esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Outlook  
### Discussion of the Presented Approach  
Within the previous chapters we described the SciFlow concept. It simplifies scientific writing and review by taking advantage of templates and cloud computing to make a slim writing client possible. Furthermore, we have described two scenarios that explain how the SciFlow concept can be used for a thesis and a scientific workshop scenario. However, the number applications for this concept is great. [24] describes a number of these applications including collaboration and peer review, keyword extraction, interactive PDFs, text-to-speech and video review, just to name a few. The choice of DocBook XML as an underlying format and the scalable asynchronous communication between multiple services, encourages the integration of these and more features. At the same time the use of web services in the SciFlow architecture allows for thin writing clients that may even be deployed to mobile devices. Moreover, DocBook XML and its available validation schemata (e.g., DTD, XML Schema) allow for new services to be connected without introducing the possibility of rendering existing data inconsistent. While the existing implementation was built with this validation in mind, this is one of the areas of improvement of the software. SciFlow will be extended to allow the co-creation of various other scientific documents such as research articles, book reviews or case-reports and therefore we will need to reconsider the choice of DocBook XML. There are other XML DTDs on the market that are more specifically tailored to the creation of scientific writings as mentioned above (e.g., the Journal DTD [28]) which in turn do not support the creation of theses.  
With the use of external services for data storage come concerns about data ownership as well as legal issues. To address the former, the SciFlow software architecture was designed to function with a great number of services or even custom servers in a data center of choice. Each service comes with a pre-defined interface and its server software is executable on a number of Linux/Unix distributions (e.g., Mac OS X, Debian Linux, Gentoo Linux). Those services may be integrated into the existing architecture as introduced in Section (see Scalability).  
The choice of Amazon Web Services presents one possible scenario for an implementation; one where Amazon's service level agreements [29] are deemed sufficient and there are no legal reasons for not choosing Amazon or a similar cloud service. Legal requirements may be influenced by University and company policies or even the personal opinions of an executive (e.g., CIO, head of department, _etc._). Another concern may be that, to a certain extent, one will be bound to obey by further pricing changes by the cloud service provider. The two choices at hand here are to either invest into an own IT infrastructure to control the architecture into the last detail or make use of service providers and thus accept the SLAs provide by them. Technologically this makes no difference to the SciFlow concept; legally and financially the implications may vary.
## 5 Experimental Results
### Results on Augmented Data
The results suggest that speaker recognition models are more robust in handling the complexities of emotion recognition compared to image recognition models. The findings also highlight the subtle but positive impact of data augmentation on the performance of SER systems. The improvements observed across different models and datasets suggest that expanding the training data can lead to better accuracy and generalization in the SER tasks.
## 3 Finding Models to Predict Energy Consumption  
Due to the nonlinear characteristics of the datasets used in this research, to the need for both accuracy and fast run times, and to the promising results obtained in other works that used deep learning [12; 19; 22; 23], three deep-learning techniques were selected for STLF in this study: simple RNN, LSTM, and GRU. In addition to these techniques, two different machine-learning techniques were selected for the purpose of comparison: SVR, and Random Forest. These were selected as they are featured in related works on STLF for demand-side energy consumption [6; 7]. Our models use three time series as input--(i) energy consumption, (ii) polymerisation production flow (POLY), and (iii) solid-state polymerisation production flow (SSPOLY)--and have a single output: the predicted energy consumption. For each input, the model uses data from the preceding seven days.
