esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview
## 4 Advances in Speech Recognition for Uyghur, Kazakh, and Kyrgyz
### Analysis and Discussion
#### 4.3.2 The Diversity of Data Sources  
Early databases generally contained reading speech recorded by microphones in an office environment (even high-quality speech recorded in a sound-proof studio [142, 143]). Speech data recorded in this way are characterized by clear pronunciation, small noise interference, and a single channel. THUYG-20 and a part of M2 ASR-DYGHUR were collected in this way. This type of collection approach is expensive and involves little diversity in the environment and channel. Recently published databases were collected in more flexible ways. For example, most of the data in M2 ASR were collected through  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Models** & **CER (\%)** \\ \hline Pre-training + fine-tuning (1 h) & \\ \hline CPC [153] & 41.2 \\ Wave2 Vec2.0 (XLSR) [84] & 6.1 \\ \hline Completely unsupervised learning (1.8 h) & \\ \hline Wav2 Vec2.0 + GAN [91] & 14.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: SOTA results tested on the Common Voice-Kyrgyz dataset.  
mobile apps, which are easy to use and can collect data with more channel and background diversity. KSC and Common Voice adopted a crowdsourcing approach, which allows any person to upload his/her speech and conduct annotation. This greatly speeds up the collection process, reduces the cost, and allows collecting a rich acoustic diversity. Taking Common Voice as an example, from the first version in 2019 to the latest version in 2022, the amount of data has increased 16 times and the number of languages has expanded from 19 to 100. One of the risks of the crowdsourcing scheme is that the annotation may not be accurate enough. However, studies have indicated that, when the data volume is large enough, the influence of annotation noise is acceptable for model training [161, 162].  
Finally, although the speech data of these three languages are much more diverse than before, they are not truly spontaneous. This is because the speakers will realize that they are being recorded, so they will tend to choose a relatively quiet place and speak clearly. A possible way to collect more spontaneous speech data is to utilize the rich data resources of the Internet. Recently, several public databases are being collected in this way, including GigaSpeech [163], WenetSpeech [164], VoxCeleb [165], CN-Celeb [166], and so on.
## 2 Contemporary Model- and Data Driven (Crowd) Forecasting Methods
### Data-Driven Forecasting of Crowd Movement  
Most studies featuring the forecasting of pedestrian movements design and train models that forecast pedestrian trajectories (e.g., [26; 27; 28; 29]). Generally, these algorithms derive the current movement behavior of a pedestrian by means of computer vision algorithms. Afterwards, a multitude of prediction techniques is used to identify the next steps of one or more pedestrians, ranging from Mixed Markov models to Recurrent Neural Networks (RNN). The prediction horizon of trajectory prediction algorithms is generally limited and the input is very detailed. This limits the applicability of these algorithms to large-scale crowd movements through an urban networks, whose movements are spread over a few hours while the real-time information regarding their movements is often spatially and temporally sparse.  
A very limited number of studies presented methods to predict the longer term movements of pedestrians, such as route and destination choice. Most of these works featured the prediction of destination sequences, which are often captured using a network of Wi-Fi sensors. Destination sequence prediction algorithms feature Bayesian approaches (e.g., [30]), dynamic discrete choice models (e.g., [31]), Markov chain models (e.g., [32]) and Hidden Markov models (e.g., [33]). One study determined the next link in a route using a link-based route measurement model that was trained by means of GPS traces [34].  
While the prediction horizon of the trajectory prediction algorithms is too short, the destination sequence prediction algorithms could be used to predict the movements of visitors of a large-scale event. However, the presented algorithms are known for their lack of memory. As we expect that the future movements of visitors at a large-scale event are dependent on their previous movements, also the destination sequence prediction algorithms might suffice in their current form. An algorithm with long-term memory retention capabilities is sought after.
