esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Filtering Algorithm
### LU Decomposition  
The coefficient matrix of the approximating spline equation has symmetric positive definite property. As shown in Equation (8), a positive definite matrix \(A\) can be decomposed into unit lower triangular matrix \(L\) and unit upper triangular matrix \(U\) by rewriting the Gauss elimination method into a compact form. Thus, the problem of solving \(Aw=z\) is transformed into the problem of solving \(Ly=z\), \(Uw=y\). The advantage of transforming the system of equations into the matrix form is that the matrix algorithm can be used to decompose the spline equations into simple parts, thus improving the computational efficiency. The flow chart of LU decomposition method is realized on the computer as shown in Figure 7. The elimination process of LU decomposition method uses the Gaussian elimination method, and generates the upper triangular matrix \(U\). At the same time, when the multipliers are eliminated, they are placed in a lower triangular matrix, and the main diagonal elements are set to one to generate the corresponding lower triangular matrix \(L\):  
\[A=\left[\begin{array}{ccccc}1&&&&\\ L_{2,1}&1&&&\\ L_{3,1}&L_{3,2}&1&&&\\ &\ddots&\ddots&\ddots&\\ &&&L_{n,n-2}&L_{n,n-1}&1\end{array}\right]\left[\begin{array}{ccccc}U_{1,1}&U_{1,2 }&U_{1,3}&&&\\ &U_{2,2}&U_{2,3}&\ddots&\\ &&U_{3,3}&\ddots&U_{n-2,n}\\ &&&\ddots&U_{n-1,n}\\ &&&&U_{n,n}\end{array}\right], \tag{8}\]  
Figure 6: Flow chart of Gauss elimination method.  
When the computer realizes matrix \(U\), the corresponding matrix \(L\) is also formed. Moreover, the original data \(z\) do not participate in the elimination process, and the algorithm performs elimination only once. Thus, the whole elimination process becomes mainly the formation of matrix \(U\). The elimination includes approximately 11 operations. Then, computer need to do two back substitution processes for \(z\). Compared to the Gaussian elimination, although there is an additional back-substitution, this additional calculation replaces the part of the calculation that is saved by the absence of \(z\) in the elimination process. For a set of raw data \(z\), the LU decomposition method and the Gaussian elimination method have the same calculation cost. If \(A=\) LU decomposition is adopted, it is quite convenient to use LU decomposition method to solve some equations such as \(Aw=z\) (1), \(z\) (1),..., \(z\)(1). There is no need for a double elimination process, and each solution \(Aw=z\) requires only two back substitution. However, the LU decomposition still adopts the Gaussian elimination process, which can cause numerical instability.
# Application of Convolutional Neural Network to Defect Diagnosis of Drill Bits
## 4 Results and Discussion
### Computational Time  
In this experiment, MATLAB, R2021b version was used. The PC used for all calculations was equipped with the 11 th Gen Intel(R) Core (TM) i5-1135G7 @ 2.4 GHz, with 8 GB RAM. For the 2D CNN, 206 s was used for 200 epochs, and approximately 300 s was used for 300 epochs. On the other hand, for the 1D CNN, the overall computational time was around 147 s for 300 epochs. As a result, the processing time for the 1D CNN process was shorter than the 2D CNN process due to the smaller data size.  
Table 1 shows the diagnostic accuracy under different epoch numbers for the 2D CNN and the 1D CNN classification processes. For the 1D CNN, classification accuracy increased  
Figure 9: The t-SNE visualization of features for (**a**) 1D CNN non-processed data, (**b**) 1D CNN processed test data, (**c**) 2D CNN non-processed data, and (**d**) 2D CNN processed test data.  
rapidly for the initial 100 epochs. After the accuracy reached 80%, the increase in accuracy slowed and gradually reached 96% at 300 epochs. For the 2D CNN, the accuracy stabilized at 97.33% after 200 epochs. As mentioned above, it took approximately 147s for the 1D CNN to run 300 epochs and 206 s for the 2D CNN to run 200 epochs. These results indicate that the 2D CNN has a better classification ability. Although it takes more iterations for the 1D CNN to reach a high classification accuracy, the computational complexity for each iteration is lower than the 2D CNN. In the 2D CNN, the input image size is 100 \(\times\) 100 pixels with three layers. It means that there are 10,000 \(\times\) 3 data points for a single measurement. However, for the 1D CNN, the input data array only contains 1536 data points for each measurement. As a result, the overall time cost for the 1D CNN is less than that for the 2D CNN. Additionally, the hardware requirements for computation are much less than the 2D CNN. This property will be beneficial when performing real-time data analysis, and it also provides the possibility for system integration.
