprehensive review of the existing body of relevant work, a summary of which is presented in Section 2.1. Figure 1 shows how our study integrates all the required elements to provide a robust explainable security framework for a 6G-IoT environment. While prior studies have explored similar areas, our work offers a fresh perspective by tailoring well-established methods to the intricate and evolving requirements of 6G and IoT environments, uniquely combining predictive capabilities for emerging IoT security threats with a strong emphasis on improving model transparency through XAI techniques. Our approach goes beyond conventional techniques by providing detailed insights into how specific feature values (high or low) influence model predictions, ensuring these features are effectively monitored for improved safety. Unlike prior studies, this paper establishes a clear and in-depth connection between the model's decision-making process and the explanations provided by SHAP and LIME. The interpretations are presented in an accessible manner, making them understandable even to non-technical individuals or stakeholders, without prior knowledge of XAI, thus broadening the framework's applicability and fostering trust in AI-powered security solutions.  
By cross validating the outcomes of XAI methods, we increase the model's dependability and transparency in decision-making. We also analyze the model's predictions using XAI methods to ensure alignment and consistency for both benign and attack traffic samples, ensuring robust performance across all data instances. Additionally, we prioritize fair data representation and continuously refine predictions through validation and XAI-driven insights. This integrated approach uniquely enhances both the interpretability and effectiveness of security solutions for future networks, positioning our framework as a comprehensive, trustworthy AI-powered solution for the dynamic and complex IoT security challenges in 6G environments.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
###### Abstract  
Automatic Dependent Surveillance-Broadcast (ADS-B) is the main communication system currently being used in Air Traffic Control (ATC) around the world. The ADS-B system is planned to be a key component of the Federal Aviation Administration (FAA) NextGen plan, which will manage the increasingly congested airspace in the coming decades. While the benefits of ADS-B are widely known, its lack of security measures and its vulnerability to cyberattacks such as jamming and spoofing is a great concern for flight safety experts. In this paper, we first summarize the cyberattacks and challenges related to ADS-B's vulnerabilities. Thereafter, we present theoretical and practical methods for implementing an Internet of Things (IoT)-based system as a possible additional safety layer to mitigate the presented cyber- vulnerabilities. Finally, a set of simulations and field experiments is presented to test the expected performance of the suggested IoT flight safety system. We conjecture that the presented system can be implemented in a wide range of civilian airplanes, leading to an improvement in flight safety in cases of cyberattacks or the absence of reliable ADS-B communication.
## 2 Materials and Methods
### 3D-EEMs Measurement  
For the FU dataset, the Hitachi F-4500 fluorescence spectrometer was used to measure its three-dimensional fluorescence data (the concentration of organic matter in the river water is relatively low, in this study, we selected several samples for dilution and repeated measurements to ensure that no fluorescence in-filtering effect occurred in the river water [26]). During the measurement, the excitation wavelength was set to 200-500 nm with an interval of 10 nm; the emission wavelength was set to 250-550 nm with an interval of 5 nm, the excitation and emission slits were both set to 10 nm, the PMT voltage was set to 700 V, and the scanning speed was set to 1200 nm/min. The measurement of F data and P data can be found in published articles ([PERSON] et al., [PERSON] et al.), while the PU data are accurately described in the official drFEM toolbox V0.6 documentation.  
To use convolutional neural networks for classification and component maps fitting of 3D fluorescence data, the prerequisite is to obtain 3D fluorescence data. During the 3D fluorescence measurement experiment of the FU dataset, in total we measured and obtained 45 samples (except outlier samples), each 3D-EEM consists of 31 excitation spectra and 61 emission spectra, therefore, the 3D-EEM size is 61 \(\times\) 31.  
In the other three types of datasets, the F dataset was measured by fluorescence spectrometry using a Perkin Elmer LS50B fluorescence spectrometer in 10 nm \(\times\) 10 nm quartz cuvettes with excitation spectra ranging from 250-370 nm with an excitation interval of 10 nm and emission spectra ranging from 270-600 nm with an emission interval of 1 nm. each emission-excitation matrix consists of 13 excitation spectra and 331 emission spectra, and the 3D-EEM size is 331 \(\times\) 13. In the P dataset, the excitation spectra range from 230-455 nm with an excitation interval of 5 nm, the emission spectra range from 290-682 nm with an emission interval of 4 nm, and each emission-excitation matrix consists of 46 excitation spectra and 99 emission spectra, and the 3D-EEM size is 99 \(\times\) 46. In the PU dataset, the excitation spectra range from 243-399 nm with an interval of 4 nm, the  
Figure 1: FU dataset sampling sites. The sequence of sampling sites shall be sorted according to the sampling time. The number of samples collected from each site are: Site 1 collected 10 samples, Site 2 collected 5 samples, Site 3 collected 3 samples, Site 4 collected 3 samples, Site 5 collected 3 samples, Site 6 collected 3 samples, Site 7 collected 8 samples, Site 8 collected 2 samples, Site 9 collected 2 samples, and Site 10 collected 10 samples. The number of samples shall be determined according to the actual situation of the sampling sites.  
emission spectra range from 303-499 nm with an emission interval of 4 nm, and each emission-excitation matrix consists of 40 excitation spectra and 50 emission spectra, and the 3D-EEM size is 50 \(\times\) 40.
