esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Chatter Stability of Two Boring Bar Models
### Convergence of the Chatter Stability  
As discussed in Section 4, the natural frequency of the shear boring bar was solved using the AMDM method. The result was an approximate value that was calculated by iteration of the first \(k\) terms. In Section 3, the accuracy of the \(\Omega_{1}\) value was one-ten thousandth. Whether the value of \(\Omega_{1}\) affects the chatter stability of the boring bar and the reliability of the \(\Omega_{1}\) result must be tested by experiments.  
We first selected an iteration number \(k=16\) to verify whether the resulting \(\Omega_{1}\) value was within the desired tolerance of \(10^{-6}\). The test result showed that when \(k=16\), the obtained \(\Omega_{1}\) value no longer changed. Therefore, \(\Omega_{1}\) achieved a stable value, which was chosen as the reference standard. To further verify whether the \(\Omega_{1}\) value was stable when \(k\) was assigned values smaller than 16, we calculated the results for four other iteration numbers (\(k=7\), 9, 11, and 13). Table 3 lists the iteration number, the related \(\Omega_{1}\) values, and the ultimate cutting depth.  
The chatter stability lobes are shown in Figure 7. The five chatter stability lobes converged to two curves. The chatter stability lobe when \(k=7\) was different than the others, as it did not converge to the standard curve (i.e., the curve when \(k=16\)). When \(k=9\), the chatter stability lobe basically overlapped the \(k=16\) curve. When \(k=11\) and 13, the chatter stability lobes overlapped and could not be distinguished from the reference curve.  
To distinguish these overlapping curves, we calculated the ultimate cutting depths for each iteration number, as shown in Table 3. The ultimate cutting depth difference was 0.0078 when \(k=7\), 0.0012 when \(k=9\), and 3.8881 \(\times\)\(10^{-4}\) when \(k=11\). In particular, when \(k=11\), the cutting depth difference met the accuracy requirement of \(10^{-4}\). When \(k=13\), the cutting depth difference was 0, which completely satisfied the accuracy requirement. Therefore, the results listed in Table 3 and Figure 7 is consistent with the analysis results obtained in Section 3.
# Optimal Design of the Vertical Earthing with Electrodes Arranged in Line
## 2 Methodological Aspects
### Calculation Methodology for VEG with Linearly Placed Electrodes  
The VEGL mathematical model adopted in the present research is the standardized one [1,3,5,8], and, as stated above, no significant changes are envisaged. Next, the main sizing stages of VEGL are briefly reproduced, the chosen form of equations being in accordance with normative documents [3,5].  
If the calculation variables {d, \(\ell\), q, \(\rho_{\text{P}}\)} are known, the resistance to earth \(\text{r}_{\text{pv}}\) of one vertical electrode (road or pipe) is calculated with the relation  
\[\text{r}_{\text{pv}}=\frac{0.366\cdot\rho_{\text{P}}}{\ell}\cdot\lg\left[ \frac{2}{d/\ell}\cdot\sqrt{1+\frac{1}{2(q/\ell)+0.5}}\right] \tag{2}\]  
which represents the basis of a calculation nomogram [14]. Examining the respective nomogram offers the possibilities to assess the character of the dependences of the earth electrode resistance on the involved variables, and to evaluate the magnitude of the variations determined by the independent variables.  
The combined resistance to earth \(\text{R}_{\text{Pe}}\) of all vertical electrodes, realized by a parallel binding of \(\text{n}_{\text{e}}\) same type rod or pipe electrodes, arranged in line, is determined according to the equation:  
\[\text{R}_{\text{Pe}}=\frac{\text{r}_{\text{pv}}}{\text{u}_{\text{P}}\ell\cdot \text{n}_{\text{e}}} \tag{3}\]  
wherein \(\text{r}_{\text{pv}}\) is the earth electrode resistance (2); \(\text{u}_{\text{P}}\ell\)--the coefficient of use of the combined earthing system, with the electrodes arranged in line, given by the equation:  
\[\text{u}_{\text{P}}\ell=\frac{3-2.5\beta}{3+2\beta-7.75\beta^{2}} \tag{4}\]  
in which the notation \(\beta\) represents the ratio:  
\[\beta=\frac{\rho_{\text{P}}}{2\pi\text{r}_{\text{pv}}}. \tag{5}\]  
From the expressions of the variable \(\beta\) and the coefficient of use \(\text{u}_{\text{P}}\ell\), it can be noticed that only in the case of VEGL does the coefficient of use not depend on the number of the electrodes.  
