esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cancer Multicellular Aggregates and LSFM  
In order to assess the accuracy of volume instances estimated with _ReViSP_, _ReViMS_, _MITK_, and _OpenSegSPIM_, we used the three cancer multicellular _aggregates_ already considered in [12]. These multicellular _aggregates_ were generated using A549 cells (lung adenocarcinoma human cell line, American Type Culture Collection, Rockville, MD, USA). A549 cells were cultured growing in adhesion in [PERSON]'s modification of [PERSON]'s F-12 medium (F12K, ATCC), supplemented with 10% fetal bovine serum (FBS, EuroClone, Milan, Italy), 1% penicillin/streptomycin (GE Healthcare, Milan, Italy) and 2% ampho-tericin B (Euroclone). A549 cells were expanded and maintained as a monolayer in incubator at 5% CO\({}_{2}\) humidified atmosphere at 37 \({}^{\circ}\)C and checked periodically for mycoplasma contamination using the MycoAlertTM Mycoplasma Detection Kit (Lonza, Basel, Switzerland). Today, many systems are available for generating 3D _cell cultures_[1]. They can be divided into the scaffold-free and scaffold-based platforms [24]. The A549 _aggregates_ used in this work were generated with a scaffold-free rotatory cell culture system, the RCCS-8 DQ bioreactor (Synthecon Inc., Houston, TX, USA) [25]. The rotator bases were put in a humidified 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) incubator, and connected to power supplies installed on the exterior side of the incubator. All activities were performed in sterile conditions. A single cell suspension of 10\({}^{6}\) cells/mL was placed in a 50-mL bioreactor rotating chamber at an initial speed of 12 rpm. The speed was increased over time to avoid aggregate sedimentation within the culture vessels. The culture medium was refreshed every two days. After five days, the _aggregates_ reached an approximate diameter of 300 \(\upmu\)m and were thus transferred (one _aggregate_/well) to sterile 96-well low-attachment culture plates (Corning Inc., Corning, NY, USA), with each well previously filled with 100 \(\upmu\)l of fresh culture medium. The plates were incubated at 5% CO\({}_{2}\) humidified atmosphere at 37 \({}^{\circ}\)C. The A549 _aggregates_ were then fixed using a glutaraldehyde solution, which provides a homogeneous and stable auto-fluorescent signal in the green range of wavelengths, useful to clearly visualise the 3D surface of the _aggregates_. Finally, the _aggregates_ were imaged using a Zeiss Lightsheet Z.1 with a filter at 561 \(\upmu\)m and a 20\(\times\)/1.0 NA detection optics and two-sided 10\(\times\)/0.2 NA illumination optics. The \(z\)-stacks were acquired by considering the sampling Nyquist criterion to compute the \(z\) distance between subsequent frames, and finally obtaining \(z\)-stacks of around 500, 1920 \(\times\) 1920 pixel resolution, 8-bit grey level images. Precisely, for Multicellular Aggregate 1 (hereafter, MA1) 262 sections were acquired, 492 sections for MA2, and 525 for MA3. Figure 2 shows the 3D representation of MA1, MA2, and MA3. It is worth noting that MA2 is characterised by a spherical shape, practically, it is a _spheroid_.
# Rotavirus A Genome Segments Show Distinct Segregation and Codon Usage Patterns
###### Abstract  
Reassortment of the Rotavirus A (RVA) 11-segment dsRNA genome may generate new genome constellations that allow RVA to expand its host range or evade immune responses. Reassortment may also produce phylogenetic incongruities and weakly linked evolutionary histories across the 11 segments, obscuring reassortment-specific epistasis and changes in substitution rates. To determine the co-segregation patterns of RVA segments, we generated time-scaled phylogenetic trees for each of the 11 segments of 789 complete RVA genomes isolated from mammalian hosts and compared the segments' geodesic distances. We found that segments 4 (VP4) and 9 (VP7) occupied significantly different tree spaces from each other and from the rest of the genome. By contrast, segments 10 and 11 (NSP4 and NSP5/6) occupied nearly indistinguishable tree spaces, suggesting strong co-segregation. Host-species barriers appeared to vary by segment, with segment 9 (VP7) presenting the weakest association with host species. Bayesian Skyride plots were generated for each segment to compare relative genetic diversity among segments over time. All segments showed a dramatic decrease in diversity around 2007 coinciding with the introduction of RVA vaccines. To assess selection pressures, codon adaptation indices and relative codon deoptimization indices were calculated with respect to different host genomes. Codon usage varied by segment with segment 11 (NSP5) exhibiting significantly higher adaptation to host genomes. Furthermore, RVA codon usage patterns appeared optimized for expression in humans and birds relative to the other hosts examined, suggesting that translational efficiency is not a barrier in RVA zoonosis.  
