esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Datasets  
Creating a mixed-language speech training dataset poses significant challenges, particularly for Mandarin-English speech recognition, which often needs more resources. Although there is ample monolingual data for each language, due to a scarcity of mixed data, constructing a mixed-language dataset becomes a viable solution [32; 34; 35]. To evaluate the efficacy of our system, we have created a mixed Mandarin-English dataset. The architecture of the dataset is visually depicted in Figure 1. The limitation of recording only 10 sentences per person was due to practical constraints such as time, resources, and participant availability. To address the insufficient size of the dataset, we have developed a method to combine and augment the original audio recordings, resulting in approximately 10,640 files using a MATLAB (version 2023a) script. Initially, we gathered 20 WAV files, equally prepared by two individuals, \"Person A\" and \"Person B\", each contributing 10 different sentences of Mandarin mixed with some English words, recorded at a sampling frequency of 16 kHz and a bit rate of 128 kbps. Both Person A and Person B are bilinguals proficient in both Mandarin as their first language and English as their second one. We have ensured that the ratio of Mandarin to English in each sentence was approximately 1:1.  
For Person A, the 10 recordings formed the base subset, stored in a folder named \"File 1A\". By merging any two of these base recordings, we created 100 distinct combinations, forming the second subset, \"File 2A\". Further merging any three recordings at a time produced the third subset, \"File 3A\", encompassing 1,000 audio combinations. These three subsets were combined to constitute \"Dataset A\", containing 1,110 data samples. Following a similar process, Person B's recordings were synthesized into \"Dataset B\". Subsequently, we created \"Dataset All\" by applying the same method of permutation and combination to all 20 recordings from both individuals, producing 8,420 data samples. Therefore, the comprehensive dataset, tailored for performance evaluation, houses 10,640 mixed Mandarin and English speech samples.  
Additionally, we have collected 1,019 audio files from YouTube as a second dataset for system performance testing. These files are instructional videos for Mandarin speakers aiming at speaking English and are ideal for testing the Mandarin-English bilingual ASR. This secondary dataset provides real-world examples of mixed-language usage, ensuring a non-biased evaluation of our system performance in handling natural and spontaneous speech.
# Hazard Model: Epidemic-Type Aftershock Sequence (ETAS) Model for Hungary
## 1 Introduction
### Background  
Since the initial formulation of the model by [PERSON] [5; 6], ETAS methodology has been researched extensively. Works by several authors suggested different methods and algorithms for parameter estimation [6; 7; 8; 9; 10] and investigated the optimal forms of event-triggering functions [6; 8; 11; 12]. The literature on ETAS also includes a discussion of important model properties. One such model property is magnitude independence, i.e., the assumption that the magnitude of an earthquake is independent of predecessor events [8; 13]. The stability or criticality conditions of the event triggering, as well as the question of self-similarity, have been investigated [14; 15]. Certain enhancements of the model that allow a more accurate modelling of complex processes, such as three-dimensional or finite-source ETAS models, have also been developed [9; 16; 17]; however, the benefits of these advanced features for modelling a moderately active region like Hungary are not immediately clear.  
This study significantly relies on the work of [PERSON] et al. [7; 8], who developed an iterative algorithm for ETAS parameterization, based on a kernel density estimation of the background activity in combination with stochastic declustering. The authors analyse the properties of the method in regional studies covering areas of Japan and New Zealand, and they suggest a range of methods for testing the different components of the model.  
ETAS parameter estimation is affected by known sensitivities and biases, which are discussed at length by [PERSON] et al. [18], who demonstrate these effects on actual and simulated catalogues derived from Italian and South Californian data. Typical sources of parameter bias include early aftershock incompleteness in the wake of a major earthquake, the anisotropy of aftershock clusters, and the choice of the magnitude threshold. These effects also need to be considered in this Hungarian study.
