esa-sceva/satcom-chunk-collection huggingface dataset:
# A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks +
## 6 Evaluation
### Scenario 3: Energy Depletion  
In this scenario, the mobile node in the network is required to return to its initial position due to the lack of energy. This scenario can be implemented in two cases. The first case (_Scenario 3A_) is where the mobile node is not needed and becomes idle at some point. When its energy reaches its lowest threshold, it will just send an information message to the sink to change the mobile node's status and return to its initial position. The second case (_Scenario 3B_) is where the mobile node is still needed in the network, but its battery reaches the lowest threshold. The mobile node informs the sink node that it needs replacement, and after a certain time, it moves back to its initial position. In both cases, the mobile node that returns is not reconsidered for being used again until its battery is fully charged.  
Figure 8 presents the topology of the cases of this scenario. In Figure 8a, the mobile node moves to its current position without causing any problems, whereas in Figure 8b, the mobile node needs a replacement to move back. When the sink node sends mobile node 22 to replace mobile node 21, 21 moves back to its initial position to charge its battery. In both scenarios, the new congestion problem is resolved with a new mobile node, mobile node 23, injected into the network.  
These experiments demonstrate the reuse of mobile nodes in the network. Reusing the mobile nodes provides the potential of using less energy and results in an energy-efficient solution for the entire network.  
Figure 7: Scenario with reuse. (**a**) First Congestion, (**b**) Second Congestion.
## 2 Background Knowledge
### An Advanced Variant of Conv-TasNet: DPTNet  
The Conv-TasNet framework employs a temporal convolution network, which consists of stacked 1-D dilated convolution blocks, to predict the mask for the encoder output. In recent years, some more advanced encoder SE methods have followed the encoder-decoder structure of Conv-TasNet while exploiting a more exquisite module to perform the mask estimation. Among them, the dual-path transformer network (DPTNet) [39], as the name suggests, uses an attention-based dual-path modeling network to estimate the mask and achieves superior SE performance. Here, we give a brief introduction to DPTNet, focusing on its difference from Conv-TasNet.  
As depicted in Figure 3, the masking estimation layer of DPTNet consists of three stages: segmentation, dual-path modeling, and overlap-add, whose functions are described as follows:  
1. Segmentation: In the segmentation stage, the input feature matrix \(\mathbf{W}\) from the upstream encoder is split into fix-length chunks with hopping. These chunks are then concatenated together to comprise a 3-D tensor.
2. Dual-path modeling: The 3-D tensor passes through the stack of dual-path modeling blocks containing intra-chunk and inter-chunk processing. The intra-chunk processing models the local information of the tensor with an improved transformer defined by [40]. The respective output then passes through another improved transformer to perform inter-chunk processing, which catches the information about global dependency among different chunks.
3. Overlap-add: After completing the dual-path modeling, we employ the overlap-add method to the chunks of the ultimate tensor to generate a preliminary mask. After that, a hyperbolic tangent function is subsequently applied as a non-linear function to the preliminary mask to ensure the final mask values in the range \((-1,1)\).
## 5 Discussion
### Favorable Enrichment Environment of Li in Coal  
The Li content in coal is positively correlated with SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and K\({}_{2}\)O, indicating that Li mainly occurs in aluminosilicate clay minerals (such as kaolinite, illite, etc.). The higher Li content in the parting, roof, and floor samples and the positive correlation between Li and clay minerals indicate that the parting, roof, and floor dominated by clay minerals may be more favorable to the occurrence and enrichment of lithium (Table 5). The positive correlation between Li content and ash yield in coal indicates that Li is mainly enriched in the syntepositional stage, whereas the significant difference in the Li content in coal seams indicates that the changes in the deposition environment affect Li enrichment.  
The concentration of Li shows an entirely different distribution in coal samples and non-coal samples (roof, parting, and floor). The parting exhibits the highest Li content (566 \(\upmu\)g/g), followed by Li content on the floor (369 \(\upmu\)g/g). The content of Li in coal samples is on the whole decreasing trend from bottom to top, and the content of Li in the roof is relatively low (Table 5). The distribution of Li content in Jincheng samples was similar to that in Gaoping samples (Figure 16). Based on the above analysis, it is shown that Li originated from the underlying strata of coal measures, and the parting is a good lithium enrichment site. In addition, coal acted as a sealing layer for Li, which hindered the upward migration of Li [114].  
