esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Porous Cathode Substrates' Preparation and Characterization  
For the deposition, porous cathode substrates based on \(\mathrm{LNFO_{ss}}\), \(\mathrm{LNFO_{p}}\), and \(\mathrm{LBNO_{ss}}\) electrode materials with the addition of a graphite pore former were prepared. The materials were mixed with the pore-former taken in an amount of 20 wt.% in the mill in an isopropyl alcohol medium (0.5 h), dried and compacted by a uniaxial semidry pressing with the addition of a polyvinyl butyl binder at 6 MPa, followed by sintering at 1400 \({}^{\circ}\)C, 2 h (heating/cooling rate of 100 \({}^{\circ}\)/h). The density of the compact samples, \(\rho\), was calculated from their weight and geometrical dimensions. Total porosity, P, was calculated according to the formula \(\mathrm{P}=100-\rho_{\mathrm{rel}}\)%, where \(\rho_{\mathrm{rel}}=\rho/\rho_{\mathrm{cr}}\) 100% (\(\rho_{\mathrm{cr}}\) is the crystallographic density of the sample calculated from the XRD data). The calculated porosity of the sintered substrates was in the range of 48-50%, 26-27%, and 24-28% for \(\mathrm{LNFO_{ss}}\), \(\mathrm{LNFO_{p}}\), and \(\mathrm{LBNO_{ss}}\), respectively. Before being used in the EPD process, the substrates were first polished and cleaned in an ultrasonic bath. Then, they were annealed at 900 \({}^{\circ}\)C for 1 h.  
Preparation of Suspensions Based on the Electrode and Electrolyte Powders and Their Characterization  
A suspension for the EPD of the functional electrode layer was prepared using the \(\mathrm{LBNO_{nc}}\) powder (concentration 10 g/L) in isopropanol (special purity grade, Component-Reaktiv, Russia) with the addition of a solution of 0.4 g/L molecular iodine in acetylacetone (analytically pure grade, Merck, CAS 123-54-6). Molecular iodine was added to the suspension as a charge agent. This contributed to the formation of a positive charge on the particles by increasing the current strength during deposition and therefore, increasing the deposition efficiency [17; 28]. The concentration of iodine was chosen according to the experimental data presented in [29; 30].  
Suspensions on the base of BCGCuO powder were prepared in a mixed dispersion medium isopropanol/acetylacetone (70/30 vol. ratio). Suspensions of 1 and 10 g/L concentrations were prepared by mixing the components and sonication using an ultrasonic bath UZV-13/150-TH (Reltec, Russia) for 5-125 min. Molecular iodine was also used to improve the BCGCuO suspension' properties.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 2 Intensity Image KCF Target Tracking Method Based on the Muti-Feature Fusion
### The Principle of KCF Tracking Algorithm  
#### 2.1.1 Ridge Regression  
Ridge regression is a linear regression method with biased estimation, which is essentially an improved least square method [17]. By giving up the unbiasedness of the least square method, it is a regression method with more realistic regression coefficients obtained at the cost of reducing the accuracy, of which the fitting of ill-conditioned data is better than the least square method.  
Suppose there is a training set \(\{(a_{1},b_{1}),\cdots(a_{i},b_{i})\}\) and a response function \(f(a_{i})=w^{T}a_{i}\). In order to minimize the mean square error between the response function of the sample vector \(a_{i}\) and its corresponding label \(b_{i}\), an optimization model about w is constructed as follows:  
\[\begin{array}{l}w=\underset{w}{\operatorname{argmin}}\underset{i}{\sum}(f(a_ {i})-b_{i})+\lambda\|w\|^{2}\\ =\underset{w}{\operatorname{argmin}}\underset{i}{\sum}(w^{T}a_{i}-b_{i})+ \lambda\|w\|^{2}\end{array} \tag{1}\]  
where \(\lambda\) is the regularization parameter that prevents overfitting; \(w\) is weight coefficients expressed as a column vector; \(\|w\|^{2}\) represents the 2-norm of \(w\). Equation (1) has a unique optimal solution, which can be calculated by the following equation:  
\[w=\left(A^{T}A+\lambda I\right)^{-1}A^{T}b \tag{2}\]  
Pluralize Equation (2) as follows:  
\[w=\left(A^{H}A+\lambda I\right)^{-1}A^{H}b \tag{3}\]  
where \(A\) represents a cyclic matrix consisting of cyclic shifts of the sample vector \(a\); \(I\) is a unit matrix; \(A^{H}\) is the Hermite transpose of \(A\).