## 3 Results and Validation
### Validation of the Resultant Maps  
To objectively evaluate the proposed methods, the ROC and AUC measures were used. In the ROC curve, the specificity and sensitivity are on the x- and y-axis, respectively [134,135]. Figure 16a,b demonstrate the ROC using the training and verification sets for the SWARA, SWARA-ANFIS, SWARA-ANFIS-PSO, SWARA-ANFIS-WOA, and SWARA-ANFIS-GWO methods, respectively. It can be observed that SWARA-ANFIS-WOA and SWARA-ANFIS-GWO can obtain the AUCs of 0.897 and  
Figure 14: Landslide susceptibility maps. (**a**) SWARA-ANFIS-WOA, (**b**) SWARA-ANFIS-GWO, (**c**) SWARA, (**d**) SWARA-ANFIS, (**e**) SWARA-ANFIS-PSO.  
Figure 15: Percentages of different landslide susceptibility classes.  
0.900 when using the training set and 0.856 and 0.869 when using the verification set. Furthermore, these observations are in accordance with the analysis with the RMSE results. Additionally, some methods and optimization algorithms such as SWARA, SWARA-ANFIS, and SWARA-ANFIS-PSO are used for comparison of the obtained results. Accordingly, results conclude that based on the training dataset the AUC for SWARA, SWARA-ANFIS, and SWARA-ANFIS -PSO are 0.836, 0.894, and 0.894, respectively. However, these results for validation dataset are 0.831, 0.831, and 0.850, respectively. The experimental results demonstrate that both the two proposed new hybrid evolutionary optimization algorithms can show the satisfactory performance and SWARA-ANFIS-GWO can achieve more accurate prediction accuracy than that of SWARA-ANFIS-WOA.
## 4 Discussions and Conclusions  
In this paper, we investigated the use of reflected light from the leafy vegetables after photosynthesis. A series of simulations and experimental results show the feasibility of this approach in constructing an energy-efficient RF-free environment. For the uplink, using the optical camera as a receiver, multiple smart-farming units can be monitored at the same time, exploiting the full-RoI. In addition, simultaneous reception of data from each smart-farming unit can be processed. Although there is a limited communication speed due to the innate low frame rate, adjusting RoI and tracking an individual smart-farming  
Figure 11: (**a**) BER for full RoI using 2-PAM, 4 PAM and 8 PAM without equalizer; (**b**) BER for tracked RoI using 2-PAM, 4-PAM and 8-PAM without equalizer.  
Figure 12: (**a**) Eye-diagram of the received 4-PAM signal of 840 b/s for the uplink communication at the distance of 25 cm without DFE for tracked ROI; (**b**) with DFE.  
unit upon request enables a faster data rate with a higher frame rate. For the downlink, typical ceiling light with an APD was utilized. The maximum data rates achieved in the proof-of-concept demonstration are 840 b/s and 20 Mb/s for the uplink and downlink, respectively. Future work includes improving the data rate by applying more advanced modulation and multiplexing techniques and increasing the coverage by a larger FoV camera. Also, an interesting future topic to investigate is a large-scale demonstration to check the practicality of this work compared to the RF-smart-farming systems in terms of energy efficiency and growth and health of the vegetables.  
Conceptualization, H.C., S.J. and L.I.; methodology, H.C.; software, H.C., S.C. and S.J.; validation, H.C., S.J. and S.C.; formal analysis, H.C. and S.J.; investigation, H.C., S.J. and L.I.; resources, H.C.; data curation, H.C. and S.J.; writing--original draft preparation, S.J. and H.C.; review & editing, H.C., S.J., L.I. and S.C.; project administration, H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Incheon National University Research Grant in 2021.  
Not applicable.  
