esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Rock Damage Recognition
### r-Value  
The energy of AE monitored by sensors can be used to quantify the stage of rock damage. [PERSON] (2016) used _r_-value to study damage in dry and wet sandstone, and defined the ratio of the sum of AE numbers and AE energy as follows:  
\[r=\sum N/\sum E, \tag{4}\]  
where \(\sum N\) is the sum of the AE numbers and \(\sum E\) is the accumulated AE energy. In this study, AE energy was obtained by integrated hardware electronics of a PCI-2 Card (Figure 10a). The wavelets of AE numbers with loading time (Figure 10b) show that AE number and energy had different peaks. AE number peak occurred at 236 s, the beginning of the accelerated-growth cracks stage; while AE energy peaked at 345 s, the middle of the accelerated-growth crack stage. The curve between _r_-value and loading stress increased at first, declined later, and then gradually rose again (Figure 10c). In the beginning of testing, AE events were rare; however, they carried a certain amount of energy. Therefore, the _r_-value had a rising trend. It then declined when no energy was released during the compression fissure stage. After that, the _r_-value began to rise, indicating that energy was not released only by several events, but a great many rhythmic AE events with uniform energy. In contrast to other deformation stages, the _r_-value presented a slow rising character in the accelerated-growth crack stage, which implied that AE number and energy had consistent changes; in other words, energy release channels were unobstructed because of serious damage.  
Figure 9: (**a**) Variation of AE hit average amplitude with loading time for sample SA-2 under uniaxial compression; (**b**) relationship between axial stress-strain curve and _lb_-value during different deformation stages for sample SA-2 under uniaxial compression.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 2 Background
### Computational Infrastructure for Resilience
#### 2.2.3 Fault-Tolerant Service Communication  
Under the microservices framework, it is common to find applications that are connected through the network, mainly via a cascade architecture. This highlights the need for tools that can constantly monitor the network to mitigate errors or failures at any time. This is where the mechanisms responsible for making decisions in the event of a failure, in terms of actions related to load balancing and service scalability, are most important.
The gears were hobbed and case-carburized to 700-750 HV of surface hardness. The treatment had a depth (550 HV) of 0.1 to 0.2 mm. Finally, the gears were grinded to a quality \(<\)5 according to DIN 3962 [22]. All the tests were performed on a small-gear back-to-back rig having a reduction ratio of 57-58. Based on 20 tests [23; 24; 25; 26], a modified expression for the gear size factor \(Y_{X}\) for steels was proposed (Equation (7)). \[Y_{X_{Dobler}}=1-0.45\cdot\log\Bigl{(}\frac{m_{n}}{5}\Bigr{)}\pm 0.075 \tag{7}\]  
These formulation fits the data according to FVA 162/I [26], FVA 271 [25], FVA 410/I [23], and Jeong [24] with a coefficient of determination \(R^{2}=0.9339\). It is interesting to notice that, according to the model proposed by [PERSON] et al., based on several results available in the literature, for modules over 5 mm the load-carrying capacity for tooth root bending decreases. The actual formulation of the standards (ISO 6336 and DIN 3990), in turn, does not fit the experimental observed behavior (\(R^{2}=-0.2203\) referred to the Eh curve). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Normal Module, \(m_{n}\) [mm]** & **Size Factor, \(Y_{X}\) [ –]** \\ \hline St, V, & \(m_{n}\leq 5\) & \(Y_{X}=1.0\) \\ GGG (perl., bai.), & \(5\leq m_{n}<30\) & \(Y_{X}=1.03-0.006\cdot m_{n}\) \\ GTS (perl.), & \(30\leq m_{n}\) & \(Y_{X}=0.85\) \\ \hline Eh, IF (root), & \(m_{n}\leq 5\) & \(Y_{X}=1.0\) \\ NT, NV & \(5\leq m_{n}<25\) & \(Y_{X}=1.05-0.01\cdot m_{n}\) \\  & \(25\leq m_{n}\) & \(Y_{X}=0.8\) \\ \hline GG, GGG (ferr.) & \(m_{n}\leq 5\) & \(Y_{X}=1.0\) \\  & \(5\leq m_{n}<25\) & \(Y_{X}=\\ 25\leq m_{n}\) & \(1.075-0.015\cdot m_{n}\) \\  & \(Y_{X}=0.7\) \\ \hline All materials for static stress & – & \(Y_{X}=1.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Size factor for different modules and materials.
