esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Vowel Classification Results and Analysis Based on ID3 Algorithm
Furthermore, the experimental results further demonstrate that formant features can be used to separate standard and non-standard pronunciations, indicating that employing formant structural information for pronunciation evaluation is reasonable. Figure 4: Vowel classification results based on the ID3 algorithm. The left figure (**a**) shows accuracy, and the right figure (**b**) shows recall. UCA (black line) represents advanced level learners, UCI (red line) represents intermediate level learners, and UCB (black dashed line) represents elementary level learners. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Vowel**} & \multicolumn{2}{c}{**UCA**} & \multicolumn{2}{c}{**UCI**} & \multicolumn{2}{c}{**UCB**} \\ \cline{2-7}  & **Accuracy** & **Recall** & **Accuracy** & **Recall** & **Accuracy** & **Recall** \\ \hline a & 91\% & 90\% & 90\% & 90\% & 89\% & 88\% \\ o & 91\% & 89\% & 94\% & 92\% & 90\% & 90\% \\ r & 94\% & 91\% & 93\% & 92\% & 92\% & 90\% \\ u & 90\% & 91\% & 91\% & 91\% & 89\% & 88\% \\ y & 88\% & 85\% & 90\% & 89\% & 87\% & 87\% \\ i & 90\% & 89\% & 90\% & 90\% & 89\% & 88\% \\ z & 89\% & 88\% & 90\% & 89\% & 87\% & 86\% \\ \% & 90\% & 90\% & 89\% & 88\% & 88\% & 85\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy and recall of vowels for learners at different levels based on the ID3 algorithm.
# Securing SDN-Based IoT Group Communication
## 4 Implementation and Evaluation
### Security Evaluation  
The security goal of our solution is to prevent \"fake\" advertisements. Indeed, with our solution, only authorized IoT endpoints can advertise WoT-TD files. Furthermore, because these WoT-TD files are singed, neither an active attacker nor the IoT endpoint itself can modify them.  
An active attacker in our system is able to replay valid advertisements. Although, in general, replay attacks are prevented by the use of the _nonce_, there can be cases in which the replayed advertisement is received before the real one. In these cases, an endpoint will believe that the attacker is a legitimate IoT device and that the real advertisement was a replayed one. Although this attack cannot be prevented in a straightforward way, we argue that its impact is limited, and it can be easily detected and i mitigated. Advertisements in our system do not contain any location-specific information, since \(Endpoint_{ID}\) identifiers are just public keys. Therefore, if an \(Endpoint_{ID}\) still provides the advertised resources, the attack will have no impact apart from the adeded network overhead. Moreover, advertisements in our system are broadcasted; hence, it will be trivial for a monitoring entity to detect the replay attack. Finally, we consider an SDN-based architecture, in which a controller can remove any endpoint from the network in a straightforward manner.  
Similarly, an attacker that has access to the private key that corresponds to an \(Endpoint_{ID}\) can only sent _valid_ advertisements of WoT-TD files belonging to the corresponding IoT device, i.e., since the \(Endpoint_{ID}\) is included in the WoT-TD, the attacker cannot use the breached key to sign an advertisement of another IoT device. Therefore, the impact of this attack is similar to the impact of the replay attack.  
From a security perspective, the most critical component of our solution is the private key that corresponds to \(Network_{ID}\). If this key is compromised, then it must be revoked; hence, all endpoints must be reconfigured with the new \(Network_{ID}\), and all LDPs must be regenerated.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Operation** & **Desktop** & **Raspberry Pi** \\ \hline LDP generation & 0.93 ms & 5.2 ms \\ LDP verification & 0.83 ms & 6.0 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: LDP generation and verification times.
# The Effect of Non-Transferred Plasma Torch Electrodes on Plasma Jet: A Computational Study
## 3 Results
### Thermal Efficiency
However, it is acknowledged that arc power, gas flow rate, and anode cooling intricacies play pivotal roles in determining plasma torch efficiency. [PERSON] et al. conducted experimental studies, concluding that the thermal efficiency of plasma torches typically falls within the range of 65-78% [40]. Further, from the computational study presented in this paper, the efficiency range of the plasma torch spans between 50 and 80%, with the power input and cathode shape identified as influential factors. It is noteworthy that this efficiency range appears slightly higher than those reported in prior works. This discrepancy can be attributed to the present study's focus on calculating efficiency based on the internal flow within the plasma torch while excluding the effects of plasma jet interaction with the atmosphere upon exiting.
