esa-sceva/satcom-chunk-collection huggingface dataset:
# GPU-Accelerated Fock Matrix Computation with Efficient Reduction
## 4 Proposed Method
### Thread-Local Reduction in Registers  
We also implement and evaluate thread-local reduction employed in previous studies [20,27]. Several primitive ERIs within each task share the same destination in the Fock matrix, allowing them to be aggregated in private registers for each thread. By accumulating these primitive ERIs within each register at low cost, we can reduce the number of expensive atomicAdd operations to global memory. As shown in Figure 2, when calculating the task submatrix \([\text{ss}|\text{sp}]\) as an example, the three contributing ERIs \([\text{ss}|\text{sp}_{x}]\), \([\text{ss}|\text{sp}_{y}]\), and \([\text{ss}|\text{sp}_{z}]\) share the same indices of contracted ERIs \(\mu\), \(\
u\), and \(\lambda\). Therefore, these primitive  
Figure 4: Global memory usage required for the replicated Fock matrices.  
ERIs share the same destinations in the Fock matrix at \(F_{\mu\
u}\), \(F_{\mu\lambda}\), and \(F_{\
u\lambda}\) among six locations. Consequently, by summing the primitive ERIs within each thread's registers before updating the Fock matrix in global memory, we can reduce the number of atomicAdd operations by a factor of \(1/3\). Table 1 shows the reduction rate of atomic operations in each ERI kernel. While higher angular momentum sub-matrices exhibit greater reduction rates, they also require a correspondingly larger number of additional register variables. We have also implemented the thread-local reduction and will evaluate its combination with our distributed atomic reduction in the next section.  
While our current GPU implementation has been developed for ERIs that include s- and p-orbitals, the proposed replicated Fock matrix approach is also expected to be effective for chemical systems involving higher angular momentum orbitals. Our distributed atomic reduction method does not affect the implementation of the shell-based complex ERI evaluation part, allowing it to be applied to CUDA kernels involving d-orbitals and beyond in the same way as low angular momenta. Furthermore, the proposed method should offer sufficient performance for higher angular momentum orbitals. This is because the number of atomicAdd operations for the Fock matrix update in Equation (24) increases with the total angular momentum \(L\). Specifically, the number of primitive ERIs computed iteratively in the for loop on line 5 of Algorithm 1 increases. For instance, in the \([\mathrm{dd}|\mathrm{dd}]\) kernel, each GPU thread sequentially evaluates 1296 primitive ERIs. Each integral \([ab|cd]\) contributes to six elements of the Fock matrix, resulting in 7776 atomicAdd operations. Using the thread-local reduction can reduce this number by \(1/36\), but still leaves 216 atomicAdd operations, which is four times more than the 54 operations in the \([\mathrm{pp}|\mathrm{pp}]\) kernel. Consequently, we infer that our proposed method that reduces memory contention caused by atomicAdd conflicts is effective even for the Fock matrix computation involving higher angular momentum orbitals.
## 5 Research and Development Challenges
### Collaborative Truck-Drone Delivery  
Collaboration between truck and drones is shown to be successful to address the flight range and payload limitation problem of drones [67; 68; 69]. In [15], the authors proposed a contactless collaborative truck-drone delivery to prevent the further spread of COVID-19 disease. They formulated a mixed-integer linear programming to minimize the delivery time while considering the energy consumption model of drones. In [70], the authors considered truck-drone collaborative delivery, where truck operation is restricted by street network and drone operation is restricted by its payload capacity and operation range to improve the efficiency of last-mile delivery. Utilizing the trucks helps to plan effective routes for drones to deliver the parcels in a certain area where the customer locations are widespread [69; 71]. However, multiple problems remain unaddressed, such as partitioning customers between trucks and drones and determining the location of the truck for launching and synchronizing the route planning to ensure reliable drone-based delivery [72]. Thus, routing and scheduling of the drones should be jointly considered to solve these issues [10]However, truck-drone-based delivery has its demerits. The cost of truck is high because it requires a driver and there exists fuel and maintenance cost. To solve these problems, alternatives such as CS installation [73; 74; 75] and utilization of public transportation network [18; 76; 77] have been investigated recently, which are a potential research topic. Additionally, the hybrid trunk-drone delivery problem considering the simultaneous deployment of trucks, truck-carried drones, and independent drones to design more robust truck-drone delivery still requires further study to overcome the limitations of limited battery and payload capacity of drones [78; 79; 10; 72].
