esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Validation and Results Analysis
### Simulation with Different Initial SOCs  
In actual application, it cannot guarantee the battery is always fully charged when the trip begins. Here, the proposed method is extended to consider different initial SOC values. The UDDS cycle,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Drive Cycle**} & \multicolumn{2}{c}{**CPU Time (s)**} \\ \cline{2-3}  & **DP Algorithm** & **Convex Algorithm** \\ \hline
9 HWFET & 170.5 & 3.1 \\
8 HWFET & 151.9 & 2.8 \\
7 HWFET & 133.1 & 2.5 \\
6 HWFET & 114.5 & 2.5 \\
9 NEDC & 227.9 & 7.0 \\
8 NEDC & 189.9 & 5.5 \\
7 NEDC & 176.7 & 4.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computation time comparison with standard initial SOC.  
Figure 13: (**a**) Engine efficiency comparison between the convex programming-based strategy and the strategy based on DP; (**b**) Engine efficiency comparison between charging depleting–charging sustaining (CD/CS) strategy and the convex programming-based method.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Drive Cycle**} & \multicolumn{2}{c}{**CD/CS Algorithm**} & \multicolumn{3}{c}{**DP Algorithm**} & \multicolumn{3}{c}{**Convex Algorithm**} \\ \cline{2-9}  & **F (kg)** & **Ending** & **SOC** & **Ending** & **Savings** & **F (kg)** & **Ending** & **Savings** \\ \hline
9 HWFET & 3.7004 & 0.2767 & 3.4980 & 0.3031 & 6.82 & 3.5030 & 0.2986 & 6.45 \\
8 HWFET & 3.1666 & 0.2767 & 2.9817 & 0.3027 & 7.39 & 2.9934 & 0.2995 & 6.83 \\
7 HWFET & 2.6328 & 0.2767 & 2.4719 & 0.3022 & 7.94 & 2.4768 & 0.2930 & 7.10 \\
6 HWFET & 2.0990 & 0.2767 & 1.9655 & 0.3017 & 8.62 & 1.9656 & 0.2994 & 8.40 \\
9 NEDC & 1.9803 & 0.2923 & 1.8449 & 0.3115 & 8.67 & 1.8486 & 0.3116 & 8.49 \\
8 NEDC & 1.6325 & 0.2923 & 1.4687 & 0.2772 & 8.29 & 1.5187 & 0.3034 & 8.26 \\
7 NEDC & 1.2847 & 0.2923 & 1.2059 & 0.3103 & 9.91 & 1.1910 & 0.3060 & 9.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results comparison with standard initial SOC. CD/CS: charging depleting–charging sustaining; DP: dynamic programming; HWFET: Highway Fuel Economy Driving Schedule; NEDC: New European Driving Cycle.
# Probabilistically-Shaped DMT for IM-DD Systems with Low-Complexity Fast WHT-Based PDSP
## 2 Operation Principle  
### The Principle of PS Technique  
The diagram for the PS technique is schematically plotted in Figure 1. At the transmitter, the upper (or lower) data sequence is separated into two bit streams, which contain and \(V_{2}\) bits, respectively. The upper (or lower) data sequence is shaped to non-uniform distributed symbols (\(U_{1}\)) through CCDM, and its corresponding rate can be expressed as \(R_{DM}=V_{1}/U_{1}\). In this scheme, the non-uniform distribution \(P_{\mathbf{X}}\) is applied and can be expressed as [20]  
\[P_{X}(x)=\left.e^{-\lambda|x^{2}|}\right/\sum_{x\in X}e^{-\lambda|x^{2}|} \tag{1}\]  
where \(\lambda\) is a rate parameter and these shaped symbols (\(M\)-PAM) can be expressed as \(\chi=\{\pm 1,\ \pm 3,\ \ldots\pm(M-1)\}\). In this work, \(M\) is chosen to 8. Subsequently, these shaped symbols are mapped into binary bit sequences with labeling by gray mapping and encoded by low-density parity-check (LDPC) code with the DVB-S.2 standard. Note that a bit-level interleaver is applied in our work to further improve the performance of FEC coding [15]. In the following, 2 one-dimensional \(M\)-PAM symbols are mapped in \(I/Q\) parts, respectively, to generate a two-dimension \(M^{2}\)-QAM symbol. Thus the information rate (IR) of PS-enabled 64-QAM (bits /QAM symbols), \(R_{PS}\), can be expressed as  
\[R_{PS}=2(1+R_{DM}-m(1-R_{C})) \tag{2}\]  
where \(R_{C}\) is the rate of LDPC code and \(m=\log_{2}M\).  
