esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Description of the Dataset  
The CICIDS2017 dataset [53], which contains both traditional and cutting-edge attacks, is utilized in this section. Introduced in 2018, the purpose of this dataset is to simulate real network attacks by imitating abstract network activities and injecting them into attack scenarios. The dataset consists of expert-created attack profiles and weekly network activity.  
The CICIDS2017 dataset consists of data samples with 80 characteristics, including attributes such as the number of forward packets, minimum flow length, and the maximum time between two flows. It is important to note that the dataset is highly unbalanced in terms of distribution. For example, there are over two million data samples from normal traffic, while there are only 11 instances of Heartbleed attacks. This characteristic makes the CICIDS2017 dataset more representative of real-world cyberattacks. Table 1 provides a detailed distribution of the dataset.  
Figure 2: Model-specific detection process.  
The dataset is usually divided according to the standards as 20% and 80% or as 30% and 70% [2]. However, our LVAE model aims to efficiently identify unknown types of attacks, unlike traditional methods. To achieve this, we create the training set by randomly selecting an equal number of attack samples and normal samples, which, together, account for 50% of the dataset. Similarly, we select the same number of samples for the testing set as for the training set. This ensures that we can accurately evaluate the detection ability of our method for unknown threats. Table 2 provides a detailed description of the dataset used in our research. The generated unknown attacks are only used for the experiments in this paper and are not used for any other purpose.
# Concrete Dam Deformation Prediction Model Research Based on SSA-LSTM
## 4 Case Study
### Data Preprocessing Results  
The deformation data of the four measurement points fluctuate greatly during the selected time period, and there are some particularly obvious outliers, which are not obvious with the use of a single denoising algorithm. Since each denoising algorithm has its unique advantages and applicability range, two algorithms, Gaussian filtering and Symlet wavelet filtering, were selected for overlapping denoising of the original data through the comparison of multiple denoising algorithms in order to better reflect the nature and characteristics of the monitoring data, which complement each other in terms of data characteristics and noise types and obtain obvious denoising effects. Afterwards, the missing data were interpolated using the \"interpolate\" linear function to further improve the quality and accuracy of the data. The results of preprocessing the data of EX3-4 measurement points training part were compared with the original data curve as shown in Figure 6. While denoising, the data were finally normalized in order to improve the convergence rate of the algorithm.
# P4-KBR: A Key-Based Routing System for P4-Programmable Networks
## 5 P4-KBR Implementation
### Data Plane: \"Translation of Ranges Solution\"  
The use of RANGE matching implemented by P4 enables the most efficient implementation of P4-KBR routing solution. However, we consider it might be of interest for readers the description of how the potential of P4 programming could offer an alternative solution to implement P4-KBR if only LPM matching (that is, value/mask matching) would be available.  
Figure 8 shows the _Ingress Parser_ module in this case. The main difference from the Ingress Pipeline defined in Section 5.1 is the inclusion of the _pre_key_routing_.  
Figure 7: ONOS and P4. Pipeline Independent (PI) framework allows the ONOS to manage and deploy a pipeline-aware app.  
As detailed in Section 4.3, the \"translation of ranges solution\" requires the subtraction and the addition of a value to the destination address of a received packet. Neither of these actions are included into the set of available OpenFlow actions defined by the OpenFlow standard. However, the P4 programmable data plane allows us to implement the required translation of the keys by the definition of the _pre_key_routing_ table and the _subtraction_sw_vid_value_ action. This table will be in charge of executing the shift of the destination address of the packet, a necessary task before looking up in the _key_routing_ table, which is populated by the controller after obtaining the adequate set of equivalent ranges and forwarding actions.  
The shift of the destination address and the lookup in _key_routing_ table are based on the use of a user-defined metadata field called _local_metadata_. _substracted_dst_. This metadata field will store the shifted destination value generated during the execution of the P4 program, in particular as a consequence of the _subtract_sw_vid_value_ action defined in the _pre_key_routing_ table.  
