esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Annual Energy Yields  
Figure 4 shows the annual sums of the energy received on the horizontal plane and inclined surfaces with southern orientations at four sites in Greece. The sites were selected to represent each of the four PV\({}_{0}\) zones. It is seen that maximum energy was received by a solar collector inclined to the south at an angle of either \(\beta\) = 25\({}^{\circ}\) (Larisa, Souda) or \(\beta\) = 30\({}^{\circ}\) (Agrinio, Serres). Following this example, Figure 5a presents the annual energy sum, averaged over all sites corresponding to the same PV\({}_{0}\) zone and received on flat planes inclined to the south at angles from 0\({}^{\circ}\) to 60\({}^{\circ}\) in steps of 5\({}^{\circ}\). It is seen from Figure 5a \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Position of the Solar Panel**} & \multicolumn{3}{c}{**Annual Mean Energy Sum \(\mathbf{G_{e}}\) (kWhm\({}^{-2}\) year\({}^{-1}\))**} \\ \cline{2-6}  & **PV\({}_{\text{cts}}\) Zone A** & **PV\({}_{\text{cts}}\) Zone B** & **PV\({}_{\text{cts}}\) Zone C** & **PV\({}_{\text{cts}}\) Zone D** \\ \hline Inclined at \(\beta\) in the range 5–60\({}^{\circ}\) in the setups & 1900 \(<\)\(G_{e,\beta\text{S}}\) & 1850 \(<\)\(G_{e,\beta\text{S}}\)\(\leq\) 1900 & 1800 \(<\)\(G_{e,\beta\text{S}}\)\(\leq\) 1850 & \(G_{e,\beta\text{S}}\)\(\leq\) 1800 \\ \hline Horizontal, PV\({}_{0}\) classification & 1775 \(<\)\(G_{e,\beta\text{S}}\) & 1700 \(<\)\(G_{e,\beta\text{S}}\)\(\leq\) 1775 & 1625 \(<\)\(G_{e,0\text{S}}\)\(\leq\) 1700 & \(G_{e,0\text{S}}\)\(\leq\) 1625 \\ Horizontal, TOTEE classification & 1700 \(<\)\(G_{e,\beta\text{S}}\) & 1500 \(<\)\(G_{e,\beta\text{S}}\)\(\leq\) 1700 & 1450 \(<\)\(G_{e,0\text{S}}\)\(\leq\) 1600 & \(G_{e,0\text{S}}\)\(\leq\) 1500 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Range of the annual mean solar energy sums \(G_{e,\beta\text{S}}\) received by planes of various tilt angles \(\beta\), averaged across the 33 locations to define the 4 PV\({}_{\text{cts}}\) zones in Greece for solar availability under all-sky conditions. The range of the annual mean solar energy sums received on a horizontal plane \(G_{e,\beta\text{S}}\) is also given for reference. One should notice the different ranges of the four bands in the case of the received energy on a horizontal plane in the PV\({}_{0}\) and TOTEE classifications.
## 4 Experiments
### Performance Metrics  
In speech recognition systems, evaluating model performance is crucial. To ensure that the experiments achieve the expected results, we use the word error rate (\(WER\)) as the evaluation metric. \(WER\) measures the difference between the recognized words and the actual words by calculating the minimum number of operations required to substitute, delete, or insert words, and then dividing this by the total number of reference words. The specific formula is as follows:  
\[WER=\frac{S_{w}+D_{w}+I_{w}}{N_{w}} \tag{12}\]  
where \(S_{w}\) represents the number of word substitutions, \(D_{w}\) represents the number of word deletions, \(I_{w}\) represents the number of word insertions, and \(N_{w}\) represents the total number of reference words. A lower \(WER\) value indicates higher recognition accracy.  