## Appendix L Description of Mechanisms for Vehicles
### Mixed Consensus Algorithm Based on PoW and PBFT [609]  
The proposed hybrid consensus algorithm, combining PoW and PBFT, is created to achieve efficiency in VANETs that are characterised by being resource-constrained. Due to the hybrid nature of the algorithm, PoW is applied to improve security while practical PBFT enhances efficiency. The application of PoW benefits Sybil attack resistance. PBFT, on the other hand, allows a smaller number of consensus nodes to be used which reduces processing time and improves efficiency. Participants are, furthermore, rated through a reputation scheme in which direct trust and indirect trust are taken into account.
## 2 Related Work
The authors of [51] combined the lossless and reversible schemes through a public key cryptosystem utilizing the probabilistic and homomorphic properties. In contrast to the other schemes, [52] proposed an improved RDH in encrypted images using a symmetric key cryptosystem. Bits \(n\), where \(n=2,3,4,\ldots\) are embedded into blocks of fixed side-lengths, by dividing the blocks in terms of \(n\) sub-blocks. Hence, the payload was greater and the \(PSNR\) was also improved over the block sizes \(8\times 8,16\times 16,\ldots\), when compared with [26]. The RDH scheme described in [47] had an overhead of sharing the convolutional neural network model for recovery, which was overcome in [53]. The latter authors utilized the correlation of pixels in the natural images for the recovery of the original cover, as in [26]. An integer wavelet-transform-based RDH in the encrypted domain was proposed in [54]. The orthogonal coefficients after orthogonal decomposition in the high frequency sub-band facilitated the hiding of secret information via a histogram-shifting operation. An adaptive method of data hiding in encrypted images was proposed in [55]. Here, a stream cipher and block permutation was employed to encrypt the cover image. The encrypted blocks were divided into two categories: smooth and complex. A data hider vacated spaces for the additional data by compressing the LSBs of the smooth block, keeping the pixels in the complex block unaltered. In this scheme, the directly decrypted image quality was better as only a few of the portions were used to hide the data. In the literature on RDH schemes in the encrypted domain, schemes are described that produce a high payload with full reversibility [16; 17; 18; 23; 56]. The intention of the proposed approach here is, however, not to achieve a very high payload but, instead, to achieve a good trade-off between the payload and the quality of the recovered image without changing the pixel values while embedding the data. Such a scheme can safeguard the security established using the encryption algorithm. Moreover, the data embedding is performed over the encrypted image with a Fibonacci transform, which is a widely used image-scrambling algorithm to ensure security.
## 1 Introduction  
Atmospheric pressure plasma jets (APPJs) have attracted a great deal of attention in the last several decades due to their various applications, such as biomedical tasks, surface modification, sterilization, etc. [1; 2; 3; 4; 5; 6]. In various applications, a large-scale or major plasma jet is essential to achieve high application efficiency and sufficiently low costs [7; 8]. However, limited by the shielding of the electrical intensity, gas flow turbulence, and discharge instability during long-term discharge, the longest APPJ that has been reported is about 10-12 cm in the surrounding air [9], and it is still unsatisfactory in terms of both the jet length and diameter for some applications.  