# Low-Carbon Logistics Distribution Vehicle Routing Optimization Based on INNC-GA
## 5 Comparison and Analysis of Experimental Results
### Effects of the Initial Solution
#### 5.2.3 Feasible Solution Solving Speed  
In the context of the vehicle routing problem with time window constraints, particularly when dealing with strict time window requirements, it can be challenging to identify feasible solutions for certain instances. In some scenarios, it may even be impossible to find a viable solution. This difficulty is primarily attributed to the large scale of certain instances and the stringent time windows, which make it challenging for algorithms to effectively optimize for both distance and time window constraints simultaneously. By employing random generation method, random traversal insertion, and the INNC algorithm to address the R206 instance, Table 4 showcases the constraint violations. The results indicate that the random generation method achieved a feasible solution without violating constraints by the 256 th iteration, the random traversal insertion method found a feasible solution without constraint violations by the 3 rd iteration, and the INNC algorithm consistently produced solutions that adhered to constraints from the initial iteration. This highlights that the INNC algorithm yields higher-quality initial solutions compared with the random generation and random insertion methods. At the same time, since the INNC algorithm finds a feasible solution that satisfies the constraints in the first generation, the algorithm does not need to spend time and resources searching and optimizing the solution space and can directly start the evolution process, which speeds up the convergence of the algorithm and makes the algorithm find a better solution faster; since it does not need to perform many iterations to search for a feasible solution, the computational cost and time consumption can be reduced, which is particularly important for large-scale problems and practical application scenarios with high real-time requirements.  
In practice, for urgent or time-sensitive tasks, being able to quickly find a feasible solution will improve delivery efficiency and service quality; in the field of logistics and distribution, being able to quickly find a feasible solution that meets the constraints means reducing unnecessary waiting and planning time, which reduces costs. At the same time, finding a feasible solution quickly means that the delivery task can be completed more punctually, which improves customer satisfaction and confidence, which is very important for maintaining customer relationships and brand image. All of this is very beneficial for logistics companies.
# Comparative Analysis of 5G Mobile Communication Network Architectures
## 5 Simulation Assumptions and Architecture Robustness Assessment
### Architecture Robustness Assessment  
Using the simulation model constructed, CCC, HCC, and MEC architectures are simulated. For the initial architecture assessment, we tested the robustness of each architecture with respect to the increase in task data traffic.  
For the robustness assessment, the total computer resource for each architecture was set at 60,000 MHz. For CCC architecture, all computer resources are placed in the central cloud. For MEC architecture, six edge clouds were assigned 10,000 MHz each. For the HCC architecture, the trend analysis was performed using the HCC architecture computing resource allocation module introduced in the previous section. Figure 5 shows the analysis results in the form of a log-scale. The x-axis represents the percentage of computing resources allocated to the central cloud, and the y-axis represents the \(\Delta\) value in Equation (10) for the HCC architecture with \(N=6\).  
For this particular HCC architecture, the trend analysis revealed that the optimum central cloud to edge clouds distribution ratio was 25:75, meaning 25% of the total computing resource (15,000 MHz) was allocated to the central cloud and 75% of the resources (45,000 MHz) were allocated in equal amounts to six edge clouds. It should be noted that the trend analysis for determining the optimal resource allocation ratio between the central cloud and edge clouds need to be conducted for different situations, since the optimal ratio may be different for each specific situation.  
The task data traffic experienced by individual region was varied from 1800 tasks per second to 10,800 tasks per second. For each architecture, the percentage of tasks with out-of-specification percentage was measured as the function of task traffic. Figure 6 shows the results of the robustness assessment for all three architectures under increasing data traffic.  
The results show that the CCC architecture does not always satisfy the 5% out-of-specification ratio; nevertheless, it is also most robust among the three architectures. The HCC architecture shows more sensitivity to the data traffic increase than the CCC architecture. However, for most data traffic ranges simulated, the HCC architecture processed data traffics well within the specification. The MEC architecture had the best performance in terms of the percentage of out-of-specification ratio. However, as the data traffic reaches more than 9000 tasks per second, the MEC architecture's performance deteriorates rapidly, which shows that there is an upper limit to the data traffic range in which MEC is robust. This initial assessment revealed key characteristics of each architecture.  
Figure 5: Trend analysis results for computing resource allocation in the Hybrid Cloud Computing (HCC) architecture.  
