esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Data Collection
### Data Collection
#### 3.1.5 Calendar Data  
Calendar data constitute a part of the internal memory of the prediction model and store information on the individual days of the year that are important for the quality estimation of traffic density. Primarily, this information states whether a given day is a workday or holiday. For this purpose, the calendar provides information concerning the type of the day per week and whether it is a bank holiday. As for bank holidays, actual strictly defined bank holidays repeated on the same date must be distinguished from bank holidays with moveable dates (this currently involves Good Friday and Easter Monday). The calendar also considered the date of the start and end of the Central European Summer Time (CEST).  
Furthermore, the times of sunrise and sunset were pre-calculated for each day of the year. The implementation set forth by the authors of [48] was used for the calculation. The values were saved in Central European Time (CET) and were alternatively transformed to CEST only for the specific year. Due to the fact that the prediction model resolution was in hours (see Section 5), it was only necessary to pre-calculate values for 1 location in the Czech Republic (the selected location was Prague, Old Town Square).
## 3 Test Results and Observations
### Accumulation of Carbon Powder and Ash Powder
#### 3.1.1 Results from Images of Substrate with Accumulated Carbon Powder  
The maximum allowable quantity of soot was assumed to be 6 g/L (mass per volume of substrate) before regeneration, and the volume of the substrate used in the test was calculated to be about 4.12 L based on the standard [16]. Therefore, the maximum allowable quantity of soot in the entire substrate was estimated to be 24.72 g, which was rounded up to 25 g for the convenience of the testing. In this study, contents of carbon powder, which is similar to soot that is accumulated in the substrate, in six different quantities were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Condition** \\ \hline Tube Voltage & 50 kV \\ Tube current & 10 mAs \\ Irradiation distance & 1 m \\ Sampling pitch (low scan mode) & 175 μm \\ Type of DPF substrate & Wall-flow \\ Substrate material & Cordierite \\ \hline \hline \end{tabular}
\end{table}
Table 4: The establishment of irradiation conditions.  
Figure 6: X-ray experimental setup photos: (**a**) tube voltage and tube current setting; (**b**) taking an X-ray; (**c**) insert IP into CR reader; (**d**) image output to the computer.  
compared by taking an X-ray. The images of the substrate in these six different conditions are shown in Table 5.  
* New substrate (0 g): The new substrate has nothing accumulated inside.
* Accumulation 12.5 g: 50% of the maximum accumulated quantity is accumulated in the substrate.
* Accumulation 25 g: The maximum accumulated quantity of substrate.
* Accumulation 62.5 g, 100 g and 125 g: Excessively accumulated quantity setting for better visual observation.  
In the case of Table 5a, showing the new substrate, the plug on the inlet and the outlet appeared white. As for Table 5b, displaying the substrate with 12.5 g of carbon powder, an area appearing extremely pale white on the upper part of the plug was witnessed as carbon powder was accumulated on the outlet of the substrate. Table 5c, with an image of the substrate with 25 g of carbon powder, shows a larger area colored pale white. In cases of Table 5d, with the substrate with 62.5 g of the powder, Table 5e, with 100 g, and Table 5f, with 125 g, it was visually observed that carbon powder, accumulated in the shape of a mountain on the outlet of the substrate, appeared in pale white.
## 4 Design of STUN
### Parameter Filtering  
Not all of the parameters affect the performance, even if the values change according to the scheduler policy or the workload. For example, Figure 3 increases the sched_rr_timeslice_ms value by 10 within the range of the FIFO policy and records the resulting value by executing Sysbench. The x-axis represents the parameter value and the y-axis represents the total number of events resulting from Sysbench. In the FIFO policy, CPUs are occupied until termination in the order in which all jobs come in. In addition, sched_rr_itemslice_ms is a parameter that determines the CPU usage time for each task in the RR policy. As shown in Figure 3, Sysbench represents a constant value of between 2500 and 3000, regardless of the parameter value, which can be from 0 to 1000.  
The filtering process shortens the training time by minimizing unnecessary training for performance improvement, and reduces memory usage to perform optimization more efficiently. Using all five scheduler policies and nine parameters in the Q-learning algorithm wastes a large amount of memory and increases the learning time. It is therefore necessary to find a parameter that significantly affects the performance. To achieve an efficient learning, STUN selects the scheduler policy and parameters for optimization through a filtering process.  
STUN changes the individual parameter values to the minimum, maximum, and default values one by one for each policy and records the performance changes in the test workload. At this time, the other parameters are fixed as the default values. The filtering process removes the parameters that do not affect the overall performance of the application. To identify the parameters, the filtering process uses a threshold value as 20% due to the measurement error. As there may be an error range of 10-15% depending on the systemsituation, we decided the performance difference of 20% is meaningful, and the parameter is tested for the optimal value.  
