esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation
### Comparison of Pressure Drop in Conformity with Impact Energy under Off-State
However, the dissipated energy increases as the input current increases in the case of the \(\Delta P_{maj}\) model. The reason for this is that the area between the \(\Delta P_{total}\) and \(\Delta P_{maj}\) curve for the stroke decreases as the dissipated energy for the yield stress increases as the input current increases, as shown in Figure 8a,c,e. Regardless of all the input currents in Figure 8b,d,f, the \(\Delta P_{total}\) model does not show a significant difference from the experimental result as the stroke velocity increases. On the other hand, the error between the \(\Delta P_{maj}\) model-based pressure drop and the experimental result increase depending on the stroke velocity increase. In Table 3, the RMS values of the error and relative error are calculated over the stroke velocity range of 0.1 m/s or more. The input current and the pressure drop models are independent; the RMS errors and relative errors in Table 3 are due to the error of the magnetic analysis. As a result of the experiment, the controllable force is estimated at 0.6137 kN, 1.382 kN, and 2.128 kN for 0.5 A, 1.0 A, and 1.5 A, respectively, based on the stroke velocity of 2 m/s. The maximum compression velocity estimated by experiments from 0 A to 1.5 A in 0.5 A units is 2.390 m/s, 2.352 m/s, 2.359 m/s, and 2.338 m/s. The error in the estimated velocity occurred due to the friction force of the drop device and MR  
Figure 6: Vertical drop test device for performance evaluation of MR shock absorber. shock absorber, but it tends to decrease as the input current increases. The RMS values of the maximum pressure drop error and relative error are compared between the pressure drop model and the experimentally measured data. For the input current, the RMS values of the error and relative error between the experimental data and the \(\Delta P_{total}\) model and the RMS values of the error and relative error between the experimental data and the \(\Delta P_{maj}\) model are as follows, in sequence, respectively: 0.3399 MPa, 9.484%, 1.889 MPa, and 52.73% for 0.5 A; 0.3052 MPa, 7.922%, 1.863 MPa, and 48.37% for 1.0 A; and 0.3666 MPa, 9.047%, 1.905 MPa, and 47.03% for 1.5 A.  
Figure 7: Pressure drop and estimated damping force considering \(\Delta P_{total}\) and \(\Delta P_{maj}\) models and experimental data: (**a**) for stroke and impact energy of 456.7 J; (**b**) for stroke velocity and impact energy of 456.7 J; (**c**) for stroke and impact energy of 937.5 J; (**d**) for stroke velocity and impact energy of 937.5 J; (**e**) for stroke and impact energy of 1538 J; (**f**) for stroke velocity and impact energy of 1538 J.  
Figure 8: Pressure drop and estimated damping force considering \(\Delta P_{total}\) and \(\Delta P_{maj}\) models and experiment data: (**a**) for stroke and input current of 0.5 A; (**b**) for stroke velocity and input current of 0.5 A; (**c**) for stroke and input current of 1.0 A; (**d**) for stroke velocity and input current of 1.0 A; (**e**) for stroke and input current of 1.5 A; (**f**) for stroke velocity and input current of 1.5 A.
## 3 Results and Discussion
### Variations due to Randomized Sub-Sets  
As observed in Figure 3, we can see that, for each choice of a subset of \(n\) species, we obtain a range of performance values, depending on the particular random selection of species. Our results show that the interquartile ranges vary as much as 12% in some cases. For instance, in case of \(n=30\) in Figure 3c, we see that the mean average precision (mAP) varies between 0.72 for one subset of 30 species to 0.84 for another subset of randomly drawn 30 species. Similarly for \(n=70\), the mAP varies between 0.6 and 0.71. We can see a similar trend in the figures for other metrics considered in this work.  
As mentioned earlier, the experiment was repeated 20 times for different randomized selections of \(n\) species. The variation in results within different \(n\) species' trials showed that the classification results can vary significantly depending on the choice of species chosen for the analysis. Consequently, it can be inferred that generic claims about the performance of a certain algorithm for a certain number of non-randomly selected species  
Figure 3: The performance measures **(a)** Precision **(b)** AUC **(c)** mAP **(d)** Recall and **(e)** Accuracy decrease as the number of species in each subset is varied between \(n=10\) and \(n=300\). Shown are the ranges from best result to worst result (whiskers) obtained for 20 different randomly drawn subsets for each value of \(n\). The red marking in each box represents the median, and the boxes indicate the middle 50% of the results.  
must be interpreted with caution. The results might not generalize for another set of \(n\) species, even when the species are drawn from the same dataset.  
