esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Resource Neural Machine Translation Improvement Using Source-Side Monolingual Data
## 4 Dataset  
We utilized parallel datasets for Wolaytta-English from the [PERSON] et al. [19] study and collected a monolingual Wolaytta dataset from several sources, such as Wolaytta Fana Radio, Wolaytta Wogeta Radio, and Wolaytta text books. Table 2 depicts the parallel and monolingual datasets used in this study and their distribution in terms of the number of parallel and monolingual sentences, types, tokens, and vocabulary size.  
As Table 2 shows, our final dataset contains 30 k parallel sentences for the Wolaytta-English language pair and 40 k monolingual dataset for Wolaytta. In the Wolaytta-English parallel dataset, there are 19,736 unique words in English, 54,825 unique words in Wolaytta, and 70,289 unique words in the Wolaytta monolingual dataset.  
Comparing unique words in the parallel dataset, Wolaytta has about three times more words than English. This is due to the morphological inflection and derivation of the Wolaytta language, which leads to a huge number of variations for a single word. Comparing the Wolaytta parallel dataset and the Wolaytta monolingual dataset, in the same way, the latter has about twice as many words as the parallel dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Languages** & **Sentences** & **Tokens** & **Types** & **Vocabulary Size** \\ \hline English & & 763,083 & 19,736 & 11,130 \\ \hline Wolaytta & & 511,426 & 54,825 & 22,706 \\ \hline Wolaytta monolingual & 40 k & 496,251 & 70,289 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Wolaytta–English parallel and Wolaytta monolingual dataset distribution.  
Figures 1 and 2 show the most common tokens in the parallel and monolingual datasets, respectively. Parallel and monolingual Wolaytta data, as shown in Figures 1a and 2, share the most common words regardless of the domain. The Wolaytta-English parallel dataset belongs to the religion domain, but the Wolaytta monolingual data belong to the sports, news, and education domains. For example, words such as, \(a\) ('she'), _ha_ ('this'), _ba_ ('go'), \(i\) ('he'), _he_ ('that'), _eta_ ('them'), _ta_ ('me'), _ne_ ('you'), _gishawi_ ('because of'), _deiya_ ('have'), _issi_ ('one'), and _mala_ ('similar') appear in both datasets, but words such as _[PERSON]_ ('[PERSON]'), _israaeela_ (Israel'), and _godai_ ('The God') are not found in the Wolaytta monolingual data because these words are only used in the religion domain.  
Figure 3 depicts the word count per sentence in the parallel datasets for each of the languages. From the combined diagram of the Wolaytta and English parallel data in Figure 4, it is evident how different Wolaytta and English sentence lengths are.  
Figure 1: Fifty most common words in the parallel corpora. (**a**) Wolaytta parallel data; (**b**) English parallel data.  
Figure 2: Fifty most common words in Wolaytta monolingual data.
## 2 Antenna Design Methodology  
### Antenna Element Design  
The initial antenna element on which the design is based consists of a microstrip patch operating at 3.5 GHz on a Rogers TMM10 substrate with a permittivity of 9.2. This antenna has a compact dimension of 22 mm \(\times\) 27 mm \(\times\) 1.270 mm. Its parameters were calculated based on the transmission line method presented in [32], where the effective dielectric constant is calculated by:  
\[\varepsilon_{\text{reff}}=\frac{\varepsilon_{r}{+}1}{2}+\frac{\varepsilon_{r}{ -}1}{2}\bigg{(}1+12\frac{h}{W}\bigg{)}^{-1/2}, \tag{1}\]  
where \(\varepsilon_{r}\) is the dielectric constant of the substrate, \(h\) is the height of the substrate, and \(W\) is the width of the patch. The length of the patch is obtained by:  
\[\Delta L=0.412h\frac{(\varepsilon_{\text{reff}}{+}0.3)(W/h+0.264)}{(\varepsilon _{\text{reff}}{-}0.258)(W/h+0.8)}, \tag{2}\]  
\[L=\frac{c}{2\,fr\,\sqrt{\varepsilon_{\text{reff}}}}{-}2\Delta L, \tag{3}\]  
where \"\(\Delta L\)\" is the length of the transmission line, \(L\) is the length of the patch, \(c\) is the speed of light in free space, and \(fr\) is the resonant frequency of the antenna. The width of the patch can be represented by:  
\[W=\frac{c}{2 fr}\sqrt{\frac{2}{\varepsilon_{r}{+}1}}. \tag{4}\]The ground plane dimensions can be calculated with the help of the following equations:  
\[L_{g} =6h+L \tag{5}\] \[W_{g} =6h+W \tag{6}\]  
The structure was modeled in the EM 3D CST Studio Suite(r) analysis software (Version 2019, Dassault Systemes, Velizy-Villacoublay, France) as shown in Figure 1. Its physical dimensions were determined to be the following values: \(L_{g}\) = 22 mm, \(W_{g}\) = 27 mm, \(L\) = 14 mm, \(W\) = 19 mm, \(W_{f}\) = 2.2 mm, and \(Y\) = 4.5 mm.
