esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application in Determination of Rock-Mass Properties
### Mohr-Coulomb Parameters  
Mohr-Coulomb model is an elastoplastic model based on the Mohr-Coulomb failure criterion and is the most common model in the context of geomaterials. When using this model, it is essential to estimate the \(c_{m}\) and the \(\hat{\mathscr{G}}_{m}\). [PERSON] correlated the rock-mass class with corresponding \(c_{m}\) and \(\hat{\mathscr{G}}_{m}\) in a discrete manner [44]. This discrete approach is used in literature for the calculation of the cohesion and internal friction angle [78; 113; 114]. The correlation between the \(\sigma_{cm}\) and the \(\hat{\mathscr{G}}_{m}\) were made through the best-fit analysis, which shows that the \(\hat{\mathscr{G}}_{m}\) tends to converge to a value of about \(50^{\circ}\) as \(\sigma_{cm}\) increases [111]. As the _RMR_ and \(Q\) values are used for calculation of \(\sigma_{cm}\), these systems can be used indirectly for the calculation of the \(\hat{\mathscr{G}}_{m}\) using Equation (45).  
\[\phi_{m}=20\times c_{cm}^{0.25} \tag{45}\]  
Later, a direct relation between the rock-mass internal friction angle and the _RMR_ value was developed [115], as shown in Equation (46), taking the intact rock friction angle (\(\hat{\mathscr{G}}_{i}\)) into consideration:  
\[\frac{\phi_{m}}{\phi_{i}}=0.3+0.7\times\frac{RMR_{89}}{RMR_{89}+\beta(100-RMR_ {89})}. \tag{46}\]  
\(\beta\) is a constant and its value varies between 0.1 and 3. The revised value for \(\beta\) is 1.0 and Equation (46) is simplified to Equation (47) [112].  
\[\frac{\phi_{m}}{\phi_{i}}=0.3+0.7\times\frac{RMR_{89}}{100} \tag{47}\]  
From the \(\sigma_{cm}\) and \(\hat{\mathscr{G}}_{m}\), [PERSON] and [PERSON] suggested Equation (48) for the calculation of rock-mass cohesion [116] as cited by [117].  
\[c_{m}=\frac{\sigma_{cm}}{2}\times\frac{1-\sin\phi_{m}}{\cos\phi_{m}} \tag{48}\]  
As _RMR_ and \(Q\) values are used for the estimation of \(\sigma_{cm}\) and \(\hat{\mathscr{G}}_{m}\), Equation (48) indirectly uses the empirical classification systems for the rock-mass cohesion (\(c_{m}\)). Later, a direct relation between \(c_{m}\) and the _RMR_ value was developed and, according to this relation, the \(c_{m}\) is a function of intact rock cohesion (\(c_{i}\)) and _RMR_[112], as expressed in Equation (49).  
\[c_{m}=c_{i}\times\frac{RMR_{89}}{RMR_{89}+6(100-RMR_{89})} \tag{49}\]  
[PERSON] and [PERSON] also suggested a continuous system for the calculation of cohesion and the frictional angle of rock masses [18] using Equations (50) and (51).  
\[c_{m}=3.625\times RMR_{73} \tag{50}\]  
\[\phi_{m}=25(1+0.01 RMR_{73}),\text{ RMR}_{73}\ \geq\ 20 \tag{51a}\]  
\[\phi_{m}=1.5\times RMR_{73},\text{ RMR}_{73}\ \leq 20 \tag{51b}\][PERSON] split the modified \(Q\) system from Equation (9) into two components and designated them the \(c_{m}\) and \(\hat{\mathcal{P}}_{m}\)[24], as shown in Equations (52) and (53), respectively.  