## 5 Conclusions  
Microwave absorbing nanocomposites based on the MWCNTs (4 wt%)/epoxy resin systems were prepared and characterized. The filler wt% was predicated on previous investigations exploring the relationship between MWCNT concentration, dispersibility, and complex permittivity. Indeed, 4 wt% represents a compromise between filler's uniform dispersion in the polymer matrix and high real and imaginary permittivity components. In fact, the formation of a conductive network throughout the composite volume allows for energy dissipation by both interfacial polarization and electron transport. Therefore, a 2 mm thick monolayer absorbing material was devised and tested for microwave reflection coefficient. The large interfacial polarization stemming from the MCWCNTs high aspect ratio (\(\approx\) 1400), and specific surface area (300 m\({}^{2}\)/g) resulted in a minimum in composite reflection coefficient at 6 GHz. Such an achievement is in good agreement with simulations. Moreover, dielectric and reflection coefficient measurements performed on epoxy resin composites comprised of morphologically-different MWCNTs demonstrated the aspect ratio, specific surface area, and purity represent dominant factors in determining the microwave absorption properties of the material (rather than the filler concentration).  
[PERSON] was responsible for permittivity and reflection coefficient measurements, Maxwell-Garnett model and microwave absorber model development and simulation, [PERSON] was responsible for the FESEM and Raman characterization, [PERSON] was responsible for samples preparation; [PERSON] and [PERSON] wrote the paper.  
This research received no external funding  
**Acknowledgments:** The authors would like to thank Nanothinx for supplying MWCNTs, [PERSON] for help in composite preparation, [PERSON] for FESEM and EDX analysis, and ing. [PERSON], [PERSON], and [PERSON] (SELEX SE) for the reflection coefficient measurements.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Candidate Universal Decoders for URLLC
### Bit-Flip Decoding (BFD)  
Bit flipping is another approach to improve the reliability of a decoder, where the block error can be corrected by flipping unreliable bits. Authors in [PERSON] and [PERSON] (2017) introduced the SC flip (SCF) decoder, which corrects the first wrong estimate of an unfrozen bit and shows that flipping the first incorrect hard choice can significantly improve decoding performance. Using additional SC decoding attempts to execute flip operations in error-prone positions is the fundamental concept behind the flip decoder, which enables it to outperform the SC decoder in error-correction performance. In [PERSON] and [PERSON] (2017), the bit-flip technique has been introduced into the SCL decoding process to further improve the error-correction performance of the CA-SCL decoder. Authors in [PERSON] and [PERSON] (2017) have introduced a diversity flip decoder (DFD) as a universal decoding algorithm for preserving diversity order. The Algorithm 4 is given for SC bit flipping as proposed in [28].  
```
1 Variable declaration
2\(y^{N}\): channel output of length \(N\)
3\(A\): information set
4T: additional number of SC decoding attempts
5\(U\): set of least reliable values of T
6 Step ISC-decoding
7 Input:\(y^{N}\)
8 Output:\(\hat{u_{1}}^{N}\)
9 Perform SC decoding on \(y^{N}\) to obtain \(\hat{u_{1}}^{N}\)
10 Step IIBit flipping
11 Input:\(\hat{u_{1}}^{N}\)
12 Output:\(\hat{u_{1}}^{N}\)
13 Perform CRC check on \(\hat{u_{1}}^{N}\)
14 if CRC(\(\hat{u_{1}}^{N}\)) = success
15 Return\(\hat{u_{1}}^{N}\)
16 else
17 Perform SC decoding for T number of attempts
18 fork\(\leftarrow\) 1 to T \(\in U\)
19 flip\(u_{k}\)
20 ifCRC(\(\hat{u_{k}}\)) = successthen
21 break;
22 endif
23 endfor
24 endif
25 Return\(\hat{u}^{N}\)
```  
**Algorithm 4**Bit-Flip decoding (BFD)
## 2 Data Description and Data Preprocessing
### Data Preprocessing  
#### 2.2.1 Missing Category  
In this study, the raw data from acceleration sensors installed on the bridge are subject to instances of data loss in which the affected sampling points are directly assigned a value of 9999. Consequently, a quantitative approach is employed to assess the occurrence and extent of missing data within the segment. The labeling of manually identified missing data anomalies is based on a criterion wherein data samples with a missing data proportion exceeding 40% are categorized as such. This specific anomaly category, amenable to direct quantification, is purposefully excluded from subsequent tasks involving anomaly detection. Consequently, the training dataset is purged of any influence stemming from this category.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### Induction of Plant Resistance to Abiotic Stress