## 2 Materials and Methods
### Wavelength Dispersive X-ray Fluorescence (WD-XRF)  
The measurements performed using WD-XRF and the validation of this method are described in detail elsewhere [15; 16], and thus we only offer a brief description here. The inorganic composition of the plant material was determined using a 1 kW WD-XRF Spectrometer (S8 Tiger, Bruker, Karlsruhe, Germany) at the Federal University of Rio de Janeiro. It was equipped with three analyzing crystals covering elements ranging from fluorine to uranium: XS-55 (F to Mg), PET (Al to Cl), and LiF200 (K to U) [17]. Two detectors mounted on a goniometer could detect fluorescent lines ranging from 0.5 eV to 50 eV: a gas proportional counter (F to V) and a scintillation counter (Cr to U). The spectrometer also featured a rhodium (Rh) tube with a beryllium window, well-suited for plant sample analysis since rhodium itself is rarely found in these samples. The spectrometer's software included a standardless application based on fundamental parameters, which was used to obtain quantitative results from the measured spectra. Quantification based on fundamental parameters makes use of incident beam intensity and tube spectra, detector efficiency, fluorescence yield, and photoabsorption cross sections, among other parameters [18; 19]. This kind of application allows for quantitative analysis without the need to develop a specific calibration curve. It is nevertheless necessary to keep in mind that this is possible but with some detriment in accuracy, which for this application has been determined to be approximately 5% in major components and as much as 20% for elements with low atomic numbers or with concentrations close to the limit of detection [20]. In order to validate this method, a standard reference material from NIST (CRM 1570a from spinach leaves) was measured. Our measurements [15] support the results previously reported in [20]. All samples were milled to a fine powder, supported by a low Z material, and measurements were performed in helium atmosphere at reduced pressure in a 17 min routine. The sample holder was assembled with a 4 \(\upmu\)m thin film (3525 Ultralene-SPEX(r)SamplePrep, Metuchen, NJ, USA), and a 34 mm mask was used to delimitate the irradiated area.  
Standardless applications require information about matrix compositions. For plant material, cellulose provides a good accuracy level for this application [20]. Measurements were made in triplicate, and the standard deviation of the mean is reported.
## 2 Materials and Methods  
### Plant Material and Treatments  
Hachailioglu apricot cultivar, which is predominantly used in dried apricot production, was harvested at maturity stage (soluble solids content: 24.35%; pH: 4.86; titratable acidity: 0.53%) from the orchard of the Apricot Research Institute Directorate (Malatya, Turkiye). After excluding damaged fruits, apricots were randomly divided into groups of 15 kg each (5 kg per replicate) and immersed in 5 L of different melatonin solutions (10 \(\upmu\)M, 100 \(\upmu\)M, and 1000 \(\upmu\)M) for 10 min at room temperature. The melatonin concentration ranges (10, 100, and 1000 \(\upmu\)M) were systematically selected based on a comprehensive review of recent post-harvest research. These concentrations represented a logarithmic scale that encompasses low, moderate, and high dosage levels, enabling a comprehensive assessment of melatonin's potential protective effects. Specifically, the 100 \(\upmu\)M concentration was informed by previous studies demonstrating its efficacy in preserving fruit quality [22], while the 10 \(\upmu\)M and 1000 \(\upmu\)M concentrations were included to explore potential dose-dependent responses and establish a comparative framework for evaluating melatonin's impact on apricot preservation. Apricots immersed in distilled water served as a control. The immersion process was conducted in a dark environment to prevent light-induced degradation of melatonin solutions.