One possible reason, among others, that could explain the variability in performance between different subsets or ensembles of randomly drawn sound samples from \(n\) species (_bag-of-birds_) is the possible degrees of similarity of sounds, or the lack of it, between species of different subsets. Ambiguity can be a consequence of the similarity of the sounds of species within an ensemble. Therefore, one possible explanation for these results is that sounds of species in the bags leading to lower performance measures have a higher degree of similarity compared to bags that generate higher classification performance.
## 3 Results and Discussion
### Effects of the Operating Conditions on the SCWG of Cellulose
#### 3.1.4 Effects of an Alkali Catalyst on the Production Yields  
The use of a catalyst may enhance the H\({}_{2}\) yields in the gas effluent from the SCWG system. The presence of catalyst can contribute to prevent some of the undesired reactions and the subsequent production of tar and/or char. Some alkali metal catalysts, mainly KOH and NaOH, have been demonstrated to have the ability to break C-C bonds and promote the water-gas shift reaction, which in turn improves H\({}_{2}\) and CO\({}_{2}\) yields [35; 47; 56; 57]. As can be seen from the results in Table 2, higher CO\({}_{2}\) and CH\({}_{4}\) yields are obtained in the presence of a catalyst. The effect of a catalyst may be a crucial factor for the success of this study, since very short residence times have been implemented and H\({}_{2}\) yields may be substantially boosted thanks to the effect of such catalyst. In order to reduce the number of experiments, the catalyst and concentration to be used have been selected according to the best data found in the literature. Thus, the effect of KOH at 0.1 wt % has been studied at 535 \({}^{\circ}\)C, 250 bar, n = 0.4, 0.3 g cellulose/L. Also, a comparison between the experiments carried out with and without catalyst in terms of H\({}_{2}\) yield is shown in Figure 7. It can be seen that the presence of a catalyst can be a key point when short residence times are implemented. In fact, there was an increment in the hydrogen yields obtained from all the experiments, and particularly from the 6-s runs, where the presence of the catalyst enhanced hydrogen yields by more than 60% in comparison to the production obtained when no catalyst was used under the same conditions. Moreover, higher LHV values were obtained using KOH, with maximum yields of 112.74% and 17393.8 kJ/Nm\({}^{3}\) from 6-s runs. Similar results have been obtained by [PERSON] et al. [51] in a study on the SCWG of timothy grass. In that case,  
Figure 6: Effect of cellulose initial concentration in SCWG processes at 250 bar, 535 \({}^{\circ}\)C, and with n = 0.4.  
different homogeneous catalysts were evaluated and an improvement of H2 yields was obtained in all the cases and particularly when KOH was used.
# Deep Learning for Predicting Traffic in V2X Networks
###### Abstract  
Artificial intelligence (AI) is capable of addressing the complexities and difficulties of fifth-generation (5G) mobile networks and beyond. In this paradigm, it is important to predict network metrics to meet future network requirements. Vehicle-to-everything (V2X) networks are promising wireless communication methods where traffic information exchange in an intelligent transportation system (ITS) still faces challenges, such as V2X communication congestion when many vehicles suddenly appear in an area. In this paper, a deep learning algorithm (DL) based on the unidirectional long short-term memory (LSTM) model is proposed to predict traffic in V2X networks. The prediction problems are studied in different cases depending on the number of packets sent per second. The prediction accuracy is measured in terms of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and processing time.  
5G; V2X; deep learning; artificial intelligence; LSTM +
Footnote †: journal: Applied
## 4 Discussion
### Orowan Bypass-Like Strengthening Mechanism in Architected Metamaterials
#### Difference in Deformation Evolution  
While atomic bonds are mimicked by the struts in architected metamaterials, several differences are noteworthy.  
Figure 9: Experimental and numerical deformation and an abstract diagram of: (**a–c**) P-0 structure, (**d–f**) P-1 structure.  
First, compared with the continuous deformation zones of crystalline materials in which only axial compression or collapse may occur, the shear bands contain strut intersections which render rigid body rotation as mentioned above. Such twist inevitably depletes extra energy and may contribute to the hindering mechanism in architected meta-materials.  