The resistance to earth, \(\text{R}_{\text{Pc}}\), owing to the horizontal strip is given by the equation:  
\[\text{R}_{\text{Pc}}=\frac{18.3}{\text{a}\cdot\text{n}_{\text{e}}}\cdot\lg(6 2.5\cdot\text{a}^{2}\cdot\text{n}_{\text{e}}^{2}), \tag{6}\]  
in which the distance between adjacent electrodes is considered known.  
The total resistance to earth, \(\text{R}_{\text{P}}\ell\), of the combined VEGL may be calculated as follows:  
\[\text{R}_{\text{P}}\ell=\frac{\text{R}_{\text{Pe}}\cdot\text{R}_{\text{Pc}}} {\text{R}_{\text{Pe}}+\text{R}_{\text{Pc}}}\ \leq\ \text{R}_{\text{Pn}}, \tag{7}\]  
verifying as much as possible the fulfillment of the condition in comparison with the design value resistance. In this paper, it is considered that only the artificial VEGL is dimensioned so that a natural earthing system does not exist.
## 2 Materials and Methods
### Reagents  
Methanol (MeOH, 99.8% LC-MS) and acetonitrile (CH\({}_{3}\)CN, 99.9% LC-MS) were purchased from Fisher Scientific (Thermo Fisher Scientific, Waltham, MA, EUA). The ultrapure water (18.2 MO.cm) was obtained from a Millipore-Direct Q3 UV system (Millipore, Billerica, MA, USA). Formic acid (HCOOH, 98% p.a.) was purchased from Merck (Darmstadt, Germany). Folin-Ciocalteu's reagent, fluorescein sodium salt, AAPH (2,2-azobis(2-methylpropionamidine)dihydro) and Trolox (6-hydroxy-2,5,7,8-tetramethyl) chroman-2-carboxylic acid) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Standards of phenolic compounds, quercetin-3-glucoside (PubChem CID: 25203368) and gallic acid (PubChem CID: 370), were obtained from Sigma-Aldrich (St. Louis, MO, USA), and chlorogenic acid (3-O-caffeoylquinic acid-PubChem CID: 1794427) was purchased from Extrasynthese (Genay, Rhone-Alpes, France).
## 2 Materials and Methods
### Bacterial Inoculation and Seed Infection  
Legume-based naturalized media have been used [31] to mimic the seed-borne conditions found in regard to _Cff_ in nature. In addition to common bean (_Phaseolus vulgaris_), which is considered to be the major host for _Cff_, soybean (_Glycine max_), cowpea (_Vigna unguiculata_), pea (_Pisum sativum_), giant white bean (_Phaseolus coccineus_), and lentil (_Lens culinaris_) have also been used to produce legume-flour. Moreover, durum wheat flour has also been tested as a non-legume comparison (Table 2). Three different concentrations (9 g/L, 18 g/L, 36 g/L) of legume and wheat flours were used as naturalized media. As a control, agarized Luria-Bertani (LB) broth [38], nutrient broth yeast (NBY) [39], and tryptone broth yeast (TBY) [40] were also used. A 5 uL suspension (OD\({}_{600}\) 0.5) of each bacterial strain (Table 1) was centrally inoculated onto a sterilized agar slant (3 mL of medium placed in 10 mL glass vials (Agilent Technologies Inc., Santa Clara, CA, USA) (Figure S1). The origin and main features of the legume and wheat flours used in this study are reported in Table 2. The bacterial cultures were incubated at 26 \({}^{\circ}\)C for 48 or 60 h.  
The same bacterial suspensions were also used to artificially infect bean seeds (Cannellino cultivar) in a vacuum chamber and under reduced pressure for 15 min, and were analyzed under the same conditions. Both symptomatic and asymptomatic Cannellino bean seeds, artificially infected with _Cff_, _Psp_, or _Xpp_, were used for the VOC analysis. The presence of _Cff_ in each tested bean seed was assessed at the end of the VOC analysis, by using highly specific PCR-based tests for _Cff_[28; 29].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strain**} & **International** & **Colony** & \multicolumn{2}{c}{**Origin**} & \multirow{2}{*}{**Year**} \\  & **Code 1** & & & **Color and Type** & **Host** & **Country** \\ \hline \multirow{3}{*}{_Cff_ TS} & ICMP 2584 & yellow–fluidal & _Phaseolus vulgaris_ & Hungary & 1957 \\  & CFBP 3418 & ICMP 22071 & red–fluidal & _Phaseolus vulgaris_ & Iran & 2014 \\  & CFBP 8819 & ICMP 22053 & yellow–fluidal & _Capsicumanum_ & Iran & 2015 \\  & CFBP 8820 & orange–fluidal & _Phaseolus vulgaris_ & USA & 2004 \\  & J24 & creamy dark & _Phaseolus vulgaris_ & Italy & not available \\  & Typ & 95-61 & yellow–fluidal & _Phaseolus vulgaris_ & USA & not available \\ \hline \hline \end{tabular}  
* Abbreviations: ICMP, International Collection of Microorganisms from Plants; CFBP, Collection Française de Bactéries Phytopathiophenes; J24 and 95-61, Dept. Plant Pathology, University of Nebraska, USA; IPV-BO, ex-lstituto per la Protezione delle Plante, University of Bologna, Italy.  