# Analysis, Evaluation and Simulation of Railway
## 3 Results and Discussion
### Last Mile Applications  
Another significant application of OESS is the so-called \"last-mile\". Hybrid or electric locomotives with this feature can cross non-electrified track sections without making use of overhead line, improving the flexibility and energy efficiency of the locomotive. This ESS can also be used in the hybrid locomotive  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Storage** & **Energy (kWh)** & **Weight (kg)** & **Volume (m3)** & **Price (f)** & **Lifespan** \\
**System** & & & & & **(Cycles)** \\ \hline Batteries & 350 & 2300 & 1.75 & 73,150 & 10,000 \\ \hline Batteries + Supercapacitors & 245 + 15 & 1633 + 6520 & 1.23 + 0.59 & 51,205 + 120,000 & 10,000 (1,000,000 1) \\ \hline Supercapacitors & 150 & 65,200 & 5.9 & 1,200,000 & 1,000,000 \\ \hline \hline \end{tabular} \({}^{1}\) Lifecycles of supercapacitors.  
\end{table}
Table 16: ESS characteristics.  
Figure 19: Power peaks reduction (blue line), braking profile (red line) and energy requirements (purple line) for the S-730 unit.  
Figure 20: Energy stored in the ESS during the whole itinerary: (**a**) BESS; (**b**) HESS.  
when switching between the diesel and the electric engine, producing smoother transitions (or in terminals with different catenary voltage, e.g., 3 kV DC vs. 25 kV AC for SNCF, RENFE operators).  
The ESS allows the locomotive to circulate in sidings, terminals, or factories, when normally another supporting diesel locomotive is used to help the train reach the last non-electrified part of the itinerary (called last-mile). In addition, it can be applied when there is a change in the nominal voltage of the catenary, e.g., in trains crossing borders (3 kV DC to 15 kV or 25 kV AC systems). Otherwise, it can also be used when the train is arriving at terminal stations, reducing in this way emissions and noise nearby the train station. Finally, another important application is its use as a backup and DER system (to be developed in Section 3.5), increasing the flexibility of the train and allowing the operation of trains when unforeseen catenary instabilities or failures are produced.  
The energy that is required for circulating during different distances (5, 10 and 15 km) at 50 km/h, with different gradients (between \(-\)15% and 15%) is presented in Figure 21.  
In last-mile applications, normally the slope is going to be near to 0, so the energy that is needed for circulating 15 km is about 30 kWh. In order to maintain the SOC of the batteries between 80% and 40% and increase their lifespan, it should be necessary to install a battery system, sized at 75 kWh. There is also the possibility to install a HESS equipped with a supercapacitor of capacity of 7 kWh (that allow the train accelerates and reaches 50 km/h) and a battery of capacity of 50 kWh that could maintain the speed for 15 km (Section 3.5). It is also possible to install a 30 kWh supercapacitor system, that increases the lifespan of the system, but the space required, and its weight increase substantially. The characteristics of the systems proposed are presented in Table 17.
## 4 Role of DAPG-Producing _Pseudomonas_ spp. in Natural Soil Suppressiveness
### Role of DAPG-Producing Pseudomonas in Other Suppressive Soils  
In addition to soils suppressive to take-all and tobacco black root rot, DAPG-producing _Pseudomonas_ spp. were isolated from other suppressive soils across the world. Numerous genotypes of DAPG-producing _Pseudomonas_ spp. were isolated from the rhizosphere of pea plants grown in soils suppressive to Fusarium wilt [97], suggesting that they could play a role in the natural suppressiveness. DAPG-producing _Pseudomonas_ spp. were also found in the rhizosphere of flax and tomato grown in the natural suppressive soils of Chateaurenard, in France [98]. However, they were also found in the conducive soils of Carquefou (France) at similar population levels and probably play a minor role in the suppression of Fusarium wilt.
## 3 Model Development and Validation
### Establish the IPSO-SVR Prediction Model
The improved particle-swarm-algorithm-optimized SVR was trained using 130 sets of training sample data to obtain the optimal parameters, the relative errors in the prediction model for the predicted and true values are shown in Table 7, and the prediction results of the established prediction model, IPSO-SVR, are shown in Figure 6.  
