esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This task represents a formidable challenge due to the inherent complexity in discerning and delineating these minute visual differences, even for human experts [16]. In the realm of geology, the objective expands beyond simply determining the type of rock under investigation. It demands the implementation of subtle, ultra-fine-grained classifications to thoroughly comprehend the intricate geological attributes of a specific region [17]. The ultra-fine-grained visual classification of rocks exhibits significant applicability in geological studies. However, the field grapples with certain unresolved issues, such as the scarcity of ultra-fine-grained image datasets and the constraints on sample sizes for each category. These limitations surpass the processing capabilities of neural network methods that are dependent on extensive training data [18]. The principal objective of Ultra-FGVC is to discern considerable intra-class differences and minor inter-class variances among identical or similar species, such as identifying disparate subtypes of limestone or sandstone rather than merely determining their overarching category. As depicted in Figure 1, relative to a standard rock classification task, an ultra-fine-grained visual classification dataset presents larger intra-class variance and smaller inter-class variance, posing greater challenges to models tasked with distinguishing between different types of rocks. This process requires the model to capture both global and local feature information concurrently. Nonetheless, current CNN models continue to encounter difficulties in recognizing global features. Self-attention mechanisms, which excel at processing sequential data [19], and the recent Vision Transformer [20], along with its variant model, the Swin Transformer [21], have demonstrated superior learning capabilities in fine-grained classification compared to traditional convolutional neural networks [22; 23]. The Swin Transformer employs a window self-attention mechanism that divides the input image into multiple windows, subsequently performing self-attention computations within these windows rather than globally. This strategy notably decreases computational complexity while preserving local information, empowering the model to excel in image-related tasks. However, when tasked with the ultra-fine-grained classification of rocks, this method hampers the ability to capture long-range dependencies within the image. In response to these findings, we propose an enhanced ultra-fine-grained rock classification model that incorporates a dynamic sparse attention mechanism based on the Swin Transformer framework. This model initially captures related information within a broader area, subsequently processing the fine-grained information within these regions. Consequently, it effectively balances the acquisition of global and local information, ultimately enabling the ultra-fine-grained classification of rocks.
# CAL: Core-Aware Lock for the big.LITTLE Multicore Architecture
## 2 Background and Research Motivation
### Lock Classification  
Since the invention of concurrent programming, a lot of research has been carried out on the design of lock algorithms. At present, the most widely used locks are mainly divided into three categories:  
Figure 1: Traditional locking algorithms suffer from throughput degradation and latency increase under the big.LITTLE multicore architecture. The CPU in our experiment includes 6 big cores and 4 little cores. The first 6 threads are bound to different big cores and the last 4 threads are bound to different little cores. Threads acquire the same lock to access the critical section, and they execute a predefined number of non instructions outside the non-critical section to simulate program operations. The CAL algorithm is open-source: [[https://github.com/nsq974487195/CAL-Lock.git](https://github.com/nsq974487195/CAL-Lock.git)]([https://github.com/nsq974487195/CAL-Lock.git](https://github.com/nsq974487195/CAL-Lock.git)) (accessed on 17 July 2024). The throughput in (**a**) means the operation of critical sections has been done in 1 us. The latency in (**b**) is the P99 tail latency of staying in the critical section.  
1. FIFO-based locks, which assign the lock to the thread of request lock in FIFO order. This representative FIFO lock contains ticket lock [6], CLH (Craig, Landin and Hagersten) lock [7], and so on.
2. Competition-based locks, which is a lightweight lock. It contains tas lock [8], simple spinlock [9], pthread lock and so on. These threads themselves call a single atomic operation (e.g., CAS (compare and swap)) to capture the lock. If the atomic instruction executes successfully, the thread enters the critical section; if the atomic instruction fails, the thread continues to execute this atomic instruction until success.
3. Cohort lock [10], which is also known as hierarchical locks and designed for NUMA architecture [11, 10, 12]. This lock aims to improve the data validity lifetime in the CPU cache. This lock generally consists of two layers: the top layer uses a global lock, and the bottom layer uses a local lock for each NUMA node. The hierarchical lock prioritizes threads in the same NUMA node for a fixed period, avoiding cross-node traffic overhead while periodically rotating the global lock between NUMA nodes to achieve long-term fairness.
