esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 3 Pressure Support Ventilation (PSV)  
The procedure of adapting raw information into numerical attributes that can be employed in place of the original data is referred to as feature extraction. It helps to reduce the computational burden during a signal analysis while maintaining the integrity of the data confined to the original statistics. The feature extraction from HRV data is commonly accomplished using various methods, including but not limited to (i) time-domain methods, (ii) frequency-domain methods, and (iii) non-linear methods. The HRV features extracted from both time and frequency domain approaches are considered linear HRV features [24].  
The linear HRV characteristics are studied following the international guidelines made available by the North American Society of Pacing and Electrophysiology task force and the European Society of Cardiology [6]. On the other hand, the non-linear HRV features are calculated per the recommendations presented in the most recent research literature [25; 26; 27]. Apart from the methods mentioned above, researchers have also attempted to extract proper HRV parameters using signal decomposition techniques and time-series modelling techniques [28]. The following subsections describe the popular HRV feature extraction methods.
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
###### Abstract  
Fuel cell electric vehicles (FCEVs) are among the devices that have emerged in recent years. To provide electricity to the electric motors, they use a proton-exchange membrane fuel cell (PEMFC) as the primary energy source and a secondary source consisting of an energy storage system (battery or supercapacitors). The addition of these sources to the motors and accessories of a vehicle requires the association of static converters to condition the different power sources. In addition, a high-efficiency and enhanced-reliability power converter is essential to connect the PEMFC to the vehicle's DC bus. This paper proposes a robust feedback controller for a four-phase interleaved boost converter used with PEMFC. The proposed controller has double loops based on a state-feedback controller, and an inner loop which translates the differential equation of the system into a state representation by linearization around its operation points. The reference current is generated by state feedback in the outer loop; the state variable is defined by using a change variable. The strong robustness and highly dynamic characteristics of the proposed controller are demonstrated through its performance in terms of output voltage, source current, and settling time. The findings indicate that the proposed controller achieves a response time of 20 ms, resulting in an over 50% improvement compared to the controllers referenced in the literature. Additionally, it reduces both current and voltage ripple, keeping them each below 10%. Further, the controller gains synthesis is validated using the linear quadratic regulator (LQR) technique as well as boundary conditions, and its robustness is verified, taking into account the uncertainty of various operating conditions and discrepancies in circuit components. A double-loop super-twisting sliding mode controller, a backstepping control algorithm, and a PI controller are selected for comparison and discussion. Subsequently, the effectiveness of the proposed controller is evaluated through simulation with the parameters of a 500 W fuel cell system.  
proton-exchange membrane fuel cell; interleaved DC-DC boost converter; energy storage system; robust feedback controller; dynamic feature; linear quadratic regulator (LQR)
## 6 Conclusions  
In this paper, an UWA communication system based on OSDM technology is proposed and designed, and a special data frame format based on PN sequence is designed. The OSDM signals with different vector lengths are analyzed. For the problems of long delay, strong multipath, and large-scale Doppler frequency shift in underwater channels, OMP-based compressive sensing channel estimation, MMSE equalization, sliding correlation Doppler estimation and resampling compensation methods are adopted. As shown in the simulation results, compared with a traditional OFDM system, the proposed UWAOSDM/PN system has low PAPR and ICI When the OSDM vector length M is bigger than the channel length, the system subcarrier flexibility is guaranteed, and the bit error rate of the OSDM system is lower than that of the OFDM system and the single-carrier system. The PN sequence-based compressive sensing channel estimation and equalization using the OMP and MMSE algorithms has a good performance to resist the multipath effect of underwater acoustic channel.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & & & & & & & & & \\ \hline UWAOSDM/PN & & Convolutional & 1/2 & PN & QPSK & OMP-MMSE & Low & 6 kHz & 10\({}^{-5}\) & 5.09 \\ [7] & Convolutional & 1/2 & LFM & QPSK & SID/PID-MMSE & High & 4 kHz & 10\({}^{-5}\) & 2.965 \\ [16] & RS & 4/5 & - & 16 QAM & - & - & 5 kHz & 10\({}^{-4}\) & 2.3 \\ Turbo & 1/3 & & QPSK & - & - & 5 kHz & 10\({}^{-4}\) & 1.4 \\ [17] & SFBC & - & LFM & QPSK & & - & - & 10\({}^{-4}\) & - \\ [18] & - & - & Chu & QPSK & CE-BEM & Low & - & 10\({}^{-4}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the UWAOSDM/PN and the other underwater acoustic (UWA) OSDM systems.  
The OSDM modulation technology has potential application prospects in underwater acoustic hybrid single- and multi-carrier modulations and adaptive modulation. In the future, on the basis of the UWA OSDM system studied in this paper, an adaptive multimode UWA communication technology study based on the OSDM modulation will be carried out.  
