esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Results
### Relative Importance of MBES and SSS Data Models for Acoustic Habitat Mapping. Allies or Not?  
Figure 12 presents the classification accuracy assessment results of the ten data models. An 85% Kappa coefficient was achieved in _data model m10_, i.e., when all MBES and SSS features have been used, for which the classification map is shown in Figure 13. When only MBES features were used (_data model m8_), the classification accuracy reached about 77% while when only SSS features (regarding both 100 and 400 kHz frequencies) were used (_data model m3_), it was 10 percent lower, about 67%. The above supports the rationale of this work that was to fill the gap concerning if and how SSS and MBES could complement each other towards improved habitat mapping in shallow habitats, as in the NMPZ case, a single-system survey would offer results from 10% to 20% less accurate. MBES backscatter information alone (either mosaic texture or angular range analyses) have been shown to provide the worse results (_data models m5 (texture): 39% and m7 (angular responses): 50%_). However, if combined with the auxiliary bathymetric analysis features, the above offered the best single-system automated habitat mapping results (_data models m8_). Regarding the contribution of multi-spectra backscatter mosaics to the classification performance through the texture information, it was shown that individual frequencies provided very poor results, with Kappa coefficients around or below 50% (_data models m1, m2 and m5_). Combination of the 100 kHz and 400 kHz SSS mosaic texture features increased classification accuracy more than 15% in comparison to the single frequency results (_data model m3: 67%_). Using all three mosaics allowed for a slight improvement of less than 5% over the latter data model (_data model m9: 70%_). Finally, the contribution of the angular response analysis features to the overall performance of the classifier was very low and more precisely about 3%, as indicated by comparing data _models m6_ and _m8_ that concerns MBES single-system scenarios, the former without and the latter with backscatter angular responses consideration.  
Figure 11: The frequency dependent separation of bottom types as made evident in a sub-region (_Area B_ in Figure 1) available backscatter mosaics: (**a**) 100 kHz SSS, (**b**) 400 kHz SSS and (**c**) 180 kHz MBES. _P. oceanica_ and _C. nodosa_ beds are indicated with arrows.
## 2 Background
### Reliability Block Diagram
#### 2.3.2 Sum of Disjoint Products (SDP)  
The Sum of Disjoint Products (SDP) method is based on Boolean algebra and evaluates the probability of system operation by the union of the minimal paths or system failure by the union of the minimal cuts. We will call minimal path and minimal cut as event to facilitate explanation of the example. If two or more events have no components in common, the probability of at least one of the events will occur is the sum of the probabilities of the individual events [22, 23]. If two events A and B have components in common, we have the following equation for evaluation of the probability of union of the events A and B (\(A\cup B\)) [23]:  
\[R(A\ \cup B)=\ R(A)+R(\bar{A}B) \tag{6}\]In this way, a system with \(n\) events (\(A_{l}\), \(A_{2}\),..., \(A_{n}\)) has:  
\[R(A_{1}\ \cup A_{2}\ \cup\...\ \cup\ A_{3}\ )=\ R(A_{1})+R(\overline{A_{1}}\,A_{2})+ \ \cdots\ +\ R(\overline{A_{1}}\,\overline{A_{2}}\ \cdots\ \overline{A_{(n-1)}}\,A_{n}) \tag{7}\]
## 3 SDN/VNF HTS Architecture  
In this work, the number of required gateways \(N\) is increased with additional \(P\) gateways, which provide redundant paths and capacity. As with the ++N+P site diversity scheme, the \(n=N+P\) gateways are concurrently active. Routing occurs at the payload level (i.e., via packet switching) as decided by a VNF. No particular constraints are assumed about the symmetry of the links, so the system model applies to diverse applications. The user beams and the gateway beams are assumed to occur in different bands, with the latter using either EHF (Q/V band) or FSO. The user beams are located at a lower frequency band (e.g., Ka-band), so it is less affected by atmospheric attenuation than the feeder links.  