## 4 Discussion
Advanced 3D models that fully consider the soft tissue structures of the knee and the complex geometry of the articular surfaces are necessary to estimate how the net TF shear force is distributed among the different knee structures, including the ACL [19; 20; 21]. However,the adopted simplified model may be considered adequate for the specific purpose of the study, which concerns the determination of the joint torques and the overall TF shear force acting in the sagittal plane during all the possible wall-squat positions and variants. [PERSON] and co-workers estimated the cruciate ligament and patellafemoral force during the dynamic wall squat performed with a 12-repetition-maximum external resistance provided by a dumbbell weight held in both hands [22; 23]. In this loading condition, the wall squat generates negligible ACL tensile force. This result is consistent with the findings of the present study that revealed only low values of maximum theoretical ACL-loading TF shear force in a very limited range of wall-squat conditions. In addition, this limited anterior tibial pull can in part be resisted by knee structures other than the ACL, as well as by the active and passive force developed by the hamstrings [24]. Figures 2-5 refer to an ideal subject 175 cm tall, with a body mass of 75 kg, and other anthropometric parameters deduced from extensive anatomic studies [9; 10]. The data recorded from the force plates were only used to determine reliable values of the point of application (\(x_{\text{GR}}\)) of the ground reaction force when the participants were asked to displace the body weight toward the rearfoot or forefoot. These values were rescaled for the adopted ideal subject and used in the simulation. The determination of the joint torques for each of the 20 participants would have required the knowledge of the mass, dimension, and position of centre of mass of each body segment of each individual participant (Equations (18)-(20)). Aside from acknowledging this limitation, one critical key of the provided analytical model is that it may be directly applied to any real subject when their anthropometric data is given.
# Survey and Perspectives of Vehicular Wi-Fi versus Sidelink Cellular-V2X in the 5G Era
## 2 Overview of the Technologies and Comparative Discussion
### IEEE 802.11p
#### 2.1.1 Next Generation: IEEE 802.11 bd  
In May 2018, IEEE and the IEEE Standards Association announced the formation of a new study group focused on the evolution of 802.11 technology for next-generation V2X communications, named IEEE 802.11 Next Generation V2X (NGV) and now preparing the amendment IEEE 802.11 bd.  
This activity was also strongly encouraged by the CAR 2 CAR Communication Consortium, which in its early studies towards an ITS-G5 Release 2, noted that IEEE 802.11 ac is already the state of the art for Wi-Fi technologies. Therefore, the idea was to take this version of the standard as a starting point and adapt it to high mobility and OCB mode, while guaranteeing backward compatibility with legacy devices. Emphasis was posed, in particular, to multiple input multiple output (MIMO) techniques, improved channel coding, and better pilots placing (specifically designed for the vehicular wireless channel). Expected gains have been estimated in about 6 dB by low density parity check (LDPC) (advanced channel coding) and 2-3 dB by space time block codes (STBC) (MIMO space diversity).  
The IEEE study group is presently in its initial stage [19], with the goal to investigate the usage of more advanced PHY technologies in amendments after 802.11p, in order to enhance the throughput (one transmission mode that at least doubles the throughput) and the transmission range (one transmission mode providing at least 3 dB lower sensitivity). In particular, the following technology candidates have been listed [19; 20]:  
* OFDM numerology re-design: definition of new optimized tone spacing and guard interval duration to better cope with 5.9 GHz frequencies and high-speed mobility;
* LDPC codes to improve coding effectiveness;
* MIMO diversity through STBC codes or cyclic shift diversity (CSD);
* Addition of midambles (control sequences in the middle of the packet) to improve Doppler recovery;
* Dual carrier modulation (DCM) and 20 MHz channels.  
