esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Quantifying the Burden on Nodes
By observing Figure 19, a possible workaround is to determine the instantaneous burden on a node from the combination of its current and previous buffer states. However, there is no guarantee that the previous buffer states would be obtained at points in time when the buffer would be able to reflect the actual relative burden on the node. These results also make it easier to imagine how inferring node burden from buffer occupancy would be a problem in a scenario where gateways are located at bus stops, for instance. Devices carried by bus drivers would then be able to encounter more destinations directly and free up their buffers. Without a steady flow of incoming messages, the buffer occupancy on such nodes may drop at a relatively higher rate, thereby rendering buffer occupancy momentarily inaccurate for determining the relative burden on them. Their number of transmissions (i.e., the number of messages sent and received on behalf of other nodes), on the other hand, better reflect their relative burden. This is because transmissions account for a considerable amount of energy consumed in routing. Figure 20 shows the cumulative number of transmissions for Node 114 and Node 177. At any point in time, the relative number of transmissions on both nodes corresponds to their relative energy consumption (cf., Figure 18). This correlation is the idea behind our proposed ReBurD mechanism. ReBurD is oblivious of buffer information, and is able to overcome related drawbacks by considering the number of transmissions within a time frame. As shown in Figure 21, ReBurD's relative burden on Node 177 does not exceed that on Node 114, even when their buffer occupancies suggest otherwise. These results correspond to the energy consumed on both nodes (cf., Figure 18) and their number of transmissions (cf., Figure 20). ReBurD also takes into account the possibility that node resources may have been restored after periods of inactivity. In other words, the computed burden decreases with reducing participation in routing, and vice versa. This explains the intersection of the ReBurD's relative burden on both nodes around the 73 rd hour, during which the burden on Node 114 continues to fall, since it is experiencing a reasonable period of inactivity--the assumption is that the device may have been recharged during this period. The burden on Node 177 on the other hand, starts to rise (cf., Figure 21) in response to incoming messages (cf., Figure 18), and equals that on Node 114, which is still inactive. Another possible event in real-life scenarios where ReBurD's dynamicity comes to play is when a node newly joins the network. In that case, it would take a very long time for newcomer's number of transmissions to catch up with that of nodes that have been in the network, even if it is a popular node. With our proposed ReBurD mechanism, the burden on the newcomer would to be comparable with the others in the next reset interval of the transmission counters. Figure 20: Cumulative number of transmissions for Node 114 and Node 177.
## 2 Framework Design of Gateway
### Gateway: Functional Requirements Analysis
#### 2.1.4 Back-Up Function to Switch from Ethernet to GSM Messaging  
As indicated above, the gateway has the capacity to communicate via Ethernet or GSM messaging. If the physical communication network (Ethernet) between the remote server and the target area fails, the system should be able to automatically execute communication failover (CF) and switch to a GSM-based communication model to ensure continued monitoring.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 2 Surveying TDMA-Based V2V Communications
### Analytical Models of Medium Usage  
The authors of [3] model analytically the probability of successful packet delivery [ratio] (PDR) in IEEE 802.11p scenarios, as a function of the number of nodes and size of contention window. PDR is dependent on two phenomena: channel collisions and dropped packets (i.e., packets not transmitted during the CCH slot). As the number of nodes increases so do collisions, leading to a degradation of PDR; and while increasing the contention window should alleviate this problem of nodes, it is observed that it also leads to a drop in PDR, as the nodes are not able to seize the CCH channel during its slot due to expiry time. In [4], the authors propose analytical models of the CSMA/CA performance for periodic broadcasting in IEEE 802.11p scenarios. The authors also report PDR as a function of the number of nodes, for a scenario in which all nodes are in range of each other. The PDR decreases linearly until a tipping point, when it degrades sharply. Collisions are the main responsible for packet losses up to the tipping point; for more nodes, the increasing probability of a packet being dropped (as nodes are unable to access the medium) is the main reason for the sharp decline in PDR. The authors of [18] produce models of packet collision probability and average contention delay for a distributed coordination mechanism (CIDC) that uses deterministic backoff values, computed as a function of the contention intensity. Considerable reduction of collisions and delay are shown through the analytical framework and simulations.  