# Impact of Combine Harvester Technological Operations on Global Warming Potential
## 2 Data and Methods  
### Telematics Data Collection and Analysis  
This study analyzed telematics data from CHs (one of the most popular models in the region with a tangential threshing apparatus) operating in Lithuania and Latvia between 2016 and 2020. Access to the relevant databases required a username and password. Such access was obtained from the manufacturer's representative. In order to be able to objectively compare the telematics data with the data obtained during the field tests, a set of telematics data was selected only for CHs of the same model. In total, our filtering conditions were met by 67 different machines (from an array of 239 different machines). The information was then organized into a unified database to statistically process the collected data. In this study, the data for each individual machine for each year were downloaded to a personal computer; these were then imported into a data array for further processing. Only the information needed for the study was selected from the large telematics database, including working time and fuel consumption in different operational modes. A machine's working time (h year\({}^{-1}\)) can be categorized as follows: idle with a partially full grain tank (T1), idle with a full grain tank (T2), unloading and not harvesting (T3), harvesting and unloading (T4), harvesting (T5), headland turn separator engaged (T6), transportation below 16 km h\({}^{-1}\) (T7), and transportation above 16 km h\({}^{-1}\) (T8). The categorizations for fuel consumption (l year\({}^{-1}\)) were as follows: idle with a partially full grain tank (F1), idle with full grain tank (F2), unloading and not harvesting (F3), harvesting and unloading (F4), harvesting (F5), headland turn separator engaged (F6), transportation below 16 km h\({}^{-1}\) (F7), and transportation above 16 km h\({}^{-1}\) (F8).  
In the analysis, the T1 and T2 times were obtained during the CH's idle mode, T7 and T8 were collected during the transport mode, and T3, T4, T5, and T6 were acquired during the CH's working mode. Fuel consumption was calculated: F1 and F2 represent the fuel consumed at idling, F7 and F8 are the quantities of fuel consumed during the transport mode, and F3, F4, F5, and F6 represent the fuel consumed during direct work.
# Recombination of Poly(Acrylic Acid) Radicals in Acidic Aqueous Solutions: A Pulse Radiolysis Study
## 3 Results and Discussion
### Physicochemical Properties of the Studied Systems
#### 3.1.1 Molecular Weights and Dimensions of PAA and OAA  
The polymeric substrates of our study, PAA and OAA, were characterized by GPC to determine their basic physicochemical parameters. Results listed in Table 1 indicate that the obtained \(M_{w}\) values were not very far from the nominal ones. The \(M_{w}/M_{n}\) ratios of above 3 indicate that samples were polydisperse. The Z-average \(R_{g}\) was around 100 nm for PAA and below the detection limit of 10 nm for OAA. It should be noted that the \(R_{g}\) values are valid in the solvent used for molecular weight determination by GPC and may differ from \(R_{g}\) at pH 2, for which most of the kinetic measurements were performed, despite the fact that in both these conditions the PAA chains are expected to be in the coiled conformation (while the buffer used as the eluent is of high pH, it provides high ionic strength, and high ion concentration screens the repulsive forces between dissociated carboxylate groups along the chain). Dynamic light scattering measurements on our PAA in water at pH 2 yielded a hydrodynamic radius, \(R_{h}\), of 29 nm. Assuming a typical \(R_{g}/R_{h}\) ratio for polydisperse statistical polymer coils to be about 1.8 [70], this would yield an estimate of about 52 nm as \(R_{g}\) at pH 2, i.e., significantly lower than in the buffer.
## 3 Methodology
### Model Structure  
In this section, we provide a detailed overview of the architecture of the CCDA (Cross-Correlation in Dual-Attention) model, as shown in Figure 1. We first use three unimodal encoders to obtain the utterance representation \(U_{m}^{d_{m}\times n}\) and embedding \(F_{m}^{T_{m}\times d_{m}\times n}\) by using feature sequences \(X_{m}\) for each modality separately, which \(m\in\{t,a,v\}\), \(U_{m}^{d_{m}\times n}\) originate from the feature representation in each unimodal encoder. This helps the model understand the semantic and sentiment information in each modality.  
Figure 1: The structure of CCDA. The global representation \(G\) consists of three unimodal representations \(\{U_{t},U_{t},U_{t}\}\). The model processes the global representation \(G\) and the unimodal features \(F_{m}\) using the dual-attention to obtain new global and unimodal representations \(\{G^{\prime},\widetilde{U}_{t},\widetilde{U}_{t},\widetilde{U}_{t}\}\) and fuses these representations for sentiment prediction. The unimodal features \(\{F_{t}^{S},F_{t}^{S},F_{t}^{S},F_{t}^{C},F_{a}^{C},F_{v}^{C}\}\) generated during this process are used to learn the correlation between the two attention mechanisms. The final objective function consists of the MSE loss \(\mathcal{L}_{MSE}\) and the cross-correlation loss \(\mathcal{L}_{c}\).  