At the receiver, the corresponding inverse operations for the PS technique are also shown in Figure 1. It should be mentioned that the generalized mutual information (GMI) under bit-metric decoding (BMD) is estimated by using log-likelihood ratios (LLRs) [7]. Assuming that \(N\)-points samples are obtained with the Monte Carlo simulation method, the \(GMI\) for \(I/Q\) parts can be written as [21]  
\[GMI_{I(Q)}\approx\frac{1}{N}\sum_{k=1}^{N}\left(-\log_{2}P_{X}(x_{k})\right)- \frac{1}{N}\sum_{k=1}^{N}\sum_{i=1}^{m}\left(\log_{2}\left(1+e^{(-1)b_{ij}L_{ kj}}\right)\right) \tag{3}\]  
where \(b_{kj}\) and \(L_{k,j}\) represent the input bit and output bit, respectively. According to Equation (3), \(GMI_{I}\) and \(GMI_{Q}\) have the same calculation method, and the total \(GMI\) for the \(M^{2}\)-QAM symbol equals to \(GMI_{I}+GMI_{Q}\).
# Dynamic SDN Controller Load Balancing
## 4 DCC Problem Formulation
### Notations  
We consider a control plane \(C\) with \(M\) controllers, denoted by \(C=\{C_{1},C_{2},...,\ C_{M}\}\) where \(C_{i}\) is a single controller. We assume that the processing power of each controller is the same and equal to \(P\), which stands for the number of requests per second that it can handle. Let \(\ d_{ij}\) be the distance (number of hops) between \(C_{i}\) and \(C_{j}\). We denote by \(G_{i}\) the \(i_{th}\) cluster and by \(G=\{G_{1},G_{2},...,\ G_{K}\}\), the set of all clusters. We assume that \(\frac{M}{K}\) is an integer and is actually the number of controllers per cluster. Thus, the size of the CV is \(\frac{M}{K}\), i.e., we assume that each cluster consists of the same number of controllers. \(Y\) denotes a matrix, handled by SC, which consists of the matching of each controller to a single cluster. Each column of \(Y\) represents a cluster and each row a controller.  
Figure 2 shows an example of such a matrix \(Y(9X3)\) corresponding to nine controllers split into three clusters. On the left part we see \(Y\) matrix before re-clustering process and on the right after it is completed. Thanks to \(Y\) one can know which controller is in which cluster as follows. If a controller is included in a cluster then in the corresponding row of the controller and corresponding column of the cluster the value will be 1, otherwise it will be 0. For instance, we see that before re-clustering, controller number 5 is in cluster b. After re-clustering (on the right matrix), we see that controller 5 is no longer in b but has been moved to cluster c.  
Figure 1: Three-tier control plane.  
Therefore, \(Y\) is a binary \(MXK\) matrix as follows:  
\[Y(t)_{ij}=\left\{\begin{array}{cc}0&C_{j}\in G_{i}\\ 1&\text{else}\end{array}\right\} \tag{1}\]  
\[\forall_{1\leq j\leq M}\ \sum_{i=1}^{K}Y(t)_{ij}=1\quad\text{and}\quad\forall_{1 \leq j\leq K}\ \sum_{i=1}^{M}Y(t)_{ij}=K \tag{2}\]  
The load of controller \(j\) in time slot \(t\) is denoted \(l\left(t\right)_{j}\). This information arrives to the SC from the controllers. Its value is the average of requests per second from all the switches associated to the controller, in time slot \(t\). \(CVL_{i}\) denotes the Cluster Vector Load of \(Master_{i}\). The SC contains the addresses of the masters for each cycle in the Master Vector (MV). Table 2 summarizes the key notations for ease of reference.
## 5 Evaluation
### Comparison with Existing Detection Methods  
As we have now calculated all the relevant metrics, it is possible to assess the effectiveness of a large language model on its ability to identify a document as either benign or malicious if it gets passed as much available information about the document as possible. Even though one could now compare those results with commonly used antivirus software like Bittdefender or Microsoft Defender, the relevance of such a comparison is questionable. This lies in the fact that the approach presented here is very much different in the use case and methodology. Commercial antivirus solutions typically report detection rates exceeding 99%. This is due to the large quantity of classified malware samples gathered from the wild that were used for training. However, their performance on novel malware exploitation techniques with strategies implemented to evade detection is not thoroughly explored, though it is expected to underperform on those samples. Similarly, YARA rules seem to have an F1-score of about 0.791, according to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] et al. [49]. As of today, large language models are not capable of completely substituting the aforementioned AVs and established detection methods, but rather complement them and assist users in understanding the potential threat with a textual explanation rather than a simple binary action, such as block or pass. It further helps security analysts to accelerate their procedures. An overview of the use cases can be found in the concluding Section 6.1. Moreover, the sample set employed in this study is insufficiently large to allow for a meaningful comparison with other approaches. The one false negative and one false positive could alternatively be regarded as outliers, resulting in all metrics reaching 100%. Regardless of the exact numbers, the evidence suggests that large language models are generally capable of identifying the nature of an OOXML document.
