esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 RSS Difference-Aware Graph-Based Semi-Supervised Learning RSS Smoothing Method  
The G-SSL method tries to set the same value for \(\hat{r}_{i}\) and \(\hat{r}_{j}\) if the coordinates \(c_{i}\) and \(c_{j}\) at locations \(S_{i}\) and \(S_{j}\) are similar. However, since the distance between each RP and each of the unlabelled locations is known, we can use this information to estimate the expected difference in RSS based on the known locations of the APs and the radio propagation model. Thus, we define \(\hat{R}_{d}(S_{i},S_{j})\) as the estimated RSS difference between \(r_{i}\) and \(r_{j}\) at location \(S_{i}\) and \(S_{j}\). We change the smoothing constraint to reflect that the difference \(\|\hat{r}_{i}-\hat{r}_{j}\|\) of estimated RSS values \(\hat{r}_{i}\) and \(\hat{r}_{j}\) should be close to \(\hat{R}_{d}(S_{i},S_{j})\). Accordingly, (4) can be written as:  
\[\hat{\textbf{R}}^{*}= \text{arg}\ \min_{\hat{R}}\bigg{\{}\sum_{i=1}^{\ell}\|\hat{r}_{i}-r_{i} \|^{2}+\frac{\gamma}{2}\sum_{i=1}^{\ell+1}\sum_{j=1}^{\ell+u}w_{ij}\left(\| \hat{r}_{i}-\hat{r}_{j}\|-\hat{R}_{d}(S_{i},S_{j})\right)^{2}\bigg{\}}. \tag{18}\]
## 4 Design of Multipoint Displacement Monitoring Algorithm for Bridge Structure
### The Method of Establishing the Space-Time Relationship of Image Sequence Data
#### 4.2.1 Dataset Construction Based on Spatiotemporal Static Image Sequences
The rich feature points and the SIFT feature points of the test bridge structure must be matched [46; 47] in the image data. As shown in Figure 16, the yellow line is the corresponding relationship between the local feature points of the structure in different fields of view and the overall feature points of the structure. The matching results of the feature points of the structure itself and the feature points of the structure SIFT are good. The red line in Figure 11 is the most similar line between the field of view \(n\) of the test bridge and the feature points of the whole bridge. The line after matching according to the similarity in the calculation process of the algorithm is used as the constraint condition of edge registration theory and the basis of the displacement measurement information (the red line is the need of explanation, but the actual line should be the yellow line). However, many mismatches in the calculation of the algorithm remain, such as the connection of several characteristic points on the bridge tower and the reaction frame. Therefore, the greedy algorithm is used in this study to re-express the matching set of feature points, and the matching similarity rate is calculated by traversing the proximal and the sub proximal points in the process of filtering. The error matching points in the feature matching set are eliminated by optimizing the selection of each calculation in the process of traversing. Euclidean Distance: \(d_{i,j}=\sqrt{\sum\limits_{i=1}^{n}{(S_{i}-S_{j})^{2}}}\)  
HMF: \(Obj(x)=\sum\limits_{i=1}^{m}{\sum\limits_{j=1}^{n}{\left|{\frac{{H_{ij}(x,y,z)- H^{i}(x,y,z)}}{{H_{ij}(x,y,z)}}}\right|}{(S_{i}-S_{j})^{2}}}\)  
Figure 15: The spatiotemporal features of static image sequences. According to the Euclidean distance calculation, the correlation degree between the spatial feature points of the test bridge structure is used for the optimal matching of the feature point set, where \(d_{i,j}\) is the Euclidean distance between feature points, \(S_{i}\) and \(S_{j}\) are the spatial feature point set. _HMF_ is the control equation of superposition analysis of displacement calculation in the space-time domain, where \(m\) and \(n\) are the serial numbers of spatial feature points; \(x,y,\) and \(z\) are the spatial coordinates of feature points; \(H_{ij}(x,y,z)\) is the structural holographic morphological response measurement of a certain time in a certain field of view; and \(H^{\prime}{}_{ij}(x,y,z)\) is the reference state of the structural holographic morphological response measurement.
