esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methodology
### License Plate Character Recognition
In addition, it is noted that the convergence time is also improved by using the concept of deep layers aggregation. Therefore, we propose an improved CNN at the lines of AlexNet, as shown in Figure 13 and parametric detail is discussed in Table 2. In improved CNN, the feature of convolution layer 4 and 5 are merged and one extra max-pooling layer is also introduced to match the features dimensions of both convolution layers. Figure 11: Proposed architecture for multinational vehicles license plate character recognition. Figure 12: Time efficiency of five well known convolutional neural networks (CNNs). The next most important step is to choose the classifier that accepts the feature vector from CNN feature learning module and generates output labels. In [46; 47; 48], the authors claimed that the SVM is a strong and fast classifier for real-time classification applications and great attention has been paid to the fusion of neural networks and SVM [49; 50]. That is why the same is used in our proposed system. For the support vector machine algorithm, kernel and hinge loss function is used as described in (16) and (17) [51]. \[G(xj\,\ xk)=xj^{\prime}xk \tag{16}\]  
where \(G(xj,xk)\) is element (\(j,k\)) of the Gram matrix, where \(xj\) and \(xk\) are \(p\)-dimensional vectors representing observations \(j\) and \(k\) in X.  
\[\ell[y,f(x)]=max[0,1-yf(x)] \tag{17}\]  
where \(f(x)=x\beta+b\), \(\beta\) is a vector of \(p\) coefficients, \(x\) is an observation from \(p\) predictor variables and \(b\) is the scalar bias. \begin{table}
\begin{tabular}{c c c c c} \hline
**Layers** & \multicolumn{3}{c}{**Configuration**} \\ \hline Input & FM: 1 & OS: \(227\times 227\times 3\) & KS: - & S: - \\ \hline Convolution1 & FM: 96 & OS: \(55\times 55\times 96\) & KS: \(11\times 11\) & S: 4 \\ Max Pooling1 & FM: 96 & OS: \(27\times 27\times 96\) & KS: \(3\times 3\) & S: 2 \\ Convolution2 & FM: 256 & OS: \(27\times 27\times 256\) & KS: \(5\times 5\) & S: 1 \\ Max Pooling2 & FM: 256 & OS: \(13\times 13\times 256\) & KS: \(3\times 3\) & S: 2 \\ Convolution3 & FM: 384 & OS: \(13\times 13\times 384\) & KS: \(3\times 3\) & S: 1 \\ Convolution4 & FM: 384 & OS: \(13\times 13\times 384\) & KS: \(3\times 3\) & S: 1 \\ Max Pooling4 & FM: 384 & OS: \(6\times 6\times 384\) & KS: \(3\times 3\) & S: 2 \\ Convolution5 & FM: 256 & OS: \(13\times 13\times 256\) & KS: \(3\times 3\) & S: 1 \\ [PERSON] & FM: 256 & OS:\(6\times 6\times 256\) & KS: \(3\times 3\) & S: 2 \\ \hline Concatenation & OS: \(6\times 6\times 640\) & Layer4 \(\copyright\) Layer5 & \\ \hline FC6 & OS: \(1\times 1\times 4096\) & & \\ \hline FC7 & OS: \(1\times 1\times 4096\) & & \\ \hline FC8 & OS: \(1\times 1\times 3\) & & \\ \hline \end{tabular}
\end{table} 
Table 2: The parametric detail of improved CNN. Figure 13: The improved CNN network structure.
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 3 Case Study on QKD in P2 MP Topology
### Time-Bin-Encoded Discrete-Variable (DV)-QKD with GPON
Since Raman backscattering from the DS GPON signal was the primary noise source for the QKD signal, the launched power of the DS GPON signal was adjusted to ensure a received power of \(-25\) dBm (i.e., receiver sensitivity level). Without a feeder fiber, where the Raman scattering effects were minimal, the QKD operation was feasible for \(N\leq 32\), as shown in Figure 4b. These experimental results were obtained for an OAN consisting solely of drop fiber with a 1 km reach. Figure 4b illustrates a significant decrease in the secret key rate with increasing \(N\), primarily due to splitting loss. This suggests that the TDM scheme was unsuitable for QKD signal distribution. The effects of Raman scattering from the DS GPON signal were further analyzed in Figure 4c. When \(N\) increased from 2 to 4, the secret key rate decreased from 3.0 kbps to 1.2 kbps in the absence of a feeder fiber. When \(N\) is fixed to 2, a 13.4 km feeder fiber, inducing 2.6-dB loss, resulted in a secret key rate of 1.24 kbps. The 2.68-dB fiber loss was lower than the increased splitting loss of 3.6 dB observed when the splitting ratio increased from 2 to 4, potentially due to Raman backscattering from the DS GPON signal. For \(N=16\), even a 0.6 km feeder fiber induced effects comparable to those observed when doubling \(N\) without a feeder fiber. In this scenario, the launched power of the DS GPON signal needed to be increased to \(-3.4\) dBm, leading to a higher Raman backscattering noise. Since DV-QKD is highly sensitive to Raman scattering, it requires a large wavelength separation from PON signals. Consequently, DV-QKD cannot be seamlessly integrated with legacy PON systems operating in the S- or C-band. Although higher speed PONs (e.g., 25G- or 50G-class PONs) would operate only in the O-band to mitigate CD penalties, the US  
Figure 4: (**a**) Configuration of a QKD-integrated GPON (BSF: band-stop filter). (**b**) Secret key rate as a function of the number of ONUs without a feeder fiber. (**c**) Required feeder fiber length to achieve the same secret key rate as that obtained when increasing the number of ONUs from \(N\) to \(2N\) in the absence of a feeder fiber. (**b**,**c**) are plotted based on the data in [9]. DV-QKD remains incompatible with TDM-PONs due to its susceptibility to high splitting loss. The increased splitting loss necessitates a higher launched power for the PON signal, further exacerbating the Raman scattering effects on DV-QKD. Additionally, crosstalk from time-interleaved QKD signals transmitted by different ONUs presents another major challenge to seamless QKD integration within PON systems.
## 2 Methods
### Recruitment and Data Collection  
Investigators from the Swinburne University of Technology, in collaboration with a local non-government organization, the Organization for Rural Community Development (ORCD) in the Narail district of Bangladesh, recruited participants and collected data in 2021. Data were collected through face-to-face interviews. The ORCD investigators and trained data collectors communicated with the potential participants over the telephone or by direct contact. Upon establishment of contact with potential participants, they were assessed for inclusion and exclusion. The inclusion criteria were the following: (i) clinical blood pressure more than, or equal to, 130/80 mm Hg for people who were not taking medication, (ii) blood pressure < 130/80 mm Hg but using anti-hypertensive medication for a minimum of six weeks, and (iii) living in the Banshgram Union only. The exclusion criteria were the following: (i) aged >75 years of age, (ii) pregnant, (iii) advanced CVDs or had any serious condition that restricted their participation in the study. The study location, the source population's demographic characteristics, and the cluster RCT have been described in detail [51; 52].
## 5 Discussion
### Organic Matter Enrichment Model  
The enrichment of organic matter in shale is not controlled by a single factor but by multiple elements such as paleo-water depth, paleo-climate, and paleo-redox conditions. Higher paleoproductivity can provide abundant sources of organic matter, while anoxic depositional environments can offer favorable preservation conditions. During the deposition of the Qiongzhusi 1 submember, the southern Sichuan Basin was characterized by predominantly clayey mud shale and black shale. This period coincided with a rapid rise in sea levels, resulting in relatively deep depositional water bodies with low terrigenous  
Figure 11: Line chart of biological barium content in shale of the Qiongzhusi formation in the southern Sichuan Basin. Caption: Q\({}_{2}\); Qiong 2 member, Q\({}_{1}\)\({}^{2}\): Qiong 1-2 submember, Q\({}_{1}\)\({}^{1}\): Qiong 1-1 submember.  
