esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Surrogate Models for Design Optimization
### Hybrid Surrogate Model for Multi-Fidelity Approach  
In this section, the hybrid surrogate model (Figure 1) is proposed for the multi-fidelity approach. The proposed approach constructed the local deviation estimated by the kriging method and the global model approximated by the RBF. It employs an RBF to represent the global model, \(\mu+fr(\mathbf{x})\), based on a dataset obtained from low-fidelity evaluation. The proposed approach combines the kriging method and the RBF by the following equation:  
\[\hat{y}(\mathbf{x})=[\mu+fr(\mathbf{x})]+\mathbf{r}^{\mathrm{T}}\mathbf{R}^{- 1}(\mathbf{F}_{\mathrm{h}}-\hat{\mu}-\mathbf{F}\mathbf{R}). \tag{11}\]  
The local deviations \(\mathbf{r}^{\mathrm{T}}\mathbf{R}^{-1}(\mathbf{F}_{\mathrm{h}}-\hat{\mu}- \mathbf{F}\mathbf{R})\) are evaluated on the basis of a high-fidelity dataset using the kriging method, where \(\mathbf{F}_{\mathrm{h}}=\left[f_{\mathrm{h}}(x_{1}),f_{\mathrm{h}}(x_{2}),f_ {\mathrm{h}}(x_{3}),...,f_{\mathrm{h}}(x_{n})\right]^{\mathrm{T}}\) is the value of the high-fidelity function at \(\mathbf{x}=\left\{x_{1},x_{2},x_{3},...,x_{n}\right\}\); \(\mathbf{F}\mathbf{R}=\left[fr(x_{1}),fr(x_{2}),fr(x_{3}),...,fr(x_{n})\right] ^{\mathrm{T}}\), \(fr(\mathbf{x})\) the function predicted from the low-fidelity data that predicted by RBF, can be expressed by (12):  
\[fr(\mathbf{x})=a_{0}+a_{1}f_{1}(\mathbf{x}), \tag{12}\]  
where \(f_{1}(\mathbf{x})\) is a function predicted by an RBF [19; 20] using low-fidelity data and \(a_{0}\) and \(a_{1}\) are correlation terms between the low-fidelity data and the high-fidelity data. Further, \(\sigma^{2}\) can be defined as:  
\[\sigma^{2}=\frac{(\mathbf{F}-\hat{\mu}-\mathbf{F}\mathbf{R})^{\mathrm{T}} \mathbf{R}^{-1}(\mathbf{F}-\hat{\mu}-\mathbf{F}\mathbf{R})}{n}. \tag{13}\]  
The unknown parameters (\(\theta,a_{0}\) and \(a_{1}\)) for the hybrid surrogate multi-fidelity model can be estimated by MLE, as expressed by (8).  
The RBF is used to approximate the global model of the hybrid surrogate-model. An RBF is used to predict the low-fidelity function (\(f_{1}(\mathbf{x})\)) by:  
\[f_{1}(\mathbf{x})=\sum_{m=1}^{n}w_{m}\Phi(\mathbf{x}-\mathbf{x}_{m}) \tag{14}\]  
where \(\Phi(\mathbf{x})\) is an RBF, \(\mathbf{x}_{m}\) is a sample point and \(w_{m}(m=1,2,3,...,n)\) is a weighting function. A multi-quadratic function is applied as an RBF in this study. The weighting function \(\mathbf{w}=[w_{1},w_{2},w_{3},...,w_{n}]^{\mathrm{T}}\) is determined from the interpolation conditions:  
\[A\mathbf{w}=\mathbf{F} \tag{15}\]  
where \(A=\begin{pmatrix}a_{1,1}&a_{1,2}&\cdots&a_{1,j}\\ a_{2,1}&a_{2,2}&\cdots&a_{2,j}\\ \vdots&\vdots&\ddots&\vdots\\ a_{i,1}&a_{i,2}&\cdots&a_{i,j}\end{pmatrix}\), \(a_{i,j}=\Phi(\mathbf{x}_{i}-\mathbf{x}_{j}),i=1,2,3,...,n,j=1,2,3,...,n\)  
Here, \(\mathbf{F}=\left[f(x_{1}),f(x_{2}),f(x_{3}),...,f(x_{n})\right]^{\mathrm{T}}\) is the value of the low-fidelity function at \(\mathbf{X}=\left\{x_{1},x_{2},x_{3},...,x_{n}\right\}\).
