esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Eddy-Generation Mechanism on the Characteristic of On-Source Fire Whirl
## 3 Results
### Velocity
#### 3.3.1 Axial Velocity  
The axial velocity contour of a cross-sectional plane generated by the two cases is presented in Figure 13 for comparison. The contour plot depicts that the maximum axial velocity of the case with 02 slit configuration is higher, i.e., increased by 6.81%, when compared with that of the baseline model. The region in which axial velocity is dominant was also extended with the introduction of additional air entrainment slit. The axial velocity dominant region, determined as the vertical location at which the excess axial velocity decreases to half of the maximum recorded value, increased by 46.14% to 0.5739 m, compared with 0.3927 m for the baseline model.  
Figure 12: Peak value of W-velocity of the two cases, at all monitoring HABs.  
Figure 13: Axial velocity contour of a cross-sectional plane of the 01 slit case (**a**), and the 02 slit case (**b**).  
The promotion of axial velocity in the 02 slit case is further justified by assessing the spatial distribution of the axial velocity plotted against the lateral direction at the monitoring HABs, presented in Figure 14. The literature reveals that the axial centreline velocity accelerates upwards to maximum in the continuous flame region. Due to the velocity fluctuation, the axial centreline velocity decreases sharply as the HAB increases to intermittent regions, and further decelerates to ambient conditions as it enters the plume region [56; 57]. Such a transition can be clearly seen in the velocity profile yield from the baseline model, indicating that the continuous and intermittent region of the fire whirl generated in the baseline case should be well below HAB 0.5 m. Meanwhile, the peak axial centreline velocity reported in the comparison case is presented in an incremental pattern as the flame developing in the downstream direction, suggesting an extended continuous region when compared with the baseline case.  
In addition, experimental data of PIV measurements confirmed that within the continuous flame region, the axial velocity reaches its maximum value in the vicinity of the reaction layer, i.e., away from the flame centreline, and its profile resembles a hump-like distribution along the radial direction. With increasing height from the intermittent flame region and beyond, the location of maximum excess temperature shifts toward the whirl's centreline and forms a Gaussian profile [27]. The transformation of the axial velocity profile from hump-like to Gaussian form, evidently observed in the baseline model as the HAB increased from 0.3 m to 0.5 m, however, had not yet occurred in the 02 slit model. The hump-like profile at all monitoring HABs up to 0.5 m in the comparison case again confirms the extension of the continuous flame region, i.e., to and beyond HAB 0.5 m, when compared with single slit configuration.
# A Review of Artificial Intelligence in Enhancing Architectural Design Efficiency
## 2 Materials and Methods
### Analysis of Search Results  
Shown in Figure 2 is the PRISMA flow chart tracking and summarizing the article selection process. In the first stage, duplicate papers were deleted, leaving 657 papers. In the second phase, articles on programming, medicine, and agronomy were excluded. At the same time, the literature was filtered according to the influence of the source of the article, reducing the count to 366. In the third stage, the development history of AI and architectural design was systematically expounded, and the application of specific AI technology in architectural design was studied in detail, excluding 283 papers, leaving 83 for detailed analysis and review.  
Figure 2: Meta-analysis process.  
The 83 articles, classified by year, as shown in Figure 3, reveal that traditional architectural design methods include five parts--\"model and theory\", \"problem\", \"thinking\", \"philosophy and ontology\", and \"tool\"--and AI can transform this information into vectorized representations in computer-processable formats, thus greatly improving the processing and generation efficiency. Across all stages--program design, construction, and operation--AI can provide substantial technical support, such as AIGC, artificial neural networks (ANNs), deep neural networks (DNNs), Random Forest (RF), etc.
## 4 Motion Verification
### Control Method  
To implement any model-based control method, we require the state-space equations that represent robot motion, as well as the model for our selected actuator (DC motors).  