## 3 Materials and Methods
### Experimental Details  
The dataset was split into a training set (80%) and a test set (20%). The training set was further divided using a 5-fold cross-validation strategy to avoid the risk of overfitting. In this scheme, the 1000 training subjects were partitioned into five equally sized folds: in each iteration, four folds were used as training data and the remaining one was used for the validation phase. This process was repeated five times, resulting in five models, each trained and validated on different subsets of the data. In the final evaluation phase, predictions from these five independently trained models were combined using a majority voting ensemble strategy to produce the final segmentation on the held-out test set. The segmentation pipeline was developed in Python (version 3.12) and executed through the Anaconda interpreter using the Spyder integrated development environment. The training process was performed on the LEONARDO supercomputer infrastructure utilizing an NVIDIA A100 GPU.  
The model was trained for up to 200 epochs, starting with an initial learning rate of \(1\times 10^{-4}\). A Cosine Annealing scheduler was used to progressively reduce the learning rate, helping stabilize the training and improving generalization. Early stopping with a patience of 20 epochs was also implemented, ending training if no improvement in validation performance was observed, thereby preventing overfitting and unnecessary computational costs.  
Training was carried out with a batch size of 1, which is common choice in volumetric medical image segmentation due to memory constraints. The model was optimized using the AdamW optimizer with a weight decay of \(1\times 10^{-5}\). AdamW decouples the weight decay from the gradient update step, allowing for better control over regularization and learning dynamics, which improves both convergence speed and generalization [45]. The initial loss function was the Dice loss, which is particularly suitable for segmentation tasks with class imbalance.
# A Review of Electrochemical Sensors for the Detection of Glyated Hemoglobin
## Abbreviations  
\begin{tabular}{l l} ARS & Alizarin red s \\ arPES & Amine-reactive phenazine ethosulfate \\ AQBA & Anthraquinone boronic acid \\ AI & Artificial intelligence \\ BSA & Bovine serum albumin \\ CHIT & Chitosan \\ CMOS & Complementary metal oxide semiconductor \\ DM & Diabetes mellitus \\ DPV & Differential pulse voltammetry \\ ESM & Eggshell membrane \\ Fc & Ferrocene \\ Fc[CO-Cys(Trt)-OMel2 & Ferrocene diformylcysteine \\ Fc[CO-Glu-Cys-Gly-OH] & Ferrocene glutathione \\ FcAb & Ferrocene-tagged anti-HbA1c antibody \\ FIA & Flow injection analysis \\ FTO & Fluorine-doped tin oxide \\ FAO & Fructosyl amino acid oxidase \\ FPOX & Fructosyl peptide oxidase \\ FV & Fructosyl valine \\ FVH & Fructosyl valine histidine \\ GHA & GHb-targeted aptamer \\ GC & Glassy carbon \\ GCE & Glassy carbon electrode \\ GOx & Glucose oxidase \\ GA & Glutaraldehyde \\ HbA1c & Glycated hemoglobin \\ GPP & Glycated pentapeptide \\ AuNFs & Gold nanoflowers \\ AuNPs & Gold nanoparticles \\ GO & Graphene oxide \\ GS & Graphite sheet \\ HP & Haptoglobin \\ Hb & Hemoglobin \\ HJNH & Heterojunction nano hybrid material \\ HPLC & High performance liquid chromatography \\ H2O2 & Hydrogen peroxide \\ IFG & Impaired fasting glucose \\ IGT & Impaired glucose tolerance \\ ITO & Indium tin oxide \\ IFCC & International Federation of Clinical Chemistry \\ MPBA & Mercaptophenyl boronic acid \\ MIC & Molecularly imprinted catalyst \\ MIP & Molecularly imprinted polymer \\ NWCNT & Multiwalled carbon nanotube \\ Nf & Nafion \\ NSPC & N, S-doped porous carbon \\ OGTT & Oral glucose tolerance test \\ PtNPs & Platinum nanoparticles \\ PBA & Phenylboronic acid \\ PBS & Phosphate buffered saline \\ \end{tabular}
