esa-sceva/satcom-chunk-collection huggingface dataset:
# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
## 4 Controlled Fault Current Interruption Sequence
### Double-Sample Time Interruption Sequence
Analysis of the Integrated Application of the Half-Cycle Elimination Method with Double-Sampling Time Interruption Sequences  
In Section 2.2, the half-cycle elimination method introduced allows subsequent calculations to be updated based on the previous results by employing the recursive least squares method. This method, in combination with the double-sampling-based fault current interruption sequence, is able to fully utilize the waiting time and the remaining relay protection response time to further enhance the calculation accuracy of the non-periodic component parameters through RLS. The computational flow of the combination of the two is shown in Figure 9.  
The results show that the half-cycle elimination method combined with the controlled fault current interruption sequence with double-sampling time improves the accuracy of the calculation of the target AC zero by an order of magnitude compared to the method combined with the conventional controlled fault current interruption sequence. Table 2 shows the sampling time required by several different zero prediction algorithms and their corresponding zero prediction errors, from which it can be seen that the half-cycle elimination method has a higher prediction accuracy. In addition, the half-cycle elimination method with double-sampling sequence can achieve a huge improvement in prediction accuracy without increasing the sampling time. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method Name** & **Sampling Time** & **Inaccuracies** \\ \hline Adaptive RLS Algorithm [18] & 15 ms & 0.4 ms \\ Improved Half-wave Fourier Algorithm [23] & 10 ms + 2 sampling points & 1 ms \\ WLMS Algorithm [24] & 10 ms & 1 ms \\ Half-cycle elimination method & 15 ms & 0.2 ms \\ Half-cycle elimination method with double-sampling sequence & 15 ms & 0.04 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sampling time and error comparison of different zero prediction algorithms. Figure 8: Comparison of the CFI on–off sequence with dual sampling time. The waiting time is mainly caused by the mismatch between the target phase and the sequence of the breaking time and the arc burning time, etc. Therefore, the performance of the combination of the half-cycle elimination method and the double-sampling time CFI sequence should be evaluated for different fault phase angles when testing the performance of the half-cycle elimination method and the double-sampling time CFI sequence. The test is conducted using the same parameter settings as reported in Section 2.1, and the results are shown in Figure 10.  
Figure 9: Flowchart of half-cycle elimination method with double-sampling time interrupt sequence.
## 4 Results and Discussion
### Effect of Spindle Speed on Tool Chatter  
While the above section demonstrates the prediction accuracy of the established FE model, subsequent FE simulations of diamond micro-milling of the Cu workpiece with different spindle speeds are performed to investigate the effect of spindle speed on the tool chatter behavior. Figure 8a,b present the simulated results of the maximum absolute feed force and the main frequency of feed force for spindle speeds ranging from 6000 rpm to 30,000 rpm, respectively. Figure 8 demonstrates that the maximum absolute feed force increases with the increase in spindle speed, indicating the increase in milling tool deformation with spindle speed. However, the main frequency of the feed force first increases with the increase in spindle speed and reaches the maximum at the spindle speed of 18,000 rpm, but it then decreases slightly with the further increase in the spindle speed.  
Figure 8: FE simulation results of the effect of spindle speed on (**a**) the maximum absolute feed force and (**b**) the main frequency of feed force.  
Figure 7: Groove bottom profile by the (**a**) FE simulation and (**b**) experiment of diamond micro-milling of the Cu workpiece.  
Figure 9 presents the morphologies of the machined surface under different spindle speeds by FE simulations. It can be clearly observed from Figure 9 that the damage at the workpiece edge increases with spindle speed, which is consistent with the maximum deformation of the milling tool. Therefore, it indicates that the milling tool deformation is closely associated with the workpiece edge failure. Figure 9 also indicates that the distance between the discontinuous pits remaining on the machined surface caused by tool chatter is gradually increasing with spindle speed, which indicates that the frequency of tool chatter decreases with the increase in spindle speed.  
