esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Field Quantization
### Linearized Quantization  
#### 3.2.1 Optical Field  
The standard method to linearize the interaction Hamiltonian can be now used by making the substitutions \(\hat{a}\rightarrow\overline{a}+\hat{a}\) where the new \(\hat{a}\) operator from now on stands for the non-classical perturbations and \(\langle\hat{a}\rangle\) is a measure of optical field amplitude. Then ignoring higher-order terms and retaining only the lowest-order interacting terms, we get  
\[\mathbb{H}_{3}=-\hbar g_{3}\left(\hat{b}^{\dagger}+\hat{b}\right)\left(e^{i \varphi}\hat{a}^{\dagger}+e^{-i\varphi}\hat{a}\right), \tag{50}\]  
as well as  
\[\mathbb{H}_{4}=+\hbar g_{4}^{+}\left(\hat{b}^{\dagger}+\hat{b}\right)^{2} \left(e^{i\varphi}\hat{a}^{\dagger}+e^{-i\varphi}\hat{a}\right),\omega\gg\Omega, \tag{51}\]  
Here, \(\varphi=\angle\overline{a}\) and the coupling frequencies are defined as  
\[g_{3}=\frac{\alpha}{\sqrt{2}}\left|\overline{a}\right|\equiv g_{0}|\overline{ a}\rangle\equiv G,\,g_{4}^{+}=\frac{\beta}{2}\left|\overline{a}\right|=\theta G,\,g_{4}^{-}=\,R\frac{\Omega^{2}}{\omega^{2}}g_{4}^{+}. \tag{52}\]
# Enhancement of Convection Heat Transfer in Air Using Ultrasound
## 1 Introduction
[32] reported an increase in the heat transfer coefficient between the coil and water in the range of 22-104%. [PERSON] et al. [33] obtained an improvement in the heat transfer coefficient between water flowing through tubes in the range of 10-40%. [PERSON] et al. [40] examined the forced flow of LiBR solution outside tubes. They received an increase in the heat transfer coefficient in the range of 10-45%. In these publications [32; 33; 40], the phenomena that cause the improvement of heat transfer have not been investigated. In other publications, an enhancement of heat transfer due to cavitation [30; 32; 33; 38] and acoustic streaming [33; 36; 37; 38; 39] was observed. Generally, an enhancement of heat transfer in liquids from 22% to 390% has been reported [32; 37; 39]. There are a lack of studies on the influence of ultrasound on the convective heat transfer coefficient in the gas phase--achieved through utilising an airborne ultrasound. This is because the generation of power ultrasound in liquids is easier and much more efficient than in gases [42]. In the case of heat transfer in air, the enhancement of free convection (cooling) was studied [29; 31; 41]. The authors of these works did not provide numerical values of the obtained enhancement of heat exchange. However, attention was paid to the importance of the relative location of the source of ultrasound to the source of heat [29]. The same problem of the effect of ultrasound transducer placement on heat transfer in liquids was analysed in [28; 36]. It was stated that the placement of the ultrasound source plays an important role in the enhancement of heat transfer. The aim of this work is to study the influence of ultrasound on both the convective heat transfer as well as the ultrasound absorption (US thermal effect) during convective heating of material in a dryer.
## 3 Observation of Scintillation in OEM7 Measurements  
To characterize ionospheric scintillation's impact and its impairing effect on GNSS measurements, a data collection campaign was conducted. Several NovAtel OEM7 receivers  
Figure 4: Polar plot of multiple satellites and their associated S4 values.  
Figure 3: S4 values for August 2023 for 24 h UTC.  
were used including single- and dual-antenna cards and were located on the rooftop of the Hexagon office in Hyderabad, India. These units are multi-frequency, multi-constellation receivers that track all GNSS bands. To understand and investigate scintillation's impact on RTK solutions, a medium baseline scenario was considered (base station: 6.5 km distance). A short baseline dual-antenna receiver on the rooftop (baseline < 10 m) was also used to characterize the carrier phase correlation for short baseline applications (such as heading) under ionospheric scintillation.
