esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods  
### Fabrication of Microchannels  
A square geometry was chosen to ensure uniform inertial lift forces along both channel walls, enhancing the predictability of particle focusing behavior. This geometry promotes predictable particle behavior, improving the separation between cells for efficient focusing and counting [24], as shown in Figure 1. Channel sizes of 150 \(\upmu\)m, 200 \(\upmu\)m, and 250 \(\upmu\)m were selected to optimize inertial focusing and the separation of microcells, which typically measure 6-20 \(\upmu\)m in diameter. These dimensions ensure that the particle-to-channel size ratio (a/Dh) remains within the ideal range of 0.05-0.2, facilitating efficient inertial migration for particle focusing [25]. The selected microchannel geometry incorporates advanced features beyond a simple straight channel to leverage inertial microfluidics, a passive technique for manipulating particles [26]. This structured, curved geometry generates inertial lift forces and secondary Dean flows, which promote the lateral migration of particles to well-defined equilibrium positions within the channel [27]. This passive focusing effect significantly improves the particle alignment and ensures a more uniform distribution across the channel's cross-section as particles travel through the observation zone.  
Figure 1: Geometry of the 150 \(\upmu\)m microchannel and fabricated device; all dimensions in mm.  
The microchannels were fabricated using the soft lithography method, which involves creating a patterned mold through 3D printing technology. A 3D printer (Form 3, Form Lab SLA 3D printer) was utilized to produce the master mold with high precision, as shown in Figure 1[28]. This technique allows for intricate designs and accurate replication of the microchannel patterns.
## 3 Results and Discussions  
The least squares estimation of deformation rates of the identified TCPs is shown in Figure 7a. The reference point was selected at an island which was assumed to be stable (shown as the triangle in Figure 7c). It is observed that the Donghai Bridge experienced a deformation range from \(-2.3\) to \(0.5\) cm/year along the line-of-sight (LOS) direction, equaling to the cumulative deformation between \(-1.2\) and \(0.3\) cm during the investigated period. For most of the body of the bridge, which is built on the piers, the deformation is detected within about \(1\) cm/year. Figure 7b shows an example of this pier-stayed bridge. However, an obvious deformation rate appeared on a cable-stayed bridge (Figure 7e) over the main navigation channel, of up to \(-1.2\) cm (see Figure 7d).
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results
### Ablation Study
#### 3.5.1 The Effectiveness of Multi-Scale Deep Features
It is noteworthy that the enhancement in performance from two scales to three scales was marginal, signifying diminishing returns in multi-scale feature fusion. This underscores the necessity of striking a balance between enhancing accuracy and maintaining computational efficiency. This phenomenon aligns with the principle of information entropy in information theory, whereby further elimination of uncertainty requires significantly increased computational complexity and resource investment, making the process increasingly challenging. Therefore, when designing models, it is crucial to consider both the potential for performance improvement and the associated resource costs in order to determine the optimal method for feature extraction and fusion.
## 2 Theory
### DMAS Beamforming in Multi-Angle PW Imaging  
In multi-angle PW imaging, each image pixel comes from a 2D signal matrix comprising echoes in both RxCh and TxEt directions. Any combination of DAS and DMAS beamforming can be applied to the 2D echo matrix in the RxCh direction and the TxEt direction to determine the final B-mode image pixel. The resultant four beamforming methods include 2D-DAS (i.e., CPWC) for 2D summation of the echo matrix, 2D-DMAS for DMAS beamforming in both of RxCh and TxEt directions [13], Rx-DMAS for coherent summation in the TxEt direction and then DMAS processing in the RxCh direction [12], and Tx-DMAS for coherent summation in the RxCh direction and then DMAS processing in the TxEt direction [11]. Given the time-compensated baseband signal \(s_{nk}=a_{nk}e^{j\phi_{nk}}\) for the \(n\)-th RxCh in the \(k\)-th TxEt (i.e., the \(k\)-th PW transmit angle), the four beamforming methods can be formulated as:  
\[y_{\text{2D-DAS}}=\frac{1}{M}\sum_{k=1}^{M}\left(\frac{1}{N}\sum_{n=1}^{N}a_{nk }e^{j\phi_{nk}}\right), \tag{2}\]  
\[y_{\text{2D-DMAS}}=\left[\frac{1}{M}\sum_{k=1}^{M}\left(\frac{1}{N}\sum_{n=1}^ {N}\sqrt[]{a_{nk}}e^{j\phi_{nk}}\right)\right]^{p}, \tag{3}\]  
\[y_{\text{Rx-DMAS}}=\left(\frac{1}{N}\sum_{n=1}^{N}\sqrt[]{c_{n}}e^{j\alpha_{n} }\right)^{p}\text{with}\ c_{n}e^{j\alpha_{n}}=\frac{1}{M}\sum_{k=1}^{M}a_{nk}e ^{j\phi_{nk}}, \tag{4}\]  
\[y_{\text{Tx-DMAS}}=\left(\frac{1}{M}\sum_{k=1}^{M}\sqrt[]{b_{k}}e^{j\beta_{k} }\right)^{p}\text{with}\ b_{k}e^{j\beta_{k}}=\frac{1}{N}\sum_{n=1}^{N}a_{nk}e ^{j\phi_{nk}}, \tag{5}\]  
Here, similar to Equation (1), the \(p\) value also represents the importance of the signal coherence in the image output of DMAS beamforming. Compared to the traditional CPWC (2D-DAS) beamforming in multi-angle PW imaging, all three DMAS images will have superior image resolution and CR. Specifically, for image resolution, CR, and artifact suppression, 2D-DMAS has the best performance and Tx-DMAS has the worst performance among these three DMAS methods. On the contrary, Tx-DMAS has the best contrast-to-noise ratio due to its higher speckle smoothness and suffers less from the dark-region artifacts around strong point reflectors [23].
## 2 Research Methodology
### General System
#### 2.3.3 Network Connection and Quality of Service (QoS)  
There are various techniques that have been used to manage network traffic and prioritize certain types of data, which ensure that the critical applications receive the necessary resources and network bandwidth for optimal performance. QoS is important in networks where different types of traffic, such as voice, video, and data, are competing for limited network resources. Furthermore, QoS can be implemented at various levels of the network, including network connection levels, and cellular and wireless networks. QoS for network connections typically involves configuring network devices, routers, switches, and firewalls. They prioritize certain types of traffic based on their characteristics, such as the type of application, the size of the data packets, and the source and including destination addresses. Consequently, QoS can help to ensure that critical applications receive the necessary network bandwidth and resources for optimal performance, while minimizing delays, packet loss, and other network issues.
## 7 Discussion
### Feasibility of LQRC and LQRIC Controllers  
The experimental results in Figure 8, Figure 9 and Table 4 verify the feasibility of LQRC and LQRIC for \(LC\)-HAPF under 50V and 40V DC-link voltage. Followed by the IEEE standard [31], the LQRC- and LQRIC-controlled \(LC\)-HAPF can compensate the \(\text{THD}_{i\text{x}}\) to less than 15%, while the reactive power and neutral current are small after compensation. These results show that both LQRC and LQRIC can achieve optimal control and can compensate harmonics and reactive power even in low DC-link voltage conditions. The proposed LQRIC also shows a better steady-state and reactive power compensation performance compared with the LQRC.
## 5 Experimental Results
### Memory Analysis
#### 5.2.3 Proposed Algorithm  
In the pattern-matching process with the PA, the CC and CO tables must be held in the GPU memory. All state codes kept in the CO table are combined into a character array on the GPU memory. The starting and ending points of each code in the character array are kept in an integer array. Moreover, the character block that needed to be processed when moving from one state to another on the automaton is obtained from the subpattern table. All subpatterns are combined into a character array on the GPU memory so that the subpatterns in the target text and subpattern table can be compared character by character. The starting and ending points of each subpattern in the character array are kept in an  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pattern Count** & **AC (KB)** \\ \hline
100 & 2625,512 \\
200 & 5181.12 \\
300 & 7785.044 \\
400 & 10,515.412 \\
500 & 13,271.48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Memory space required for the AC tables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pattern Count** & **WM (KB)** \\ \hline
100 & 4331.49 \\
200 & 8619.935 \\
300 & 12,908.622 \\
400 & 17,197.241 \\
500 & 21,486.196 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Memory space required for the WM tables.  
integer array. Table 9 shows the memory space required for the PA tables specially created for datasets containing different numbers of patterns.