\end{table}
Table 1: Bacterial plant pathogens of legumes used in this study and their main features.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.2 Ant  
ANT [63] is a low-power proprietary wireless technology designed and developed for a broad range of wireless sensor network (WSN) applications. ANT is specifically appropriate for low data rate battery powered sensor nodes and covers a range of network topologies from simple peer-to-peer to complex mesh networks. ANT is a candidate for wireless connectivity in battery powered applications such as health monitoring where ultra-low power consumption is required. ANT operates in the 2.4 GHz frequency band, supports a data rate of 1 Mb/s and employs TDMA scheme to address interference issues. ANT+ facilitates wireless communication of devices from different companies by providing predefined network parameters and data payload structures including device profiles. Existing ANT+ device profiles consist of heart rate monitors, stride-based speed and distance monitors, bike speed and power. Several upcoming device profiles include weight scales, multi-sport speed and distance, and environment sensors.
## 4 Results and Discussion
### Performance Evaluation Matrices
#### 4.2.2 F1 Score  
To identify the classification worth of the 3-TierDCFNet, we use the F1 score. F1 score considers both precision and recall. The higher the value of the F1 score indicates that the proposed model has a better prediction efficiency regarding classification. Equation (5) describes the mathematical formula for the F1 score:  
\[\text{F1 Score}=2\times\frac{\text{precision}\times\text{recall}}{\text{ precision}+\text{recall}} \tag{5}\]  
The 3-TierDCFNet got 92.57%, 95.62%, 95.87%, and 98.12% for Tier-I, Tier-II, Tier-III, and global, respectively.
## 2 Materials and Methods
### Volatile Organic Compound Profiling  
Non-targeted VOC profiling was conducted in triplicate by headspace/solid-phase microextraction coupled to gas chromatography and time-of-flight mass spectrometry (HS/SPME-GC-TOF-MS), using a Trace 1300 gas chromatograph (Thermo Fisher, Waltham, MA, USA) equipped with a Stabiliwas-MS column (30 m by 0.25 mm, film thickness of 0.25 \(\upmu\)m; Restek, Bellefonte, PA, USA) and coupled to a BenchTOF-HD mass spectrometer operating with electron impact ionization at 70 eV (Markes International, Llantrisant, Wales, UK). For analysis, approximately 10 g of the fermented meat product sample was frozen using liquid nitrogen (Airliquide, Paris, France) and grounded into a fine powder with a coffee grinder (DeLongi KG49, Treviso, Italy). Subsequently, 1.5 g of grounded powder was incubated in a 10-mL screw-top headspace vial at 60 \({}^{\circ}\)C for 10 min, after which the sample was exposed to an SPME fiber (DVB/CAR/PDMS; Sigma-Aldrich, St. Louis, MO, USA) at 60 \({}^{\circ}\)C for 20 min. To each sample, 10 \(\upmu\)L of a 10 ppm toluene-D8 solution (Sigma-Aldrich) was added as an internal standard. The VOCs adsorbed to the SPME fiber were thermally desorbed at 250 \({}^{\circ}\)C with a split rate of 50 mL/min. The GC oven temperature program consisted of an initial step at 40 \({}^{\circ}\)C for 1.5 min, followed by a continuous increase to 225 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, and finally, the temperature was kept constant at 225 \({}^{\circ}\)C for 15 min. Helium gas (Praxair, Danbury, CT, USA) was used as the carrier gas at a flow rate of 1 mL/min. The TOF-MS was scanned in the _m/z_ range of 35 to 400 after 2 min. The raw data were deconvoluted by TOF-DS software (Markes). Afterward, peaks were identified based on the NIST library (National Institute of Standard and Technology, Gaitherburg, MD, USA) and the use of commercially available standards [37; 41]. For each identified compound, the peak area was normalized to the peak area of the internal standard.