Keywords:reassortment; phylogeny; virus evolution; genome constellations; genetic diversity; codon bias +
Footnote †: journal:
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Analysis of Detection Failures
#### 4.5.2 Space Occupancy Classification Failures  
Figure 27 shows examples of space occupancy classification failures. Figure 27a shows an example involving standing water. Reflections in the water resulted in a large number of edges in the parking space, and the space was mistakenly classified as occupied. Figure 27b shows an example involving a severely distorted car. The car in the parking was distorted because it was too close to the camera. This caused the image of the car to be extended to the adjacent parking spaces, and the spaces were mistakenly classified as occupied. Figure 27c shows an example involving a motorcycle. According to regulations, heavy motorcycles must be parked in a parking space. However, because motorcycles are substantially smaller than cars, they are often outside the AVM's field of vision if they are parked too deep in the parking space. Thus, this space was mistakenly classified as vacant.  
Figure 26: Examples of parking space detection failures. (**a**) Dark shadows; (**b**) Severely damaged markings; (**c**) Puddle.
# Optimal Cluster Expansion-Based Intrusion Tolerant System to Prevent Denial of Service Attacks
## 6 Conclusions and Future Works  
In this study, we proposed an optimal cluster expansion-based ITS in order to provide stable and reliable services under massive DoS attacks. We took queuing theory into account for incoming packet numbers in order to determine the optimal number of increased or decreased VMs. Moreover, our proposed scheme increases system performance by minimizing resource waste while preserving good QoS. Our simulation results show that the proposed ITS scheme outperforms conventional ITS schemes such as SCITs, ACTs, and exponential-ACTs. Our future work will include the development of a new cluster expansion scheme that can control the rotation scheduling of VMs under a massive attack. For example, when the rotation of VM is rapid, system security will be enhanced because of the short exposure time of the online VMs. However, the VM rotation overhead will increase, and more resources will be required for the fast VM rotation. Therefore, we aim to develop an optimal scheme for maintaining appropriate VM rotation and system performance, while minimizing resource waste. We will examine how to improve system performance and security by optimally controlling the VM rotation schedule, and we will also investigate different types of attack models.  
This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIT) (No.2017-0-00380, Development of next generation user authentication and No. 2016-0-00173, Security Technologies for Financial Fraud Prevention on Fintech).  
[PERSON] designed the entire architecture and performed the experiments. [PERSON], [PERSON] and [PERSON] gave some advice and ideas about the article. All authors analyzed the result of this article. [PERSON] and [PERSON] wrote the article. All authors discussed the contents of the manuscript. [PERSON] supervised the whole process as a corresponding author.  
The authors declare no conflict of interest.
# Hierarchical Optimization Method for Energy Scheduling of Multiple Microgrids
## 4 Optimization Scheduling
### Stackelberg Game for EMOs
#### 4.2.1 Formulation of a Stackelberg Game  
The hour-ahead energy sharing within the MMG is formulated as a Stackelberg game. The EMO is the leader of the game, and stimulates energy sharing by setting the internal prices with the goal of maximizing profits, while the MGs act as followers that optimize their utility through properly responding to internal prices. The game between the EMO and the MGs can be defined by its strategic form as  
\[G=\Big{\{}\big{(}MGO\cup\{EMO\}\big{)},\Big{\{}L_{i}^{h}\Big{\}}_{i\in N^{ \prime}}\Big{\{}P_{cb}^{h}\Big{\}},\Big{\{}P_{cs}^{h}\Big{\}},\Big{\{}U_{i}^{h }\Big{\}}_{i\in N^{\prime}}\text{\emph{Pro}}_{MGC}^{h}\Big{\}}, \tag{17}\]  
where \(\Big{\{}L_{i}^{h}\Big{\}}_{i\in N}\) is the set of load strategies adopted by each MG \(i\) in the time slot \(h\) constrained by \(l_{i}^{h,\text{min}}\leq l_{i}^{h}\leq l_{i}^{h,\text{max}}\), \(\Big{\{}P_{cb}^{h}\Big{\}}\) and \(\Big{\{}P_{cs}^{h}\Big{\}}\) are the strategic set of the EMO, which ensures that the internal prices are constrained by \(p_{gs}^{h}\leq p_{cs}^{h}<p_{cb}^{h}\leq p_{gb^{h}}^{h}\); \(\Big{\{}U_{i}^{h}\Big{\}}_{i\in N}\) and \(\text{\emph{Pro}}_{EMO}^{h}\) are the utility of the MG and the profit of the EMO which are expressed by (2) and (9), respectively.  