For this, using the model presented in [25], we express the relationship between the motor's torque load and its angular velocity as  
\[T_{L}=-\frac{k^{2}}{R}\omega+\frac{k}{R}E \tag{6}\]  
where \(T_{L}\) is the torque load, \(\omega\) is the angular velocity, \(E\) is the voltage applied, \(R\) is the motor's resistance, and \(k\) is the torque constant. The torque \(T_{L}\) is proportional to each acceleration \(\dot{\omega}\) with the moment of inertia \(I_{r}\) as a coefficient. Assuming that the moment of inertia \(I_{r}\) applied to the motor is constant for each motor, Equation (6) can be transformed using the relationship between load torque and angular acceleration and Equation (4).  
\[\begin{pmatrix}\dot{V}_{x}\\ \dot{V}_{y}\\ \dot{\Omega}\end{pmatrix} =-\frac{k^{2}}{I_{r}R}\begin{pmatrix}V_{x}\\ V_{y}\\ \dot{\Omega}\end{pmatrix}+\frac{k}{I_{r}R}T^{+}\begin{pmatrix}E_{1}\\ E_{2}\\ E_{3}\\ E_{4}\end{pmatrix} \tag{7}\] \[\dot{\mathbf{V}} =A\mathbf{V}+B\mathbf{E}\]  
where \(A=diag(-\frac{k^{2}}{LR}),B=\frac{k}{LR}T^{+}\).  
When the robot is running at the desired speed, \(\mathbf{V}_{e}\), we have that \(\mathbf{\hat{V}}=\mathbf{V}-\mathbf{V}_{e}=0\). Linear quadratic regulator (LQR) is a control regulator often used to solve problems formulated like this. LQR is a method that aims to minimize  
\[J=\int_{0}^{\infty}\mathbf{x}^{\top}Q\mathbf{x}+\mathbf{u}^{\top}R\mathbf{u} \ dt \tag{8}\]  
where \(\mathbf{x}\) and \(\mathbf{u}\) are the state and the input of the system and \(Q\) and \(R\) are the weight matrix of the function. In this case, \(\mathbf{x}\) is \(\mathbf{\hat{V}}\), and \(\mathbf{u}\) is \(\mathbf{\hat{E}}=\mathbf{E}-\mathbf{E}_{e}\) where \(\mathbf{E}_{e}\) is the ideal voltages applied to each motors to move the robot at the target speed \(\mathbf{V}_{e}\). It is needed to solve the following Riccati equation for the evaluation function minimization.  
\[PA+A^{T}P-PBR^{-1}B^{T}P+Q=0 \tag{9}\]  
where \(A=diag(-\frac{k^{2}}{LR})\), \(B=\frac{k}{I_{r}R}T^{+}\) and \(Q\) and \(R\) are the weight matrix; generally, with the identity matrix in the evaluation function, \(P\) is a solution to the equation and is uniquely determined by \(A\), \(B\), \(Q\), and \(R\). Using the matrix \(P\) satisfies this equation, the following equation expresses the voltages \(E\) applied to each motor to move the robot.  
\[\mathbf{E}=\mathbf{E}_{e}-R^{-1}B^{T}P(\mathbf{V}-\mathbf{V}_{e}) \tag{10}\]
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 4 Datasets
### RadioML2018.01a  
This dataset contains 24 modulation modes: 32 PSK, 16 APSK, 32 QAM, FM, GMSK, 32 APSK, OQPSK, 8 ASK, BPSK, 8 PSK, AM-SSB-SC, 4 ASK, 16 PSK, 64 APSK, 128 QAM, 128 APSK, AM-DSB-SC, AM-SSB-WC, 64 QAM, QPSK, 256 QAM, AM-DSB-WC, OOK and 16 QAM. Each modulation mode contains 26 SNRs, 4096 data in each SNA, two IQ signals in each datum and 1024 points in each signal, so the dataset contains (24 \(\times\) 26 \(\times\) 4096) \(\times\) 1024 \(\times\) 2 = 5,234,491,392 data. The dataset is stored in three dimensions (X, Y, Z) in the HDF5 file. The X dimension stores a three-dimensional array that stores signal modulation data (that is, data used to recognize signal types) in the shape and size of (2,555,904, 1024, 2), representing a total of 25,555,904 signal data, with 1024 signal modulation data containing two IQ signals per signal. The Y dimension stores a two-dimensional array that stores signal types in the shape and size of (2,555,904, 24), representing 2,555,904 signal data of each signal type, represented by 24 digits. The Z dimension stores a one-dimensional array that stores the SNR (2,555,904, 1), representing 2,555,904 signal data. The SNR of each signal sample ranges from \(-\)20 dB to 18 dB. The parameter description of the dataset is shown in Table 9.  
