esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
(1) Aim: To evaluate the failure reason and surface modifications of a retrieved knee prosthesis; (2) Methods: Rapid confocal Raman spectroscopy screening was applied on the surface of a retrieved knee prosthesis (both titanium and UHMWPE (Ultra-high-molecular-weight polyethylene) component) in order to determine predominate implant damage, along with the chemical composition of synovial fluid accumulated in the stem of the tibial component during the implantation period. Correlations between the medical records of the patient (clinical and radiographic information) and spectroscopic results are pointed out, the parameters being interpreted in the context of proper functioning and life span of knee prosthesis; (3) Results: The metallic tibial component does not show any modification during the implantation period, as demonstrated by the well preserved titanium component with signature of anatase phase detected in retrieved component, compared to unused piece. The spectral features of polymeric component (ultrahigh molecular weight polyethylene, UHMWPE) revealed structural modification in crystallinity and amorphous phase accompanied by insignificant level of oxidation (OI \(<\) 1). Scratching, pitting and persistent organic spots as a result of mechanical and chemical deterioration were noticed on the surface of retrieved insert. Acrylic cement deterioration was also noticed. Synovial fluid collected from the stem of the tibial component demonstrated a lipidomic profile; (4) Conclusions: Combining the clinical evidences with confocal Raman spectroscopy allowed a rapid screening with high sensitivity and nondestructive measurements in the case of failure in TKA (total knee arthroplasty). The third body wear and lipidomic profile of synovial fluid are cumulative factors of failure in this case, resulting in an osteolysis that finally leads to an aseptic loosening.  
knee prosthesis failure; arthroplasty; confocal raman spectroscopy 2020, 10, 5343; doi:10.3390/app10155343
# Fault Diagnosis of Roller Bearings Based on a Wavelet Neural Network and Manifold Learning
###### Abstract  
In order to improve the accuracy of the fault diagnosis of roller bearings, this paper proposes a kind of fault diagnosis algorithm based on manifold learning combined with a wavelet neural network. First, a high-dimensional feature signal set is obtained using a conventional feature extraction algorithm; second, an improved Laplacian characteristic mapping algorithm is proposed to reduce the dimensions of the characteristics and obtain an effective characteristic signal. Finally, the processed characteristic signal is inputted into the constructed wavelet neural network whose output is the types of fault. In the actual experiment of recognizing data sets on roller bearing failures, the validity and accuracy of the method for diagnosing faults was verified.  
roller bearing; manifold learning; wavelet neural network; fault diagnosis Article
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 4 Case Study  
### Study Area and LPR Data Analysis  
Parameter calibration belongs to the research on the micro-characteristics of traffic flow, which requires high data accuracy. Therefore, the accuracy of traffic survey data significantly impacts the results. Based on survey experience, on-site manual surveys often result in large errors and many uncertainties, and the reproducibility of the data is weak, which can easily lead to the failure of the final calibration results. Therefore, more accurate survey methods should be used for traffic flow characteristic surveys.  
This section includes a case study to demonstrate the model and solution suggested in this paper. The study was conducted in the central area of Yuyao City, China, as shown on the map in Figure 2. The study area covered an area of approximately 40 m\({}^{2}\) and consisted of 31 LPR detectors. The data analyzed in this case study included almost 80,000 records from June to July 2018. Table 3 illustrates the LPR data used in the study. The case study was performed using Matlab 2021b running on a desktop with an Intel(R) Core (TM) i5-11400F CPU @ 2.60 Hz, 2.59 GHz, and 16.00 G RAM.  
The LPR data were used to calculate the traffic flows, which were calculated by aggregating the same origins and destinations within the study area.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 3 Experiments and Results
### Environment  
The environment for the experiment was set up as in Figure 7.  
A Windows machine was set up as the data collector. The data collector implemented a custom application written in Python 3.11. The parser comprised several functions to parse various formats of source files including.pcap, csv, syslog, and evtx. For this experiment, the CSV file of features and labels was read line by line and a Python dictionary was constructed for each flow record. Each flow dictionary was then immediately streamed using Apache Kafka.  