_Accuracy_ is another commonly used evaluation metric. The higher the accuracy, the better the model performs in recognizing speech. It can be calculated using the following formula:  
\[Accuracy=\frac{N_{E}}{N_{w}} \tag{13}\]  
where \(N_{E}\) represents the number of incorrectly recognized words, which is the sum of word substitutions, deletions, and insertions. Therefore, there is a corresponding relationship between \(Accuracy\) and \(WER\):  
\[Accuracy=\frac{N_{w}-(S_{w}+D_{w}+I_{w})}{N_{w}}=1-WER \tag{14}\]
# Simulation-Based Analysis on Operational Control of Batch Processors in Wafer Fabrication
## 4 Simulation Experiments and Results
### Results from Simulation Experiments
#### 4.2.3 Performance of Look Upstream Control Rule (LKUP)  
This rule attempts to reduce the waiting time at the batch processing station by considering jobs incoming shortly. The loading with a partial batch is delayed until a new job arrives shortly if this delay is expected to lead to less waiting time. Table 2 compares the waiting time at the BP station when MBS1 and LKUP rule are utilized for BP loading, under the CONTIME job release environment. It is seen that the LKUP rule provides consistently less waiting time at the BP station than the base case regardless of BP utilization levels.  
Figure 8: Performance of different MBS levels batch sizes in the MBS control in terms of (**a**) waiting time at BP station and (**b**) system lead time.  
Production operation managers are more likely interested in such global performance measures as system lead time and throughput than the local performance measures, such as waiting time at the BP station. From Table 2, it is found that the LKUP performs better than MBS1 when the BP utilization is high. However, MBS1 and LKUP rules provide almost the same lead time with lower BP utilization around 60% or 70% (Note that in these BP utilization ranges, The LKUP performs better than MBS1.) The simulation results indicate that the locally good control strategy may not lead to good system performance. This insight into the control scheme may be especially true when the product flow has a reentrant characteristic. To examine the effect of reentrant flow on the performance, we have carried out some simulation experiments with scenarios with short serial product flow. To have a serial flow system, the original product flow requiring 52 operations is divided into five independent jobs where each job visits the bottleneck machine and BP station only once, respectively. Table 3 compares the BP waiting time and lead time for the serial and reentrant product flows. Here, the lead time for the serial product flow is multiplied by five to make a fair comparison. It is seen that the local waiting time is reduced for both reentrant and serial product flow environments with the LKUP control rule. However, the system-level lead time remains almost unchanged in the reentrant flow case, whereas the lead time is reduced in the serial product flow case.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 2 Related Works  
### Research on Cloud Storage System Architecture  
Cloud storage services adopt a master/slave structure, which is the central storage architecture. For example, the infrastructure of Google is mainly composed of four parts: the Google File System [10], the parallel data processing model Map/Reduce [11], the structured data table BigTable [12], and the distributed lock Chubby, as shown in Figure 0(a).  
In Figure 0(b), each GFS cluster is constituted by multiple chunk servers and one master. The chunk servers store the block data, and the master stores the metadata of the file system. When the system needs to access the file, it communicates with the master to obtain the chunk server information of the file and the location information of the file replica. Then, it directly communicates with the chunk server to obtain the corresponding file. Yahoo's HDFS [13], Amazon's S3 [14], Microsoft's SkyDrive, IBM's Blue Cloud, and KFS [15] also use this architecture.  
This single storage system has high performance with central storage architecture, which is suitable for providing dedicated storage services. The disadvantage is that centralization brings a single point of failure, which makes the system unable to cope with service interruptions caused by unforeseen sudden failures, and many data centers are unable to cope with failures [16]. For example, in February 2011, Gmail was unavailable to 0.02% of users due to a failure.
## 2 Mathematical Foundations
### Translation of the Acoustic Field  
Figure 1 shows the vector \(\mathbf{x}^{\prime}\), which identifies the origin of a relative coordinate system corresponding to the centre of the listener's head. The vector \(\mathbf{x}_{\text{rel}}\) defines the same point in space in the relative coordinate system as is identified by the vector \(\mathbf{x}\) in absolute coordinates.  