# Phenotype of White Sika Deer Due to SCF Gene Structural Variation
## 3 Results  
### Resquencing Data and Genetic Variation  
The sequencing results were filtered for quality control, yielding 98.4 Gb of clean data, with 99.19% genome coverage and 39.06\(\times\) sequencing depth. After allele frequency analysis, 780,147 SNP loci, 83,287 InDels, 4380 SVs, and 448 CNVs were screened. A total of one SV and 19 SNPs were located on the exon sequence of the coat color candidate gene, including five nonsynonymous mutations and 14 synonymous mutations in SNPs (Table 1). The SV was located within the SCF gene (chr3:16,444,201-16,454,300), which lost a 10.099 kb DNA fragment. The nonsynonymous mutations were _TYRP1_ (c.C137T:p S46F), _LYST_ (c.C166T:p.S556L), _ICAT_ (c.C268G:p.P90A), _WRN_ (c.T1529C:p.I510T), and _EPG5_ (c.A4762G:p.S1588G), of which the first three were albino pathogenic genes [23].
# Targeted Sentiment Classification Based on Attentional Encoding and Graph Convolutional Networks
## 4 Experiments
### Ablation Study
#### 4.4.1 Ablate Graph Convolution Network  
For AEGCN, a graph convolution network was deployed between the Bi-LSTM and multi-head self-attention in the proposed model, since it can reconstruct the representation of each word using syntactic information. We ablated the graph convolution network to examine the performance of AEGCN without it.  
As a result of removing the GCN, the accuracy and F1-score on three datasets (lap14, rest14, and rest15) decreased, while the accuracy and F1-score on the twitter and rest16 datasets increased. This is because the sentences from the twitter dataset are colloquial and less grammatical. Since the syntactic structure of most sentences in the twitter dataset is not perfect, the introduction of syntactic information would interfere with the prediction of the sentiment polarity of a specific target. TNet-LF is able to learn more abstract contextualized word features from deeper networks, and it achieved comparable results on the rest16 dataset in terms of both accuracy and macro-F1 measure (Table 2). We can conclude from this that the twitter and rest16 datasets are less-sensitive to syntactic information. Our experimental results demonstrate that syntactic information is quite helpful for targeted sentiment classification on most datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**TWITTER**} & \multicolumn{2}{c}{**LAP14**} & \multicolumn{2}{c}{**REST14**} & \multicolumn{2}{c}{**REST15**} & \multicolumn{2}{c}{**REST16**} \\ \cline{2-10}  & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** & **Accuracy** & **F1** \\ \hline
**AEGCN** & **73.16** & **71.82** & **75.91** & **71.63** & **81.04** & **71.32** & **79.95** & 60.87 & 87.39 & 68.22 \\ \hline w/o GCN & **73.55** & **71.88** & 74.86 & 70.65 & **80.74** & **70.78** & 79.82 & **61.12** & **87.66** & **69.57** \\ w/o PWC & 73.06 & 71.81 & 75.02 & 70.55 & 80.68 & 70.64 & 79.70 & 60.76 & **87.71** & **69.77** \\ w/o MHSA & 72.49 & 70.77 & **75.28** & **71.71** & 80.17 & 70.18 & **80.13** & **62.40** & 87.60 & 68.24 \\ w/o MHIA & 73.12 & 71.20 & 72.62 & 67.57 & 79.94 & 70.18 & 78.16 & 58.34 & 86.79 & 68.65 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results (%). PWC: point-wise convolution; w/o: without. The best two results with each dataset are highlighted in bold.
## 3 Material and Methods
### Research Instruments
#### 3.3.2 MindWave EEG Device  
This study used the MindWave EEG headset developed by NeuroSky as the EEG detecting instrument. The headset employs ThinkGear technology to collect electronic signals from the brain and is as portable as a regular headset for music listening. In addition, the headset can be paired with a computer wirelessly through Bluetooth, and the device's user-friendliness favored the participants' usage and enabled the research team to collect the data in a natural environment. The electrodes are distributed in three touch points across the MindWave headset. In addition to detecting brainwaves in the frontal lobe, the frontal electrode also detects the user's blinking. The other two electrodes are normal electrodes that are primarily used to detect alpha and beta waves. Conductive adhesives are not required for the MindWave headset, leading to reduced test time by eliminating preparation.