This dataset is an improved version of the previous datasets. Unlike the three datasets mentioned earlier, this dataset uses 24 different modulation types, divided into analog and digital. The single carrier motion scheme is also included. Firstly, the model is built to generate several analog wireless channels and dataset signals. Then the OTA test is carried out on the clean signal channel without the damage of synthetic signals. The way to test is this to first modulate the signal and then let it transmit. The signal is set to offset about 1 MHz and stored in baseband. The test transmitter is stored with the transmitter modulated real-world label on the ground. The next part classifies the signals. The specific classification methods include baseline method, CNN and ResNet [47]. After signals are classified, appropriate data can be obtained.
# LaRaWAPS: A Wide-Area Positioning System Based on LoRa Mesh
## 4 System Design
### Hardware Design  
The system consists of gateways, anchors, and targets. The gateway and anchor consist of a LoRa radio module, a control module, an interface module, a low noise amplifier (LNA), and a high-gain antenna. The hardware structure of the target is much the same as the anchor, except that the LNA is removed, as shown in Figure 2.  
The radio module uses the SX1280 chip. SX1280 is an RF-integrated chip developed by SEMTECH, which can carry out ultra-long distance communication in the 2.4 GHz band with linearity and can withstand severe interference. The receiving gain of the low noise amplifier is 10 dB and the transmitting gain is 11 dB. It has a built-in automatic discrimination circuit for high-speed transceiver switching, which can automatically switch the transceiver state and work automatically without the control signal given by the control chip. The control module is based on STM32L476 RGT6, a low-power ARM Cortex-M4 microprocessor. The control module is responsible for controlling the RF module and uploading the data and is connected to the radio module through serial peripheral interface (SPI). The interface module uses the serial port to network port chip to provide wired network access and data reporting (as a gateway).  
The current design focuses on functional verification, so its size and low-power characteristics are not optimized. Despite that, all the following proposed algorithms are device-independent by adding a hardware abstraction layer, making it easy to port to other platforms for cost control or power optimization.
# An Analysis Method for Interpretability of CNN Text Classification Model
## 2 Interpretability Analysis Method
### CNN Text Classification Model  
Word embedding in the CNN-based text classification model is a method of converting words in the text into digital vectors. In order to use the standard machine learning algorithm to analyze them, it is necessary to take these vectors converted into numbers as input in digital form [17]. The method in this paper is to analyze based on the CNN text classification model. The structure of the CNN text classification model is generally as shown in Figure 2. In order to store word embeddings, we need a V*D matrix, where V is the size of the vocabulary, and D is the dimension of word embedding. The dimension of word embedding is a user-defined hyperparameter. The larger D is, the stronger the expressive ability of word embedding. In the model, the matrix is called the embedding layer. According to the text length and the classification performance indicators, multiple convolutional layers and pooling layers can be adopted to build the model. In general, for short text with a length of less than 50 words, we recommend using one convolutional pooling layer. Two convolutional pooling layers can be adopted for text with a length of 500 words or less. The structure and hyperparameters of the model can be determined with the deep learning model training method. The convolutional pooling layer is generally followed by a densely connected layer. Among them, common general pooling methods include max-pooling and average pooling. In this paper, the max-pooling method is used to achieve the effect of obtaining the maximum value, in each feature graph by the feature graph  
Figure 1: Analysis method of model interpretability.  
unit before the classification layer through global maximum pooling. Finally, the classification layer is connected according to the actual classification number, which can be two-class or multi-class.  
It shall be noted that when it is required to perform multi-classification tasks at the classification layer, the number of target labels for multi-label classification is generally uncertain. For this reason, we recommend determining a target label S0. When predicting, it is still the k classes with the largest output scores, but only the classes with scores greater than S0 are output using S0 as the threshold.