These compounds are responsible for the elimination of reactive oxygen forms (ROS), maintenance of the proper structure of proteins and cell membranes, and regulation of osmotic and metabolic homeostasis of plants [176; 177; 178]. Another mechanism used by _Trichoderma_ fungi to increase the sensitivity of plants to stressful environmental conditions is the induction of production and control of the level of salicylic acid (SA) levels in the plant. According to [PERSON] et al. [163], SA regulates antioxidant metabolism, thereby inducing an antioxidant response and modifying the level of photosynthesis efficiency. [PERSON] et al. [179] also indicate that the acid improves the ratios of K\({}^{+}\) to Na\({}^{+}\) and reduces lipid peroxidation. In contrast, [PERSON] and [PERSON] [180] report that increasing the salicylic acid content of the plant causes the accumulation of reactive forms of oxygen, hydrogen peroxide, and calcium ions, which lead to the closing of the stomata in situations of water deficits and drought. Another phytohormone regulated by _Trichoderma_ sp. that controls the conductivity of the stomata, the water management of plants and the hydraulic conductivity of the roots, is abscisic acid (ABA). The high concentration of ABA causes the closing of the stomata, thus protecting the plant from the loss of a large amount of water [181]. Many analyses show that one of the mechanisms responsible for increasing plant tolerance to stressful environmental conditions is the production of ACC deaminase (ACCD) by _Trichoderma_ sp., which reduces the concentration of ACC acid in plant tissues. In turn, this acid is a precursor of ethylene (ET), the accumulation of which accelerates the ripening process. Therefore, the production of ACCD enzymes delays the aging of plants [135; 182; 183]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Stress** & **Species of** & **Crops** & **Mechanisms of Action** & **Reference** \\ \hline Drought & _Trichoderma harzianum_ & rice & \(\bullet\) & Increasing the activity of antioxidant enzymes of plants; Regulating the level of proline in the plant; Reducing lipid peroxidation. & [172] \\  & & & \(\bullet\) & Regulating the level of proline in \\  & & & \(\bullet\) & Reducing lipid peroxidation. & \\ \hline Drought & _Trichoderma harzianum_ & tomato & \(\bullet\) & Regulating the level of proline, soluble proteins, phenols and flavonoids in the plant; Increasing the level of gibberell, auxins and abscisic acid in the plant. & [173] \\  & & & \(\bullet\) & Increasing the level of gibberell, auxins and abscisic acid in the plant. & \\ \hline Presence of & _Trichoderma_ & sunflower & \(\bullet\) & Increasing the activity of antioxidant enzymes of plants. & [174] \\  & & & & Reducing lipid peroxidation; Regulating the level of proline in & [175] \\  & & & & the plant. & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Materials and Methods
### Presented CAD
#### 3.3.6 Breast Cancer Identification  
The breast cancer identification phase of the suggested CAD system employs five various SVM classifiers, each adopting a separate kernel function to improve classification efficacy. The selected kernel functions are Linear (LSVM), Quadratic (QSVM), Cubic (CSVM), Medium Gaussian (MGSVM), and Coarse Gaussian (CGSVM), each selected for their distinct capacities to elucidate complex relationships in the data. The classifiers are evaluated using a five-fold cross-validation method, guaranteeing a rigorous and impartial assessment of their performance. This validation method divides the dataset into five equal segments, with each of them serving as the testing set in turn, while the other segments are employed for training, facilitating thorough performance evaluation across all data samples. This method employs various kernel functions to investigate the influence of numerous mathematical transformations on the model's capacity to differentiate between classes, ultimately determining the most effective kernel for precise breast cancer classification. This strategy enhances the reliability of the CAD system and ensures generalizability to novel data, rendering it a valuable asset in clinical applications.
# Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
###### Abstract  
Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott's index of agreement (WIA), coefficient of determination (R\({}^{2}\)), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3 LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.  
artificial neural network (ANN); extreme learning machine (ELM); multilayer perceptron (MLP); discrete wavelet transform (DWT); fault classification; fault location +
Footnote †: journal: Applied Physics Letters
# Large Constellations of Small Satellites: A Survey of Near Future Challenges and Missions
## 4 Discussion  
The increasing trend of launching very small satellites into space has been clear and well established for the last two decades. Smaller satellites are in turn paving the way to constellations, which are gaining widespread interest. Spacecraft constellations are appealing, especially in three fields, namely, (i) communications, for global coverage, (ii) Earth observation, for near real time measurements and (iii) space observation for continuous monitoring/surveillance. During this survey, about one hundred companies have been found proposing constellations with varying numbers of satellites. Satellite sizes range mainly from nano to micro-sizes, i.e., from 1 kg up to 100 kg. Most of them are expected to be active in orbit before 2025. The most common number of elements for the constellations is below 150 units. Figure 5: Galaxy group NGC 5353/4 seen with a telescope at Lowell Observatory in Flagstaff with Starlink satellites passing in front, Arizona, on 25 May 2019 (Courtesy of EarthSky Voices). A review was carried out on the challenges that satellite constellations will have to face to become more sustainable, with a focus on three categories: constellation management, communication and space traffic. The level of maturity reached by these three areas is, however, not homogeneous: communication is probably the most matured, where relevant work is being done concerning infrastructure integration and protocol efficiency. Some significant past experience in constellation management was reviewed during this survey; still, the need for an improved level of automation is clear. In this respect, artificial intelligence seems to be a valuable option, together with infrastructure sharing for rapid development and commercial viability. On the other hand, space traffic management is mostly unprepared, with significant developments only in terms of debris countermeasures. Some extensions of the air traffic regulation are expected in the years to come to mitigate the current free-space policy covered by the Outer Space Treaty. Although many technical challenges are still being addressed, the amount of work that has been analyzed during this review suggests good chances of success for large constellation missions. Conceptualization, G.C. and P.T. ; methodology, G.C. and D.M. ; writing--original draft G.C. and D.M; writing--review and editing, D.M. and G.C. ; supervision: P.T. All authors have read and agreed to the published version of the manuscript. This research was internally funded by the University of Bologna. The authors declare no conflict of interest. List of constellations surveyed in this work. \"?\" stands for unknown; \"*\" stands for \"dormant or cancelled project\".
# Improved Solar Operation Control for a Solar Cooling System of an IT Center
## 3 Results--Model Predictive Control
### Dry-Cooler Model
Consequently, for serial cooling water flow, the flow rates in the absorber and condenser and in the dry-cooler are the same, i.e., \(W_{A}=W_{C}=W_{K}=V_{K}\cdot\rho_{K}\cdot c_{p,K}\):  
\[t_{Ki}=t_{Co}=t_{Ai}+\frac{Q_{E}+Q_{D}}{W_{A}}. \tag{15}\]  
Combining Equations (14) and (15) results in:  