The correlation between Li and deposition environmental parameters shows that Li is negatively correlated with Sr/Ba and C-values (Figure 17), indicating that Li enrichment is related to terrigenous detritus, i.e., the terrestrial deposition environment is conducive to Li enrichment. The positive correlation of Li with V/(V+Ni), Cu/Zn, and V/I (Figure 17) suggests that a reduced sedimentary medium is favorable for Li enrichment. There is a significant negative correlation between Li and Sr/Cu (Figure 17), indicating that a stable and arid climate is conducive to Li enrichment. The Li and GWI show a positive correlation (Figure 17), implying that Li in coal is influenced by water flow transport and deposition, indicating its terrigenous origin. The terrigenous detrital quartz, anatase, and layered clay minerals (Figure 4, Figure 6, and Figure 7) provide strong support for terrigenous clas  
Figure 16: Comparison of Li content between Gaoping samples and Jincheng samples (Jincheng sample data from [PERSON] et al. (2019) [6]).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Vitrinite** & **Inertinite** & **Liptinite** & **Clay Minerals** & **SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\)** **Molar Ratio** \\ \hline Li & \(-\)0.17 & \(-\)0.26 & 0.50 & 0.66 & \(-\)0.55 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlations between Li and macroal, clay minerals, and SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) molar ratio.
## 2 Basic Principles of Digital Image Correlation (DIC)
As reported in Reference [53], one function is the magnitude of intensity value difference,  
\[R(x,\ y,\ x^{*},\ y^{*})=\ \Sigma\left|F(x,\ y_{*})-G(x^{*},\ y^{*})\right| \tag{1}\]  
and another function is the normalized cross-correlation (\(NCC\)),  
\[NCC(x,\ y,\ x^{*},\ y^{*})=\frac{\sum F(x,\ y)G(x^{*},\ y^{*})}{\sqrt{\sum F(x,\ y)^{2}\sum G(x^{*},\ y^{*})^{2}}} \tag{2}\]  
where \(F(x,\ y)\) and \(G(x^{*},\ y^{*})\) represent the intensity values within the subset of the pre-event and post-event images, respectively; \((x,\ y)\) and \((x^{*},y^{*})\) are the coordinates of a point on the subset before and after deformation, respectively; and the symbol \(\Sigma\) represents the sum of the values within the subset. The coordinate \((x^{*},y^{*})\) after deformation is related to coordinate \((x,\ y)\) before deformation. Hence, the displacement components are obtained by determining the best setting of the coordinates after deformation \((x^{*},y^{*})\) that minimize or maximize the \(R\) or \(NCC\) parameters [53]. The subset chosen for the DIC analysis has to be tracked in the post-event images. The subset shape during the entire correlation process must be able to change its geometrical parameters (such as its size, profile and position). Different numbers of parameters define the method by which the subset can deform during the correlation process. If the first-order partial derivatives are considered,  
Figure 1: Schematic diagram of the basic principle of DIC. the general form of an affine transformation is retrieved. The affine transformation accounts for rigid body motion (translation and rotation), shear and normal stretching. Next, by adding the terms \(\Delta x\) and \(\Delta y\), an expression similar to that of a four-node bilinear Lagrange interpolation function in a finite element analysis is yielded and mapping onto an irregular quadrangle deformation is allowed. Finally, second-order terms can be included in the shape function to introduce the effects of full bending of the subset [52] (Figure 2). In the case of a robust and homogeneous deformation, an affine transformation order and a large subset will be able to describe the deformation process. When the deformation is more complex and heterogeneous, smaller subset sizes should be used or a higher transformation order needs to be introduced for a precise description of the deformation process. Most of the DIC algorithms use a subset-based scanning method to measure the displacements, thereby allowing for spatially continuous deformation detection with sub-pixel accuracy [54]. The same approach can be applied with only two images or when using stacks of images collected at different times, thereby achieving time series of deformations.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 5 Results of Attack-Specific Two-Class Models
### Exposure to Unseen Data
#### 5.2.6 Web Attacks  
The web attacks are a harder attack type to classify within CIC-IDS2017. Most methods were not able to reach perfect classification scores. Although day 5 and 6 contain web attacks, the dataset documentation does not mention what the differences between the two days are. They both contain web brute force attempts, cross-site scripting (XSS) and structured query language injections (SQLis).  