Second, in crystalline materials, the slip plane bows out under the influence of the applied stress into an arc when the dislocation encounters the precipitates [46]. Such arc-shaped dislocation may further expand and interact with other obstacles before it could no longer endure the force acting upon it and break free, which drains additional expansion energy and impedes the dilation of slip band. On the contrary, the radian of the shear band in architected materials after plastic deformation is restrained, since the printed struts could only bend to a limited extent due to its shear strength.  
Third, unlike the slip band of crystalline materials in which Orowan loops are formed to let through the dislocation, the shear band of architected metamaterials is incapable of bypassing the embedded lattices entirely, due to the limitation of edges and boundaries. Instead, the shear front ceases to proceed after it reaches the buckling limit. When the structure is further compressed, a new shear plane is generated, which is derived from the side edge of the structure. The new shear plane is in a mirror symmetry relationship with the previous plane to transfer further deformation. The evolution of the new plane is still along the diagonal but hindered by the meta-precipitates.  
The main cause of the difference is possibly related to the connectivity of the nodes and struts in our work which is unable to completely simulate the atomic bonds in crystallographic microstructures, the boundaries of architected metamaterial we design and the limited meta-precipitates that are introduced.
# A Comprehensive Worst Case Bounds Analysis of IEEE 802.15.7
## 5 Numerical Analysis
### Delay Analysis  
Concerning the timeliness support of IEEE 802.15.7 via its GTS mechanism, it is vital to understand the delay bounds of the system, in to order accurately setup the network to meet the expected timeliness requirements. In Figure 10, we represent the delay bounds of the VLC network for various superframe orders and different burst sizes. In accordance  
Figure 8: Maximum throughput of a GTS allocation as a function of burst size (low data rates).  
Figure 9: Maximum throughput of a GTS allocation as a function of burst size (higher data rates).  
with PHY III and II, we fixed an arrival rate of 100 Mbps and checked the impact of various burst sizes on the delay.  
From Figure 10, we can see that the delay increases with the increase in the superframe order. In the case of lower superframe orders, the variance in delay is only 10-30 optical clocks. The increase in the value of the superframe order results in the increase in the size of the superframe and the timeslots, which in turn can introduce wasted bandwidth. This can result in a significant (10 fold) increase in the network delay for larger SO, if the network designer does not carefully consider this issue.
# Multi-Label Classification for Predicting Antimicrobial Resistance on _E. coli_
## 2 Predicting Antimicrobial Resistance
### Data Preprocessing  
Data preprocessing is an essential stage in ML. The different operations performed during this stage included data cleaning and data transformation. Data cleaning involves identifying and checking categorical and missing values in the data. Data transformation involves one-hot and label encoding. The VITEK 2 system was used for antimicrobial susceptibility testing, and interpretation was performed based on the EUCAST guidelines. The isolates were then filtered to obtain the missing antibiotic resistance information. The variants of the samples were called using bcftools software [22] version 1.12, Samtools [22] version 1.12 was used for sorting the aligned reads and vcftools [23] version 0.1.13 was used for filtering the raw variants.  
Figure 1: The overall architecture of the AMR prediction approach. The architecture constitutes three main stages in the prediction of AMR. The collection stage involves gathering the whole-genome sequence and drug phenotypes of _E. coli_ from various environmental and biological sources and databases. The next stage involves data preprocessing, which consists of cleaning the datasets, encoding the sequences, and normalizing the data using bcftools. In the prediction stage, various machine learning algorithms are used to model the data to learn and make predictions from the features. The performance of the models is then evaluated using accuracy, F1-score, recall, precision, and hamming loss. Consequently, the output determines the antimicrobial resistance or susceptibility of the four antibiotics (GEN, CTX, CIP, and CTZ).  
To prepare the SNP data for subsequent machine learning, label encoding and one-hot encoding were utilized. Nucleotide bases A, G, C, T, and N were encoded into numerical values 1, 2, 3, 4, and 0, respectively, using label-encoding techniques. The final output was an SNP matrix that is structured such that each row corresponds to a sample and each column represents a variant allele. Subsequently, all isolates were merged according to the reference allele positions, facilitating feature integration for machine learning models. One-hot encoding was applied to convert the DNA sequences (A, G, C, T) into a binary matrix format, composing 0 and 1, for the vectorization of the sequence data, and the matrix was then vectorized.