\[c_{m}=\bigg{(}\frac{RQD}{J_{n}}\times\frac{1}{SRF}\times\frac{\sigma_{ci}}{100} \bigg{)} \tag{52}\]  
\[\phi_{m}=\tan^{-1}\bigg{(}\frac{J_{r}}{J_{a}}\times\frac{J_{w}}{1}\bigg{)} \tag{53}\]
## 2 Background and Motivation
### Comparison of RDMA Technology and TCP
In contrast to traditional DMA's internal bus I/O, RDMA can remotely transfer data directly between application software at two endpoints over a network; Compared with traditional network transmission, RDMA can easily achieve ultra-low latency and ultra-high throughput transmission between remote nodes without the intervention of the operating system and protocol stack, and basically does not require CPU resource participation, and in a high-speed network environment, it is no longer necessary to consume too many system resources for the processing and repeated copying of network data. In short, these three characteristics of RDMA, i.e., CPU offloading, kernel bypass, and zero copy [32], make its advantages self-evident, and the transmission performance it can achieve far exceeds that of the traditional TCP, which, indeed, become a major tool for improving distributed file systems. In summary, using RDMA communication instead of the original TCP can avoid the time delay caused by frequent data copying, thereby improving the performance of the distributed file system.
## 2 Materials and Methods
### Methods  
#### 2.3.1 CASA Model  
The CASA model belongs to the LUE models. It has been widely used due to its good biophysical basis and simplicity of calculation [4]. It works by taking two variables, including absorbed photosynthetically active radiation (APAR) and a light use efficiency factor (\(\epsilon\)), as the main drivers of the model. Additionally, the model considers the influence of water and temperature stress on vegetation [46; 47]. The relevant equations are as follows:  
\[NPP=APAR\times\epsilon \tag{1}\]  
where \(NPP\) denotes the net primary productivity (g C/m\({}^{2}\)/month); \(APAR\) denotes the absorbed photosynthetically active radiation (MJ/m\({}^{2}\)/month); and \(\epsilon\) denotes the actual light use efficiency (g C/MJ).  
\[APAR=SOL\times FPAR\times 0.5 \tag{2}\]  
\[\epsilon=T_{\epsilon 1}\times T_{\epsilon 2}\times W_{\epsilon}\times\epsilon_{max} \tag{3}\]  
where \(SOL\) denotes the total solar radiation (MJ/m\({}^{2}\)/month); \(FPAR\) is the fraction of incident photosynthetically active solar radiation absorbed by the vegetation canopy; and the constant 0.5 denotes the fraction of available active solar radiation (from wavelengths between 0.4 and 0.7 \(\upmu\)m) from the total solar radiation utilized by the vegetation. \(T_{\epsilon 1}\) and \(T_{\epsilon 2}\) denote the stress effect of low and high temperatures on light use efficiency; \(W_{\epsilon}\) is the water stress effect coefficient; \(\epsilon_{max}\) is the maximum light use efficiency under ideal conditions (g C/MJ).
# The Energy Efficiency of Heterogeneous Cellular Networks Based on the Poisson Hole Process
## 5 Simulation Results
From the simulation results, it can be seen that the quadratic interpolation algorithm has fewer iterations and faster convergence speed. When \(\alpha=3.5\), the optimal transmit power allocations of the PBS corresponding to the two cellular networks are 7.60 W and 7.63 W, respectively. When \(\alpha=4\), the optimal transmit power allocations of the PBS corresponding to the two cellular networks are 6.44 W and 6.46 W, respectively. In the PPP-PPP HetNets, for the two cases \(\alpha=3.5\) and \(\alpha=4\), when the PBS transmit power is optimal, the EE values of HetNets are 1.3% and 2.9% higher than when the PBS transmit power is optimal. Figure 4: Energy efficiency \(\eta_{EE}\) versus the PBSs transmit power \(\mu_{p}\) under different \(\alpha\). Figure 3: Coverage probability versus SIR. power is fixed at 10 W, respectively. In the PPP-PHP HetNets, for \(\alpha=3.5\) and \(\alpha=4\), when the PBS transmit power is the optimal value, the EE values of HetNets are 1.25% and 2.8% higher than when the PBS transmit power is a fixed value of 10W, respectively. Therefore, the EE of HetNets can be improved by setting an appropriate PBS transmit power allocation. Whether \(\alpha=3.5\) or \(\alpha=4\), the EE of the two-tier PPP-PHP HetNets is higher than that of the PPP-PPP network.
