esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Model Fitting
#### 4.3.5 Optimization of the Regression Model  
Equation (2) was derived for each response (\(Y_{i}\)) using the \(a_{i}\) values of Table 8. It was done using commercial software Statgraphics Centurion version 16.2. The optimization lets to the optimal value of \(X_{i}\) normalized between \(-1\) and \(+1\).  
In the case of FMR and FPG, optimization aimed to obtain the maximum value. On the other hand, for the SPG and CPG responses the minimum value was obtained (Table 9). By means of a single flotation stage, the optimal values for each of the responses showed outstanding values highlighting a FMR of 70.12% and a FPG of 74.50%.  
In the case of the flotation process carried out in this research, the optimization of the model has short utility if the optimization of multiple responses is not performed. Through multiple optimization of FMR and FPG, assigning an equal weight to each response, the optimal factors values are shown in Table 10. The results obtained for each response were 68.99% FPG and 60.45% FMR.  
With the optimization, the final regression equation can be defined as  
\[\begin{array}{l}\text{FMR=}a_{01}\text{+}a_{11}0.45\text{+}a_{21}0.58\text{+} a_{31}\text{+}a_{41}\text{+}a_{51}0.22\text{+}a_{61}0.45^{2}\text{+}a_{71}0.45\\ \text{-}0.58\text{+}a_{81}0.45\text{+}a_{91}0.45\text{+}a_{101}0.45\text{-}0.22 \text{+}a_{111}0.58^{2}\\ \text{+}a_{121}0.58\text{+}a_{131}0.58\text{+}a_{141}0.58\text{-}0.22\text{+}a _{151}\text{+}a_{161}\\ \text{+}a_{171}0.2\text{+}a_{181}\text{+}a_{191}0.22\text{+}a_{201}0.22^{2}\\ \end{array} \tag{4}\]  
\[\begin{array}{l}\text{FPG=}a_{02}\text{+}a_{12}0.45\text{+}a_{22}0.58\text{ +}a_{32}\text{+}a_{42}\text{+}a_{52}0.22\text{+}a_{62}0.45^{2}\text{+}a_{72}0. 45\\ \text{-}0.58\text{+}a_{82}0.45\text{+}a_{92}0.45\text{+}a_{102}0.45\text{-}0. 22\text{+}a_{112}0.58^{2}\\ \text{+}a_{122}0.58\text{+}a_{132}0.58\text{+}a_{142}0.58\text{-}0.22\text{+}a _{152}\text{+}a_{162}\\ \text{+}a_{172}0.22\text{+}a_{182}\text{+}a_{192}0.22\text{+}a_{202}0.22^{2} \\ \end{array} \tag{5}\]  
Based on these results, it has gone from having a non-marketable by-product with 41% of FPG to recover, in a single flotation stage, 60.45% of the fluorite content with an FPG of 68.99%. The product obtained is marketable as metallurgical grade fluorite.