For each user, two routing decision elements are required for each communication direction (i.e., data from or towards the user). Examples of the services include high-definition video streaming (i.e., data flowing mainly towards the user) and a large userbase generating and reporting data to an Internet-of-things cloud server (i.e., data flowing from the user to the gateways). Figure 1 depicts the main components of the system. Each routing decision element consists of an intelligent agent that learns how to select the optimal routing decision just by observing the performance of the past transmissions. As a result, one distinct feature of the proposed approach compared to the existing routing methods considered for the HTS is that it does not require the knowledge of the current system state, including the atmospheric conditions at the gateway sites or the bit-error-rates of the channels. Another advantage is that the agent does not need to be aware of physical-layer changes, such as those handled by adaptive-coding modulation. Through the learning mechanism, the agent observes the outcome of those changes and modifies its forwarding policy. This functionality is achieved by defining agents as VNF that implement reinforcement learning.  
Figure 1: System architecture with a virtual network function (VNF) providing a routing function for a High Throughput Satellite (HTS) system.
###### Abstract  
This paper aims to evaluate the influence of preparation and extraction methods on the phytochemical profile and microbiological activity of 12 medicinal plants from the west side of Romania. First, the proximate composition (humidity, proteins, lipids, and ash) and elemental composition of the raw material were evaluated. Two levels of plant shredding were used: coarse shredding (CS) and fine grinding of plants to obtain powder (FG), and three extraction methods: conventional solvent extraction (CES), ultrasound-assisted extraction (UAE), and microwave extraction (MWE). The phytochemical profile investigated referred to antioxidant activity (AA), total polyphenol content (TPC), and flavonoid content (TFC), using spectrophotometric methods, and individual polyphenols detected using the LC/MS method. The preparation/extraction method for each medicinal plant was optimized using statistical analysis. The optimized extracts for each medicinal plant were tested to evaluate the antimicrobial potential against 9 standard strains. The results showed that the sample preparation method before extraction (shredding or grinding) influences the content of phytocompounds by increasing them in powder form. The use of green technologies, especially MWE, leads to the highest content of TPC, TFC, and AA. The TPC value ranged between 4.83-19.2 mgGAE/g DM in the samples CS and between 19.00-52.85 mgGAE/g DM in the samples FG. The highest antioxidant value was found in the _Origimum vulgarte_ extract, both in the case of the crushed sample (22.66 mM Fe\({}^{2+}\)/100 g) and the powder sample (81.36 mM Fe\({}^{2+}\)/100 g), followed by _Malsa officinalis_, The TFC values varied in the range of 1.02-3.46 mgQE/g DM when CES was used, between 2.36-12.09 mgQE/g DM for UAE, and between 1.22-9.63 mgQE/g DM in the case of the MWE procedure. The antimicrobial activity highlighted the effectiveness of the extracts, especially on the strains of _H. influenzae_, _C. albicans_, _C. parapslossis_, _S. aureus_, _S. flexneri_. Reduced antimicrobial activity was recorded for the strains of _S. pyogenes_ and _P. aeruginosa_. The best antimicrobial activity was registered by _Thymus serpyllum_, with an inhibition rate of 132.93% against _E. coli_ and 78.40% against _C. albicans_.  
conventional solvent extraction phytocompounds; microwave extraction ultrasound-assisted extraction 2023132530[[https://doi.org/10.3390/app13042530](https://doi.org/10.3390/app13042530)]([https://doi.org/10.3390/app13042530](https://doi.org/10.3390/app13042530))
# Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method
## 2 Proposed Methods
### Theory of VMD
#### 2.2.1 Model Framework  
In the VMD process, the input signal is estimated to consist of components with different central frequencies and limited bandwidth [18]. Simply, The sum of the components obtained by the constraint is equal to the original signal.  
Figure 1: SNR enhancement experiment of Teager energy operator. (**a**) Pure shock signal, (**b**) Impulse signal with white noise, (**c**) Original TEO signal, and (**d**) Complete TEO signal.  