Summing up, the possibility offered by P4 of storing and manipulating data pertaining to each received packet as a user-defined structure, and the flexibility in the definition of tables (matching fields and actions) have made it possible to overcome the limitations associated with OpenFlow standard.
## Materials and Methods
### Skin Preparation and Collagen Extraction  
The skin was prepared according to the method [47]. The skin used was bigeye tuna (_Thunnus obesus_) skin, which was a fishery by-product. The skin was from Ambon and brought to the research laboratory in Bogor. The skin was stored in the freezer (\(-\)28 \({}^{\circ}\)C). The skin was cleaned, cut into small pieces (0.5 \(\times\) 0.5 cm\({}^{2}\)), and then lyophilized for 14 h using a TFD5503 Bench-Top freeze dryer (ilShinBioBase, Ede, Netherlands). The lyophilized skin was used for collagen extraction.  
Collagen extraction was carried out according to the method in [47]. The first stage was the pre-treatment stage, namely soaking of the skin in 0.1 M NaOH solution, followed by soaking in 10% (\(v/v\)) butyl alcohol solution. Soaking was carried out in the refrigerator at 4 \({}^{\circ}\)C for 24 h. The ratio of skin and soaking solution was 1:10 (\(w/v\)). The soaking solution was changed every 12 h. At the second stage, the pre-treated skin was extracted using dilute acetic acid (0.5 M) containing 0.1% protease (\(w/v\)). The proteases used were bromelain and pepsin. Extraction was carried out at 4 \({}^{\circ}\)C (in the refrigerator) for 72 h. The ratio of pre-treated skin to extraction solution was 1:40 (\(w/v\)). During the pre-treatment and extraction processes, the solution must be shaken frequently. The suspension was then centrifuged using a refrigerator centrifuge Hermle Z 32 HK (Benchmark Scientific, Sayrevill, NJ, USA) at 6000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C. The supernatant was separated from the precipitate. The supernatant was then salted-out by sodium chloride to a final concentration of 2.0 M. The precipitate was collected by centrifugation at 6000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C and then dialyzed with cold distilled water using a dialysis membrane, Spectra/Por(r) Dialysis Membrane MWCO 6-8000, Regenerated Cellulose (Spectrum Laboratories, Inc., Rancho Dominguez, Los Angeles, CA, USA). The obtained wet collagen was then lyophilized for 14 h using TFD5503 Bench-Top freeze dryer (ilShinBioBase, Ede, The Netherlands), so that dry collagen (freeze dry collagen) was obtained. Collagen obtained from skin extraction using acetic acid and bromelain was called bromelain soluble collagen (BSC). The collagen obtained from skin extraction using acetic acid and pepsin was called pepsin soluble collagen (PSC).
## 1 Introduction  
Natural disasters arise from various natural processes and phenomena in the biosphere, lithosphere, hydrosphere, and atmosphere interacting and adversely affecting humans, infrastructure, and resources [1; 2]. These events create hardship through loss of life and property, along with adverse impacts on agriculture through loss of yields [3; 4]. In 2017, the United States incurred more than USD 306 billion of damage due to severe weather-induced disasters, including wildfires, hail, tornadoes, and flooding from extreme precipitation [5]. Flood damage from extreme precipitation following three severe weather events during the specified year (Hurricanes Harvey, Irma, and Maria) incurred the majority of this damage [5]. Several studies have shown that the frequency, intensity, and impact of natural disasters, especially flood hazards resulting from severe weather events, have increased over the years and are projected to increase in the coming decades, partly due to human-induced alterations to the environment and climate change [2; 4; 6; 7; 8; 9]. Moreover, largely anthropogenic-ledactivities, including extreme fluid withdrawal rates, have induced surface deformation processes that have modified the land surface and have further intensified the impact and extent of inundation following landfalling tropical cyclones and other coastal flood regimes, as the impact of storm surges is intensified by a rise in sea levels and further exacerbated by long-term high subsidence rates [10; 11]. Furthermore, flooding in areas proximal to the courses of streams and rivers following severe weather events may be aggravated by the subsidence of flood control structures built along the courses of these streams and rivers [11; 12; 13; 14]. subsidence processes on such structures could alter the flow dynamics and enhance the spatial extent and depth of floodwaters [12; 13].