input, tranquil water conditions, weak hydrodynamic action, and abundant nutrients. The values of biogenic barium and TOC were relatively high, favoring high primary paleo-productivity levels. Concurrently, the paleo-redox conditions were anoxic and sulfidic, gradually creating favorable conditions for organic matter preservation, marking a phase of increasing organic matter enrichment. During the deposition of the Qiongzhusi 1-2 submember, the southern Sichuan Basin was characterized by the predominant deposition of black shale, siltstone, and sandy shale in both deep and shallow water continental shelf environments. This period coincided with the early stage of sea-level regression, resulting in shallower depositional water bodies compared to the deposition of the Qiongzhusi 1-1 submember. Terrigenous input was lower with finer sediment grains relative to the Qiongzhusi 1-1 submember, yet paleoproductivity levels remained high. The depositional environment was predominantly a continental margin, representing a key stage of organic matter enrichment. During the deposition of the Qiong 2 member, as sea levels gradually decreased, the evolution shifted from deep to shallow water continental shelf environments, characterized mainly by the deposition of shallow water siltstone and sandy shale. In combination with oxidizing conditions, paleoproductivity levels were relatively lower compared to the Qiongzhusi 1 submember, resulting in a decrease in the degree of organic matter enrichment. This could be attributed to slow oxidation of organic matter during sedimentation or an increase in water column circulation and terrigenous sediment supply, diluting the enrichment of organic matter in both seawater and sediments (Figure 12).  
Figure 12: Sedimentary evolution model of the Qiongzhusi formation in the southern Sichuan Basin.
###### Abstract  
With the advancement of space exploration and optical communication toward deep space, the high-precision evaluation and image stabilization of space optical payloads under micro-vibration have become increasingly critical. To address these challenges and ensure sub-micro-radian pointing accuracy for high-precision space optical payloads (HPSOPs), this paper proposes a high-precision micro-vibration testing scheme and a two-stage image stabilization system. The micro-vibration testing scheme is based on an automated quasi-zero stiffness suspension device (AQZSSD), which enhances testing sensitivity and environmental disturbance resistance, ensuring the accuracy of the results. The two-stage image stabilization system integrates three bipod vibration isolation legs (BVILs) and a decoupled fast steering mirror (FSM), extending control bandwidth and achieving comprehensive vibration suppression. Micro-vibration testing and image stabilization experiments were conducted under disturbances from multiple vibration sources. Experimental results demonstrate that the AQZSSD introduces disturbances below 0.4 Hz, confirming its quasi-zero stiffness characteristics in alignment with theoretical predictions. Furthermore, the line-of-sight (LOS) jitter root mean square (RMS) value is reduced from 1.253 \(\upmu\)rad to 0.276 \(\upmu\)rad, achieving sub-micro-radian stability. Additionally, due to the coupling effect of the micro-vibration response, the collaborative testing results were found to be lower than the linear superposition of individual sources. This work offers critical theoretical and technical support for the development of HPSOPs, with potential applications in future space missions and advanced optical technologies.  
control moment gyro; cryocoolers; micro-vibration test; high-precision space optical payload; two-stage image stabilization +
Footnote †: journal: Applied Sciences
# Research on Dynamic Modeling and Vibration Characterization of Integrated Bearings
## 2 Integrated Bearing Dynamic Performance Analysis
### Influence of Structural Parameters on Vibration in Integrated Bearings  
The structural parameters of integrated bearingsings significantly influence their vibration characteristics. To investigate the specific effects of these parameters, this section examines the impact of the curvature radius coefficients of the inner and outer raceways, as well as the cage pocket clearance, on the vibration characteristics of integrated bearings. The analysis is conducted at a spindle speed of 50,000 rpm, with a radial force of 200 N in the Y-direction and no radial force in the Z-direction or axial force in the X-direction. The results focus on the radial vibration acceleration level and vibration velocity at the center of mass of the bearing spindle to describe the overall vibration levels of the bearing.