## 5 Conclusions  
Based on the exergy analysis of cascaded phase change heat storage, the experimental system of a cascaded phase change heat storage dehumidifier was established, and some experimental studies were carried out. The conclusions are as follows.  
1. The more stages of phase change materials exit in the cascaded phase change heat storage, the greater the exergy efficiency will be, but the relationship between them is not linear. The increase rate of the exergy efficiency decreases with the increase of the stage numbers. Considering the above factors, the complexity of a multi-stage phase change heat storage device and the difficulty of selecting phase change materials, it is recommended to select the number of phase change material stages as three. When the \(\Delta T\) is 5 K, and the \(n\) is equal to 3, the \(\eta_{n}\) can reach a maximum of 53.0%.
2. With the increase of the temperature and volume flow of the wet air at the inlet of the dehumidifier, the time required for the air temperature at the outlet of the dehumidifier to reach stability is shortened, and the heat transfer capacity of the phase change heat storage dehumidifier is enhanced.
3. The cascaded layout of PCMs can improve the water production performance of a dehumidifier. When the hot and wet air temperature and the volume flow rate at the inlet of the dehumidifier are 338.15 K and 5.0 m\({}^{3}\)/h, compared with dehumidifiers with three kinds of PCM single-stage layouts, the GOR of the dehumidifier with a PCM three-stage layout is increased by 9.69%, 3.86% and 2.40%, and the WPC is reduced by 8.82%, 3.69% and 2.31%, respectively. If the heat in the phase change heat storage device is used for the secondary water output, the water output and GOR will be increased by 25%, and the WPC will be reduced by 20%.  
Conceptualization, L.Z.; methodology, L.Z.; software, Z.F. and Y.J.; validation, L.Z. and Y.J.; formal analysis, L.Z., K.W. and Z.F.; investigation, Z.F., L.Z. and Y.J.; data curation, Y.J.; writing--original draft preparation, Z.F., L.Z. and Y.J.; writing--review and editing, L.Z. and K.W.; visualization, Y.J. and supervision, L.Z. All authors have read and agreed to the published version of the manuscript.  
Figure 13: WPC1 and WPC2 under different arrangements of PCMs.  
This research received no external funding.
## 6 Model Validity  
As MSK models and their associated modelling and simulation methods estimate parameters which cannot easily be measured _in vivo_, direct validation is not always straightforward. As such, various simulation standards are set out within the MSK modelling and simulation community to ensure feasible and valid solutions. These include assessment of errors between acquired and modelled marker trajectories, and assessment of residual forces following inverse dynamics [103]. More advanced validation steps include comparing estimated muscle activations to acquired EMG data to illustrate muscle on-off timings and muscle activation magnitudes. These comparisons are done using correlations where current methods show correlations ranging from 0.46-0.99 [104]. Along with these stand-alone validation steps, some initiatives have provided validation data sets for joint contact forces and joint kinematics derived from instrumented endoprosthesis [105; 106; 107; 108]. Using these validation datasets, estimation of joint contact loading magnitude within 0.1-0.5 [3; 109] and 0.5-1.0 [110; 111] body weights for the knee and hip joints, respectively, have been reported. These studies, although showing good agreement with validation data, cannot be carried out for each subject in large cohort studies. A number of studies using these datasets have already provided not only confidence in the modelling and simulation approaches but important methodological advances and consideration [109; 112; 113]. With such datasets available, and becoming more common, the refinement of methods, and the validity of simulations will continue to improve.
