esa-sceva/satcom-chunk-collection huggingface dataset:
# Temperature Effects in Packaged RF MEMS Switches with Optimized Gold Electroplating Process
## 4 Measurements and Discussion
### Temperature Effect on Mechanical Performance
Next, under different temperatures, the pull-in voltage shift of the switch during 1 h continuous operation is measured. The actuation voltage used here is a 1 kHz, 65 V square wave. A sawtooth wave is generated every 5 min to obtain the pull-in voltage of the switch. Figure 10 shows the pull-in voltage shift with the operation time at different temperatures. Firstly, the pull-in voltage continuously increases with the operation time of the switch, which is consistent with the dielectric charging effect of RF MEMS switches [23; 24]. Secondly, the shift of the pull-in voltage significantly increases with the increase in environment temperature, which is consistent with the test results in Ref [18]. The phenomenon of charge injection is widely used in ohmic contact switches and capacitive switches with a dielectric layer between the driven electrodes [25; 26]. The switch designed here has no dielectric layer between the driven electrodes, but the test results show that the dielectric charging still happens. In order to analyze the source of charge injection, COMSOL is used to perform electrostatic simulation on the switch, as shown in Figure 11. From the simulation results, most of the electric field exists in the air between the cantilever beam and the bias electrode. But there are also large electric fields of 60-80 MV/m around and underneath the bias electrode. These large electric fields are sufficient to inject charges into the dielectric layer below the bias electrode. After that, the lifetime of the switch is tested at different temperatures. The driven signal used for the lifetime test is a 10 kHz, 80 V square wave. The lifetime test results of the switch at different temperatures are shown in Figure 12. From the test results, it can be seen that the switch lifetime is about 2 \(\times\) 10\({}^{9}\) within the range of 25-45 \({}^{\circ}\)C. In this temperature range, the effect of temperature on switch lifetime is not significant. When the temperature reaches 55 \({}^{\circ}\)C, the lifetime of the switch decreases significantly to 0.5 \(\times\) 10\({}^{9}\), and the switches all failed due to unchanged high resistance. The reason may be the acceleration effect of temperature on charge injection. Figure 10: The pull-in voltage shift with the operation time at different temperatures.
## 2 Dynamic Modeling of Trans-Medium Process
### Reference Frame
 \tag{1}\]  
\[\mathbf{R}_{2}= \begin{bmatrix}1&\text{s}\phi\cdot\text{t}\theta&c\phi\cdot\text{t }\theta\\ 0&\text{c}\phi&-\text{s}\phi\\ 0&\text{s}\phi/c\theta&c\phi/c\phi\end{bmatrix}  \tag{2}\]  
Here, \(c(\cdot)\), \(\text{s}(\cdot)\), and \(\text{t}(\cdot)\) denote \(\cos(\cdot)\), \(\sin(\cdot)\), and \(\tan(\cdot)\), respectively. It should be noted that \(\mathbf{R}_{1}\) is an orthogonal matrix, so we have \(\mathbf{R}_{1}^{-1}=\mathbf{R}_{1}^{\text{T}}\). Therefore, the kinematic equation of HAUV is as follows:  
\[ \begin{array}{l}\dot{\mathbf{\eta}}_{1}=\mathbf{R}_{1}\mathbf{V}_{1}\\ \dot{\mathbf{\eta}}_{2}=\mathbf{R}_{2}\mathbf{V}_{2}\end{array}  \tag{3}\]  
During the cross-domain process, the HAUV does not carry out severe maneuvering, so reasonable assumptions can be made to simplify the dynamic model for the convenience of controller design. The following assumptions are made:  
**Assumption 1**. : _The mass of the HAUV is evenly distributed over the cylindrical watertight cabin, and the tank body is rigidly symmetric with respect to the three coordinate planes._  
**Assumption 2**. : _Due to the small maneuvering speed, the Coriolis term of translational motion and complex coupling hydrodynamic parameters can be ignored._
## 4 Efficient Band Ordering  
Each band in a hyperspectral image can be seen as a two-dimensional image. By visualizing the content of each band it becomes clear that the spectral correlation is high between many bands, but it is also clear that a few consecutive bands are not strongly spectrally correlated.  
In Figure 9, for example we show band 160 of the Lunar Lake AVIRIS image.  
If we compare Figure 9 and Figure 2 we see that there is very low correlation between the two.  