## 2 Preliminary Experiments
### Reactor Pumping Performance and Heater Flow Resistance  
The parts composing the reaction loop in Figure 1 were fabricated, and the liquid pumping capability of the reactor was measured at relatively low temperatures. If the dehydrogenation reactor could provide a sufficient liquid flow rate, the reaction loop could be operated without an additional pump. The dehydrogenation reactor had an inner diameter of 400 mm and a height of 78 mm. The catalyst bed had an outer diameter of 370 mm, an inner diameter of 270 mm, and a height of 60 mm. The reservoir had an inner diameter of 400 mm and a height of 500 mm. The flow control ball valve and flow meter (model HIT-4 from Hoffer Flow Controls, Elizabeth City, NC, USA) were installed downstream of the reactor.  
The results of the pumping capability measurements using H0-DBT are displayed in Figure 7. The pressure difference did not change significantly, either with the liquid temperatures of the test range or with flow rates <5 LPM. At a rotation speed of 200 rpm, the reactor provided a flow rate >3.5 LPM despite the flow resistance due to the 12-mm-diameter plumbing around the flow meter. The pressure differences did not differ significantly from the values measured during the gas infiltration experiments (Figure 5). The pressure difference in Figure 5b with maximum air infiltration varied between 12.0 and 13.0 kPa depending on the amount of water within the reactor. The pressure difference for the same rotation speed in Figure 7 was within the range 8.8-9.8 kPa, even for varying flow rates. The densities of H0-DBT (Table 1) for the test range were within 2% of the water density at ambient conditions.  
Figure 7: Pressure difference and maximum flow rate established by reactor at H0-DBT temperatures of (**a**) 70 \({}^{\circ}\)C; (**b**) 97 \({}^{\circ}\)C; (**c**) 112 \({}^{\circ}\)C.  
The parts comprising the heating loop in Figure 1 were fabricated and experiments were conducted to measure the flow resistance of the heater. Here, H0-DBT started to flow from the reservoir, then passed through the pump, flow meter, pressure transmitter, and heater before returning to the reservoir. The experimental results are displayed in Figure 8. Sufficient convective heat transfer along the heating surface within the heater was achieved at H0-DBT flow rates >40 LPM. The power required to overcome the flow resistance was calculated to be <19 W. Most of the driving power from the motor was consumed in the form of friction, which was exerted by the high-temperature sealing.
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 2 Materials and Methods
### Multi-Objective Optimization Framework  
In this study, a collaborative optimization framework combining RSM, NSGA-III, CRITIC objective assignment, and VIKOR compromise sorting is constructed. This framework addresses significant limitations in conventional concrete optimization approaches by integrating complementary methodologies that collectively resolve the complex trade-offs between critical performance indicators in extreme environments. The RSM reveals that the parameter non-linear interaction mechanism, NSGA-III, generates the global Pareto solution set, CRITIC quantifies the dynamic weights of the indicators, and VIKOR achieves conflict elimination to systematically solve the multi-objective collaborative optimization challenges.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 3 Proposed Method
The flowchart of the proposed method is presented in Figure 2.  
```
Input: Observed data \(\mathbf{Y}\in\mathbb{R}^{m\times n}\) and \(\lambda>0\) Initialization: compute \(\mathbf{L}^{(0)}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{T}\) and \(\mathbf{S}^{(0)}\), \(\mu_{0}=10^{-4}\), \(\rho>1\), \(\mu_{max}=10^{4}\), \(i=0\)  While \(\frac{\|\mathbf{D}-\mathbf{L}-\mathbf{S}\|_{\text{F}}}{\|\mathbf{D}\|_{\text{F }}}>10^{-8}\) and \(i<\) inneriter do \[\mathbf{L}^{i+1} =\underset{\mathbf{L}}{\text{arg}\,\text{min}}\|\mathbf{L}\|_{ \gamma}+\mu^{i}\bigg{\|}\mathbf{L}-\frac{1}{2}\bigg{(}\mathbf{D}+\mathbf{Z}^{ i}-\mathbf{S}^{i}+\frac{\mathbf{\Lambda}_{1}^{i}}{\mu^{i}}+\frac{\mathbf{\Lambda}_{2}^{i}}{ \mu^{i}}\bigg{)}\bigg{\|}_{\text{F}}^{2};\] \[\mathbf{S}^{i+1} =\underset{\mathbf{S}}{\text{arg}\,\text{min}}\lambda\|\mathbf{S }\|_{1}+\frac{\mu^{i}}{2}\bigg{\|}\mathbf{S}-\bigg{(}\mathbf{D}-\mathbf{L}^{i+ 1}+\frac{\mathbf{\Lambda}_{1}^{i}}{\mu^{i}}\bigg{)}\bigg{\|}_{\text{F}}^{2};\] \[\mathbf{Z}^{i+1} =\underset{\mathbf{Z}}{\text{arg}\,\text{min}}\tau\|\mathbf{Z}\|_ {TV}+\frac{\mu^{i}}{2}\bigg{\|}\mathbf{Z}-\bigg{(}\mathbf{L}^{i+1}-\frac{\mathbf{ \Lambda}_{2}^{i}}{\mu^{i}}\bigg{)}\bigg{\|}_{\text{F}}^{2};\] \[\mathbf{\Lambda}_{1}^{i+1} =\mathbf{\Lambda}_{1}^{i}+\mu^{i}\Big{(}\mathbf{D}-\mathbf{L}^{i+1}- \mathbf{S}^{i+1}\Big{)};\] \[\mathbf{\Lambda}_{2}^{i+1} =\mathbf{\Lambda}_{2}^{i}+\mu^{i}\Big{(}\mathbf{L}^{i+1}-\mathbf{Z}^{ i+1}\Big{)};\] \[\mu^{i+1} =\text{min}\Big{(}\rho\mu^{i},\mu_{max}\Big{)};\] \[i=i+1;\] end while Output:\(\mathbf{L}^{i}\), \(\mathbf{S}^{i}\). ```  