## 3 Materials and Methods
### Evaluation Metrics and Feedback Adjustment Strategies
#### 3.4.2 Constraint-Based Feedback Adjustment  
In addition to the reward-based feedback, constraint-based feedback is used to ensure that the optimization process adheres to the defined sustainability constraints. If the system violates any constraints, the weights of the violating objectives are adjusted accordingly to mitigate the problem:  
\[w_{1}(t)=w_{1}(t-1)-\alpha\cdot\Delta C_{1}(t) \tag{15}\]  
where \(w_{1}(t)\) is the updated weight for the environmental objective at time \(t\), \(w_{1}(t-1)\) is the previous weight, \(\alpha\) is a learning rate that determines how quickly the system adapts the weight, and \(\Delta C_{1}(t)\) is the change in the environmental constraint violation over time.
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 4 Hardware Design  
This paper implements the adaptive controller and performance monitor, which are the key modules of the adaptive reconfigurable cache scheme, by synchronous finite state machines (FSM) using Verilog HDL.  
The state transition diagram of the adaptive controller is shown in Figure 9a. The cache is initialized to 16-way set-associative (maximum associativity) after the arrival of the RST_N signal. The state machine starts with the PROFILE state to wait for statistics from the performance monitor. After the duration of PERIOD1 (corresponding to reconfiguration period), the state transfers to DECISION. The adaptive controller determines the current optimal associativity. We compare whether the current optimal associativity is consistent with the last optimal associativity: The state transfers to CONFIG when inconsistent, the cache is controlled to tune associativity, and dirty blocks are written back after PERIOD2 (corresponding to the reconfiguration overhead). After the cache flush operation is completed, the state transfers to STABLE with the current optimal associativity. Then, the state returns to PROFILE. If the current and last associativity are consistent, the state returns to NCONFIG without any operation, returning to the PROFILE state after PERIOD2 + PERIOD3.  
The performance monitor is implemented by counters, and the implementation of the high-performance counters is beyond the scope of this paper. Figure 9b shows the state transition diagram of the performance monitor. Counters are reset to 0 after the arrival of RST_N. The state machine starts with the COLLECT state, collecting application run time information. Then, the state transfers to the CLEAR state after PERIOD1. Run time information is stored to related registers, and counters are cleared to 0. After PERIOD2, the state transfers to RE-COLLECT to collect information during the stable-running period, which is used to evaluate the reconfiguration effect. Counters are reset to 0, and the state machine returns to the COLLECT state at the end of the stable-running period.
## 1 Introduction
The work presented in [20] solves this problem in a scenario-based stochastic unit commitment (SUC) formulation, which uses a number of possible scenarios to cover a credible range of the future wind power availability. However, it is time-consuming to run the crude Monte Carlo simulation (MCS) for all input scenarios, especially when high computation accuracy is needed. Robust optimization is employed in [21] solely for the worst-case scenario, which may lead to highly conservative results that are far from reality. Another critical concern of the committed generation reserve is whether it is deliverable when needed. When the generation reserve is undeliverable due to the binding power flow constraints, operators may resort to wind curtailment and load shedding [22]. To address this issue, a risk-based constraint could be embedded in each line based on the line flows calculated via the probabilistic optimal power flow (P-OPF) [23; 24]. Reference [25] proposes a power system partitioning method to commit the reserves locally in the accrued zones by minimizing the risk of intra-zonal transmission congestion. In the resent study [26], transmission reserves have been introduced and have served as a complementary tool in case of generation reserve undeliverability. In this paper, with the goal of limiting the risk of uncertainty-induced transmission congestion and reserve undeliverability, we propose a risk-averse predictive method for dynamic transmission reserves in a SUC framework. The proposed model is formulated as a bi-level optimization which can be decomposed into day-ahead unit commitment (UC) and probabilistic real-time redispatch. The redispatch problem in the lower level is solved to capture the statistical features of line flows using a high-dimensional probabilistic collocation method (PCM) with correlation control. The results are then used to quantify the predictive transmission reserve requirements as a constraint of UC in the upper level. In summary, the main contributions of our work can be outlined as follows:  
* The risk of transmission congestion is formulated in the form of predictive transmission reserves, which is incorporated in the SUC framework to improve the operational flexibility and capability of facing unexpected wind power realizations. * The transmission reserve requirements are dynamically quantified in the UC based on the probabilistic real-time line flow information, rather than using the deterministic way of assigning a predefined portion of total transmission capacity. * The statistical features of line flows in response to the multi-correlated wind power uncertainty are systematically characterized in a probabilistic redispatch problem using the high-dimensional PCM, which avoids the complexity induced by numerous scenarios in the traditional MCS-based approaches.