# Recent Advancements in Anaerobic Digestion and Gasification Technology
## 4 Recent Studies on Anaerobic Digestion Focusing on Biogas Production
The findings from the study provided an efficient method for textile sludge utilization in biogas production. The potential of using chicken manure as the sole feedstock for biogas production under anaerobic digestion was studied by [PERSON] et al. [16]. The choice of chicken manure in the study was because of its high biogas potential yield using a biochemical methane test. A methane yield of 508 mL CH\({}_{4}\) g\({}^{-1}\) VS was recorded throughout the 217 days of monitoring. The novelty of the study is associated with the adaptation of the same operational conditions (organic loading rate and retention time) during the fermentation process. To this end, approximately 93% of chicken manure was digested, with an organic loading rate of 3.14 kg VSL\({}^{-1}\)d\({}^{-1}\) and total ammonium nitrogen of 5.5 g L\({}^{-1}\). This proves that it is possible to digest chicken manure as a stand-alone feedstock as long as the ammonium concentration is carefully controlled, which is achieved via the freshwater dilution process. In terms of the design parameters, [PERSON] et al. [17] mentioned that the biogas digester dimension and material of construction are important factors of consideration. The authors developed a design equation that considered the volume of the digester, inlet, and outlet chamber dimensions, as well as the shape of the digestion chamber. In terms of construction, the understudied biogas digester was fabricated using high-density polyethylene (HDPE) plastic, whereas the inlet and outlet chamber was constructed using bricks/cement. The developed plastic biogas digester and the appropriate dimension were found to be suitable for anaerobic digestion for biogas production (methane and carbon dioxide reported to be 60% and 30%, respectively) using cow dung as the substrate. To stabilize organic waste while generating biogas, the anaerobic digestion of poultry litter was carried out by [PERSON] et al. [18]. The study originated as a result of technical challenges faced, which included seasonality of litter production, low carbon/nitrogenratios, and limited digestibilityibility of bedding. From the study, biomethane potential of 0.24-0.30 L/gVS and 0.15-0.16 L/gVS was obtained for litter leachate and whole litter, respectively, whereas the insoluble bedding material left after leaching was reported to be 0.08-0.13 L/gVS. The information from the results indicates that poultry litter leachate can be amended for digestion despite having a low carbon/nitrogen ratio. Hence, litter can be stored before digestion, although this leads to biomethane losses. The authors opined that the loss is because of the decomposition of organics during storage.
# Mechanisms of Tunnel Rockburst Development Under Complex Geostress Conditions in Plateau Regions
## 3 FLAC3D Secondary Development and Validation  
### Visualization of Elastic Strain Energy  
The mechanisms and development of rockbursts are closely related to energy. When rock masses are subjected to external disturbances, internal deformation and cracking induce energy redistribution, and the releasable elastic strain energy stored within the rock becomes one of the driving forces for rockburst occurrence [29]. Although FLAC3D can calculate major physical parameters of rock, such as stress and displacement, it is unable to compute the magnitude of internal energy or generate strain energy contour maps. This limitation hinders the investigation of energy transformation mechanisms within the surrounding rock during tunnel excavation. Therefore, in this study, secondary development was conducted using the built-in FISH programming language to achieve intuitive visualization of elastic strain energy density.  
To calculate the elastic strain energy density (\(U_{\mathrm{ez}}\)) of each zone, the model was first divided into two groups: the tunnel and the external surrounding rock. Upon completion of tunnel excavation, each zone element in the external surrounding rock was traversed, and the elastic strain energy density within the surrounding rock was calculated using Equation (1). The contour extra method was then employed to generate contour maps of elastic strain energy density, thereby achieving the visualization of elastic strain energy.  
\[U_{\mathrm{ez}}=\frac{1}{2E_{0}}[\sigma_{1}^{2}+\sigma_{2}^{2}+\sigma_{3}^{2 }-2\mu(\sigma_{1}\sigma_{2}+\sigma_{2}\sigma_{3}+\sigma_{3}\sigma_{1})] \tag{1}\]
## 4 Application in Fastener Quality Control
### Reliability  
Since reliability focuses on the overall performance and defective probabilities of products or systems, a strong quality control process contributes to achieving the desired reliability of a product. For square nuts and large flange rivets with batch sizes of 500, we can define the defective product numbers as in Equation (16) by using the determined number of components (\(k\)), proportions (\(\pi\)) and parameters (\(\alpha\)) of each Poisson mixture distribution. By utilizing these probability density functions, we can calculate the probabilities of having the number of defective products greater or less than a certain value for both products within a batch.
