esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 An Overview of the LCPV System Based on an SSLFR  
The \(LCPV\) system studied in this paper is based on an \(SSLFR\). The characteristics of this \(LCPV\) system are described in [14]. The elements comprising the \(SSLFR\) are shown in Figure 1a. The system is roughly composed of two main parts: a primary reflector system and a secondary system. In Figure 1a, the secondary system is aligned with a north-south orientation.  
Rows of mirrors are used in this type of solar concentrator to redirect the Sun's rays during the day to a secondary system on top of the fixed structure. The movement of the mirror rows is computer-controlled to keep their reflective surface perpendicular to the angle divider between the directions of the Sun and the secondary system [26].  
To more easily understand the \(SSLFR\), this study was divided into two planes [26; 27]; a transversal plane and a longitudinal plane. Therefore, the angle of incidence of solar irradiance is divided into the transversal angle of incidence (\(\theta_{t}\)) and the longitudinal angle of incidence (\(\theta_{t}\)).  
The primary reflector (\(PR\)) system (see Figure 1a) is composed of the parallel mirror rows and the tracking system. This design has a central mirror and an equal number of mirrors on either side of it. The \(PR\) system was installed on a mobile structure to provide longitudinal movement.  
The parameters required for the transversal design of the \(PR\) system are shown in Figure 1b and can be defined as follows: \(f\) is the height to the receiver, \(N\) is the number of mirrors on each side of the central mirror (therefore, the total number of mirrors is \(2N+1\)), \(W_{Mi}\) is the width of the \(i\)-th mirror, \(\beta_{i}\) is the angle which mirror \(i\) forms with the horizontal, \(L_{i}\) is the position of each mirror with respect to the central mirror (in central mirror \(i=0\), \(L_{0}=0\), and \(\beta_{0}\)), \(d_{i}\) is the separation between two consecutive mirrors, and \(\theta_{i}\) is the angle between the vertical at the focal point and the line connecting the centre point of each mirror to the focal point. The parameters required for the longitudinal design of the \(PR\)system are shown in Figure 1c and can be defined as follows: \(\beta_{M}\) is the angle between the mirror axis and the horizontal plane, and \(L_{M}\) is the length of the mirrors.  
The secondary (\(S\)) system (see Figure 2) is composed of the \(PV\) system, the active cooling system, the secondary structure, the isolation material, the protective casing, and the shaft. The shaft of the \(S\) system is installed on a fixed structure.  
The parameters required for the transversal design of the \(S\) system are shown in Figure 1b and can be defined as follows: \(W_{PV}\) is the width of the \(PV\) cells, and \(W_{AC}\) is the width of the active cooling system. The parameters required for the longitudinal design of the \(S\) system are shown in Figure 1c and can be defined as follows: \(\beta_{S}\) is the angle between the \(PV\) system and the horizontal plane, \(L_{PV}\) is the length of the \(PV\) system, and \(L_{AC}\) is the length of the active cooling system.
Due to the complexity of the three-dimensional analysis model, the theoretical researches of fracture mechanics still focus on the state of the two-dimensional analysis model. Therefore, based on the X-SBFEM algorithm, this paper introduces a nonlinear crack model which adopts the linear superposition of iterative methods to incorporate the cohesive interactions within the FPZ. The proposed approach was then implemented to model mixed-mode fracture of concrete beam and gravity dam structures. The results show improvements comparing to other methods. Close agreements were found between the numerical and experimental results. The contents of this paper are arranged as follows. In Section 2, we explain the principle of X-SBFEM. In Section 3, a nonlinear crack model with iterative method for cohesive interactions in the FPZ is introduced and a flowchart for solving the cohesion is given. In Section 4, four numerical simulations (a three-point bending beam, a four-point shear beam, an experimental concrete gravity dam with single crack expansion, and a static cohesive crack propagation simulation of Koyna Dam) are modeled to validate the nonlinear model. In Section 5, we conclude that this paper has developed the X-SBFEM with the nonlinear model to improve the modeling of crack propagation.