Conceptualization and methodology, L.L., H.Z., and M.L.; software and validation, H.Z., Z.L., H.R., and J.M.; writing--original draft preparation, L.L., H.Z., M.L., and L.Z.; writing--review and editing. J.J. and J.Z.; visualization, L.Z. and Z.L.; funding acquisition, L.L., L.Z., J.J., and J.Z.  
The research is supported by the National Natural Science Foundation of China (61431005, 41427803, 51409235 and 61401111).  
The authors declare no conflict of interest.
## Nomenclature \begin{tabular}{l l} Abbreviation & Significance \\ \(CoG\) & center of gravity (of a vehicle or, in general, of a body) \\ \(CoG^{\prime}\) & projection of the \(CoG\) of a vehicle (or, in general, of a body) on the vehicle support plane \\ \(CoG^{\prime\prime}\) & projection of the \(CoG\) of a vehicle (or, in general, of a body) on the vehicle support plane \\  & when the support plane is horizontal \\ \(CoR\) & center of rotation (of the rotating system) on the support plane \\ \(CoR^{\prime\prime}\) & center of rotation (of the rotating system) at a height \(h\) from the support plane \\ DoF(s) & degree(s) of freedom \\ OECD & Organization for Economic Co-operation and Development \\ Q\({}_{1},\ldots\) Q\({}_{4}\) & quadrants of the turntable on which a vehicle under test is positioned, subdividing the support plane \\
**Symbol** & **Significance, units** \\ \(\alpha\) & inclination angle (always positive) of the support plane (in the frame of reference of the test system); together with \(\beta\) is one of the \"_operative angles_\" of the test rig [\({}^{\circ}\)] \\ \(\alpha_{lim,L},\alpha_{lim,R}\) & limit angle of inclination of the support plane \(\alpha\), corresponding to an incipient rollover condition on the left (\(L\)) or right (\(R\)) side of the vehicle [\({}^{\circ}\)] \\ \(\beta\) & rotation angle (positive if counter-clockwise) of the turntable, measured between the \(y_{SP}\) \\  & axis of the turntable and the maximum slope direction of the support plane; together with \(\alpha\) is one of the \"operative angles\" of the test rig [\({}^{\circ}\)] \\ \(\beta^{\prime}\) & rotation angle (positive if counter-clockwise) of vehicle longitudinal axis with the \\  & maximum slope direction; it coincides with the rotation angle of the turntable only when \\  & the vehicle longitudinal axis \(x_{t}\) is parallel to the \(y_{SP}\) axis of the turntable [\({}^{\circ}\)] \\ \end{tabular}
# GUI for Analysis of Parameters, Accurate Design and Optimization of Microstrip Filters
## 4 Computer Algorithms
### Calculation of Total Length, \(Tl\)  
Another important value in the results is the _total length_, \(TL\), of the filter.  
The \(TL\) parameter is computed with the summation of the physical length of all the lines, using the following equation  
\[TL=2l_{Z0}+\sum_{i=1}^{n}l_{Li}+\sum_{j=2}^{n}l_{Cj}, \tag{20}\]  
where \(l_{Z0}\) is the length of the source/load impedance lines, \(l_{Li}\), for \(i\) odd, are the lengths of inductive lines (high impedance lines) and \(l_{Cj}\), for \(j\) even, are the lengths of capacitive lines (low impedance lines). This parameter is expressed in millimeters.
## 3 Hypotheses and Model Development
### Security  
Despite the boom in CC with new features and market access, security in CC remains the biggest problem hindering the adoption of CC services [31; 37].  
Security is one of the crucial technical problems concerning CC adoption. Cloud vendors are trying to simulate the classic principles of confidentiality, availability, and integrity, which are commonly found in physical systems for distributed, virtualized, and dynamic cloud systems that are accessed online [150]. Three service models are used in CC (SaaS, PaaS, and IaaS) and four deployment models (public, private, community, and hybrid), which require different levels of security for each model to protect the user's data [151]. Internet security vulnerabilities have been an issue for users for years, such as e-commerce and online banking. Hence, the importance of security in IT environment of HEIs is critical [148; 152; 153]. Because CC is based on Internet technology, the same security issues hinder its adoption. However, the advanced security algorithms used in CC have been identified as the main differentiators of CC [139] that can influence its adoption. Previous studies have considered security as an influencing factor in adopting CC services [31; 33; 37; 38; 147]. Accordingly, it is hypothesized that:  
**Hypothesis 8 (H8).**_Data security negatively influences CC adoption in HEIs._
## 4 Cascaded Weighted Sum and Its Field of Application
### Classification of Application Scenarios and Examples  
Optimization projects can be classified into three different types:  
1. The nonrecurring type, which is performed once with little or no prior knowledge of e.g., the impact and relevance of decision variables or the behavior of objectives. This type requires many decisions regarding e.g., the number and ranges of decision variables, the number and kind of objectives, of restrictions, and more.
