esa-sceva/satcom-chunk-collection huggingface dataset:
# Weak Fault Feature Extraction and Enhancement of Wind Turbine Bearing Based on OCYCBD and SVDD
## 3 Singular Value Decomposition Denoising  
### Basic Theory of Singular Value Decomposition  
The special property of singular value decomposition (SVD) has attracted a lot of attentions in the field of signal denoising. And this approach is capable of clearing off the redundant interferences by simply reconstructing the matrix with the chosen effective singular values [29].  
Assume that \(X=[x(1),x(2),\cdots,x(L)]\) is a given discrete signal, and the constructed Hankel matrix for conducting SVD is as follows:  
\[A=\left[\begin{array}{cccc}x(1)&x(2)&\cdots&x(l)\\ x(2)&x(3)&\cdots&x(l+1)\\ \vdots&\vdots&\cdots&\vdots\\ x(L-l+1)&x(L-l+2)&\cdots&x(L)\\ \end{array}\right]_{m\times l} \tag{22}\]  
where \(l=L/2\) and \(m=L/2+1\).  
The orthogonal decomposition of matrix \(A\) is the essence of SVD, it can be described as:  
\[A=UEV^{\mathrm{T}} \tag{23}\]  
where \(E=(diag(\sigma_{1},\sigma_{2},\cdots,\sigma_{p}),0)\in R^{m\times l}\) is the diagonal matrix, \(U=(u_{1},u_{2},\cdots,u_{m})\in R^{m\times m}\) and \(V=(v_{1},v_{2},\cdots,v_{l})\in R^{\times l}\) refer to the orthogonal matrixes, \(\sigma_{1}\geq\sigma_{2}\geq\cdots\geq\sigma_{p}>0\) indicates the gained singular value sequence.  
Another expression form of matrix \(A\) can be described as:  
\[A=\sigma_{1}u_{1}v_{1}^{\mathrm{T}}+\sigma_{2}u_{2}v_{2}^{\mathrm{T}}+\cdots+ \sigma_{p}u_{p}v_{p}^{\mathrm{T}}=A_{1}+A_{2}+\cdots+A_{p} \tag{24}\]For the purpose of removing the interference components, the matrix \(A^{\mathbb{R}}\) corresponding to the denoised signal needs to reconstruct through the selected former \(i\) singular values as follows:  
\[A^{\mathbb{R}}=\sigma_{1}u_{1}v_{1}^{\mathbb{T}}+\sigma_{2}u_{2}v_{2}^{\mathbb{ T}}+\cdots+\sigma_{i}u_{i}v_{i}^{\mathbb{T}}=A_{1}+A_{2}+\cdots+A_{i} \tag{25}\]  
And the purified signal is able to be gained through diagonal mean operation of \(A^{\mathbb{R}}\).
## 5 Experiments
### Experiment 4: Static Splitting Factors with Stopping Criteria
#### 5.4.2 Replica Nodes Criterion
Likewise, as depicted in Figure 11 and Table 12, in the scenario of a 10% pollution factor, our scheme reaches a complete solution for every splitting factor for connection pools up to 4K. Again, within the range of low-to-medium partitioning factors, complete recovery is also accomplished for connection pools of 5K, while splitting factors of 2, 4, and 12 generate identical outcomes for connection pools of 6K. Finally, within the spectrum of 9K and 10K connections, for yet another time, splitting factors of 10 and 20 appear as the best (87%) and worst (77%) performing ones, respectively. Nevertheless, for 10K connections, the first consumes 8.33 min less and spawns 1071 fewer nodes; this translates as \(\approx\)28% less time and \(\approx\)51% less resource utilization. Similarly, the latter needs 150.66 min less and 1170 fewer nodes, meaning a significant decrease in time (\(\approx\)91%) and resource (\(\approx\)54%) requirements.
## 3 Proposed CNN-Based Power Allocation Method
### Context-Aware Power Inference via CNN  
The proposed CNN enables spatially adaptive power inference by interpreting complex network features in real time. Instead of simply reinforcing strong links, the model learns to identify UEs that require greater power due to unfavorable conditions such as weak channels, far distances from APs, or high interference levels. This behavior emerges as a result of supervised training based on max-min optimized power allocations, which inherently prioritize fairness by allocating more power to disadvantaged users. During training, the CNN receives input tensors representing spatial and signal features and minimizes the discrepancy between its predicted allocations and the max-min references. Through this process, the model implicitly learns the relationship between environmental conditions and required power levels to meet fairness-driven SINR targets. After the AP-to-UE power matrix is determined, the required CPU-to-AP power is derived by aggregating the per-AP transmission demands. This hierarchical inference allows the network to flexibly and efficiently allocate power while respecting hardware limitations and service constraints. In this way, the proposed design ensures improved user-level performance and EE through globally optimized and context-aware decision making.
