esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Comparison with Electrical Quality Metric  
Evaluating the performance of SRAM cells involves various metrics, most of which maintain trade-off relationships. Increasing CR to enhance stability or reduce delay can lead to greater leakage power and, in the worst case, compromise resistance to SEU or SEMNU. Consequently, a universal metric, known as electrical quality metric (EQM), is commonly utilized to reflect all these indicators [4; 5]. A higher EQM indicates better cell performance.  
\[EQM=\frac{Q_{c}\times RSNM\times WWTV}{H_{PWR}\times T_{WA}\times T_{RA}\times Area} \tag{3}\]  
As shown in Table 2, since the performances of these cells generally exhibit trade-off relationships, RSLP16T, possessing similar levels of EQM, shows good performance in read stability and leakage power while demonstrating some disadvantages in delay and area. However, the internal nodes maintain robust resilience against SEU effects, leading to a higher level of EQM than other cells.
## 4 Examples of Critical Constructability Features in Facade Panels of the Orchard Commons Project
### Feature 5: Minimum Panel Types  
**Constructability Constraints and Experts' Knowledge:** The fabrication of panels with many different types is costly. Therefore, in the OC project, the designers aimed to have the minimum possible panel types on the facade and defined this as a constructability constraint. This constraint is an example of constructability constraints related to the \"variety of component types\". Earlier, it was explained that the main focus in this research is on cases in which the studied components are used multiple times, such as repeating facade panels. The main reason for this scoping is to enhance the research scope and include the mass-fabrication aspect, which would not be represented in cases with unique components. Moreover, the focus was on cases in which the components have minor differences from each other while all of them belong to the same building object category. Through these considerations, it would be possible to enhance the research scope and capture a significant fabrication constraint, namely the \"typing\". This constraint requires having the lowest possible number of types for a specific design task.  
In consultation with practitioners, the OC project stakeholders agreed on establishing a pattern with only 18 different panel types that correspond with the number of building floors. In this way, each vertical panel bay covers the 18 floors of the building with 18 different panels. With this decision and by vertically shifting the panel bays, the outcome became a diverse and unique pattern which is created by only 18 panel types. Figure 6 shows the distribution of these 18 types in different bays, where each number corresponds to a specific panel type.  
Figure 7: Details of a panel’s embeds from the shop drawings designed without computational optimization (**left**); workers adjusting the panel embeds on-site due to the misalignment with the receiving embeds (**right**).  
**Constructability Feature and Computational Implementation:** The implementation of this feature in the computational algorithm was combined with the implementation of the feature for vertical shift between the panel bays introduced earlier. The typing constructability features can be described in computational design through a function that controls the minimum total number of component types (\(f_{min}(\Sigma\ t_{n})\)). This function can be implemented in many different ways. One way is to collect \(\Sigma\ t_{n}\) for each design variant and then find the overall minimum, as it was implemented in the OC project.  
Certainly, there is a direct relationship between the fabrication costs and the number of component types. Therefore, typing as a constructability feature is a common computational optimization task in many projects. \"Minimum window wall types\" was another typing feature in this case study which was computationally designed.