Figure 1: Vector \(\mathbf{x}\) is represented as \(\mathbf{x}_{\text{rel}}\) in the relative coordinate system \(\mathbf{x}^{\prime}\).  
The sound field translation operator is derived by considering two plane wave expansions of the same sound field, but centred at different points in space, specifically at the origin of the absolute and relative coordinate systems. In this case, the difference between the two plane wave expansions is given by the plane wave amplitude densities \(q(\mathbf{\hat{y}},\omega)\) and \(q_{\text{rel}}(\mathbf{\hat{y}},\omega)\), respectively. Therefore, the objective is to express one density in terms of the other. This is achieved by expanding \(\mathbf{x}_{\text{rel}}\) as \(\mathbf{x}-\mathbf{x}^{{}^{\prime}}\), leading to:  
\[q_{\text{rel}}(\mathbf{\hat{y}},\omega)=q(\mathbf{\hat{y}},\omega)e^{i\mathbf{ \hat{y}}\mathbf{x}^{{}^{\prime}}\mathbf{\hat{y}}}. \tag{2}\]  
Equation (2) indicates that \(e^{i\mathbf{\hat{y}}\mathbf{x}^{{}^{\prime}}\mathbf{\hat{y}}}\) is the translation operator for the plane wave expansion from the origin to \(\mathbf{x}^{{}^{\prime}}\). Its equivalence in the time domain can be easily found by using the shifting property of the Fourier transform:  
\[Q_{\text{rel}}(\mathbf{\hat{y}},t)=Q\left(\mathbf{\hat{y}},t-\frac{\mathbf{x} ^{{}^{\prime}}\mathbf{\hat{y}}}{c}\right). \tag{3}\]
## 5 System Equilibrium Analysis under Arbitrary Transmission Limit
### Equilibrium Analysis for S-ALOHA System  
We define the system load as \(\rho=\frac{\lambda}{L/\delta}\). When ACB is enabled, the average number of contending devices on a data channel is defined as \(s=\frac{Xp}{L/\delta}\). Let \(p=1\), i.e., disable the ACB scheme, we can have the same system balance equation as Equation (11) in [44]:  
\[\begin{array}{ll}f&=e^{-\frac{X}{L/\delta}}=e^{-s}\\ \begin{array}{ll}\frac{X}{\lambda}&=\frac{s}{\rho}=\frac{1-\left(1-e^{-s} \right)^{Q}}{e^{-s}}\end{array}\end{array} \tag{35}\]  
That is to say if the ACB is not considered, we can obtain all the system equilibrium points by solving Equation (35). Then according to [44], there may be multiple solutions and some of which are on \(s\in(1,+\infty)\). However, when ACB scheme is enabled, there must be \(s=\frac{Xp}{L/\delta}\leq 1\). Therefore, we have to discuss the system equilibrium under different system load and retransmission limit case by case.
### Bell Shapes and the Degree of Bell Shape (DoBell)  
In this work, a monotonic rise to a peak followed by a monotonic fall on a histogram (see the envelopes in Figure 1) is depicted by the term **bell shape**. We do not name such curves as \"normal distributions\" because of their asymmetry and non-smoothness. Being more appropriate than other names that simulate this shape visually, such as pyramid, triangle, and arch, \"bell\" has the symbolism of sound, suggesting musicological research.  
Monotonic trends can be tested by applying the Mann-Kendall test [22; 23; 24], which is, however, unnecessarily complicated for this study. We propose a novel feature with a simple implementation, Degree of Bell Shape (_DoBell_), manifesting the degree that a histogram approximates a bell shape with a value between 0 and 1.  