Figure 10a,b present the simulated results of the maximum deformation of the milling tool and the microscopic roughness Rz of the machined surface for spindle speeds ranging from 6000 rpm to 30,000 rpm, respectively. Figure 10a shows that, with the increase in spindle speed, the maximum deformations of the milling tool in both the feed direction and the cross-feed direction increase and are substantially equal to each other. Figure 10b demonstrates that, with the increase in spindle speed, the microscopic roughness Rz of the machined surface also increases, which follows a linear relationship. Specifically, the relationship between the spindle speed and the microscopic roughness of the machined surface is fitted as \(y=4.5\times 10^{-5}x-0.177\), where \(y\) is the microscopic roughness of the machining surface Rz (\(\mu\)m) and \(x\) is the milling spindle speed (rpm).  
Figure 9: FE simulation results of machined surface morphologies under different spindle speeds: (**a**) 6000 rpm, (**b**) 18,000 rpm, (**c**) 24,000 rpm and (**d**) 30,000 rpm.
## 7 Summary of Findings  
In this study, the applicability of the MSCR test on fatigue resistance of asphalt binder was evaluated. Six binder types with different modifications and additive contents were utilized. Mixture tests were also conducted to evaluate if the MSCR test could represent mixture fatigue performance. Based on the analysis, the following conclusions can be drawn:  
* The binder MSCR test can be conducted at intermediate temperatures (20 \({}^{\circ}\)C or higher) with a smooth data curve and good repetitions. However, testing temperatures at 15 \({}^{\circ}\)C or lower may result in a steep curve and needs to be performed with more caution.
* The binder MSCR test at intermediate temperatures was sensitive enough to differentiate binder type and additive content at different aging durations.
* The binder MSCR test at intermediate temperatures showed a similar ranking to the mixture's fatigue test, indicating that the binder MSCR test could be potentially utilized to represent a mixture's fatigue resistance where binder selection is a major concern.  
Figure 8: Ranking comparison between MSCR \(R_{3,2}\) and mixture fatigue test results.  
* The binder MSCR test, with binder fracture energy test and mixture's tests, all proved that the SBS modified binder was less affected by aging and showed excellent performance in improving the mixture's durability.
* The above conclusions indicate that the MSCR test could be a promising method to evaluate the fracture properties of asphalt mixture. Further study on the correlation between MSCR and any field test roads could be necessary.
## 5 Design Aspects
### Sample size requirements for different estimands
#### Sample size and power for testing significance of the slope  
A sample size that may be viewed as sufficient for the purpose of treatment comparison may not be sufficient for other objectives, such as testing significance of the linear slope \(\gamma\). Consider the same experimental setting as above (underlying distribution of engagement is normal with mean \(=0.6\) and SD \(=0.3\), and the model parameters are \(\mu_{E}=-1\), \(\mu_{C}=0\), \(\gamma=-0.5\), and \(\sigma=1\)).  
Figure 5 shows box-plot distributions of power of the test in Equation (8) testing significance of the slope: \(H_{0}:\gamma=0\) (top panel) and similar distributions of the test of mean difference: \(H_{0}:\Delta_{0}=0\) (bottom panel) for the four choices of the sample size (\(n=98,110,150,\text{ and }200\)). With \(n=200\), the power for testing \(H_{0}:\Delta_{0}=0\) is \(>90\%\); however, the power for testing significance of the slope is in the range \(20\)-\(45\%\).  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Total Sample Size (\(n\))**} \\ \hline \(\mathbf{x}\) & **Q50** & **Q80** & **Q90** \\ \hline
0 & 98 & 110 & 114 \\ \hline
0.3 & 40 & 46 & 50 \\ \hline
0.5 & 24 & 26 & 30 \\ \hline \(\overline{x}_{E}\) & 22 & 24 & 24 \\ \hline
0.8 & 24 & 28 & 30 \\ \hline \(\overline{\text{Q50}}=\) median; Q80 = 80 th percentile; Q90 = 90 th percentile. & & \\ \end{tabular}
\end{table}
Table 2: Summary statistics for the total sample size distributions (cf. Figure 4).  
Figure 4: Distributions of the sample size to achieve 80% power of ANCOVA \(t\)-test of \(H_{0}:\Delta_{x}=0\) (using 2-sided \(\alpha=5\%\)) for \(x=0,0.3,0.5,0.8\), and \(x=\overline{x}_{E}\). Responses were simulated from model (1) with \(\mu_{E}=-1\), \(\mu_{C}=0\), \(\gamma=-0.5\), and \(\sigma=1\). Engagement values were simulated from a normal distribution with mean \(=0.6\) and SD \(=0.3\).
