esa-sceva/satcom-chunk-collection huggingface dataset:
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### AOC-Based Results  
Tables 2 and 3 show our results when using AOC data for training and fine-tuning for the 4-way and 3-way dialect identification using all the experimented classifiers and the different word embedding techniques. It can be observed from Table 2 that the LR classical classifier is still a good candidate for the application of dialect identification due to its simplicity and efficiency for model training. Furthermore, most of the dialect effect can be captured just from the top frequent 5000 words. Using larger word lists would not provide better performance. Due to the stochastic behavior of several processes of neural network training, we applied each neural network-based experiment three times. The results illustrated in Tables 2 and 3 that are related to neural-based experiments are the average values for each experiment. The neural networks-based models significantly improve performance with the best result achieved with the pre-trained BERT-based models using the MARBERT base and embedding, which achieved an accuracy of 85.86%. By excluding the MSA part, which represented a dominant segment of the data, and repeating the experiments for 3-way dialect identification, the fine-tuned MARBERT-based model also achieved the best performance with an accuracy of 90.45%. To check the stability of the proposed models, we conducted several experiments. Using our best model (MARBERT based), we checked the sensitivity of the model via changing two of the main parameters: Dropout rate and batch size. First, we fixed the batch size as 16 and applied the dropout rate values 0.0, 0.3, 0.5, and 0.8. The best accuracy was achieved using these settings with a dropout rate of 0.5. So, the value of dropout was maintained at 0.5 and batch size values of 24, 32, 48, 64, and 84 were applied. Table 4 illustrates the results of parameters sensitivity experiments. As shown in Table 4, the accuracy values change from as low as 83.91% to as high as 86.17% when changing the values of dropout rate and batch size. Although this change in the accuracy value is not small, the whole range of values is higher than the state-of-the-art result of [13] (82.45%). However a careful selection of hyperparameters should be made to maximize the achieved gain. Figure 5 illustrates confusion matrices of MARBERT-based model result errors. The right confusion matrix shows the four dialects classification errors while the left one presents the errors of the three dialect classification task. As we can see, the confusion of dialects between each other in the 3-dialect experiment is relatively low. However in the 4-dialect experiment, there is a notable perplexity between MSA & LEV (19%) and MSA & GLF (18%). This is aligned with the fact that there is a considerable overlap of vocabulary between MSA & LEV and MSA & GLF as we have seen in Figure 4.
## 4 ITS Architecture Proposed for the FMCS
### Identification of FMCS Requirements
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Requirement** & **Related to** & **Proposal** & **Observation** \\ \hline Location technology of & & & Although there are some \\ transit vehicles must allow & Transit vehicle & & alternative location technologies, \\ continuous and & location & & these would not allow continuous \\ efficient monitoring. & & & vehicle tracking. \\ \hline FMCS must allow speed & & Use of GNSS to measure & Through speed alerts, the risks of \\ control of the transit vehicle & Transit vehicle speed & speed, use of speed alerts and & accidents can be reduced. \\  & & detection of inappropriate & By detecting inappropriate \\  & & driving behaviors & driving behaviors, steps can be taken to reduce fuel consumption. \\ \hline FMCS must allow & & & \\ interoperability and & & & \\ integration (with other & & \\ systems or services) in the & & & \\ context where it & & & \\ is implemented. & & & \\ \hline FMCS must use an adequate & & & LoRa offers some notable \\ communication technology & & Use of LoRa technology & advantages over other \\ for the application context & & (using the network & technologies for the specific \\  & & (using the network & service. LoRaWAN protocol can \\  & & protocol LoRaWAN) & be a viable option that improves \\  & & & communication security \\  & & & and efficiency. \\ \hline \hline \end{tabular} \
\begin{tabular}{c} The exchange of data \\ between the FMCS modules \\ must be secure. \\ \end{tabular}  Information security
\begin{tabular}{c} Secure information exchange \\ (through a network protocol \\ such as LoRaWAN, HTTPS, \\ TCP). Access to the data with \\ SHA passwords and \\ user hierarchy. \\ \end{tabular}
\end{table} 
Table 3: Requirements determined for the FMCS.