**Algorithm 1** Solving (7) by ADMM
## 3 Simulation Results and Discussion
### Effects of Particle Orientation  
In this subsection, the influence of particle orientation varying between 0 degrees, 22.5 degrees, and 45 degrees related to the x-axis on the mechanical properties of MGMCs with a 25% particle volume fraction is studied under uniaxial compressive loading along the y-axis. Five different microstructural finite element models with a constant particle shape ratio of two are generated through the developed program for different particle orientations, respectively. It can be seen from Figure 4 that the particle orientation plays a small role on the stiffness of the MGMC, while the strength of the MGMCs increases with the increase of particle orientation. Meanwhile, when MGMCs enter the yield stages, the hardening stages increase with the increase of the particle orientation angle. Zero-degree orientation will cause biggest stress concentration among the three different orientations; thus, there is much more plastic deformation in MGMCs with zero-degree particle arrangement than those with 45 degrees, as  
Figure 3: Simulation results of model with 100 identical circular particles under uniaxial compress loading. (**A**) Nominal stress–strain relations of bulk metallic glass composite (BMGC) and bulk metallic glass (BMG). (**B**) (**a–d**) equivalent plastic strain distributions in BMG corresponding to the positions (I–IV).  
shown in Figure 5. From Figure 5, one can also see that the particles in MGMCs block the propagation of shear bands in BMG.
## 5 Precoder and Combiner Design Based on Reinforcement Learning
### State Space  
In the \(t\)-th iteration, given the environment state \(s_{t}^{i}\in\mathbb{R}^{dim_{i}^{state}}\) of the current agent \(i\), the joint state space of multiple agents at iteration step \(t\) is represented as \(\boldsymbol{S}_{t}=\left\{s_{t}^{1},s_{t}^{2},\cdots,s_{t}^{2N_{t}}\right\}\). One precoding and combining agent only obtains its own state information, which is the L2-norm value of the row vector corresponding to the \(\boldsymbol{W^{\text{T}}}\) or \(\boldsymbol{F^{\text{T}}}\) matrix according to the agent allocation situation, while the states as well as actions information of other agents are unknown. That is, the state observed by the agent \(i\) is \(\hat{s}_{t}^{i}=\{||\boldsymbol{e}_{i}\boldsymbol{W}||\}\) or \(\hat{s}_{t}^{i}=\{||\boldsymbol{e}_{i}\boldsymbol{F}||\}\).
# Impacts of Material Orthotropy on Mechanical Behaviors of Asphal Pavements
###### Abstract  
Material anisotropy significantly impacts the mechanical behaviors of asphalt pavements. However, most current asphalt pavement design methods treat the material properties only as isotropic, which could significantly skew the mechanical behaviors. There is a need to evaluate the impact of material anisotropy on pavement mechanical behaviors. In this study, we first developed a new and efficient 3-dimensional finite element (FE) model of anisotropic material. Then, the feasibility of the proposed FE model was verified using field data collected with a falling weight deflectometer. Finally, using this model, the contributions of each layer anisotropy to the mechanical properties were determined. The results showed that the mechanical behaviors were more sensitive to the orthotropy than to the transverse isotropy of the material. The all-layer orthotropy was the most unfavorable combination. In addition, the subgrade orthotropy showed the most significant effect on increasing the surface deflection and compressive strain of the subgrade top (by about 10%). Based on the study results, we recommend that the homogeneity degree of the filling subgrade should be strictly controlled to ensure adequate pavement capacity and anti-rutting performance during construction.  