## 3 Sensor Node Position Estimation
### Uncertainties in GPS Positioning
#### 3.1.2 Closeness Algorithm  
Our second algorithm used to obtain the reference points is called closeness. In this algorithm, the sensor node creates every possible combination among the intersection point pairs. In Figure 4, the sets created after this combination are \((a,b,c)\), \((a,b,e)\), \((a,c,f)\), \((a,e,f)\), \((b,c,d)\), \((b,d,e)\), \((c,d,f)\), and \((d,e,f)\). Subsequently, the sensor node calculates the distance between all pairs of points in each set. Then, it sums the distances within a set. The intersection points in the set with the smallest sum are considered as reference points, as can be seen in Equation (3).  
\[rp=ip(a,b,c):ip\in s\ \ and\ \ ip\ \ has\ \ min(d(a,b)+d(a,c)+d(b,c)) \tag{3}\]  
where \(rp\) represents the set of reference points, \(ip\) denotes each combination of three intersection points (\(a\), \(b\), and \(c\)), \(s\) represents the set of these combinations, and \(d(x,y)\) represents the distance between two intersection points \(x\) and \(y\).
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 2 Study Area and Dataset
### Dataset  
Based on the distribution of crop categories contained in the selected parcel samples, we chose 18 categories for the parcel recognition system in France. As shown in Figure 2, parcels that did not belong to these 18 categories were labeled as \"void\".  
In Figure 3, a yearly image time series reveals the annual cyclical changes in crops and also highlights how different land features have similar representations in single-temporal remote sensing images. Based on these observations, this study selects SITS as the focus of research for agricultural land semantic segmentation.  
The dataset used in this study is PASTIS, which comprises SITS and labels for the semantic segmentation of agricultural land. The image data were collected using Sentinel-2 and consist of 2433 multi-spectral image sequences. Each image in the sequence comprises 10 channels, and during training, normalization is required for each channel of the image. Each image consists of \(128\times 128\) pixels. The data were collected from September 2018 to November 2019, with varying observation frequencies ranging from 38 to 61 times and an average interval of 5 days. Each multi-spectral image sequence is organized into a four-dimensional tensor of shape \(T\times 10\times 128\times 128\), where T represents the number of observations (\(T\in[38,61]\)). There are 19 annotated classes in total, including 18 different crop classes and one background class. The class labels were sourced from a publicly available land parcel identification system in France, with an accuracy rate exceeding 98%.
# MTRSRP: Joint Design of Multi-Triangular Ring and Self-Routing Protocol for BLE Networks
## 4 The Proposed MTRSRP  
### System Architecture  
The MTRSRP system architecture utilizes multi-tiered rings to organize nodes into structured layers, thereby enhancing routing and connectivity. The key components are:  
* Piconets: Fundamental network units, each comprising one master node and several slave nodes;
* Rings: Higher-level structures comprising interconnected piconets managed by intra- and inter-ring bridges;
* Coordinator: A master node with additional responsibilities, such as collecting network data and coordinating the formation of the scatternet.  
The MTRSRP method includes two primary phases to establish the network topology: leader selection and scatternet formation.  
The leader selection phase involves selecting the coordinator node through a node discovery process and establishing the initial piconet structures, which are detailed as follows.  
1. Node Discovery: Nodes alternate between advertiser and scanner roles to discover neighbors and exchange IDs and neighbor counts. Advertisers broadcast their IDs on designated channels, and scanners collect the information. This process continues until the nodes fully understand their immediate neighbors.
2. Coordinator Election: Nodes compete to become coordinators based on the collected neighbor information. The node with the highest neighbor count is elected as the coordinator. The coordinator then gathers timing information, node addresses, and neighbor counts from all the nodes.  
In contrast, the scatternet formation phase utilizes the coordinator's information to form multi-tiered ring structures that efficiently connect the piconets. Specifically, the coordinator uses the collected data to organize the nodes into interconnected rings. Each ring comprises multiple piconets connected by intra-ring bridges, whereas inter-ring bridges link different rings, creating a multitiered structure.  
Additionally, the self-routing protocol of MTRSRP uses coding-addressing techniques to optimize packet transmission paths. This protocol ensures that packets follow the shortest path within triangular rings and between rings, thereby reducing communication latency and enhancing network efficiency.  
