esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Theoretical Case with 6 Sensors  
The trained models were deployed to predict the test data with the shifted frequencies, resulting in the predictions seen in Figure 9. While, in this case, the biLSTM was not able to make adequate predictions, as can be seen in Figure 9c, the other models predicted the element force mostly accurately. It can be noted that the NARX model's predictions of the first frequency steps used as delays are not accurate, while the remaining curve is correctly predicted, as in Figure 9b. The pretrained ANN as well as the frequency-dependent ANN make slightly wrong predictions about the height and the form of some of the peaks (see Figure 9a,d).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **R** & **RMSE (N)** \\ \hline Frequency-dependent ANN & 0.9950 & 0.0248 \\ NARX & 0.4969/0.9644 & 0.3164/0.0305 \\ biLSTM & 0.9879 & 0.0353 \\ Pretrained ANN & 0.9574 & 0.2076 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression coefficients and RMSE for the different models trained on the academic case with 68 sensors.  
Figure 9: Finite element analysis (FEA) and predicted element force over frequency steps for the academic case with 6 sensors: (**a**) frequency-dependent ANN, (**b**) NARX with increased number of frequency steps, (**c**) ANN with biLSTM layer, and (**d**) pretrained ANN.  
This second theoretical case proves that most of the ANNs are also successful in the case, where the number of sensors is restricted, as is often the case in reality.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 1 Introduction
As a baseline, LDAS-Monde is capable of assimilating LAI and SSM satellite products. The objective of this paper is to (1) validate an aggregated 1 km \(\times\) 1 km S1 SSM product and (2) evaluate, for the first time, the effect of assimilating S1 SSM in ISBA-A-gs at the kilometer scale using the LDAS-Monde tool driven by a high-resolution atmospheric forcing. The study was carried out in two regions in France and one region in Spain where in situ measurements of soil moisture are available. The assimilation of S1 SSM products alone, LAI alone, and S1 SSM together with LAI was performed in order to assess the added value of the S1 SSM assimilation. The paper is organized as follows: Section 2 presents the ISBA model and the LDAS-Monde tool, the satellite and in situ datasets, as well as the study areas. Section 3 describes the methodology and the experimental design. Section 4 evaluates the S1 SSM products using in situ measurements of SSM and presents the assimilation results. Section 5 contains a discussion about how results can be interpreted. