esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Additively and subtractively manufactured resin-based crowns have emerged as a new option for pediatric patients. In this study, it was aimed to test the effect of the production technique on the marginal and internal gap values of resin-based crowns for primary teeth. Methods: A total of 30 crowns were produced with additive (3D printing; DLP and SLA) and subtractive (milling) technologies (\(n=10\)). The crowns were cemented on the resin dies. Three STL files were obtained from the scans of the die, crown, and crown cemented on the die. STL data of the die and crown were superimposed on the STL data of the crown cemented on the die, and gap volume calculations in the marginal and internal areas were performed in the software. The results were statistically analyzed with one-way ANOVA and Tukey HSD tests (\(\alpha=0.05\)). Results: The results showed that the production technique was significantly effective on the marginal and internal gap values. The highest marginal and internal gap value was observed in the milling group, followed by the SLA and DLP groups. Conclusion: The production technique affected the fit of the resin-based crowns for primary teeth. Compared to subtractive techniques, lower marginal and internal gap volumes were found with additive production techniques.  
Keywords:adaptation; CAD-CAM; digital light process; pediatric dentistry; primary tooth crown; stereolithography; triple-scan protocol +
Footnote †: journal: Applied sciences
# Multi-Scale Residual Depthwise Separable Convolution for Metro Passenger Flow Prediction
## 3 Experiments
### Baseline Models  
In this study, we assess the performance of the proposed MRDSCNN model by comparing it with the following baseline models.  
HA [7]: this is a simple baseline model that predicts future passenger flow based on historical average values.  
CNN [17]: this applies convolutional operations to extract spatial and temporal features from the passenger flow time series. By stacking multiple convolutional layers, the CNN can capture complex and hierarchical representations of the input sequence.  
GRU [16]: this has gating mechanisms that control the flow of information within the network, allowing it to selectively retain and update important information from past time steps.  
BiGRU [30]: this is a model extended from GRU to deal with sequence data by introducing a bidirectional loop structure. It effectively captures bidirectional dependencies in sequence data by combining two GRU modules, forward and backward.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Passenger Inflow** & **Passenger Outflow** & **AttBiGRU** \\  & **Prediction** & **Prediction** & **AttBiGRU** \\ \hline Kernel Size & 3 × 3 & 3 × 3 & - \\ Dilation rate & 3 & 3 & - \\ Residual Module1 & 32 filters & 32 filters & - \\ Residual Module2 & 64 filters & 64 filters & - \\ Batch Size & 64 & 64 & 64 \\ Activation Function & ReLu & ReLu & Linear \\ Number of Layers & - & - & 1 \\ Number of Neurons & - & - & 128 \\ Number of Neurons in FC1 & 80 & 80 & - \\ Number of Neurons in FC2 & 80 & 80 & - \\ Number of GCN Layers & 1 & 1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameters of MRDSCNN model.  
TCN [31]: this employs residual concatenation and causal convolutions to accelerate model convergence and improve modeling capabilities.  
ResLSTM [32]: this is a multi-branch deep learning architecture that combines the residual module, GCN, and attention LSTM.  
Conv-GCN [26]: this is a fusion of multi-graph GCN and 3D CNN, allowing it to effectively capture high-level spatiotemporal features among different patterns of passenger flow.
## 3 Results and Discussion
### Response Surface Methodology
#### 3.2.1. Thrust Force
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Sum of Squares** & **df** & **Mean Square** & **F-Value** &
\begin{tabular}{c} _p-Value_ \\ **Prob > F** \\ \end{tabular} & **Observation** \\ \hline Model & 447,346.21 & 11 & 40,667.83 & 98.51 & \textless{}0.0001 & Significant \\ Drill tip angle (\(X_{1}\)) & 4110.97 & 1 & 4110.97 & 9.96 & 0.0070 & Significant \\ Tooth bite (\(X_{2}\)) & 37,989.69 & 1 & 37,989.69 & 92.02 & \textless{}0.0001 & Significant \\ Drill type (\(X_{3}\)) & 168,825.11 & 1 & 168,825.11 & 408.95 & \textless{}0.0001 & Significant \\ \(X_{1}X_{2}\) & 1037.29 & 1 & 1037.29 & 2.51 & 0.135 & Not Significant \\ \(X_{1}X_{3}\) & 1005.84 & 1 & 1005.84 & 2.44 & 0.140 & Not Significant \\ \(X_{2}X_{3}\) & 26,335.85 & 1 & 26,335.85 & 63.79 & \textless{}0.0001 & Significant \\ \(X_{1}^{2}\) & 997.94 & 1 & 997.94 & 2.42 & 0.142 & Not Significant \\ \(X_{2}^{2}\) & 2508.90 & 1 & 2508.90 & 6.08 & 0.02 & Significant \\ \(X_{1}X_{2}X_{3}\) & 43.78 & 1 & 43.78 & 0.11 & 0.749 & Not Significant \\ \(X_{1}^{2}X_{3}\) & 3324.29 & 1 & 3324.29 & 8.05 & 0.0132 & Significant \\ \(X_{2}^{2}X_{3}\) & 4315.11 & 1 & 4315.11 & 10.45 & 0.006 & Significant \\ \hline R\({}^{2}\) & & & & 0.98 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results obtained during ANOVA analysis of the model selected to predict the trust force.
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 5 Power Profiling Model
### Analysis

Table 4: Performance of power profiling based on the daily load factor (\(l_{f}\)) and DTW similarity to the weekend load pattern set. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Measures** & \multicolumn{2}{c}{**Power Profiles**} \\  & **Workday Profile (WoLP)** & **Weekend Profile (WeLP)** \\ \hline Sensitivity/Recall (\%) & 81.82 & 65.38 \\ Precision (\%) & 85.71 & 58.62 \\ F-Score & 0.8372 & 0.6182 \\ Accuracy (\%) & 77.17 & 77.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of power profiling based on the daily load factor (\(l_{f}\)) and DTW similarity to the workday load pattern set. In comparison to workday load-patterns-based clustering, having a small set of weekend power load time series for training and setting WeLP is the main reason for the low accuracy in the second clustering model. In this work, the number of sample power load time series for the weekend used for training was almost half the number of sample time series for the workday. DTW similarity calculation based on a small set of symbols leads to poor performance in weekend load-patterns-based clustering. This problem can be addressed by incorporating more samples from additional datasets from a broader demographic of households.
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 3 AI and Ophthalmology: View of the Ensemble
### AI and Anterior Segment Diseases
#### 3.2.2 AI and Keratoconus  
AI for keratoconus identification is a modern field of research, with certain methods used throughout years to identify the disease, differentiating the normal from the fruste forms, and even grading the disease [35].  
[PERSON] et al. used DL to classify the presence or absence of keratoconus based on numerical computations of output data by using color-coded corneal maps on anterior segment swept-source OCT (AS-OCT). An AUC of 0.991 was generated for keratoconus detection and classification of disease grade in keratoconic patients [36].  
[PERSON] et al. researched corneal curvature and astigmatism in keratoconic patients after intracerebral ring implantation to foresee visual performance by means of ML [37].  
[PERSON] et al. analyzed corneal characteristics on the anterior and posterior surfaces, identified unique non-overlapping clusters by means of an unsupervised ML method, and performed a post hoc analysis for the corresponding clusters to predict the probability of needing further keratoplasty surgery [38].