A Linux server machine was set up in the public cloud. The server was equipped with 64 GB of RAM, 2 TB of hard drive space, and eight processors with two threads per core. On this server, three virtual machines were set up. The first machine implemented the correlation and detection component of the experiments. A Python script was developed as a Kafka consumer to read live events as they arrived at the Kafka server. The events then were passed on to the analytics application, running inside the same virtual machine. The analytics application then performed the correlation. At the end of each correlation iteration, the raw events and the completed analytics were sent to a MongoDB database which was set up in a separate virtual machine. The third virtual machine was running a snort IDS. This was used to process the same original packet capture file to check for intrusions, for comparison with the performance of the developed detector. The table (Table 6 below summarizes the environment setup.  
Figure 7: Experimental setup.
## 3 Results and Discussion  
### Optical and Morphological Analysis  
The images of wheat flour and coffee silverskin (2.5-20 wt.%) and SEM images of coffee silverskin aqueous extract composite films are presented in Figures 1 and 2. The images indicate that while the pure wheat flour film is colorless and transparent, the composite films with coffee silverskin have evenly distributed filler particles and are brown in color. Compared with pure wheat flour, all the films exhibited lower light transmission, making them suitable for packaging photo-sensitive materials [31]. The coffee silverskin particles exhibited varying sizes and were evenly distributed within the matrix. Nevertheless, in certain areas, some agglomeration was also observable, as indicated by a larger particle size when the concentration of CS was increased. This clustering in higher concentrations imparted roughness on the surface of the wheat flour films (WFFs). The films with CS aqueous extracts exhibited a homogeneous and continuous structure, with no fragility or pores and cracks, owing to the glucose concentration in the matrix. As shown in the SEM images, glucose, glycerol and sorbitol as plasticizers in biofilms, yielded commensurate outcomes, with more uniform surface [19; 32].  
Figure 1: Images of biocompposite wheat flour films with coffee silverskin particles.
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### Blockchain
#### 2.1.4 Cryptography Technology  
Blockchain can essentially be understood as a distributed ledger or a decentralized database [52]. As its unique technological advantages continue to drive research and adoption across various industries, blockchain primarily relies on cryptographic techniques to manage and secure vast amounts of data [53].  
Cryptography employs advanced mathematical principles and algorithms to ensure the secure storage and transmission of data [54], allowing only authorized recipients to access and process the information [55]. Among its core components, encryption plays a crucial role by encoding data in a manner that renders it unintelligible to unauthorized parties, thereby protecting it from potential attacks and unauthorized modifications.  
In blockchain systems, encryption techniques are generally classified into two main categories: symmetric encryption and asymmetric encryption. Symmetric encryption utilizes a single key for both encryption and decryption, whereas asymmetric encryption employs a pair of keys--a public key for encryption and a private key for decryption--enhancing security and data integrity during transmission. These cryptographic techniques serve as the foundation of blockchain security, safeguarding data privacy and ensuring the reliability of transactions [56]. The comparison table between symmetric and asymmetric encryption is shown in Table 7.
## 1 Introduction
SSDA comprises three modules: the spectral-spatial module for learning robust features, the domain attention mechanism to amplify relevant features while mitigating the influence of irrelevant ones, and the multifaceted loss module, implemented to tackle domain adaptation challenges. Ref. [39] presents a novel network that synergistically combines residual modules with attention mechanisms. Separately selecting features from each band by weight-adaptive learning and enhancing the importance of the center pixel are executed in both networks. Furthermore, considering the problem that available training samples for training DL models are generally very limited, [PERSON] et al. [40] propose a depth-wise separable fully convolutional residual network (DFRes) combined with convolutional conditional random fields and region growing to tackle the question of relying on limited training samples, where residual learning and depth-wise separable convolution effectively addresses the challenges of gradient vanishing, overtraining, and high memory requirements. Consequently, augmenting the capacity for feature extraction and bolstering the classification accuracy of limited training samples have emerged as pressing issues in the realm of HSI classification, particularly within the framework of deep learning networks. In this paper, inspired by the hybrid 2D-3D CNN and feature fusion [41], we construct a hybrid depth-wise separable convolution and dual-branch feature fusion network (HDCDF) for classification. The proposed architecture contains two branches based on 2D-3D depth-wise separable convolutions, combined with an attention mechanism and hybrid residual blocks separately, which both capture joint spectral-spatial features. By fusing the extracted features of the two branches, we obtain further features and send them to the classifier to determine the final classification result. In addition, we evaluate the efficacy of the proposed HDCDF with a reduced number of training samples. The primary contributions of this study are as follows:  
1. We propose an innovative network architecture that leverages 2D-3D depth-wise separable convolution along with dual-branch feature fusion for HSI classification;
2. The proposed model incorporates 2D depth-wise separable convolution attention (2D-DCAttention) and hybrid residual blocks, which effectively improve the classification performance;
3. The classification performance of the HDCDF network is analyzed using limited training data, achieving state-of-the-art results across three public datasets.