Definition: Consider the game G defined in (17) as a set of strategies \((L_{i}^{h*},P_{cb}^{h*},P_{cs}^{h*})\) constituting a Stackelberg equilibrium (SE) if (and only if) the following set of inequalities are satisfied:  
\[U_{i}^{h}(L_{i}^{h*},P_{cb}^{h*},P_{cs}^{h*})\geq U_{i}^{h}(l_{i}^{h},L_{-i}^{ h*},P_{cb}^{h*},P_{cs}^{h*})\;\forall i\in N,\forall l_{i}^{h}\in L_{i}^{h}, \tag{18}\]  
\[\text{\emph{Pro}}_{EMO}^{h}(L_{i}^{h*},P_{cb}^{h*},P_{cs}^{h*})\geq\text{\emph {Pro}}_{EMO}^{h}(L_{i}^{h*},P_{cb}^{h},P_{cs}^{h*})\;\forall p_{cb}^{h}\in P_{ cb}^{h}, \tag{19}\]  
\[\text{\emph{Pro}}_{EMO}^{h}(L_{i}^{h*},P_{cb}^{h*},P_{cs}^{h*})\geq\text{\emph {Pro}}_{EMO}^{h}(L_{i}^{h},P_{cb}^{h*},P_{cs}^{h})\;\forall p_{cs}^{h}\in P_{ cs}^{h}, \tag{20}\]  
where \(L_{i}^{h*}=[l_{1}^{h*},...,l_{i}^{h*},...,l_{N}^{h*}]\), \(L_{-i}^{h*}=[l_{1}^{h*},...,l_{i-1}^{h*},l_{i+1}^{h*},...,l_{N}^{h*}]\). When all the players in \((MGO\cup\{EMO\})\) reach the SE, the EMO cannot improve its profit by adjusting the internal prices from the SE prices \(P_{cb}^{h*}\) and \(P_{cs}^{h*}\). Likewise, no MGs can enhance their utilities by selecting different strategies from \(L_{i}^{h*}\).
## 4 Discussion  
\"Unsuitable\" CF length will limit efficient and safe of tunnel excavation. In tunnel design, mine technicians and mining experts generally focus on pillar dimensions [36], support technology [37], stress control [38], etc., with little attention paid to the \"raw\" parameters of the tunneling process, i.e., the CF. Reference [16] considered the effect of CF during the study of the stability of the excavated surface, but it was not the focus of their study, and they did not give a recommendation for choosing the length of the cyclic progression. Reference [14] proposes a new method for determining the length of CF in coal mines, using the maximum principal stress in the tunnel roof as the main consideration, and states that most engineers [39; 40] have recognized that the choice of CF has a considerable influence on the likelihood of collapse of the working face and the tunnel, as well as on the stability of the main support in the vicinity of the working face. Unfortunately, however, to the best of our knowledge, studies focusing on this parameter are still relatively few.  
The selection of CF not only affects the rate of the boring process, but is also closely related to the bearing capacity of the tunnel itself. CF can not only affect the displacement distribution of the tunnel (Figures 8 and 9), but also has a more significant effect on the vertical displacement (Y-Displacement) (Figure 10) and vertical stress (Stress-YY) (Figure 13) of the palm face, and this effect is not linear. It is clearly seen that when the CF length is 1.5 m, both the displacement values and stress state are better than other CF lengths. This gives us some insights: We can use numerical simulation software to establish a model that fits the actual engineering scenario, select correct and reasonable parameters, carry out simulation and analysis of the stability of the tunnel under different CF length schemes, obtain the corresponding displacement and stress parameters, and carry out a comprehensive comparative analysis to obtain the optimal CF length. This approach can overcome the limitations of theoretical derivation, or engineering experience in determining the worthiness of CF, and can avoid the cost of money and time consumed by similar simulation tests.  
However, pure numerical simulations are often unconvincing, so experiments are usually required to verify the simulation results. Unfortunately, we are currently unable to conduct experimental tests according to the simulation scenarios, which is too \"time-consuming and labor-intensive\". Therefore, we should recommend the formulation of a complete set of specifications to define the above process, including but not limited to the degree of quantification of the test scenarios and the calibration of the consistency of the results. In addition, this study only refers to the simulation process under rectangular tunnels, but in actual production, there are many other common tunnel section shapes. All these issues will be the focus of our next work.