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 1 Introduction
[PERSON] extended the BLS to semi-supervised learning for classification using a large number of unlabeled samples combined with the theory of manifold regularization to improve the accuracy of the model for image classification [17]. The graph convolutional broad network (GCB) proposed by [PERSON] and [PERSON] has been used to explore the deep information of image structure to analyze data more effectively [18]. Up until now, the BLS has not been used for damage detection in wind turbine blades. Although deep learning algorithms have been widely used in image processing, with the deepening of model structure, time complexity of back propagation is becoming higher  
Figure 1: Global cumulatively installed wind power capacity and rate of growth in 2001–2020 (GW, %). and higher [19]. Deep learning algorithms also rely heavily on labelled images that are getting increasingly difficult to develop; therefore, it is necessary to develop a new model to better analyze images [20]. The CBNLM-BLS was proposed as a novel model based on the BLS and has been applied to damage detection in wind turbine blades in this work. The main contribution of this paper consists of the following two aspects. On one side, to solve the problem of the low computational efficiency in NLMs, the method of improving NLMs using the chunked, in-parallel approach is proposed. It solved the problem of repeated calculations for finding the difference value of pixels pair by pair in the NLM. The use of chunked operations sped up the operation of the integral map, which, in turn, improved the overall operation efficiency of the NLM. On the other side, to solve the problem of the poor generalization ability caused by little image data, the CBNLM-BLS-based damage detection for wind turbine blades was established. The proposed model had a single hidden layer structure, and the model performance could be improved only by adjusting the number of feature nodes and enhancement nodes. By calculating the pseudo-inverse of the feature nodes and enhancement nodes to the target value to calculate the weights, the training speed of the model was improved rapidly.
# Compensation for Group Velocity of Polychromatic Wave Measurement in Dispersive Medium
## 3 Compensation of Group Velocity in Dispersive Medium  
For the compensation of group velocity, we measure the IF curves of incident and transmitted signals. To acquire IF curves, the incident and transmitted signals are transformed through Hilbert transform for as following analytic representation:  
\[z(t)=r(t)+jH_{t}\{r\}=Me^{i\Phi} \tag{5}\]  
where, \(H_{t}\{x\}\) is a output at a time \(t\) of Hilbert transform filter applied the signal \(r\), \(M\) and \(\Phi\) are the magnitude and the instantaneous phase of the transmitted signal. And then, the instantaneous phase is derived as following:  
\[\xi_{2}(t-d)^{2}+\omega(t-d)+\phi=\tan^{-1}\frac{abs(j*Hilbert(r(t)))}{abs( Hilbert(r(t)))} \tag{6}\]  
where, \(abs(\cdot)\) means the absolute function. Figure 1 shows the illustration of the compensation of IF curves. In Figure 1, \(f_{s,c}\) and \(f_{r,c}\) are the center frequency of incident signal and transmitted signal. To derive the time shifted terms, \(a(f),b(f)\), of transmitted signal according to operation frequency, we obtained the shifted term using the delayed incident signal by the time delay, \(d\), and the reflection signal as follows.  
\[a(f)=\frac{f_{r,c}-f_{0}}{\xi_{1}},\quad b(f)=\frac{f_{r,c}-f_{0}}{\xi_{2}}, \quad c(f)=\frac{f_{s,c}-f_{r,c}}{\xi_{1}} \tag{7}\]  
The concept of group velocity of polychromatic signal is associated with narrow-band pulses concentrated in the neighborhood of center frequency, \(\omega_{0}\), with an effective frequency band \(|\omega-\omega_{0}|\leq\Delta\omega\), where \(\Delta\omega\ll\omega_{0}\). Furthermore, the compensated time delay between incident signal and transmitted signal is derived as following:  
\[t_{com}=d+(b(f)-a(f))-c(f) \tag{8}\]  
And then, compensated group velocity is derived as follows: \(v_{com}=l/t_{com}\). where, \(l\) is the known length of target cable. In future work, we derive the frequency and group velocity, \(v(f)\), curve based on (1). And then, we assume that the center frequency of transmitted signal is linearly decrease with time. According to this assumption, the center frequency of transmitted signal can be expressed as: \(f_{r,c}(t)=a\cdot t+b\) where, \(a<0\) and \(b\) are the constant. The travelling distance, \(D\) can be derived as: \(D=\int_{t_{0}}^{t_{0}+t_{com}}v(f_{r,c}(t))dt\). This article compensates the speed with time delay compensation, and next it will develop this to estimate the distance through accurate propagation speed and time delay without travelling distance information.