## 4 Validation of General ESS-Based VSM Control
### Simulation Test of Hybrid ESS-Based VSM under Renewable Generation Fluctuation
Frequency performance is similar, and the SoC of FAESS remains approximately 0.5. The wear and tears of the SG are reduced, and the demand for response speed and energy capacity on FAESS are also lower than constant VF control. Control performances in the above tests are summarized in Table 4. As revealed, VF controlled FAESS features the best frequency control performance, but the largest energy deviation. This is opposite, for ESS with solely grid-following control. The VSM-controlled ESS without energy recovery, behaves better in frequency, but there is energy deviation compared with grid-following case. With energy recovery control added, better SoC performance (controlled near 0.5) is achieved. \begin{table}
\begin{tabular}{c c c} \hline
**Devices and Key Control Parameters** & **Frequency Variation** & **SoC Deviation of FAESS** \\ \hline FAESS adopts VF control, no SAESS & Almost constant & SoC increases from 0.5 to 0.9 within 20 s \\ \hline All ESS with PLL synchronization and no frequency support & Maximum \(\pm\)0.22 Hz fluctuation & Kept at 0.5 \\ \hline Hybrid ESS adopts VSM control, without SoC recovery & Maximum \(\pm\)0.1 Hz fluctuation & Mean SoC increases to 0.51 within 45 s \\ \hline Hybrid ESS adopts VSM control, with SoC recovery & Maximum \(\pm\)0.1 Hz fluctuation & Mean SoC near 0.5 within 45 s \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of frequency performance in the power system with renewable generators for ESS with different control options. Figure 19: The control performance when all ESS adopt purely grid-following control. The waveforms show that frequency fluctuation is large due to the PV generation variation.
## 3 Results and Discussion
### Inhibitor Releasing Characteristics and Mechanism of OIMs@PAM
#### 3.2.1 Release Behavior of GE-EP@OIMs in Different pH Environment
EP@OIMs is nearly completely released in both 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C environments. In the 50 \({}^{\circ}\)C environments, the cumulative release of GE-EP@OIMs continues to increase throughout the entire immersion period (1-60 days), with OIMs being stably released at this temperature. However, in the 80 \({}^{\circ}\)C environments, the release rate slows down after 40 days, which can be attributed to the reduction in release kinetics caused by the decreasing residual content of OIMs. The differences in the cumulative release curves at different temperatures suggest that the release mechanisms of GE-EP@OIMs may vary with temperature. Within the 30-60-day interval, the release concentration of OIMs exhibits a pattern where it is in the order of 50 \({}^{\circ}\)C \(>\) 80 \({}^{\circ}\)C \(>\) 20 \({}^{\circ}\)C. This phenomenon can be ascribed to the circumstance that the cumulative release of OIMs within GE-EP@OIMs attains 81% during the initial 30 days at 80 \({}^{\circ}\)C. This, in turn, leads to a precipitous decline in the residual quantity of the OIM corrosion inhibitor within the specimen, diminishing from 34.75% to 6.60%. As illustrated in Figure 8, upon immersion of GE-EP@OIMs in solutions at 80 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, and 20 \({}^{\circ}\)C for a duration of 60 days, the cumulative release amounts are 96%, 86%, and 36%, respectively. Subsequent to 30 days of immersion in the 20 \({}^{\circ}\)C solution, the cumulative release of the sample ceases to exhibit a significant increment, signifying that the release of GE-EP@OIMs has halted, whereas GE-EP@OIMs is nearly entirely released in both the 50 \({}^{\circ}\)C and 80 \({}^{\circ}\)C environments. In the 50 \({}^{\circ}\)C environment, the cumulative release of GE-EP@OIMs persists in augmenting throughout the entire immersion period (ranging from 1 to 60 days), with OIMs being released in a stable manner at this temperature. However, in the 80 \({}^{\circ}\)C environment, the release rate decelerates after 40 days, which can be imputed to the attenuation of release kinetics consequent to the diminishing residual content of OIMs. The disparities observed in the cumulative release curves at diverse temperatures imply that the release mechanisms of GE-EP@OIMs might be subject to variation with temperature.