# Improved Bilateral Filtering for a Gaussian Pyramid Structure-Based Image Enhancement Algorithm
## 2 Improved Retinex Model  
### Single-Scale Retinex Model  
The SSR model is a color constant visual image enhancement method based on [PERSON]'s theory of human visual perception. In this model, the color of an object is considered to be fixed, while the corresponding image obtained by the camera is composed of a low-frequency incident light component and a high-frequency component resulting from the reflections of light on the surface of the object, as shown in Figure 1.  
For a given image, the relationship between incident and reflected light components can be expressed as  
\[S(x,y)=L(x,y)\cdot R(x,y), \tag{1}\]  
where \(S(x,y)\), \(R(x,y)\), and \(L(x,y)\) denote the full color component, the reflected light component, and the incident light component of a given image, respectively.  
The principle of traditional Retinex image enhancement is to remove or reduce the impact of incident light components on an image by deconstructing this into separate incident and reflection components. The contrast of the reflection image is consequently enhanced, the texture information of the image is compressed within a certain range, and at the same time, the basic tone of the original image is retained. For ease of calculation, logarithms of both sides of (1) are taken, yielding the following:  
\[\ln(S(x,y))=\ln(L(x,y))+\ln(R(x,y)). \tag{2}\]  
The mathematical form of the reflection component of the image can be obtained by rearranging (2) as follows:  
\[\ln(R_{i}(x,y))=\ln(S_{i}(x,y))-\ln(L_{i}(x,y)) \tag{3}\]  
where \(S_{i}(x,y)\), \(R_{i}(x,y)\), and \(L_{i}(x,y)\) denote the color component, the reflection component, and the incident light component of the \(i\)-th color channel, respectively.  
Inspection of (3) shows that the reflection component of the image can be obtained by deducing the incident light component. Hence, the accuracy of incident light estimation directly affects the fog removal effect. Since \(L\) is low-frequency information, a Gaussian convolution function can be used to better estimate the incident light component from the image, i.e.,  
\[L(x,y)=G(x,y)*S(x,y), \tag{4}\]  
where \(*\) represents the convolution operation, \(G(x,y)\) is a Gaussian function, defined as \(G(x,y)=\frac{1}{2\pi\sigma^{2}}\exp(-\frac{x^{2}+y^{2}}{2\sigma^{2}})\), and \(\sigma\) is the standard deviation of the Gaussian function, also known as the scale factor.
# Improved Scheduling Algorithm for Synchronous Data Flow Graphs on a Homogeneous Multi-Core Systems
## 3 Related Work
The self-time scheduling method studies SDFG, which completes scheduling based on actor readiness and completion times, and is a scheduling strategy with many real-time requirements. Ref. [25] proposes an offline heuristic strategy for speeding up algorithm scheduling rather than optimizing scheduling quality. Ref. [26] is oriented towards a specific DSP multiprocessor architecture. Scheduling with other real-time tasks interfering is considered in [27], where the scheduling algorithm proposed in [27] relies on special cases. Ref. [28] considers multi-mode multi-processor scheduling techniques that can schedule all SDFGs in all modes simultaneously. Ref. [29] is targeted at specific embedded stream processing applications using a symbolic approach for SDFG analysis. DAG-based scheduling is a type of static scheduling method. PAPS [3] and DONF [4] are methods for scheduling SDFG on a multi-core systems based on the DAG model. The PAPS method uses a Hu-level [7] based approach in the task-selection phase to calculate the level values of individual vertices. Priority is given to the vertex with the larger level value for scheduling. However, no core selection policy is specified with PAPS. The DONF method only calculates the priority of tasks that are locally in the ready state during the task-selection phase. The DONF method ignores the global information of the computed graph and also specifies the handling strategy when tasks of equal priority conflict with the number of processors. The DONF method studies single DAG, whereas HSDFG is transformed from SDFG and multiple DAG will exist. SDF3 [30] studies the SDFG random generator, which generates SDFGs that satisfy consistency requirements for the evaluation of scheduling policies. However, the SDFG model save file of this generator is in xml format, and a corresponding xml parser is required to be used as the input source of the scheduling algorithm. We have designed the SDFG random generator without the use of an xml parser.