## 3 Ibwo-Lssvr Online Model  
### Lssvr  
To address the challenge of hyperplane parameter selection, which often results in an unwarranted expansion of the solution space within SVR, the Least Squares Method is harnessed for optimization. The optimization criterion centers around the quadratic term of the error factor, thereby transforming inequality constraints into an equivalent set of equation constraints. Notably, the resolution of the optimization problem is reformulated as the solution to a system of linear equations, a transformation facilitated by the Karush-Kuhn-Tucker (KKT) condition. This adaptation renders the method particularly suitable for the precise fitting of limited sample data. The optimization objective function is expressed in Equation (5).  
\[\begin{split}\min\J(\omega,\xi)=\frac{1}{2}\|\omega\|^{2}+c \sum_{i=1}^{m}\xi_{i}^{2}\\ s.t.\,y_{i}=\omega^{\mathrm{T}}\cdot\varphi(x_{i})+b+\xi\end{split} \tag{5}\]  
where: \(x_{i}\) is the sample vector; \(c\) is the penalty factor; \(x\) is the slack variable; \(\omega\) is the normal vector; \(b\) is the bias vector. By introducing the _Lagrange_ multiplier method, the transformed optimization problem is shown in Equation (6):  
\[L(\omega,b,e,\alpha)=J(\omega,b,e)+\sum_{i=1}^{m}\alpha_{i}\Big{(}y_{i}-\Big{(} \omega^{\mathrm{T}}(x_{i})+b\Big{)}\Big{)} \tag{6}\]  
where \(\alpha\) is the _Lagrange_ operator; \(e\) and \(\pi\) are irrational numbers.  
The optimal solution is obtained through the KKT condition, and the regression function of LSSVR can be obtained by solving using the least squares method as shown in Equation (7):  
\[f(x)=\sum_{i=1}^{m}\kappa\big{(}x_{i},x_{j}\big{)}\alpha_{i}+b \tag{7}\]where \(k\left(x,x_{i}\right)\) is the kernel function. In this paper, we choose the radial basis kernel function (\(RBF\)), which has good generalization ability and significant advantage when dealing with nonlinear problems, as shown in Equation (8):  
\[\kappa(x_{i},x_{j})=\exp(-\frac{\parallel x_{i}-x_{j}\parallel^{2}}{2\sigma^{2}}) \tag{8}\]  
where \(\sigma^{2}\) is the width parameter of the \(RBF\). The selection of \(\sigma\) and \(c\) directly affects the performance of LSSVR. Therefore, to improve the performance of SVR, the optimal solution search for the values of \(\sigma\) and \(c\) is required. Meta-heuristic algorithms are widely used for selecting optimal solutions for parameter optimization due to their superior ability to find optimal solutions.
# Single vs. Multi-Label: The Issues, Challenges and Insights of Contemporary Classification Schemes
## 2 Overview of State-of-the-Art Approaches  
This section encompasses a brief overview of state-of-the-art approaches which provides a fair idea about the current trends in the research articles' classification community as every scientific study is dependent upon the study of erudite peers in the field. The document classification community is focused on proposing innovative ideas for document classification as the number of documents in digital form is increasing. Text classification is a very old dilemma. As early as the 1800s, studies were completed on verifying the authorship of the works of [PERSON] [20]. When the first document classification approach was proposed, thereafter the process started to emerge into different branches. As a result, the community began the classification of a specific type of document, for instance: (1) magazines, (2) newspapers, [12; 21; 22; 23] and hierarchical classification [24]. Since then, the document classification community has diverted its attention specifically to the research papers' classification due to subsequent inventions in scientific literature.  
The contemporary approaches that address the issue of research articles' classification broadly rely on two categories: (1) a content-based approach; (2) a metadata-based approach, as described subsequently. It is also worth mentioning that the content-based approaches usually outperform the metadata-based approaches due to the rich text features, etc. However, the limitation is in terms of open-access articles only. Although many publishers and journals offer an open-access facility nowadays, a huge number of journals are still non-open-access. So, this is where the content-based schemes fail and the metadata-based approaches are used, because they only rely on the available metadata of the article such as the title, abstract, keywords, references, etc. Moreover, metadata-based approaches have proven comparable to the content-based approaches in both single label and multi-label approaches.
## 4 Experimental Setup
### Datasets  
**IEMOCAP**[42]: A multimodal dataset of two-speaker conversations. There are six emotion labels, including neutral, happiness, sadness, anger, frustration, and excitement. Since there is no validation set for this dataset, we follow the method of [PERSON] et al. [43], who use the last 20 conversations in the training set for validation.  
**MELD**[44]: A corpus of multi-speaker conversations collected from the TV show Friends. The emotion category contains seven types: neutral, happiness, surprise, sadness, anger, disgust, and fear.  