It can be noted that all mentioned modifications are at the PHY layer.  
In addition, co-existence and interoperability with legacy IEEE 802.11p is remarked as mandatory: any NGV device must communicate with legacy devices, granting fairness in channel access.
# An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
## 4 Validation and Result Analysis  
In order to verify the effectiveness of the TME algorithm, we repeated the simulation experiment several times, and then it was compared with the original TE (TE without traffic prediction and migration) algorithm and the classical network TE algorithms: A* Search [21] and HPR [33]. This paper focuses on KPIs such as network throughput, run time and average delay of data traffic transmission. When the network runs one time, we count one round. The simulation environment was a Win7 64-bit system with CPU being Intel Core i5-7500 3.4 GHz, and memory being 4.00 GB. The simulation software used in this research were Visual C++ 6.5 and MATLAB 2017B. Table 2 shows the simulation parameters.
## 3 Classification of Optical Waveguide Sensors
### MZI-Structured Optical Waveguide Sensor
#### 3.2.4 Polymer-Based MZI Type Optical Waveguide Sensor
Because the sensitivity of the MZI sensor affects the change in the effective refractive index, the change in the effective refractive index of the sensor is in the range of 0.5-2.0 (\(\times 10^{7}\)), and the corresponding output optical power change can reach the order of \(10^{-4}\). Consequently, the MZI sensing platform has significantly high sensitivity and is suitable for applications in biochemical sensing. [PERSON] et al. [113] selected polymer materials for the waveguide core and upper cladding, resulting in improved sensing performance and economical and efficient mass production. When the polymer is dry-etched, the upper cladding in the sensing window is removed with ease, and the rectangular sensing arm can be approached by the analyte to form a three-dimensional (3D) structure. This structure significantly expands the sensing area and increases the sensitivity by 3.5 times. In addition, by studying the dry-etching parameters, the hydrophilicity of the polymer surface was increased, which enhanced the interaction between the guided light and the test solution. The MZI waveguide sensor proposed in this study uses the SU8 polymer as the core layer, polymethyl methacrylate-glycolidyl methacrylate (PMMAAGMA) as the upper cladding, and silica as the lower cladding. The refractive index measurement range at a wavelength of 1550 nm was 1.31-1.42, and the sensitivity was 225.4 dB/RIU. By optimizing the structure of the sensing arm and performing a surface treatment, the obtained sensing response time can be less than 20 s. This sensor has many advantages, such as high sensitivity and fast linear response, which are suitable for refractive index biochemical sensing. [PERSON] [114] proposed an asymmetric MZI sensor that designed the straight waveguide part of a Y-shaped branch as a curved structure. When the length difference between the two branches is 19.8 \(\upmu\)m, the refractive index is in the range of 1.470-1.545, thus maximizing the internal sensing sensitivity. The device was then fabricated using photolithography and wet etching, and the encapsulation process was optimized using PMMA films. The output power of the device was tested with benzaldehyde and ethanol mutual solutions with different refractive indices. The actual sensitivity value was 791 dB/RIU, and the sensor resolution was 1.26 \(\times\) 10\({}^{-6}\) RIU. Theoretically, two polymer sensor structures of symmetrical MZI and asymmetrical MZI were optimized, and the theoretical value of the sensing sensitivity increased to 1200 dB/RIU.
## 4 Discussion
### Discussion on the Spatial Morphology of Xingdi Village Settlement  
The analysis of the overall fractal dimension in the study area represents only a small part of fractal dimension analysis. Previous research has indicated that urban areas exhibit fractal structures and characteristics, and similarly, sub-regions within cities also display these traits. Therefore, it is feasible to conduct detailed studies on different directions and regions within urban settings [16; 42]. In contrast, prior spatial analyses within settlements have utilized methods such as space syntax [43] without incorporating fractal dimensions into their evaluations. This study shifts its focus from densely populated and extensive urban systems to relatively discrete settlement systems characterized by smaller village areas. Compared to urban environments, settlements are smaller in scale but exhibit similarly complex spatial forms. They can also be regarded as complex systems, making them appropriate for conducting fractal dimension analysis at sub-regional scales. By performing such analyses, the complexity of spatial forms within sub-regions of settlements can be effectively revealed.  