2. The extended nonrecurring project, where some variants of the first optimization task are handled as well. Frequently, the modifications of the original project are motivated by the experience gained in the first optimization runs. As in the first type, decisions are usually made by humans.
3. The recurring type, usually based on experience gained from a predecessor project and frequently part of an automated process without or with minor human interaction only.  
Examples of Types I and II are design optimization tasks like the design of micro-optical devices as described in [22] or problems from such a challenging field like aerodynamic design optimization, see e.g., [23]. A typical example of type III is the task of scheduling jobs to be processed on a computational grid as introduced in the last section and described in detail in [6]. Normally, nobody is interested in the details of the actual schedule, which usually will be replaced soon by a new one due to a replanning event like a new job to be planned or the introduction of new resources. Another example is the planning of collision-free paths for movements of industrial robots, as described in detail and for different industrial robot types in [14,15,24,25]. This example also shows that in some cases human judgment is possible in addition to a pure consideration of the achieved objective figures of a generated robot move path. The decision maker can take a look at the resulting movement using a robot simulator or the real device. Assessing this movement is much more impressive and illustrative than reading objective figures. On the other hand, such a well-fitting visualization is not always available.
## 2 Materials and Methods
### Proposed GP-Based Feature Construction Approach
#### 2.3.3 Fitness Function  
Usually, the fitness function for GP-based feature construction only assesses the classification performance obtained from the constructed features, i.e., at the trees' outputs. However, the classification performance of the original radiomic feature set at the terminal nodes is ignored. Thus, it is possible to obtain solutions in which the constructed features may underperform the original ones. Aiming to ensure that constructed features improve the original radiomic features, we propose maximizing the classification performance gain output-to-input ratio, where the area under the ROC curve (AUC) evaluates the classification performance of two independent logistic regression (LR) classifiers trained with constructed and original radiomic features.  
The AUC represents the probability that a given classifier correctly predicts an input from the positive class. Additionally, the majority class does not influence the AUC in class imbalance scenarios, such as survival prediction [40]. The Wilcoxon-Mann-Whitney test provides a direct estimator for AUC. Let \(\mathbf{p}^{+}=[s_{1}^{+},\ldots,s_{n_{+}}^{+}]\) be the predicted scores of the positive class (i.e., non-survivor) with \(n_{+}\) samples. Likewise, define \(\mathbf{p}^{-}=[s_{1}^{-},\ldots,s_{n_{-}}^{-}]\) for  
Figure 3: An example of multi-tree representation to construct a feature set with \(m\) predictors. For instance, the first tree constructs the feature \(t_{1}=\max(x_{30},x_{35})\div(x_{12}\times x_{61})\).  
the \(n_{-}\) samples that belong to the negative class (i.e., survivors). Then, the combined set \(\{\mathbf{p}^{-},\mathbf{p}^{+}\}\) is ranked in ascending order, and the AUC is calculated as [41]  
\[\text{AUC}=\frac{\sum_{i=1}^{n_{+}}r_{i}-n_{+}(n_{+}+1)/2}{n_{+}n_{-}}, \tag{1}\]  
where \(r_{i}\) is the rank of the \(i\)th sample belonging to the positive class. The AUC is bounded to the range \([0,1]\), where 0.5 indicates a random classification, and 1.0 is a perfect classification.  
Let us denote \(\text{AUC}_{\text{in}}\) and \(\text{AUC}_{\text{out}}\) as the AUC values obtained with the original radiomic features at the terminal nodes and GP-constructed features at the output nodes, respectively. The AUC's gain output-to-input ratio is expressed as  
\[f(\mathbf{z})=\frac{\text{AUC}_{\text{out}}}{\text{AUC}_{\text{in}}}, \tag{2}\]  
where \(\mathbf{z}=[t_{1},\dots,t_{m}]\) is a potential solution with multi-tree representation. To evaluate this fitness function, the AUC is trunked to the range \([0.5,1.0]\), so the fitness value ranges from 0.5 to 2.0; the higher the value, the better the gain. It is worth mentioning that several trees can share some radiomic features at the input nodes; thus, unrepeated features are considered to obtain \(\text{AUC}_{\text{in}}\).
# Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
## 3 Experiment and Results
### Spatial Distribution of Visual Indexes  
The weighted average method is adopted to aggregate visual indexes in the research units in Beijing and Chongqing. As shown in Figure 4, taking the GVI as an example, on the whole, the spatial distribution of the GVI is similar to that of the house price. The GVI is divided by the fourth Ring in Beijing. In the northwest, on both sides of Houhai and near Shichahai Park, the GVI value is higher than in other areas. In the southeast, the GVI value is higher in the area around Longtan Park. The GVI values near Louquat Mountain Park, Zourong Park, and Jialing Park in Chongqing were higher than those in other areas. Both cities showed that the GVI near the park was higher than other areas. The possible reason is that there is more vegetation and dense trees around the park, so the street view images captured more vegetation and accounted for more vegetation pixels.  