s +
Footnote †: journal: Applied Sciences  
**Keywords:** asphalt pavements; material; orthotropy; finite element; mechanical behavior
# Status of BIPV and BAPV System for Less Energy-Hungry Building in India--A Review
## 2 PV Technologies for BIPV/BAPV
\begin{table}
\begin{tabular}{|l|c|c|c|} \hline \multicolumn{1}{|c|}{**Solar cell**} & \multicolumn{2}{c|}{**Advantages**} & \multicolumn{2}{c|}{**Disadvantages**} \\ \hline Polycrystalline silicon solar cells & \(\bullet\) & The production process is & \(\bullet\) & Due to low level of silicon \\
[50; 51; 52] & simpler than the & & purity, performance is only \\  & & monocrystalline cells. & & around 13–16\%. \\  & \(\bullet\) & Highly durable. & & \\ \hline Amorphous silicon solar cells & \(\bullet\) & Low manufacturing costs. & \(\bullet\) & The efficiency is typically \\
[53; 54; 55] & \(\bullet\) & The cell can be produced in & 6–8\%. \\  & & various shapes. & \(\bullet\) & They have shorter lifetime \\  & \(\bullet\) & At high temperature, & compared to other solar cells. \\  & & performance degradation is & \(\bullet\) & Required twice, the space to \\  & & lower than crystalline silicon. & get same PV power than that \\  & & & of crystalline silicon. \\ \hline CdTe solar cells [56; 57; 58; 59; 60; 61] & \(\bullet\) & Cadmium is abundant. & \(\bullet\) & The efficiency operates in the range 9–11\%. \\  & & \(\bullet\) & The manufacturing process & \(\bullet\) \\  & & is simple. & \(\bullet\) & Tellurium is not abundant. \\  & \(\bullet\) & It can absorb light of & \(\bullet\) & Cadmium telluride is toxic \\  & & shorter wavelength. & & and not \\  & & & environmentally benign. \\ \hline CIGS solar cells [52; 53; 62; 63; 64; 65; 66; 67] & \(\bullet\) & CIGS solar cells use lower & \(\bullet\) & The efficiency ranges in \\  & & levels of cadmium, in the & between 12–14\%. \\  & & form of cadmium sulphide. & \(\bullet\) & High fabrication and \\  & \(\bullet\) & CIGS solar cell substrates are & \(\bullet\) & production costs. \\  & & more versatile in comparison & \\  & & with c-Si. & & \\  & \(\bullet\) & CIGS solar panels show & & \\  & better resistance to heat & & \\  & compared to silicon & & \\  & solar cells. & & \\ \hline Organic solar cells [36; 37; 38] & \(\bullet\) & The PV modules are low & \(\bullet\) & Lifetime is short. \\  & & weight and flexible. & \(\bullet\) & Very low efficiency around \\  & & Lower production costs than & 4–5\%. \\  & & traditional inorganic & \\  & & technologies, such as silicon & \\  & & solar cells. & \\ \hline DSSC [68; 69; 70] & \(\bullet\) & It consists of low-cost & \(\bullet\) & Low efficiency around 12\%. \\  & & materials easy fabrication. & \(\bullet\) & The liquid electrolyte has \\  & & It works even in low light & temperature \\  & conditions such as the & & stability problems. \\  & cloudy weather. & \(\bullet\) & The liquid will quickly \\  & \(\bullet\) & Tunable transparency is & & dry up. \\  & possible by tuning the & \(\bullet\) & Long term stability \\  & thickness and dye. & & is questionable. \\  & \(\bullet\) & Use of flexible substrate & \\  & makes it flexible and suitable & & \\  & for BIPV. & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._
## 4 Experiments and Results  
### Data  
#### 4.1.1 Data Acquisition and Description  
We have utilized a construction cost dataset that the Public Procurement Service has been providing on the open site ([[http://pcae.g2b.go.kr/pbs/psa0000/index.do](http://pcae.g2b.go.kr/pbs/psa0000/index.do)]([http://pcae.g2b.go.kr/pbs/psa0000/index.do](http://pcae.g2b.go.kr/pbs/psa0000/index.do)) accessed on 15 November 2020) in South Korea. This site discloses the construction cost information of new construction or extension construction projects procured by the Public Procurement Service.  