# Conceptual Design Evaluation Considering Confidence Based on Z-AHP-TOPSIS Method
## 6 Conclusions  
Given the subjectivity and uncertainty in conceptual design evaluation, this paper proposes an evaluation method of different DMs using different evaluation confidence scales, which makes up for the deficiency of quantitative information reliability when using the fuzzy method.  
In conceptual design evaluation, DMs evaluate the concept and indicate the degree of evaluation confidence. Z-number is used to represent the evaluation information of DMs, and it is transformed into a regular fuzzy number to express the fuzziness and reliability of the linguistic evaluation. The reliability of Z-numbers is used as the confidence level of DMs, making the decision more in line with the objective facts and providing more uses for the fuzzy linguistic quantification method. Therefore, a concept design evaluation method using DMs self-confidence is proposed. Different DMs can use different confidence scales to make practical decisions and avoid mistakes. In conceptual design evaluation, the evaluation framework of conceptual design based on customer requirements is constructed using the Z-AHP and Z-TOPSIS methods.  
Finally, a design example is given to demonstrate the feasibility and superiority of the proposed method. The proposed method can reflect the opinions of DMs more comprehensively. With the subdivision of DMs' confidence level and confidence scale, a more reasonable and accurate ranking of design schemes can be obtained.  
The proposed conceptual design evaluation framework can be integrated into many different stages of the product development process, such as customer participation evaluation. In this process, the use of confidence can better reflect the design problems. With the complexity of the product, the evaluation index will increase, which may exhibit the phenomenon of index coupling. In the future, we need to consider adding a correlation between the indexes to obtain a more realistic and more effective product scheme evaluation.  
Q.L.: conceptualization, methodology, investigation, formal analysis, writing--original draft, writing--review and editing. J.C.: conceptualization, methodology, data curation, project administration, supervision, writing--review and editing, funding acquisition. W.W.: formal analysis, writing--review and editing, funding acquisition. Q.Q.: investigation, formal analysis, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Youth Science and Technology Talent Growth Project by the Department of Education of Guizhou Province, grant number KY [2017]106 and KY [2018]112, and by the Science and Technology Foundation of Guizhou Province ([2020]1Y262).  
The authors declare no conflict of interest.
# Systematic Review of Anomaly Detection in Hyperspectral Remote Sensing Applications
## 5 Unstructured Background Models
### Kernel-Based Models  
If the background and the anomalies can not be adequately separated in the data space, it may be useful to seek the simple decision boundary in the higher dimensional feature space. That is the basic idea of the kernel-based anomaly detection methods that rely on the so-called \"kernel trick\" [89]. We want to replace complex anomaly detection models in the data space with much simpler models in the higher dimensional feature space, which is generated using the non-linear mapping function \(\underline{\Phi}(\cdot)\). The mapping can be done to \(M\)-dimensional feature space \(\mathcal{F}\), where the dimensionality of the \(\mathcal{F}\) can be indefinite (but usually \(M\gg K\), where \(K\) denotes the number of spectral bands of the input hyperspectral image). The goal is to find the appropriate feature space \(\mathcal{F}\) where the background and the anomaly class can be easily and more accurately separated. Simple decision boundaries in the higher dimensional space project to more complex boundaries in the lower dimensional space. That is the main benefit of the kernel-based methods, as they are able to reduce a non-linear algorithm in the data space to a linear one in the higher dimensional \(\mathcal{F}\). However, it is not computationally feasible to directly implement any algorithm in the \(\mathcal{F}\), due to its high dimensionality. Luckily, there is a way to implicitly compute dot products in the feature space \(\mathcal{F}\) without actually performing the non-linear mapping \(\underline{\Phi}(\cdot)\) of the input spectral vectors \(\{\underline{x}_{i}\}\). It is called the kernel trick, and it is an effective method to implement the dot product in the feature space using the kernel functions. The dot products in \(\mathcal{F}\) can be kernelized as [26]:  
\[k\big{(}\underline{x}_{i},\underline{x}_{j}\big{)}=\underline{\Phi}( \underline{x}_{i})^{T}\cdot\underline{\Phi}\Big{(}\underline{x}_{j}\Big{)}. \tag{22}\]  
From (22), it can be seen that the dot product in \(\mathcal{F}\) can be replaced by a non-linear kernel function \(k\), which can be computed without explicitly defining the mapping function \(\underline{\Phi}(\cdot)\). One of the most commonly used kernel functions is the Gaussian radial basis function (RBF) kernel [18]:  
\[k\big{(}\underline{x}_{i},\underline{x}_{j}\big{)}=\ \exp(\frac{-\| \underline{x}_{i}-\underline{x}_{j}\|^{2}}{\sigma^{2}}) \tag{23}\]  
where \(\sigma\) denotes the kernel bandwidth parameter.