## 4 Discussion  
The STFD framework achieves robust infrared target detection through a collaborative design of temporal and spatial analysis. By combining temporal coherence evaluation and gradient-enhanced contrast measurement, the method effectively addresses key challenges like transient noise interference and complex background suppression. The temporal components (TRC-RIA) enhance target persistence through multi-frame energy integration while filtering short-term clutter, while the spatial module (GELCM) distinguishes targets from textured backgrounds through adaptive gradient analysis. Critically, comprehensive experimental validation demonstrates STFD's consistent effectiveness across diverse operational scenarios--from highly cluttered environments with overlapping terrain reflections to relatively uniform backgrounds under clear atmospheric conditions.  
This balanced architecture ensures adaptability across varied operational conditions. Unlike methods specialized for either moving platforms or static scenes, STFD dynamically adjusts its emphasis on temporal versus spatial features based on scenario characteristics. The modular design not only simplifies computational implementation but also maintains interpretability--critical for practical systems compared with black-box deep learning models. Notably, STFD is particularly effective in high-frame-rate infrared small target tracking and detection tasks, such as aircraft IRST platform detection, space-based early warning systems, and ground-based UAV tracking, making it a versatile solution for military and surveillance applications.  
Despite its strong performance, STFD has certain limitations that warrant further investigation. First, the computational complexity, though moderate compared with iterative optimization methods, may still pose challenges for extremely resource-constrained systems. To address this, future work could explore GPU acceleration or lightweight parallel computing architectures to enhance real-time performance. Second, the framework's sensitivity to parameter settings, particularly in the GELCM's gradient thresholds and  
Figure 10: ROC curve for ablation study.  
TRC's temporal window sizes, could affect its adaptability in highly dynamic environments. Potential solutions include integrating adaptive parameter optimization techniques, such as lightweight neural networks or reinforcement learning, to dynamically adjust these parameters based on scene characteristics.  
These advancements could further expand STFD's applicability in emerging domains such as autonomous drone navigation, satellite-based reconnaissance, and next-generation military surveillance systems, ensuring its relevance in the rapidly evolving field of infrared target detection. Such hybrid approaches would retain interpretable physical principles while adapting to evolving sensor technologies, striking a practical balance between reliability and adaptability. These enhancements would strengthen STFD's applicability in even more challenging operational environments, ensuring its continued relevance in the evolving field of infrared target detection.
## 3 Analysis Method
### Analysis Conditions
In the experimental measurement, the laser beams emitted in the order from right to left, as shown in Figure 10, and when they reached the left end of the generation range, they instantly returned to the right end and repeated the same behavior toward the left end, as in the previous case. The detecting point also moved in the \(y\) direction, so the reciprocal theorem described earlier was no longer applicable. In addition, even if the defect geometry is the same, the analysis must be redone depending on the measurement conditions such as a traveling speed change. Figure 8: Defect shape in the FEM model and its example of the actual shape. (**a**) X-ray CT results. (**b**) Defect shape in the \(xy\) cross-section. (**c**) Defect shape in the \(xz\) cross-section. Figure 9: Position of generation and detection in the analysis. In this study, we devised a method to evaluate the results of the traveling measurement by picking up data, as shown in Figure 10, from a dataset obtained by repeating the calculation of static measurement at a position shifted by 0.04 mm in the weld line direction, according to the traveling speed. As a result, not only can the reciprocal theorem be used, but evaluation can also be performed simply by changing the pickup location, even if conditions such as the traveling speed change.