The distribution pattern of the fractal dimension in subspaces reflects more internal spatial morphological characteristics compared to the overall fractal dimension of the settlement. The study of Xingdi Village reveals that the core area of this settlement, due to its developed and optimized morphology, exhibits a higher degree of spatial filling and complexity. Similar to urban models, it is observed that the filling degree deteriorates as one approaches the boundary of the settlement. Throughout the development process of settlements, there inevitably arises a need for morphological renewal. This progression has led to two distinct developmental pathways--one involves continuous improvement upon existing settlements, leading to new ones, while the other entails establishing new zones in newly designated areas. Xingdi Village has opted for the latter approach, resulting in a clear demarcation between old and new villages along north-south axes. The newly constructed village benefits from well-planned designs and relatively complete infrastructure support, featuring more organized courtyard spaces with higher overall space utilization rates. In contrast, although older villages exhibit greater complexity within their core areas, there exists a significant disparity between their edge regions and core areas. Overall, it can be concluded that the spatial filling degree in new villages surpasses that found in old ones. Furthermore, we have observed that near Dongzhai and Xibao regions within Xingdi Village, spatial patterns are well-preserved; traditional courtyards remain largely intact with favorable configurations and have not undergone substantial modernization alterations. Consequently, these areas demonstrate significantly better spatial filling degrees compared to other ancient village regions.
# Examining Attention Mechanisms in Deep Learning Models for Sentiment Analysis
## 6 Discussion  
The experimental study shed light on the performance of attention-based models. In the first experiment, the focus is aimed at self-attention and global-attention methods. The results show that baseline methods like CNNs and LSTMs without the use of attention mechanisms report the lowest performance on all three datasets. Both attention mechanisms improve the performance of baseline models by a fair percentage. The use of global-attention mechanisms in LSTM models increases the performance of LSTM in all experiments when compared to the baseline methods. The use of a self-attention mechanism resulted in even better results and the experimental study indicates that self-attention LSTMs outperformed the global-attention LSTMs in all experiments. The global-attention LSTM model reports an accuracy of 88.29%, while the self-attention one boasts an accuracy of 89.12% on the IMDB dataset. Furthermore, the self-attention-based LSTMs with the addition of word2 vec embeddings performed the best across all datasets, reaching an accuracy as high as 89.71% on the IMDb dataset.  
In the second experiment, we examine the performance of the hierarchical-attention mechanism. The results are quite interesting and showcase that baseline GRU models can achieve satisfactory performance. The implementation of hierarchical-attention mechanisms in GRUs can greatly outperform baseline GRUs in all the examined datasets. Indeed, the hierarchical attention GRUs improve performance over baseline GRUs by almost 2% in terms of accuracy. Additionally, utilizing a dropout technique proves to be quite useful in improving the accuracy of attention-based GRUs even further.  
Figure 6: Performance of the GRU-based models on the SUBJ, MR and IMDB datasets.  
Ultimately, the experiments showcase that the attention-based methods can lead to performance gains of up to 3.5% in terms of accuracy. In addition, it is worth noting that the attention mechanism achieves the highest accuracy gain on the IMDb dataset. The main reason for this is the dataset's particular high sequence length. Furthermore, self-attention reports the best performance gains as far as the examined attention mechanisms are concerned. In general, a self-attention mechanism can relate different positions of a single sequence to compute a representation of the same sequence. Thus, self-attention can achieve higher performance when compared to the global-attention model, mainly due to its capability to focus on very specific parts of a sequence.