# Laser Absorption Sensing Systems: Challenges, Modeling, and Design Optimization
## 3 Fundamentals of Different LAS Measurement Schemes
### DAS-Calibrated WMS  
From the discussion above, one may conclude that DAS and WMS have their own advantages and limitations. While being more noise-immune in practical environments, calibration-free WMS methods rely on extensive laser characterization, and the nonlinear response to the sinusoidal modulation over the entire wavelength scan could lead to significant distortion in the 2\(f\) lineshape, especially with wide wavelength scans. [PERSON] et al. have thus proposed a DAS-calibrated WMS method, which combines the simplicity and accuracy of the intrinsically calibration-free direct TDLAS (dTDLAS) with the enhanced precision of WMS-2_f_[63]. A rapid (125 Hz) time-division multiplexed scheme alternating between triangular scan and scanned modulation was used to realize quasi-simultaneous DAS and WMS measurements. The concept of this on-the-fly calibration is illustrated in Figure 7. An absorbance level of 0.1 was chosen as the decision criterion of whether DAS only or DAS-calibrated WMS will be used. This largely extends the dynamic range of a single sensor and is especially advantageous for in-field measurements without the need for reference gases or measurement interruption.  
Figure 6: Flow chart of the recovery process of the absorbance profile based on higher-order harmonic signals.  
Figure 7: Schematic of the on-the-fly WMS calibration using time-multiplexed direct tunable diode laser absorption spectroscopy (dTDLAS) and WMS. Figure adapted from [63].
## 3 Results and Discussion
### Optimization of Preparative-Scale Parameters for Ultrasound-Assisted Extraction Process
Similarly, the response-surface plots showed that total anthocyanin content increased with increasing water content, from 10 to 30%; it seems that mass transfer between the solid and liquid phases is hampered by high solvent viscosity in NADESs with 10% water, while anthocyanins are much less stable in NADESs with higher water contents. [PERSON] et al. have also reported on the relationship between water content in NADESs and anthocyanin stability [25], while [PERSON] et al. noticed that solvent viscosity is influenced by water content, and thus impacts upon mass transfer [33]. MW and US irradiation also decrease the viscosity of NADESs. Extraction time had a significant effect on total anthocyanin content. Increasing the extraction time led to decreased anthocyanin content, indicating that long extraction times and prolonged heating increase degradation [34]. Furthermore, [PERSON] et al. reported polyphenol yields using conventional solvents like ethanol and water mixtures, with yields ranging significantly based on the material and extraction conditions [35]. The comparison highlights NADESs as a promising alternative that can achieve competitive or superior yields, especially considering their environmental and operational benefits. While much of the existing research on NADES and UAE has been conducted at laboratory scales, studies focused on scaling up (e.g., [PERSON] et al.) provide valuable insights into the challenges and opportunities of larger-scale operations [36]. Our findings indicate that with careful optimization, NADES-based UAE processes can be effectively scaled, aligning with the industry's move towards more sustainable extraction methods. Comparing the outcomes from laboratory-scale experiments, where 26.07 mg\({}_{\text{GAE}}\) g\({}^{-1}\) of polyphenols was extracted [30], to the results obtained in the preparative scale in this study, which recorded a similar yield of 21.6 mg\({}_{\text{GAE}}\) g\({}^{-1}\) of polyphenols, suggests that the NADES process holds promise for effective extraction on larger scales. An economic analysis from our prior work highlights that while the initial setup costs for using NADES may be higher due to the need for specialized equipment, the long-term operational costs are significantly lower. This is attributed to reduced energy consumption  
Figure 3: Pareto chart for the yield of total polyphenols (TPs) depending on amplitude strength (A), percentage of NADES BGlc (NADES), mass-to-volume ratio (MV), and time (t). and minimal waste treatment requirements, making NADESs a viable option for industrial applications [10].