## 3 Experimental Results and Discussion
### Fiber Laser Shielding  
The environmental air vibration and acoustics wave may affect both the fiber laser power and linewidth. In order to determine the optimum length of EDF and SMF, various length of two kinds of fibers were tested by trial and error, as described in Section 3.1. Many fiber connectors were used to connect discontinuous points of the fiber laser module. These fiber connectors may induce loss and reflection in the fiber laser cavity. Once the lengths of EDF and SMF had been determined, all the connection points were replaced by splicing method to reduce the optical loss and reflection effect. Thus, the laser power could be increased to 56.3 mW. In this measurement, all connection points are spliced to reduce the fiber-to-fiber interface reflection and connection loss. By adjusting the VOA near the FRM to obtain single longitude mode from multiple longitude modes, the optical power is attenuated from 56.3 mW to 9.3 mW. As shown in Figure 6a, to investigate the impact of air perturbation and acoustic vibration, we set up a transparent shielding box to partially un-shield or fully shield the  
Figure 4: (**a**) OSNR variation with different SMF lengths. (**b**) Electrical spectra of fiber laser show the single longitude mode and multi longitude modes conditions.  
Figure 5: (**a**) Laser linewidth variation for different lengths of SMF. (**b**) Laser line widths using Rayleigh backscattering methodology as SMF lengths of 120 m.  
fiber laser. The red arrow indicates the laser traveling direction along the fiber spool. As shown in Figure 6b, when the fiber laser is fully shielded, the linewidth is stable at 1.46 KHz after one min measurement. The output power is 9.3 \(\pm\) 0.3 mW with 3.3% power variation. In the unshielded case, the average measured laser linewidth is 4.97 KHz during one min measurement. The laser output power has larger variation and is unstable. The output power has larger variation so not easy to measure. Figure 7 shows that the frequency and linewidth are stable in 80 MHz and 1.46 KHz. respectively, for one minute when the fiber laser is fully shielded.
# Origin and Composition of Exosomes as Crucial Factors in Designing Drug Delivery Systems
## 2 Exosomes and Their Release Routes  
Exosomes are recognized as a source of various nucleic acids and proteins, acting as local and body-wide signalization and regulation systems. Exosomes (30-150 nm in diameter) are the smallest subpopulation of extravascular vesicles, which also comprise microvesicles (50 nm-1 \(\upmu\)m) and apoptotic bodies (50 nm-5 \(\upmu\)m) [3,4].  
The source of the exosomes is in the endosomal system. In the beginning, the so-called early endosomes form as a result of inward membrane budding, and they are subjected to sorting, facing their final destination. The early endosomes can fuse with endocytic vesicles, thus leading their cargo toward recycling, degradation, or secretion. If their cargo is meant to be recycled, they form recycling endosomes [11]. Other early endosomes maturate into late endosomes. During their maturation to late endosomes, the inward budding of the vesicle membrane occurs, resulting in multivesicular bodies (MVBs) creation, containing numerous intraluminal vesicles (ILVs) [6]. At this stage, the MVB can either fuse with the lysosome--with onward degradation of ILVs--or fuse with a plasma membrane, resulting in the release of ILVs to the extracellular space (Figure 1). These released ILVs are called exosomes and contain many proteins, nucleic acids, lipids, and polysaccharides derived from the cell interior [12]. Another release mechanism is due to the outward budding of the plasma membrane, resulting in the formation of so-called shedding microvesicles or exosomes [12].
# Remanufacturing of Industrial Electronics: A Case Study from the GCC Region
## 3 Industrial Product Remanufacturing
### Uncovering the Causes of Industrial PCB Problems  
Modern industrial machines are commonly outfitted with embedded electronic motherboards that serve multiple purposes. These motherboards provide power to the machine's controller, such as through switched mode power supply (SMPS) and inverters, while also managing various industrial processes. For instance, the operations performed by the controller in industrial machines include temperature control, monitoring of digital I/O data flow via programmable logic controllers (PLCs), regulation of the speed of three-phase asynchronous motors through variable frequency drives (VFDs), and display of essential process parameters through human-machine interfaces (HMIs). Examples of industrial EoL products are shown in Figure 3.  