## 3 Materials and Methods
### RNA Extraction, Reverse Transcription, Primers, and qPCR Analysis  
The RT-qPCR analyzes of gene expression were performed as described elsewhere [71]. Briefly, _V. cholerae_ (wild type) was cultivated aerobically at 37 \({}^{\circ}\)C overnight, then moved to a fresh medium (1:100). The culture was grown to OD\({}_{600}\) = 0.1. and compounds were added as indicated. Samples were taken at time zero, 15, 45, and 60 min. Total RNA was extracted using RNeasy Mini Kit (Qiagen GmbH, Hilden, Germany) according to the manufacturer's protocol. The total RNA concentration and purity of samples were assessed using Agilent RNA Kits (2100 Bioanalyzer System, Agilent Technologies, Santa Clara, CA, USA). Next, reverse transcription was performed using EvoScript Universal cDNA Master (Roche, Basel, Switzerland). Subsequently, quantification of transcripts in samples was performed by real-time qPCR analysis using SYBR Green-based method. The experiment was carried out in LightCycler(r) 480 instrument (Roche Diagnostics AG, Rotkreuz, Switzerland). Briefly, the qPCR reaction (10 uL) contained: 1\(\times\) SYBR Green I Master Mix, oligonucleotides (primer list, Table S1), and 1 uL of cDNA template (20 ng/uL) obtained in a previous step. The qPCR assay was performed with the following amplification program: activation of the enzyme at 95 \({}^{\circ}\)C for 10 min., followed by 45 cycles of 95 \({}^{\circ}\)C for 10 s, 60 \({}^{\circ}\)C for 10 s, and 72 \({}^{\circ}\)C for 10 s. Melting curve analysis of the PCR products was performed to ensure the specificity of the products. Normalization was done by the amount of the total RNA in each RT reaction and by the level of expression of a reference gene _recA_.
# Smart Helmet-Based Personnel Proximity Warning System for Improving Underground Mine Safety
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**BLE Signal Recognition Distance (m)**} & \multicolumn{3}{c}{**Facing Angle between the Smart Helmet and the Bluetooth Beacon (Degree)**} \\ \cline{2-6}  & \(\mathbf{0}^{\circ}\) & \(\mathbf{45}^{\circ}\) & \(\mathbf{90}^{\circ}\) & \(\mathbf{135}^{\circ}\) & \(\mathbf{180}^{\circ}\) \\ \hline Mean & 27.1 & 25.1 & 22.8 & 19.4 & 10.0 \\ STD \({}^{1}\) & 3.0 & 4.7 & 6.6 & 5.9 & 4.3 \\ Max \({}^{2}\) & 30.0 & 30.0 & 31.0 & 29.0 & 15.0 \\ Min \({}^{3}\) & 22.0 & 18.0 & 9.0 & 13.0 & 4.0 \\ \hline \hline \end{tabular} \({}^{1}\) Standard deviation \({}^{2}\) Maximum value \({}^{3}\) Minimum value.  
\end{table} 
Table 5: Statistical analysis results of the BLE signal detection distance (m) of the smart helmet according to the facing angle between the smart helmet and Bluetooth beacon. personal PWS. In particular, the frustration score differed the most because they were unfamiliar with wearing smart glasses (glasses slipping and wearing ordinary glasses under smart glasses), whereas the feeling of wearing a smart helmet was similar to that of a general safety helmet. Figure 11: Average BLE signal detection distance of the smart helmet according to the facing angle between the smart helmet and Bluetooth beacon (m). Figure 12: Average value of the evaluation scores of the six workload parameters of the NASA-TLX according to the type of experiment. (**a**) Type 1: workload of truck drivers when using the smartphone-based PWS; (**b**) Type 2: workload of the truck drivers when using the smart helmet-based PWS; (**c**) Type 3: workload of the pedestrian workers when using the smart glass-based PWS, and (**d**) Type 4: workload of the pedestrian workers when using the smart helmet-based PWS. Figure 13 shows the total workload score calculated in the four experiments divided into the driver side (a) and the pedestrian side (b). The drivers who used the smartphone-based personal PWS scored approximately 32 points, whereas drivers who used the smart helmet-based personal PWS scored approximately 6.3 points. Workers using the smart glasses-based personal PWS scored approximately 30.6 points, whereas workers using the smart helmet-based personal PWS scored approximately 5.9 points. The smart helmet helped to increase work efficiency by effectively providing proximity warnings for equipment or vehicles to the driver and worker simultaneously while freeing both hands of the driver and worker. Moreover, compared to smart glasses, the wearability of the smart helmet was convenient, which helps reduce worker stress.