**DailyDialog**[45]: A multi-turn dialogue dataset written by humans, covering a variety of topics in daily life, with emotional labels including neutral, happiness, surprise, sadness, anger, disgust, and fear. Since it has no speaker information, we consider the utterance turns as the speaker turns by default.  
**EmoryNLP**[46]: Like MELD, it was collected from the script of the TV show Friends but differs in the selection of scenes and emotion labels. The emotion labels of this dataset include neutral, sad, mad, scared, powerful, peaceful, and joyful.  
To verify the effectiveness of SETD-ERC, we evaluate our model on the corpora listed above. We only focus on the textual modal information of the corpus in our work. The statistics of the corpus are listed in Table 2.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 4 Diamond-Based Electrodes
### Multi-Component Analysis: Dopamine, Adrenaline and Noradrenaline  
A similar setting to that previously described, that is, comprised of a boron-doped microelectrode coupled with capillary electrophoresis, was reported to simultaneously detect three main neurotransmitters: dopamine, adrenaline, and noradrenaline [46]. The system exhibited high separation efficiency for these three catecholamines and a good detection performance, with high reproducibility for the current response over ten repetitive injections of mixtures of the catecholamines, at 50 \(\upmu\)M each, and detection limits as low as 20 nM.  
Towards In Vivo Applications: Noradrenaline and Adenosine Detection in Excised Tissue 4.5.1. Noradrenaline  
In an attempt to bring sensors closer to real-life applications in living organisms, a diamond-coated carbon fibre microsensor was developed and tested regarding noradrenalin detection in an in vitro setting with excised rat mesenteric tissue. Following electrical stimulation of the mesenterium (60 pulses at 20 Hz for a total time of 3 s, repeating the stimulation after 10 min), noradrenaline was released from sympathetic nerves innervating the mesenteric artery and was measured as an electric response, presenting a peak of _c.a._ 10 pA. The response exhibited good reproducibility and recyclability over time [49; 50].
## 3 Experimental Results
### Evaluation of Synchronization Performance
Figure 8: Comparison of the time and frequency domains for three sensors positioned on the top storey, as depicted in Figure 4. (**a**) the time history of \(x\)-axis accelerations recorded over a duration of 15 min. (**b**) the linear magnitude spectrum computed using the Fast Fourier Transform (FFT) of the \(x\)-axis accelerations. (**c**) the spectrogram of the \(x\)-axis accelerations. A meticulous examination of the initial two peaks in the acceleration responses reveals a discernible millisecond drift between each peak. The primary objective of this research is to optimize the alignment path using DTW to enhance the quality of the time series data, as discussed in Section 2.3.5. In Figure 10, we present the results obtained from comparing the preprocessed acceleration and the aligned acceleration using DTW for three low-cost sensors. Figure 10a illustrates the time history of the preprocessed acceleration along the \(x\)-axis, while Figure 10b illustrates the time history of the aligned acceleration obtained using DTW.
# Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading
###### Abstract
To address these issues, we propose Light Graph Convolutional Recommendation Algorithm Based on Hybrid Spreading (LGCNHS). The algorithm has an optimized embeddings layer based on LightGCN, effectively learning latent embedding representations of users and items through user features and item features. These embeddings are transformed into an allocation weight matrix, which is further incorporated into the second round of the hybrid spreading process. This enables LGCNHS to not only utilize user-item interaction data but also comprehensively consider the semantics of user features and item features. By flexibly adjusting the allocation weight matrix based on users' historical behaviors and interest preferences, LGCNHS generates more accurate and personalized recommendations for each user. This innovation effectively addresses the shortcomings of traditional spreading-based recommendation algorithms in handling personalized user features and latent item information, thereby improving recommendation accuracy and diversity. More importantly, by introducing the allocation weight matrix into the second round of the hybrid spreading process, LGCNHS captures more complex potential relationships between users and items, fully considering user-item interaction data. The algorithm proposed in this paper is compared with four other related algorithms in a comparative study, and the effectiveness of the algorithm is verified by quantitative analysis using two real datasets, which illustrates that the combination of spreading-based recommendation algorithms and deep learning has certain potential and provides new ideas and directions for future research.