In conclusion, the architecture and phases of the MTRSRP method were designed to optimize the formation and routing of BLE networks. The protocol aims to improve routing efficiency, reduce latency, and enhance network stability by organizing nodes into structured multi-tiered rings and using a well-defined leader selection and scatternet formation process.
###### Abstract  
Several stainless-steel slags have been successfully employed in previous studies as substitutes for lime in the treatment of industrial acidic wastewaters. This study deepens the knowledge of such application, by analyzing the neutralizing capacity of different slags related to their mineral compositions. To do so, firstly the chemical and mineral compositions of all the slag samples are assessed. Then, 0.5 g, 1 g 2 g of each slag and 0.25 g and 0.5 g of lime are used to neutralize 100 g of 0.1 M HCl or HNO\({}_{3}\) solutions. After the has neutralization occurred, the solid residues are extracted and analyzed using XRD spectroscopy. Then, the solubility of the minerals is assessed and ranked, by comparing the XRD spectra of the residues with the obtained pH values. The results show that minerals such as dicalcium silicate and brodigite are highly soluble in the selected experimental conditions, while minerals such as mervinite and akermanite, only partially. Moreover, Al-rich slags seem to perform poorly due to the formation of hydroxides, which generate extra protons. However, when the weight of slag is adequately adjusted, Al-rich slags can increase the pH values to higher levels compared to the other studied slags.  
steelmaking slag; solubility; minerals; wastewater treatment; leaching Article
## 3 Results and Discussions
### Characterization of Fine Lateritic Materials
#### 3.1.2 Water Content and Density Parameters  
The values of water content and density parameters are given in Table 2. The lowest and highest values of water content (\(\omega\)) which were obtained in the BG1 soil profile were 15.3 and 26.8%, respectively. According to [74], materials with 10 \(<\)\(\omega\) \(<\) 30 are considered as silts but those with 20 \(<\)\(\omega\) \(<\) 50 are rendered as clays. Therefore, the water content of the studied soil samples confirms the presence of silty and clayy particles as also revealed by the macroscopic description, mineral association, and grain size distribution. Moreover, there is a noticeable increase in water content with an increase in kaolinite proportions (Figure 6). Thus, the water absorption of these materials is significant and, therefore, unsuitable for the construction of pavement bases [75].  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Profiles** & \multicolumn{6}{c}{**BG1**} & \multicolumn{6}{c}{**BG2**} \\ \hline
**Geotechnical parameters** & BL15 & BL17 & BL23 & BL24 & BL26 & BL32 & BL13 & BL16 & BL20 & BL27 & BL29 & BL31 \\ \hline Water content (\(\omega\)) (\%) & 25.3 & 26.8 & 26.1 & 22.3 & 15.3 & 21.5 & 22.3 & 25.1 & 18.6 & 18.2 & 22.6 & 22.5 \\ Specific gravity (Gs) & 2.79 & 2.09 & 2.092 & 2.11 & 2.03 & 2.021 & 2.058 & 2.057 & 2.077 & 2.083 & 2.094 & 2.025 \\ Bulk density (\(\gamma\)) (t/m\({}^{3}\)) & 1.93 & 1.58 & 1.91 & 1.61 & 1.61 & 1.598 & 1.91 & 1.68 & 1.77 & 1.11 & 1.65 & 1.66 \\ Gravel & 0.0 & 2.95 & 15.11 & 12.22 & 0.14 & 2.6 & 0.0 & 0.7 & 15.11 & 0.0 & 4.99 & 2.4 \\ Coarse sand & 0.79 & 12.31 & 3.6 & 20.91 & 8.21 & 3.4 & 2.87 & 8.8 & 6.72 & 9.96 & 13.89 & 6.6 \\ Fine sand & 7.54 & 6.54 & 8.83 & 11.97 & 21.92 & 15.8 & 11.48 & 20.63 & 5.71 & 22.383 & 10.75 & 15.7 \\ Silt & 27.32 & 24.85 & 11.34 & 14.89 & 20.16 & 24.2 & 27.98 & 27.09 & 11.34 & 23.427 & 12.97 & 27.5 \\ Clay & 57.45 & 46.54 & 61.12 & 40.00 & 48.34 & 50.0 & 54.00 & 40.45 & 61.12 & 41.67 & 57.4 & 47.8 \\ \hline
**Alterberg limits** & & & & & & & & & & & & \\ \hline LL (\%) & 69.01 & 59.8 & 76.8 & 59 & 48.25 & 71.7 & 59.2 & 65.5 & 60.4 & 61.6 & 68.3 & 71.8 \\ PL (\%) & 40.31 & 30.46 & 41 & 35.15 & 28.78 & 41.7 & 35.38 & 31.1 & 36.76 & 37.4 & 45.02 & 41.7 \\ PI (\%) & 28.7 & 29.4 & 35.8 & 23.87 & 19.47 & 30.0 & 23.82 & 34.4 & 23.63 & 24.2 & 23.28 & 30.0 \\ CI & & 2.01 & 0.92 & 1.64 & 1.86 & 1.8 & 1.0 & 1.75 & 1 The bulk density and specific gravity values are also given in Table 2, and the values vary from 1.586 to 1.930 t/m\({}^{3}\) and from 2.000 to 2.800, respectively. Thus, the studied laterites are considered good and satisfactory, as their specific gravity values range between 2.000 and 3.000 (as recommended for clayey soils) [76].