Figure 6: Robustness assessment for CCC, HCC, and MEC architectures.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 10 Mitigation Techniques of Beam Systematics at the Neutrino Detectors
### Flux Measurement at the Detector
The corresponding cross section for deep-inelastic events (DIS) is:  
\[\frac{d\sigma_{\
u_{\mu}}}{d\
u}=\frac{G_{F}^{2}M}{\pi}\bigg{(} \int_{0}^{1}F_{2}dx-\frac{\
u}{E_{\
u}}\int_{0}^{1}[F_{2}+xF_{3}]dx\] \[+\frac{\
u}{2E_{\
u}^{2}}\int_{0}^{1}\bigg{[}\frac{Mx(1-R_{L})}{1 +R_{L}}F_{2}\bigg{]}dx+\frac{\
u^{2}}{2E_{\
u}^{2}}\int_{0}^{1}\bigg{[}\frac{ F_{2}}{1+R_{L}}+xF_{3}\bigg{]}dx\bigg{)} \tag{18}\]  
where \(G_{F}\) is the Fermi constant, \(M\) is the proton mass, \(x\) is the [PERSON]'s scaling variable and \(F_{1,2,3}\) are the structure functions. \(R_{L}\) is a function of the previous variables and is proportional to the longitudinal structure function \(F_{L}\)[159]. Equation (18) indicates that for \(\
u\to 0\), i.e., for \(\
u\) smaller than a given threshold \(\
u_{thr}\), the cross section is independent of the neutrino energy \(E_{\
u}\). As a consequence, the measurement of the low-\(\
u\) interaction rate \(N(\
u<\
u_{thr},E_{\
u})\) as a function of the neutrino energy is equivalent to a measurement of the shape of the neutrino flux because:  
\[N(\
u<\
u_{thr},E)\sim\phi(E_{\
u})\int_{0}^{\
u_{thr}}\frac{d\sigma}{d\
u}= \phi(E_{\
u})\sigma(\
u<\
u_{thr},E_{\
u})\sim\phi(E_{\
u}) \tag{19}\]  
Even if this method only provides the spectrum of the flux and is plagued by the systematics associated with the choice of \(\
u_{thr}\), it provides useful information, especially in the energy region where the systematics due to the beamline simulation are particularly high. Besides, \(\sigma(\
u<\
u_{thr},E_{\
u})\) is much larger than the corresponding \(\
u_{\mu}-e^{-}\) cross-section and statistics is not an issue even in low-power beams. The technique works provided that Equation (18) holds, i.e., the experiment records DIS events. This is the reason why it has been used mostly in high \(E_{\
u}\) experiments like NuTev [160], NOMAD and MINER\(\
u\)A, and may be particularly useful for DUNE.
# Smart Poly(acrylic acid)/Poly(acrylamide) Microgels with Interpenetrating Polymer Network Structure
## 2 Materials and Methods
### Asymmetrical Flow Field-Flow Fractionation (AF4)  
Fractionation was performed on an Eclipse DUALTEC AF4 system (Wyatt Technologies Europe, Dembach, Germany) with an Agilent pump system (1260 er series) in a short channel with a height of 350 \(\upmu\)m and 0.02% aqueous NaN\({}_{3}\) solution as eluent.  
The spacer material for the channel, made of poly(tetrafluoroethylene) (PTFE), had a thickness of 490 \(\upmu\)m, with channel dimensions extending 26.5 cm in length and tapering from a width of 2.1 cm to 0.6 cm. Regenerated cellulose membranes with a molecular weight cutoff of 10 kDa (Superon GmbH, Dembach, Germany) served as the accumulation barrier. Flow rates were precisely managed using an isocratic pump (1200 series, Agilent Technologies, Santa Clara, CA, USA) equipped with a vacuum degassing unit. Detection was carried out with a DLS detector (DynaPro NanoStar, Wyatt Technologies, Santa Barbara, CA, USA) and a refractive index (RI) detector (Optilab T-rEX, Wyatt Technology Europe GmbH, Dembach, Germany), both operating at a wavelength of 658 nm.  
During standard AF4 operations, the channel flow rate (Fc) was maintained at 0.8 mL/min. Unless indicated otherwise, the focus flow rate (Ff) was set at 2.5 mL/min and applied for 5 min. Each fractogram presented reflects data from three independent measurements, showing the elution phase starting immediately after the transition from focusing to elution. Typically, an injection volume of 50 \(\upmu\)L was employed. For online DLS analysis, the sample loading was increased to 150 \(\upmu\)g (corresponding to a 1 mg/mL solution) to ensure reliable data and reduce scatterings.  
Overloading effects are not considered as they do not affect the R\({}_{\text{h}}\) data. Unless otherwise stated, the cross-flow rate (Fx) during the elution step was optimized by an exponential Fx decay with a curve factor of 1 from 1 to 0 mL/min in 30 min (method A). To improve the void peak separation of sample PC75, the initial Fx was increased to 2 mL/min with an exponential decay, with a curve factor of 1 to 0 mL/min in 30 min (method B). Data processing was performed by using Astra software (version 6). The angular dependence was calculated by using a 2 nd order Berry fit. The profile of the AF4 separation method is shown in Figure 1.  