Throughout the filtering process, the relationships between these policies and parameters are known, and thus only the parameters that most affect the performance as learning variables are used. As a result, the filtering process shortens the learning time and reduces the memory usage, allowing the optimization to be achieved more efficiently.
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 4 Investigated Methods to Support RTA
### Preliminary Channel Access (PCA)  
The second method is PCA [15], shown in Figure 1. Let the RTA STA expect the arrival of a data frame during some interval \(T_{exp}\). According to PCA, the RTA STA starts channel access \(T_{\text{PCA}}\) before the beginning of this interval to transmit an RTS frame. Let us denote the duration of the RTA data frame transmission, including the acknowledgment, by \(T_{s}\). In case of timely RTS transmission, the AP responds with CTS, and the channel becomes virtually busy for all other STAs until the end of the possible transmission, i.e., \(T_{exp}+T_{s}\) interval. As a result, the STA can transmit a data frame immediately after its generation. Otherwise, if the data frame arrives before RTS transmission, the STA discards the RTS frame and starts the channel access to transmit the data frame. To avoid waste of channel resources, when the data frame transmission ends before the end of \(T_{exp}+T_{s}\) interval, the STA transmits a CFend frame which signals all STAs to zero their NAVs.  
The time \(T_{\text{PCA}}\) for which an RTA STA starts PCA equals  
\[T_{\text{PCA}}=\text{TXOP Limit}+AIFS[\text{RTA}]+W_{min}[\text{RTA}]\cdot T _{e}+T_{RTS}+SIFS+T_{CTS}.\]  
Figure 1: Scheme of the PCA.  
This \(T_{\text{PCA}}\) value guarantees a successful channel reservation by the data frame arrival time if only one RTA STA starts PCA (generates an RTS frame) while the channel is busy with a non-RTA transmission. The disadvantage of the PCA is the fact that one RTA STA can occupy the channel for a long time while other RTA STAs have data frames to transmit. As a result, delay components associated with waiting for the end of RTA transmission and contention with RTA STAs become significant.
###### Abstract  
A method of deicing using microwave heating is proposed to make scientific and economical road deicing in a cold area, and to make up for deficiencies in the existing methods for melting snow and ice. This paper proposes to define microwave deicing efficiency as the heating rate of a concrete surface when heated to 0 \({}^{\circ}\)C (the efficiency of deicing is equal to the difference divided by heating time, which is between 0 \({}^{\circ}\)C and the initial temperature at the junction of ice and concrete). Based on the mechanism of microwave heating and deicing, a method combining the finite element simulation model with indoor experiments was proposed to study the deicing efficiency of microwaves, and the effects of different microwave frequencies and different road structure materials on microwave deicing efficiency were analyzed. The results show that the microwave frequency and road structure materials have a great influence on microwave deicing. For asphalt concrete, the ice melting efficiency of 5.8 GHz is 4.31 times that of 2.45 GHz, but the heating depth is less than that of 2.45 GHz. At 2.45 GHz, the melting efficiency of cement concrete is 3.89 times that of asphalt concrete. At 5.8 GHz, the melting efficiency of cement concrete is 5.23 times that of asphalt concrete. Through the consistency of the simulation and experimental results, the validity of the simulation model based on the finite element theory is verified. The results provide theoretical guidance and a practical basis for future applications of microwave deicing.  
microwave de-icing efficiency; microwave frequency; road structure materials; simulation model; indoor simulation experiment
## 2 Literature Review
### Research on Pipelines  
Pipeline transportation is the most prevalent mode for transporting petroleum. Due to the hazardous nature of petroleum cargo, pipeline transportation poses social, economic and environmental risks, particularly in the event of pipeline failure leading to the leakage of harmful components. Leakage risks can often be mitigated by installing sectional valves along the pipeline, which help to reduce losses and associated risks. A tricky issue is determining the reasonable amount and location of valves depend on the risks present in each component. [PERSON] et al. (2018) tackled the valve location problem (VLP) by modeling it as a shortest path problem and applying the Bellman-Ford algorithm.  
Resilience indicators and process descriptions were studied in ([PERSON] et al., 2019) for natural gas pipeline network system (NGPNS), considering different types of interference, a comprehensive structure was proposed to evaluate the power supply elasticity of NGPNS, including topology and operating conditions. The study explored the features of certainty and uncertainty perturbation. The maximum flow method and the shortest route approach were combined with operational and configurable coefficients to calculate the gas that supply volumes and paths before and after the disturbances.  