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 3 Enhanced PCP Feature
### Enhanced PCP with Singing-Voice Separation  
To attenuate the effect of the singing voice and consider the temporal correlations of the chord, we first separate the singing voice from the accompaniment before calculating the PCP or LPCP. This is denoted as enhanced PCP (EPCP) or enhanced logarithmic PCP (ELPCP). The framework of singing voice separation is shown in Figure 3.  
In general, because of the underlying repeated musical structure, we assume that music is a low-rank signal. Singing voices offer more variation and have a higher rank but are relatively sparse in the frequency domain. We assume that the low-rank matrix \(A\) represents the music accompaniment and the sparse matrix \(E\) represents the vocal signals [45]. Then, we perform the separation in two steps. First, we compute the spectrogram of music signals in matrix \(D\), which is calculated from the STFT. Second, we use the inexact augmented Lagrange multiplier (ALM) method [45], which is an efficient algorithm for solving the RPCA problem, to solve \(A+E=\left|\right|\), given the input magnitude of \(D\). Then,  
Figure 3: Calculation of enhanced PCP (EPCP).  
Figure 2: PCP (pitch class profile) (**left**) and LPCP (logarithmic pitch class profile) (**right**) of **C** major triad.  
using RPCA, we can separate matrices \(A\) and \(E\). The low-rank matrix \(A\) can be exactly recovered from \(D=A+E\) by solving the following convex optimization problem:  
\[\text{Minimize}\mid\mid A\mid\mid_{*}+\lambda\mid\mid E\mid\mid_{1}\text{Subject to }A+E=D \tag{3}\]  
where \(\lambda\) is a positive weighting parameter. The inexact ALM method is as follows [45]:  
```
Input: matrix \(D\), parameter \(\lambda\)  1: \(Y_{0}\)* \(D/J(D);E_{0}=0;\mu_{0}>0;\rho>1;k=0\).  2:while not converged do  3: // lines 4-5 solve \(A_{k+1}=\underset{A}{\text{argmin}}L(A,E_{k},Y_{k},\mu_{k})\).  4: \((U,S,V)=svd(D-E_{k}+\mu_{k}^{-1}Y_{k})\);  5: \(A_{k+1}=US_{\mu_{k}^{-1}}[S]V^{T}\).  6: // line 7 solves \(E_{k+1}=\underset{E}{\text{argmin}}L(A_{k+1},E,Y_{k},\mu_{k})\).  7: \(E_{k+1}=S_{\lambda\mu_{k}^{-1}}[D-A_{k+1}+\mu_{k}^{-1}Y_{k}]\).  8: \(Y_{k+1}=Y_{k}+\mu_{k}[D-A_{k+1}-E_{k+1}]\).  9: \(\mu_{k+1}=\rho\mu_{k}\).  10: \(k=k+1\).  11:endwhile Output: (\(A_{k},E_{k}\)).
```  
**Algorithm 1**********Inexact ALM Algorithm  
Figure 4 shows the PCP and EPCP of an audio music piece (the music is \"Baby It's You\", from the Beatles' album _Please Please Me_). Figure 5 shows the LPCP and ELPCP of the same musical piece.  
Figure 4: PCP (**left**) and EPCP (**right**) of test music.  
Figure 5: LPCP (**Left**) and ELPCP (**Right**) of test music.  
Figure 4 shows that the EPCP has improved continuity compared with the PCP. Figure 5 shows that the ELPCP is further enhanced compared with the LPCP. In Figure 4, it is shown that the EPCP of audio music is more obvious than the PCP. Figure 5 shows that the ELPCP of audio music is clearer than the LPCP. Thus, PCP features for chord recognition are improved by singing voice separation before calculating the PCP or LPCP.