The scale of an APPJ can be affected by the applied voltage, type of excitation voltage, frequency, gas flow rate, and electrode configuration [10; 11; 12; 13; 14; 15; 16]. Some studies have indicated that the length of an APPJ is strongly correlated with the applied voltage at lower levels; however, when the applied voltage exceeds a certain threshold, the APPJ's length remains constant, despite further increases in the voltage [11; 12; 13]. In this process, the space charges play an important role in preventing further increases in the APPJ's length, because the electric field built by the space charge can suppress the applied electric field at the trail of the plasma jet, when a long microsecond pulse or unipolar pulse voltage is employed [17]. The polarity of the excitation voltage pulse is another critical parameter in the generation of large-scale plasma jets [11; 18]. It has been revealed that a plasma jet at positive polarity is typically composed of \"bullet-like\" plasma volumes, which can be considered as \"guided ionization waves\", propagating with speeds of 10\({}^{3}\)-10\({}^{6}\) m/s, and the length of the APPJ,which depends on the travel distance of the plasma bullet, is typically in the range of 3-8 cm [19]. Meanwhile, a plasma jet at negative polarity is only about 2-3 cm, with a shape similar to an arrow [20]. Moreover, theoretical and experimental studies in recent years have indicated that the flow patterns (laminar flow and turbulence flow) also have obvious effects on the scale of the plasma jet [11; 21]. When the turbulence gas flow is formed by the electrode's appearance or the gas flow rate, the fast diffusion of the working gas can limit the length expansion of the plasma jet significantly.  
In this work, to achieve a large-scale atmospheric pressure plasma jet (LS-APPJ), we design a plasma jet structure featuring a single-spiral high-voltage electrode without the need for a ground electrode. The LS-APPJ is excited by a bipolar pulse voltage, positive unipolar pulse voltage, and negative unipolar pulse voltage in atmospheric-pressure helium. The waveforms of the voltage and current, discharge images, and temporally and spatially resolved spectra are investigated. Additionally, we discuss the effects of the pulse voltage polarity and the jet tube's length on the length of the plasma jet.
## 1 Introduction
Through simulation analysis, we validate the effectiveness of the proposed algorithm, particularly by comparing its performance with traditional recovery strategies in two scenarios: random node failures and deliberate attacks. The results further demonstrate the superior performance of our algorithm in network recovery. 4. This study provides a novel approach and solution for damage recovery in air-sea cross-domain communication networks, significantly contributing to the enhancement of network reliability and stability.
## 2 Materials and Methods
### DPPH Radical Scavenging Activity  
The assessment of DPPH radical scavenging activity was carried out as previously outlined in our methods [17]. In brief, a solution of DPPH radical was created by blending 7.89 mg of DPPH with 200 \(\mu\)L of 80% aqueous methanol, resulting in a concentration of 0.1 mM. This DPPH solution was then appropriately diluted with 80% aqueous methanol until the absorbance reached 0.700 \(\pm\) 0.020 at 517 nm using a Shimadzu spectrophotometer (Shimadzu, Kyoto, Japan). Next, a total of 50 \(\mu\)L of ACE or each ACE fraction was combined with 2.95 mL of the DPPH solution, and the mixture was incubated at room temperature in darkness for a period of 30 min. Subsequently, the absorbance at 517 nm was gauged utilizing the Shimadzu spectrophotometer (Shimadzu). For reference, 80% aqueous methanol served as the blank, while vitamin C was utilized as the standard. The DPPH radical scavenging activity was expressed in terms of mg of vitamin C equivalents (VCE) per g of dry weight of either the ACE or its individual fractions.
# Cryptographic Protocol with Keyless Sensors Authentication for WBAN in Healthcare Applications
## 3 Protocol Design and Description  
In the previous section, different protocols focused on protecting WBASNs and TMIS were presented. These protocols are primarily focused on ensuring the authentication of entities. The protocol presented below focuses on guaranteeing not only entities' authentication but also information. In the same way, the confidentiality and integrity of sensitive information that travels through the network are guaranteed. This is achieved by using four cryptographic primitives to ensure secure communication between the entities participating in the WBASN. Therefore, sensitive information that is transmitted between nodes is protected. Likewise, the protocol applies a key-free or non-cryptographic method of authentication of the nodes, which makes it possible to distinguish whether the sensors are on the patient's body.
# Flexural Performance of Novel Nail-Cross-Laminated Timber Composite Panels
## 3 Experimental Results and Analysis
### Effects of Different Panel Design Parameters
#### 3.3.3 Effect of Number of Nails  
Specimens KWL30070235480 and KWL30070235240 were used to investigate the effect of the number of nails. As the number of nails doubled, the ultimate bearing capacity, ultimate bending moment, and maximum displacement at mid-span decreased by 6%, 5.8%, and 5%, respectively. From Figure 12a, the maximum displacement of specimen KWL30070235240 with 240 nails was 130.69 mm. The maximum displacement of specimen KWL30070235480 with 480 nails was 124.09 mm. An increase of the number of nails could effectively reduce deformation under same load of the NCLT panels. From the analysis in Figure 12b, Strain in the tension zone of the specimen KWL30070235480 was lower than that of specimen KWL30070235240 under similar load at both mid-span section and one-third section. This indicates that an increase in the number of nails improved the interlaminar force of NCLT panels, making their composite behavior more effective. Therefore, the integrity of NCLT panels was consequently enhanced, which is advantageous for maintaining their effective bending rigidity.