## 2 Technological Aspects of Smart Shoes for Digital Health
### Acquisition Systems
#### 2.1.2 Data Generation  
Reliable data generation is an important basis for all subsequent analysis and application pipelines. A wide variety of sensors are applicable in the context of smart shoes:  
* Relative location and orientation determination using inertial-magnetic measurement units (IMMUs, consisting of an accelerometer, a gyroscope, and a magnetometer) and data fusion algorithms [18]. This data can be used, for example, for gait analysis that is explained later.
* Absolute location determination using satellite navigation systems (GPS, GLONASS, GALILEO) [19], which can also provide time information [20]. Relative and absolute location and orientation determination can be fused using loosely or tightly coupled data fusion algorithms [21]. This data can be used, for example, for wide-range activity tracking in a daily living outdoor context.
* Foot plantar pressure determination using various forms of pressure sensors, which provide information regarding how effectively and efficiently individuals control the distribution of the body weight during gait [22]. This data can be used, for example, for rehabilitation purposes, when a patient should not put too much weight onto a leg after surgery.
* Ambient environmental sensors, such as atmospheric pressure sensors for altitude-dependent activities (e.g., stair climbing or hiking) and local weather information (changes over minutes and hours), and light and sound sensors for context-related information generation.
* Internal status sensors, for example for battery and memory capacity (not discussed further).  
Besides sensor data recording, data transmission and storage are also of central importance. The obtained sensor data are typically stored locally on the acquisition system with limited capacity. Therefore, data acquisition systems often have cloud-based data transmission capabilities [23]. For maximum usability, wireless transmission is typically employed, e.g., Bluetooth, Wi-Fi, or proprietary solutions. For our own system, we envision a smart shoe data concentrator that uses wireless battery charging as well as safe and secure cloud data transmission during non-usage periods (Figure 3). It would also contain an offline-data storage module in cases where no cloud data transmission is available or required. Lastly, the data transmission and storage implementation needs to adhere to data safety and security requirements, especially in the case of medical technology systems that manipulate sensitive patient health information.  
An overview of sensing technologies with more details (accelerometer, gyroscope, compass sensor, pressure sensors,... ) can be found in the literature, for example in [24; 25].
## 3 Data Protection Risks of ML Systems and Relations with GDPR Provisions  
From the previous discussion, it has become evident that automated decision-making systems pose several risks with respect to the rights of privacy and personal data protection. More precisely, the widespread use of ML algorithms that are used as \"core primitives\" for these systems makes these systems even more vulnerable to new privacy threats; MLand, especially, deep learning algorithms by themselves, as also discussed in Section 2.3, raise concerns with respect to these fundamental rights, whilst our understanding of their nature and impact on individuals is still limited [13]. These algorithms aim to extract the characteristics and properties, under a specific context, of individuals, toward reaching specific conclusions that are subsequently used to derive a decision about them. To this end, an ML algorithm is based on large volumes of input data, which, in turn, also correspond to individuals, and thus, they are also personal data.  
Bearing in mind the aforementioned provisions stemming from the GDPR, a main data protection challenge for these systems stems from the data minimization principle. Indeed, to ensure compliance with the GDPR, it is necessary to develop machine learning applications that receive as input the absolutely necessary personal data; for instance, the training dataset must, at any time, consist of samples that contain the absolutely necessary personal data and no more than those that are necessary to achieve the purpose of each application [40]. More precisely, the training dataset should not allow the reidentification or singling out of an individual since such a process would violate the data minimization principle. However, performing robust anonymization is not always an easy task as stated above; the simple removal of direct identifiers (e.g., identity card numbers, names, etc.), although it is a prerequisite, does not ensure an anonymous dataset (see, e.g., some famous incidents related to improper anonymization, such as [34]).  