## 2 Literature Review
### Voice Analysis for Emotion Detection in Neurodegenerative Diseases
The databases used for testing are Emo-DB, Savee [54], and RAVDESS [55], with a separate model built for each of the datasets. The reported average accuracies go as high as 96%, but these are tested only on data from the same dataset. The best reported performances were obtained using a mix of frequency and time domain features. In [56], using different approaches like Deep CNN combined with other ML techniques, the authors achieve a performance of 90.50% average accuracy for identifying emotions from the Emo-DB dataset, 66.90% for emotions from the Savee dataset, 76.60% on the IEMOCAP, and 73.50% on the RAVDESS. Classifiers are built only on one dataset and then tested on data from the same dataset. In [57], different approaches are presented for classifying emotions in different datasets. For RAVDESS, the reported average accuracy is 71.61%, believed to be the highest achieved for the reference dataset at the time of writing (2020). The same paper reports achieving an accuracy of 86.1% for Emo-DB and 64.30% for IEMOCAP. It is worth mentioning that the studied literature that performed testing on multiple datasets, did not get the best results on each dataset using the same feature selection and classifier building approach. The authors of [58] report the best average accuracy of 72.2% for classifying data from RAVDESS and 97.1% for data from Tess [59] using CNNs, outperforming their own models built using long short-term memory models (LSTMs) or GRU (advanced version or recurrent neural networks), SVMs, or RF. The work presented in [60] achieves high classification accuracy by using divided patches from images that contain coordinate information concatenation as part of a vision transformer-based classification model. A table that centralizes results presented in other works in the area of SER algorithms is presented in Table 1.  
The approach selected for the presented emotion recognition algorithm is based on a CNN built using multiple datasets. For this reason, a straight comparison of performances may not be feasible; as seen from the work presented above, the same approaches performed differently on different datasets. Regarding emotion recognition, the interest of this work is to build a speaker-independent emotion recognition algorithm that performs well on new data that can be integrated into the NeuroPredict platform in an easy-to-use manner for the intended users: medical personnel, formal and informal caregivers, and patients.
# Distributed Orbit Determination for Global Navigation Satellite System with Inter-Satellite Link
## 4 Whole-Constellation Centralized Extended Kalman Filter
Finally, the method of WCCEKF that combines the satellite-to-satellite measurements and satellite-to-GAS measurements can be expressed as [29]:  
\[\overline{\mathbf{P}}_{k}=\mathbf{\Phi}_{k-1}\hat{P}_{k-1}\mathbf{\Phi}_{k-1}^{\mathrm{ T}}+\mathbf{G}_{k-1}\mathbf{Q}_{k-1}\mathbf{G}_{k-1}^{\mathrm{T}} \tag{35}\]  
\[\mathbf{K}_{k}=\overline{\mathbf{P}}_{k}\mathbf{H}_{k}^{\mathrm{T}}\left(\mathbf{H}_{k}^{ \mathrm{T}}\overline{\mathbf{P}}_{k}\mathbf{H}_{k}+R_{k}\right)^{-1} \tag{36}\]  
\[\delta\hat{\xi}_{k}=\mathbf{K}_{k}\mathbf{z}_{k} \tag{37}\]  
\[\hat{\mathbf{X}}_{k}=\overline{\mathbf{X}}_{k}+\delta\hat{\xi}_{k} \tag{38}\]  
\[\hat{P}_{k}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})\overline{\mathbf{P}}_{k} \tag{39}\]  
The dimension of state vector \(\mathbf{X}_{k}\) is  
\[N_{W}=6n+\sum_{i=1}^{n}D^{i} \tag{40}\]  
where \(D^{i}\) is the number of dynamic parameters for the _ith_ satellite. In the method of WCCEKF, each satellite is correlated with the other satellites through the state vector covariance matrix which has the dimension of \(N_{W}\times N_{W}\). Furthermore, matrix \((\mathbf{H}_{k}^{T}\overline{\mathbf{P}}_{k}\mathbf{H}_{k}+R_{k})\) with the dimension of \(m\times m\) (\(m\) is the dimension of the measurement vector) needs to be inversed during the process, and a huge computation amount is expected. The computation amount for a process of WCCEKF is:  