Appl. Sci._**2015**, \(5\), 817-824; doi:10.3390/app5040817  
[400]**CUPANCGASS**  
[40]**_applied sciences_  
[40]**ISSN 2076-3417**  
[41]**www.mdpi.com/journal/applsci  
[42]**Kerr-Lens Mode-Locked Femtosecond Yb:GdYSiO\({}_{5}\) Laser Directly Pumped by a Laser Diode**  
[43]**[PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 2,*, [PERSON] 3, [PERSON] 3 and [PERSON] 4**  
[44]School of Physics and Optoelectronic Engineering, Xidian University, Xi'an 710071, China; E-Mails: [EMAIL_ADDRESS] (J.Z.); [EMAIL_ADDRESS] (Z.G.);[EMAIL_ADDRESS] (W.T.); [EMAIL_ADDRESS] (J.W.)  
[45]Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China; E-Mail: [EMAIL_ADDRESS]  
[46]Key Laboratory of Transparent and Opto-functional Inorganic Materials, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 201800, China; E-Mails: [EMAIL_ADDRESS] (L.Z.); [EMAIL_ADDRESS] (L.S.)  
[47]School of Physics Science and Engineering, Institute for Advanced Study, Tongji University, Shanghai 200092, China; E-Mail: [EMAIL_ADDRESS]  
[48]Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel./Fax: +86-10-82648115.  
[49]Academic Editor: [PERSON]  
[40]**Abstract:** We demonstrate the first Kerr-lens mode-locked operation in a diode-pumped Yb:GdYSiO\({}_{5}\) oscillator. Under a diode pump power of 5 W, 141 fs pulses with an average power of 237 mW were obtained at a repetition rate of 118 MHz. The central wavelength was at 1094 nm with a bandwidth of 10.1 nm. Shorter pulses were obtained by adjusting the cavity to operate at a shorter wavelength, resulting in 55 fs pulse duration at the central wavelength of 1054 nm with a bandwidth of 23.5 nm.  
**Keywords:** ultrafast laser; diode-pumped; Kerr-lens mode-locking; all-solid-state laser
###### Abstract
designed a vehicle side slip angle state estimator based on interacting multiple model unscented Kalman filter (IMM-UKF) and analyzed the applicability of the nonlinear tire model under large tire side slip angle operating conditions [22]. In previous research work, interacting multiple model methods are often combined with nonlinear Kalman filter and have shown good performance in areas such as target tracking, navigation, etc., but there have been no applications to extend the IMM-SCKF algorithm to the field of vehicle state estimation. Therefore, this paper proposes an interacting multiple model algorithm combined with square root cubature Kalman filter to design interacting multiple model sets based on nonlinear vehicle dynamics models for the case where the statistical characteristics of system state noise and measurement noise are unknown. The SCKF algorithm is used to estimate the yaw rate, sideslip angle, and longitudinal and lateral vehicle speeds for each sub-model, ensuring that the fusion estimation results always maintain the output of the sub-model with the least tracking error.
###### Abstract  
Unmanned aerial vehicles (UAVs) can obtain high-resolution topography data flexibly and efficiently at low cost. However, the georeferencing process involves the use of ground control points (GCPs), which limits time and cost effectiveness. Direct georeferencing, using onboard positioning sensors, can significantly improve work efficiency. The purpose of this study was to evaluate the accuracy of the Global Navigation Satellite System (GNSS)-assisted UAV direct georeferencing method and the influence of the number and distribution of GCPs. A FEIMA D2000 UAV was used to collect data, and several photogrammetric projects were established. Among them, the number and distribution of GCPs used in the bundle adjustment (BA) process were varied. Two parameters were considered when evaluating the different projects: the ground-measured checkpoints (CPs) root mean square error (_RMSE_) and the Multiscale Model to Model Cloud Comparison (M3C2) distance. The results show that the vertical and horizontal _RMSE_ of the direct georeferencing were 0.087 and 0.041 m, respectively. As the number of GCPs increased, the _RMSE_ gradually decreased until a specific GCP density was reached. GCPs should be uniformly distributed in the study area and contain at least one GCP near the center of the domain. Additionally, as the distance to the nearest GCP increased, the local accuracy of the DSM decreased. In general, UAV direct georeferencing has an acceptable positional accuracy level.  