The data presented in this study are available on request from the corresponding author and on his personal website.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Discussion
This study reports site-specific \(\varepsilon_{\text{site}}\) values based on LRC curves for a near-natural peatland during extremely dry and normal climatic years, which could be useful for the development of additional GPP models. The mean annual ecosystem scale light use efficiency parameter values of 0.39 (dry year) and 0.38-0.42 g C MJ\({}^{-1}\) (normal years) were lower than the literature values ranging from 0.61 to 1.2 g C MJ\({}^{-1}\)[20; 21; 22]. The annual ecosystem scale light use efficiency values reported for Clara West were not used to model GPP, as it is calculated using EC-derived GPP and PAR. Validating modelled GPP with EC-derived GPP would have induced circularity into the model. However, the \(\varepsilon_{\text{site}}\) values from this study can be used for modelling similar sites across Ireland and other temperate peatlands. This study did not consider the environmental stressors, such as temperature and vapor pressure deficit, while estimating GPP to reduce model complexity. As mentioned by [PERSON] et al. [21] and [PERSON] et al. [8], scalars developed for other ecosystems may not be appropriate for peatlands, and there is a need of longer dataset from multiple sites to develop or refine existing scalers. A recent study by [PERSON] et al. [22] uses a water use efficiency approach to estimate ecosystem maximum light use efficiency across 52 FLUXNET sites. Their results for seven biomes spread across globe agree well with experimental and modelling methods [22]. As a next step, the approach can be tested on satellites with limited band availability for remote locations and across drainage gradient of peatlands.
## 4 The Global SAC and Grid Systems
### Hexagonal Grid System  
As we mentioned before, constructing a global grid system can use different partition method. There are two kinds of partition method: triangle partitions and hexagon partitions. In some disciplines, such as GIS, hexagonal global grid systems are more common and useful than triangular based grid  
Figure 14: The icosahedron version of the initial global grid and region index.  
Figure 15: Four-fold DGGSs based on the icosahedron version of SACs (level = 3, 4, 5).  
systems. This subsection will give you some visual results that use SACs to create hexagonal based global grid. Figure 16 illustrates three levels of hexagonal based DGGSs that are based on SACs.  
Hexagonal based grid has some advantages. They are more compact than triangles and squares and also have the smallest average error. Because hexagons are more symmetry, they provide the highest angular resolution. Unlike square and triangle grids, hexagon grids have uniform adjacency; each hexagon cell has six neighbors. But the hexagonal based grids also have some disadvantages. The grid's cells are not all hexagon, some pentagons exist in the initial vertices of Platonic solid. A simple data structure, like quadtrees, octrees cannot use to index the grid systems. In this paper, we focus on triangular based grid systems.
# Design of Particle Dampers for Laser Powder Bed Fusion
## 2 Review  
One possibility to increase the damping in laser beam melted structural components is to integrate the effect of particle damping [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. By means of particle dampers, produced by laser powder bed fusion (LPBF), vibrations can be reduced by more than a factor of 20 in some cases [7; 10; 12]. The powder bed-based process allows the particles to be integrated directly into the cavities of the component during the manufacturing process, thus further increasing the high degree of functional integration. In the field of product development, the use of additively manufactured particle dampers offers potential in that the conflict of goals between high stiffness and simultaneous high damping can be solved [7; 8]. For example, particles can be integrated in the area of the neutral fibre and components can be optimised in terms of mass, stiffness and damping. Figure 1 shows an exemplary manufacturing process of laser beam melted particle dampers using the example of a damped gear wheel.  
With particle dampers, the energy is dissipated by combined loss mechanisms due to friction and impact processes between the particles themselves and the interaction between the particles and the wall. A schematic representation is shown in Figure 2.  
In addition to the already mentioned advantages of simple and low-cost design [21] and broadband damping [7; 27; 28; 29; 30], particle dampers are characterised by good performance in harsh environmental conditions. These include, in particular, use under the influence of high temperatures [21; 31; 32] (from cryogenic to high-temperature applications of 2000 \({}^{\circ}\)C) and contamination [21; 33] (oil, dust, etc.). In addition, they show hardly any wear and are low-maintenance [21]. Furthermore, particle dampers can increase component life due to the increased damping.  
Due to these and other advantages, particle dampers have been used successfully for several hundred years to reduce vibrations [21]. In contrast, the use of particle dampers in additive manufacturing is not yet widespread and is described briefly below.