Among the dataset, projects with more than KRW 30 billion are excluded because, in terms of Korea's public bidding system, the contract method for projects costing more than KRW 30 billion is complex, and the number of cases is small. As a result, the construction cost data for 775 building projects during the period 2015-2021 were utilized. Figure 2 shows the distribution of the collected cost data. We can find that the number of construction cases decreases when the construction amount exceeds KRW 20 billion. If the number of data is small, such as in a construction project of more than KRW 20 billion, the prediction accuracy may be lowered.
# Probabilistic Ship Detection and Classification Using Deep Learning
## 1 Introduction
(1) This was the first attempt to detect and classify various classes of ships in a deep learning framework. (2) The confidence from a deep learning detector was considered as a probability and their values from the consecutive images were combined over time via Bayesian fusion. (3) Missed ships were recovered using the IoU of the bounding boxes between consecutive frames. (4) Large-scale ship detection dataset has been built by collecting ship images from google image search and annotating ground-truth bounding boxes. The remainder of this paper is organized as follows: in Section 2, the background for the Faster R-CNN and the basic idea underlying this paper are outlined. In Sections 3 and 4, the details about the proposed method are explained. In Section 5, the experimental results, performance, and discussion are presented. Finally, the conclusions drawn from this study are presented in Section 6.
## 5 Discussion  
The results described in the previous section make a question appear--why the simulation _Case 1_ seems to follow more closely the behaviour of the vortex rope observed experimentally than the simulations _Case 2_ and _Case 3_, which use a refined time integration set up.  
The Root Mean Square (RMS) and the maximum RMS of the pressure equation are compared in Figure 10. The RMS reach values lower than \(10^{-3}\) for all the simulations except simulation _Case 1_ before the simulation crashes. For _Case 2_, the residuals reach values below \(10^{-5}\), which shows the role plays by the number of internal loops per time step. The maximum RMS are quite high for all simulations with some values above 0.1. Interestingly, the maximum RMS of _Case 1_ and _Case 3_ are around 0.4, but _Case 3_ does not crash.  
Figure 9: Magnitude of the Fourier transform of the pressure signal in the runner and the draft tube.  
The time history of the dimensionless mass flow \(Q^{*}=Q/Q_{inlet}\) at the inlet of the runner and at the outlet of the computational domain are compared for the three simulations in Figure 11. In the runner inlet section, upstream from the region where cavitation occurred, the mass flow is constant and equal to 1. Downstream, due to the phase change, the mass flow oscillates. The oscillation is stronger for _Case 1_ with a strong decrease of the mass flow, which is a consequence of the vapour volume growth (see Figure 5).  
By assuming a density of the vapour negligible compared to the liquid density, the 1D mass conservation equation applied for the whole computational domain writes:  
\[Q_{1}-Q_{2}=-\frac{dV}{dt} \tag{2}\]  
with \(Q_{1}\) and \(Q_{2}\), respectively, the mass flow rate at the inlet and outlet of the computational domain and \(V\) the volume of vapour. The left- and right-hand terms of Equation (2) are plotted in Figure 12 for the three cases. For _Case 2_ and 3, the mass conservation is respected contrary to _Case 1_ for which the time variation of the volume of vapour is smaller than the difference of the flow rate. This mass unbalanced seems to be responsible for the collapse of the vortex rope and the crash of the simulation. As the boundary condition are fixed, the mass flow rate unbalanced cannot be supported infinitely since it is a non-equilibrium state. Therefore, the collapse of the vortex is the only way to return to an equilibrium state.  
Figure 11: Time history of the dimensionless mass flow at the runner inlet section (left) and at the outlet of the computational domain (right).  
Figure 10: RMS (left) and maximum RMS (right) of the pressure equation.
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 5 Simulation Results
### Simulations for ITU Standards
#### 5.1.3 Ng-Gpn2  
For simulation of NG-PON2 (the activation process is described in Section 4.3), the PLOAM in-band message transmission option (i.e., a common transmission) was selected. In principle, the simulation is very similar to that of XG-PON, the only difference being the use of other types of messages. Time transmitted messages are shown in Figure 6.  
For the NG-PON2 simulation, the same equations as the XG-PON simulation were used.  
Figure 5: Visualization of the XGPON activation.