Because energy companies explore more remote resources, petroleum and gas production systems have become increasingly complex, with pipeline systems getting deeper and farther offshore. The complex pipeline networks need to adapt to topological complexities encountered and significantly reduce total investment costs across various areas. In Ref. ([PERSON] et al., 2019) research, the A* algorithm was introduced to calculate shortest path for pipeline placement optimization. In addition, the design of subsea pipeline paths is critical in the offshore oil and gas industry due to the complex seabed topography. Environmentally friendly, economical and safe pipeline path designs are crucial. The use of 3D underwater terrain models with various obstacles facilitates the design of the subsea pipeline roadbed. Based on 3D technique, [PERSON] et al. (2019) proposed a method for offshore pipeline path design combines the [PERSON]'s LCP algorithm with Laplacian smoothing algorithm to the presence of obstacles.  
Pipelines are the main way of oil and gas transportation. Researches on pipeline transportation primarily focuses on the safe laying of pipelines, shortest path calculation, obstacle avoidance and cost reduction. Most of researches have concentrated on long-distance, buried and submarine pipelines.
## 4 Motivation  
As described in Section 2, the core network (EPC) and the control plane segment of the RAN in 5G NSA are based on LTE. Therefore, theoretically, the NSA version of 5G is likely to be vulnerable to known 4G attacks.  
Some research has already been carried out to practically verify the working of 4G attacks on 5G NSA. The work in [34] validated three vulnerabilities that are based on implementation flaws on 5G: key-stream reuse, null-ciphering, and IMS IP security off. The work in [35] tried to verify two design flaws and several implementation flaws on 5G NSA. The authors verified the RRC Blind DoS and EPC scanning attacks on two commercial networks. Similarly, the work in [36] also analyzed the possibility of performing the Blind DoS Attack on 5G NSA and suggested some countermeasures. Furthermore, one more work [30] recently verified the Null Ciphering attack on 5G NSA.  
However, so far, no literature has systematically explored the state of the art and provided a comprehensive taxonomy of possible attacks on 5G NSA over the radio interface. We addressed this gap in the literature and presented the first taxonomy of attacks on 5G NSA in Section 3.  
The sheer number of theoretically possible attacks on 5G NSA motivated us to the next step. We selected and implemented three attacks that have severe consequences for the user and have not been practically verified on 5G NSA architectures yet.  
The attacks we selected are the _Downgrade Attack_, the _Numb Attack_, and the _IMSI Leak Attack_. These are active message attacks using a fake BS (cf. Section Fake Base Station).
## 10 Comparison between Developed Models  
As mentioned in earlier sections, the effectiveness of the suggested new models was evaluated using four statistical parameters (R\({}^{2}\), MAE, RMSE, and SI). Compared to the NLR and MLR models, the ANN model had the lowest RMSE and MAE values and the highest R\({}^{2}\) values. The coefficient of determination for all models using training and test sets is shown in Figure 9. Figure 9 demonstrates that the ANN model's predicted and measured compressive strength values are much more comparable, demonstrating the ANN model's superior performance to other models.  
The SI assessment parameter values for the proposed models during the training and testing phases are shown in Figure 9. The SI values for all models and phases (training and testing), as displayed in Figure 9, were less than 0.2, indicating excellent performance for all models. Additionally, the SI values for the ANN model and the other performance parameters are lower compared to other models. The ANN model has smaller SI value values across all phases compared to the NLR model, for instance, 208 percent lower in training and 68 percent lower in testing datasets.