# HP-LSTM: Hawkes Process-LSTM-Based Detection of DDoS Attack for In-Vehicle Network
## 4 HP-LSTM Detection Model
### Training Module
While for HP-LSTM ht-chm, we modified the Hawkes parameter \(\beta\) treated with an exponential function to linear processing, aiming to investigate whether exponential operations can better reproduce the dynamics described by the Hawkes process, thus benefiting the model learning. Finally, for HP-LSTM hte-chp, we changed the influence of the Hawkes coefficients on the cell state of the HP-LSTM, replacing the multiplication relationship with an addition relationship to explore its impact on neural network learning. Moreover, it is worth mentioning that in Section 4.3, we partition network traffic data into multiple windows, each representing data within a specific time interval. We stipulate that each window comprises 128 packets from different IP addresses. In the training process, the Hawkes parameters contained in the Hawkes coefficients corresponding to each packet (denoted as \(X_{t}\)) within the window represent the parameters of the Hawkes process for the source IP address of that packet. Additionally, we introduce an RSAB (residual attention block), a neural network module designed for processing sequential data. The essence of the RSAB lies in its utilization of the self-attention mechanism, enabling the model to concurrently consider the correlations among each element within the input sequence. Through this mechanism, the RSAB can effectively capture global information in sequential data, thus enhancing the model's representation and generalization capabilities. Within our model, the RSAB dynamically weights input features, effectively highlighting the unique patterns exhibitedby DDoS attacks within vast network data flows, thereby facilitating subsequent layers of the model to more accurately identify and classify them, thus improving the model's detection performance. The equation of the RSAB is based on the self-attention mechanism, typically using the Scaled Dot Product Attention mechanism to compute attention weights [41; 42]. Specifically, the equation of the RSAB is as follows:  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{17}\]  
where \(Q\), \(K\), and \(V\) represent the query, key, and value vectors, respectively, obtained through a linear transformation of the input sequence. \(d_{k}\) denotes the dimensionality of the query and key vectors, typically the length of the vectors. \(\frac{QK^{T}}{\sqrt{d_{k}}}\) denotes the dot product of the query and key vectors, divided by the scaling factor \(\sqrt{d_{k}}\). This step is intended to scale the attention scores to improve the stability of the model. The softmax function is used to convert the scaled attention scores into weights, representing the importance of each position. After passing through the RSAB and a multiple perceptron, the final output of each model is the probability of the window being in either a normal state or a DDoS-attack state.
## 3 Results and Discussion
### Antimicrobial Properties
#### 3.7.2 Antifungal Activity  
Results of antifungal activity tests against a colony of _Chaetomium globosum_ (ATCC 6205) of PETE fabrics and PETE-Cu(\({}^{0}\)) composites are illustrated in Figure 9 and listed in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & \multicolumn{2}{c}{**Average Inhibition Zones for Bacterial Growth (mm)**} \\ \cline{2-3}  & _E. coli_ & _S. aureus_ \\ \hline PETE & 0 & 0 \\ PETE-Cu(\({}^{0}\))-1 & 1 & 1 \\ PETE-Cu(\({}^{0}\))-2 & 2 & 1 \\ \hline \end{tabular} Concentration of inoculum (bacterial suspension) number of live bacteria: _E. coli_—CFU/mL = 1.2 \(\times\) 108 and _S. aureus_—CFU/mL = 1.7 \(\times\) 108.  
\end{table}
Table 8: Results of tests on the antibacterial activity of PETE-Cu(\({}^{0}\)) composites, procedure in accordance with the EN ISO 20645:2006 [119].  
Figure 8: The PET samples coated with copper modifier antimicrobial activity tests. Inhibition zones of _Escherichia coli_ bacterial growth on Petri dishes: (**a**) PETE, (**b**) PETE-Cu(\({}^{0}\))-1; (**c**) PETE-Cu(\({}^{0}\))-2.  
The results revealed strong visible inhibition zones of fungal growth on Petri dishes (Figure 9), and therefore proved antifungal protection of metallized surface of PETE-Cu\({}^{(0)}\) against _Chaetomium globosum_ (Table 9).  
The results revealed that the unmodified PETE sample induced the strong fungal growth-covering of the entire surface of the control sample (Figure 9a). At the same time PETE-Cu\({}^{(0)}\)composites equipped with copper layers/clusters on the composite surface provided antifungal properties for both--PETE-Cu\({}^{(0)}\)-1 and PETE-Cu\({}^{(0)}\)-2 composites.