## 3 Storjohann's Improvements
### The LLL-Reduction with Fraction Free Computations
The **FFSwap** step of the algorithm reduces \(\Delta\) by a factor \(\delta\)[7]. This is because when \(b_{k}\) and \(b_{k-1}\) are swapped, \(\|b_{k-1}^{*}\|^{2}\,\|b_{k}^{*}\|^{2}\) remains constant, and the new value of \(\|b_{k-1}^{*}\|^{2}\) is reduced at least by a factor \(\delta\). As a consequence \(d_{k-1}\) is reduced by a factor \(\delta\), while all other \(d_{i}\) do not change. The value of \(\Delta\) is unchanged in the **FFReduce** step of the algorithm because \(B^{*}\) does not change after this step. Since \(1\leq\Delta\leq M_{sc}^{n-1}\), Case 1 in the **ModifiedLLL** algorithm occurs only \(O(n\log_{1/\delta}M_{sc})\) times. Hence this part of the algorithm is executed in \(O(n^{2}\log_{1/\delta}M_{sc})\) arithmetic operations. Case 2 of the algorithm can also occur at most \(O(n\log_{1/\delta}M_{sc})\) times, each requiring \(O(n^{2})\) arithmetic operations. Hence, this part of the algorithm is executed in \(O(n^{3}\log_{1/\delta}M_{sc})\) arithmetic operations. Finally a \(\delta\)-LLL reduced basis is generated by \(UB\), which is performed in \(O(n^{2}d)\) operations under standard matrix multiplication, and in \(O(n^{1.376}d)\) using the algorithm of [PERSON] and [PERSON] [14, 22]. [PERSON], [PERSON], and [PERSON] [7] showed that the bit length of the numbers on which the arithmetic operations are performed is bounded by \(O(\log_{2}M_{sc})\). This gives the complexity result in Table 1, where \(d=O(n)\) for simplicity. The following lemma gives bounds on the size of intermediate lattice bases generated during the **LLL** and **ModifiedLLL** algorithms. This property is used when using computations with modular arithmetic. **Lemma 1**: _[_7_]__. Let \(B\) be an input basis to the_ **LLL** _and_ **ModifiedLLL** _algorithms. The quantities \(\max_{i}\{\|b_{i}^{*}\|\}\) and \(\max_{i}\{d_{i}\}\) are non-increasing in the_ **LLL** _and_ **ModifiedLLL** _algorithms. Furthermore, upon termination_  
\[\|b_{i}\|^{2}\leq nM_{0},\;\mathrm{for}\;1\leq i\leq n\]  
**Proof:** Recall that size reduction/subtract does not change \(B^{*}\), consequently for all \(i\), \(\|b_{i}^{*}\|\) is unchanged in this step. Swapping \(b_{i}\) and \(b_{i-1}\) decreases \(\|b_{i-1}^{*}\|\) by a factor of \(\delta\) and the updated \(\|b_{i}^{*}\|\) is bounded by old \(\|b_{i-1}^{*}\|\). Hence, the non-increasing property is established. We have \(\|b_{i}\|^{2}=\|b_{i}^{*}\|^{2}+\sum_{j=1}^{i-1}\Gamma_{j,i}^{2}\|b_{j}^{*}\|^ {2}\leq nM_{0}\), since \(\|b_{i}^{*}\|\leq\|b_{i}\|\leq M_{0}\) in the beginning, and throughout the **LLL** and **ModifiedLLL** algorithms. The bounds obviously hold at termination. \(\square\)
# Authentication and Billing for Dynamic Wireless EV Charging in an Internet of Electric Vehicles
## 5 Proposed Authentication and Billing Scheme
### Key Pre-Distribution Phase  
To begin with, the CSC generates pseudonyms and the corresponding key sets, \(\mathcal{P}_{i}\) and \(\mathcal{K}_{i}\), daily based on the traffic volume and sends the message \(m_{1}\):\(\{\mathcal{P}_{i},\mathcal{K}_{i},t_{C},\text{SIG}_{gk_{c}}\}\) to each PO, denoted as \(PO_{i}\), where the subscript \(i\) is the index of the PO owning the DWC lane, and the subscript \(C\) indicates that the message is initiated by the CSC. This message is signed using the signing key of the CSC and is encrypted using the public key of \(PO_{i}\). Therefore, only the PO is able to decrypt the message using its private key. After receiving the message, \(PO_{i}\) verifies the signature of the CSC and forwards the pseudonyms and the corresponding key sets to the charging segments under its control in a message \(m_{2}\):\(\{\mathcal{P}_{i},\mathcal{K}_{i},t_{PO_{i}}\}\). This phase can be done during light traffic hours.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 3 Logistics Warehouse Object Detection
### Dataset and Training  
#### 3.2.1 Dataset  
The quality and quantity of the dataset is critical for deep learning detection models. In the context of the logistics warehouse scene under investigation, the first dataset included 273 randomly selected videos on websites, depicting workers and parcels. However, the object detection performance of the trained model was poor. Therefore, a logistics warehouse operated by a company was used as the research scene, as shown in Figure 9. There, 790 photos were captured, which included goods and workers, encompassing diverse angles. Light blocking conditions were employed for obtaining complete data.  