## 4 Discussion  
Based on the results above, two material removal mechanisms can be considered responsible for the surface smoothing process during the ultrasonic abrasive polishing of additive manufactured components. The first is the material removal due to cavitation collapse pressure, which is effective in dislodging the partially melted powders from the surface. The bonding neck between a partially melted powder and the surface acts like a crevice, which entraps gas and improves the cavitation erosion [4]. The second material removal mechanism is based on the impact of abrasive particles suspended in the slurry. The abrasive particles are accelerated by the cavitation pressure and the ultrasonic energy of the horn, and tend to penetrate the workpiece in different directions. The SPH simulation in this study only demonstrated the vertical impact action of abrasive particles, which, it is believed, would smooth the surface by plastic deformation. However, it should be noted that the impact from other directions would result in material removal by micro-cutting or sliding [27]. All of these make abrasive particles capable of smoothing and further removing large structures and discontinuities on the surface of AM components. Figure 13 shows the schematic of material removal mechanisms in the ultrasonic abrasive polishing of additive manufactured components explained above.  
The two material removal mechanisms are greatly influenced by the process conditions. Even though the general polishing performance under different conditions has been investigated through the experiments, the nature of the change is not fully understood. The real velocities of the abrasive particles accelerated by the cavitation collapse pressure and the ultrasonic energy of the horn should be further studied, so that the polishing process can be improved. In addition, the cavitation erosion is often associated with damage on smooth specimens based on earlier studies, so it may also have a tendency to worsen the surface smoothness in the polishing process. The control of the two conflicting effects of cavitation on AM surface finish is important to ensure efficient polishing, and should be investigated in the future study. It is believed that the technology could be an ideal solution for the surface modification of AM components. Moreover, as the ultrasonic abrasive polishing process is conducted uniformly on the whole working area by using suitable settings, this polishing method can be used to process surfaces with various complex shapes and internal features.
## 3 Problem Formulations  
Two out of four routing techniques proposed different routing paths that directly connect the source and sinks through intermediate, relay, and forwarder nodes. The probability of packet losses, node failure, energy tax (extra), and inefficient data gathering in these schemes is common. Furthermore, the direct path through mediators' nodes is an old technique in which EBER\({}^{2}\), WDFAD-DBR, and AVH-AHH-VBF are inline. With reference to their simulation, unsatisfactory results are achieved. Moreover, the AEDG and ASEDG both introduce the atomic shape path for relay nodes and sinks. Still, it is not rotational, so there is a need to design a routing path that is shortest in length and covers the parameters mentioned above. In our proposed work, there was some consideration because of our focus on the avoidance of void nodes by introducing inter-node adjustment and providing alternative paths via rotational orbit-based shapes (as orbits are much denser then atomic and elliptical shapes and cover a larger area [11; 13]). The first consideration is: (i) every node has its localization information, (ii) symmetric communication was assumed between two nodes, for example, '\(i\)' and '\(j\)', (iii) sinks can receive multiple buckets at the same time, (iv) the vertical direction in water was mostly not considered, but due to water current, the horizontal direction was considered, (v) dead nodes were near the sinks called Sonobuoys Neighboring Nodes (SNNs), and its orbit path was considered for these SNNs when introducing inter-node adjustment (which is the proposed scheme), and (vi) if the SNNs zone is closer to the sinks, then the internode has sent the data directly without taking an alternative path to improve network throughput [19]. The major contribution of our work is as follows.  
Our contribution in this regard was to make the path officially rotational (based on inter-nodes adjustment) and have orbits shape, not atomic and elliptical [11]. It is interesting to know about atomic that its particle is itself surrounded by rotation. However, the atomic particles that move around its central point also have the shape of orbits. The major advantage of the orbit-based path covers much area as compared to atomic path and elliptical. The two-way motion described in the literature according to _Heisenberg's Principle_[16] is possible, in which they state that _\"every particle have two-way motion, first itself and other around the material in which they occupied\"_[20].  