## 3 IoT Traffic Models
As this kind of application has no requirement to share information with peers from time to time, traffic generated online is hard to foresee. 7. _Smart Retail (event trigger)_ saves customer interest and shopping history in the database. Once a customer walks into the store, a scan of member card causes the end device on the trolley to download personal details from the server. Traffic between the gateway in the branch and server reflects customer flows, which could be studied from statistics in the past. 8. _Smart Grid (periodical/event trigger)_ collects the usage information during the day so as to formulate a dynamic strategy for power supply. Various sensors on the transmission line monitor the change of environment and impact on the power line, they can access to the public network via mobile or optical networks [43]. As the number of sensors is solid in a region and the coverage of the server is constant, traffic flow received on the server is normally steady. 9. _Healthcare (periodical/event trigger)_ via IoT is achieved by sensors attached to the user to monitor real-time body functions. The results are sent to the server through a mobile network or broadband [44]. As these devices may connect to the server via different access points, traffic is unpredictable in this case. 10. _IoT in Farming (periodical/event trigger)_ fetches data from sensors and cameras in the poultry house to maintain a comfortable environment. A notification is sent to the farmer via the smartphone, and the operation is triggered due to exceeding certain thresholds [45]. Data collection is similar to IoT in agriculture, it can be accomplished without Internet access. Traffic is predictable due to the limited number of sensors and switches.
## 4 Methodology
### Analysis Strategies
#### 4.6.2 Scenario #2  
The scenario is identical to the previous one, except for the initial state of the TDRS satellites. In this investigation, the initial states of the TDRS satellites were perturbed by more than 40-\(\sigma\) of the current TDRS accuracy requirement. To create this perturbation, an offset of 1000 m in position and 10 cm/s in velocity was applied in each of the three directions. These offsets were chosen in a way that they introduced a significant degree of error in the satellites' initial state without causing the solution to diverge. If not accounted for, a small modeling error could accumulate over time and result in significant deviations from the expected trajectory. The purpose of this investigation was, therefore, to determine whether TT&C-only solutions can meet TDRS operational requirements when the state of the satellites is poorly known. Section 5.1.2 presents the results of this investigation.
## 4 Computational Experiments
### Results
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**PS**} & \multirow{2}{*}{**SD**} & \multicolumn{2}{c}{**Operating Room**} & \multicolumn{2}{c}{**ICU**} & \multicolumn{4}{c}{**Ward**} \\ \cline{3-11}  & & **PP** & **Makespan** & **POR** & **OB** & **EP** & **POR** & **PICU** & **OB** & **EP** \\ \hline Small & Small & 12 & 1063 & 1 & 3 & 4 & 13 & 1 & 2 & 5 \\ Small & Medium & 9 & 1119 & 0 & 5 & 2 & 10 & 1 & 4 & 4 \\ Small & Large & 9 & 1221 & 0 & 5 & 2 & 10 & 1 & 4 & 6 \\ Medium & Small & 26 & 2542 & 1 & 5 & 5 & 25 & 2 & 4 & 7 \\ Medium & Medium & 20 & 2572 & 1 & 5 & 5 & 19 & 2 & 2 & 11 \\ Medium & Large & 17 & 2923 & 1 & 4 & 6 & 16 & 1 & 5 & 12 \\ Large & Small & 33 & 3307 & 2 & 5 & 8 & 32 & 2 & 19 & 15 \\ Large & Medium & 28 & 3621 & 1 & 6 & 8 & 26 & 3 & 21 & 13 \\ Large & Large & 22 & 3685 & 1 & 5 & 9 & 21 & 4 & 24 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary results of the lower-level plan when the ICU becomes the bottleneck. \begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \multirow{2}{*}{**PS**} & \multirow{2}{*}{**SD**} & \multicolumn{2}{c}{**Operating Room**} & \multicolumn{2}{c}{**ICU**} & \multicolumn{4}{c}{**Ward**} \\ \cline{3-11}  & & **PP** & **Makespan** & **POR** & **OB** & **EP** & **POR** & **PICU** & **OB** & **EP** \\ \hline Small & Small & 14 & 1220 & 1 & 3 & 2 & 13 & 1 & 8 & 6 \\ Small & Medium & 10 & 1212 & 0 & 3 & 2 & 9 & 2 & 12 & 5 \\ Small & Large & 7 & 1238 & 0 & 3 & 2 & 7 & 1 & 15 & 4 \\ Medium & Small & 23 & 2273 & 1 & 2 & 5