# Redefining Archaeological Research: Digital Tools, Challenges, and Integration in Advancing Methods
## 3 Types of Digital Tools
### Global Positioning Systems (GPSs)  
The first global navigation satellite system (GNSS), commonly known as a GPS, provides efficient, precise, and accurate measurements [18]. By employing real-time kinematic positioning (RTK), archaeologists can record the exact locations of artefacts with high precision and detail [19; 20]. However, the effectiveness of a GPS is sometimes limited in environments with dense vegetation, urban areas with obstructed sky views, or deep trenches that can block or weaken GPS signals [21]. Typically, two devices are used in RTK setups, a base station and a receiver rover, which work together to ensure accurate positioning data (Figure 3).  
Figure 3: Measurement of control points with GPS RTK for photogrammetric flight in the city of Utica (Tunisia). Source: [20].  
Figure 2: Thematic map of least-cost paths calculated for mule cart transport in Early Roman sites in the Kromme Rijn area, the Netherlands, derived from GIS. The map presents the Betweenness centrality measurements of all sites in the Middle Roman mule cart network. Source: [16].  
Before the advent of GPSs, researchers relied on manual surveying, which required significant time and effort and was prone to inaccuracies. Printed topographic maps were often used to identify and mark locations, but these often lacked the precision needed for archaeological work. The introduction of GPSs revolutionized this process, allowing researchers to easily acquire precise coordinates at any point within an excavation or archaeological site. GPSs are also highly valuable when combined with other techniques, serving as a complementary tool for establishing ground control points (GCPs) and aiding in georeferencing processes, as demonstrated in the works of [22] (Figure 4) and [20].  
In most cases, the coordinates provided by GPSs are based on a global reference system and must be converted to a local reference system to meet specific project requirements [23]. For example, in the Galilee Prehistory Project (Israel) [24], researchers used a GNSS to set up excavation areas and record control points for drone surveys. However, limitations such as the need for stable Bluetooth or Wi-Fi connections can occasionally affect its functionality.  
Another noteworthy application of GNSS techniques can be found in the restoration of Pasha's Bridge, one of Greece's largest stone bridges [25]. Researchers employed a GNSS to obtain precise location coordinates in both the national reference system and the World Geodetic System 1984 (WGS84), including ellipsoidal heights, to create the digital terrain model (DTM) and contour lines for the site.