In addition, the orbit is larger than a circle, and quite similar to an elliptical shape; on the contrary, the atomic path is also under the umbrella of eclipse shapes. From this reference, we can say that the ancestor of these shapes (including the atomic shape path used in ASEDG [13]) of the orbit has more advantages and result-oriented features in all relative parameters.  
Furthermore, the area covered by the atomic path is not much greater than the orbit path mentioned in _'Bernoulli's Equation'_[17]. It states that the area covered by its volume is always directly postponed to its _'pressure and performance.'_
## 3 Prediction Results of Mining Surface Subsidence of Northern Pei County for Reusing Land Resources
### The Influence of Geological and Mining Conditions on Prediction Parameters
#### 3.1.1 Lithology of Overlying Strata  
The lithology overlying strata has an impact on the surface subsidence coefficient. If the overburden is a hard stratum, it can appear a large number of separations in the process of rock stratum movement and destruction. If the overburden is a soft stratum, it is not easy to separate in the process of rock stratum movement and destruction. Therefore, the harder the overlying strata, the smaller the subsidence coefficient. The average consistency coefficient \(f\) of the overlying strata is usually used to reflect the overburden lithology. As \(f\) increases, the subsidence coefficient will reduce. The calculation method of \(f\) is as follows [26]:  
\[f=\frac{\sum M_{i}R_{i}}{10\sum M_{i}} \tag{4}\]  
In which:  
\(M_{i}\) is the normal thickness of the \(i\) stratum, m;  
\(R_{i}\) is the uniaxial compressive strength of the \(i\) stratum, _Mpa_;  
\(f\) is the average consistency coefficient.  
The lithology of overlying strata also has an influence on the tangent of major influence angle. When the mining depth is the same, the overlying strata is harder, and the tangent of major influence angle \(tan\beta\) is smaller. The empirical formula of the relationship between \(tan\beta\) and the lithology of overlying strata is given in the literature [26]:  
\[tan\beta=(D+0.0032H)(1-0.0038\alpha) \tag{5}\]  
where: \(D\) is the lithology influence coefficient, and it has a relation with the stratum comprehensive evaluation coefficient \(P\); \(H\) is the mining depth, m; \(tan\beta\) is the tangent of the major influence angle; \(\alpha\) is the inclined angle of the coal seam, \({}^{\circ}\).  
The strata are at the state of the cantilever beam due to the existence of the voussoir beam in the goaf boundary. This part of the strata is hard to cave under the protection of the voussoir beam. If the overlying strata are hard, the voussoir beam structure possibly is bigger, and the inflection point offset should be larger.  
In general, the tangent of the major influence angle has a correlation with the coal seam dip angle. However, the lithology of overlying strata also has a certain influence on the value of the tangent of the major influence angle. When the overlying strata are very soft, no matter how large the coal seam dip angle is, the value of the tangent of the major influence angle is \(90^{\circ}\).
## 3 Results and Discussion
### Characterization of Sorbent Material  
#### 3.6.1 XRD Analysis  
XRD designs of the treated bleaching clay and 3-amino-5-hydroxypyrazole impregnated bleaching clay (AHIBC) are exposed in Figure 5. The two designs were chiefly composed of montmorillonite with a small quantity of quartz and kaolinite. The treated bleaching clay involves mostly of montmorillonite with individual peaks at 20 = 7.2\({}^{\circ}\), 19.76\({}^{\circ}\), 27.42\({}^{\circ}\), 36\({}^{\circ}\), and 50.31\({}^{\circ}\)[47], and kaolinite has a peak at 20 = 12.33\({}^{\circ}\)[48]. Moreover,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Temperature, K** & **298 K** & **303 K** & **308 K** & **313 K** & **318 K** & **323 K** & **328 K** \\ \hline \(\Delta\)G\({}^{\circ}\), kJ-mol\({}^{-1}\) & \(-\)4.43 & \(-\)4.40 & \(-\)4.37 & \(-\)4.35 & \(-\)4.32 & \(-\)4.29 & \(-\)4.26 \\ \hline \(\Delta\)H\({}^{\circ}\), kJ mol\({}^{-1}\) & & & & \(-\)6.01 & & & \\ \hline \(\Delta\)S\({}^{\circ}\), kJ mol\({}^{-1}\) K\({}^{-1}\) & & & & \(-\)0.53 \(\times\) 10\({}^{-2}\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermodynamic parameters of the Y(III) adsorption on the AHIBC adsorbent.  