## 2 Materials and Methods
### Viscoplastic Self-Consistent  
The incompressible viscoplastic behavior at each material point was characterized using the following non-linear, rate-dependent equation:  
\[\varepsilon(\overline{\mathbf{x}})=\gamma_{0}{\sum_{k}}m^{k}(\overline{ \mathbf{x}})\left(\frac{m^{k}(\overline{\mathbf{x}}):\sigma(\overline{\mathbf{ x}})}{\tau_{o}^{s}(\overline{\mathbf{x}})}\right)^{n}\]  
\(\varepsilon\) and \(\sigma\) are the deviatoric strain rate and stress. \(\gamma_{0}\) is a normalization factor, and \(n\) is the rate-sensitivity exponent. \(\tau_{o}^{s}\) and \(m^{k}\) are the threshold resolved shear stress and the symmetric Schmid tensor associated with the slip system (\(k\)).  
Assuming a linear relationship between strain rate and stress within the statistically representative (SR) grains, the equation simplifies to the following:  
\[\varepsilon(\overline{\mathbf{x}})=M^{(r)}:\sigma(\overline{\mathbf{x}})+ \varepsilon^{0(r)}\]  
\(M^{(r)}\) and \(\varepsilon^{0(r)}\) are, respectively, the viscoplastic compliance and the back-extrapolated term of SR grain (\(r\)). In this study, which adopted affine linearization, \(M^{(r)}\) and \(\varepsilon^{0(r)}\) were chosen [18]:  
\[M^{(r)}_{affine}=n\gamma_{0}{\sum_{k}}\frac{m^{k(r)}\bigotimes m^{k(r)}}{\tau _{0}^{k(r)}}\left(\frac{m^{k(r)}:\sigma^{(r)}}{\tau_{0}^{k(r)}}\right)^{n-1}\]  
\[\varepsilon^{0(r)}_{affine}=(1-n)\gamma_{0}{\sum_{k}}\left(\frac{m^{k(r)}: \sigma^{(r)}}{\tau_{0}^{k(r)}}\right)^{n}\]  
Figure 3: Pixel arrangement for orientation gradient analysis. The selected pixels have a minimum of 0.5 confidence index.  
In the experimental context, affine first-order self-consistent approximations are linearization schemes at the local level that make use of information on field averages only, disregarding higher-order statistical information inside the grains. The underlying physical significance of using affine simply lies in the fact that it was proven to work well for the case of polycrystalline materials (including the case where viscoplastic flow is highly non-linear) submitted to complex thermo-mechanical loading paths [15].  
For the calculation to determine rotational axis, the active slip system here is the single \(\{112\}<111>\), which is known to be the most common slip system to be activated during cold rolling [19]. The \(\{112\}<111>\) family is detailed in Table 1.  
Figure 4 summarizes the proposed method, with the central concept being the determination of the rotation axis from both experimental data and calculations. These rotation axes are then plotted on a pole figure to assess whether they are aligned parallel to each other.  
Figure 4: Schematic illustration of suggested method for single \(\{112\}\)\(<\)\(111\)\(>\) slip system activity.  
Statistical analysis using analysis of variance (ANOVA) was conducted to examine the effect of position on the application of the current method, considering both top and center positions. A significance level of 0.05 was used, followed by a Tukey post hoc analysis to identify specific differences.
## 2 Related Work
### EH-WSN Routing Algorithms  
Routing algorithms in EH-WSNs mainly focus on optimizing network performance within the constraint of energy harvesting rate. In recent years, many routing protocols for EH-WSNs were also proposed. Typically, the authors of [7] proposed the Energy Harvesting Aware Ad hoc On-Demand Distance Vector Routing (AODV-EHA) protocol, which integrates the AODV protocol with energy harvesting. In this method, the \"hop count\" is replaced by the \"energy count\" (the predicted transmission cost of a packet) to find a path with the lowest transmission cost. The authors of [1] give an Effective Energy-Harvesting-Aware Routing Algorithm (EHARA). This algorithm extends the backoff period in the MAC layer IEEE 802.15.4 CSMA/CA by adding an \"extra backoff\" time, which can ensure the nodes have more time to collect energy and maintain the energy balance. The authors of [8] propose a Stability-Aware Geographical Routing Protocol (SAGREH). In this method, the candidate paths are determined through considering the residual energy and energy harvesting rate. Then, it utilizes the active link quality measurement to evaluate the quality of each path and chooses the transmission route with the highest reliability. The authors of [6] propose an Energy Harvesting Routing (EHR) algorithm. In this method, it constructs an energy rank model, which ranks the residual energy of nodes and selects the node with the highest energy level as candidate nodes. At the same time, it designs a node information update mechanism, which can save the extra energy consumed by the information exchange of nodes. Finally, the relay node is selected according to the energy level and energy harvesting density. The authors of [11] propose a Physarum-inspired routing protocol for EH-WSN (EHPRP). This algorithm establishes the Poiseuille equation by consideringthe residual energy, angle and distance factors of the node. Based on this equation, the best next hop node can be selected, which can reduce energy consumption and preserve the energy-neutral state of the network. The authors of [14] propose a routing algorithm based on the LA theory (DEH-LA-SERA). It constructs a multi-factor measurement model for sensor nodes, including node stability model, energy ratio function, expected harvesting energy model and direction judgment model, thereby deriving node weighted value to determine the next hop node. This algorithm guarantees an overall energy balance and savings while maintaining a stable selected path.  