# AlGaN Ultraviolet Metal-Semiconductor-Metal Photodetectors with Reduced Graphene Oxide Contacts
## 3 Results and Discussion
The spectral photoresponsivity (\(R\)) is calculated with the ratio of the measured output current for a given input optical power as follows [25]:  
\[R=\left(I_{photo}-I_{dark}\right)/P_{inc}\]  
where \(I_{photo}\) is the photocurrent, \(I_{dark}\) is the dark current, and \(P_{inc}\) is the incident optical power. The peak in photoresponsivity is shifted to lower wavelengths (i.e., higher energies) for the rGO/AlGaN samples with higher Al mole fractions. The samples show the peak photoresponses of 0.176, 0.143, and 0.111 A/W at the wavelengths of 335 nm, 325 nm, and 315 nm, respectively. Note that the difference in the peak wavelengths can be associated with the difference in the energy bandgaps of Al\({}_{x}\)Ga\({}_{1-x}\)N. The energy bandgap of AlGaN can be calculated by linear interpolation between the theoretical bandgap values of GaN (3.43 eV) and AlN (6.2 eV) [26; 27]. Energy bandgaps of 3.69 eV, 3.8 eV, and 3.93 eV are obtained for the samples with \(x=0.095\), 0.134, and 0.179, respectively. These values show high correspondence with the peak photoresponsivity wavelengths in Figure 5. The photoresponsivity curves show gradual decreases for further decreasing wavelengths, which is because the photoresponsivity is proportional to the wavelength [5]. All of the experimental results in Figure 5 agree with the theoretical prediction that is expected for the AlGaN UV MSM photodiodes with various Al mole fractions. Small kinks (only visible in a logarithmic-scale plot of Figure 5) in the photoresponsivity curves were also observed near the wavelengths corresponding to the energy bandgap of GaN, which is possibly because of absorption by the underlying GaN layer, as in the previous report by [PERSON] et al. with a graphene/AlGaN Schottky diode [28]. As another figure of merit of photodetectors, the detectivity (\(D^{\bullet}\)) shows how much minimum signal can be detected by the photodetector. The detectivity is calculated by using the simplified equation [29]:  
\[D\ \sim\frac{A^{\frac{1}{2}}R}{\left(2 qI_{dark}\right)^{\frac{1}{2}}} \tag{1}\]  
where \(A\) is the junction area and \(q\) is the elementary charge. At the peaks in photoresponsivity, the MSM photodiodes with the Al mole fractions of 0.095, 0.134, and 0.179 showed the detectivity of 3.9 \(\times\) 10\({}^{12}\), 3.17 \(\times\) 10\({}^{12}\), and 2.46 \(\times\) 10\({}^{12}\) cmHz\({}^{1/2}\)W\({}^{-1}\), respectively, which are values that are comparable to the literature [29]. Figure 5: Spectral photoresponses of the rGO/AlGaN MSM photodiodes with various Al mole fractions.
# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
###### Abstract  
A model to forecast the underground temperature in a mine ventilation circuit was developed on the basis of a case study and actual data describing temperature, airflow, and drift length collected over several years. A mathematical model featuring seven variables with interactions provided reliable predicted temperatures, achieving a correlation of \(R^{2}=0.933\) with an estimation error of \(\pm 2\,^{\circ}\)C. Its soundness was proven using both the node-to-node analysis and the multi-node approach. The multi-node approach was shown to be an interesting option to model underground mining environments. This model can be very useful to predict the temperature evolution along the main ventilation system, determine the best workplace conditions in terms of temperature, and analyze different planning scenarios of the mine. Moreover, some recommendations are presented for obtaining reliable data when using temperature sensors and the model in a U-shaped ventilation system.  
Keywords: underground mining; mine ventilation; predictive model; temperature prediction; workplace environmental conditions +
Footnote †: journal: Applied sciences
# AR Presentation of Team Members' Performance and Inner Status to Their Leader: A Comparative Study
## 6 Results
### Questionnaires
#### 6.2.2 Strategy  
After each trial, participants were asked to explain their task assignment strategy with open feedback. It revealed that when stress feedback was not provided, the main focus was on the Success component when participants needed to choose to whom to assign a task, as an indirect way to assess operators' stress and chances to succeed in tasks. Eighteen participants reported doing so after the _Textual No Stress_ condition and 17 after the _Graphical No Stress_ condition. Otherwise, the Stress component was the main focus when available. Ten participants stated that their strategy was based on the Stress Component after the _Textual Stress_ condition and 16 reported the same after the _Graphical Stress_ condition. Following operators' stress feedback, participants aimed at assigning tasks to operators with a low or medium_simulated stress_ level. Sixteen participants reported doing so after the _Textual Stress_ condition and 12 after the _Graphical Stress_ condition. The presentation seems to have little effect on the task assignment strategy except for the Stress component: more participants reported focusing on the stress indicator when it had a graphical presentation than a textual one, which supports **H2**.