# A Theoretical Comparative Study of CO\({}_{2}\) Cascade Refrigeration Systems
## 2 Materials and Methods
### Followed Methodology  
The present work evaluates different cascade refrigeration systems with a developed model in Engineering Equation Solver (EES) [48] which is validated by literature data (see Section 2.4). The refrigeration production is 50 kW and the refrigeration temperature is examined parametrically for \(-\)35, \(-\)25, \(-\)15 and \(-\)5 \({}^{\circ}\)C. The condenser temperature is studied at various temperature levels from 10 \({}^{\circ}\)C to 45 \({}^{\circ}\)C in the parametric analysis, while in the yearly evaluation of Athens it is 5 K higher than the ambient temperature. In every study, the high-temperature level of the lower temperature circuit (\(T_{m1}\)) is the optimization variable, while for the CO\({}_{2}\)/CO\({}_{2}\) system also the high transcritical pressure is optimized. The optimization is conducted using the conjugate directions method or \"Powell's method\" which is supported by the simulation tool (EES) [48]. The relative convergence tolerance is chosen at 10\({}^{-8}\) and the maximum number of iterations (function calls) at 5000. About the yearly performance, the ambient temperature of Athens (Greece) is used. These data are given in Figure 3 and they have been taken by the TRNSYS libraries [49]. These weather data have been taken from past measurements and they correspond to the typical meteorological year for Athens. It can be said that the most usual value is 14 \({}^{\circ}\)C with 486 h, while the mean yearly temperature is around 18.4 \({}^{\circ}\)C. About the CO\({}_{2}\)/CO\({}_{2}\) system, the transcritical operation regards 19% of the year period.  
In the end, it is useful to summarize the main assumptions of this work [13; 14]:  
* The system is examined in steady-state conditions.
* There is no pressure drop in the evaporator, condenser and heat exchanger.
* The throttling valve is assumed to be adiabatic and so the enthalpy is conserved.
* The outlet streams from the evaporator, condenser and cascade heat exchanger are assumed to be saturated state points.
* The temperature difference of the streams in the cascade heat exchanger is 5 K.
* The condenser temperature is 5 K higher than the ambient temperature and for the CO\({}_{2}\)/CO\({}_{2}\) system, the gas cooler outlet temperature is 5 K higher than the ambient.
* The cooling capacity is 50 kW in all the cases.
* All the compressor isentropic efficiencies are calculated according to Equation (7).
* In the yearly analysis, the system is assumed to operate during all the year period.
# A Review of Machine Learning Applications in Land Surface Modeling
## 4 Major Applications of Machine Learning in Land Surface Modeling
### Benchmarking the LSMs  
As the LSMs become increasingly complex and observational data volumes rapidly expand, there is a growing need for frequent and intensive testing and evaluating the models to fully utilize the richness of large Earth System data sets like satellite or FLUXNET measurements. Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsT-MIP) [65; 66] and Land Model Testbed (LMT) [67] are two such initiatives that incorporated ML techniques for this purpose. [PERSON] et al. [68] quantified divergence as the spread in output from multiple models and an observational constraint. Considering inter-model spread as a measure of the approximations in physical and biogeochemical processes in the models, the ML experiments highlighted the uncertainties in the structures of the carbon pools and advised against the hard parametric limits on ecosystem function. Their results confirmed that model intercomparison projects like MsMTIP ensemble provide a proper framework to link model skill to structure, excluding confounding factors while taking into account inter-model spread. RF helped to find model structural characteristics, which serve as important factors of skill for several MsTIP variables. LMT, with a similar objective, leveraged existing tools of International Land Model Benchmarking (ILAMB) and was able to launch thousands of ensemble simulations simultaneously while perturbing the parameters of LSMs. They developed the ML-based benchmarking workflow to increase the diagnostic capacity of LMT. New relationship metrics were designed using ML methods to benchmark the LMT produced ensemble simulations against in-situ measurements of GPP, ET, and SH. The initiative was an outstanding attempt to accelerate the model development cycle by means of careful assessment of model fidelity and analysis of model outputs.