If we are able to analyze the band correlation and if we can re-order the bands, this can lead to improvements to the SLSQ algorithm.
# Earth Rotation Parameters Estimation Using GPS and SLR Measurements to Multiple LEO Satellites
## 4 ERP Estimation Based on the Combination of Multiple LEO Satellites  
In this section, we focus on the ERP estimation using the onboard data from multiple LEO satellites. The quality of ERP solutions, based on the different combinations of LEO satellites, is firstly investigated. Then, we perform a combined solution with LAGEOS and LEO satellites in order to explore the contribution of LEO satellites. Finally, a comparison between the one-step solution and two-step solution is presented.
## 6 Open Challenges, Future Prospects and Research Directions
### Open Challenges
#### 6.3.1 Mitigation Techniques for Atmospheric Turbulence  
AT remains one of the primary obstacles in FSO communication, causing signal degradation and fluctuations in signal strength. Future research should focus on developingadvanced AO systems that can dynamically adjust the optical beam in real-time to compensate for turbulence-induced distortions. Also, enhancing error correction techniques to effectively mitigate the impact of turbulence on data integrity is important. Likewise, extensive research on the utilization of ML algorithms to predict atmospheric conditions and adjust FSO parameters proactively to maintain optimal performance is required. Also, investigating hybrid systems that combine FSO with other technologies, such as RF, to provide redundancy and maintain connectivity in turbulent conditions is important to realize the full potential of FSO communication.
# Geochemical Analysis of a Multi-Layer Hydrocarbon Reservoir in the Wuerhe Area, Junggar Basin
## 4 Results and Discussion
### Steranes  
The different content of sterane in crude oil may be related to its sedimentary environment [13]. During the deposition period of the Fengcheng Formation in the Wuerhe area, the climate became dry, and evaporation began, forming an alkaline lake sedimentary environment, and therefore, the content of C29 steranes in crude oil increased. In addition, the distribution of regular steranes of C29 and C30 compounds became relatively concentrated (see triangle diagram; Figure 7). This indicates that the crude oils in this area originated from the source rocks of Permian Fengcheng Formation with the abundance of algae in the parent materials.  
Figure 5: Triangles of group composition contents of crude oil samples in Wuerhe area, showing a decrease of the saturated hydrocarbon content and an increase of non-hydrocarbons and asphaltene contents from Permian source rocks to Cretaceous bitumen.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Type**} & \multicolumn{1}{c}{**Permanian Source Rocks**} & \multicolumn{1}{c}{**Light Oil**} & \multicolumn{3}{c}{**Normal**} & \multicolumn{1}{c}{**Heavy Oil**} & \multicolumn{1}{c}{**Bitumen**} \\ \hline
**NO.** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline
**A** & 0.45 & 0.36 & 0.65 & 0.75 & 0.73 & 0.68 & 0.66 & 0.66 & 0.96 \\
**B** & 1.13 & 1.05 & 1.05 & 1.03 & 1.09 & 1.09 & 1.05 & 1.01 & 1.01 \\
**C** & 0.24 & 0.28 & 0.18 & 0.38 & 0.35 & 0.31 & 0.29 & 0.20 & 0.16 \\
**D** & 1.11 & 0.66 & 0.74 & 0.39 & 0.29 & 0.36 & 0.39 & 0.39 & 0.41 \\
**E** & 0.59 & 0.54 & 0.56 & 0.53 & 0.52 & 0.53 & 0.54 & 0.49 & 0.59 \\ \hline \hline \end{tabular} Note: A: Pristane/Phytane (Pr/Ph); B: Odd-Even Predominance (OEP); C: Ts/(Ts + Tm); D: gammacerane/C30-BOB; E: C31-BOBANE 225/(225 + 22R).  
\end{table}
Table 3: Biomarker parameters of source rocks and crude oil samples in Wuerhe area.
## 3 System Design
### Acquisition in High-Sensitivity Mode
#### 3.4.1 Theory of Operation
The acquisition Doppler search step is chosen considering a trade-off between computational load at the receiver and accuracy in the estimation of the received Doppler frequency. A typical value for the Doppler search step is \(f_{st}=\frac{1}{2|C|}\), where \(T_{CI}\) is the CI time [7]. When \(T_{CI}\) is set to 100 ms, this formula yields a Doppler search step of 5 Hz. As a result, the acquisition must conduct a frequency sweep across all potential carrier frequencies within a \(\pm 5\) kHz range, in 5 Hz increments. This process involves a vast number of combinations, leading to a significant computational burden. In practice, the use of assistance data and Doppler prediction reduces the acquisition Doppler search space and consequently the acquisition latency.