## 3 Methods
### AnimalEnvNet Network  
We created a new neural network structure named AnimalEnvNet. The network has three essential modules: trajectory feature extraction, environment feature extraction, and feature fusion with dimensionality reduction. The trajectory feature extraction module leverages LSTM to capture the temporal information in animal movement trajectories, while the environment feature extraction module utilises multiple CNNs. LSTM excels at analysing and capturing temporal patterns in animal movement trajectories, whereas CNN is proficient in extracting spatial characteristics from remote sensing images. The AnimalEnvNet model combines the benefits of processing time-series data and high-resolution image data. This allows for the integrated utilisation of environmental and trajectory data, resulting in improved prediction accuracy and adaptability of the model. The feature fusion module, which includes dimensionality reduction, utilises attention processes to selectively combine and compress features, resulting in reduced duplication. Compared to existing models, AnimalEnvNet is able to process both animal trajectory data and high-resolution remotely sensed image data through multimodal data fusion. This combination allows the model to fully capture the complexity of animal behaviour and its interaction with the environment. The model being suggested, AnimalEnvNet, is shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Field Name** & **Range of Values** & **Remarks** \\ \hline
1 & Month & 1–12 & One-Hot Encoding \\
2 & Day & 1–31 & One-Hot Encoding \\
3 & Lunar day & 1–31 & One-Hot Encoding \\
4 & Hour & 0–23 & One-Hot Encoding \\
5 & Minute & 0–59 & One-Hot Encoding \\
6 & Seconds & / & Normalise \\
7 & XDIFF & / & Normalise \\
8 & YDIFF & / & Normalise \\
9 & Height & / & Normalise \\ \hline \hline \end{tabular}
\end{table}
Table 1: Trajectory dataset fields.  
Figure 4: Histogram depicting the correlation between the discrepancies in XDIFF and YDIFF.
## 2 Theoretical Foundations
### Ultrasonic-Assisted Techniques  
Ultrasonic-assisted machining differs from traditional machining techniques by combining two or more forms of energy to create a new composite machining method. This approach significantly enhances machining precision and efficiency, making it a form of advanced composite cutting technique.  
In ultrasonic-assisted cutting, the contact mode between the cutting tool and workpiece involves a micro reciprocal vibration interaction during the cutting process. The ultrasonic waves continually impact and squeeze the workpiece material being machined through high-frequency vibrations, resulting in advantages such as uniform cutting volume, shorter cutting strokes, easy chip removal, effective heat dissipation, and cutting tool wear reduction. Additionally, when ultrasonic-assisted machining is used with cutting fluid, a pumping effect phenomenon occurs, allowing the cutting fluid to more easily enter the cutting area. Studies have shown that incorporating appropriate ultrasonic vibrations during the cutting process can effectively reduce burrs, lower cutting forces and vibrations, improve surface quality, reduce tool-workpiece friction, minimize dimensional accuracy errors, and extend cutting tool life. A schematic diagram of the ultrasonic-assisted cutting model is shown in Figure 1.
## 4 Experimental Results
### Comparison of Average Precision with Deep Learning Methods  
This section shows the comparison among diverse deep learning methods used in the field of defect detection. The paper [20] also provided the performance evaluation of the state-of-the-art methods. Based on the experimental results in their work, the comparison between our method and the state-of-the-art methods, which are SSD, Faster-RCNN, YOLO-V2, YOLO-V3, EDDN, and Xception, is shown in Table 4. The table shows how our method performed well for defects such as patches, crazing, rolled-in scale, and pitted surfaces. Although the proposed method scored lower in scratch and inclusion, the overall mAP of the proposed model is the best among the other state-of-the-art methods. The Figure 7  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Defects** & **HOG + NNC [20]** & **HOG + SVM [20]** & **LBP + NNC [20]** & **LBP + SVM [20]** & **Proposed Method** \\ \hline Pitted Surface & 0.438 & 0.328 & 0.446 & 0.515 & 0.8504 \\ Inclusion & 0.576 & 0.580 & 0.412 & 0.378 & 0.7117 \\ Patches & 0.612 & 0.630 & 0.538 & 0.601 & 0.8987 \\ Rolled-in Scale & 0.358 & 0.330 & 0.237 & 0.330 & 0.6794 \\ Crazing & 0.400 & 0.412 & 0.321 & 0.335 & 0.6928 \\ Scratches & 0.474 & 0.463 & 0.326 & 0.432 & 0.7942 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of average precision between traditional models and the proposed method.  