Next, we delve into the dual-attention mechanism (which contains self-attention and cross-attention), a core component of CCDA. By utilizing self-attention and cross-attention, CCDA can capture sentiment information and dynamics within a single modality (intramodality) and across different modalities (intermodality), respectively. This dual-attention mechanism enables the model to comprehensively analyze multimodal data and sentiment information, thereby improving the accuracy of sentiment analysis.  
Following that, CCDA calculates cross-correlation losses between the embeddings generated by the two attention mechanisms while obtaining information about the intramodality and intermodality dynamics. This contributes to the indirect interaction between the two attention mechanisms and, thus, improves the model's performance. CCDA then uses relevant coefficients strategy to fuse the unimodal and multimodal representations obtained from these two attention mechanisms to generate the final sentiment representation.  
In the following parts, we elaborate on the three main components of CCDA: unimodal encoders (Section 3.2.1), dual-level attention (Section 3.2.2), and fusion and prediction units (Section 3.2.3).
# Review of Ka-Band Power Amplifier
## 3 Topologies  
### Common Source  
Common source (CS) is one of the three basic configuration circuits of the field-effect tube amplifier. The input and output circuits of the common source circuit contain the MOS tube source; the input signal is input from the gate, and the output signal is output from the drain. A typical basic CS amplifier is shown in Figure 6. In the practical design and application of the common source amplifier, multistage circuit is usually used to obtain high gain [44].  
Figure 5: SiGe BiCMOS structure profile.  
In 2008, [PERSON] et al. proposed to use 180 nm CMOS design to produce 3-stage CS PA [45]. In order to reduce the loss of a conductive silicon substrate, a substrate-shielded microstrip line is used as the matching network in the amplifier (see Figure 7a). The three-stage amplifier achieved a record gain of 14.5 Db for the Ka-band power amplifier using CS transistors, with a corresponding efficiency of 13.2% and output power of 14 dBm.  
In [46], [PERSON] et al., on the basis of the one-stage CS circuit, proposed a frequency reconstruction matching network and reconfigurable coupling-coefficient-based transformer (see Figure 7b). These technologies make efficient PA possible in 60 nm CMOS, which is working at 24 GHz and 28 GHz with better than 40% power added efficiency (PAE). However, only a one-stage CS amplifier is used, resulting in a relatively low gain of less than 9.1 dB at 24 and 28 GHz, and P\({}_{\mathrm{sat}}\) of 14.7 dBm at 24 GHz.  
In 2021, work [47] proposed a 3-stage CS PA in 90 nm CMOS, achieving high gain and good back-off PAE at 28 GHz. [PERSON] et al. presented three techniques to improve 1 dB compression point output power (P\({}_{\mathrm{1 dB}}\)) efficiency at low-, medium- and high-power regions, respectively. The three techniques are adaptive bias network (ABN), predistortion linearizer (LIN) and g\({}_{\mathrm{m}}\) compensation technique (the cross-couple pair for the g\({}_{\mathrm{m}}\) compensation technique). These techniques have an obvious effect, the efficiency of P\({}_{\mathrm{1 dB}}\) approaches the efficiency of P\({}_{\mathrm{sat}}\). The measured P\({}_{\mathrm{1 dB}}\) and P\({}_{\mathrm{sat}}\) are 16.73 and 17.68 dBm at 28 GHz, and gain is 28 dB.  
A summary comparison with CS PA measurements is given in Table 1.  
Figure 6: CS amplifier circuit schematic diagram.  
Figure 7: (**a**) Three-stage CS amplifier in 180 nm CMOS [45]. (**b**) One-stage CS amplifier in 60 nm CMOS [46].  
As can be seen from Table 1, the area of CS PA in CMOS is small. Among them, Ref. [46] achieved high PAE and gain with an area of 0.11 mm\({}^{2}\). However, silicon-based Pas have narrow bandwidth and lower efficiency at the Ka band.