Let \(P_{max}\) denote the pitch of a song \(s\)'s highest bin in its histogram with zero bins removed, \(bin(p)\) corresponds to each pitch \(p\)'s bin height, and \(min_{\text{pitch}}(s)\) and \(max_{\text{pitch}}(s)\) represent \(s\)'s lowest and highest pitches. We define \(p\)'s monotonic trend (MT) as  
\[MT(p)=\left\{\begin{aligned} & 1,\text{if }bin(p)\leq bin(p+1)\\ & 0,\text{if }bin(p)>bin(p+1)\end{aligned}\right.\text{ if }p<P_{max} \tag{1}\]with zero bins removed and the Degree of Bell Shape of song \(s\) as  
\[DoBell(s)=\frac{\sum_{p\text{ in }[min_{\text{phch}}(s),\,max_{\text{phch}}(s)-1]}MT(p)} {max_{\text{pitch}}(s)-min_{\text{pitch}}(s)}. \tag{2}\]  
The \(DoBell\) of a V-shaped distribution generates a value near 0.5, while 1 indicates a perfect bell shape, where adjacent equivalent values do not destroy the monotonicity. In this article, a histogram is announced conforming to the bell shape only in the perfect case that its \(DoBell\) is precisely equal to 1.  
The pronunciation of the abbreviation \(DoBell\) is reminiscent of the bell-shaped pattern drawn on a doorbell, and the \"Do\" comes with a prompt for the movable do Solfgege.
## 3 Proposed Intrusion Detection System
### Auto-Encoder for Intrusion Detection  
We use the preprocessed network packet features as input to the deep auto-encoder intrusion detection. Deep auto-encoder consists of two parts, encoder and decoder. Encoder compresses the raw data into a low-dimensional representation. Decoder reconstructs the low-dimensional representation of the encoder compression. Algorithm 5 describes the flow of Auto-encoder based on on packet2 vec learning for intrusion detection  
**Encoder:**\(X=[x_{1},x_{2},\cdots,x_{n}]^{T}\) is the input data vector of encoder, \(X^{\prime}=[x^{{}^{\prime}}_{1},x^{{}^{\prime}}_{2},\cdots,x^{{}^{\prime}}_{m }]^{T}\) is the output data vector of encoder. It is worth noting that \(n\) is larger than \(m\). Encoder uses \(tanh\) as the activation function.  
\[X^{\prime}=f(x)=\tanh(WX+b). \tag{9}\]  
where \(W\) represents the encoder weight matrix with size \(m*n\) and \(b\) is a bias vector of dimensionality \(m\).  
**Decoder:** The decoder function maps the hidden representation \(X^{\prime}\) to a reconstruction \(\mathrm{Y}=[y_{1},y_{2},\ldots,y_{n}]\). Decoder uses \(tanh\) as the activation function.  
\[Y=g\left(x^{\prime}\right)=\tanh\left(W^{\prime}X^{\prime}+b^{\prime}\right). \tag{10}\]where \(W^{\prime}\) represents the decoder weight matrix with size \(n\times m\) and \(b^{\prime}\) is a bias vector of dimensionality \(n\).  
**Object function:** The training goal of deep auto-encoder is to minimize the error between input \(X\) and output \(Y\).  
\[\mathcal{L}(\theta)=\sum_{x\in D}L(x,g(f(x))), \tag{11}\]  
\[\mathcal{L}(x,y)=\|x-y\|^{2}. \tag{12}\]  
In addition, we find the optimal parameter \(\theta\) through the objective function.  
\[\theta=\left\{W,W^{\prime},b,b^{\prime}\right\}=\arg_{\theta}\min L(x,y). \tag{13}\]  
```
0: vectorVec, which contains \(q\) network packets in the network traffic, each network packet is a vector (\(vec_{i1}\),\(vec_{2}\),\(\cdots\),\(vec_{i\prime}\)).
0: Evaluate result in test dataset.
1:Step 1:Create auto-encoder model
2: Add the \(1^{st}\) encoder layer of \(l_{1}\) units whose activation function is tanh.
3: Add the \(2^{nd}\) encoder layer of \(l_{2}\) units whose activation function is tanh.
4: Add the \(3^{rd}\) dense layer of \(l_{3}\) units.
5: Add the \(4^{th}\) decoder layer of \(l_{4}\) units whose activation function is tanh.