# Domain Adaptation for Arabic Machine Translation: Financial Texts as a Case Study
## 2 Background
### Domain-Specific MT  
Domain translation is a challenging task due to the fact that language varies across different domains, genres, and styles. For example, texts in the financial domain often contain specific terminologies and jargon that may not be extensively used in legal or health domains. Therefore, researchers have proposed different methods to improve the quality of translations in domains such as medical and biomedical [7; 33; 34], legal [35], and financial texts [36]. Several domain adaptation approaches have been proposed (for more comprehensive survey see [3]). Domain adaptation methods can intervene in various stages of NMT system design, training and use and can be classified into three main categories: data-centric methods, architecture-centric adaptation methods, and inference schemes for adaptation [3]. In data-centric methods, the objective is to select or generate appropriate in-domain data. Large generic monolingual data can be filtered to select domain-representative datasets based on some unique characteristics of the target domain. However, selecting a small in-domain dataset may be more domain-relevant, but the impact of any deviation from the target domain will be magnified [37]. Another approach is to construct partially synthetic bilingual training corpora by forward- or back-translation. Ref. [38] observed that models trained exclusively on back translations can perform similarly to models trained on natural data. Recently, the use of pre-trained large language models (LLMs) to generate large amounts of synthetic data at very low cost has emerged to be an effective approach [7].  
Architecture-centric adaptation typically involves adding trainable parameters to pre-trained models to avoid training models from scratch. A common approach is to fine-tune an existing well-performing NMT model on small in-domain data. Extensive fine-tuning can lead to catastrophic forgetting. Ref. [39] proposed mixed-fine tuning which involves two steps: (1) training an NMT model on out-of-domain data until convergence and then (2) fine-tuning the NMT model from step 1 on a mix of in-domain and out-of-domain data (by oversampling the in-domain data) until convergence. Mixed-fine tuning approaches can be helpful to prevent two major issues notably overlooking the specificity of each domain [1] and forgetting previously learned knowledge when exposed to the new training examples as reported in [40].  
Lastly, the inference schemes for adaptation develop a separate NMT model for each domain and combine them at inference time.
# Isolation and Properties of _Enterobacter_ sp. LX3
## 2 Materials and Methods  
### Isolation of IAA-Producing Bacteria from Maize Rhizosphere  
Soil samples were collected from a maize rhizosphere after harvest in October in an experimental field of biochar amendment at Shenyang Agricultural University, China. Biochar was evenly scattered on soil surface at a ratio of 16 t/ha and homogenized to a depth of about 15 cm by rotary tillage. Maize crops were sown in early May and harvested in early October from 2012 to 2016. Each soil sample (1 g) from the maize rhizosphere was suspended in 10 mL of sterile saline (Kemiou, Tianjin, China) and stirred at room temperature for 10 min. The suspension was serially diluted to 10\({}^{-10}\) with sterile saline and these diluted samples (100 \(\mu\)L) were spread on Luria-Bertani (LB) agar plates. Colonies were randomly chosen and incubated at 30 \({}^{\circ}\)C for 36 h and inoculated in King's B agar plates [10]. After colonies were grown at 30 \({}^{\circ}\)C for 72 h, 1 mL of Salkowski reagent was spread on the agar plate and incubated for 1 h in the dark [11]. IAA-producing colonies that developed a pink color were collected. In order to obtain a single colony, isolates were streaked on fresh LB agar plates and one IAA-producing colony was selected for the following studies.  
LB agar plates consisted of 10 g peptone (Aoboxing, Beijing, China), 10 g sodium chloride (Kemiou, Tianjin, China), 5 g yeast extract (Aoboxing, Beijing, China) and 20 g agar (Aoboxing, Beijing, China) in 1000 mL distilled water at pH 7.0. King's B agar plates contained 10 g glycerol (Kemiou, Tianjin, China), 1.5 g K\({}_{2}\)HPO\({}_{4}\) (Kemiou, Tianjin, China), 1.5 g MgSO\({}_{4}\) (Kemiou, Tianjin, China), 20 g peptone and 20 g agar in 1000 mL distilled water at pH 7.2. Salkowski reagent was prepared by mixing 49 mL of 35% perchloric acid (Kemiou, Tianjin, China) with 1 mL of 0.5 M FeCl\({}_{3}\) solution (Kemiou, Tianjin, China).