# A Setting Optimization Ensemble for a Distributed Power Grid Protective Relay
## 1 Introduction
The adaptive modified firefly algorithm is used in document [28] to set the optimal overcurrent relay, which greatly reduces the overall relay operation time. The meta heuristic optimization method correctly solves the coordination problems between each relay offered by reference [29]. In document [30], a method optimizing the coordination direction of overcurrent protection in the bus system is designed using the PSO algorithm. Reference [31] employs the DE algorithm to optimize the setting and coordination of overcurrent relays. Additionally, reference [32] combines the TLBO algorithm to propose a comprehensive microgrid overcurrent relay (OCR) coordination the method, considering three optimization parameters. Reference [33] develops an improved genetic algorithm for the directional overcurrent relays in active distribution networks, considering a thermal equivalent short-circuit current. Even with the advantages of the genetic algorithm (GA), it has complexity and lower computational efficiency. This paper introduces a fixed-value distributed optimization method for overcurrent protection, combining independent factor evaluation (IFE) and quantum genetic optimization (QGO). Initially, a global tuning optimization model is established based on fault characteristics and overcurrent protection device features. Subsequently, the combined method of IFE and QGO is applied to solve the optimization model, offering the following characteristics:  
* The IFE algorithm extracts independent components from measurement signals, enhancing the sensitivity of the relay protection system by identifying abnormal conditions;* The inherent signal separation capability of IFE facilitates a more precise localization of faults and anomalies within intricate power system environments, thereby furnishing accurate inputs for subsequent value optimization;
* QGO utilizes qubit representation for quantum chromosomes, enhancing search space coverage and algorithm convergence while reducing computational burden;
* The quantum gate-based dynamic iterative process in QGO significantly improves global optimization efficiency, making the algorithm more flexible and adaptive. In a practical example within a regional power grid, the integrated application of the fixed-value distributed optimization method incorporating IFE and QGO ensures the \"four characteristics\" of the relay system and markedly enhances the efficacy of the optimization process. This integrated approach offers a new solution for power system safety and reliability, accelerating smart grid construction. Effectively combining the strengths of both methods, this approach holds promising prospects in the field of relay protection.
## 1 Introduction
With reference to the T-L model modeling method, this paper proposes a new conjunctive compensation method based on INS/GPS and fluxgate magnetometer information. Through modeling, inertial navigation information is introduced into the T-L model, and the compensation model with 18 coefficients is transformed to the compensation model with 36 coefficients. The negative effect of the fluxgate error in the compensation model is suppressed. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data and it is also a real-time compensation method. Compared with the traditional compensation method only based on fluxgate magnetometer information, the improved ratio is increased significantly. This paper briefly introduces the T-L model algorithm and analyzes theoretically the influences of fluxgate noise on compensation. Then, the influence of different amplitude fluxgate noise on the compensation results is compared by experimental flight data. Finally, the conjunctive compensation method based on INS/GPS is described in detail and the compensation results and analysis are given.