While most works on VANETs performance develop analytical formulations of network performance, we take the approach of exploring simulations to produce and fit models over empirical data. Most analytical formulations produce nominal performance bounds that are often not observed in practice, while empirical models can produce fitting models that are closer to reality. This is further stressed by our option to present the performance metrics as a function of the number of RF neighbours, a metric that we do not control directly, that is hard to model analytically, and that is practical, as it is a medium-state metric easily available to any IEEE 802.11p node.
## 3 Network Architecture and Topology
### Decision Making Subsystem  
The Decision Making subsystem (DMS) is in charge of planning the necessary actions in order to achieve the objectives of the system. The tasks which are assigned to this subsystem can be divided into three different groups. The first of them comprises tasks aimed at data storage and preprocessing. It deals with the huge amount of data which arrives at the subsystem, filtering and storing only relevant information and subsequently accessing to it. The second group handles traffic information from different sources and processes it according to the aim of the application. Finally, the third group of tasks is responsible for addressing control commands as well as for managing the network.  
The DMS can be executed at different levels. In a top level it can be implemented at centralized TMCs. This implies that all data gathered by the sensing subsystem is sent, via the Distribution subsystem, to the DMS, which must support an asymmetric data flow. The main advantage of this approach is the possibility of performing complex calculations over a great amount of information. Conversely, if only simple processing is to be applied, the DMS can be distributed among the sensor nodes. This enables performing simple collaborative algorithms between neighbor nodes which allow the execution of real-time traffic safety applications. Finally, another solution is the use of smart devices (smartphones, _etc._) in vehicles, which may receive raw data from road networks and use them, for example, to plan routes.
## 4 CFD Optimisation
### General Verification  
In the following table, the pressure drop in the different sections is compared, calculated analytically and by CFD. This serves to verify the theoretical results obtained by two different methods. The results can be seen in Table 2 numerically or in Figure 14 graphically.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Section** & \multicolumn{2}{c}{**Analytical Pressure Drop**} & \multicolumn{2}{c}{**CFD Pressure Drop**} & \multicolumn{1}{c}{**Difference\%**} \\  & **Pa** & **\%** & **Pa** & **\%** & **\%** & \\ \hline Settling chamber & 0.1 & 0.0 & 0.1 & 0.0 & — \\ Honeycomb & 4.6 & 0.8 & 4.6 \({}^{\star}\) & 0.8 & — \\ Mesh screen & 25.9 & 4.3 & 25.9 \({}^{\star}\) & 4.3 & — \\ Contraction & 0.1 & 0.0 & — & 0.0 & — \\ Test section & 75.0 & 12.4 & 95.0 & 16.1 & +26.6 \\ Offuser with corners & 407.1 & 67.5 & 407.5 & 69.3 & +0.1 \\ Adaptation to fan diffuser & 21.6 & 3.6 & 19.9 & 3.4 & \(-8.1\) \\ Fan and mesh & 9.0 & 1.5 & 9.1 \({}^{\star}\) & 0.1 & +1.4 \\ Exit & 60.0 & 10.0 & 26.3 & 4.5 & \(-56.2\) \\ Total & 603.4 & 100.0 & 588.3 & 100.0 & \(-2.5\) \\ \hline \hline \end{tabular}  
* This pressure drop was not calculated by CFD, so the value obtained by analytical calculations has been used instead.  
\end{table}
Table 2: Pressure loss comparison between analytic model and CFD calculations.  
Figure 14: Drop of pressure along the IWT.  
Figure 13: CFD simulation of the IWT with boundaries and inflation detail.