Figure 3: Industrial EoL Products for oil and gas and others.  
Figure 2: Troubleshooting of Faulty PCB.  
On the other hand, there are several reasons why controllers in industrial machines often fail, resulting in different symptoms and signs. These reasons include:  
1. Manufacturers use bad quality parts, such as electrolytic capacitors, with short lifetimes that lose their capacitance and internal series resistors over time;
2. Controllers are poorly designed and do not have sufficient protection components, such as metal oxide varistors (MOV), transient voltage suppressor (TVS) diodes, Zener diodes, fuses, thermistors, etc., which are vulnerable to damage caused by voltage surges and spikes resulting from electrical fluctuations. Such damage can impact electronic components severely;
3. Operators manipulate the controller without taking safety precautions, damaging many ICs and some field-effect transistors, such as metal oxide semiconductor field effect transistor (MOSFET), due to external phenomena such as static electricity;
4. The corrupted software or program of the controller is generally stored in embedded micro-controllers and programmable ICs, which sometimes need an upgrade;
5. Industrial environmental conditions, such as temperature, dust, water, rain, moisture, exposure to various chemicals and gases, including H2S, and electro magnetic interference (EMI) influence the PCB and affect the product and components' lifespan;
6. Damage caused by stress, mechanical shock, and vibration to the electronic board and plastic enclosures, leading to broken PCB traces and causing an open or a short circuit while powering on;
7. Animals such as rats and cockroaches, as well as their eggs and faeces, can cause harm to the internal electrical components of the product, leading to costly repairs. In particular, cockroaches are known to be attracted to the warmth of electronic motherboards and may inadvertently touch high-voltage parts, causing a short circuit and further damage.
## 1 Introduction
This study used the SWAT model on the upper reaches of the Heihe River basin with parameters screened using four typical parameter sensitivity analysis methods: Morris screening, Sobol analysis, the Fourier amplitude sensitivity test (FAST), and the extended Fourier amplitude sensitivity test (EFAST) to conduct sensitivity analysis on the parameters related to runoff simulation in SWAT model. The objectives of this work are to: (1) compare the sensitivity analysis results of the four methods, (2) define a series of sensitive parameters that most affected the monthly runoff simulation of the SWAT model, and (3) verify whether the calibration results of the four methods were better than that of the single method. The results of this study have reference significance for the parameter optimization of the SWAT model when studying alpine rivers by constructing the SWAT model.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 4 Related Work
### Reparameterization Methods
Employing DoRA enhances LoRA's learning capacity and training stability while avoiding additional inference overhead. When fine tuned using LLaMA, LLaVA, and VL-BART, DoRA outperforms LoRA in various downstream tasks, such as common sense reasoning and visual instruction tuning. GLoRA [26] enhances the low-rank adaptation approach with a more generalized prompt module to optimize pretrained model weights and adjust intermediate activations. Employing a generalized prompt module design per layer offers enhanced capability and flexibility in fine tuning across diverse tasks and datasets. It learns the individual adapters of each layer and facilitates efficient parameter adaptation by employing a scalable, modular, and layer-wise structure search. Its structural reparameterization ensures inference efficiency without incurring any additional cost. GLoRA [26] shows considerable enhancements in the language domain compared to the original LoRA using the LLaMA-1 and LLaMA-2 models. By adopting a generalized low-rank adaptation and reparameterization framework, GLoRA significantly reduces the number of parameters and computation resources required for fine tuning. This makes it a more resource-efficient and practical method for real-world applications. KronA [27] and COMPACTER [28] use Kronecker product decomposition, which is another factorization method. KronA [27] replaces low-rank decomposition in LoRA with Kronecker product decomposition for efficient model fine tuning. This results in improved accuracy without increasing the inference latency. COMPACTER [28] inserts task-specific weight matrices into a pretrained model's weights. These weights are generated by adding Kronecker products between shared \"slow\" weights and \"fast\" rank-one matrices specific to each COMPACTER layer. Ref. [28] shows that despite learning 2127.66 \(\times\) fewer parameters compared to full fine tuning, it achieves better or comparable performance at full-data settings while outperforming it at data-limited settings. A unique advantage of Kronecker products is their multiplicative ranks, which help to overcome the limitations of low-rank assumptions.