Figure 1: AF4 flow profiles for the separation of IPN MGs.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 3 Deep Fuzzy Echo State Networks for Fault Classification  
In this section, a DFESN model [29] is introduced for fault classification. The fault types can occur in three sensors of the leader, three sensors of the follower, the actuator of the follower, and communication (see Figure 1). To achieve fault detection and fault diagnosis simultaneously, we make the normal working condition as another fault type. The classification process inherently identifies whether the system is in a healthy state or experiencing a fault; therefore, fault classification also effectively performs fault detection. The input of the training network utilized for training is \(u(t)=\left[\varphi_{1}^{\prime}(t)\ \varphi_{2}^{\prime}(t)\ \varphi_{3}^{\prime}(t)\right]\) from the data of the follower sensor.
# Welding Image Data Augmentation Method Based on LRGAN Model
## 2 LRGAN Model
### The Generator Part of the LRGAN Model
#### 2.2.3 Nine-Layer Residual Network  
To prevent the model from collapsing due to the accumulation of network layers, this paper introduces a residual module into the generator, with the number of residual network layers set to nine. The last layer uses a tanh activation function, while the other layers use the ReLU activation function. With this nine-layer residual network design, the module can avoid introducing additional computational complexity or increasing the risk of overfitting when expanding the network's depth and width. At the same time, the residual connections ensure the continuity and effectiveness of deep features, allowing information to flow freely without the need for complex nonlinear transformations. The structure of the nine-layer residual generator is shown in Table 1.  
In the generator, the residual block allows the neural network to skip one or more layers without adding extra parameters or increasing computational complexity. The residual module increases the network's depth, enhancing its expressive power, while effectively preventing overfitting and addressing training instability caused by gradient explosion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Layer** & **Input Feature Map (H \(\times\) W \(\times\) C)** & **Output Feature Map (H \(\times\) W \(\times\) C)** \\ \hline ReLU, Conv1, BN & 128 \(\times\) 128 \(\times\) 1 & 64 \(\times\) 64 \(\times\) 64 \\ ReLU, Conv2, BN & 64 \(\times\) 64 \(\times\) 64 & 32 \(\times\) 32 \(\times\) 128 \\ ReLU, Conv3, BN & 32 \(\times\) 32 \(\times\) 128 & 16 \(\times\) 16 \(\times\) 256 \\ ReLU, Conv4, BN & 16 \(\times\) 16 \(\times\) 256 & 8 \(\times\) 8 \(\times\) 512 \\ ReLU, Conv5, BN & 8 \(\times\) 8 \(\times\) 256 & 16 \(\times\) 16 \(\times\) 512 \\ ReLU, DeConv1, BN & 8 \(\times\) 8 \(\times\) 1024 & 16 \(\times\) 16 \(\times\) 256 \\ ReLU, DeConv2, BN & 16 \(\times\) 16 \(\times\) 512 & 32 \(\times\) 32 \(\times\) 128 \\ ReLU, DeConv3, BN & 32 \(\times\) 32 \(\times\) 256 & 64 \(\times\) 64 \(\times\) 64 \\ Tanh, DeConv4 & 64 \(\times\) 64 \(\times\) 128 & 128 \(\times\) 128 \(\times\) 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Input and output of each layer in the residual module.  
Figure 7: Generator activation function.
## 3 Methodology
### Evaluation Metrics  
We used three metrics to evaluate classification performance: accuracy and F1 Score (F1), formulated in Equations (15) and (16), and area under curve (AUC).  
\[Accuracy=\frac{TruePositive+TrueNegative}{TruePositive+FalsePositive+TrueNegative+ FalseNegative}\times 100\% \tag{15}\]  
\[F1=\left(2\times\frac{Precision\times Sensitivity}{Precision+Sensitivity} \right)\times 100\% \tag{16}\]  
* TruePositive (TP) refers to the number of TB cases correctly classified as TB.
* TrueNegative (TN) refers to the number of normal cases correctly classified as normal.
* FalsePositive (FP) represents the number of normal cases incorrectly classified as TB.
* FalseNegative (FN) denotes the number of TB cases missed by our method.  