\[4N_{W}^{2}(N_{W}^{2}-1)+(N_{W}-1)N_{W}(N_{W}+1)/6+(2N_{W}^{2}+7N_{W}+1)\times m \tag{41}\]  
If the WCCEKF algorithm is employed, the on-board computer of the main satellite would need to process all the ISL measurement and satellite-to-GAS measurement data to finish the task of orbit determination and navigation message generation for satellites in the constellation. Due to the huge computation amount and great complexity of communication, WCCEKF is difficult to be implemented in a satellite constellation with limited on-board computation ability. In addition, the WCCEKF is also vulnerable. Once the main satellite and its backup satellite failed, the entire navigation constellation would stop working. To avoid the drawbacks in the WCCEKF method, many researches nowadays are focusing on developing a distributed data processing algorithm.
## 1 Introduction
### Our Contributions and Paper Organization  
Our attempt in this paper is to consider the communication channel errors and the fusion rule as a whole in a decode-then-fuse sensor network. The data uncertainty at the FC due to channel errors, modulation and reception mode are characterized by the BER. The main contributions of this work are summarized as follows.  
* An optimal LRT fusion rule with channel errors characterized by BER (LRT-BER) in a decode-then-fuse sensor network is derived. The relation between the decode-then-fuse fusion strategy and the physical layer specifications (modulation, reception mode and channel statistic characteristics of the flat fading communication channels between sensors and the FC) is established.
* The detection performance analysis of the decode-then-fuse sensor network in the presence of channel errors is presented.
* For identical local detection performance indices, the closed-from solution of the system detection performance and threshold choice method are derived by randomized test. For non-identical local detection performance indices, the central limit theorem (CLT) approximation is utilized to perform the performance analysis.  
In addition, the detection performance of the proposed fusion rule is compared with the existing fusion rules, e.g., MRC, EGC, two-stage CV and LRT-CS. The simulation results show that better detection performance can be achieved by using the LRT-BER fusion rule.  
The rest of this paper is organized as follows. Section 2 gives the detection model and the local decision rule at each sensor. Several previous fusion statistics are reviewed in Section 3. The LRT-BER fusion rule is presented in Section 4. The performance analysis of the proposed fusion rule based on the Neyman-Pearson (N-P) criterion is studied in Section 5. Numerical results are organized in Section 6, and some conclusions are drawn in Section 7.
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 4 System of Detections
### Clustering and Community Based Detection
If the system can be effectively clustered, i.e., all the satellites are closely related and assumed to be spoofed, it would have a low modularity \(Q\). As such the detection problem can be formulated as:  
\[\lambda_{\mathrm{mod}}\underset{H_{0}}{\gtrless}Q\,, \tag{36}\]  
where \(\lambda_{\mathrm{mod}}\) is the detection threshold for the modularity. If the values are above the threshold, the system cannot be clustered effectively. Hence, the system is diverse and the null-hypothesis \(H_{0}\) of no spoofing is accepted. Otherwise, the alternative hypothesis \(H_{1}\) is accepted, where the system is spoofed. Clustering forms an intuitive approach to spatial spoofing detection, hence, it forms part of the new detection metrics proposed by this article.