Figure 4: Distribution of quantitative results of six visual indices in Beijing and Chongqing.  
Figure 3: Spatial distribution of the house prices in Beijing and Chongqing.
## 2 Materials and Methods
### HPLC-DAD-MS/MS Analysis  
A Thermo-Finnigan SpectraSystem HPLC (Thermo-Finnigan, Waltham, MA, USA) equipped with a P2000 binary gradient pump, an SCM 1000 degasser, an AS 3000 automatic injector, and a Finnigan Surveyor PDA Plus detector, coupled in tandem with an API 3200 QTRAP with a Turbo V source (Applied Biosystems Sciex, Foster City, CA, USA), was used. Separation was achieved on a Kinetex 5 \(\upmu\)m Phenyl-Hexyl 100 A 150 \(\times\) 4.6 mm (Phenomenex, Castel Maggiore, Italy) equipped with a SecurityGuard(tm) analytical guard cartridge system (Phenomenex). The mobile phase was composed of solvent A (formic acid 0.1% in ultrapure water) and solvent B (methanol) at a flow rate of 1 mL/min with injection volume of 10 \(\upmu\)L. The elution programme was as follows: A 90% kept in isocratic for 0.5 min, A 70% in 2.5 min, A 65% in 5 min, A 60% in 3 min, A 20% in 19 min, and A 90% in 1 min maintained in isocratic for 1 min. Photodiode array (PDA) spectra were recorded in a full scan modality over the wavelength (\(\uplambda\)) range of 220-600 nm, with quantification performed using a calibration curve obtained for the trans-5-CQA analytical standard. MS/MS conditions for the identification of caffeoylquinic acid (CQAs) were optimised using the _trans_-5-CQA standard. The ion source was operated in a negative ion mode usingthe following conditions: ion spray voltage, \(-4500\) V; turbo spray temperature, 500 \({}^{\circ}\)C; curtain gas, 2.07 \(\times\) 10\({}^{5}\) Pa; interface heater, on; nebuliser gas, 2.4 \(\times\) 10\({}^{5}\) Pa; and heater gas, 10 \(\times\) 10\({}^{5}\) Pa. Nitrogen was used as the nebuliser, heater, curtain, and collision gas. Masses were recorded in the range of \(m/z\) 100-700 amu using an enhanced mass spectrum scan experiment with a declustering potential of \(-20\) V and an entrance potential of \(-10\) V. Product ions (MS/MS) were generated according to the information-dependent acquisition mode, with a threshold of 50,000 cps and a collision energy of \(-30\) eV, and were collected in enhanced product ion mode.
# Reverse Knowledge Distillation with Two Teachers for Industrial Defect Detection
## 5 Conclusions  
We propose to use reverse knowledge distillation with two teachers for industrial defect detection. In the teacher-student network, a two-teacher network with inconsistent architectures is employed to extract features for diverse feature learning. To focus on the important features and improves the accuracy of the model, an attention mechanism is adopted in the teacher network and an iterative attentional feature fusion is used for the feature fusion of the two teacher networks. In the student network, the student network is designed to prevent overfitting. Extensive experiments are conducted on Mvtec and BTAD datasets. From the results, both image-level and pixel-level anomaly scores are improved. However, the pixel-level performance improvement is not high, and it needs to be investigated in the future.  
Conceptualization, N.L. and M.P.; methodology, N.L. and H.S.; software, M.P.; validation, M.P.; formal analysis, N.L.; investigation, M.P.; resources, N.L.; data curation, M.P.; writing--original draft preparation, M.P.; writing--review and editing, N.L. and P.G.; visualization, M.P.; supervision, N.L.; project administration, N.L.; funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported in part by the Natural Science Foundation of Jiangsu Province of China (BK20222012), Guangxi Science and Technology Project (AB22080026/2021 AB22167), National Natural Science Foundation of China (No. 61375021) and the Natural Science Key Project of Anhui Provincial Education Department (No. KJ2020A0636, No. KJ2021A0937, No. 2022 AH051683, No. 2022 AH051670, No. 2022 AH051669).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the first author, [PERSON], upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline** & **New Sudden** & **Two Teachers** & **Mscam** & **Iaff** & **Value** \\ \hline \(\surd\) & & & & & 98.72/97.78 \\ \(\surd\) & \(\surd\) & & & & 98.90/97.83 \\ \(\surd\) & \(\surd\) & \(\surd\) & & & 99.18/97.78 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & 99.29/97.75 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 99.43/97.87 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study of designed networks and modules for image-level and pixel-level anomaly detection results.