Additionally, we rated the classifier, using the Kappa Index [55], which takes all elements in the confusion matrix into account, whereas accuracy counts only those on the main diagonal. The Kappa Index was computed:  
\[Kappa\ Index=\frac{\left(P_{observed}-P_{expected}\right)}{1-P_{expected}}\ \times 100\% \tag{17}\]  
\(P_{observed}\) is the observational probability of agreement, \(P_{observed}=\frac{TP+TN}{TP+TN+FP+FN}\), _and_\(P_{expected}\) is the expected probability of agreement, \(P_{expected}=\frac{\left(TN+FP\right)\left(TN+FN\right)+\left(TP+FP\right) \left(TP+FN\right)}{\left(TP+TN+FP+FN\right)^{2}}\). Classification performance based on Kappa Index values is shown in Table 2, as defined by [PERSON] and [PERSON] [56].
## 2 Related Works
### Tracking Data Analysis
#### 2.1.1 Tracking Data Application  
Tracking data can analyze the physical performance of players by extracting their running distance, running heat map, and running speed distribution. [PERSON] and [PERSON] [18] investigated the velocity distribution of elite players to statistically generate velocity ranges that could be consistently used for soccer games, while [PERSON], et al. [19] statistically compared the high-speed running activity demands between training and matches for different positions of players. [PERSON], et al. [20] built a data-driven framework that uses tracking data as a core indicator, to offer a multi-dimension and role-aware evaluation of the performance of players.  
Another powerful application of tracking data is for tactical analysis [17]. Team tactics are mainly determined by team formations (such as 4-3-3), which can inevitably affect various aspects of team performances, such as by defining players' roles, increasing  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & Name & Game Type & Players & Length (s) & FPS & Resolution & Field Size (m\({}^{2}\)) & Field & Split Type \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 2: NPSPT Video Statistics.  
coverage, and determining strategies. Considering the significance of team formation, coaches and analysts are motivated to analyze formation changes [4]. FC Barcelona [2] built a novel parametric pitch control model that incorporates motion information, relative distance to the ball, and player positions in order to provide a smooth surface of potential ball control. They employed the full spatio-temporal dynamics of each player to construct two spatial value creation metrics, accounting for both occupation and generation of spaces. [PERSON], et al. [5] presented a method which allows for large-scale analysis of team formations across large volumes of player and ball tracking data. By using a role-representation, they clustered the plays of a team to describe their common motion patterns.  
The construction of data analysis methods and research models are based on a large amount of original tracking data. Due to the cost constraints for obtaining raw data, most research focuses on the top professional leagues. However, young and non-professional players, who make up a large proportion of the soccer population, also have an urgent need of data analysis. The analysis methods of this part of the population are very different from those of professional players.  
Therefore, designing a low-cost and high-efficiency acquisition of tracking data is important.
## 3 Results and Discussion
### Antimicrobial Activity of AgNP Evaluated in an Ex Vivo Skin Model
Although the skin fractions treated with only EtOH looked more preserved, their overall skin viability was much lower. The viability of the skin cells was almost 80% with 100% EtOH and 5% SDS. The lower skin cell viability (<60%) was obtained with AgNP + 70% EtOH. In contrast, AgNP and extract provide a skin viability similar to PBS, almost 100%. This result shows that AgNP (non-diluted) do not compromise the skin cell viability as measured in an ex vivo model. The swab tests (Figure 8c) had the purpose of assessing the sterilization that each solution provided during the time course of the assay. On day 0, skin samples treated with 70% EtOH, 100% EtOH, AgNP + 70% EtOH, and AgNP + 100% EtOH solutions show no growth of microorganisms. The skin slices treated with the TE or 5% SDS presented similar results, showing some microbial growth. PBS and AgNP presented comparable results, showing more colonies than the previous solutions. On days 7 and 14, plates with PBS, TE, 5% SDS, and AgNP show similar aspects, and the microbial growth is equivalent. The histological results were crucial in determining the cutaneous irritation caused by the solvents used (Figure 8d). It is recognized that the source of porcine skin can have a substantial impact on contamination, epidermis structure, and usable time. Compared to PBS (Figure 8d-A), after treatment with 70% EtOH, 100% EtOH, AgNP, and 5% SDS, the skin's superficial layer seems to be contracted. The extract, AgNP + 70% EtOH, and AgNP + 100% EtOH did not cause changes in the thickness of the skin layers when compared to PBS. These results indicate that the presence of AgNP with EtOH can decrease ethanol's cutaneous irritation potential.