## 3 Results
### Model Performance Results  
The XGBoost model demonstrated strong predictive performance (Table 4). It explained 83.5% of the variance in the test data set, indicating strong generalisation to different data while showing a small variation in the testing phase, indicating minimal overfitting. Pearsons's value showed a robust linear correlation between predicted and actual densities, emphasising reliability. RMSE values revealed a moderate level of error. These metrics indicated that the model accurately forecasted the target variable (relative density), maintaining a balance between model accuracy and generalisation. The performance of the model can be compared with that of a similar method applied in [37], as it has better RMSE and MAE values, but has a worse R\({}^{2}\) score, showing space for improvement.  
The resulting XGBoost model achieved usable results (test R\({}^{2}\): 0.835, MAE: 0.728) while showing a significant dependence on influence interactions, especially on laser power \(\times\) scan speed (35.9%) and laser power \(\times\) layer thickness (29.0%), which enabled it to model complex patterns in the experimental data set effectively. The expected versus actual values of relative density for the training and test sets are shown in Figure 8.  
The scatter plots present the model's effectiveness when forecasting the relative density (89.29-99.975%). The training set (a) depicts a strong match, with the points tightly clustered around the ideal line (y = x). The test set (b) had shown some variability at the extreme values (90-92% and 98-99%) showing places for potential additional research. The slight reduction in R\({}^{2}\), which is addressed using regularisation, additionally emphasises the need for additional data to improve precision.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Training** & **Testing** \\ \hline R\({}^{2}\) Score & 0.872 & 0.835 \\ \hline MAE & 0.683 & 0.728 \\ \hline RMSE & 1.118 & 0.892 \\ \hline MAPE & 0.73\% & 0.76\% \\ \hline Pearson’s correlation coefficient & 0.941 & 0.929 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Metrics for the best model achieved.
# Central Kurdish Text-to-Speech Synthesis with Novel End-to-End Transformer Training
###### Abstract  
Recent advancements in text-to-speech (TTS) models have aimed to streamline the two-stage process into a single-stage training approach. However, many single-stage models still lag behind in audio quality, particularly when handling Kurdish text and speech. There is a critical need to enhance text-to-speech conversion for the Kurdish language, particularly for the Sorani dialect, which has been relatively neglected and is underrepresented in recent text-to-speech advancements. This study introduces an end-to-end TTS model for efficiently generating high-quality Kurdish audio. The proposed method leverages a variational autoencoder (VAE) that is pre-trained for audio waveform reconstruction and is augmented by adversarial training. This involves aligning the prior distribution established by the pre-trained encoder with the posterior distribution of the text encoder within latent variables. Additionally, a stochastic duration predictor is incorporated to imbue synthesized Kurdish speech with diverse rhythms. By aligning latent distributions and integrating the stochastic duration predictor, the proposed method facilitates the real-time generation of natural Kurdish speech audio, offering flexibility in pitches and rhythms. Empirical evaluation via the mean opinion score (MOS) on a custom dataset confirms the superior performance of our approach (MOS of 3.94) compared with that of a one-stage system and other two-staged systems as assessed through a subjective human evaluation.  
deep learning; central Kurdish text to speech (TTS); transformers; end-to-end autoregressive transformers +
Footnote †: journal: _arxiv:10.1111
# An Approach for Anomaly Detection in Network Communications Using _k-Path_ Analysis
## 4 Three-State Markovian Approach
### Definition of the States
The threshold values thus defined can then be used to detect periods where the data volume significantly exceeds expected levels, indicating the presence of a flux peak. In the future, we plan to propose a study on selecting the threshold \(X_{\text{Peak}}(e)\) for detecting data traffic peaks. In our simulation, we will use the mean (\(\mu\)) and standard deviation (\(\sigma\)) to define this threshold. The mean represents the typical value of data flux, while the standard deviation measures the dispersion of data around the mean. By setting the threshold at a certain number of standard deviations above the mean, we can identify periods where the data volume exceeds what is considered moderate. We define:  
\[X_{\text{Peak}}(e)=\mu(e)+h\times\sigma(e),\]  
where \(\mu(e)\) is the mean of the data \(X_{t_{i}}(e)\), \(\sigma(e)\) is the standard deviation of the data \(X_{t_{i}}(e)\), \(h\) is a coefficient that determines the number of standard deviations above the mean to define the threshold. The larger \(h\) is, the higher the threshold will be, and thus more stringent in terms of peak detection.