###### Abstract  
In this study, nanocomposites of spherical gold nanoparticles (AuNPs) and graphene oxide (GO) nanosheets were fabricated by a simple one-step reduction method. The characterisation by UV-visible spectroscopy of the plasmonic sensing properties pointed out to a strong interaction between graphene and metal nanoparticles in the hybrid GO-AuNP, as confirmed by nuclear magnetic resonance. Moreover, atomic force microscopy analyses demonstrated that the gold nanoparticles were mostly confined to the basal planes of the GO sheets. The response of the nanoassemblies at the biointerface with human neuroblastoma SH-SYSY cell line was investigated in terms of nanotoxicity as well as of total and mitochondrial reactive oxygen species production. Confocal microscopy imaging of cellular internalization highlighted the promising potentialities of GO-AuNP nanoplatforms for theranostic (i.e., sensing/imaging + therapy) applications.  
Keywords:theranostics; Au nanoparticles; GO; neuroblastoma cells; NMR; AFM; nanotoxicity; ROS; confocal microscopy +
Footnote †: journal: Applied Sciences
## 2 Power Converter Principle  
The HPLDDC system configuration is depicted in Figure 2, including the CVOM and CCOM controllers, a DC-DC convertor, and safety standard components. The on-board battery supplies a DC-power to the HPLDDC inlet; the HPLDDC outlet connects three HPLDs in series. To protect the SBBC, CVOM, and CCOM controllers, the SBBC input side must place safety standard components because voltage impulses and spikes or EMI/EMC noises from the battery power bus can be filtered out. In addition, the DC-DC converter topology is the SBBC; an SBBC circuit is shown in Figure 3; this circuit composition includes an output capacitor, \(C_{o}\), power switches, \(Q_{1}\)-\(Q_{4}\), and an inductor, \(L_{1}\). Principles and analyses in the buck and boost modes are discussed in the following.  
Figure 2: High-power laser diode driving controller (HPLDDC) system configuration.
## 3 Our Approach
### Delay-Resilient Remote Memory Attestation  
When the remote device is far away from the verifier, the end-to-end delay will be significantly large. This makes the verifier have to increase the iterations of the verification procedure in order to make an accurate decision. In order to eliminate the impact of network delay, we develop a delay-resilient remote memory attestation scheme, which can evaluate the real-time network delay by using the time differences reported by relay nodes in the process of the \"challenge-response\" protocol.  
As shown in Figure 4, delay-resilient remote memory attestation (DRMA) consists of the following steps:  
* Step 1: challenge generation: The verifier sends a random challenge message to the remote device;
* Step 2: challenge transmission: The relay node forwards the challenge message to the destination node and records the time when the challenge message is received by them;
* Step 3: checksum generation: The remote device computes the checksum according to the challenge issued by the verifier and returns the response to the verifier;* Step 4: checksum transmission: The relay node forwards the checksum from the remote device to the verifier, records the time when the response message is received by the relay nodes and then reports the time difference between the challenge and the response to the verifier;
* Step 5: checksum verification: The verifier separately computes the corresponding checksum and verifies the correctness of the checksum returned by the remote device. If checksums are the same, the verifier will continue to verify the time for computing;
* Step 6: determination: The verifier computes the network delay using the time differences reported by relay nodes and compares the response time with the normal one. In this way, the remote device can determine whether it is compromised or not. DRMA obtains the computing time of the remote device with the consideration of the network delay, in order to achieve greater accuracy for the detection.  