# Multi-Antenna Jammer-Assisted Secure Short Packet Communications in IoT Networks
## 3 Secrecy Performance Analysis  
In this section, we give a comprehensive analysis on the secrecy performance of the three proposed schemes. We first derive closed-form approximations for the secrecy throughput. Then, we analyze the asymptotic secrecy throughput in the case of infinite blocklength to gain further insights. Furthermore, the optimization problem in terms of maximizing the secrecy throughput is investigated under the latency and reliability constraints.
## 3 Intrinsic and Extrinsic Factors Associated with Hair Graying
### Reduced Capacity of Melanin Synthesis and Transfer  
GeneChip Array analysis of mRNA expression profiles between pigmented, gray, and white scalp hair follicles from human donors identified 194 and 192 downregulated genes  
Figure 1: Intrinsic and extrinsic factors associated with the incidence or progression of hair graying in humans. Abbreviations: Cu, copper; Fe, iron; MSCs, melanocyte stem cells; UV, ultraviolet.  
and 186 and 177 upregulated genes in gray and white hair follicles, respectively, compared to the pigmented hair follicles. The downregulated genes included tyrosinase (TYR), tyrosinase-related protein 1 (TYRP1), premelanosome protein (PMEL, Pmel17, GP100), melan-A (MLANA), KIT (c-Kit, receptor for stem cell factor), and MET (c-Met, receptor for hepatocyte growth factor) [41].  
Genome-wide comparison of the gene expression profiles of gray and black hair follicular cells of human patients with premature hair graying identified 127 differentially expressed genes (DEGs). Many melanogenesis-associated proteins, such as TYR, MLANA, PMEL, TYRP1, solute carrier family 45 member 2 (SLC45A2), KIT, G protein-coupled receptor 143 (GPR143), and coulocutaneous albinism 2 (OCA2) were found to be downregulated in gray hair follicles compared to black hair follicles [42].  
These studies support the view that the expression of genes related to melanin synthesis, melanosome biogenesis, and other melanocyte-relevant functions is reduced in hair follicles producing white or gray hair, resulting in insufficient melanin supply and reduced melanin incorporation into hair filaments during the anagen. Most DEGs in the above-mentioned studies are target genes of the transcriptional regulation by microphthalmia-associated transcription factor (MITF); hence, reduced MITF activity in melanocytes causes hair graying.  
Placental sphingolipid induced the expression of MITF and TYR via the p38 mitogen-activated protein kinase (MAPK) signaling pathway in murine melanoma B16F10 cells and its topical application increased MITF expression in follicular melanocytes and the growth of fresh dense black hair in age-onset gray-haired C57 BL/6] mice [43]. Therefore, the melanin synthesis and transport capacity of melanocytes, which are controlled by MITF, are the key variables most directly related to hair pigmentation and graying.
# How Can We Achieve Query Keyword Frequency Analysis in Privacy-Preserving Situations?
## 4 Kfa-Peks  
### System Model  
The following entities are included in our scheme: a key generation center (KGC), a matching server (MS), a keyword frequency server (KFS), a time server (TS), data owners (DOs), and data users (DUs). A brief description of our system model is given in Figure 1.  
* KGC: The role of the KGC is to generate public parameters and distribute the corresponding keys to the various entities involved in the scheme.
* MS: The MS mainly provides secure file storage services for the DOs and secure file search services for the DUs.
* KFS: The KFS collaborates with the MS in processing the DUs' file query requests, and conducts keyword frequency analysis on the DUs' queries, while ensuring the privacy of the DUs, in order to provide enhanced file storage and lookup services to both the DOs and DUs.
* TS: The TS periodically sends time tokens embedded with the current timestamp to the MS, which are then used to recover keyword information from the DUs' queries in order to conduct keyword frequency analysis.
* DO: Each DO generates their own public-private key pair based on the public parameters. They then extract keywords from a file to generate searchable ciphertext, which is sent to the MS along with the file for secure storage and search services.  
Figure 1: Our scheme consists of four main steps. In the first step, the DOs encrypt their data using their public key to generate searchable ciphertexts, which are then uploaded to an MS. In the second step, the DUs generate query trapdoors for the set of keywords of interest and send them to the MS. In the third step, the MS and the KFS cooperate to calculate the query results, which are then sent back to the corresponding DU. Finally, in the fourth step, the TS periodically sends time tokens to the KFS. The KFS can use time tokens to recover the keywords that satisfy the query trapdoor for the current timestamp, and then analyze these keywords. The analysis results are sent back to the DOs.  