## 4 Summary  
An LV DSSE tool was developed and proposed in this paper, focusing on the critical aspects of finding accurate and coherent information on network topology with automated management of information systems, real LV network implementation for power flow calculation and managing portions of the network characterized by uncertain or inconsistent line lengths. Using pandapower and the Gauss-Newton algorithm, SE were run in four Hungarian LV supply areas. For testing the effects of different levels of measurement uncertainty, different pseudo-measurement scenarios were defined with different combinations of AAC and SLP usage. A 50-50% (odd week-even week) estimation-validation split was applied for each scenario to run from the dataset of 52 weeks. For all four networks, modeling was performed with three different load placement accuracies, due to the data storage method of the DSO. In this regard, an INIS model refining algorithm was implemented to find a balance between reducing human resources and achieving accurate estimations. The presented method is able to estimate node voltages with a relative error of less than 1% in case of using AACs. A meter-placement method is published as well to reduce the maximum value, and this way, the deviation of the absolute value of errors per node is reduced. The main conclusion of the paper is that the usage of AACs and SLPs, as well as optimal meter placement, can be a significant step toward real-time network monitoring.  
Future research will focus on the extension of the presented approach toward more realistic scenarios. Pseudo measurements will be partially replaced by real measurements from actual metering devices, and the rest of the measurements will be yielded by load-flow calculations to ensure a plausible load configuration model. The validation procedure will also be extended to include load-flow results from the same dataset as reference.  
Conceptualization, G.B.B. and B.H.; methodology, G.B.B. and B.H.; software, G.B.B. and L.B.; validation, G.B.B. and L.B.; formal analysis, G.B.B. and I.T.; investigation, G.B.B., L.B. and B.H.; resources, I.T. and B.H.; data curation, L.B.; writing--original draft preparation, all authors; writing--review and editing, all authors; visualization, G.B.B. and B.H.; supervision, B.H.; project administration, B.H.; funding acquisition, B.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hungarian Academy of Sciences and E.ON Hungary grant number LENDULET_2019-111. The APC was funded by Budapest University of Technology and Economics.  
Not applicable.  
The datasets used for the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.  
Appendix AFigure 13: Topology of area 44600 (MV/LV represents the supplying medium/low-voltage transformer).
## 3 Analysis of Channel  
In a complex urban environment, the distribution density of buildings and vegetation varies with height. This leads to different main factors for signal fading at different heights, and channel characteristics are more complex and changeable. The fading characteristics of wireless communication channels also have great randomness. In addition to direct radiation, when radio waves encounter obstacles, they will reflect, diffract, and scatter according to their wavelength and the size of the obstacle. This leads to a certain amount of radio wave energy loss and cause signal fading. This section gives a brief overview of the fading model that will be used.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### Unsw-Nb15  
UNSW-NB15 was created using a commercial penetration tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS). This tool can generate hybrid synthetically modern normal activities and contemporary attack behaviors from network traffic [9]. They collected tcpdump traces for a total duration of 31 h. From these network traces, they extracted 49 features categorized into five groups: flow features, basic features, content features, time features, and additional generated features. Feature and statistical analyses are the most common methods used in several published papers employing UNSW-NB15 [46; 47; 48]. While [46] could obtain 97% accuracy by using 23 features, [47] incorporated the XGBoost algorithm for feature reduction, using several traditional machine learning algorithms for evaluation, such as Artificial Neural Network (ANN), Logistic Regression (LR), k-Nearest Neighbor (kNN), Support Vector Machine (SVM) and Decision Tree (DT).