# Understanding the Seismic Resilience of Metallic Cylindrical Tanks Through Parametric Analysis
## 4 Discussions on FE Results and Parametric Sensibility
### Tank H/R = 0.75  
As observed in Figure 7a, the pressure profiles exhibit a similar trend across the five different \(R/t\) ratios investigated. Significant peaks of pressure occurred at the top of the wall due to convective waves both for rigid and flexible wall tanks. The largest peak occurred for the rigid tank (\(R/t=600\)), at approximately 0.9 normalized height (\(z/H\)), and this peak decreased as the flexibility of the wall increased, reaching its lowest value for the flexible tank (\(R/t=2000\)). In the lower part of the tank, the pressure profiles remained relatively uniform, showing no significant differences across the different \(R/t\) ratios. Figure 7b presents a comparison between the numerical results and the pressure distribution recommendations from the various standards along the wall. In general, the results obtained from the FE analyses aligned well with the analytical predictions in the lower part of the tank wall. However, as in previous cases, the impact of convective waves on the tank shell caused a pressure peak at the top of the wall that cannot be predicted by the design codes. Under the assumption of flexible circular tanks, EC8-4 predicted  
Figure 6: Horizontal pressure distribution of \(H/R=0.5\) tank ratio considering different \(R/t\) thickness ratios: (**a**) FE simulations and (**b**) predictive pressures computed according the international standards compared with FE simulations.  
the highest pressure values, while for rigid circular tanks, EC8-4 underestimated the pressure profiles compared to the FE simulations. The NZSEE standard resulted in similar pressure values along the wall for both rigid and flexible tanks. In contrast, the AWWA code consistently underestimated the pressure profiles when compared to the FE results. These findings further emphasize the limitations of existing design codes, particularly in capturing the effect of convective waves at the top of the tank wall, which is critical for accurately predicting the seismic response of tanks.
# VLSM-Net: A Fusion Architecture for CT Image Segmentation
## 1 Introduction
The results show that the 3D detection methods generally outperform the 2D method, but the segmentation results are not smooth and fine enough. To solve these problems, we added the hybrid LSM into V-Net. The LSM is a segmentation method which combines image information into an energy equation [16]. It implements the segmentation of the target region by solving the minimization energy equation and driving the initial contour curve evolution. The Mumford-Shah (MS) proposed to use not only the edge gradient information of the whole image, but also the global location information and length information of the ROI to optimize the global energy equation of the  
Figure 1: Upper line is the three-view drawing of the liver CT, lower line is the corresponding label. (**A**) Top view of the CT. (**B**) Side view of the CT. (**C**) Front view of the CT. (**D**) Top view of the label. (**E**) Side view of the label. (**F**) Front view of the label. image [17]. The Chan-Vese (CV) model was proposed by improving the energy equation to simplify the model-solving process [18]. An LSM without re-initialization was used to further improve the application in segmenting medical images [19]. Based on this, other scholars combined the LSM and neural network model together [20; 21; 22]. By using a neural network to extract the deep information of the image, obtaining a priori information on the image, and using the LSM to segment the target image, a smoother and more accurate segmentation result can be obtained. In this paper, we propose the VLSM-Net model by combining the V-Net model and hybrid LSM in order to solve the problems of ROI segmentation of medical images. In the training period, a priori V-Net model is trained by using the labeled dataset, and the outputs of the priori are regarded as preliminary segmentations. Then, we send the preliminary segmentations to the LSM for curve evolution, and treat the evolved results as the real label. In the test period, the unlabeled data are first sent to the V-Net model to obtain the coarse segmentation results, and then the coarse segmentation results are sent to the LSM for curve evolution to obtain the final fine segmentation results. The next sections will introduce the VLSM-Net in detail.