## 2 Materials and Methods
### Statistical Evaluation  
Data from microsatellite analysis were used to calculate allele frequencies, number of different alleles, number of effective alleles, probability of identity, Shannon's information index, fixation index, and heterozygosity using the software GenAlEx v. 6.5 [21]. GenAlEx was also used for calculating the allelic patterns across geographical origin groups. The polymorphic information content was calculated using Cervus 3.0.7 software [22]. The population structure was assessed using STRUCTURE v. 2.3.4 software [23], with the default admixture model setting for the ancestry of individuals and correlated allele frequencies. The models were tested for K-values ranging from one to fifteen, with five independent runs each. Burn-in and Markov Chain Monte Carlo iterations were set to 10,000 and 100,000, respectively. The most likely number of clusters was selected by plotting the LnP(D) values against \(\Delta\)K values with the best K value selected according to the Evanno test [24]. The tree in STRUCTURE was estimated using the program NEIGHBOR, which implemented the Neighbour Joining method [25]. The plot was generated using DRAWTREE [26]. The dendrogram was constructed using neighbour joining with simple matching using the statistical software DARwin 5.0.158 [27]. Pairwise comparisons using the Bray-Curtis similarity index of standard control genotypes with collected samples were assessed using the PAST (PAleontological STatistics) software version 3.19 [28].
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 7 Experimental Result  
### Experimental Set-Up  
We used 11,147 chip component image data sets exhibiting the six SMT assembly defects. Figure 1 shows the chip component defects used in this paper, and Table 2 shows the dataset used to train and test the CNNs mentioned in this paper. Of these, 6838 sets were used as training data and the remaining 4309 were used as test data. The Microsoft Cognitive Toolkit (CNTK), was used for learning and testing CNNs, and nvidia titan xp was used for training and testing. The learning rate of CNN \(l_{r}\) was the same for all CNNs with \(l_{r}=5\times 10^{-5}\). The mini-batch size of the CNN was equal to 20 and the number of epochs was 100.  
First, we divided the chip component dataset into a training dataset and a testing dataset. Using the training dataset, we then trained single-stream CNN and dual-stream CNN, respectively. We computed the precision, recall, and F1-score using the trained model and testing datasets. We repeated the experiment 5 times and have presented the average value as the experiment result.
## 3 Results and Discussion  
### Preparation of SDexF on a Semi-Industrial Scale  
The application of a semi-industrial scale resulted in a 100-fold increase in the amount of product in one process cycle, from approximately 70 g on a laboratory scale to 7 kg, while reducing the process time. The efficiency of SDexF production process was around 70%. Some of the most important research from the point of view of future applications were performed for individual batches to check the repeatability of the process.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 4 Experiments and Evaluation
### Ablation Study  
#### 4.4.1 Effectiveness of the Component Pair Constraint  
In Section 3.2, we introduce the component pair generation strategy. The purpose of this section is to study whether component pair constraints affect network correspondence results. Figure 14 visualizes the geodesic error on the original FAUST dataset. The correspondence error on the basis of the geodesic distance to the ground truth is demonstrated with a color scale of 0 to 0.1. As shown in this figure, our model achieves a lower geodesic error on the original FAUST dataset by using a component pair constraint, which proves the effectiveness of the component pair constraint.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**FAUST**} & \multirow{2}{*}{**SCAPE**} & \multirow{2}{*}{**KIDS**} & \multicolumn{6}{c}{**TOSCA**} \\ \cline{5-12}  & & & & & **[PERSON]** & & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline FMNet & 0.3601 & 0.3709 & 0.3402 & 0.3413 & 0.3712 & 0.3100 & 0.3752 & 0.3574 & 0.3745 & 0.3522 & 0.0360 \\ SURFMNet & 0.3952 & 0.3895 & 0.