The unconstrained variational optimization procedure is obtained by introducing Lagrange multipliers \(\lambda\) and penalty terms \(\alpha\):  
\[\begin{array}{c}\mathrm{L}(\{u_{k}\},\{\omega_{k}\},\lambda):=\alpha\underset{ k}{\sum}\|\hat{\alpha}t[F(t)]\|_{2}^{2}\\ \qquad+\left\|f(t)-\underset{k}{\sum}u_{k}(t)\right\|_{2}^{2}+\left\langle \lambda(t),f(t)-\underset{k}{\sum}u_{k}(t)\right\rangle\end{array} \tag{5}\]  
where \(\{u_{k}\}=\{u_{1},u_{2},\times\times,u_{K}\}\) and \(\{\omega_{k}\}=\{\omega_{1},\omega_{2},\times\times\times,\omega_{K}\}\) represent the mode components and center frequencies, respectively. Hence, the saddle point of Equation (5) is obtained by updating \(\Big{\{}u_{k}^{n+1}\Big{\}}\),\(\Big{\{}\omega_{k}^{n+1}\Big{\}}\), and \(\lambda^{n+1}\) with the alternative direction method of multipliers (ADMM). Then, the process obtains _K_-independent representation items \(\{u_{k}\}\).
# Residual Stress Relaxation of DRWDs in OSDs under Constant/Variable Amplitude Cyclic Loading
## 3 Analysis Model of the Residual Stress Relaxation Effects
### Cyclic Plasticity Constitutive Model
#### 3.1.3. Hardening Rule  
Once the mechanical stress combined with welding residual stress exceeds the elastic limit under cyclic loading, the phenomenon of material hardening occurs. The kinematic hardening rule for overall backstress associated with isotropic hardening is adopted [31]:  
\[\dot{\alpha}=\sum_{k=1}^{N}\left(\dot{\alpha}\right)_{k}=\sum_{k=1}^{N}\gamma_ {k}\left(\frac{3}{2}C_{k}\dot{\epsilon}^{p}-(\dot{\alpha})_{k}\dot{p}\right), \tag{4}\]  
where \(N\) is the number of back stresses; \(\gamma_{k}\) is the ratio of the decrease of the kinematic hardening modulus with the increase of plastic deformation; \(C_{k}\) is the initial kinematic hardening modulus. The isotropic hardening variable and kinematic hardening variable depicts the nonlinear deformations at different stages of plastic deformation. The accumulated plastic strain rate (\(\dot{p}\)) in Equation (4) can be obtained as follows [32]:  
\[\dot{p}=\left(\frac{2\cdot p}{5}^{p}\cdot\dot{\epsilon}^{p}\right)^{\
icefrac{{ 1}}{{2}}}. \tag{5}\]  
[PERSON] [33] adopts kinematic hardening rule and nonlinear isotropic hardening to take into account cyclic hardening and its effect on the cyclic stress-strain curves of materials. The isotropic hardening rule is described as:  
\[\dot{\sigma}^{0}=Q_{\infty}\left(be^{-bp}\right)\dot{p}, \tag{6}\]  
where \(Q_{\infty}\) is the maximum change in the size of the yield surface; b is the ratio of yield surface to plastic strain development, which indicates the rapidity of the isotropic hardening.  
For Q345 qc steel [34], which is widely used in bridge engineering in China, the isotropic and the kinematic hardening parameters (\(C_{i(i=1\sim 4)}=1800\), \(\gamma_{1}=245\), \(\gamma_{2}=155\), \(\gamma_{3}=50\) and \(\gamma_{4}=30\)); steel parameters (\(Q_{\infty}=21\), b = 10) [35; 36] are used in this section. The steel plate and weld materials adopted the same cyclic plasticity constitutive model.
## 3 Materials and Methods
### Anomaly Detection Techniques
#### 3.4.2 Isolation Forest (IF)  
From the date of its publication until today, this algorithm has had relevance in different areas. The authors announced it as a new approach based on the concept of isolating anomalies using binary trees [40]. The main idea of this algorithm is that anomalous points will be isolated into shorter paths in the binary trees within the ensemble or forest of isolation trees. This technique is efficient for large datasets, as well as medium or small datasets. This algorithm does not make any assumptions about the distribution of the data, is effective in high-dimensional spaces, and provides an anomaly score that can be analyzed in depth to achieve a more detailed interpretation of the analysis.  
This technique analyzes each observation within the data and measures how quickly it can be isolated or separated from the other observations. Observations that are significantly different are more likely to be quickly isolated. Within the algorithm, different random trees are created where the data are divided into smaller groups each time. When there is an anomalous observation, it tends to have a shorter path within the random trees, or in other words, fewer steps are necessary to separate that anomalous observation from the others. By analyzing all random trees, the algorithm identifies all observations that consistently have the shortest paths. These observations will probably be anomalous compared to the rest.  