# NeuChain+: A Sharding Permissioned Blockchain System with Ordering-Free Consensus
## 4 Deterministic Transaction Execution  
### Read Sets Transmission  
For cross-shard transactions, some of them cannot be executed directly locally but require the read set from remote shards. We denote such transactions as transactions with remote read-write dependencies. This requires the read sets transmission between shards. For state sharding, this communication overhead is inevitable. A straightforward idea is to send a request to the required shard when such a transaction is executed, requesting the required read set. For example, there is transaction \(T\) that updates a record in shard \(S_{1}\), and the update depends on a read in shard \(S_{2}\). When the block server in \(S_{1}\) executes \(T\), it will send a read request to \(S_{2}\). After receiving the request, \(S_{2}\) will return directly if \(T\) has been executed. Otherwise, it will wait for \(T\) to be executed and generate a read set to return.  
In NeuChain+, when the block server executes a transaction with remote read-write dependencies, it caches its _tid_ and the corresponding remote shard, waits for a batch of transactions to process, and then sends a request to each block server in the remote shard. After receiving the request, if all transactions in the batch complete execution, it will directly package all read sets into a batch and return it. If executions have not been completed, it needs to block and wait until the executions are completed. After receiving the read set, it wakes up the pending transaction to complete execution.
## 3 Non-Thermal Technologies in Winemaking Process
### Pulsed Electric Field (PEF) Effects on Microbial Populations, Phenolic Extraction, and Wine Quality
However, PEF treatment notably increased the extraction of aroma compounds (esters, alcohols, and aldehydes) from wood chips, resulting in the enhanced overall sensory quality of wines. In summary, most of the recently available studies deal with the ability of PEF technology to inactivate spoilage microorganisms in wine, but its effectiveness has also been recognized in the field of extraction of valuable components by enhancing mass transfer, shortening maceration time and reducing SO2 during winemaking while maintaining/improving wine quality and sensory characteristics. Several reviews have already summarized the recent advances and applications of PEF technology to improve the extraction of various phenolic compounds during winemaking [79; 84; 85]. Many of the conclusions from these reports are confirmed by the new findings presented here. In addition, the working properties of PEF, such as continuous flow, constant temperature, and very short treatment time, are suitable for grape/wine processing. Moreover, the potential of this technology was recognized by the OIV in 2020, which passed a special Resolution OENO 634-2020 [77] on the application of PEF on red and white destemmed and crushed grapes to improve the extraction of phenolic and aroma compounds (precursors), yeast-available nitrogen, and other components contained inside the grape cells, as well as to reduce maceration process. However, despite numerous reports on the effectiveness of PEF in improving the microbial stability of musts and wines, the OIV resolution does not refer to the application of the PEF system for microbial control in wineries. Furthermore, the potential of combining PEF with other new technologies to improve wine quality and safety was emphasized and recommended as a further direction for the development of this technology [9; 11; 12].
## 3 Communication Performance Analysis of Hybrid FSO/RF System
### Outage Probability  
Outage probability is the probability when the end-to-end output SNR of the communication system is lower than a specific threshold \(\gamma_{th}\)[45]. Therefore, the OP of the hybrid system can be expressed as:  
\[P_{out}=\Pr(\gamma^{MRC}<\gamma_{th})=\int_{0}^{\gamma_{th}}f_{\gamma^{MRC}}( \gamma)d\gamma=F_{\gamma^{MRC}}(\gamma_{th}). \tag{11}\]  
By substituting Equation (7) into Equation (11), the OP of the hybrid system can be rewritten as:  
\[P_{out}^{hy}=A_{1}\sum_{k=1}^{\beta}c_{k}\sum_{i=0}^{\infty}C_{1}\gamma_{th}^ {D_{1}}G_{r+2,3\gamma+2}^{3\gamma,2}\bigg{(}E_{1}\gamma_{th}\bigg{|}\begin{array} []{c}1-D_{1},1,\kappa_{1}\\ \kappa_{2},1-D_{1},-D_{1}\end{array}\bigg{)} \tag{12}\]  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Binary Modulation Scheme** & **Detection Type** & \(\delta\) & \(p\) & \(q_{t}\) & \(n\) \\ \hline OOK & DD & 1 & 1/2 & 1/2 & 1 \\ \hline M-PSK & HD & \(\frac{2}{\max(\log_{2}M,2)}\) & 1/2 & \(\sin^{2}\bigg{(}\frac{(2t-1)\pi}{M}\bigg{)}\) & \(\max\bigg{(}\frac{M}{4},1\bigg{)}\) \\ \hline M-QAM & HD & \(\frac{4}{\log_{2}M}\bigg{(}1-\frac{1}{\sqrt{M}}\bigg{)}\) & 1/2 & \(\frac{3(2t-1)^{2}}{2(M-1)}\) & \(\frac{\sqrt{M}}{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for different modulation schemes.  