# Numerical and Field Investigations of Tremors Induced by Thick-Hard Strata Fracture
## 2 Numerical Simulation  
### Modelling  
The UDEC can be used to simulate the deformation and failure process of discontinuous materials. In this software, the general numerical model is mainly composed of block elements and the interfaces among them. The block can be deformed but not destroyed. When the shear stress/tensile stress of interface exceeds its shear/tensile strength, the interface will be damaged, and the blocks bonded by that interface will be separated, which means that cracks have occurred in the material. Therefore, UDEC can be used to simulate the fracture process of rock and the occurrence of rockburst.  
Based on the actual geological conditions of the 73 upper07 working face, a simplified numerical model was established to study the occurrence of rockburst around the roadway due to the disturbance of strong mining tremors by the fracture of thick-hard stratum. As shown in Figure 1, the height and width of the numerical model were 650 m and 437 m, respectively, and contained seven layers of rock. The goal in the middle of the model with a height of 8 m and a width of 250 m was represented by a rectangular cave. The roadway with a width of 4 m was 200 m from the left boundary. In order to reduce the computing resource consumption while ensuring calculation accuracy, the different areas of the model were finely divided through different types of blocks. The thickness of the floor, 3# coal seam, immediate roof, primary roof #1, primary roof #2, red bed, and overburden were 19 m, 8 m, 10 m, 25 m, 35 m, 100 m, and 240 m, respectively.  
There was a red bed with a thickness of 250 m above the 3# coal seam. The micro-parameters of the blocks and interfaces of different strata were calibrated based on the laboratory experiment, as shown in Tables 1 and 2. After the goal was formed, the stress around the goat would be transferred as the fall of the immediate roof and primary roof #1, causing the concentration of shear stress at the end of the red bed and the accumulation of a large amount of elastic energy. Under the influence of lateral abutment pressure, the stress around the roadway also rose sharply. When the concentrated stress reached the bearing strength of the red bed, it would fracture and generate mining-induced tremors. Besides, when the stress wave was interacted with the pre-existing high static load, the risk of the roadway rockburst would obviously increase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stratum** & **Lithology** & **Density/(kg/m\({}^{3}\))** & **Bulk Modulus/GPa** & **Shear Modulus/GPa** & **Thickness/m** \\ \hline Floor & Silstone \#1 & 2600 & 5.47 & 2.82 & 19 \\ Coal & & 1400 & 2.87 & 1.48 & 8 \\ Immediate roof & Silstone \#2 & 2500 & 5.47 & 2.82 & 10 \\ Primary roof \#1 & Medium sandstone & 2600 & 6.42 & 2.56 & 25 \\ Primary roof \#2 & Mudstone & 2600 & 4.58 & 2.06 & 35 \\ Thick-hard rock & Fine sandstone & 2600 & 8.51 & 2.36 & 100 \\ Overburden & Clay & 2600 & 3.65 & 1.6 & 240 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Micro-parameters of the block.  
Figure 1: Numerical model.
## 4 Experimental Environment and Preliminaries
### Experimental Setup
The utilized metrics were the mean absolute error (_MAE_) shown in Equation (29), \(MSE\) Equation (30), root mean squared error (\(RMSE\)) Equation (31), and coefficient of determination (\(R^{2}\)) Equation (32). Two additional metrics were included in the analysis, the index of alignment (\(IoA\)) Equation (33) and Euclidean distance error (\(EDE\))  
Figure 2: Detailed flowchart of the proposed framework—XGBoost (**left**) and LSTM (**right**). Equation (34), which was used to determine the distance between the predicted vessel location and the actual location. \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|, \tag{29}\]  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}, \tag{30}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{31}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\hat {y})^{2}}, \tag{32}\]  
\[Recall=\frac{TruePositives}{TruePositives+FalseNegatives}, \tag{25}\]0  
where \(y_{i}\) denotes the observed values, \(\hat{y}_{i}\) the forecast value, and \(\hat{y}\) the mean of the observed values. Finally, \(n\) denotes the number of observed samples. The \(EDE\) metric helps determine the distance between the predicted value and the actual value, as described in Equation (34)  
\[Recall=\frac{TruePositives}{TruePositives+FalseNegatives}, \tag{25}\]1  
where \(x_{i}\) and \(y_{i}\) represent the actual vessel coordinates, while \(\hat{x}_{i}\) and \(\hat{y}_{i}\) denote the predicted values for each of the coordinates. To guide the optimization process, the \(MSE\) metrics were selected as the objective function, with the goal of the optimization being minimization. The mean value of the EDE was utilized as the indicator function in the same experiment.