* DU: Each DU generates their own public-private key pair based on the public parameters and generates a query trapdoor for a set of keywords of interest. Only the DU has the ability to decrypt the queried results received from the MS in order to obtain the corresponding file index, thereby ensuring privacy and security.
## 5 ALD on Prefunctionalized EG
### Ozone Prefunctionalization  
[PERSON] et al. [38] evaluated the effect of in-situ ozone (O\({}_{3}\)) pretreatment on the uniformity of ALD grown Al\({}_{2}\)O\({}_{3}\) onto EG. Firstly, a preliminary study of the influence of different O\({}_{3}\) dosing conditions (i.e., number of O\({}_{3}\) pulses and temperature) on the structural properties of EG was carried out. Figure 11a shows the comparison between two C1s core level X-ray photoelectron spectroscopy (XPS) measurements on as-grown EG and after exposure to 20 O\({}_{3}\) pulses at a temperature of 250 \({}^{\circ}\)C, respectively. The two spectra show two prominent peaks associated to the EG and to the SiC substrate (covered by EG), respectively, and a shoulder associated to the interfacial BL. The two spectra are almost perfectly overlapped, indicating a negligible effect of the O\({}_{3}\) treatment at 250 \({}^{\circ}\)C on EG structural and chemical properties. Figure 11b shows a C1s core level spectrum acquired on EG after 20 O\({}_{3}\) pulses at a higher temperature of 350 \({}^{\circ}\)C, with a deconvolution of the different spectral contributions, associated to the SiC substrate, to EG and to the BL. In addition to the components present in the pristine EG sample and in the O\({}_{3}\) -functionalized one at 250 \({}^{\circ}\)C (Figure 11a), another peak at lower binding energy, associated to SiC uncovered by EG, was observed in the EG sample subjected to the 20 O\({}_{3}\) pulses at 350 \({}^{\circ}\)C. From the XPS peaks intensity ratio, these bare SiC regions due to partial etching of EG by O\({}_{3}\), were found to correspond to 45% of EG surface. The degradation of the structural integrity of EG after this O\({}_{3}\) pretreatment (20 pulses at 350 \({}^{\circ}\)C) was also confirmed by Raman spectroscopy, showing a strong increase of the defects-related D peak and a large broadening of the G peak. Noteworthy, further experiments showed that EG damage can be mitigated by properly reducing the number of O\({}_{3}\) pulses [38]. As an example, the percentage of etched EG at 350 \({}^{\circ}\)C was found to be reduced to 2.5% by using only 2 O\({}_{3}\) pulses. Figure 11c shows a representative AFM image of a 50 nm thick Al\({}_{2}\)O\({}_{3}\) film  
Figure 10: C-AFM morphology (**a**) and current map (**b**) of Al\({}_{2}\)O\({}_{3}\) deposited by 25 ALD cycles on the bare EG surface. Morphology (**c**) and current map (**d**) of Al\({}_{2}\)O\({}_{3}\) deposited with the same number of cycles on the PTCDA/EG surface. Images adapted with permission from Ref. [36], copyright American Chemical Society 2011.  
obtained by 500 TMA/water cycles on the EG sample prefunctionalized using 20 O\({}_{3}\) pulses at 250 \({}^{\circ}\)C, i.e., the non-destructive conditions illustrated in Figure 11a. Although a uniform Al\({}_{2}\)O\({}_{3}\) coating was obtained on the entire EG surface, the higher roughness of the oxide film in the 2 L EG region close to the SiC steps indicated a limit for further reduction of O\({}_{3}\) exposure and temperature.
# Internet of Things (IoT) and the Energy Sector
## 4 IoT in the Energy Sector
### Intelligent Transportation  
One of the major causes of air pollution and energy losses in big cities is overuse of private vehicles instead of public transportation. As opposed to a traditional transportation system where each system works independently, applying IoT technologies in transportation, so called \"smart transportation\", offers a global management system. Also, the real-time data processing plays a significant role in traffic management. All the components of the transportation system can be connected together, and their data can be processed together. Congestion control and smart parking systems using online maps are some applications of smart transportation. Smart use of transportation enables passengers to select a more cost-saving option with shorter distance and the fastest route, which saves a significant amount of time and energy [120]. Citizens will be able to determine their arrival time and manage their schedule more efficiently [125]. Therefore, time of city trips will be shortened, and the energy losses will be reduced significantly. This can remarkably reduce CO\({}_{2}\) emissions and other air polluting gases from transportation [119].  