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### The Classifier System
#### 3.2.2 LSTM and BiLSTM  
The Recurrent Neural Networks (RNN) is one type of effective model for sequential data classification, such as our application of dialect identification of a stream of text. For long sequences, such as the text input on social networks, RNN has the drawback of vanishing gradients. Long Short Term Memory (LSTM) neural networks are designed to extract long-term dependencies by integrating a memory state into a regular RNN. The LSTM uses a previous state h_(t\(-\)1) and an input x_t to measure the hidden state h_t, as illustrated in Figure 2. We used a word-based LSTM in this study, with an architecture of 100 hidden units in 100 dimensions.  
Conventional LSTMs have the drawback of being able to decide predictions based only on previously seen content. Bidirectional LSTMs (BiLSTMs) are a form of LSTM that solves this problem by data processing in both directions in two different hidden layers. These two hidden layers are feedforwarded to the same output layer. The forward hidden sequence, the backward hidden sequence, and the output sequence are all computed by BILSTMs.  
The architecture we used for the BiLSTM classifier is:  
* Embedding Layer: Each word represented with 300 dimensions;
* Sequence length 30 time stamp;
* Dropout rate = 0.8;
* L2 norm of regularization;
* Relu activation;
* Dense Layer: 4 units with a softmax activation function.  
In addition, with the recent notable success of combining attention mechanisms with neural networks in a variety of NLP problems, including speech recognition, machine translation, and image captioning [33; 34], we investigated employing an attention mechanism with our BiLSTM models to boost the models' accuracy. We used an implementation inspired by [35], in which we applied attention to the LSTM layers output vector.  
The architecture we used was the following: Tanh activation function on the outputs of BiLSTM in sequence, calculate the softmax of the outputs (attention weights), multiply the attention weights with outputs of BiLSTM, then sum the outputs of the multiplication.
## 3 Results
### Rapid Chromosome Duplication Has a Greater Requirement for Rep than for RecA  
The above data do not address the relative importance of Rep and recombination enzymes in underpinning efficient fork movement. The time taken to replicate chromosomes during a single cell cycle was therefore estimated using flow cytometry in strains lacking either Rep or RecA. Upon synchronising replication initiation using the temperature-sensitive _dnaA46_ allele, wild type cells take 40-50 min for their DNA content to increase from 1 to 2 chromosome equivalents but \(\Delta\)_rep_ cells take more than 80 min [31] (see also Figure 3A,B). This extended duplication time reflects the impact of nucleoprotein complexes on fork progression in the absence of Rep [22; 30]. In contrast to \(\Delta\)_rep_ mutant cells, we found that the majority of _recA_ mutant cells had completed genome duplication after 40-50 min (Figure 3C). We also tested the time taken for chromosome duplication in _recB_ mutant cells. The requirement for either Rep or RecBCD for survival implies that one or the other of these enzymes provides an essential means of underpinning fork progression [71; 72]. However, _recB_ mutant cells had chromosome duplication times similar to those found in wild type and _recA_ mutant cells (Figure 3D).  
These data demonstrate that absence of either RecA or RecBCD does not lead to significant slowing of the mean time taken for replication forks to travel from _oriC_ to the terminus region. Processing of blocked replication forks by either RecA or RecBCD is therefore not critical for rapid chromosome duplication.  
Figure 2: The chromosome content defects of _rep_ and _recA_ mutant cells at mid-logarithmic phase in rich medium are resolved by the time stationary phase is reached. Strains (**A–D**) are the same as those used in Figure 1A (i–iv).
# Recurrence Quantification Analysis (RQA) of Toroidal End Tool Milling Process
## 2 Methodology of the Experiment
### Experimental Setup  
The experimental tests were conducted on a DMU 100 monoBLOCK (Hoffman Estates, IL, USA) machining center (Figure 3). In each test, three cutting force components were recorded using a Kistler 9257B piezoelectric force sensor (Winterthur, Switzerland). The F\({}_{\text{f}}\) force component was measured in the direction of feed motion (F\({}_{\text{f}}\)), the F\({}_{\text{f}\text{N}}\) component in the direction normal to the feed, and the F\({}_{\text{a}}\) component along the tool axis. The signals were then amplified using a Kistler 5070 load amplifier. The force signals were converted into digital form with a National Instruments NI USB-6003 digital-to-analog converter (Austin, TX, USA). The data recording and analysis of cutting force components were performed using SignalExpress 2013 V7.0 software.  