6: Add the \(5^{th}\) decoder layer of \(l_{5}\) units whose activation function is tanh.
7:Step 3: Train model
8:while early stop condition is not met do
9:while training dataset is not empty do
10: Update weights and bias using adadelta gradient descent optimization algorithm.
11:endwhile
12:Step 4:Test model
13:Test fine-tuned hyper-parameters with test dataset.
14:return Evaluate result in test dataset.
```  
**Algorithm 5** Auto-encoder based-on packet2 vec learning for intrusion detection
## 4 Results and Discussion
### Analysis of Factors Affecting the Interaction Capability Between Asphalt and Mineral Filler
#### 4.4.3 Influence of Frequency on the Interaction Capability Between Asphalt and Mineral Filler  
Figure 13 illustrates the variation in adsorbed asphalt film thickness with experimental frequency for three types of asphalt mastics at different temperatures. As the influence of the filler-to-binder ratio is not considered in this analysis, the film thickness results at a constant filler-to-binder ratio were selected for investigation. Given that the mineral filler volume fraction of FAM, CAM, and LAM falls within the critical range at a filler-to-binder ratio of 0.4, this value was chosen to minimize the influence of inter-particle interactions on the analysis results.  
Figure 13: Variation in adsorbed asphalt film thickness with experimental frequency at different temperatures: (**a**) 28 \({}^{\circ}\)C; (**b**) 40 \({}^{\circ}\)C; (**c**) 52 \({}^{\circ}\)C.  
Figure 13 demonstrates that adsorbed asphalt film thickness varies with experimental frequency, reflecting changes in interaction capability. Thus, the interaction capability between asphalt and mineral filler is frequency-dependent. Figure 13a,b indicate that at 28 \({}^{\circ}\)C and 40 \({}^{\circ}\)C, adsorbed asphalt film thickness decreases as frequency increases. This suggests that at lower temperatures, increasing frequency weakens the interaction capability between asphalt and mineral filler. As frequency increases, the shear modulus of asphalt and asphalt maskic rises, causing asphalt to gradually transition from viscoelastic to elastic behavior [44]. This transition reduces the wetting ability of asphalt, thereby weakening its interaction with the mineral filler. Figure 13c reveals that at 52 \({}^{\circ}\)C, adsorbed asphalt film thickness increases with frequency. Since this temperature surpasses the asphalt softening point (48 \({}^{\circ}\)C), the material transitions from a semi-solid to a viscous flow state. At this stage, the wetting effect of asphalt outweighs the influence of frequency, enhancing its interaction with the mineral filler.
## 3 Wound Healing Phases  
The wound healing process has been widely discussed in the literature; in brief, four stages (Figure 3) have been identified in the wound healing process, known as hemostasis, inflammation, proliferation, and remodeling [26]. After injury of the lymphatic vessels, leading to hemorrhage, blood clotting occurs through the clotting cascade and thrombocyte aggregation by exposed collagen during the hemostasis phase [27]. Simultaneously, platelets decrease blood flow via vasoconstriction, reducing the gaps between tissue points [1,28]. The formed clot is composed of growth factors, fibrin, fibronectin, fibroblasts, and other molecules that create a matrix. The inflammation phase, occurring in two to five phases, is important, since it disinfects the wound with the emergence of neutrophils. These neutrophils have the ability to phagocytize and release proteases (e.g., elastase), which help to remove any debris or residues and break down the bacteria. Moreover, to enhance the inflammatory phase, neutrophils produce mediators (TNF-\(\alpha\), IL-1, and IL-6) that trigger VEGF and IL-8. Furthermore, the presence of macrophages stimulates inflammation, eliminates apoptosis, and encourages cell growth and tissue healing [29]. During the inflammatory phase, edema, erythema, and pain are common symptoms. The next stage, proliferation, is the most significant phase, lasting from six to twenty-one days [30]. During this phase, the wound heals by the formation of new collagen and extracellular matrix tissue. As days pass, the wound reduces