## 3 Results
### Expression of Up- and Down-Regulated Genes
Figure 3: Heatmap of the 5 datasets of endometriosis and recurrent pregnancy loss showing the expression of the upregulated and downregulated significant genes as depicted by R software using the complete heat package of R. Effect value refers to the change of the log ratio of gene expression compared to control or study-wide mean or median. In a parallel workflow, all the target GEO datasets were analyzed using Geo2R. The expression profiles contained genes that were significantly expressed in comparison to the control. Following this, the expression profiles of all the datasets were overlapped using the Venn diagram (Figure 4A); it was seen that only 19 significantly overexpressed genes were common among the five datasets. Interestingly, when these 19 genes were compared with the differentially expressed genes from EA and NA analysis results (Figure 4B), then, surprisingly, only a single gene, i.e., _TWIST_2, was found to be commonly present among all the three analyses, viz. EA, NA and Geo2R. This outcome shifted our focus towards the _TWIST_2 gene and triggered our interest in exploring the biological role of this marker, especially in the context of human reproductive health. It should be noted here, with respect to Figure 4B, that all the genes that are considered for comparative analyses between the three different software-based approaches demonstrated significant fold change in the patient sample compared to the control. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gene Symbol** & **Entrez ID** & **Log Ratio Combined** & **Fold Change** & **FDR*** \\ \hline _NDRG2_ & NDRG Family Member 2 & 0.31 & 2.042 & \(1.71\times 10^{-5}\) \\ _H4C3_ & H4 Clustered Histone 3 & \(-\)0.304 & 2.014 & \(4.67\times 10^{-7}\) \\ _RSPO3_ & R-Spondin 3 & \(-\)0.3029 & 2.009 & 0.004831 \\ _TSPAN2_ & Tetraspanin 2 & 0.2999 & 1.995 & 0.0251 \\ _CPXM1_ & Carboxypeptidase X (M14 Family), & \(-\)0.2865 & 1.934 & \(4.13\times 10^{-6}\) \\ _FBLN7_ & Fibulin 7 & \(-\)0.2862 & 1.933 & \(5.63\times 10^{-6}\) \\ _HOXD11_ & Homeobox D11 & \(-\)0.2822 & 1.915 & 0.0406 \\ \hline \hline \end{tabular}  
* FDR: False discovery rate.  
\end{table} 
Table 2: _Cont._  
Figure 4: Venn diagrams based on the expression profiles of the study datasets. (**A**) The number of common genes obtained by Geo2R from the 5 endometriosis and RPL datasets as visualized by a Venn diagram—19 genes were found common amongst the 5 datasets; (**B**) common genes of individual analyses of the 5 datasets of endometriosis and RPL by 3 different software programs, ExAtlas, Network Analyst and Geo2R. Only 1 gene was found common among the individual results obtained across the 3 programs—_TWIST_2.
## 2 Challenges, Trends and Constraints in WSAN Hardware Design
### Open-Source-Hardware Architectures versus Open-Architecture Systems
#### 2.3.2 Power Management Constraints
To emphasize this problem, a single LED indicator that is blinking inside of a closed plastic box, installed in the agriculture field, is useless for the users and it consumes more energy than that consumed by e.g., a ZigBee RF transceiver.