According to the definition of outage probability, the OP of the only FSO system can be directly given by substituting Equation (2) into Equation (11).
# Detection of Sub-Aroma Threshold Concentrations of Wine Methoxyprazines by Multidimensional GCMS
## 2 Materials and Methods
### Method Optimization
#### 2.3.4 Second Dimension Heart-Cutting MDGC-MS  
The second dimension separation utilized the work of [PERSON] et al. [10] for the target flow rates and column selection. An Rtx-5 ms (5% diphenyl 95% dimethyl polysiloxane) 30 m \(\times\) 0.25 mm ID \(\times\) 0.25 \(\upmu\)m film thickness (Restek, Bellefonte, PA, USA) was connected to the outflow of the Dean's style switch in the first dimension GC oven via a short transfer line held at 62 \({}^{\circ}\)C extending into the oven of a Shimadzu QP2010 Ultra GCMS (Shimadzu NZ, Ltd. Auckland, New Zealand), where it was connected to the mass spectrometer detector. The ion source and transfer line in the mass spectrometer were held at 200 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. The column oven was programmed to increase the initial temperature of 35 \({}^{\circ}\)C (30 min hold) to 90 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and hold for 4.67 min before a final increase to 250 \({}^{\circ}\)C at 50 \({}^{\circ}\)C/min with a final hold time of 5.8 min for a total run time of 62 min. The column flow rate in the second dimension was initially 1.69 mL/min, however as the pressure increased this slowed to a rate of 1.3 mL/min at 25 min and was maintained at this flow rate throughout the remaining run time. The pressure applied to the Dean's style switch was also programmed to ensure 100% of the column outflow could be cut to the second dimension at any time. This was therefore increased from an initial pressure of 92 kPa to a final pressure of 163 kPa. There were two heart-cuts made for the three MPs, the first occurred at 30.50-32.00 min and the second at 35.25-38.40 min. The MS was operated in SCAN mode to elucidate the retention times of the desired compounds and to ensure that no interfering peaks were present, prior to switching to selective ion monitoring (SIM) mode for the final proposed methodology (Table 1). The second dimension elution times for the three MPs were 43.78 min (IPMP), 47.74 min (SBMP) and 48.38 min (IBMP), respectively. The two deuterated internal standards eluted at 43.69 min (d\({}_{3}\)-IPMP) and 48.29 min (d\({}_{3}\) IBMP).
_applied_  
_sciences_  
_Article_  
**Dual-Mode Control Scheme to Improve Light Load Efficiency**  
**for Dual Active Bridge DC-DC Converters Using**  
**Single-Phase-Shift Control**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\)**, [PERSON] \({}^{2}\) and [PERSON] \({}^{2,*}\)\({}^{\copyright}\)**  
\({}^{1}\) Department of Power Mechanical Engineering, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwan  
\({}^{2}\) Department of Vehicle Engineering, National Formosa University, No. 64, Wunhua Rd., Huwei Township, Yunlin County 632, Taiwan  
\({}^{*}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +886-5-631-5692  
**Abstract:** In vehicle-to-grid (V2G) applications, dual active bridge (DAB) converters are commonly used as the power interface because they offer high efficiency, galvanic isolation, and bidirectional power flow. For the DAB control strategy, phase-shift control is the mainstream, especially the single-phase-shift (SPS) method because of its ease of implementation. However, due to the phase shift, a DAB converter operated under this control method has relatively high backflow power, resulting in poor efficiency. The SPS control method has the drawback of high backflow power, especially at light loads. Thus, this paper proposes a new dual-mode control scheme to improve the light load efficiency of DAB converters by taking advantage of the pulse-width modulation (PWM) strategy in combination with the conventional SPS strategy for DAB converters based on load conditions. In other words, when the DAB converter operates under light load conditions, the PWM control strategy is used to avoid considerable backflow power. A prototype DAB converter with a power rating of 1 kW under a switching frequency of 100 kHz interfacing a DC bus (400 V) and a battery pack (50 V) is designed and implemented to verify the feasibility of this control strategy. A detailed analysis of the working principle and design parameters of the proposed converter is provided in this paper. Experimental results show that the highest efficiency of the proposed converter at light loads (10-200 W) was 96.2% for the forward power conversion and 97.3% for the backward power conversion.  