# On Predictive Maintenance in Industry 4.0: Overview, Models, and Challenges
## 4 Challenges of Predictive Maintenance
### Limits in the Deployment of Industrial Predictive Maintenance Models  
Generally, there are three challenging steps after the development of intelligent failure prediction models: their integration, monitoring, and updating. Model integration in the industry is a challenge because this task is often performed by an information technology (IT) team which is usually dissociated from the team of researchers and developers who developed the predictive maintenance models. Building such an IT infrastructure to maintain the data pipelines can be laborious and is not usually factored into the project planning. Regarding the monitoring step, it consists of ensuring that the model is updated. To do this, a feedback loop is added to the model so that the new incoming data are learning inputs. This continuous re-training of the prediction models has the disadvantage of weakening the reliability of the results. Indeed, neither the integrity nor the relevance of the data is verified in production, which allows the introduction of outliers and thus distorts predictions in the long run. Finally, as for the updating of the models, it is necessary in order to avoid the conceptual drift phenomenon that affects machine learning models. To update the prediction models, the company must modify the code, the model, and the data at the same time. Compared to traditional enterprise software updates, this cycle of improving predictive maintenance models is much more complex [54].
## 3 Implementation and Experimental Results
### Comparison among Feature Extractors  
When designing the architecture of our deep model, several CNN-based models that are frequently used in image featuring were evaluated. In this subsection, five popular backbones, namely, VGG-16 and VGG-19 [12], InceptionV3 [16], MobileNet [18], and ResNet-50 [14], were chosen for comparison. For fairness, their inputs were normalized to an identical size and followed by the same classifier. Their training and validation accuracy are presented separately in Figures 16 and 17. Notably, the training and validation accuracy were relatively high in the early epoch because these compared backbones were pre-trained on the ImageNet dataset [22]. It can be seen that ResNet-50 outperformed other  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Label** & **of Training Samples** & **of Validation Samples** \\ \hline Background & 417 & 83 \\ Alignment mark & 375 & 75 \\ Normal & 560 & 140 \\ Abnormal & 417 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Data distribution in Dataset-I.  
Figure 15: Training and validation accuracy.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**True** & **Predicted** & **Background** & **Alignment Mark** & **Normal** & **Abnormal** & **Accuracy (\%)** \\ \hline Background & 83 & 0 & 0 & 0 & 100 \\ Alignment mark & 0 & 64 & 0 & 0 & 100 \\ Normal & 0 & 0 & 138 & 2 & 98.57 \\ Abnormal & 0 & 0 & 0 & 83 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for additional 370 test data.  
approaches after six epochs, whereas VGG-16 and VGG-19 showed comparable accuracy. Moreover, a computational comparison between these backbones is listed in Table 6. Here, the minimum, maximum, and average computational time for identifying a pattern image and the total number of parameters of different models are also summarized. The VGG-16 and VGG-19, whose accuracies were comparable to ResNet-50, have a much larger number of model parameters. To consider the balance between accuracy and computational time, ResNet-50 was chosen as the standard subnetwork for the image feature extractor applied in our proposed method. More discussion on different CNN-based networks is provided in Section 4.1.
## 3 Materials and Methods
### Calibration of Standard Curves  
Similar truffle matrix was prepared by adding 2.4 mg/g glucose, 48 mg/g mannitol, 5.2 mg/g malic acid, 2.4 mg/g alanine, 3.7 mg/g glutamate and 1.8 mg/g glycine in Milli-Q deionized water before external standard quantification [38; 39]. A recombination containing all volatile compounds was diluted with methanol to 1:5, 1:10, 1:20, 1:50, 1:100 and 1:200 strengths. Then, 5 \(\upmu\)L 1,2-dichlorobenzene (100 mg/L) was introduced to the 5 g of model matrix in a 20 mL vial to establish the calibration curves. Equally, 5 \(\upmu\)L 2-methyletrahydrofuran-3-thiol (100 mg/L) was added to establish the calibration curves for sulfur compounds. These mixture models were extracted by HS-SPME. The standard curves, coefficient of determination (r\({}^{2}\)) and validated linear range for the volatile compounds were set up. All experiments were repeated three times. The calculation formula was as the following:  
\[(A_{x}/A_{i})=a\ (C_{x}/C_{i})+b \tag{1}\]  
(\(A_{x}\)/\(A_{i}\)) equaled peak area of volatile compounds standard/peak area of internal standard; (\(C_{x}\)/\(C_{i}\)) represented concentration of volatile compounds standard/concentration of internal standard; \(a\) is the slope and b is the intercept on y axis of the standard curve.