## 4 Discussion
### Summary of Evidence
These data can also impact the retention time; the literature reports that the retention period after expansion ranges from 2 to 12 months [35]. All studies included in this meta-analysis evaluated suture density up to 6 [21; 25; 26] or 7 months [16; 25] after expansion. Despite being a plausible period for suture reorganization in cases of RME [13; 15], this time may not be sufficient for patients treated with SARPE and MARPE. Density values show that 7 months after SARPE, remineralization was still incomplete and it was questionable whether the ossification present up until that point would be able to resist the forces exerted by adjacent bone structures and whether recurrence could be avoided [15; 16]. The MPS fusion first occurs posteriorly and then progresses anteriorly with new bone formation. For this reason, the opening of the suture takes place in the form of a triangle with the base located in the anterior part of the maxilla and the expansion occurring predominantly in the anterior and middle regions [29]. The bone density in the posterior region examined via occlusal radiographs was reported to decrease more than in the anterior region [35], similar to our results. It is reasonable to assume that after RME or SARPE, the density of the maxillary suture may differ depending on the level of bone density prior to expansion, which, according to the Misch classification, would be D3 (350 to 850 Hounsfield units) [36]. Specifically, if the posterior part of the maxilla has already undergone ossification, then the suture density in this region is likely to be higher after expansion compared to the anterior and middle regions, where the lower pre-expansion density may result in little to no difference in suture density. More studies are needed to prove this theory and a longer period of anterior retention may be recommended to prevent relapses.
## 3 Analysis of Test Results
### The Force-Displacement Curves for SBS-Modified Asphalt Mastic for Different Numbers of Cycles
Based on the peak load of the glued pulp specimen with a ligament width of 5 mm and the geometry of the specimen, the net sectional stress of the specimen can be calculated according to Equation (6):  
\[\sigma_{n}=P_{peak}/(\overline{B}\,\overline{L}) \tag{6}\]  
In the above formula, \(\sigma_{n}\) is net sectional stress; \(P_{peak}\) is peak load. Figure 9: The force–displacement curves for SBS-modified asphalt mastic for different numbers of cycles. CTOD can be calculated by the following equation:  
\[\delta_{t}=w_{e}/\sigma_{n} \tag{7}\]  
In the above formula, \(\delta_{t}\) is CTOD, mm. The crack tip opening displacement, also known as CTOD. Due to the high correlation between CTOD value and fatigue performance of asphalt mixtures. Therefore, the CTOD value can characterize the medium temperature fracture resistance of asphalt. Observing Figure 9 reveals that the load-displacement curve of SBS-modified asphalt mastic exhibits three distinct stages of fluctuation. In the first stage (or segment), the load increases rapidly with displacement. In the second stage (ab segment), after reaching the yield load, the specimen continues to stretch with displacement while the load decreases slowly. In the third stage (bc segment), the load drops sharply until the specimen fractures. This phenomenon could arise due to the formation of a cross-linked network structure by the SBS modifier within the mastic. With the rise in the quantity of temperature-humidity coupling cycles, the failure deformation of the mastic decreases, and the peak load increases, indicating that after the temperature-humidity coupling cycle, the SBS-modified asphalt mastic becomes harder, and its ductility deteriorates. As shown in Table 5, the CTOD of the SBS-modified asphalt mastic decreases with the increase in the number of temperature-humidity coupling cycles. The lower the CTOD, the closer the mastic is to a brittle state, making it more susceptible to permanent damage. The temperature-humidity coupling cycle diminishes the mid-temperature fracture resistance of the SBS-modified asphalt mastic, with the magnitude of this impact escalating proportionally with the number of cycles, aligning with the analysis findings regarding peak load and failure deformation. On the one hand, this may be due to the increased number and size of internal pores in the SBS-modified asphalt mastic under the combined action of temperature and humidity, providing channels for water to enter the mastic and ultimately destroying the internal structure of the mastic. On the other hand, it is because hydrophilic polar molecules and some water-soluble substances in the SBS-modified asphalt mastic combine with water, causing the mastic to become brittle and hard [33; 34].