\[t_{Lo}=P_{L}\cdot(t_{Co}-t_{Li})+t_{Li}=P_{L}\left(t_{Ai}+\frac{Q_{E}+Q_{D}}{W _{A}}-t_{Li}\right)+t_{Li}. \tag{16}\]  
and subsequently inserting Equation (16) into Equation (13) with the ambient air temperature as the air inlet temperature \(t_{Li}=t_{amb}\) leads to:  
\[V_{L}=\frac{W_{A}}{\rho_{L}\cdot c_{p,L}\cdot P_{L}}\cdot\frac{Q_{E}+Q_{D}}{Q _{E}+Q_{D}+W_{A}\cdot(t_{Ai}-t_{amb})}. \tag{17}\]  
However, Equation (17) determines \(V_{L}\) only implicitly, because \(P_{L}\) is a function of \(V_{L}\) since the dimensionless heat transfer capability \(NTU_{L}\) and heat capacity flow rate ratio \(R_{L}\) in Equation (14) depend on \(V_{L}\):  
\[NTU_{L}=\frac{Y_{L}}{W_{L}}=\frac{U_{L}\cdot A_{L}}{\rho_{L}\cdot c_{p,L}\cdot V _{L}}\qquad\quad R_{L}=\frac{W_{L}}{W_{A}}=\frac{\rho_{L}\cdot c_{p,L}\cdot V _{L}}{W_{A}}. \tag{18}\]  
Figure 5: Nomenclature for dry-cooler model. In order to solve Equation (17) explicitly for \(\mathrm{V_{L}}\), an approximation \(\mathrm{P_{L}^{\prime}}\) for the dimensionless temperature glide, \(\mathrm{P_{L}}\) is used from [12], appendix O:  
\[\mathrm{P}_{\mathrm{el,DC}}=\frac{\mathrm{\Delta}\mathrm{p}_{0}\cdot\mathrm{V }_{\mathrm{L,0}}}{\mathrm{\eta}_{\mathrm{DC,tot}}\big{(}\mathrm{V}_{\mathrm{L,0}}\big{)}^{2}\cdot\mathrm{V}_{\mathrm{L,0}}}\cdot\big{(}\mathrm{V}_{\mathrm{ L}}\big{)}^{3}+\mathrm{P}_{\mathrm{el,DC}}^{\mathrm{aux}}=\mathrm{P}_{\mathrm{el,DC,0}} \cdot(\mathrm{C}_{\mathrm{DC}})^{3}+\mathrm{P}_{\mathrm{el,DC}}^{\mathrm{aux}}, \tag{12}\]0  
In this equation, \(\mathrm{W_{L,0}}=\mathrm{\rho_{L}\cdot c_{p,L}\cdot V_{L,0}}\) holds for the air side capacity flow rate at the nominal condition (Index 0) and \(\mathrm{P_{L,0}}\) is calculated with \(\mathrm{W_{L,0}}\) (i.e., also at the nominal condition). Thus, at the normalized flow velocity \(\mathrm{C_{DC}}=\mathrm{W_{L}}/\mathrm{W_{L,0}}=1\), the approximation \(\mathrm{P_{L}^{\prime}}\) equals \(\mathrm{P_{L,0}}\) (i.e., the exact value). For \(\mathrm{C_{DC}}=0\), Equation (19) converges to the same limit value \(\mathrm{P_{L}^{\prime}}=1\) as \(\mathrm{P_{L}}\) in Equation (14). Between \(\mathrm{C_{DC}}=0\) and \(\mathrm{C_{DC}}=1\), the exponential dependency is considered by a reciprocal approach.
## 4 Materials and Methods
### Biomass Yield on Glucose Measurements  
Glucose consumption was quantified using High-Performance Liquid Chromatography (HPLC) [55]. The ancestor and resistant strains were cultured in 5 mL of medium with an initial OD\({}_{600}\) of 0.05 in test tubes, allowing growth until the OD\({}_{600}\) reached 1.0. Subsequently, the supernatant was obtained by centrifuging at 12,000 rpm for 10 min and filtered through 0.22 \(\upmu\)m filters to prepare the samples. To make the calibration curve, glucose standards were prepared, ranging from 0 to 50 mM. These standards, along with the samples, were analyzed using an HPLC instrument (LC-20 AT, Prominence, Shimadzu; Kyoto, Japan), which was equipped with a 300 \(\times\) 7.8 mm Ion exclusion Rezek ROA Organic Acid H+ (8%) column (Phenomenex), a UV detector (SPD-20 A, 210 nm), and a refractive index detector (RID-20 A, 40 \({}^{\circ}\)C). The mobile phase consisted of 5 mM aqueous H\({}_{2}\)SO\({}_{4}\), with a flow rate of 0.5 mL/min, and the analysis was conducted at 55 \({}^{\circ}\)C. The correlation of OD\({}_{600}\) to cell dry weight (DW) was CDW = 0.56 \(\times\) OD\({}_{600}\) g/L [56]. The biomass yield on glucose was calculated using 0.56 g L\({}^{-1}\)/180.156 g mol\({}^{-1}\)\(\times\) Glucose consumption mol L\({}^{-1}\) (gDW/gGlu). Each sample had at least three replicates. Data are presented as means \(\pm\) SD; statistical significance was determined using a one-way ANOVA, * \(p\) < 0.05, ** \(p\) < 0.01, *** \(p\) < 0.001.