## 1 Introduction
The scatterometer can provide TC intensity at the sea surface level, while the multi-channel polarized microwave radiometer can provide additional vertical information of TC structure. Therefore, we try to develop a TC intensity estimation method that synergistically uses active and passive microwave observations. In this study, satellite observations and best-track data of TCs in the Northwestern Pacific Ocean are collocated. Several predictors of TC intensity are proposed, based on statistical analyses, and a multivariate linear model is built, based on these predictors. Then, the proposed method is tested, with satellite and aircraft observations. The remainder of this paper is organized as follows. The study area and data are described in Section 2. Then, we describe the methodology in Section 3. In Section 4, the experiments are described. Lastly, Sections 5 and 6 include discussion and conclusions.
# The Impact of Spatial Resolutions on Nature-Based Solution Suitability Mapping for Europe
## 2 Study Area and NBS
Firstly, a soil depth at a minimum of 0.6 m and a mean depth of 3.2 m allow trees to grow roots [35], whilst a bulk density greater than 1.6 g/cm is restricting to root growth [36]. Regarding the water retention potential, soil textures such as silts and loans have an efficient water intake and water holding rate [37]. * _Built-up areas._ Limiting factors for the planting of riparian forest buffers are existing buildings, whether industrial, public, or private houses. In addition, transport infrastructures such as railways or roads may be excluded. Transportation ways are further causing fragmentations of habitats since they function as barriers for wildlife. * _Water._ Riparian forest buffers are treed corridors along water bodies. The recommended width of a buffer is dependent on the purpose (e.g., flood reduction, bank stabilisation, biodiversity enhancement) and the size of a flowing water body (e.g., streams and rivers). Buffer widths are recommended to start at 12 m, but for supporting biodiversity, buffers should be at least 30 m wide. For water bodies with a width of 2 m or less, a buffer could already be starting at 5 m [25; 26; 28; 38]. In this study, suitable areas for riparian forest buffer zones are mapped for flood risk reduction. For this purpose, it could be argued that flood risk areas should be added as a suitability criterion. However, we did not include this criterion in this analysis because, firstly, riparian buffers can be implemented in upstream areas to reduce the flood risk downstream [39], and secondly, at the European scale, the flood hazard maps [31] do not include smaller streams and ditches; thus, the dataset was not considered to be completely suitable for this analysis.
## 3 1-vs.-1 WVR Air Combat Engagement Modelling
### State Space of 1-vs.-1 WVR Air Combat  
System state of air combat is defined by the current motion state parameters of UCAV\({}_{1}\) and UCAV\({}_{2}\), the relative position and angle parameters and future motion state parameters. Firstly, the current motion state parameters of the two UCAVs in air combat are considered to form the basic state space \(\mathcal{S}_{base}\):  
\[\mathcal{S}_{base}=[x_{1},y_{1},x_{2},y_{2},v_{1},v_{2},\phi_{1},\phi_{2}, \psi_{1},\psi_{2}]^{T} \tag{11}\]  
where, \((x_{1},y_{1})\) and \((x_{2},y_{2})\) are the current positions of UCAV\({}_{1}\) and UCAV\({}_{2}\). The positions of UCAVs have no limits, thereby allowing for flight in all directions in the x-y plane. Notably, \(v_{1}\) and \(v_{2}\) are the current speeds of both UCAVs, \(\phi_{1}\) and \(\phi_{2}\) are the current heading angles of both UCAVs, and \(\psi_{1}\) and \(\psi_{2}\) are the current bank angles of both UCAVs. The bank angle is limited based on the maximum capabilities of the actual UCAV, and based on the need to limit the maximum turning capabilities of the UCAV, the heading angle is allowed to take any value between \(\pm 180^{\circ}\).  
Subsequently, the distance and relative angle parameters of the two UCAVs are introduced as components of the state space of 1-vs.-1 WVR air combat. The distance parameter is the two-norm of the LOS vector \(\mathbf{\rho}\), and the relative angle parameters are \(\lambda\) and \(\epsilon\). These components of the state space are depicted in Equation (12):  
\[\mathcal{S}_{relative}=[|\mathbf{\rho}|\,,\lambda_{1},\lambda_{2},\epsilon_{1}, \epsilon_{2}]^{T} \tag{12}\]  
Finally, a prediction of the target position is introduced, and the predicted target position is used as a component of the state space. A precise prediction of the target's future position is crucial for preemptive action. However, accurate predictions can be difficult to achieve owing to uncertainty of the target's position and intention. The exact model and the characteristics of a combat aircraft are completely hidden. Due to individual differences in the behaviors of the pilots, the combat strategies can contain unpredictable randomness. Furthermore, wrong predictions can worsen the combat situation. Therefore, the target positions should be carefully and precisely predicted.