Figure 6: Comparison of mean average precision of traditional models with ours.  
displays the overall mAP compared to the other approaches. The figure asserts that the proposed model performance is better than the others by a 5% difference.
### Relative Fundamental Frequency (RFF) as an Estimate of Laryngeal Muscle Tension
[24] compared the effects of different \(f_{o}\) estimation techniques on resulting RFF estimates, determining that \(f_{o}\) estimation via the Auditory-SWIPE\({}^{\prime}\) algorithm (an algorithm that estimates the \(f_{o}\) of an input sound by first filtering the sound using a auditory-processing front-end then comparing the spectrum of the filtered sound to a sawtooth waveform) [25] led to smaller errors between manual and semi-automated RFF estimates compared to autocorrelation. The results of this work led to a refined version of the aRFF algorithm that employs Auditory-SWIPE\({}^{\prime}\) for \(f_{o}\) estimation, as well as using the acoustic metric, pitch strength [26], to account for differences in voice sample characteristics (e.g., recording location, overall severity of dysphonia). Incorporating both Auditory-SWIPE\({}^{\prime}\) and pitch strength-based sample categories, this algorithm is called \"aRFF-AP\". In both manual and semi-automated RFF estimation methods, the most tedious step of the RFF computational process is identifying the boundary between voiced and unvoiced speech. As RFF depends on the termination and initiation of voicing within a VCV production, these points in time must be identified from the acoustic signal prior to collecting vocal cycles for RFF estimation. Manual RFF estimation relies on trial-and-error techniques of trained technicians to locate this boundary (requiring 20-40 min of analysis time per RFF estimate; [11]), whereas the semi-automated RFF algorithms (aRFF, aRFF-AP) take advantage of a faster, more objective approach. Specifically, three acoustic features (normalized peak-to-peak amplitude, number of zero crossings, and waveform shape similarity) are examined in time to identify where a state transition in feature values occurs to locate voiced/unvoiced boundary. The aRFF and aRFF-AP algorithms assume that each acoustic feature will exhibit a substantial change in feature values over time and that this change will occur at the boundary between voiced and unvoiced segments.
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 2 Methods
### Model Description and Numerical Case Design
#### 2.1.2 Boundary Conditions  
Software ICEM 18.0 is used to build the model and the unstructured mesh is generated. A total of 14 million elements is chosen after the grid independence test. The boundary conditions of numerical simulation are depicted in Table 2. The air supply flow rate, air supply temperature and walls temperature are obtained through literature research [24; 25; 26; 27; 28]. It is worth noting that the recirculated air is filtered by absolutely perfect HEPA filters, which means that the air supply diffusers provide 100% fresh air. The size of droplets generated by respiration is in the submicron level [29; 30; 31; 32; 33]. In this paper, the size of droplets is set to 1 \(\upmu\)m, and the concentration of droplets is 1000/L. It is assumed that the droplets exhaled by the infectious source are composed of water and sodium chloride, and the density of droplets are 1010 kg/m\({}^{3}\). The droplets evaporate in the cabin and finally form the droplets nuclei.
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## Materials and Methods
### Application of the Method  
We also demonstrated applicability of our method by quantification of individual IsoP in six different human cancer cell lines (AsPC1, MiaPaca, BxPC3, Panc1, S2013, and Capan-1). We have also analyzed six plasma samples to measure endogenous concentrations of each individual IsoP.