From now on, we shall refer to the notion of an adversary as any entity that can mount a so-called privacy attack, i.e., can extract more personal information than it is entitled to do so. By considering the legal provisions of the GDPR, an adversary can be either a malicious third party (i.e., an attacker) or even an entity that legitimately processes personal data and does not have a malicious purpose (e.g., the ML service provider); if this legitimate entity is practically able, e.g., to reidentify an individual in violation of the data minimization principle, then it is still considered an adversary.
## 4 Approaches to the reconstruction problem from protein contact maps
### Methods based on distance geometry and local search  
_Distance Geometry_ deals with the characterization of mathematical properties which can be derived from distance values between pairs of points. Distance Geometry can be seen as the mathematical foundation for a geometric theory of molecular conformation. One of the most relevant results in this context is that, given a consistent set of distances in the three-dimensional space, the problem to find a set of coordinates which satisfy such exact distance constraints can be solved by a polynomial-time algorithm [5] (the problem becomes NP-complete when the given set of distances is sparse [19]). One of the best known application of this theory is the determination of molecular conformations from experimental data such as NMR spectroscopy and X-ray crystallography. Because of experimental errors, we can usually obtain only a set of lower and upper bounds to inter-atomic distances rather than exact values. Even with such relaxed distance constraints, the problem to compute a set of consistent coordinates is intractable [16]. While the problem is intractable, in the eighties [PERSON] and [PERSON] [14; 15] developed a polynomial-time recovering algorithm from a sparse set of lower and upper bounds. This is the first use of a distance geometry based approach for protein structure recovering. Their algorithm first uses some bound smoothing technique to estimate bound values for the missing distances. Then it uses an algebraic technique known as the Embed algorithm to generate an approximate set of 3D coordinates. The 3D structure recovered is, in some sense, the best three-dimensional fit for the set of distances considered. One important feature of Embed is that it provides a reasonable solution also when the set of distances in not embeddable in the three-dimensional space.  
The Embed algorithm cannot be directly used with great success for the contact map reconstruction problem because, even in the presence of maps without errors, the set of lower and upper bounds which can be inferred from contact maps is usually coarse and not that accurate. Anyway, the Embed algorithm can be still used to compute a good initial solution better than a random one to be used as a starting point for a successive refinement procedure. To our knowledge there is only one method reported in literature which uses this approach.
# Effect of Residual Stress in Surface Layer on Plastic Yield Inception
## 4 Discussion
### Experimental Evidence  
Various existing experimental studies have provided evidence for the results of the present study. The result of [PERSON] [14] showed that with compressive residual stress of \(-602\) MPa to \(-768\) MPa in the surface layer of 17 Cr2 Ni2 MoVNb steel, the wear rate decreased up to \(80\%\) compared to the untreated one (no residual stress was introduced intentionally). The result of [PERSON] [16] showed that with residual stress change from \(-323\) MPa to \(-159\) MPa, the cross-sectional wear track area doubled. These results confirm the result in the present study--that the compressive residual stress can strengthen the contact system by increasing the critical yield load of the contact system. The result of [PERSON] [18] showed that with residual stress of \(-4.01\) GPa, the wear depth almost doubled in the nanowear test compared to the residual stress of \(-1.48\) Gpa. The result of [PERSON] [19] showed that with residual stress of \(-0.7\) GPa, the PVD coating showed smaller wear volume than those with \(-0.2\) GPa and \(-1.2\) GPa. The result of [PERSON] [20] showed that with residual stress of about \(-4\) GPa, the TiSiN coatings showed better damage resistance and adhesion strength than those with \(-1\) GPa and \(-10\) GPa. These results confirmed that although compressive residual stress can increase the critical yield load of the contact system, excessive compressive residual stress will cause it to decrease.  
The results of locations of yield inception are in accordance with the experimental observation of [PERSON] [20] on nanocomposite TiSiN coatings on steels. Ring cracks apparently initiated at the contact edge and propagated into the coating were observed for coatings that possessed high compressive residual stress. This may be due to the yield locations of a, e, and h in Figure 8. When compressive residual stress decreased, ring cracks were reduced, but lateral cracks formed at the interface remained, and may be due to the yield locations of c and f in Figure 8. With a further decrease in compressive residual stress, microcracks became excessive in coatings, and may be related to the yield locations of g and i in Figure 8.