Some advantages over other methods are as follows:  
* Isolation forest allows the building of partial models, i.e., some large parts of the trees do not need to be constructed.
* IF does not use distance or density measures to detect anomalies, which eliminates the significant computational cost involved in calculating distances in other density- or distance-based methods.
* Isolation forest has a linear time complexity with a low constant and a low memory requirement.  
The algorithm calculates an anomaly score by repeating this process; the anomaly is standardized between zero and one and is given by Equation (4):  
\[s(x,n)=2^{-\frac{E[h(x)]}{c(n)}} \tag{4}\]  
where  
\(s(x,n)\) Anomaly score for observation \(x\);  
\(E[h(x)]\) Path length to isolate observation \(x\);  
\(c(n)\) Normalization factor, calculated as follows:  
\(c(n)=2H(n-1)-(2(n-1)/n)\).  
\(H(i)\) is the harmonic number equal to \(\ln(i)\) + 0.5772 (Euler constant).  
To use the technique in the context of anomaly detection, a limit value must be determined, where if the score is lower than said value, the point in question will be considered anomalous, as shown in Equation (5).  
\[\begin{split}\text{If }s(x,n)<\text{limit},\\ \text{then }x\text{ is an anomaly.}\end{split} \tag{5}\]
# Accuracy Analysis of GNSS Hourly Ultra-Rapid Orbit and Clock Products from SHAO AC of iGMAS
###### Abstract  
With the development of the global navigation satellite system(GNSS), the hourly ultra-rapid products of GNSS are attracting more attention due to their low latency and high accuracy. A new strategy and method was applied by the Shanghai Astronomical Observatory (SHAO) Analysis Center (AC) of the international GNSS Monitoring and Assessment Service (iGMAS) for generating 6-hourly and 1-hourly GNSS products, which mainly include the American Global Positioning System (GPS), the Russian Global'naya Navigatsionnaya Sputnikova Sistema (GLONASS), the European Union's Galileo, and the Chinese BeiDou navigation satellite system (BDS). The 6-hourly and 1-hourly GNSS orbit and clock ultra-rapid products included a 24-h observation session which is determined by 24-h observation data from global tracking stations, and a 24-h prediction session which is predicted from the observation session. The accuracy of the 1-hourly orbit product improved about 1%, 31%, 13%, 11%, 23%, and 9% for the observation session and 18%, 43%, 45%, 34%, 53%, and 15% for the prediction session of GPS, GLONASS, Galileo, BDS Medium Earth Orbit (MEO), Inclined Geosynchronous Orbit (IGSO), and GEO orbit, when compared with reference products with high accuracy from the International GNSS service (IGS).The precision of the 1-hourly clock products can also be seen better than the 6-hourly clock products. The accuracy and precision of the 6-hourly and 1-hourly orbit and clock verify the availability and reliability of the hourly ultra-rapid products, which can be used for real-time or near-real-time applications, and show encouraging prospects.  