# See-Through Near-Eye Display with Built-in Prescription and Two-Dimensional Exit Pupil Expansion
## 1 Introduction  
Following in the footsteps of smartphones, smart glasses are highly anticipated as the next mobile computing device. Unlike smartphones, which widely adopt the flat panel displays (FPDs) [1; 2; 3], smart glasses are equipped with see-through near-eye displays (NEDs) [4; 5; 6]. The major difference between these two types of display technologies is something called an exit pupil (EP). In FPDs, there is no such thing. In NEDs, an exit pupil defines the boundaries to which the eye can be moved. Unfortunately, for the sake of its compact form factor, almost every optical component of NEDs needs to be miniaturized. This inevitably results in a tiny exit pupil. Even when the form factor is overridden, it will not be easy to achieve a big exit pupil owing to the paradox between the field of view (FOV) and the exit pupil [7]. To enlarge the exit pupil, the most common method is to duplicate a single exit pupil into many through multiple reflections and/or diffractions. Of all the techniques that have been reported [8; 9; 10; 11; 12; 13; 14; 15; 16], the most successful ones are the cascaded semi-reflectors [8] and slanted gratings [10]. The former is a proprietary technology patented by Lumus. However, the cascaded semi-reflectors are only able to duplicate the exit pupil in one dimension. The latter was originally developed by Nokia and later commercialized by Microsoft's HoloLens. Though HoloLens pulled off the two-dimensional exit pupil duplication, it requires three layers of waveguides with slanted gratings to realize the full color [15]. Another gripe about HoloLens is it is not very friendly for those who have to wear the prescriptionlens for correcting the refractive errors [17]. For wearable devices, bulkiness is always a deal breaker. Towards the said issues, we hereby introduce a see-through NED that integrates the prescription lens with a two-dimensional exit pupil expander (EPE). In what follows, its structure, design rules, and overall performance are to be expounded.
## 2 Bearing Load Testing Method Based on Redundant Strain and Jacking Data Fusion  
### Testing Scheme  
The bearing load testing method based on the fusion of redundant strain and jacking data utilizes both the JM and the SGM in a comprehensive manner to complete the testing process. To enhance the measurement precision of the SGM, this paper introduces the RSGM. Specifically, building upon the strain measurement section design in the SGM [11], the number of strain measurement sections is increased to ensure an equal count at both ends of the bearing. By applying multi-sensor data fusion technology to process the redundant strain data, the reliability of the SGM's measurement outcomes is improved. The layout of the sensors is depicted in Figure 1.
# Fuzzy-Based Multi-Modal Query-Forwarding in Mini-Datacenters
## 4 Modeling the Query Flow Within the Mini-Datacenter
### Fuzzy Modeling of Granular Selection in Query Forwarding
#### 4.3.2 Modeling Rules  
We have formulated a set of rules, as tabulated in Table 1, to define how the input variables \"G_availability\" and \"G_overloaded\" affect the state of the output variable, such as low, medium, and high. A low availability of gateways and a high congestion at the gateway buffers mapping to 'low' output state, leading to the selection of coarse-grain forwarding. The rules are designed to cover a range of input conditions to ensure that the fuzzy system provides appropriate outputs for various scenarios.  
In the case of fine-grain forwarding, the medium and higher values of the fuzzy output are constrained by the redundant gateways, which are estimated from Equations (2)-(5) in Section 4.2.1.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization
#### 2.2.3 Impedance Matching with Loading  
In-body antennas commonly require the matching of impedance at the anticipated frequency of operation using loading techniques. The loading can possibly be inductive or capacitive, which can effectively minimize the imaginary part of the impedance by nullifying the effect of reactance, helping in size reduction. In Ref. [29], a circularly polarized implantable patch antenna was presented, where the size was reduced due to the use of capacitive loading compared to traditional square patch antenna. However, the impedance matching of a high-quality-factor (\(Q\)-factor) in-body antenna can lead to performance issues and may require a separate compensation network. This can also reduce the bandwidth of the antenna.