unmanned aerial vehicle (UAV); structure from motion (SfM); direct georeferencing; ground control point (GCP); accuracy assessment; point cloud +
Footnote †: journal: Advances in Neural Information Processing Systems
## 3 Results
### Phylogenetic Analysis of the Strain CPCC 401429  
The _left-1_\(\alpha\) DNA sequence retrieved from the genome of CPCC 401429 strain was used to preliminarily determine the identity in a BLAST nucleotide search of NCBI and exactly matched the _left-1_\(\alpha\) region of \(D\). _tabscens_ HKAS86603 (GenBank accession KT822441) with 99.6% similarity. A phylogenetic dendrogram of _left-1_\(\alpha\) recovered from CPCC 401429 and other _Armillaria_ species or lineages (Figure 1) was generated, as shown in this phylogenetic tree, the \(D\). _tabscens_ is more closely clustered to \(D\). _ectypa_ species and formed a distinct group (clade V). Interestingly, [PERSON] et al. (2017) performed six loci (_28S_, _EF1\(\alpha\), _RPB2_, _TUB_, _actin-1_ and _gpd_) phylogenetic analysis to investigate and resolve the evolutionary status of diverging lineages _Guyanagaster_ and _Armillaria_ subgenus _Desarmillaria_[25]. To  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Genome Feature** & **No./Value** \\ \hline Genome size (Mb) & 50.36 \\ Number of scaffolds & 713 \\ Scaffold N\({}_{50}\) (bp) & 125,925 \\ Scaffold N\({}_{90}\) (bp) & 33,591 \\ Longest scaffold (bp) & 839,203 \\ Shortest scaffold (bp) & 526 \\ GC content (\%) & 47.14\% \\ Protein-coding genes & 15,145 \\ Average gene length (bp) & 1959 \\ Complete BUSCOs (\%) & 92.4\% \\ Genes of KEGG & 3441 \\ Genes of COG & 9507 \\ tRNA No. & 255 \\ rRNA No. & 4 \\ TE No. & 438 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Genome feature of the _Desarmillaria tabscens_ CPCC 401429.  
further investigate the armillarioid phylogeny, 18S-ITS-28S regions and ten housekeeping genes (_tf-1\(\alpha\)_, _rpb2_, _act1_, _gpd_, _tub_, _atp_, _pgi_, _rpb_ and _ubq_) were selected for MLST-based phylogenetic tree construction. As shown in Figure 2, markedly, the species _D. tabscens_ CPCC 401429 and the species _Guyanagaster necorrhizus_ MCA 3950 clustered as a well-supported branch. However, _Armillaria_ sensu stricto includes annulate mushroom-forming armillarioid species, exemplified as _A. mellea_, _A. ostoyae_, _A. borealis_, _A. cspistipes_, _A. solidipes_, _A. fuscipes_, _A. altimontana_, and _A. gallica_ species. Our result reinforced that _D. tabscens_ was supposed to belong to the genus of _Desarmillaria_[25; 51].  
Figure 1: Dendrogram generated from elongation factor 1-\(\alpha\) (Tef-1\(\alpha\)) DNA sequence data showing the phylogenetic relationships of _Armillaria_ species and lineages. The phylogenetic tree was constructed by the Neighbor-Joining method with MEGA 7.0 programme [47]. The bootstrap scores are based on 1000 reiterations. The Tef-1\(\alpha\) DNA sequence of _A. fuscipes_ CMW2740 acts as the outgroup.
# An Overview of Variants and Advancements of PSO Algorithm
## 3 Parameters of PSO
### Velocity Clamping  
It is the property of a good optimization algorithm to create an optimal balance between the exploration and exploitation objectives. It was found in the basic PSO algorithm that the velocity quickly explodes to larger values and, as a result of it, the position of the particle changes rapidly, that leads to the divergence of the whole swarm. To overcome this problem, [PERSON] and [PERSON] (2016) introduced the concept of velocity clamping i.e., the velocities are clamped to stay within the boundary constraints. \(\mathrm{V_{max}}\) denotes the maximum allowed velocity that creates a better equilibrium between the global exploration and local exploitation. If the velocity of the particle reaches beyond this specified limit \(\mathrm{V_{max}}\), then that velocity of the particle is set to the maximum velocity value (\(\mathrm{V_{max}}\)). \(\mathrm{V_{max}}\) is an important parameter as it controls the explosion of velocity. Higher values of \(\mathrm{V_{max}}\) encourage global exploration, but it may risk the possibility that the particles may miss a better search area. Since the particles are moving faster, they may jump over the optimal solution and continue to roam in a useless region in the global search area, and on the other hand, the smaller values of \(\mathrm{V_{max}}\) encourage local exploitation but may risk the possibility that the particles become trapped in the local optima, resulting in the particles not exploring a better search space. So, to create a balance between exploration and exploitation, an appropriate value of \(\mathrm{V_{max}}\) is required.  