The test samples (3 in Figure 3) were made of 42 CrMo4 steel, with dimensions of \(150\times 50\times 20\) mm. Two 8 mm diameter holes were drilled into each specimen to allow for rigid attachment to the force gauge. This clamping method ensured maximum fixture stability. Two machining passes were performed on each specimen with defined cutting parameters.  
For cutting tests, a Sandvik R300-050Q22-08H toroidal cutter (Stockholm, Sweden) with a diameter of 42 mm was used. A single R300-0828E-PL 1030 circular cutting insert was installed on the cutter. This insert features a relief angle of 8 degrees and a rake angle of 7 degrees and is made of tungsten carbide coated with TiAlN using PVD technology. The insert is specifically designed for machining materials from the P group. Only one cutting edge (one insert) was utilized to eliminate the impact of the tool run-out. Additionally, the use of a single insert removed the potential interference from overlapping force signals caused by multiple cutting edges, thereby improving the accuracy of the force signal analysis. Additionally, to ensure consistent and invariant testing conditions, each cutting test was performed using a new cutting insert. This approach eliminated the influence of tool wear on the cutting process and the analyzed parameters and indicators. Such an experimental setup enabled the investigation of dynamic process variations without the impact of tool wear while maintaining constant workpiece material parameters and ensuring the stability of the cutting process.  
In the modal analysis of the tool holder-cutter system, the modal hammer type PCB 086C03 and the accelerometer type PCB M353 B16 were used. Force and acceleration signals were recorded using Carrier NI USB-9162 (Palm Beach Gardens, FL, USA) with an AC/DC NI 9233 measurement card coupled.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 5 Performance Analysis
### Heterogeneous Networks
Figure 14: Results for heterogeneous networks: eight nodes in total, half operating under one variant. Each plot contains the aggregate normalized airtime of each variant (first two bars), the overall channel efficiency, and fairness. The coexistence results are summarized in Table 3. In this study, fairness is the most important metric. The highest fairness is achieved by random-muting FBE. Fortunately, it exhibits quite good efficiency. Meanwhile, low fairness with near-perfect channel efficiency is meaningless, as indicated by standard FBE's results. Greedy-enhanced FBE has the second highest efficiency, but is aggressive in acquiring channel resources. Thus, we conclude that there is still room for improvement in designing an FBE variant with good coexistence qualities.
## 6 Conclusions  
Taking full advantage of the multiple coverage characteristic of LEO constellation, this paper proposed two cooperative downlink transmission methods. In detail, beam-edge UTs could directly combine the signals as one useful signal from the accessible satellites in the DC method. With the help of DC, signals arrived at the UT could be enhanced. When the system applies TDM in downlink transmission, beam-edge UTs are able to receive signals in different time-slots from the satellites. To obtain a better performance, we further study an SC-based method where the receiver at the UT would select the strongest signal to demodulate. However, the selection overhead is not friendly toward the ergodic sum-rate. Specifically, as there exists a significant equivalent channel gain gap at the beam-edge and beam-center, we exploited the co-carrier scheme for both methods where the beam-edge and beam-center UTs are able to occupy the same resource block. To analyze the ergodic sum-rate and outage probability, we further derive the analytical expressions for the new methods. Simulation results illustrate that the co-carrier-based DC could provide a remarkable ergodic sum-rate or ergodic rate for beam-edge UTs. In terms of the outage probability, the co-carrier-based SC method outperforms the DC since the diversity order of the SC increases with the number of the satellites. Overall, the proposed co-carrier cooperative methods could fully use the energy resource without consuming additional power onboard compared with the non-cooperative method.  
Conceptualization, T.L. and G.L.; methodology, T.L. and G.L.; validation, T.L. and X.Y.; writing--original draft preparation, T.L., X.Y. and B.D.; writing--review and editing, T.L., X.Y. and B.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 62201533.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
Figure 10: Outage probability with \(M=9\) and \(\alpha=0.2\).
# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
## 3 Experimental Setup and Dataset Description
### Decoding Strategy and Language Models  
Different strategies were considered during decoding: greedy decoding and beam search n-gram language model decoding. For the beam search, 3-gram and 4-gram KenLM [76] models were constructed for each language. For each configuration, two models were trained depending on the data: one included only training transcription data, and the other included primary transcription data and text data from other corpora. Moreover, we constructed extensive language models for each target language by crawling text corpora spanning diverse sources like speech transcriptions, online texts, and books. The size of the text corpora collected for training the language models was relatively modest, with fewer than 100k words secured for each language. Beam search hyperparameters \(\alpha\) and \(\beta\) were selected based on the dev set and searched during 50 trials with Bayesian optimization techniques, by fixing a beam width of 128.  
However, due to the lack of a standard normalization of the transcriptions and the low amount of data, this optimization did not lead to significant improvement and even degraded performance for some languages. Therefore, the final decoding strategy was only based on greedy search and heuristic corrections applied to correct textual errors such as capitalization, punctuation, and reducing multiple spaces or letters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{3}{c}{**Train**} & \multicolumn{2}{c}{**Dev**} & **Test** \\ \cline{2-6}  & **Primary** & **Speed Augm.** & **External** & **Total** & **Primary** & **Primary** \\ \hline Bribri & 0.49 h & 0.98 h & 1.14 h & 2.61 h & 0.04 h & 0.19 h \\ Guarani & 0.32 h & 0.64 h & - & 0.97 h & 0.02 h & 0.12 h \\ Kotiria & 2.69 h & 5.43 h & 21.8 h & 29.92 h & 0.5 h & 0.3 h \\ Wai’khana & 1.49 h & 2.98 h & - & 4.39 h & 0.1 h & 0.21 h \\ Quechua & 1.67 h & 3.34 h & 7.04 h & 12.09 h & 0.2 h & 2.08 h \\ \hline \end{tabular}
\end{table}
Table 1: Detailed description of train-dev-test splits for each language and data source. Primary refers to the corpus provided by the AmericasNLP organization. Speed augmentation is performed only on the primary database and external refers to corpus collected from other sources.
## 2 Related Work
### Parallel Implementations for Big Data Visualization  
Along with data reduction, researchers have also recently proposed parallel implementations to address scalability issues; these approaches include many-core GPUs, distributed clusters, or hybrid architectures.  
Compared with multi-core processors, many-core GPU processors exhibit a higher degree of explicit parallelism and higher data throughput and have been widely used to accelerate index generation, offline preprocessing, and rendering during visualization. [PERSON] et al. proposed a novel indexing scheme that makes use of modern GPUs to efficiently support spatio-temporal queries over massive point datasets, and that also achieves interactive, sub-second response times for queries over 1.1 billion tweets [26]. Moreover, [PERSON] et al. designed a distributed visualization framework with Hadoop/Spark that leverages GPU to compute data aggregates with kernel density estimation [27]. The open-source project MapD [28] uses GPU to accelerate the processing of complex, real-time spatio-temporal data and can process billions of rows of data in milliseconds. Finally, imMens [24] also uses the GPU-based WebGL to conduct data processing and rendering.  
Distributed clusters with commodity hardware are also very commonly used in big data visualization applications. For example, VisReduce is built on a distributed NoSQL database and implements both online aggregation and data compression [29]. Moreover, the tile-based visual analytics system (TBVA) performs the offline preprocessing of big data through a Spark cluster, generating the tile data of different spatial levels in advance and calculating the statistical values of the attribute dimensions of each tile to achieve an interactive visualization effect [30]. GeoMesa/GeoWave uses NoSQL databases to store spatio-temporal data in a distributed manner and can further use MapReduce or Spark to accelerate data processing and visualization [31; 32].  
Most of these implementations concentrate on using parallel/distributed platforms for data processing and online rendering, while very few of them consider using these resources to develop a unified solution for big data visualization, from quick pre-processing to scalable storage, and further to efficient online query/filtering.