###### Abstract  
Underwater wireless sensor networks (UWSNs) have become a new hot research area. However, due to the work dynamics and harsh ocean environment, how to obtain an UWSN with the best systematic performance while deploying as few sensor nodes as possible and setting up self-adaptive networking is an urgent problem that needs to be solved. Consequently, sensor deployment, networking, and performance calculation of UWSNs are challenging issues, hence the study in this paper centers on this topic and three relevant methods and models are put forward. Firstly, the normal body-centered cubic lattice to cross body-centered cubic lattice (CBCL) has been improved, and a deployment process and topology generation method are built. Then most importantly, a cross deployment networking method (CDNM) for UWSNs suitable for the underwater environment is proposed. Furthermore, a systematic quar-performance calculation model (SQPCM) is proposed from an integrated perspective, in which the systematic performance of a UWSN includes coverage, connectivity, durability and rapid-reactivity. Besides, measurement models are established based on the relationship between systematic performance and influencing parameters. Finally, the influencing parameters are divided into three types, namely, constraint parameters, device performance and networking parameters. Based on these, a networking parameters adjustment method (NPAM) for optimized systematic performance of UWSNs has been presented. The simulation results demonstrate that the approach proposed in this paper is feasible and efficient in networking and performance calculation of UWSNs.  
UWSN; nodes deployment; networking; systematic performance +
Footnote †: journal: Sensors
## 4 Results and Discussion
### Radiation Patterns Obtained with Placement of Cylindrical Rods  
The simulated radiation patterns thus obtained after keeping cylindrical rods for 10 GHz, 11 GHz, 12 GHz, 13 GHz, 14 GHz and 15 GHz are shown in Figure 6a-f in various angles of rotations as of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\) for WR-75 waveguide. As observed, a relative decrease in the peak value is noted this might be due to the arrangement of cylindrical rods. Additionally, the main beam is deviated to the average elevation angle (\(\theta\)) of 0\({}^{\circ}\), 32\({}^{\circ}\), 48\({}^{\circ}\), 79\({}^{\circ}\) and 94\({}^{\circ}\) for a counter clockwise rotation angle (\(\psi\)) for WR-75 of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 75\({}^{\circ}\) and 90\({}^{\circ}\), respectively.  
Figure 5: (**a–f**) Simulated steered radiated beam for circular rings pattern in 10 GHz, 11 GHz, 12 GHz, 13 GHz, 14 GHz and 15 GHz frequencies.  
In order to verify the concept, we have fabricated the proposed prototype through a Stratasys J750 3D printer using VERO CMYK material, whose permittivity (\(\epsilon_{r}\)) is 2.8 and loss tangent (tan\(\delta\)) is 0.124, as described in [23]. The beam deviation has been observed for five different rotation angles of the waveguide. To verify our proof of concept, we have considered two rotation angles namely 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\) in the measurement scenario whose radiation plots are shown in Figure 7. The radiation plots are well matched for the required 10 to 15 GHz frequencies range except at the corner frequencies. Especially at 10 and 15 GHz frequencies, there is a slight deviation in the peak values, as noticed in Figure 7a,f respectively, which might be due to the frequency limitation and alignment of WR-75 waveguide to the gap of parallel plates. However, these slight variations have no effect on the measured results for other frequencies of interest. During the experimental setup, the fixed gap is maintained between the two parallel plates through a 3D printed prototype. The WR-75 waveguide is maintained across the edge of parallel plates for fixed values of rotation angles through paper tapes. These experimental results are modeled and numerically solved through a Nearfield Systems Incorporated 2000 antenna measurement  
Figure 6: (**a**–**f**) Simulated steered radiated beam obtained after the placement of cylindrical rods in 10 GHz, 11 GHz, 12 GHz, 13 GHz, 14 GHz and 15 GHz frequencies.  
system by noting the Amplitude, in dB, versus Azimuth, in degrees. The measurement of the proposed prototype inserted between two parallel plates is performed in NSI-700S-50 spherical near-field measurement system at the Australian Antenna Measurement Facility. The experimental setup is shown in Figure 8.  
Figure 7: (**a–f**) Simulated and measured steered radiated beam obtained after rotation of feed waveguide in 10 GHz, 11 GHz, 12 GHz, 13 GHz, 14 GHz and 15 GHz frequencies.