# Thermographic Non-Destructive Evaluation for Natural Fiber-Reinforced Composite Laminates
## 3 Experiments
### Continuous Wave Terahertz  
The principles of continuous wave (CW) terahertz (THz) and millimetre-wave imaging have been known for several decades. The electromagnetic spectrum of millimetre-wave generally ranges from 30 GHz to 300 GHz, which has a region of overlap between 100 GHz and 300 GHz with THz spectroscopy [28; 29; 30]. Compared to pulsed THz imaging which can provide depth, frequency-domain or time-domain information of the object, CW THz imaging can only yield intensity data when a fixed frequency source and a single detector are used. However, the CW imaging system is compact, simple, fast and relatively low-cost without a pump-probe system [31; 32].  
The CW THz system (Figure 7) uses IMPAct ionization Transit Time (IMPATT) diodes to exploit \(\sim\)0.1 THz CW imaging, and the system resolution is \(\sim\)3 mm. It consists of a detector 'TeraSense Linear-1024', a CW emitter 'TeraSense TS 1603', and a robot 'Funac LR Mate 200 iD 7L' to perform a line scanning. The detector has a spectral range of 40 GHz-0.7 THz and a frame rate of \(\sim\)8.2 fps. The pixel size of the detector is 1.5 mm \(\times\) 1.5 mm, and the number of pixels is 1024 (\(256\times 4\)). The emitter has an optimized frequency of \(\sim\)0.1 THz and 1 us rise/fall time (TTL Modulation). The detector records 256 \(\times\) 4 pixels (384 mm \(\times\) 6 mm) into one frame. The distance from the specimen to the CW emitter/detector was \(\sim\)10 cm [32].
## 2 Materials and Methods
### Characterization Methods
#### 2.2.3 Evaluation of Absorptivity along the Cut Front  
The inclined cut front throughout the plate thickness characterizes the transition from solid to molten phase in laser beam cutting. The angle of incidence of the laser radiation on the cut front determines the local absorptivity (see Figure 7), which is associated to the energy deposition and the resulting temperature distribution. According to Figure 6, microscopic imaging using focus stacking recorded a 3D profile of the cut front. Based on these profiles, the local angle of incidence \(\varphi_{\text{in}}\) along the symmetry plane of the cut front was determined (see Figure 7a).  
The dependency between the angle of incidence and the reflectivity of polarized laser radiation specifies the Fresnel equations. The present trend of the absorptivity shown in Figure 7b was calculated according to the dissipation of laser energy on metallic surfaces [2]. The absorptivity relates to the polarization state of the laser beam. In case of randomly polarized, respectively non-polarized radiation, it was appropriate to calculate the circularly polarization. The average absorptivity \(A_{\text{Cir}}\) for circularly polarized laser radiation defines the relation of reflectivity for parallel and perpendicular radiation \(R_{\text{P}}\) and \(R_{\text{S}}\):  
\[A_{\text{Cir}}=1-\frac{R_{\text{P}}+R_{\text{S}}}{2}. \tag{1}\]  
Whereas the angle dependent reflectivity's results from the Fresnel equations:  
\[R_{\text{P}}=\frac{(n\cdot\cos\varphi_{\text{in}}-1)^{2}+(k\cdot\cos\varphi_{ \text{in}})^{2}}{(n\cdot\cos\varphi_{\text{in}}+1)^{2}+(k\cdot\cos\varphi_{ \text{in}})^{2}}, \tag{2}\]  
\[R_{\text{S}}=\frac{(n-\cos\varphi_{\text{in}})^{2}+k^{2}}{\left(n+\cos\varphi _{\text{in}}\right)^{2}+k^{2}}, \tag{3}\]  
Figure 7: Derivation of absorptivity based on cut front topography: (**a**) Scheme of the angle of incidence \(\varphi_{\text{in}}\); (**b**) Dependence between angle of incidence \(\varphi_{\text{in}}\) and absorptivity \(A_{\text{Cir}}\) for liquid iron and radiation wavelength \(\lambda\) = 1.07 \(\upmu\)m [2].  
with the local angle of incidence \(\varphi_{\text{in}}\), the refractive index n, and the extinction coefficient k. Specific material constants, the wavelength of the laser radiation, and the temperature define the optical parameters n and k. For the present case of laser beam cutting of steel material with a wavelength of \(\lambda\) = 1.07 \(\mu\)m, it was proven to estimate average values for the optical parameters with n = 5.3 and k = 3.82 based on liquid iron [2].