GNSS; hourly ultra-rapid products; iGMAS; orbit; clock; IGS 2021  
31 December 20203 March 2021  
8 March 2021  
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# Proxy-Based Semi-Supervised Cross-Modal Hashing
## 4 Experiments
### Performance Comparison  
We compared the PSSCH method with baseline methods on three public datasets. The mAP results are shown in Table 2. PSSCH generally outperforms other baseline methods, achieving satisfactory performance. On the MIRFLICKR-25K dataset, the PSSCH method achieves an average mAP that is 2.5% higher than the best baseline method in the image-to-text retrieval task. Additionally, PSSCH achieves the best performance in the 64-bit text-to-image retrieval task. However, in the text-to-image retrieval task, PSSCH performed slightly worse than GCSCH at 16 bits and 32 bits. We speculate that the size of the hash space may affect the performance of our method. Moreover, since the MIRFLICKR-25K dataset is not very large, the use of data augmentation in GCSCH on the training image data may also be a reason why it performs better in the text-to-image retrieval task. On the NUS-WIDE dataset, PSSCH also shows an average mAP improvement of about 1% over the best baseline method in both retrieval tasks. On the MS COCO dataset, PSSCH performs most significantly, with an average mAP that is 3% higher than the best baseline method. We speculate that this may be because the MS COCO dataset contains 80 categories, which is much higher than the other two datasets. In scenarios with a larger number of categories, learning the relationships between data and categories may yield better results. To demonstrate that the improvements regarding our method are significant and not due to randomness, we conducted experiments on three datasets at 64 bits to obtain 10 mAP results. We then performed a paired t-test with the best baseline method, and the test results are presented in Table 3, which proves that the performance enhancement regarding the PSSCH method is significant. Figure 2 shows the precision-recall curve results for the MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets with 64-bit codes. Precision refers to the proportion of relevant items in the retrieval results, while recall indicates the proportion of all relevant items that are correctly retrieved by the model. The PR curves for the PSSCH method outperform other baseline methods in both retrieval tasks across the three datasets. Figure 3 shows the mAP of the semi-supervised cross-modal hashing methods under different percentages of labeled samples on the MIRFLICKR-25K, NUS-WIDE, and MS COCO datasets. As can be seen, with the reduction in the number of labeled samples, the mAP of the PSSCH method decreases sharply, which was expected. We must acknowledge the importance of true labels. Our feature proxies require real labeled samples for constraints in order to ensure the accurate generation of pseudo-labels for unlabeled samples. Moreover, the mAP
###### Abstract  
According to an in-depth analysis of the relationship among n-level polybinary transformation, the time-packing factor and the performance of the decoding algorithm, we find that the appropriate n-level polybinary transformation can improve the performance of the decoding algorithm within a certain range of the time-packing factor in the Faster than Nyquist (FTN) system. In this paper, we explain the reason that this phenomenon occurs. Based on the above analysis, we propose a modified blind phase search (BPS) algorithm to compensate for phase noise (PN) in the FTN system with an extremely small time-packing factor. As a result, the modified-BPS algorithm can cope with the PN with the linewidth \(\times\) symbol rate at 1.07 \(\times\) 10\({}^{-5}\), 1.79 \(\times\) 10\({}^{-5}\), 2.86 \(\times\) 10\({}^{-5}\) and 3.57 \(\times\) 10\({}^{-5}\) under a time-packing factor of 0.55, 0.50 and 0.45, respectively. At the same time, the spectrum efficiency (SE) is improved to 3.27 bit/s/Hz, 4 bit/s/Hz and 4.88 bit/s/Hz.  
polybinary transformation; time-packing factor; decoding algorithm; modified-BPS algorithm 2022212211227. 202  
that the technique could cope with a 0.53 and 0.71 time-packing factor for the FTN-QPSK system [15]. The concept of non-orthogonal WDM was proposed, which, respectively, realized the detection of a 112 GBaud 8 QAM signal and a 83 GBaud 16 QAM signal in 100 GHz and 75 GHz frequency grids by using polybinary transformation, and the time-packing factors, respectively, were 0.89 and 0.93 [16]. Therefore, polybinary transmission can enhance the performance of decoding algorithms for the FTN system with a small time-packing factor. However, the research works in the literature do not clearly explain the relationship among the polybinary transformation, time-packing factor and performance of decoding algorithms.  
In this paper, we analyze the relationship among the polybinary transformation, time-packing factor and performance of the decoding algorithms, in order to determine the influence of an extremely small time-packing factor on the performance of the decoding algorithm. Based on the above analysis, we find that the method of performing an n-level polybinary transformation combined with a designed MLSE results in better performance than that without doing so. However, it is worth noting that when the time-packing factor changes from 1.00 to 0.45, the best working range of the method will be quite different. Based on the above analysis, we propose a modified-BPS algorithm to compensate for PN in the FTN system under a smaller time-packing factor. Utilizing the modified-BPS algorithm, we find that this method can equalize the PN with the linewidth symbol rate at 1.07 \(\times\) 10\({}^{-5}\), 1.79 \(\times\) 10\({}^{-5}\), 2.86 \(\times\) 10\({}^{-5}\) and 3.57 \(\times\) 10\({}^{-5}\), in which the time-packing factor equals 0.55, 0.50 and 0.45, respectively.