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
### Research Problems and Contributions  
Currently, although research on ETC data-based traffic sensing and analysis technologies has made certain progress, it still faces numerous challenges. In theoretical research, traffic flow theories and analysis methods targeting ETC data characteristics are not yet sufficiently developed, lacking systematic theoretical frameworks. In technical applications, most research remains at the level of data processing and simple statistical analysis, lacking deep, intelligent analysis and prediction capabilities. In practical applications, although some cities have conducted related pilot work, the application scope is relatively limited, and the promotion and replication of successful experiences need to be strengthened.  
Based on the above background analysis, this paper aims to review the research status, key technologies, and development trends of ETC data-based urban traffic state sensing and analysis technologies, focusing on examining and analyzing the following three core research areas.  
First, how existing research constructs multi-level traffic sensing methodology systems based on ETC. This includes reviewing the complete feature extraction research frameworks covering three levels: cross-section traffic state representation, road segment traffic state representation, and road network traffic state representation, ranging from microscopic vehicle individual behavior recognition to mesoscopic road segment traffic state analysis and macroscopic overall road network operation assessment.  
Second, how current studies achieve the effective fusion of ETC data with other traffic data sources. This review focuses on comprehensive traffic analysis methods based on ETC+GPS data, including deep feature extraction technologies for vehicle operation state monitoring, travel time distribution analysis, and road segment operation reliability assessment.  
Third, how researchers construct urban traffic dynamic balance diagnosis systems supported by ETC and big data. By examining the deep fusion and intelligent analysis of multi-source data, this review analyzes the transformation from traditional passive-response mode to proactive-prediction mode, and the establishment of information-led, intelligence-driven multi-system collaborative control mechanisms.
## 3 Results  
### Surface Morphology Analysis  
#### 3.1.1 Three-Dimensional Surface Analysis  
The characterization of coal's pore structure is crucial for studying its gas storage capacity, permeability, and chemical reactivity [27]. Traditional characterization techniques, such as Scanning Electron Microscopy (SEM) and Mercury Intrusion Porosimetry (MIP), have significant limitations: SEM can only provide two-dimensional surface morphology information, while MIP infers pore structure through indirect calculations; both fail to achieve quantitative analysis of three-dimensional pore networks at the nanoscale [28]. Atomic Force Microscopy (AFM) can achieve true three-dimensional morphology reconstruction with a vertical resolution of \(<\)1 nm through its unique probe-surface interaction mechanism, making it suitable for revealing the spatial connectivity and topological characteristics of micro/mesopores in coal. Compared to traditional methods, AFM does not require conductive coating of samples, thus avoiding the introduction of pore structure artifacts due to sample preparation. Meanwhile, AFM can achieve sub-nanometer resolution in atmospheric environments, providing the possibility for in-situ characterization. Although AFM technology still faces challenges, such as a single scan area typically limited to 100 \(\times\) 100 \(\upmu\)m\({}^{2}\), making it difficult to cover the macroscopic heterogeneous characteristics of coal samples; in addition, when the sample surface roughness (R\({}_{\text{a}}\)) exceeds 500 nm, probe drift may lead to morphology distortion, which requires optimization of surface flatness through ultra-thin sectioning or ion polishing. Despite these challenges, the unique advantages of AFM in in-situ characterization of nanopores make it an important tool for improving the multi-scale analysis system of coal structure.  
By constructing the three-dimensional morphology of coal samples, the pore morphology characteristics of the coal surface can be obtained. As shown in Figure 1A, it exhibits the surface characteristics of the AH-1 coal sample. It can be observed that the surface of the AH-1 sample features rugged gullies, with the highest point reaching 103.2 nm and the lowest point reaching \(-\)82.6 nm, resulting in an altitude difference of 185.8 nm.  
Moreover, an obvious fence-like bedding structure can be observed on its surface, with pores appearing as sparse and irregular macropores. In Figure 1B-D, the samples exhibit similar morphological structures, but the altitude differences decrease to 117.0 nm, 96.6 nm, and 69.0 nm, respectively, with the decrease in V\({}_{\text{daf}}\). In Figure 1E, the fence-like bedding structure disappears, the surface tends to be smooth, and pores begin to show a trend of transitioning from irregular macropores to smooth micropores, with an altitude difference of 74.6 nm. In Figure 1F, pores mainly appear as dense and regular micropores, the surface roughness decreases, and the altitude difference is only 14.2 nm, far lower than the 185.8 nm observed in the AH-1 sample.