## 4 Results and Discussion
### Effect of Feed Rate of Sand on the Flow Pattern
#### 4.3.2 Distribution of the Solid Volume Fraction  
As discussed in Section 4.1.2, a few sand particles are trapped in the flow passage of the lower part of the primary impeller. Here, the solid volume fraction in the flow passage of the upper part of the primary impeller is monitored. In the lower part of the volume chamber, a relatively high concentration of sand particles is evidenced, as shown in Figure 7. This situation is more apparent at \(q_{s}=3.12\) m\({}^{3}\)/min. Sand particles cannot enter the flow passage of the lower part of the primary impeller, and the resistance originates from the centrifugal force generated through the rotation of the primary impeller. Generally, the increase in the feed rate of sand may aggravate the blockage of the inlet segment of the impeller pump [46]. In comparison, in the flow passage of the upper part of the primary impeller, local solid volume fractions differ remarkably, as shown in Figure 13. With the rotation of the two impellers, sand particles tend to travel toward the outlet of the primary impeller. After a sudden expansion of the flow space, sand particles enter the solute chamber and are then dispersedly distributed with the interaction between the mixture discharged from the impeller and that remaining in the solute chamber.  
Sand particles continue to enter the flow passage of the secondary impeller through the vertical convergent inlet pipe. It is appreciable that when the feed rate of sand is too high, the flow passage of the upper part of the primary impeller may be blocked. This event may be preceded by the blockage of one or two blade channels. When severe blockage occurs, the operation of the pump has to be halted. On the contrary, when the solid volume fraction is low, the risk of the blockage of the flow passage is low, and the delivery of the sand-water mixture through the pump is convenient. However, in this case, the performance of the hydraulic fracturing will be degraded because insufficient sand particles cannot sustain the opening of the fractures.  
Figure 13: Cross-sectional distributions of solid volume fractions at different feed rates of sand.
## 5 Experimental Results and Evaluation
### Experimental Setup  
To evaluate our proposed method, we conducted a series of experiments on the dataset in order to answer the following research questions:  
1. How does BERT affect the performance of hierarchical structure in the document-level SA task?
2. Is our Query-Guided Dual Attention (QGDA) mechanism leveraging manually-defined financial queries able to improve model performance?How does the concept-level explainable basis derived from the QGDA mechanism (specifically, the dominant query categories) affect the analysis of stock market predictions and their consistency with actual market fluctuations?  
Our evaluation pipeline is designed to systematically address these questions by leveraging the outputs of our model, for which the architecture and logic are detailed in Figure 1 and Algorithm 1, respectively. To answer RQ1 and RQ2, we assess the model's core classification performance using the widely-adopted F1-score. For RQ3, we evaluate the practical utility of the model's explainable basis; specifically, we analyze the dominant query categories derived from the attention weights in Algorithm 1 and measure their consistency with actual stock price fluctuations using the accuracy metric.  
For our experiments, we set the maximum sentence length to 32 tokens and the maximum document length to 24 sentences. Sentences exceeding 32 tokens were truncated, and documents containing more than 24 sentences were processed by considering only their initial 24 sentences. This approach was adopted based on an empirical analysis of our dataset, which showed that the majority of financial review documents are relatively concise and that the key sentiment-bearing information is often concentrated in the earlier sentences. This design choice also balances computational efficiency with retaining essential content for sentiment analysis. We utilized the BERT model as our base, with 12 transformer layers, a hidden size of 768, and 12 attention heads. The subsequent Bi-GRU sentence encoder was configured with two layers, each having a hidden size of 384.  
The final training hyperparameters were determined through a systematic tuning process. We performed a grid search on a dedicated validation set, exploring values for key parameters including the learning rate ([5 \(\times\) 10\({}^{-5}\), 1 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-3}\)]), batch size ([16; 32; 64]), and dropout rate ([0.1, 0.2, 0.3]). The optimal configuration employed an Adam optimizer with a learning rate of 0.001 and was selected based on the best F1-score achieved on the validation set. We also utilized an early stopping mechanism with a patience of five epochs to prevent overfitting. All models were implemented in PyTorch 1.13 and trained on a single NVIDIA RTX 3090 GPU.