In the following subsections, we describe those steps in detail.
## 1 Introduction
To overcome this shortcoming, the stochastic or probabilistic branch of network calculus employs statistical multiplexing of independent traffic flows to smooth out burstiness with a high probability. The stochastic network calculus allows the derivation of performance bounds with a certain violation probability. Having derived the performance bounds for a single-hop network, the earlier approaches of deriving end-to-end performance bounds for a multi-hop network involved using an iterative application to add up per-stage delay bounds and concatenating probabilistic service curves. Most of these approaches resulted in loose bounds, and the bounds decreased with the number of stages involved. Easy scaling of stages and the derivation of tighter end-to-end performance bounds for concatenated systems are critical aspects of multi-hop networks. We provide a system theoretic formulation of the probabilistic network calculus with moment-generating functions (MGFs) for general \((\sigma(\theta),\rho(\theta))\)-traffic models, allowing us to estimate multi-hop performance without assuming anything beyond MGFs' existence and their statistical independence. The contributions of this work are as follows:  
1. We derived closed-form, end-to-end, probabilistic performance bounds for PDV using MGFs and (min, +) algebra. The limited literature search showed that applying MGFs to derive bounds for PDV is a novel approach. The analytical expression for PDV can be easily scaled to analyze the performance of multi-hop networks. 2. The delay and PDV analytical expressions were derived using a general traffic model to describe through (of interest) and cross (background) traffic. A wide variety of traffic models can replace the general traffic model. Thus, PDV computation can easily be extended for arrival processes such as periodic, fluid, on-off, and regulated sources. 3. The analysis based on simulations showed how different network parameters impact end-to-end PDV. The analysis also revealed that, if certain parameters are controlled in a systematic manner, the background traffic can be shaped in a way that results in bounded PDV for the desired traffic. This knowledge can be utilized to shape the background traffic so that PDV can be kept in the desired range by controlling the network parameters. The paper is organized as follows. First, the related work is presented in Section 2. Section 3 describes the preliminaries of network calculus. Section 4 establishes end-to-end probabilistic bounds for delay and PDV, and Section 5 evaluates the performance based on simulations. Conclusions are provided at the end.
## 4 Methodology
### Implementation of LET Plus TDMA in Multicore Systems  
In a multicore real-time system, write/read operations can be synchronized to the channel using any notification mechanism, enabling data exchange between cores independently of the task-executing algorithms and at a fixed rate. This synchronization allows for predicting data flow behavior based on this model. In real hardware, timing depends on memory cache times and the selected synchronization mechanism (e.g., ISRs (Interrupt Service Routines), OS task, or specific hardware). Despite these dependencies, the minimal latency between operations can be calculated and further optimized.  
In this work, we develop a method to address these challenges by combining the predictability and data consistency of the LET model with the composability of TDMA. This implementation is shown in Figure 7 and is described as follows:  
1. Determine the TDMA time intervals for communication between tasks on different cores that require sharing information. Assign specific time windows during which a task can transmit data through the communication channel.
2. Assign an execution period to each task (LET), defining when the task should start its execution and when its results must be available. Additionally, set and fix the reading and writing times statically, allowing the system to behave predictably, as each task's operations on shared memory have a defined execution period.
3. Coordinate and plan the TDMA intervals with the LET execution times of the tasks to ensure that communication occurs without conflicts, thereby enabling communication within the TDMA intervals without interference.
4. Implement mechanisms to synchronize the LET task groups with the same LET periods with their corresponding TDMA slots, maintaining the execution of tasks within the TDMA and LET processes.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
## 1 Introduction
By emphasizing important spatial regions and feature channels, CBAM enhances the precision of segmentation and ensures better boundary delineation. Importantly, CBAM achieves these improvements with minimal computational overhead. The remainder of the paper is structured as follows: The state-of-the-art of sun glare detection and segmentation is briefly introduced in Section 2. Section 3 describes the proposed architecture called \"GCBAM-UNet\". The dataset, implementation details, experiments, and a comparison of the findings are introduced in Section 4. The discussion is introduced in Section 5. Finally, we conclude the paper in Section 6.