The prediction results show that the back-prediction accuracy of IPSO-SVM is 94.00%, the root mean square error (RMSE) is 0.37, the mean absolute value error (MAE) is 0.32, and the goodness-of-fit (R2) is 0.92, indicating that optimizing the relevant parameters of the support vector machine using the improved particle swarm algorithm has obvious advantages in predicting comfort. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Predicted Value** & **True Value** & **Relative Error** \\ \hline
1 & 5.268905 & 5 & 0.053781 \\
2 & 5.942431 & 6 & 0.009595 \\
3 & 5.035716 & 4.5 & 0.119048 \\
4 & 5.400769 & 5 & 0.080154 \\
5 & 6.316997 & 6 & 0.052833 \\
6 & 7.522472 & 7 & 0.074639 \\
7 & 5.628436 & 6 & 0.061927 \\... &... &... &... \\
30 & 5.37114 & 5 & 0.074228 \\
31 & 5.588755 & 5.5 & 0.016137 \\
32 & 5.795578 & 5.5 & 0.053742 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Relative error between predicted and true values. Figure 6: IPSO-SVR model prediction results.
## 5 Conclusions  
In the paper the feedback control law was designed ensuring the regulation of the relative satellites motion in a swarm. Unlike previous papers, the limited data transmission rate over the communication channels was taken into account. The adaptive coding/decoding procedure for the transmission of position between the satellites in the formation, employing the kinematics process description was studied for the cases of the ideal and erasure channel. Note that an adaptive coder used in the paper was not new since it was employed earlier for other problems in [77; 78]. However such a coder was not applied to the control of satellite swarms previously and this is an additional novelty in the paper.  
The dependence of the closed-loop system performance and accuracy of the data transmission algorithm on the data transmission rate was numerically evaluated. It was shown that both data transmission error and regulation time depend approximately inversely proportionally on the communication rate.  
In future research, disturbances and noise in the communication channel will be taken into account.  
Conceptualization, B.A. and A.L.F.; Data curation, B.A.; Formal analysis, E.V.K.; Funding acquisition, A.L.F.; Investigation, B.A.; Methodology, A.L.F.; Project administration, A.L.F.; Software, B.A. and E.V.K.; Supervision, A.L.F.; Writing--original draft, B.A. Writing--review & editing, B.A. and A.L.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Government of Russian Federation (Grant 08-08) and by the Ministry of Science and Higher Education of Russian Federation (Grant FZWF-2020-0015).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LTI & Linear Time Invariant \\ DTN & Delay Tolerant Networking \\ MAC & Medium Access Control \\ ECC & Elliptic Curve Cryptography \\ LEO & Low Earth Orbit \\ SDR & Software-defined Radio \\ IRM & Implicit Reference Model \\ \end{tabular}
# Single Trace Analysis on Constant Time CDT Sampler and Its Countermeasure
## 2 Preliminaries
### Discrete Gaussian Sampling
Hence, there is no need to sample from \(x\in(-\infty,\infty)\). Instead, \(x\) is selected from \(x\in(-\tau\sigma,\tau\sigma)\) for some tailcut parameter \(\tau\), depending on \(\lambda\). Cumulative Distribution Table Sampling  
There are many sampling algorithms such as rejection sampling and Knuth-Yao sampling [20,21]. However, since the Gaussian sampler plays a crucial role in the performance of LWE-based cryptosystems, CDT sampling is one of the widely chosen sampling algorithms due to its efficiency. CDT sampling is an instantiation of inversion sampling that requires a precomputed cumulative density function table. The cumulative distribution function (CDF) of a random variable \(X\) is defined as follows. **Definition 9**. : _(Cumulative Distribution Function (CDF)) The CDF \(F_{X}\) of a random variable \(X\) is the function given by_  