# Tuning of Graphene-Based Optical Devices Operating in the Near-Infrared
## 2 Graphene Optical Properties
### Graphene Conductivity
Following [8; 9], the refractive index change (\(\Delta n\)) and the phase shift (\(\varphi\)) can be calculated using the following equations  
\[\Delta n(V_{G})=\frac{\lambda}{L}.\frac{\Delta\lambda(V_{G})}{d}\quad\varphi= \Delta n_{eff}L.\frac{2\pi}{\lambda} \tag{5}\]  
where \(L\), \(d\) and \(\Delta\lambda\) are the graphene length, the spacing between minima or free spectral range (~6 nm [8]) and the wavelength shift with voltage \(V_{G}\), respectively. In order to better understand the behavior of graphene's complex permittivity, the influence of parameters such as the chemical potential, relaxation rates/scattering time, temperature and thickness of spacer materials between GEs is discussed in the next subsection. In this work, the positive axis of the chemical potential (when \(\mu_{c}>0\) eV) was considered, since the optical conductivity of graphene is symmetric for the positive and negative electrochemical potentials due to the symmetric band structure in graphene [8]. The optical behavior of graphene, in terms of conductivity or complex permittivity, has been implemented in several numerical methods such as the finite element method (FEM) and the finite-difference time-domain (FDTD), to mention a few, exploiting both commercial and open source codes. In particular, the FEM is a numerical method that is used to solve boundary value problems. It must be characterized by a set of boundary conditions and/or a partial differential equation. A set of form functions is used to represent the primary unknown variable in the element set. For each element from the discretized area, a set of linear equations is calculated. After assembling all of the elements, a global matrix system with generalized properties is formed [58]. Conversely, FDTD is a widely used technique in which space is divided into a discrete grid and the fields are evolved in time using discrete time steps as the grid and the time steps are made finer and finer, this becomes a closer and closer approximation for the true continuous equations, and one can, essentially, simulate many practical problems exactly [59; 60].
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.3 Adaptive Neuro-Fuzzy Inference System (ANFIS)  
ANFIS, as a hybrid algorithm combining fuzzy logic and neural networks, was a central focus of the study. Its capacity to model complex, non-linear relationships while maintaining interpretability made it a promising choice for this context. It was widely analyzed by [PERSON] et al. (1997) [48], [PERSON] et al. (2022) and [PERSON] et al. (2023) [1; 3]. The dataset underwent a filtration process as a preliminary step, ensuring its suitability for constructing a fuzzy system within the MATLAB R2023b, software environment. A rigorous assessment of performance metrics led to selecting the gbellmf membership function in conjunction with a hybrid algorithm, primarily due to its remarkable capacity to minimize Root Mean Square Error. This choice facilitated the establishment of a well-structured four-input to one-output system. Each of the input variables, denoted as K1 through K4, was subsequently employed as input data for the Adaptive Neuro-Fuzzy Inference System (ANFIS), where individual nodes were assigned specific roles to address the unique functionalities associated with each input provided.
# Haze Removal Based on Refined Transmission Map for Aerial Image Matching
## 1 Introduction
Image dehazing algorithms can be classified as shown by the table in Figure 1.  
In this article, a novel algorithm is introduced that can remove haze from a degraded single image. The proposed single-image-based algorithm restores a hazy image by refining a transmission map using an approach based on a hidden Markov model [45,46]. The proposed strategy enhances the image contrast by reducing the block artifacts from the patch-based image dehazing method. As a second contribution, the proposed de-hazing method is suitable for image matching based on local feature-embedded colors using the C-SIFT operator. Finally, image matching was evaluated using aerial-haze images, the NH-Haze dataset, and a manually annotated dataset. The remainder of this article is organized as follows. In Section 2, a refined transmission map using the proposed method and its implementation are described. In Section 3, the experimental results are presented and discussed. Section 4 concludes the work. Additionally, the assessment of the dehazed images was evaluated and compared using the PIQE [47]. Figure 1: Classification table for image dehazing algorithms [26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46].
# An Integrated Adaptive Kalman Filter for High-Speed UAVs
The results show that SAKF has greater advantages in anti-interference and accuracy, which is suitable for the integrated navigation of UAV in a high-dynamic environment.