## 3 Results  
The electronic search identified 361 references. After removing the duplicates, 308 references were selected for eligibility based on the titles and abstracts, excluding in vitro studies, animal studies, and ongoing and unpublished clinical trials. After searching in the references, 28 additional articles were identified. For the full text review, 129 articles were selected. Of these, 38 clinical trials were excluded because oral health was not the focus of the study, because the study design was unclear, there was no information about allocation, the intake of probiotics was evaluated in animals, and studies were performed in vitro. The 91 randomized controlled trials (RTCs) included in the review involve data from 5374 participants with gingivitis (\(n=14\)), periodontitis (\(n=21\)), peri-implantitis (\(n=8\)), carie (\(n=23\)), orthodontic conditions (\(n=6\)), halitosis (\(n=4\)), oral conditions associated with chemo-radiotherapy (\(n=6\)) and changes in the oral ecosystem (\(n=9\)). Sample sizes range from 10 and 321 participants. The duration of interventions varied between 7 days and up to 2 years. There were 59 studies performed in adults, 24 in children, and 8 in more than one age group. Regarding the probiotic/s administered, 53 studies have implemented a combination of probiotics and 37 have administered a single probiotic. Two studies used symbiotic/prebiotic (1 with a single probiotic and 1 with a combination). The concentrations of the probiotics were not reported in 29 studies and one study did not specify the probiotic that was used. In two studies the duration of administration of probiotic(s) was not given. Adverse effects were reported in 13 trials.  
Regarding the study design, there are 11 cross-over trials, 80 placebo-controlled trials and 11 trials compared with another intervention. Regarding the blinding, there are 18 studies without indication of blinding, five single blinding, 61 double-blind, six triple-blind and one with no blinding. The primary results of the studies are mainly focused on clinical, microbiological, and immunological parameters. No data were analyzed regarding financing, for-profit bias, and location of studies.
# Multivalent Aptamer Approach: Designs, Strategies, and Applications
## 2 Multivalent aptamer Structures
### Spherical Multivalent Aptamers  
For spherical multivalent aptamers, the aptamers are connected onto a hollow or solid core particle in an organized and radial polyvalent arrangement [31]. The core particles can be gold [32], silver [33], metal oxides [34; 35; 36], silica [37; 38], polymers [39], or DNA self-assemblies [40]. Spherical carriers allow the greatest directional extension of aptamers into sample solutions, resulting in enhanced contact surface areas and thus better aptamer-target interactions. Furthermore, additional plasmonic [41], magnetic [24], or luminescent [42] properties of the particles can also offer added advantages for applications such as imaging and detections.
## 5 Conclusions  
For improved fault diagnosis of rolling bearings, a hybrid method based on ICEEMDAN, nonlinear dynamics entropy, and ESVM for multi-fault diagnosis is presented in this paper. The ICEEMDAN algorithm is utilized to decompose the nonlinear and nonstationary bearing vibration signals, and three kinds of entropy features are extracted from the obtained IMFs. Then, the discriminative features are selected based on the interclass and intraclass distances, and the multi-fault conditions of rolling bearings are intelligently recognized using the multi-class EVSM. The effectiveness of the proposed method is validated with extensive experimental case studies of rolling bearings with different fault modes as well as fault severities under multiple operational conditions (i.e., speeds and loads). Comparisons with some other decomposition algorithms and classifiers further demonstrate the performance of the proposed approach. In the future, the above method for multi-fault rolling bearing diagnosis across different operational conditions will be addressed to bridge the gap between research and real applications.  
Conceptualization, R.L. and B.Z.; methodology, R.L. and B.Z.; software, R.L. and B.Z.; validation, B.Z. and C.R.; formal analysis, L.H., R.L., and S.F.; investigation, B.Z.; resources, R.L.; data curation, B.Z.; writing--original draft preparation, B.Z.; writing--review and editing, R.L. and C.R.; visualization, B.Z.; supervision, B.Z.; project administration, B.Z.; funding acquisition, R.L. and B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Research Program of Chongqing Municipal Education Commission under grant KJ1704109 and KJZD-K201900602, and in part by the Open Found of The State Key Laboratory of Mechanical Transmissions under grant SKLMT-KFKT-201809.  
The authors would like to appreciate CWRU for their kind permission to use their bearing data.  
Figure 12: The average recognition rates by the four classifiers for the ten bearing fault classes.