\[F_{X}=P(X\leq x).\]  
The right-hand side of the equation represents the probability of the random variable \(X\) takes on a value less than or equal to \(x\). The CDT sampler first constructs the CDT table evaluated at some positive integer points including zero. Given a random number \(x\), sampled from uniform distribution, the idea of the CDT sampler is to return the smallest index \(i\) of the precomputed CDT table \(\Psi\) such that \(\Psi[i]<x\leq\Psi[i+1]\). The length of the table depends on the Gaussian parameters, tailcut, and precision. An example of sampling by CDT is as follows:  
Suppose we want to sample from some distribution \(f\), by sampling eight bits uniformly. The first seven bits correspond to a uniform random integer in \([0,128)\), and the last eighth bit is used to determine the sign of the sampled value. Suppose that, by sampling \(0,\pm 1,\pm 2\), and \(\pm 3\), we can obtain the desired statistical distance between \(f\) and our CDT sampler. Then, we first obtain the probability of \(0,1,2\) and \(3\): \(\mathrm{P}[i]=\{0.4,\,0.3,\,0.2,\,0.1\}\) according to \(f\). Then, we map this probability to integers between \([0,128)\): \(\mathrm{P}[i]=\{51,38,26,13\}\). From this probability density table, we obtain: \(\Psi[i]=\{26,64,90,103\}\). Note that we halved the probability of \(0\) since \(-0=+0\) so that \(0\) is sampled twice. By sampling \(x\in[0,128)\), CDT sampler returns the smallest \(i\in[0,3]\) such that \(x\leq\Psi[i]\). The previous implementation used a binary search to find \(i\) efficiently. Note that the greater the number of bits used to sample, the more accurate the sampler is to the Gaussian distribution. To conclude the section, CDT sampling is a popular choice for implementing lattice-based cryptographic schemes due to their efficiency. Rejection sampling requires high-precision floating-point arithmetic [6]. Knuth-Yao sampling is fast, but it requires large precomputed tables when the standard deviation is large. Therefore, the CDT sampler is widely chosen when implementing lattice-based cryptography.
# Recent Advances in Optical Detection of Aminoglycosodies
## 5 Amplification Strategies
### Nanomaterials as the Signal Amplification Carrier  
Nanomaterials have a large surface area and thus have been employed as a carrier to load a large amount of signaling molecules. [PERSON]'s group employed a magnetic separation technique and Au NPs for signal amplification. By taking advantage of Au NPs with a large surface-specific area, a large amount of HRP can be carried on the surface of Au NPs. Thus, the proposed colorimetric method exhibited a LOD for kanamycin as low as 1.7 fM [84]. Liposomes with a large cavity were employed to encapsulate a large amount of signaling molecules. [PERSON]'s group established a signal-on competitive colorimetric immunoassay for the detection of streptomycin, in which an anti-streptomycin antibody-coated microplate and glucose-capsulated liposomes were employed as the immunoreaction carrier and signal-generation tags, respectively. In the absence of target streptomycin, the signal generation tag, coupled with streptomycin-boxine serum albumin (BSA) conjugate, formed an immunocomplex with the anti-streptomycin antibody coated on the microplate. After the rupture of liposomes with Triton X-100, glucose was released and reacted with glucose oxidase to produce H\({}_{2}\)O\({}_{2}\), which oxidized Fe (II) to Fe (III), leading to the color change of the complex ion [Fe(phen)\({}_{3}\)]\({}^{2+}\) from orange-red to light yellow in pH 7.4 solution. In contrast, the addition of target streptomycin caused an orange-red color. Based on the liposome-based amplification strategy, a good linear correlation between the absorbance at 510 nm and the logarithm of the streptomycin concentration was achieved in the dynamic range from 1.7 nM to 34 uM with a LOD of 0.68 nM [85].