The poor generalization obtained by single decision trees is the root cause for the feeble results of the methods that build on top of it. Recall is so consistently below 40%, with spiking precision scores making it impossible to recommend any of these methods. These results did not improve with more training, different scaling or less feature reduction. The worst performers are randomized decision trees. There is no learning, because they do not try to set optimal splitting points. It is clear that for a harder-to-classify attack class, this method does not work. The web attack models built on decision trees typically had 90-100% recall after some training within CIC-IDS2017. The relative 50% drop-off is disconcerting.  
Nearest neighbors starts off with some signs of learning, but levels off quickly at low to very low precision-recall pairs. The method had good scores within CIC-IDS2017 (85-95% recall and 75-85% precision), but that performance does not carry over into CSE-CIC-IDS2018. Nearest centroids had robust 85+% recall on the web attack traffic of CIC-IDS2017, at all combinations of training volume, scaling and feature reduction. The associated precision was never good, so it is expected that this will continue. Unfortunately, the nearest centroids loses much in terms of recall. Only the models with min-max-scaled features stay stable at 57%. Other methods of scaling have recall stable at 15%. All precision is lost.  
The near-perfect recall and moderate precision of logistic regression models within CIC-IDS2017 is not retained on CSE-CIC-IDS2018. Recall drops below 40%, often crashing to 0%, and precision is at 0% more often than not. This conclusion also applies to a linear SVM and rbf SVM. All of these methods struggled in terms of precision within CIC-IDS2017, but did reach near-perfect recall. None of this translates into generalization performance when classifying the web attacks in CSE-CIC-IDS2018, despite the fact that both datasets contain the same types of web attacks.  
It is clear that, for this type of attack, which typically has a lower network footprint (unless it is a brute force login), is much harder to classify from network-related features. Within the dataset, however, the performance can be very good and a subsequent recommendation for use in real-world systems would be logical, but ultimately misguided.
# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 4 Current State-of-the-Art Schemes
### Adaptive PIT Management
Not considering processing delays might result in the degradation of ISR in some applications. Likewise, not considering the content type [10; 26; 27] while replacing \(PITE\) could lead to life loss and disabilities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & **PIT** & \multirow{2}{*}{**PIT Management Strategy**} & **Simulation** & \multirow{2}{*}{**Limitations**} \\  & **Management** & & & **Environment** & \\ \hline \multirow{3}{*}{[14; 15]} & \multirow{3}{*}{Static} & \(\square\) & Neighbour-based forwarding & \(\square\) & NS-2, & \(\square\) & Contention problem due to \\  & & selection strategy & \(\square\) & Highway scenario & & periodic beacon overhead \\  & & \(\square\) & PIT entries are managed by & & with mobility & \(\square\) & Stale PIT entries in high \\  & & selecting limited forwarder(s) & & & density networks. & \\ \hline \multirow{3}{*}{[16; 17; 18]} & \multirow{3}{*}{Static} & \(\square\) & Receiver-based & \(\square\) & ndnSIM, & \(\square\) & Stale PIT entries due to more \\  & & (position-based) forwarding. & \(\square\) & Urban scenario & & chances of Reverse Path Partitioning \\  & & \(\square\) & PIT entries are managed by & & with mobility & problem as distant neighbor is \\  & & forwarder selection & & & selected as forwarder. & \\ \hline \multirow{3}{*}{[19]} & \multirow{3}{*}{Static} & \(\square\) & To proactively cache the content & \(\square\) & ndnSIM, with & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & & near to the requester to reduce the & & infrastructure & & \\ \cline{1-1}  & & number of PIT entries & environment & & \(\square\) & Stale PIT lifetime \\ \cline{1-1} \cline{2-5}  & & \(\square\) & _PITE_ Replacement policy is same & & & \\ \cline{1-1}  & & as presented in [26; 27]. & & & & \\ \cline{1-1}  & & Adaptive PEL scheme proposed & & & application popularity \\ \cline{1-1}  & & (i) When PIT not overflow & \(\square\) & ndnSIM & \(\square\) & The received valid Interest _PITE_ is \\ \cline{1-1}  & & - Threshold is set as an average of & \(\square\) & No mobility & replaced with the _PITE_ in the PIT \\ \cline{1-1}  & & Interest LT and default LT. & \(\square\) & Rocket fuel & which might be about to \\ \cline{1-1}  & & (ii) When PIT is in overflow & & mapped topology & be satisfied. \\ \cline{1-1}  & & \(\square\) & PEL threshold is updated by & & \(\square\) & Type of content, and \\ \cline{1-1}  & & calculating the average LT between & & & environmental conditions \\ \cline{1-1}  & & the LT threshold value and the & & & not considered. \\ \cline{1-1}  & & incoming packet LT. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PIT Management Schemes in NDN-based VANETs.