# Towards a Reference Architecture for Cargo Ports
## 6 Validation of the RA  
RA and patterns are abstract models, not software, and such models cannot be evaluated in terms of performance, reliability, or security using traditional experimentation or testing methods [20; 26]; it does not make sense to experimentally validate an instance of a pattern or RA because evaluating its properties verifies them only for that instance, not for all the instances of the pattern or RA. On the other hand, other criteria can be used to validate RA and patterns. We can validate abstract models' completeness by comparing an RA model to models of commercial cargo port systems, explicitly comparing the fundamental components of our cargo port RA with the elements of these systems. If the RA includes all the fundamental features and functions offered by existing cargo port architectures, it means the abstractions faithfully represent their key aspects. Our patterns include \"Known Uses\" subsections, which indicate examples of commercial cargo port models where we have verified, pattern by pattern, that all the components of our RA are present in these ports. We studied descriptions of the ports of Shanghai, China; Singapore; Rotterdam, Netherlands; Los Angeles, Long Beach, Miami, and Everglades, USA; all these ports represent modern ports, including automation and other advances. Following a standard way to find patterns [7; 8], the first four ports were used to build the patterns, and the other three were selected to confirm that real ports used these patterns. None of these descriptions used patterns or UML, so we needed to abstract their components and functions.  
In general, specific patterns are normally evaluated by submitting them to a pattern conference. In these conferences, a pattern paper is developed with the help of a shepherd and then discussed in a workshop of about 8-12 subject matter experts. The pattern is exposed for criticism, revised, and then published. In terms of precision, we created our RA through UML modeling and a well-defined pattern template for describing the pattern solutions. This method is widely used in the software engineering discipline and is regarded as more precise as compared to other representations such as textual or block diagram-based representations.  
In addition, we have presented our work in several architecture-oriented meetings, such as Pattern Languages of Programming (PLoP) conferences, open Web application security project (OWASP) meetings, and in the pattern language community, where we received significant feedback on the usefulness and benefits of this work. Ultimately, the final validation of this work will come from practitioners using it to build concrete architectures.  
Another type of validation refers to the usefulness of the RA [23; 26; 59], shown in Section 7.
## 4 High-Speed On/Off Valve Orifice Erosion Influence Factors Analysis
### Effect of Pressure Difference at the Valve Port on Valve Port Erosion  
As the pressure difference at the valve port increased, the inlet flow rate and particle mass flow rate of the high-speed on/off valve also increased rapidly, and the number of particles impacting the wall per unit time increased, resulting in severe erosion wear at the valve port. As shown in Figure 12, the maximum erosion rate at a pressure difference of 5 MPa was about 10 times higher than that at a pressure difference of 1 MPa. This shows that the pressure difference at the valve port had a significant effect on the erosion.  
By fitting the data with the least-squares method, the relationship between the maximum erosion rate and the pressure difference at the valve port was obtained as \(R_{\text{ecoion}}=1\times 10^{-9}\times\left(1.837x^{3}-1.675x^{2}+47.56x-4.084 \right)\left(\text{kg}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\right)\)  
When the pressure difference at the valve port was less than 2 MPa, the spool erosion wear parts were mainly concentrated in the vicinity of the seal line. With the increase in the orifice pressure difference, the spool cone end and the valve body inner wall erosion wear increased (Figure 13). This is because with the increase in the pressure difference at the valve port, the speed of the fluid at the valve port and the speed of the contaminated particles in the fluid increased rapidly, and the particles, due to their inertia, continued to move forward in the original direction of motion after flowing out of the valve port, increasing the number of particles hitting the end of the spool cone and the wall of the valve body. The kinetic energy at the time of impact was also greater, resulting in more serious erosion wear at the end of the spool cone and the inner wall of the valve body.  
Figure 12: The curve of the maximum erosion rate with the pressure difference at the valve port (inlet pressure—5 MPa, outlet pressure—4 MPa, 3 MPa, 2 MPa, 1 MPa, and 0, valve opening—0.2 mm, spool taper angle—120\({}^{\circ}\), oil contamination class—10, particle size range—1–10 \(\upmu\)m).
## 5 Experimental Results
### Cnn
#### 5.2.2 Convolution Kernel Size Selection  
The results in Figure 18 show how this study used five CNN layers, the activation function of ReLU, and max pooling to evaluate the performance of CNN models with different convolution kernel sizes. The convolution kernel sizes tested in this study were 1 \(\times\) 1, 2 \(\times\) 2, and 8 \(\times\) 8. The test results are shown in Figure 19. The convolution kernel size significantly influenced the recognition capability of CNN models. A convolution kernel that was too small limited the ability of CNN models to search for picture features. If the convolution kernel were too large, there would be high computational costs. Figure 19 shows that the 1 \(\times\) 1 convolution kernel resulted in the best accuracy and the least training time. As such, this study selected the 1 \(\times\) 1 convolution kernel as the architecture for the CNN models.