Figure 4: Graph of log K\({}_{\rm d}\) versus 1/T for Y(III) adsorbed on the AHIBC adsorbent.  
quartz has a reflection peak at 20 = 28.15\({}^{\circ}\). The peak at 20 = 7.2\({}^{\circ}\) with d(001) = 11.12 nm spacing specified that the treated bleaching clay is sodium montmorillonite [49]. The XRD design of AHIBC shows a shift of the 20 from 7.2\({}^{\circ}\) to 6.22\({}^{\circ}\) by a distinct growth in the d(001) from 11.12 nm to 13.34 nm for AHIBC. It approved the combination of 3-amino-5-hydroxypyrazole in the interlayer of Na-montmorillonite.
## 3 Results
### Case II: Healthy vs. Epilepsy Subjects  
In our investigation, to classify healthy and epilepsy subjects using CNN, we considered two different scenarios. Specifically, we evaluated the effectiveness of the CNN in classifying various combinations of healthy (Z and O) and epilepsy (N, F, and S) states. The performance metrics for each case are shown in Figure 7. In the first case, when distinguishing between a collective group of healthy subjects (Z and O) and interictal subjects (N and F), the model achieved an Acc of 95%. Pre and Rec metrics, both at 95%, indicate the model accuracy while balancing false positives and negatives. This balance is further evidenced by its F1 of 95%. In the second case, we aimed to classify the combined healthy subjects (Z and O) from the interictal (N and F) and ictal (S) states of epileptic subjects. The model achieved 97% Acc, indicating a high degree of overall correctness. Further, Pre and Rec metrics were also 97%, indicating the model's ability to accurately identify relevant data while managing false alarms.  
The CNN training and validation metric curves shown in Figure 8a for healthy vs. epileptic subjects demonstrate effective learning. For the (Z+O) vs. (N+F+S) scenario, the model showed continuous improvement, reaching an NTA of 1.0 and an NTL of 0.0 by the 7 th epoch. Although the NVA curve showed fluctuations, it also showed growth. Similarly, in (Z+O) vs. S, the model maintained stability during its learning process, with the NTA reaching 1.0 by the 6 th epoch and the NTL decreasing to 0.0. The NVA continued to grow and remained stable. These results emphasize the model's effectiveness in accurately classifying the chosen state combinations.  
Figure 7: Radar plot representation of the CNN performance metrics for classifying healthy vs. epileptic subjects.  
The normalized confusion matrices for the two significant classification scenarios considered in the CNN model are shown in Figure 8b. The model accurately classified Z+O from N+F combinations. It had a TPR of 98.57% for the identification of N+F states, with a low FNR of 1.43%. The TNR was 96.19% and the FPR was effectively minimized to 3.81%. In the complex classification scenario Z+O vs. N+F+S, the model demonstrated robust performance, yielding 100% TNR to accurately identify N+F+S states and maintaining a 5% FPR. This indicates minimal errors in the classification of (Z+O) as (N+F+S). These results demonstrate the model's proficiency in the classification scenarios with remarkable overall Acc and F1 values.
# Review on Engineering Application Status of Gob-Side Entry Retaining Technology in China
## 3 Parametric Study of GSER Application Status
### Entry-Side Filling Width  
In the application of GSER technology, the entry-side filling width is one of the key parameters that directly affect the stability of the surrounding rock and the entry retaining speed. If the filling width is too small, the bearing strength and stability of the filling body are difficult to adapt to the strong ground pressure during the GSER. Conversely, if the filling width is too large, it leads to higher construction material costs and slower construction speed, thereby impacting the mining efficiency of the working face. Therefore, the reasonable filling width should be scientifically determined based on key engineering parameters such as burial depth, thickness, and dip angle of coal seams. The mechanical properties of different materials used in the entry-side filling can also affect the determination of the filling width. Among 587 cases of GSER engineering projects, 149 cases provide the data on actual entry-side filling width. Figure 9 shows the application of GSER engineering projects with different filling widths at a statistical interval of 1 m.  