dual-mode control; dual active bridge converter; single-phase-shift pulse-width modulation
# Early Ransomware Detection with Deep Learning Models
## 5 Experimental Evaluation
### Metrics  
These indicators represent different outcomes for binary-classification model predictions:  
* TPs (true positives) are the correct predictions of the positive class (ransomware).
* TNs (true negatives) are the correct predictions for the negative class (benign processes).
* FPs (false positives) are the incorrect predictions for the negative class (ransomware).
* FNs (false negatives) are the incorrect predictions for the positive class (benign processes). Accuracy is the overall proportion of correctly classified instances [59]: \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\] We utilized the following metrics in our evaluation. Sensitivity (or recall) assessed the models' ability to correctly identify positive predictions (actual ransomware activities). In contrast, specificity measured their effectiveness in correctly classifying negative predictions (non-ransomware activities) [60]:  
\[\text{Sensitivity} =\frac{\text{TP}}{\text{TP}+\text{FN}}\] \[\text{Specificity} =\frac{\text{TN}}{\text{TN}+\text{FP}}\] Precision measured the accuracy of positive predictions: \[\text{Precision} =\frac{\text{TP}}{\text{TP}+\text{FP}}\] F1 score combines precision and sensitivity into a single metric [61], offering a balanced measure of model performance: \[\text{F1 Score}=2\times\frac{\text{Precision}\times\text{Sensitivity}}{ \text{Precision}+\text{Sensitivity}}\] We also measured execution times (in seconds) to get a better understanding of the models' performance: * Test time measured the time it took for the models to evaluate all samples in the test set, and * training time measured the duration required for the models to complete training on the entire training dataset, allowing us to assess the computational resources needed for model training.
## 1 Introduction
### Contributions of the Work  
1. Adaptive control method for permanent magnet synchronous motors based on superhelical sliding mode control: A novel adaptive control system for permanent magnet synchronous motors, employing super-helical sliding mode control, is proposed. This system exhibits superior performance in terms of response speed, robustness, and steady-state behavior when compared to conventional control systems.
2. Optimization of adaptive control parameters for permanent magnet synchronous motors: Two optimization algorithms, NSGA II and MOPSO, are utilized to optimize the parameters of the established system. Simulation results indicate a notable decrease in rotational speed and torque overshooting of permanent magnet synchronous motors under both optimization algorithms, highlighting significant optimization outcomes.
3. Partial Sample Shannon Entropy Evaluation: To facilitate a comprehensive comparison of torque performance improvement under different optimization algorithms, this paper introduces the Shannon entropy-based torque evaluation strategy, PSSEE. This strategy effectively evaluates the motor's output torque, revealing torque data accumulation through conversion into a standard fractional \(Z\)-curve. The optimization algorithm with the best performance is screened on the basis of torque analysis, which effectively improves the robustness of the motor.
# Damage and Failure in a Statistical Crack Model
## 2 Statistical Crack Models
### Failure  
BCM also uses the concept of damage to determine when the computational cell has failed. From the viewpoint of ensuring mesh independence (an assumption of BCM), failure must depend on a dimensionless moment of the crack size distribution in Equation (7); any other moment would involve a length scale that would have to be compared to the computational cell size \(DX\). A first-order effective modulus theory puts no limit on crack growth, but a second-order theory with self-consistent field corrections produces percolation thresholds that may be interpreted as failure [2]. However, those thresholds predict values of damage at failure much larger than are seen experimentally [5; 6].