# A Hybrid Traffic Sensor Deployment Model with Communication Consideration for Highways
## 3 The Solution Algorithm for the Hybrid Sensor Deployment
### The Greedy Search Algorithm for Step II  
In this step, the maximum budget for purchasing servers is assumed to be provided in advance, i.e., \(W-\omega_{1}\). According to the unit cost of a server, the maximum number of installed servers could be evaluated. The main purpose of applying the greedy search is to enumerate all possible deployment scenarios and select the optimal one for connecting sensors. If the number and location of servers are decided, the service scope of each server can be defined with the communication range. To increase the utilization of each server, sensors should be placed in specific regions. This situation can be described by modifying  
Figure 3: The framework of the proposed solution algorithm.  
road cell constraints (see Equation (15)). This repression reflects the influence of the service scope on variable \(x_{ij}^{k}\). By adding Equation (15) into sub-model I, an efficient deployment plan could be derived from the restricted model (Equations (13) and (15)).  
\[\sum_{i\in\Lambda^{m},j\in\mathbf{N}\cup\{n+1\},k\in\mathbf{S}}x_{ij}^{k}\geq 1,m\in\Delta H \tag{15}\]  
where \(\Delta H\) is the set of servers and \(\Lambda^{m}\) denotes the set of road cells covered by the \(m\)th server. Based on Equation (15) and sub-model I, the procedure of the greedy search algorithm is listed in Algorithm 1.  
```
Input: Set \(\mathbf{N}\) and \(\mathbf{S}\), the total budget \(\mathbf{W}\), matrix \(\pi\), installation cost \(c\) and \(\varpi\), server capacity \(F\), expected memory usage \(\alpha\). Output: The deployment plan \(x_{ij}^{k}\) and \(y_{i}\).
1\(R\leftarrow\lfloor\frac{W-up_{1}}{\varpi}\rfloor\)
2\(H\leftarrow\{1,2,\ldots,R\}\)
3\(\Theta\leftarrow\emptyset\) //\(\Theta\) means the set of server locations
4\(\Lambda\leftarrow\emptyset\)
5//Part I: site selection
6 for\(h\) in \(H\)do
7 Enumerate all possible positions for h servers and record positions in set \(\Theta_{h}\). //\(\Theta_{h}\) means that the set of sever locations contains \(h\) elements.
8 end for
9//Part II: service scope determination
10 for\(h\) in \(H\)do
11 for\(g\) in \(\Theta_{h}\)do
12 Calculate the service scope for each server and update set \(\Lambda^{hg}\).
13 end for
14
15 end for
16//Part III: comparison of sensor deployment plans
17 for\(h\) in \(H\)do
18 for\(g\) in \(\Theta_{h}\)do
19 Add set \(\Lambda^{hg}\) into Equation (15) and solve restricted sub-model I.
20 end for
21
22 end for
23 Select the deployment plan with the minimum.
```  
**Algorithm 1**The Greedy Search Algorithm
# Proxy Re-Encryption Scheme for Decentralized Storage Networks
## 5 Experiment
### Performance Comparison  
Python 3.8 is used to implement our PRE scheme and WDLC10. Since [PERSON] uses a different theory over the large prime numbers, it was hard to make a fair comparison by choosing the parameters. The code is modified from an open-source pure Python library named python-ecdsa, which is licensed in the public domain.  
We implemented the ECC version of our PRE scheme and the practical modification in Python 3.8 and tested it on a MacBook Air of Intel Core i5 at a processor speed of 1.3 GHz. For every 64 bytes of data (including 48 bytes clear message \(m\) and 16 bytes of random initialization vector \(w\)), we repeat encryption 100 thousand times and note the time cost.  