Table 3 summarizes the applications of IoT in the energy sector, from smart energy grids to the end use of energy. The IoT-based digitalization transforms an energy system from a unidirectional direction, i.e., from generation through energy grids to consumers, to an integrated energy system. Different parts of such an integrated smart energy system are depicted in Figure 5.
## 4 DEM Validation Based on ICESat-2 Data
### Influence of Elevation on the DEM Error  
To analyze the relationship between elevation and DEM error, the DEM errors were divided into four groups according the elevation: 0-200 m, 200-400 m, 400-600 m and \(>\)600 m. Figure 3 shows the ME, RMSE and STD values of the DEMs in different elevation ranges. The ME values exhibit general upward trends when the terrain height increases. The RMSE and STD values of the DEMs, except AW3D30, increase with the elevation. This can be explained by: (1) with the elevation increase,  
Figure 2: Histogram plot of the elevation errors (ICESat-2 minus different digital elevation models (DEMs)) for (**a**) SRTM1; (**b**) SRTM3; (**c**) ASTER; (**d**) AW3D30; (**e**) TDX30 and (**f**) TDX90. (TDX30: 30-m TDX DEM, TDX90: 90-m TDX DEM).  
the terrain becomes rougher, which makes the optical photogrammetry and InSAR techniques difficult to measure the ground elevation; (2) the ground cover of higher area is dominated by forest (Figure 1), which prohibits the optical photogrammetry and short wavelength SAR (e.g., X- and C-band) from measuring the sub-canopy ground elevation. In particularly, a significant increase of RMSEs and STDs is observed when the terrain height is under 400 m (except ASTER GDEM), but the error increases slightly when the height is >400 m. The AW3D30 DEM shows immunity to the elevation change. In addition, the DEM errors with 90 m resolution increase more significantly with elevation than those of DEM at 30 m resolution, indicating that DEM with higher resolution has better tolerance to the elevation change.
## 3 Real-Time Problem Description
This fact may introduce priority inversions at sending nodes where a flow with a relative deadline further away is scheduled before a more urgent one but is being blocked at the destination server. The scheduling algorithm is abstracted from the application layer, which means that, at the network level, each flow can be identified by its origin, destination, size and period, regardless of the source or data format as stated at the beginning of this section. When one or more applications request different flows with the same source and destination servers, all flows are treated as independent ones. At each slot, however, the scheduler should choose one flow to send, and this decision is independent of the size of the flow but on the priority determined by the period or deadline. In the following example, there are three nodes interacting. Nodes 1 and 3 send flows to node 2. However, the priority of the flow with origin in 3 is higher than that of the flow with origin in 1. Server 1 also has other flows to different destinations. Table 1 shows the flow identification, source, destination, size, period and deadline. In both nodes, the utilization factor is below 1. The symbol \(*\) indicates the destination is to some other server (different from 2) in the DCN. As can be seen in the scheduling graph in Figure 2, the A flow from server 1 to server 2 is delayed at server 1 because D flow with source in server 3 has a higher priority at receiving server 2. The time used in server 2 for receiving flow D is used in server 1 to transmit flow B and the beginning of flow C. Only when flow D is completed, can server 1 send flow A to server 2. As shown, there are five slots of priority inversion. Flow scheduling in DCN should be done almost on the fly and usually involves hundreds of flows and servers. In such scenarios, computing blocking times and interferences \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flow** & **Source** & **Destination** & **Size** & **Period** & **Deadline** \\ \hline A & 1 & 2 & 3 & 10 & 10 \\ B & 1 & * & 4 & 12 & 10 \\ C & 1 & * & 3 & 15 & 15 \\ D & 3 & 2 & 5 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Flows description. is almost impossible to be made on time. Additionally, path selection is made to reduce the energy demand of the network infrastructure evaluated as the sum of active switches during the scheduling period. In [8], the authors showed that this is an NP-hard problem, and thus the resolution time is likely to be prohibitive when the number of servers and flows increases. Despite this, in Section 4, an Integer Lincal Programming ILP model is presented and used to validate the results of the heuristics proposed. In fact, in Section 6, a small system with only a few flows is solved first using CPLEX optimization tool ([[https://www.ibm.com/docs/en/icos/20.1.0](https://www.ibm.com/docs/en/icos/20.1.0)]([https://www.ibm.com/docs/en/icos/20.1.0](https://www.ibm.com/docs/en/icos/20.1.0)), accessed on 15 February 2022), and the solutions are compared to the results obtained with the heuristic methods proposed in Section 5.