We can write \(\mathrm{Vmax}\) as written in Equation (4):  
\[\mathrm{V_{max}}=\delta\left(\mathrm{X_{max}}-\mathrm{X_{min}}\right) \tag{4}\]  
where \(\mathrm{X_{max}}\) and \(\mathrm{X_{min}}\) are, respectively, the maximum and minimum values of \(\mathrm{X}\) and \(\delta\ \varepsilon\ (0,1]\).
# Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor
## 1 Introduction
### Motivation and Main Contribution
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensors** & \multicolumn{2}{c}{**Microprocessor**} & \multicolumn{2}{c}{**Radio Chip**} \\ \hline Components & Power consumption & \multirow{2}{*}{Components} & Power consumption & \multirow{2}{*}{Components} & \multirow{2}{*}{Components} & \multirow{2}{*}{Transmit power consumption} \\ ADXL35 & & MSP430C2553 & & CC2430 of & & 2 V @ 27 mA \\ (Accelerometer of & \multirow{2}{*}{1.8 V @ 0.35 mA [29]} & of Texas & & & Instrument \\ Analog Devices & & & MSP430L092 & & CC2520 of & 1.8 V @ 18.5 mA \\ (Temperature & \multirow{2}{*}{2.4 V @ 0.008 mA} & of Texas & & & Texas \\ sensor of ST) & & Instrument & & Instrument \\ MPL115A & \multirow{2}{*}{3.3 V @ 0.005 mA} & ATMega128 & 2.7 V @ 8 mA [35] & & & 2.1 V @ 25 mA \\ (Pressure sensor & [34] & & ATMega128 & 2.7 V @ 8 mA [35] & & Semtech [36] \\ of Freescale) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some IWSs commercial off-the-shelf components and nominal power consumption specifications. Figure 1: Conceptual view of the proposed system, which is comprised of a piezoelectric transducer (PT), Predictor of the Harvestable Energy from Vibrations (PHEV), Power Management Module (PMM), Industrial Wireless Sensors (IWS), and Base Station (BS). IWS to transmit a certain amount of data in each measurement cycle; the different stages of this new design method are described in the next subsection.
## 4 Evaluation
### Results and Discussion
#### 4.2.4 Signaling Overhead  
Signaling overhead is an important aspect to consider when designing a mobility support scheme. There is a tradeoff between signaling overhead and user experience. In fact, minimizing signaling messages can help reduce network congestion and conserve resources, leading to a more efficient use of the network infrastructure. On the other hand, increasing signaling messages for frequent updates on the network's condition can enhance user experience by maintaining a consistent quality of service. This tradeoff is a fundamental challenge in designing a mobility support scheme that provides a high-quality user experience while efficiently minimizing unnecessary signaling traffic. Signaling overhead is defined as the number of packets needed to ensure that the consumer's Input can reach the MP when handoff occurs. We compared the signaling overhead generated by the three schemes while varying the producer speed, as shown in Figure 11. Multi-RV KITE generates a higher signaling overhead compared to MAP-ME and DMMS. This is because of the periodic sending (every 2 s) of TI/TD packets to refresh the trace path between  
Figure 9: (**a**) Average path stretch vs. mobile producer speed, (**b**) average hop count vs. mobile producer speed.  
producer and rendezvous servers. In contrast, both the DMMS and MAP-ME schemes generate considerably less signaling overhead. The DMMS uses at least one Input packet to remove the old path, notify the Anchors about the handoff event, and inform Anchors about the predicted location of the MP after a handoff. Meanwhile, MAP-ME uses a single Input packet to update the path between the new and old producer's PoA.  
It is worth noting that DMMS is affected by the prediction accuracy of the producer's next location, causing an additional signaling overhead.  
Figure 11: Signaling overheads vs. producer speed.  
Figure 10: Average data packet delay vs. mobile producer speed.