# A Survey of Wi-Fi 6: Technologies, Advances, and Challenges
## 6 TWT in Wi-Fi 6
### Related Works
#### 6.4.1 User Distribution  
Distributing the stations to different TWT SPs helps to minimize the collision in the network. [PERSON] et al. [151] proposed an uplink OFDMA random access grouping scheme by utilizing the TWT feature to minimize the collision rate in random access mode. The objective was to maximize performance by optimizing the group size of the stations within one TWT SP. Stations are distributed in different SPs and a random access process takes place in each of them. If too many stations compete, they will create collisions. Conversely, a small group may reduce network capacity, the transmissions being inefficiently multiplexed. The adaptive algorithm considered lower and upper bound grouping sizes and group stations to achieve the highest system efficiency.  
The authors of [152; 153] planned random access uplink scheduling to minimize contention and, thereby, power consumption. The access point accepts or rejects the TWT requests using a mathematical model based on the number of stations, the RUs, and the listen intervals. In particular, stations that wake up at the same time would collide. Therefore, the access point will schedule them in separated TWT sessions to reduce the collision probability.  
[PERSON] et al. [154] avoided collisions by limiting the number of selected stations to wake up in every target beacon close to the number of available RUs. They derived a throughput optimization problem with delay and instant throughput constraints. They solved the problem using a genetic algorithm and the outcome was the optimal listen interval schedule for TWT broadcast agreements.  
The authors of [155] applied max-rate and proportional fairness scheduling to the TWT mechanism in order to maximize throughput, save energy, and improve fairness. Max-rate scheduling divides the nodes into two groups and assigns them to two timeslots within an SP. Proportional fairness scheduling selects the appropriate station at the beginning of each timeslot.
## 3 Semi-Empirical Model of Retired Battery Capacity Degradation Based on Interval Number Similarity
### Interval Number Similarity Degree Computing Model
\begin{aligned} & SOH_{1}=1-\alpha_{1}\cdot\exp\Big{[}\frac{ \alpha_{1}\cdot C_{Rate}+b_{1}}{R\cdot T_{1}}\Big{]}\cdot C_{Rate}\,\,^{\hat{p}_ {1}}\cdot DOD_{1}\,^{\gamma_{1}}(N\cdot DOD_{1}\cdot Q_{b})^{z_{1}}\\ & SOH_{2}=1-\alpha_{2}\cdot\exp\Big{[}\frac{\alpha_{2}\cdot C_{Rate }+z_{2}}{R\cdot T_{2}}\Big{]}\cdot C_{Rate}\,\,^{\hat{p}_{2}}\cdot DOD_{2}\,^{ \gamma_{2}}(N\cdot DOD_{2}\cdot Q_{b})^{z_{2}}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \qquad\qquad\qquad\qquad\vdots\\ & SOH_{k}=1-\alpha_{k}\cdot\exp\Big{[}\frac{\alpha_{k}\cdot C_{Rate }+b_{k}}{R\cdot T_{k}}\Big{]}\cdot C_{Rate}\,\,^{\hat{p}_{k}}\cdot DOD_{k} \,^{\gamma_{k}}(N\cdot DOD_{k}\cdot Q_{b})^{z_{k}}\end{aligned} \right. \tag{15}\]  
Then, the interval number similarity is assigned to each fitting parameter as a weight, and the new fitting parameters can be obtained as follows:  
\[c_{j}=S^{\prime}_{m,n_{1}}\times c_{1,j}+S^{\prime}_{m,n_{2}}\times c_{2,j}+ \cdots+S^{\prime}_{m,n_{k}}\times c_{k,j}\,(j=1,2,\cdots n_{c}) \tag{16}\]  
where \(c_{j}\) denotes the fitting parameter of the semi-empirical model and \(n_{c}\) is the number of fitting parameters. Finally, the semi-empirical model of the capacity degradation of retired batteries based on the parameter interval number similarity method is obtained by the following Equation (17):  
\[SOH_{parm}=1-\alpha_{j}\cdot\exp\Big{[}\frac{a_{j}\cdot C_{Rate}+b_{j}}{R\cdot T }\Big{]}\cdot C_{Rate}\,^{\hat{p}_{j}}\cdot DOD_{j}\,^{\gamma_{j}}(N\cdot DOD _{j}\cdot Q_{b})^{z_{j}} \tag{17}\]  
where \(\alpha_{j},\beta_{j},\gamma_{j},a_{j},b_{j},z_{j}\) denote the new parameters calculated by the interval number similarity model, and \(SOH_{parm}\) is the result of the similarity method based on the parameter interval number. An Improved Semi-Empirical Model of Retired Battery Capacity Degradation Based on Least Square Curve Fitting  
The previous part proposed two semi-empirical models based on interval number similarity for the capacity degradation of retired batteries, which are divided into two types: the model interval number method and parameter interval number method. Because the interval number similarity-based semi-empirical modeling method can only obtain a capacity degradation model under a certain operating condition, it is impossible to obtain a general semi-empirical model to simulate the capacity decline of retired batteries under various working conditions. Therefore, to directly obtain the degradation of retired battery  
Figure 1: Flow chart of semi-empirical model of capacity degradation of retired batteries based on model interval number similarity. capacity in different \(\Delta\)SOCs through the empirical models, it is necessary to improve the empirical models based on the data-driven methods.