# Time-Dependent Alternative Route Planning: Theory and Practice +
## 2 Preliminaries
### Computing Time-Dependent Shortest Paths
#### 2.1.2 Reversed Time-Dependent Dijkstra  
The reversed version of TDD (RTDD) grows a full shortest path tree rooted at a node \(d\) for a given arrival time \(t_{d}\) at \(d\). The differences from the original (forward) TDD are the following:  
1. The arc relaxations are performed for the incoming arcs of the currently settled node.
2. The algorithm computes latest departure times from the arc tails \"on the fly\" during an arc-relaxation, by evaluating the inverse of the arc-arrival-time function (which is strictly increasing due to the strict FIFO property).
## 3 Results  
### Clsm  
Both pristine M and MRS rendered minimal autofluorescence signal, same as samples immersed in BHI, without receiving any further treatment. Contaminated untreated MRS discs rendered more fluorescence compared to their machined counterpart. All treated surfaces showed significant reduction in fluorescence signal compared to untreated surfaces, regardless of the treatment provided.  
Regarding M samples, overall reduced mean fluorescence reported exceeded 70%. The percentages of the disc surfaces free from the biofilm were for group 6, 71.6% (SD \(\pm\) 5.4%); for group 2, 78.8% (SD \(\pm\) 1.0%); for group 1, 80.5% (SD \(\pm\) 0.9%); for group 5, 83.4% (SD \(\pm\) 3.5%); for group 7, 86.8% (SD \(\pm\) 1.8%); for group 4, 88.6% (SD \(\pm\) 3.9%); and for group 3, 87.2% (SD \(\pm\) 1.8%). Group 6 was found to be significantly less effective than distilled water and all the other tested powders. Group 3 was significantly more efficacious than groups 2 (_p_-Value 0.0395) and 6 (_p_-Value < 0.0001). In addition, group 4 was noticed to be significantly more active than groups 2 (_p_-Value 0.0121) and 6 (_p_-Value < 0.0001). Mean percentages of remaining bacteria are represented in Figures 3 and 4.  
As for the MRS sample, overall reduced mean fluorescence reported exceeded 80%. Respectively, the remaining biofilm decreased by 81.7% (SD \(\pm\) 7.8%) for group 5; by 83% (SD \(\pm\) 7.3%) for group 4, by 84.8% (SD \(\pm\) 3.4%) for group 1; by 86% (SD \(\pm\) 0.9%) for distilled water; by 86.1% (SD \(\pm\) 5.3%) for group 3; by 88.2% (SD \(\pm\) 1.9%) for group 2; and by 89.3% (SD \(\pm\) 1.3%) for group 6 without statistically significant differences among any of the tested treatment modalities. Mean percentages of remaining biofilm are represented in Figures 5 and 6.  
Figure 4: Autofluorescence of bacterial biofilm on M discs before and after treatment with air abrasive unit delivering different powders: (**A**) pristine disc; (**B**) contaminated untreated disc; (**C**) group 7; (**D**) group 1; (**E**) group 2; (**F**) group 6; (**G**) group 3; (**H**) group 4; (**I**) group 5; (**J**) group 4 with boxes for fluorescence measurement; (**K**) diagram showing disc with fluorescence measurement rectangles. Magnification 4\(\times\) and rectangle area of 11.5 mm\({}^{2}\).  
Figure 5: Percentage of remaining biofilm after decontamination of titanium discs with a moderately rough surface (MRS) by means of air abrasive unit delivering different powders.
## 3 Results and Discussion
### Optimization of Fermentation Process of Green Tea Wine  
Fermented tea wine taste is mostly catalyzed by yeast cells, thus factors affecting yeast fermentation activity can cause significant differences in tea wine taste. Therefore, this research investigated the effect of fermentation conditions on tea wine taste in different factors, such as yeast inoculum, initial sugar level, and fermentation temperature, and then determined the optimal fermentation process. As shown in Figure 6, the optimal fermentation conditions were determined by the single-factor experiments as 1.5% inoculum, 30 \({}^{\circ}\)Brix initial sugar level, and 25 \({}^{\circ}\)C fermentation temperature. The green tea wine quality obtained from fermentation under optimal conditions was 14.4% volume alcohol, 26.23% polyphenols and 8.7 \({}^{\circ}\)Brix sugar, respectively.  