## 2 Method
### Helical Path Generation
To generate the points \(P_{helix}\) on the path with a helical regularity, the points and vectors on the offset contour need to be close to the next layer of the contour in different ratios depending on where they are located. After the preparation work is completed, the generation of the path starts. The first step is to calculate the coordinates on the path. The point \(P_{i}^{\prime}\) on the offset contour, its direction of travel \(P_{i}^{\rightarrow}Q_{c}\) and length ratio \(L_{i}/L\) are known. The point \(P_{helix}\) on the helical path is calculated according to Equation (4). \[P_{helix}=P_{i}^{\prime}+\frac{L_{i}}{L}\overrightarrow{P_{i}^{\prime}Q_{c}} \tag{4}\]  
In generating the tool orientations \(n_{helix}^{\rightarrow}\) on the helical path, the angle \(\delta\) of rotation of the vector \(\overrightarrow{n_{p}}\) toward \(\overrightarrow{n_{Q}}\) is first calculated according to Equation (5). Then, a new vector is obtained by rotating the rotation of \(\overrightarrow{n_{p}}\) according to this angle. The operation used for vector rotation is the quaternion definition [20], which gives \(n_{helix}^{\rightarrow}\) according to Equations (6) and (7). \[\delta=acrcos\left(\overrightarrow{n_{p}},\overrightarrow{n_{Q}}\right) \times\frac{L_{i}}{L} \tag{5}\]  
Figure 10:0  
\[n_{helix}^{\rightarrow}=q\overrightarrow{
## 3 Results and Discussion
### Seasonal Variations
The statistical analysis of the seasonal impact on individual PAHs for the entire area, as well as for individual stations, revealed statistically significant differences between the warm and cold seasons (\(p<0.05\)). Significant differences (\(p<0.05\)) were observed between winter and summer, winter and spring, winter and autumn, as well as between spring and summer for the individual PAHs, suggesting that seasonal changes affect their concentration. No significant differences (\(p>0.05\)) were found between spring and autumn, indicating that sources and atmospheric conditions may influence PAH levels in these two seasons in a similar manner. No direct effect of atmospheric precipitation on the concentration of PAHs was recorded at individual stations (Tables S2-S9, Supplementary Materials). Seasonal differences are influenced by several factors. This may result from a greater shift to vapor in the warmer season, meaning increased pollution in the colder months is a result of poorer dispersion conditions and lower mixing heights. Additionally, higher emissions of pollutants may occur in winter due to increased combustion of fossil fuels and biomass for heating purposes. In the winter season, photolysis and photochemical degradation are also reduced due to lower temperatures, less radiation, and lower levels of atmospheric oxidants [14]. PAHs can experience photolysis and oxidation through interactions with hydroxyl radicals, ozone, nitrogen oxides, or other potent oxidizing agents. The duration of photolysis reactions is significantly shorter for PAHs found in airborne particulate matter compared to their presence in water or organic solvents [7]. In other Central and Eastern European countries located in the temperate climate zone with a similar geographical latitude, such as the Czech Republic, Slovakia, Hungary, Ukraine, and the Baltic States, during the heating season (winter, early spring, and late autumn), emissions of particulate matter will also increase due to the heating of households. This leads to a seasonal rise in PM\({}_{10}\) concentrations and other air pollutants such as PAHs. This is due to similar climatic conditions, including a clear division into four distinct seasons.
## 1 Introduction
This paper proposes a zero-shot learning methodology based on the above second approach by using the embedding model with images as visual data and word vectors as semantic data. The proposed scheme has the following two main contributions. * We propose a hierarchical semantic loss in the training phase. It uses hierarchical knowledge in semantic data to calculate loss function. It shows the more meaningful embedding results in performing zero-shot cross-modal retrieval. * We propose an unseen confidence estimator in the inference phase. It estimates the confidence score for input query image and adjusts the distances between a query image vector and unseen class semantic vectors to improve the performance of generalized zero-shot learning. Generally, in embedding-based zero-shot learning using word vectors as semantic data, the embedding model is trained such that the distance between the positive pair of visual data and the semantic data such as word vector of labels is closely embedded into common vector space by using mean squared error loss or triplet margin loss. However, as the existing triplet margin loss uses random sampling in the process of selecting a negative sample, unnecessary training occurs because the semantic data having no relation to it as a negative sample are used in training the embedding model [23]. In our way, hierarchical semantic loss selects a semantically more closely related data as a negative sample of the triplet margin loss and can create a better zero-shot learning model. The proposed methodology has a novelty in that it utilizes the common hierarchical knowledge of semantic data, which is easy to obtain, compared with the zero-shot learning methodologies using attributes that are relatively difficult to collect. We also propose a generalized zero-shot learning methodology using a confidence estimator, which estimates whether the input visual data are seen class or unseen class. The proposed methodology increases the influence of the class label of unseen data if their confidence on the unseen class data is high. Experiments conducted using CIFAR-100 and CIFAR-10 datasets show that the proposed hierarchical semantic loss and confidence estimator can perform more efficient embedding-based zero-shot learning.
## 6 Results and Discussion
### Impact of Network Density on NDR  
Figures 11 and 12 show the evolution of FND and HND occurrence under the three protocols when the WSNs size increases. The plotted results reveal that the speed at which FND and HND occur in all protocols increases with the increase of the WSNs size but the speed increase varies. If we compare the FND occurrence between the WSNs of 50 and 500 nodes, we find that the death speed increases by a factor of 3.37, 1.36 and 1.67 in DT, LEACH and LEACH-PRO respectively. For HND, its occurrence speed has similar trend to FND where it increases by a factor of 5.52, 1.28 and 1.96 in DT, LEACH and LEACH-PRO respectively. The analysis of these results tell us that although LEACH-PRO is still outperforming DT and LEACH it has a slightly higher HND speed compared to LEACH.  
Figure 11: FND evolution under varying WSN densities.  
Figure 10: NDR evolution during the network lifetime under varying network densities. Network of (**a**) 50, (**b**) 100, (**c**) 200, (**d**) 500 nodes.