# Conscious Mobility for Urban Spaces: Case Studies Review and Indicator Framework Design
## 4 Case Studies
### Mexico City Bus Electrification and Active Mobility Programs for Sustainable Mega-Cities
Besides the positive environmental impact, important economic and health benefits have been achieved from the implementation of this sustainable transport system, such as USD 141 million in regained economic productivity as a result of travel time reductions from Metrobus Line 3 [67], the elimination of more than 2000 days of lost work due to illness, four new cases of chronic bronchitis, and two deaths per year saving the city an estimated USD 4.5 million [67] and an increase in passengers walking an average of 2.75 min more per day than before the city implemented its BRT system [67]. This case study represents a starting point for the prioritization of sustainable transport systems in an attempt to achieve zero-emission conscious mobility in Mexico City. The importance of this project lies in its ability to demonstrate that an integrated system of mobility can be a feasible alternative, having a great impact on mega cities, where negative externalities derived from transport activities are more salient to its citizens. On the other hand, it also highlights the correct application of policy regulations toward a sustainable-smart mobility. However, financial and technical capacity still represent a barrier to the massive adoption of the current pilot test. Moreover, the lack of supportive technologies to gather useful insights from big data mining for example that impede the correct identification of opportunity areas for improvement, thus, lowering the sustainable transportation efficiency potential of the system. Therefore, the Mexico City case study is classified as part of stage 3 of conscious mobility, hoping to reach stage 4 as financial and technical barriers can be overcome.
## 3 Results and Discussion  
### Deflectometric Indicators  
Overall, a closer examination of the deflectometric data at the center of pavement slabs revealed a considerable range over the length of a single lane and thus over the entire area of a single toll station. This was not necessarily expected, given the limited length of the toll stations compared to the total length of the highway, but it is entirely consistent with the varying extent of surface damage at the locations examined. An example of the observed trend is shown in Figure 7, where the change in D\({}_{0}\) and D\({}_{1800}\) over the length of a particular lane is shown. It is clarified that the deflection changes shown in Figure 7 refer to the whole lane, reflecting the damage evolution over the length of the lane, rather than to each slab. Each point on the lines in Figure 7 corresponds to a unique slab. The complete deflection data from all slabs and lanes at each toll station are presented in the form of descriptive statistics for both indicators (minimum, mean, standard deviation, CV, and maximum) in Tables 1 and 2.  
Similar to the sample lane shown in Figure 7, Tables 1 and 2 show a considerable range of deflection indicators in all cases. These results are meaningful because the severity of the observed pavement damage varies, even within the same lane (as shown in Figure 7). Considering that the data measured in the field reflect the \"as-is\" (i.e., real-world) condition of the pavement structures, the observed deflection range created a need to map the condition of individual slabs and lanes as a function of the data collected through NDT. Next, laboratory testing of the quality and strength of the concrete may be required to  
Figure 7: Evolution of both indicators along the length of a representative area (lane E of toll station No. 1).  
In both cases, it was found that the generalized extreme value distribution sufficiently takes into account the available data according to the Anderson-Darling evaluation criterion for the assessment of the goodness of fit. Figure 8 shows that a characteristic value of 145 \(\upmu\)m (according to the red arrow in Figure 8), with a typical range of \(\pm\)50 \(\upmu\)m, was found for indicator D\({}_{0}\) based on the value at which the probability density function reaches its maximum. This means that the characteristic value represents the value with the highest probability of occurrence. Similarly, for the D\({}_{1800}\) indicator, a characteristic value of 13 \(\upmu\)m (according to the red arrow in Figure 9), with a typical range of \(\pm\)7 \(\upmu\)m, was determined from the peak of the curve in Figure 9. It is assumed that these values characterize the majority of the pavement slabs.