As shown in Figure 9, the entry-side filling width does not exceed 5 m in 149 GSER projects. Among them, there are 84 cases using a 1-2 m filling width, accounting for 56.38%; 39 cases with a filling width of 2-3 m, accounting for 26.17%; 16 cases with a filling width of 0-1 m, accounting for 10.74%; and 8 cases with filling width of 3-4 m, accounting for 5.37%. From the curve distribution in the figure, it can be seen that the reasonable range of entry-side filling width in China's GSER project is 1-3 m in order to meet the requirements of GSER and achieve better economic and technological benefits.
# Study of Green Synthesis of Ultrasmall Gold Nanoparticles Using _Citrus Sinnensis_ [PERSON]
###### Abstract  
Sweet orange (_Citrus sinensis_) peel, one of the most underutilized biowaste, was in this study employed for the green synthesis of gold nanoparticles (AuNPs) as an alternative source of reductant and stabilizer. Spherical AuNPs with narrow size distribution (\(1.75\pm 0.86\) nm) were obtained by controlling pH and adjusting sequence for the first time. ultraviolet-visible (UV-vis) spectrophotometer, transmission electron microscopy (TEM), energy-dispersive spectroscopy (EDS), dynamic light scattering (DLS) were applied to detect the characteristic surface plasmon resonance peak, morphological and aggregate characteristic, elementary composition and hydrodynamic diameter, respectively. The major functional groups in extract were tested by Fourier transform infrared(FT-IR) spectrophotometer to characterize the components which are responsible for the reduction and stabilization of AuNPs. The possible role of the components during the process of AuNPs synthesis is also discussed. The result of this study enriched the green source for ultra-small AuNPs synthesis, and will help to understand the mechanism of synthesis and stability of ultra-small AuNPs by fruit peels extract.  
biowaste; green synthesis; gold nanoparticles; functional groups; mechanism Article
## 6 Simulation
### The Packet Interarrival Analysis  
The packet interarrival analysis is implemented for the proposed DSCA-MC-CR technique by using the parameters of the LTE and 5G systems shown in Table 2. The calculations using the LTE system shown in Figure 16 indicate that the SU system within the OSA technique is not capable of accessing the PU system spectrum when the PU subscriber is active on its spectrum at a rate of 8 Mbps. In addition, the SU starts accessing the PU system spectrum when it senses that the PU power of a certain subscriber decreases and then the PU becomes inactive. Moreover, the DSCA technique achieves a maximum rate of 10 Mbps for the PU and a guaranteed minimum rate of 5 Mbps for the SU system. Furthermore, the OC-DSA technique achieves a maximum rate of 15 Mbps for the PU and a guaranteed minimum rate of 6.5 Mbps for the SU system. Thus, the proposed DSCA-MC-CR succeeds in achieving a maximum rate of 43 Mbps for the PU and a guaranteed minimum rate of 33 Mbps for the SU. The previous results indicate that the proposed system succeeds in achieving and guaranteeing higher rates than the other techniques in the literature.  
The calculations using the 5G system shown in Figure 17 indicate that the SU system within the OSA technique also cannot access the PU system spectrum when the PU subscriber is active at a rate 11 Mbps, as with the LTE system. In addition, the DSCA technique achieves a maximum rate of 15 Mbps for the PU and a guaranteed minimum rate of 8 Mbps for the SU system. The OC-DSA technique achieves a maximum rate of 22 Mbps for the PU and guaranteed minimum rate of 10 Mbps for the SU system. Just as importantly, the proposed DSCA-MC-CR system succeeds in achieving a maximum rate of 65 Mbps for the PU system and guarantees a minimum rate level of 50 Mbps for the SU system. Finally, the previous results indicate that the proposed system succeeds in achieving and guaranteeing higher rates than the other techniques in the literature too.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Description** \\ \hline LTE networks & Channel bandwidth = 20 MHz; minimum bandwidth = 14 MHz; \\  & downlink speed up to 1 Gbps; uplink speed up to 500 Mbps. \\
5G networks & Channel bandwidth = 300 MHz:1 GHz. \\ TX packets; SNR & 500; 4.6 dB. \\ \(P_{p}\); \(P_{s}\) & 500 mwatt; 500 mwatt. \\ \(\gamma\), \(K\) & 0.2, 0.3. \\ Used techniques & OSA [10], DSCA [10], OC-DSA [33], the proposed DSCA-MC-CR. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for packet interarrival analysis through LTE and 5G networks.  
Figure 15: PU and SU BERs of model 2 using fading channel with \(\gamma=3\%\). (**a**) PU BER of model 2. (**b**) SU BER of model 2.