Figure 6: Optimization of fermentation conditions for green tea wine. Yeast inoculums (**A**), initial sugar (**B**), temperature (**C**). Columnar, Tea polyphenols; Red square-shaped, Residual sugar; Blue square-shaped, Alcohol.  
Temperature is one of the most important factors affecting the growth and metabolic efficiency of yeast. Within the optimal temperature range, the growth rate and metabolic capacity of yeast are positively correlated with temperature. When the temperature is higher than optimal temperature, yeast cells find it difficult to resist the extreme environment and irreversible damage occurs, leading to the decline of cell function or cell death [33; 34]. Inoculum is a factor that affects the growth rate of microorganisms, and low inoculum will prolong the lag period. Within a certain range, an appropriate increase in the inoculum amount can promote the growth and metabolism of microorganisms. However, higher inoculum is not better, and too high inoculum will lead to an increase in secondary metabolites and premature death of microorganisms [35; 36]. Yeast needs nutrients to grow, and tea juice is extremely low in carbon and nitrogen, failing to match the growth needs of the yeast growth. Sugar source acts as precursor for alcohol synthesis and determine the alcoholic yield, while providing a carbon source for yeast growth and facilitating the fermentation process [37].
## Abbreviations  
The following acronyms are used in this manuscript:  
\begin{tabular}{l l} \(\alpha\) & _aBaseSuperframeDuration_ \\ \(\delta_{i}\) & Enough time to transmit the message \(M_{i}^{k}\) \\ \(\eta\) & Number of messages generated by message streams located below of \(CH_{j}\) \\ \(\gamma_{i}\) & Initial delay for message stream \(S_{i}\) \\ \(\sigma_{i}\) & Time interval immediately lower than the transmission time for one message \(M_{i}^{k}\) \\ \(\Theta\) & Interference imposed by highest priority message streams \\ \(b\) & Maximum number of backoffs \\ \(M_{i}^{k}\) & K-th message of Message Stream i generated by node \(N_{n}\) \\ \(N_{nodes}\) & Number of sensor nodes \\ \(n_{CH}\) & Number of child cluster-heads \\ \(N_{CH}\) & Number of cluster-heads \\ \(n_{l}\) & Number of leaf child nodes \\ \(\mathbb{P}_{c}\) & Probability of a node to perform two consecutive CCA analysis \\ \(\mathbb{P}_{co}\) & Collision probability of two nodes \\ \(\mathbb{P}_{s}\) & Probability of a node to perform a CCA with the maximum backoff number \\ \(q\) & Probability that a node transmits a single data at any time of the CAP \\ \(r\) & Average backoff number \\ \(R_{i}\) & Worst-case response time for message stream \(S_{i}\) \\ \(\mathbb{S}\) & Set of Message Streams \\ \(S_{below}^{j}\) & Set of message streams located in the descendant nodes of \(CH_{j}\) \\ \(S_{i}\) & Message Stream \(i\) \\ \(T_{BO}\) & Average backoff time \\ \(T_{ROL}\) & Backoff period length \\ \(T_{BOT}\) & Total backoff time \\ \(T_{radio\_transition}\) & Time duration that the radio takes to switch between the different operation modes \\ \(T_{TXD}\) & Total transmission time for a single data frame \\ \(U\) & Total Effective Utilisation \\ \(W\) & Local worst-case response time \\ \end{tabular}
## 3 Results
### Evaluation Method and Performance Metric  
We performed a 4-fold cross validation on the 120 volumes to assess the average performance of our algorithm. We hypothesized that the proposed algorithm can result in improved localization and training because of its utilization of the feature similarity between the two folds. Therefore, we use two metrics to validate our proposed contributions: (i) training efficiency (number of episodes explored until convergence) and (ii) localization performance (localization error and accuracy). Localization error is measured by the Euclidean distance of the localized landmark from the expert annotation. Localization accuracy is measured by the percentage of acceptable localization results as evaluated by the expert.  
For comparison, we also applied the standard RL-based localization [11] (with the individual training method) as our main baseline method. Furthermore, we applied the widely used deep learning based end-to-end localization approaches, e.g., direct location/coordinate regression and heatmap regression [7], to compare the localization performance.