## 3 Results and Discussion
Finally, comparing all the sample groups with the control group, after 120 h of storage, all groups showed lower hardness values (54.9% lower for bread prepared with 5% untreated wheat bran, 43.1% lower for bread prepared with 10% untreated wheat bran, and 74.5% lower for bread prepared with 15% untreated wheat bran; 41.2% lower for bread prepared with 5% extruded/fermented wheat bran, 9.8% lower for bread prepared with 10% extruded/fermented wheat bran, and 27.5% lower for bread prepared with 15% extruded/fermented wheat bran). According to [PERSON] et al. [21], a combination of bran fermentation with enzymatic treatment not only improves the loaf volume and textural properties of bread but also prolongs the shelf life [22], and these effects are mainly due to the redistribution of water among starch, gluten, and bran particles during storage [21]. Our results are in agreement with these findings as well as with those of [PERSON] et al. [23], who reported that the incorporation of bran in the main bread formula leads to a greater stability of bread texture during storage, and this could be related to the retardation of amylopectin retrogradation in the presence of bran. The results of the ANOVA test indicated that there is a significant effect of adding untreated or extruded/fermented wheat bran (\(p<0.0001\)) and in different quantities (\(p<0.0001\)), and the interaction of these factors (\(p<0.0001\)) on bread hardness after 120 h of storage.
## 3 Results  
### Mechanical Properties of Mortar/SAPs Specimens  
During the flexural strength test an abrupt, brittle failure was observed in mortars mainly due to deformation localization by the coalescence of narrow microcracks that ultimately lead to macrocracks that expand to the entire specimen [52]. The flexural strength evaluation of the mortar-based composites with SAPs in dosages 1 and 2% bwoc as well as control mortar specimens at the age of 28 days is demonstrated in Figure 4a,b. According to Figure 4b, a slight increment of about 3% can be detected on the flexural strength of the mortar-based composites that contain SAPs in dosages 1% and 2% bwoc, in comparison to the control specimens which practically reveals that the flexural strength remains intact after the SAPs incorporation in both concentrations.  
On the other hand, the compressive strength of the mortar based composite materials with the SAPs incorporation for the same mortar age (28 days) and w/c ratio (0.5) is depicted in Figure 5a,b. More specifically, Figure 5a depicts the load applied during the test as a function of the head displacement and Figure 5b shows the calculated compressive strength of the mortar based composite materials with the SAPs incorporation for the same mortar age (28 days) and w/c ratio (0.5). As shown in Figure 5b the compressive strength of the mortar-based composite materials practically remains intact for both SAPs dosages (1% and 2% bwoc) in comparison with the control materials.  
Figure 4: Maximum values of loads for mortar specimens at 28 days of age (**a**), flexural strength of mortar specimens at 28 days of age (**b**), optical image of specimen after flexural test (**c**).  
Figure 6 reveals the relationship between the flexural and compressive strength of the mortar-based control and composite materials using the corresponding average values at the age of 28 days. It can be observed that a direct relationship (R\({}^{2}\) = 0.97818) between them exists. Similar behaviors have been reported by other research works for reference mortars [53,54].