The original image set was annotated using the software called Labelimg in txt format [24]. YOLOv5 changed the annotation of the dataset from xml format to txt format, with five parameters per line in the txt file, which is shown in Table 2.  
Figure 8: SPP structure.  
Figure 7: CSP2_X structure.  
Figure 9: Photos in the logistics warehouse scene.  
* To ensure the robustness of the model, 100 photos from the first dataset are intentionally incorporated as a supplement to training.
* object_class indicates the type of object detected, which is usually numbered by a positive integer starting from zero.
* width and height indicate the width and height of the target bounding box (normalized, i.e., divided by the width and height of the whole image).
# Real-Time Circuit Breaker Availability Assessment in the Transmission Network
## 7 Conclusions  
Determining the availability of a circuit breaker as a network element in operation at complex real-time transmission events or fully performing its proper function is challenging. Very often, it is extremely difficult to determine the circuit breaker fault using standard methods and procedures.  
When analyzing and determining availability using standard methods, which detect faults rather slowly and uncertainly, the procedure success depends on the knowledge and experience of each observer. In this paper, a fuzzy-logic-based regulator was made, and with its certain rules of the conclusion, it was possible to determine the breaker availability in a new way, as shown in the examples and discussion. This has never been done using the aforementioned methodology.  
Information based the SCADA systems and protection devices is exploited. Essential information is obtained from the position and breaker state, change in current through the switching element, start or trip of a protection relay, and state of the breaker alarm. FL-based analysis provides real-time, unambiguous, and timely information regarding the state of the switch and the operation of the associated network element that the breaker switches on or off.  
This demonstrates that the above method can successfully detect the state of breakers in real time while also having many advantages over similar methods for determining the availability of breakers or other network elements.  
This significantly contributes to the faster determination of the breaker state, elimination of faults, and the increase in reliability and safety of the assigned as well as provides a list of elements with faulty breaker states.  
**Author Contributions:** Conceptualization, I.P.; methodology, I.P., H.G., R.N. and D.B.; software, I.P. and D.B.; validation, R.N., H.G. and D.B.; formal analysis, I.P. and H.G.; investigation, I.P. and R.N.; resources, I.P. and R.N.; data curation, I.P. and R.N.; writing--original draft preparation, I.P. and H.G.; writing--review and editing, R.N. and D.B.; visualization, R.N.; supervision, H.G. and D.B.; project administration, I.P. and H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
In the aerospace industry, spacecraft often serve in harsh operating environments, so the design of ultra-lightweight and high-performance structures is a major requirement in aerospace structure design. In this article, a lightweight aerospace bracket considering fatigue performance was designed by topology optimization and manufactured by 3D-printing. Considering the requirements of assembly with a fixture for fatigue testing and avoiding stress concentration, a reconstructed model was presented by CAD software before manufacturing. To improve the fatigue performance of the structure, this article proposes the design idea of abstracting the practiced working condition of the bracket subjected to cycle loads in the vertical direction via a multiple load-case topology optimization problem by minimizing compliance under a variety of asymmetric extreme loading conditions. Parameter sweeping was used to improve the computational efficiency. The mass of the new bracket was reduced by 37% compared to the original structure. Both numerical simulation and the fatigue test were implemented to support the validity of the new bracket. This work indicates that the integration of the proposed topology optimization design method and additive manufacturing can be a powerful tool for the design of lightweight structures considering fatigue performance.  
topology optimization; additive manufacturing; fatigue testing; COMSOL +
Footnote †: journal: Applied Sciences  
12 July 2021  
**Vol. 2021**, _Applied_  
**Vol. 2021**, _11_, _6671_. [[https://doi.org/10.3390/app11156671](https://doi.org/10.3390/app11156671)]([https://doi.org/10.3390/app11156671](https://doi.org/10.3390/app11156671))