# Parameterized Algorithms in Bioinformatics: An Overview
## 3 Genome Assembly and Sequence Analysis
### Multiple Sequence Alignment  
**Problem Description.** Multiple Sequence Alignment is the central alignment problem in bioinformatics, used to align DNA sequences along one another, taking insertions, deletions, and substitutions into account (see Figure 5). An _extension_ of a string \(s\) is a string obtained from \(s\) by inserting any number of _gap_ characters, denoted -. An _alignment_ of strings \(s_{1},\ldots,s_{k}\) is obtained by creating an extension \(s_{1}^{\prime},\ldots,s_{k}^{\prime}\) of each input string, all of the same size. _Column_\(i\) of an alignment is the tuple \((s_{1}^{\prime}[i],\ldots,s_{k}^{\prime}[i])\). A _cost function_ is any function assigning a positive cost to a column. The _unit cost function_ counts the total number of mismatches in a column, i.e. the number of pairs \((j,j^{\prime})\) such that \(s_{j}[i]\
eq s_{j^{\prime}}[i]\).  
**Input:** Strings \(s_{1},\ldots,s_{k}\), and some \(m\in\mathbb{N}\)  
**Question:**\(\exists\) an alignment \(s^{\prime}_{1},\ldots,s^{\prime}_{k}\) such that the sum of the cost of each column is \(\leq m\)?  
**Results.** Multiple Sequence Alignment can be solved by a straightforward dynamic programming in time \(\ell^{O(k)}\) for any cost function (where \(\ell\) is an upper-bound on the input string length). It is NP-hard for a wide range of cost functions, including all metric cost functions (even on binary strings), and in particular the unit cost function [42; 43; 44]. Due to its central position in DNA analysis, this problem has been the subject of over 100 heuristics over the last decades (this was already said in 2009 [45]), but there does not seem to be any success so far in terms of exact parameterized algorithms.  
**Open Problems.** Does Multiple Sequence Alignment admit an FPT algorithm for parameter \(k\)? If not, can \(\ell^{o(k)}\) be achieved?
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 4 Results and Discussion  
Shock waves, as a common phenomenon in supersonic flow, cause significant changes in the flow structure of supersonic channels [27]. To analyze the shock structure in the flow field, this article used numerical schlieren to represent the distribution of shock waves in the scramjet using density gradient \(\partial\rho/\partial x\). The greater the density gradient, the stronger the shock wave intensity.  
As shown in Figure 11, the oblique shock wave, which originates from the compression caused by the aircraft's front body surface, is situated just upstream of the inlet lip's leading edge. As the incident shock wave interacts with the wall surface, it triggers boundary layer separation, leading to the formation of a separation bubble in the vicinity of the wall and the generation of the first reflected shock wave. After passing through the inlet, the airflow was compressed and converged, resulting in an oblique shock wave. As the air traversed this shock wave, its velocity decreased while pressure and temperature were simultaneously elevated. This process effectively conditioned the airflow into the combustion chamber, where it will be mixed with fuel. As can be seen from Figure 11, the intensity of the shock wave gradually diminishes with the number of reflections. The non-uniformity of the airflow entering the inlet is exacerbated by the flight's angle of attack and the asymmetry of the aircraft's front body surface along the vertical axis.