## 3 Results and Discussion
### Cost Efficiency of Extraction Methods  
Based on our observations, there are notable differences in the cost efficiency of the extraction methods employed in this study. MUAE exhibited the highest cost efficiency for potential large-scale operations, primarily due to its ability to significantly reduce both processing time and solvent consumption through the synergistic effects of microwave and ultrasound. UMAE closely follows in terms of extraction efficiency, although it requires slightly higher energy consumption compared to MUAE. UAE, while utilizing less expensive equipment, is less cost-effective overall due to its longer extraction times and higher solvent requirements, making it less suitable for large-scale applications. MAE, although faster than UAE, is not efficient in terms of extraction yield, and its higher equipment costs and greater energy consumption make it the least favorable method for large-scale operations. Overall, our findings suggest that MUAE provides the best balance between cost and performance for large-scale extractions, followed by UMAE, UAE, and MAE. This ranking aligns with the extraction yields and operational efficiency observed in our study, positioning MUAE as the most appropriate method for maximizing both yield and cost efficiency in industrial-scale applications.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & **Chemical Formula** & \
\begin{tabular}{c} **Molecular Weight** \\ **(g/mol)** \\ \end{tabular} &
\begin{tabular}{c} **Retention Time** \\ **(min)** \\ \end{tabular} & **Concentration (\%)** \\ \hline Quercetin 7,4\({}^{\prime}\)-O-diglucoside & C\({}_{27}\)H\({}_{30}\)O\({}_{17}\) & 626.5 & 7.10 & 0.65 \(\pm\) 0.02 \({}^{\text{c}}\) \\ Quercetin 3,4\({}^{\prime}\)-O-diglucoside & C\({}_{27}\)H\({}_{30}\)O\({}_{17}\) & 626.5 & 7.40 & 0.73 \(\pm\) 0.02 \({}^{\text{c}}\) \\ Isorhametin 3,4\({}^{\prime}\)-diglucoside & C\({}_{28}\)H\({}_{32}\)O\({}_{17}\) & 640.5 & 7.60 & ND \\ Unknown & & & 8.20 & ND \\ Quercetin-3,0-glucose & C\({}_{21}\)H\({}_{20}\)O\({}_{12}\) & 464.4 & 8.44 & 0.36 \(\pm\) 0.01 \({}^{\text{c}}\) \\ Quercetin-4\({}^{\prime}\)-O-glucose & C\({}_{21}\)H\({}_{20}\)O\({}_{12}\) & 464.4 & 10.50 & 3.15 \(\pm\) 1.20 \({}^{\text{b}}\) \\ Isorhametin-3,0-glucose & C\({}_{22}\)H\({}_{22}\)O\({}_{12}\) & 478.4 & 12.60 & ND \\ Quercetin & C\({}_{15}\)H\({}_{10}\)O\({}_{7}\) & 302.23 & 13.60 & 5.43 \(\pm\) 0.06 \({}^{\text{a}}\) \\ kaempfer & C\({}_{15}\)H\({}_{10}\)O\({}_{6}\) & 286.24 & 16.50 & 0.46 \(\pm\) 0.08 \({}^{\text{c}}\) \\ Isorhametin & C\({}_{16}\)H\({}_{12}\)O\({}_{7}\) & 316.26 & 16.80 & ND \\ Unknown & & & 17.20 & ND \\ \hline \hline \end{tabular}
\end{table}
Table 6: Flavonoid content in extracts of red onion skin by HPLC.  
Figure 5: HPLC chromatograms of onion skin extract.