## 3 Optimization Arrangement of the One-Camera-Five-Mirror Module  
The 3D Bubble reconstruction algorithm developed in this paper was based on space rectangular coordinate system. The spatial coordinates of the cavitation bubbles in the valve was provided by the position information of the experiment images from the three directions of left (L), right (R), and back (B). The bubble position coordinates on the horizontal axis from the L and B sides' images were directly used as the \(x\)-coordinate values of the bubbles in the spatial location and the R side's image provided the \(y\)-coordinate values. In case a virtual camera has an oblique angle with the corresponding observed side of the valve, the transparent surface made by PMMA material will cause image refringence, then the 3D bubble position in the \(x\)-\(y\) plane will be inaccurate with its actual space position. The larger the oblique angle, the greater the error of the bubble in space position. To eliminate this problem, the light center axes of the three virtual cameras were all defined to be perpendicular to the observed faces.  
The resolution of the experiment videos was limited by the capability of the high-speed camera and the distance from the observed field to the camera lens. The resolution capability of the camera and the lens used in the experiment are fixed. The cavitation bubbles in the valve were quite small. In order to ensure the resolution of the bubble images, the distance of the optical path were maintained as short as possible. In addition, the distance of the optical path of the three virtual cameras was set to be equal to ensure the consistent image resolution in the L, R and B three sides.  
To optimize the arrangement of the high-speed camera and five plane mirrors to meet the expectation above, the optimization model was built. As shown in Figure 4, the point \(P\) and \(Q\) are the position of the virtual camera 3 (B) and 2 (R) in Figure 3. Due to the symmetrical relationship of the virtual camera 1 (L) and 2 (R), the optimal design of the two mirrors of virtual camera 1 was omitted. Thus, the optimization design variables in practice were the position parameters of the camera (point \(H\)) and the three mirrors (marked as A, B and C).
## 4 FTN Detection Scheme in Multipath Channels Based on Neural Network
### Proposed Deep-Bi-LSTM Architecture for FTN Signal Detection  
In the previous section, FTN signal detection is regarded as a classification problem. Consider using the CNN network to do different processing methods for different received signal forms. In this section, we treat FTN signal detection as a regression problem and explain the proposed Deep-Bi-LSTM network model in detail.
# Enhancing Real-Time Kinematic Relative Positioning for Unmanned Aerial Vehicles
## 5 Results
### Performance Improvement through Synchronization of Measurement Times  
Figure 14 shows the ratios of the test pass rates and the 3D Root Mean Square Errors (RMSE) of the relative positions between the UAVs. The flight test data was used to compare the results of using conventional and dual KFs with and without GNSS measurement time synchronization. The results reveal that neither conventional nor dual KFs without time synchronization can achieve centimeter-level positioning performance. With measurement time synchronization, both KFs had ambiguity fix rates above 40% on all datasets, while without the time synchronization process, both KFs had fix rates of 0% on 34 out of 45 datasets. The average ratio-test pass rate increased from 13.31% to 85.93% for the conventional KF and from 19.11% to 99.59% for the dual KF, representing an overall performance improvement of 545% and 421%, respectively. Also, the average 3D RMSE decreased from 57.96 cm to 7.14 cm for the conventional KF and from 57 cm to 1.93 cm for the dual KF, showing a 3D RMSE reduction of 87.7% and 96.6%, respectively. The difference in RTK performance with and without the time synchronization process is significant, except for a few limited cases where the time difference between the received measurements is sufficiently small to be negligible.
## 4 Materials and Methods
### Validation of DEGs by qRT-PCR Analysis  
The key DEGs in the terpenoid synthesis pathway were selected to validate their expression levels by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Synthesis of cDNA for qRT-PCR was performed with First Strand cDNA Synthesis Kit (ThermoFisher Scientific, Shanghai, China). The primers were synthesized by Tsingke Biotechnology Co., Ltd. (Wuhan, China) (Table S7). The reaction system of qRT-PCR was as follows: cDNA template 0.5 \(\upmu\)L at 3000 ng/\(\upmu\)L, gene-specific upstream and downstream primers 0.4 \(\upmu\)L each at 10 \(\upmu\)M, 2 \(\times\) ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) 10 \(\upmu\)L, 8.7 \(\upmu\)L of double-distilled water (ddH2O). qRT-PCR was performed by CFX96 Real Time System (Bio-Rad, Hercules, CA, USA). Young leaves were selected as the control and 26s rRNA as the internal reference genes. The relative expression of genes in the qRT-PCR experiment was analyzed by the \(2^{-\triangle\Delta\Delta\text{CT}}\) method [67]. At least three biological replicates were set for each sample.