## 4 Performance Analysis
### Transaction Security Analysis
#### 4.2.4 Security of Legitimacy Verification of the Relevant Transaction Amounts  
The BTPP scheme achieves concealment of the transaction amounts via the Pedersen commitments and range proofs based on Bulletproofs. Taking the previously described transaction as an example, for the sender's commitments \(C_{\text{b}}=[C_{1},C_{2},C_{5}]\) for the amounts and the blind factor \(r_{\text{b}}=r_{\text{b}_{1}}+r_{\text{b}_{2}}-r_{\text{b}_{\text{b}}}\), the receiver verifies the correctness of the payment amount based on Equation (14), i.e., \(v_{1}+v_{2}=v_{4}+v_{5}\), and only the sender's correctly generated commitments to the input amounts and the change amount can pass the receiver's verification of the payment amount. Meanwhile, for the sender's \(C_{\text{b}}\) and the receiver's \(C_{\text{a}}\) and \(K\), the verification nodes verify the balance of transaction inputs and outputs as follows:  
\[\begin{array}{ll}C_{1}+C_{2}&=C_{5}+C_{\text{a}}+K\\ &=r_{\text{b}_{1}}\cdot\text{G}+v_{1}\cdot\text{H}+r_{\text{b}_{2}}\cdot\text{G }+v_{2}\cdot\text{H}=r_{\text{b}_{5}}\cdot\text{G}+v_{5}\cdot\text{H}+r_{\text {a}}\cdot\text{G}+v_{4}\cdot\text{H}+(r_{\text{b}}-r_{\text{a}})\cdot\text{G} \\ &=v_{1}\cdot\text{H}+v_{2}\cdot\text{H}=v_{5}\cdot\text{H}+v_{4}\cdot\text{H} \\ &=v_{1}+v_{2}=v_{4}+v_{5}\end{array}\]  
Only when both transaction parties correctly generate commitments to the relevant amounts can the verification notes verify the balance of input and output amounts of the transaction. Additionally, the range proofs provided by both parties for the output amounts ensure that the output amounts are not negative. Therefore, the BTPP scheme can securely verify the legality of transaction amounts.
## 4 DC-DC Converter Topologies for LEV Charging
### Off-Board Charging  
Considering the potential impact of on-board LEV chargers on supply power quality and the drive to make LEVs more efficient, researchers are looking towards off-board and DC-based LEV charging. This mode of charging has a comparatively lower number of conversion stages, thus leading to more efficient charging. Furthermore, there are limited concerns with respect to supply power quality, owing to the DC power supply. DC-based charging eliminates the front-end AC/DC and PFC stage altogether, thus leaving only a DC-DC stage which controls battery charging. Normally, DC chargers exist as off-board chargers [3], and it is a relatively nascent concept in in LEVs. Thus, it has not seen widespread commercial use yet. Notable commercial uses of DC fast charging are highlighted in [5; 59].  
Another interesting aspect of off-board DC chargers is the potential for solar PV integration at the supply end. Certain works report a dual input EV charger [60], which features a three-port charger powered through a 3-phase AC grid and a standalone PV system. In the event of low solar irradiation, the EV charger operates in grid-connected mode. One of the key advantages of this setup is the ease of burden on the AC grid due to the use of solar PV power. Furthermore, this type of charger is also capable of operating in V2G mode, during times when the connected EV is idle for long hours. The PV source is connected via a unidirectional DC-DC converter, which is responsible for maximum power point tracking. A bidirectional AC/DC converter ensures charging from the grid and allows a possibility for V2G. Finally, the unidirectional DC-DC converter interfaced with the EV ensures charging control.  
Nevertheless, the same segregation applies here as in the case of on-board chargers: (i) isolated, and (ii) non-isolated chargers. There may be a valid argument for use of isolated converters if the voltage level is sufficient. However, for low power levels, it is feasible to pursue non-isolated topologies.
# Biosynthesis of Silver Nanoparticles Using _Tabernaemontana ventricosa_ Extracts
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM)  
The morphology and general particle size of the synthesized AgNPs using the various extracts of _T. Ventricosa_ were characterized and confirmed using SEM. The SEM images illustrated in Figure 3A-F substantiate the synthesis and particle size (<100 nm) of the AgNPs. The formation of AgNPs was predominantly spherical, uniform in terms of shape, and displayed extreme agglomeration. In a similar study, [PERSON] et al. [2] characterized AgNPs synthesized using _T. divaricata_ extracts. The SEM analysis showed that the synthesized AgNPs also displayed consistent spherical-shaped NPs, which ranged from 29 to 68 nm (<100 nm); however, the synthesized particles were well distributed compared to the current study, which showed high agglomeration [2]. In another study by [PERSON] et al. [41], _T. divaricata_-mediated AgNPs showed contrasting rod-like structures with an average particle size of 40-57 nm but displayed some agglomeration. It is suggested that agglomeration occurs as a result of dehydration/drying of the synthesized solutions [15; 45]. Moreover, studies have shown that the degree of agglomeration and average particle size of AgNPs are often influenced by variations in reaction temperature, extract concentration, bioactive compounds, and AgNO\({}_{3}\) concentration [46; 47; 48].