## 3 Framework Development
### Adjustment of Dispersion Parameter (\(\beta_{\text{ds}}\)) Due to Corrosion  
As part of the overall procedure for integrating corrosion effects into seismic fragility assessment, this step focuses on adjusting the dispersion parameter to account for ad ditional uncertainties introduced by corrosion. In the development of seismic fragility curves, the dispersion parameter \(\beta_{\text{ds}}\) helps to capture the uncertainty associated with the structural response to seismic events. To accurately reflect the impact of corrosion on structural capacity, it is proposed to adjust this parameter as well, in order to consider the additional uncertainty that was caused by the corrosion process and this analysis. The adjustment of \(\beta_{\text{ds}}\) in this process is aligned and based on the principles outlined in FEMA P-58 [77], which emphasizes the importance of considering uncertainties in seismic performance assessments to ensure a more comprehensive and realistic evaluation of structural vulnerability.  
The uncertainty in the structural response is heightened due to the reliance on previous studies, which included experimental research with inherent variability in material properties. These studies form the basis for the simulation models used in this research, and the variability in their results must be accounted for in the fragility analysis. In addition, corrosion leads to variability in structural performance due to several factors, including non-uniform degradation of materials and differences in construction quality. These factors contribute to increased uncertainty in the structural response, which is typically not captured by the baseline fragility curve parameters.  
To incorporate this additional uncertainty, the adjusted dispersion parameter \(\beta_{\text{ds}}^{\prime}\) is calculated using the root-sum-square (RSS) method, combining the baseline \(\beta_{\text{ds}}\) with the additional uncertainty attributed to corrosion \(\beta_{\text{cor}}\).  
\[\beta_{\text{ds}}^{\prime}=\sqrt{{\beta_{\text{ds}}}^{2}+{\beta_{\text{cor}}} ^{2}} \tag{22}\]  
Here, \(\beta_{\text{ds}}\) represents the original dispersion derived from the baseline fragility analysis, while \(\beta_{\text{cor}}\) represents the uncertainty introduced by the corrosion process. The resulting \(\beta_{\text{ds}}^{\prime}\) is then applied in the fragility curve equations to provide a more accurate representation of the seismic vulnerability of corroded structures. This adjustment ensures that the fragility curves account for the increased variability in structural performance, leading to a more realistic assessment of seismic risk.  
It should be noted that the adjustment of \(\beta_{\text{ds}}\) is not mandatory, but it is suggested to account for the increased variability introduced by the proposed corrosion analysis. The value of \(\beta_{\text{cor}}\) represents the additional uncertainty introduced by the corrosion process. A standard value of 0.25 is recommended as suitable for most cases. However, if a more detailed analysis is required, the suggested ranges are elaborated in Table 2.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 8 The Future of CMCs  
The literature review on the available CMCs and the manufacturing processes that can be used for their fabrication can be used for a better insight into the future of such materials for aero-engine applications. Based on this literature review, a technology roadmap is provided in this section. CMC material properties allow them to be the solution in a variety of today's material challenges in process industries, aerospace, power generation, transportation, and for military applications. These types of applications are essential in obtaining global competitiveness, minimizing environmental pollution, and decreasing overall energy consumption [234]. CMC prototypes and trial products become available to create test methods, classification systems, and unified terminology as well as reference materials for aero-engine applications, but the lack of design codes and their related databases have been limiting factors in obtaining approval to use more extensively CMCs in both rotary and stationary Aero-Engine applications [235].  
Every step in Figure 18 requires significant effort and time, enabling full consensus to be developed for reaching maturity to allow characterizing the developed CMC. Continuous databases need to be created to produce property and performance data using acceptable standards. A wide variety of different information levels must be assigned in the database to proceed to the creation of design codes and life prediction models to be used in the unique design requirements and applications. After the design codes and life expectancy models are determined, regulatory approval is achieved and can be used in aero-engine applications.  
There are a few steps that, according to research, are required to take place for the CMC material to meet the expectations of end-users to develop a  
Figure 18: Interrelation of standards, databases, life expectancy models, and design codes of CMCs.  
specific application [234]. This is achieved by creating an RD&D approach, which considers these steps at the same time to achieve the CMC's commercial success.  
The development process steps (Figure 19), in addition to the R&D approach, are interrelated with the material database, producing data to use in material selection and providing analytical support design and life prediction models. Furthermore, the design and life prediction models improve the methods and analytical tools, providing greater efficient component design. After the design and life prediction models, the non-destructive evaluation is an improvement method allowing for low-cost component inspection and quality assurance. When the component passes the non-destructive evaluation, the fabrication optimization method takes place, increasing material reliability, manufacturing scale-up, and cost reduction. Finally, the demonstration stage takes place, verifying the CMC component viability with long-life testing in the final work environment of the product.