## 4 FL-Based Controller: Formulation, Constraints and Algorithm
### Constraints and Formulation of Charging and Discharging Control
#### 4.2.2 Voltage Membership Function  
The controller should maintain the adequate charging and discharging voltage. For instance, as the voltage may rise due to the high penetration of PV, the controller is required to regulate the voltage and prevent the battery from any over-discharging and over-charging. Indeed, the power system may experience voltage drops due to high power demand that is related to on-peak hours. Thus, a proper controller should take into account the voltage variations and avert any damage due to excessive depth of battery discharge. In that regard, the input membership voltage is fuzzified into the corresponding fuzzy voltage signals with seven linguistic variables, as shown in Figure 7; Very High Negative (VHN), High Negative (HN), Low Negative (LN), Normal (NO), Low Positive (LP), Medium Positive (VH), and Very High Positive (VHP).  
Figure 6: Input membership function State of Charge (SoC).  
Figure 7: Input membership function Voltage.
## 3 Results
### Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item 1**} & \multicolumn{2}{c}{**Milk (M)**} & \multicolumn{2}{c}{**Fresh Cheese (FC)**} & \multicolumn{2}{c}{**Ripened Cheese (RC)**} & \multicolumn{2}{c}{**P 2**} \\  & **Mean** & **SEM** & **Mean** & **SEM** & **Mean** & **SEM** & **M vs. FC** & **M vs. RC** & **FC vs. RC** \\ \hline C40-C10:0 & 14.7 & 0.85 & 13.7 & 0.68 & 18.3 & 0.22 & ns & ** & ** \\ C12:0 & 5.21 & 0.12 & 5.03 & 0.19 & 6.20 & 0.09 & * & ** & ** \\ C14:0 & 9.46 & 0.05 & 9.83 & 0.30 & 11.1 & 0.11 & ns & ** & ** \\ C16:0 & 27.9 & 0.68 & 27.4 & 0.44 & 26.3 & 0.06 & ns & + & ns \\ C18:0 & 13.5 & 0.21 & 12.7 & 0.41 & 10.6 & 0.22 & + & ** & ** \\ C18:1 n-\(\rho\) cis & 16.9 & 0.55 & 18.00 & 0.21 & 15.6 & 0.08 & * & ns & ** \\ CLA _cis-9_, _trans_-11 (RA) & 0.54 & 0.04 & 0.53 & 0.03 & 0.61 & 0.04 & ns & * & ns \\ Others & 11.8 & 0.23 & 12.8 & 0.30 & 11.3 & 0.05 & ns & ns & ** \\ SFA & 73.3 & 0.61 & 71.1 & 0.35 & 74.9 & 0.09 & * & ns & *** \\ MUFA & 20.9 & 0.53 & 22.2 & 0.35 & 19.2 & 0.05 & * & + & *** \\ PUFA & 5.78 & 0.18 & 6.67 & 0.08 & 5.87 & 0.14 & * & ns & ** \\ n-3 & 1.36 & 0.07 & 1.54 & 0.06 & 1.26 & 0.03 & + & * & ** \\ n-6 & 3.74 & 0.13 & 4.44 & 0.05 & 3.88 & 0.17 & * & ns & ** \\ \hline \hline \end{tabular}  
* 1 SFA, saturated FAs; MUFA, monounsaturated FAs; PUFA, polyunsaturated FAs; SCFA, short-chain FAs; MCFA, medium-chain FAs; LCFA, long-chain FAs; CLA, conjugated linoleic acid; 2 Statistical probability for comparisons: ns, not significant (\(p>0.05\)); *, \(p\leq 0.05\); **, \(p<0.01\); **, \(p<0.001\); \(t,p\leq 0.10\).  
\end{table} 
Table 5: Fatty acid (FA) profile (expressed as % of total FA) of fresh and ripened Roja Mallorquina ewes’ cheese and corresponding original milk.