Our scheme is slightly slower than WDLC10 for encrypting in theory, as we described in Table 1. However, for the modification for practical cached exponential operation for \(g_{ECC}\), from Figure 4, we can observe that practical modification can speed up the encryption by avoiding ECC computation. In most cases, elliptic curve-based asymmetry cryptography is commonly used in the signature or key exchange due to its slowness. However, the decentralized storage network shows the scenarios where an asymmetry encryption is required. Meanwhile, it is nice to have the key strength and reasonable encryption/decryption speed. The result of Figure 4 shows that even for a modern CPU, the computation is insufficient for fast encryption. The practical implementation is helpful to speed up the operation while maintaining the security from ECC with less key length.  
In the experiment, we use the cyclic multiplicative group over an integer of 196 bits for both ours and WDLC10. The ECC curve is NIST192p. The message \(m\) size is 48 bytes and \(w\) is 16 bytes since the blake2b outputs up to 64 bytes (512 bits) each time.  
In Figure 5, we compare our practical modification PRE scheme with pure-Python-implemented WDLC10. PRE brings useful access control, privacy features, and better key management than symmetry encryption. Unlike WDLC10, there is no requirement for layer 1 and layer 2 ciphertext. Our scheme needs only one public/secret key pair. For the hash function, we use blake2b, which can output a flexible length of the hash digest. For the curve, we use NIST192p. Our proxy re-encryption is ready for practice. Figure 5 shows that with the modification on our scheme, the ECC computing is cached, so our scheme can be slightly faster than WDLC10 even if the theory shows our scheme was slower, shown in Table 1.  
Figure 4: Time cost for PRE ECC and practical encryption for every 1000 times of encryption of 64 bytes data on MacBook 1.3 GHz Intel Core i5.
# Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
## 6 Conclusions  
This paper proposes a fault diagnosis method for the planetary gear of wind turbines based on digital twinning. Additionally, a fault diagnosis system platform for the planetary gear of wind turbines based on digital twinning is built. The accuracy and feasibility of the proposed fault diagnosis system are verified by the experimental research on the wind turbine fault test bench conducted within the laboratory. The planetary gear fault diagnosis of wind turbines based on a digital twin is realized. The main conclusions are as follows:  
1. Digital twinning technology is applied to carry out the real-time visual monitoring of the operating state of the wind turbine planetary gear through the data acquisition of sensors, making it possible to monitor the internal operation process of the wind turbine digitally. The proposed digital twin fault diagnosis system provides a new concept and a complete solution for the visual monitoring, real-time fault diagnosis, and performance maintenance of the planetary gear of wind turbines.
2. A data-driven fault diagnosis method based on EMD-ASO-SVM is proposed to make timely and effective judgments on the health status of planetary gears by the real-time collection, diagnosis, and analysis of strain signals in the running state of planetary gears of wind turbines. The fault classification accuracy of the ASO-SVM model is 94%, while that of the traditional SVM model is only 86.67%, characterized by fewer required samples and higher diagnostic efficiency.
3. Compared with other digital twin systems, the system developed in this paper has the advantages of low delay and high efficiency, providing it with very high application universality.  
The research work after this paper will be devoted to introducing more data types to realize the continuous improvement and optimization of the service function of the wind turbine fault diagnosis system based on digital twinning.  
Conceptualization: Y.W. and L.L.; methodology, Y.W.; software, Y.W.; validation, Y.W. and S.B.; formal analysis, Y.W.; investigation, Y.W.; resources, W.S.; data curation, Y.W. and B.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, Y.W. and S.B.; supervision, W.S.; project administration, Y.W. and R.J.; funding acquisition, W.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Demonstration of Key Technologies of Digital Twinning for Wind Power Equipment Based on Industrial Big Data (Grant number: 202112142).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank Xinjiang University of Technology, Xinjiang, China, for their support throughout the research project.  
**Conflicts of Interest:** The authors declare no conflict of interest.