# Three-Dimensional Pulp Volume Analysis in Lip and Palate Cleft Population
## 3 Results
### Comparison of Pulp Volume Regarding Sex and Age  
The average dental pulp volume value in both sexes, regardless of group, is presented in the following Table 3. It can be concluded that the values were statistically significant, with a \(p<0.001\) and mean values of 30.8 for males and 21.4 for females.  
Figure 3: Comparison of the values for the pulp volume of the upper central incisors.  
Figure 2: Diagram of extremes and quartiles shows the value distribution of the medium pulpal volume for both the control and CLP groups.  
The mean dental pulp volume for both sexes in the control group also presented a statistically significant value, with the pulp volume in males significantly higher than in females (\(p=0.001\)). In males, there was a mean of 30.2, a median value of 30, with a 75 th percentile value of 34.5 and a 25 th percentile value of 21.8. In females, there was a mean of 20.8, with a median value of 19 and 25 th and 75 th percentile values of 15.1 and 22.4, respectively.  
Similarly, the mean dental pulp volume for both genders in the CLP group also presented a statistically significant value, \(p=0.007\). The mean value for males was 31.4, with a median value of 29 (\(\pm\)12.5) and values of 22.5 for the 25 th percentile and 40.2 for the 75 th percentile. Females presented a mean value of 21.9, with a median value of 20 (\(\pm\)11.2) and values of 14.4 and 25.9 for the 25 th and 75 th percentiles, respectively.  
A statistically significant linear regression can be observed (\(p<0.001\)) between the medium pulpal volume and age, with age explaining approximately 32% of the pulpal volume variance. Age represents a regression coefficient of \(-1.919\), which means that for each additional year a medium decrease of 1.919 mm\({}^{3}\) in pulpal volume is observed. The dispersion graph shows the relationship between age and medium pulpal volume (Figure 5).  
\begin{table}
\begin{tabular}{l l l l} \hline  & **Masculine (61)** & **Feminine (33)** & \(p\) \\ \hline Volume (mm\({}^{3}\)) & 30.8; 29 (11.3) 22.5/37.1 & 21.4; 19 (10.6) 14.4/22.9 & \textless{}0.001 \(\varepsilon\) \\ \hline \end{tabular} \(\varepsilon\) Mann-Whitney Test.  
\end{table}
Table 3: The medium dental pulp volume value in both sexes.  
Figure 4: Comparison of the pulp volume values on the teeth on the cleft side and the non-cleft side.
## 4 Results and Discussion
### Incompressible Taylor-Green Vortex
#### 4.1.3 Discussion on Computational Cost  
In this section the computational cost of the various methodologies in this test case is compared. Table 1 shows the runtime cost of the incompressible TGV problem presented here. Multiple runs were performed and an average value of the runtime is given. Ratio values are computed based on the averaged runtime of the iLES approach with discretization being \(30^{3}\) elements and \(p3\) solution polynomial order. These test cases run on a CPU-based platform, with an Intel E5-2680 V3 node, which consists of 24 cores. Table 1 shows that the \(p3\) schemes are slightly more computationally efficient than their \(p1\) counterparts. It can be observed that when higher order discretization is applied, under the same DOFs, the computational cost is reduced by about \(7\%\) for iLES, DB and similarity simulations and \(10\%\) for Smagorinsky simulation. An explanation may be that fewer elements in higher order computation reduces the number of interfaces, which affects the cost of interface communication. The explicit SGS models both lead to extra cost compared to the iLES computation. This trend is reasonable because these models both call for extra calculations on SGS terms. The cost of the DB model is less than the explicit SGS models because of its more simple terms.  