# Soil Classification from Piezoone Penetration Test Using Fuzzy Clustering and Neuro-Fuzzy Theory
## 4 Verification of Suggested Neuro-Fuzzy Model
### Busan New Port Site  
This site is located on the sea, and two PCPT penetrations were carried out. Soil layers were clay and a mixture of clay, sand, and gravel from the top of seabed. Thin silt lenses were found at the upper part of the deposit due to variations in seawater level. Laboratory test results from the undisturbed sample revealed the clay layer as highly compressible \"CH\". Water contents ranged from 51.5% to 75.3%, liquid limits ranged from 67.8% to 101.9%, and plastic limits ranged from 27.4% to 34.8%. Piezocone test results, boring logs, passing #200 sieve, water contents, and Arterberg limits are shown in Figures 9 and 10. From the results, [PERSON]'s method misclassified upper clay to some depth as silt, while [PERSON] and [PERSON]'s method gave a better prediction of the narrow silt layer between clay at GL-16-17 m in Figure 9, but failed to predict the lower clay layer in Figure 10 as silt. [PERSON]'s \(q_{t}-B_{q}\) chart also provided satisfactory prediction when compared to boring logs, but had unclassified zones at the upper clay in both cases. On the other hand, the proposed neuro-fuzzy model from this study successfully classified the interbedded silt layer in Figure 9 and the clay layer in Figure 10. From the results, it was found that the proposed model provided more consistent classification with boring logs than others.  
Figure 8: Optimized Shapes of fuzzy membership functions after training.  
Figure 9: Verification results at Busan new port site-1.
## 6 Security Analysis and Results
### Additional Security Discussions
#### 6.5.1 Need for the Secondary Epidemic  
In this section, we discuss the motivation behind disseminating the identities of the duplicate nodes using the secondary epidemic. This might seem like a digression from the main thread of our discussion. However, in light of the communication cost analysis above, it makes sense to explain this here. Upon a quick examination of **Discard**, it might seem that the secondary epidemic is unnecessary and clearly increases the communication costs by necessitating the exchange of the duplicate cache. We now argue in its favor and justify our design. Of course, it is true that by exchanging and merging the neighborhood caches amongst each other, nodes can identify replicas in their midst. It has been shown [42] that merely exchanging and merging the neighborhood caches among the home zone nodes creates an almost random graph \(G\) among these nodes. Here, the graph \(G\) is viewed as a directed \"who-knows-whom\" graph. A directed edge \(e\) between nodes \(n\) and \(p\) implies that \(n.NCache\) contains \(p\). This indicates that each node in the home zone \"knows\" \(c\) other random nodes in the home zone. In the course of exchanging their caches and merging, every node has been populating their duplicate caches as they find them. When an outside node requests verification from \(v\) verifiers, we would like to know how many verifiers to choose to attain sufficient confidence in the verification results. Essentially, we would like to choose \(v\) verifiers such that their union comprises the identities of all of the nodes in the entire home zone with high probability. This way, when the verification request comes in from a duplicate node, there is a high probability that at least one of the verifier nodes will identify the duplicate and trigger the appropriate revocation mechanism in place. Recall that the \(NCache\) of each node is of size \(c\) and contains almost random elements from its home zone set \(H\) of size \(h\). It has been shown that the expected size of the union of \(v\) random subsets of cardinality \(c\) drawn from a set \(H\) of size \(h\) is given by \(h\left(1-\left(1-\frac{c}{h}\right)^{v}\right)\)[44]. Suppose, \(c=5,h=45,v=3\); we get that this expected size is about \(13\). This shows that we need to increase both \(c\) and \(v\) in order to get the expected size close to 45. For instance, with a cache size of \(c=20\) and \(v=5\) verifiers, we get the expected size around 43. However, clearly, this is quite expensive in terms of communication, and hence, our decision to disseminate the duplicate caches as a secondary epidemic in **Disccard** is justified.