Although the EH-WSNs algorithms we mentioned above can reduce energy consumption and extend network lifetime, these algorithms overlook the problems of limited data buffer capacity and long transmission delay.
# New Chaotic Dynamical System with a Conic-Shaped Equilibrium Located on the Plane Structure
## 3 Systematic Design of Chaotic Oscillator and Its Experimental Verification
Describing the set of differential equations of the circuit shown in Figure 6 can be expressed as  
\[\frac{du_{x}}{dt}=\frac{1}{R_{1}C}u_{z}\quad\frac{du_{y}}{dt}=-\frac{K}{R_{2}C }\bigg{(}\frac{R_{1}}{R_{3}}u_{y}+\frac{R_{1}K}{R_{4}}u_{x}u_{z}\bigg{)}\cdot u _{z}\quad\frac{du_{z}}{dt}=\frac{K}{R_{2}C}u_{y}^{2}+\frac{1}{R_{1}C}u_{x}- \frac{V_{r}}{R_{1}C}, \tag{6}\]  
where \(K=0.1\) is an internally trimmed constant associated with each analog multiplier and \(V_{r}=1\) V is a fixed voltage, which can be directly derived from the positive voltage supply by a resistor-based voltage divider followed by a voltage buffer. To obtain chaotic waveforms, we can choose the following numerical values of passive circuit components: \(C=100\) nF, \(R_{1}=10\) k\(\Omega\), \(R_{2}=1\) k\(\Omega\), \(R_{3}=830\)\(\Omega\) (potentiometer, set of parameter \(a\)) and \(R_{4}=2500\)\(\Omega\) (potentiometer, set of parameter \(b\)). In the case of the current-mode chaotic oscillator, the unity gain factor of the integrated circuit EL2082 (which can be naturally adjusted by an external dc voltage between zero and two, see datasheet for details) is supposed, while the integrated circuit AD844 has unity current gain automatically. On the other hand, the gain factor of the \(k\)-th current multiplier is arbitrary and denoted as \(B_{k}\). Transfer function of the \(k\)-th multiplier can be expressed simply as \(I_{W}=B_{k}\cdot I_{X}\cdot I_{Y}\). The set of differential equations, which fully covers network realization shown in Figure 7 is in the form:  
\[\frac{di_{x}}{dt}=\frac{i_{z}}{R_{1}C}\quad\frac{di_{y}}{dt}=-\frac{B_{2}i_{z} }{C}\bigg{(}\frac{i_{y}}{R_{2}}+\frac{B_{3}i_{z}}{R_{1}}\cdot\frac{i_{x}}{R_{ 1}}\bigg{)}\quad\frac{di_{z}}{dt}=\frac{B_{1}}{R_{1}C}i_{y}^{2}+\frac{i_{x}}{R _{1}C}-\frac{V_{r}}{R_{3}C}, \tag{7}\]  
where \(V_{r}=1\) V is the dc voltage dedicated to change the offset of the hyperbolic function. Note that summing operation is realized by a single node. Supply voltage does not need to be lowered, since AD844, EL2082, as well as EL4083 can operate with symmetrical \(\pm 15\) V. It is evident that twelve active elements are needed for the design of this chaotic oscillator using discrete components. However, such a principle will probably be chosen in the case of full on-chip implementation, using available CMOStechnology. Practical realization of the prescribed chaotic attractor can be problematic if the fabrication of technology does not support a sufficiently wide range of the supply voltages.