Figure 6: Energy spectra of TGV simulation at \(t=9\) of TGV simulation, \(Re=1600\), \(120^{3}\) DOFs. (**a**) \(p1\); (**b**) \(p3\).
## 1 Introduction
### Fundamentals of the NOMA System  
The CD-NOMA and PD-NOMA systems enable manifold clients to access the same wireless resources in the coding and power domains. This creates non-orthogonality in client access. This work focuses on the PD-NOMA system, where users sharing the same resources are assigned different power levels [46; 47]. PD-NOMA further supports user fairness by providing the user with a high transmission power and a low channel gain. Conversely, the greater the channel gain, the less user will get broadcast power. This indicates that clients' power coefficients are allocated among corresponding clients contrariwise proportionate fashion with their station circumstances [48]. Additionally, under the NOMA system, a single subcarrier may be allotted to several clients, and each client can collect data from manifold subcarriers.  
1. NOMA's capacity to support manifold clients with the equivalent time-frequency resource makes it highly spectrum-efficient, resulting in improved spectral efficiency (SE). This, in turn, can enhance the system throughput.
2. Improved fairness for users can be achieved through the NOMA system, which allows for greater flexibility in the allocation of radio resources. By relaxing the orthogonal restraint of the OMA scheme, NOMA permits efficient resource allocation, promoting user fairness.
3. The non-orthogonal assignment of resources of the NOMA scheme implies that the available resources do not constrain the supported users or devices. With a less non-orthogonal resource allocation, NOMA can accommodate more users than OMA.
4. The NOMA technique can be considered an \"add-on\" to existing OMA methods as it uses a distinct power-domain dimension. As superposition coding and SIC methodology are well-established in theory and practice, NOMA can be linked to modern MA approaches. This makes NOMA compatible with different systems.
5. In the 6G connection, using OMA incurs significant latency and signalling costs due to the access grant required in the uplink. This is deemed unacceptable. As a solution, the NOMA approach can provide low transmission delay and signalling requirements.  
NOMA, on the other hand, facilitates confer-free upstream dissemination, reducing dissemination delay and transmitting complexity. Additionally, serving multiple users in a NOMA network helps to minimize latency. However, the following are some drawbacks of the NOMA scheme [49]:  
1. The overhead for CSI feedback is increased in the NOMA system because the base station (BS) needs to identify the top CSI to achieve the best-translating directive for the SIC scheme.
2. The SIC process increases the computational complexity of the receiver side, especially for multicarrier and multi-user systems.
3. In the NOMA system, the communication cost is heightened because the strong user needs to be aware of the energy dissemination of the feebler client to implement SIC.
4. As a result of distributing more power to the poor users, the whole system experiences increased inter-cell interference.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 4 Fault-Types Classification (FC)
### FC Based on Decision Tree (DT) Technique  
The term decision tree (DT) refers to graphs which are able to make decisions and its basics are detailed in [83; 84]. Three sorts of nodes are involved in DT namely; root node, internal nodes, and the leaf nodes. Mechanism of decision making starts from the root node for classification and class label is represented by the leaf node as shown in Figure 7 [85]. The suboptimal decision tree is obtained using training data via greedy algorithm e.g., C4.6, regression tree, ID3 and, classification and regression tree (CART) with increased accuracy in reasonably reduced time [84]. Random forest (RF) comprising of a finite number of DTs is used in [86] for fault classification in single and double circuit transmission lines. The decision-making mechanism can be performed with accuracy in less than a quarter-cycle via DT [28; 29]. DWT coefficients are used as features for CART-DT and a performance comparison was made with FNN [20]. Both of the mechanisms obtained a high degree of accuracy whereas the performance of CART-DT is found better.  
Figure 6: Basic fuzzy logic systems architecture.  
Figure 7: Decision tree layout.