## 3 Methodology
### Drone 3D Position Estimation and Outlier Frame Removal
```
0:\(D_{pos}\) set, \(D\); minimum point, \(min\)Pts; radius, \(\epsilon\)
0: clustered \(C\) and un-clustered \(UC\)
1:for each sample point \(SP\) in \(D\)do
2:if\(SP\) is not visitedthen
3: mark \(SP\) as visited
4:\(nPts\leftarrow\) sample points in \(\epsilon-neighborhood\) of \(SP\)
5:if\(sizeof\big{(}nPts\big{)}<min\)Ptsthen
6: mark \(SP\) as \(UC\)
7:else
8: add \(SP\) to new cluster \(C\)
9:for each sample point \(SP^{\prime}\) in \(nPts\)do
10:if\(SP^{\prime}\) is not visitedthen
11: mark \(SP^{\prime}\) as visited
12:\(nPts^{\prime}\leftarrow\) sample points in
13:\(\epsilon-neighborhood\) of \(SP^{\prime}\)
14:if\(sizeof(nPts^{\prime})\leq min\)Ptsthen
15:\(nPts\gets nPts+nPts^{\prime}\)
16:endif
17:endif
18:if\(SP^{\prime}\) is not yet member of any clusterthen
19: add \(SP^{\prime}\) to cluster \(C\)
20:endif
21:endfor
22:endif
23:endif
24:endfor
```  
**Algorithm 1** DBSCAN pseudocode  
```
0: Video frame, \(VF\); clustered \(C\)
0: Extracted frame, \(EF\)
1:\(nC\gets 0\)
2:for each cluster \(c\) in \(C\)do
3:if\((sizeof(c)>nC)\)then
4:\(nC=sizeof(c)\)
5:\(MC=c\)
6:endif
7:endfor
8:for each video frame \(vf\) to \(VF\)do
9:if\(vf\in MC\)then
10:\(EF\gets EF+vf\)
11:endif
12:endfor
13:return\(EF\)
```  
**Algorithm 2** Outlier frame removal pseudocode
# The Optimal Dispatch of a Power System Containing Virtual Power Plants under Fog and Haze Weather
## 4 The Mathematical Model of Dispatch under F-H Weather
### Constraints  
(1) System power balance constraints: C\({}_{1}\).  
\[\sum_{i=1}^{M}P_{Gi,t}+\sum_{k=1}^{n}P_{VPPk,t}=P_{Dt},t=1,2,...,T \tag{21}\]  
where \(P_{Dt}\) is the total load in period \(t\).  
(2) Output limits of TPPs and VPPs: C\({}_{2}\).  
\[\begin{array}{ll}P_{\text{min}-Gi}\cdot I_{Gi,t}\leqslant P_{Gi,t}\leqslant P _{\text{max}-Gi}\cdot I_{Gi,t},P_{\text{min}-VPPk}\leqslant P_{VPPk,t}\leqslant P _{\text{max}-VPPk}\\ i=1,2,...,M,t=1,2,...,T,k=1,2,...,n\end{array} \tag{22}\]  
where \(P_{min-Gi}\) and \(P_{\text{max}-Gi}\) are the minimum and maximum outputs of thermal power unit \(I\); the counterparts of VPP\({}_{k}\) are \(P_{min-VPPk}\) and \(P_{\text{max}-VPPk}\).  
(3) System reserve constraints: C\({}_{3}\).  
\[\sum_{i=1}^{M}P_{\text{max}-Gi}\cdot I_{Gi,t}+\sum_{k=1}^{n}P_{\text{max}-VPP k}\geqslant P_{Dt}+S_{Dt},t=1,2,...,T \tag{23}\]  
where \(S_{Dt}\) is the total system reserve in period \(t\).  
(4) Ramping constraints of TPPs: C\({}_{4}\).  
\[\left|P_{Gi,t}-P_{Gi,t-1}\right|\leqslant Q_{i},i=1,2,...,M,t=2,3,...,T \tag{24}\]  
where \(Q_{i}\) is the limit value of output change of unit \(i\).  
(5) The maximal start/stop count limits: C\({}_{5}\).  
The VPP is in the running state all of the time, except during maintenance in the dispatch period, so the start/stop constraints consider only TPPs. These constraints are expressed as minimal on/off time limits in published research works, which are transformed into maximal start/stop counts to simplify the complexity of calculation in this paper.  
\[\sum_{t=2}^{T}\left|I_{Gi,t}-I_{Gi,t-1}\right|\leqslant J_{\text{max}-i},i=1,2,...M \tag{25}\]  
where \(J_{\text{max}-i}\) is the maximum start/stop count of unit \(i\).