## 3 Proposed Algorithmic Approach
### Exchange of Initial Information  
Because the algorithms depend on nodes (vertices), edges (connections between nodes), and weights (battery level and received signal quality), information is required to be sent from the controller to all nodes using a broadcast frame (0 xFFFF) and a downlink. The broadcast frame is transmitted and propagates throughout the network through this  
Figure 9: Node delay components.  
link and an application, as shown in Figure 10. The receiving nodes store this frame and retransmit it to their nearby nodes, which allows for its propagation in the network, as shown in Figure 11. In this way, all nodes that receive a frame with destination address 0 xFFFF must retransmit the information until it reaches all nodes in the network. This broadcast frame allows each node to identify the nodes within its coverage range. Once the retransmission is complete, each node will have information about the battery level and signal quality of all the nodes it can reach.  
Figure 11: Sequence diagram 2: Sending the broadcast frame.  
Figure 10: Communication between nodes and controller through the downlink.  
Figure 12 shows several captured frames sent from the network's nodes to the controller node. These frames contain information on the node's battery and energy levels in their payload (neighbors table). For example, in the payload of the first frame, the first two bytes indicate the node's address that sends the neighbor table (00 07--0x0007). The next four bytes represent the battery level (33 2E 31 35--3.15v). Subsequent bytes represent the energy levels of frames previously received by the node and the node's source address that generated them. Thus, the seventh to the tenth byte indicates the reception of a frame with an energy level of 00 30, which was transmitted by node 0x0008 (00 08).
# Joint Acquisition Time Design and Sensor Association for Wireless Sensor Networks in Microgrids
## 4 Problem Formulation and Problem Solution
### Problem Solution
#### 4.3.2 Sensor Association Optimization  
When the acquisition time \(\boldsymbol{T}\) is given, the optimization problem of sensor association \(\mathbf{X}\) at this time can be expressed as follows:  
\[\begin{split}\mathbf{P2}&:\underset{\mathbf{X}}{min} \quad\sum\limits_{i}E_{CM_{i}}+\sum\limits_{j}E_{CH_{j}}\\ &=\sum\limits_{i}\Bigg{[}\frac{1}{\eta}(p_{t}+\alpha)*l*t_{i}* \sum\limits_{j}\frac{x_{ij}}{r_{ij}}\Bigg{]}+\sum\limits_{j}\Bigg{[}e_{c}* \sum\limits_{i}(l*t_{i}*x_{ij})+E_{DA}*\left(\sum\limits_{i}(l*t_{i}*x_{ij}) \right)\Bigg{]}\\ s.t.&\sum\limits_{j}x_{ij}=1,\forall i\in\mathcal{L}, \\ &\sum\limits_{i}x_{ij}\leq N_{j}^{\max},\forall j\in\mathcal{K}, \\ x_{ij}\in\{0,1\},\forall i\in\mathcal{L},\forall j\in\mathcal{K}. \end{split} \tag{19}\]  
Problem \(\mathbf{P2}\) is a 0-1 multi-knapsack problem. Dynamic programming is widely used to solve the knapsack problem, but when the dimensionality of the problem to be solved is very large, the solution efficiency of dynamic programming will be drastically reduced, which is called the dimensionality disaster.  
Based on the above description, we propose an approximate solution for problem \(\mathbf{P2}\) to obtain the sensor association \(\mathbf{X}\) based on the method in [25]. This method iteratively considers all objects that are not loaded into the backpack and calculates the Euclidean distance between the optimal and suboptimal returns each time. The object corresponding to the maximum Euclidean distance will be loaded into the backpack in this iteration until all objects are loaded into the backpack, at which time the algorithm stops iterating. See the detailed algorithm ATSAO, where \(w_{ij}\) is a measure of the \"desirability\" of assigning the item \(i\) to knapsack \(j\). From question **P2**, \(w_{ij}\) can be calculated by the following formula, which can be derived by Equation (19):  
\[w_{ij}=\sum_{i}\sum_{j}\left(\frac{\frac{1}{\eta}(p_{t}+\alpha)*l*t_{i}}{r_{ij }}+(e_{c}+E_{DA})*l*t_{i}\right). \tag{20}\]