# A Thermodynamic Model for Cryogenic Liquid Hydrogen Fuel Tanks
## 4 Analysis of Cryogenic Fuel Tanks  
### Analysis Condition  
The fuel consumption rate was calculated for the analysis of the cryogenic liquid hydrogen tank. The fuel consumption device for calculating the fuel consumption rate was selected to be a PROTIUM-300 [34] fuel cell, made by Spectronik Pte. Ltd., Singapore. The selected fuel cell produced a rated power of 300 W when the fuel consumption rate was 3.8 L/min. The hydrogen supply conditions were a temperature of 24 \({}^{\circ}\)C and a pressure of 140 kPa to 170 kPa. When the fuel consumption rate was recalculated by applying the hydrogen supply conditions, it became 7.229 \(\times\) 10\({}^{-3}\) g/s to 8.776 \(\times\) 10\({}^{-3}\) g/s. In the present analysis, the lower fuel consumption rate of 7.229 \(\times\) 10\({}^{-3}\) g/s was applied to predict the maximum pressure of the tank.  
The size of the fuel tank was set so that the fuel cell could be loaded with enough fuel to operate for one hour. As shown in Figure 4, the tank was cylindrical, and it had a diameter (\(D\)) of 80 mm and a height (\(H\)) of 120 mm. Liquid hydrogen filled 80% of the tank volume, and the remainder was filled with gaseous hydrogen at a pressure of 150 kPa. The mass of hydrogen contained in the tank was 33.857 g of liquid hydrogen and 0.201 g of gaseous hydrogen, which was a total of 34.055 g. This could supply the fuel cell for more than one hour, based on the maximum fuel consumption rate.  
The heat transfer of the tank was considered to be the heat conduction through the tank wall. The ambient temperature was set at 15 \({}^{\circ}\)C. The structural thickness of the tank was ignored because it was very thin and its materials had a higher thermal conductivity compared to the insulation. The thickness of the insulator was set to 60 mm. The thermal conductivity of the insulator was applied as 0.037 W/m\(\cdot\)K.
###### Abstract
While deep learning, which utilizes deep framing and gradient descent, can improve the accuracy of predictions, it also requires a significant amount of data and time to train the model, and there is no exception for evaluations of landslide susceptibility. To address this issue, [PERSON] proposed a novel learning system known as the Broad Learning System (BLS) [23], which does not rely on deep learning. The BLS achieves its task of prediction by varying the number of mapped features and enhancement nodes instead of the number of network layers, and by solving for a pseudo-inverse instead of a gradient descent. This approach allows for high training efficiency and good classification effects. The BLS has been shown to have similar performance to methods such as stacked autoencoders, deep belief networks, deep Boltzmann machines and multilayer perceptions of MNIST data, and has the fastest training speed [24]. BLS has been used as a promising approach in many fields, such as license plate recognition, predicting short-term wind speed, identifying wind turbine faults and industrial process fault diagnostics [25; 26; 27; 28]. The fast training speed and good predictions of BLS also make BLS highly promising for evaluating landslide susceptibility. In addition to the aforementioned methods, the development of reliable landslide susceptibility maps through ensemble learning and optimization algorithms has emerged as a crucial area of research in landslide prevention. Various algorithms, including Gradient Boosting Decision Tree (GBDT) [29], natural gradient boosting, COA-MLP and SFO-MLP, among others, have demonstrated their effectiveness in the field of evaluating landslide susceptibility. These advanced algorithms have shown promising results and hold great potential for improving the precision and dependability of landslide susceptibility maps. In this study, an ensemble learning model based on BLS was introduced for preparing landslide susceptibility maps. Ensemble learning models based on classification and regression tree (CART) and SVM were created for a comparison with method of BLS-AdaBoost. The entire workflow for this study is shown in Figure 1. Firstly, various data were processed using ENVI 5.3 and ArcGIS 10.2 software. Secondly, all landslide conditioning factors were evaluated using the random forest method. The landslide factors used in the study were also selected at this step. Thirdly, three models, namely CART-AdaBoost, SVM-AdaBoost and BLS-AdaBoost, were constructed and used to map landslide susceptibility. In the final step, all integration methods were evaluated and compared using statistical indices and ROC curves.