in size and new tissues develop. New blood vessels are required so that the granulation tissue receives oxygen and nutrients. Moreover, fibroblasts differentiate into myofiboblasts, connecting the margins of the wound, giving the granulation tissue a pink to red appearance. Additionally, the wound is resurfaced by epithelial cells, with healing accelerated by the presence of moisture. Finally, tissue restoration can occur from collagen [31]. Tissue remodeling--the last phase of the wound healing process--can continue for months or even years, despite the fact that clinicians consider wound closure as the end of wound healing [32]. The remodeling phase process determines scarring and wound recurrence; this phase involves neovascular regression, extracellular matrix deposition, and the transformation of granulation tissue into scar tissue. Although collagen type III comprises the granulation tissue, a stronger type, collagen I, replaces it [33]. The scar tissue and duration of healing at this stage depend on many factors, such as the location of the wound, possible microbial colonization, and severity and treatment. In most cases, the new tissue gradually obtains mechanical strength and flexibility. However, sometimes wounds cannot completely heal, and, therefore, treatment is quite crucial. The use of active molecules and proper dressings can enhance the wound healing process, reducing hospitalizations and incidents such as bacteremia, fungemia, and even death [34].
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 5 Improved V2I/N Link Reliability with Proposed Collaborative RSU for Resource Management
### BS-Type RSU with 3D Beamforming
The benefits of the 3D beamforming calculated through the analysis can be further enhanced by applying it to RSUs of the proposed hybrid RSU scheme, in conjunction with resource management techniques, such as dynamic ICIC and CS-CoMP, as previously suggested. Figure 5: Three\(-\)dimensional beamforming antenna pattern.
## 3 Results
### Multivariate Classification Analyses
Week 2 samples were not included in the statistical analysis comparing Weeks 1 and 3, shown in Figure 4d,e. Next, the team undertook iLDA to identify a single panel capable of distinguishing all three sample classes of interest (Control, Cancer Week 1 and Cancer Week 3). iLDA was undertaken and applied on all VOCs that were differentially expressed for all sample comparisons (including VOCs only significant in Week 3 of Cancer). LDA utilizing five compounds (damascenone, 1,3,5-trichlorobenzene (TCHB), linalool, DTBP and 2-hexanone (2-HEX)) led to accurate classification of all three sample classes (the Third, LDA Model). Linalool is a linear monoterpenoid and damascenone an isoprenoid lipid that are not listed in Table S1 because although statistically significant for Control/Week 3 classification, they were not univariately significant for either All Cancer/Control or the Cancer Week 1/Week 3 comparison. Using this third LDA model, Cancer samples were distinguished from Control samples with an AUC equal to 0.98, sensitivity = 100% and specificity = 95% (LOOCV AUC = 0.97 and fivefold cross validation = 0.95). Alternatively, Cancer Week 1 was classified from Week 3 with AUC = 0.99, sensitivity = 100% and specificity = 92% (LOOCV AUC = 0.97 and fivefold cross validation AUC = 0.97). The ROC curves can be seen in Figure 4g,h, while the two-dimensional LDA plot can be observed in Figure 4i. Urine samples collected from the Week 2 cohort were tested using this classification model and the LDA plot is shown in Figure S1. Week 2 samples are 100% distinguished from Control samples and again cluster in between Cancer Week 1 and Cancer Week 3. However, many of the mouse urine samples were classified as either Cancer Week 1 or Cancer  
Figure 3: (**a**) PCA using all VOCs with \(p<0.05\) between all Cancer samples and Control samples (44 VOCs). This panel can distinguish Cancer Weeks 1\(-\)3 from Control with high accuracy. (**b**) PCA using 10 VOCs separating Cancer Week 1, Cancer Week 3 and Control samples. This smaller panel of VOCs can separate these classes with good accuracy. Week 3, showing that some of the mice in Cancer Week 2 had tumors that may have progressed faster.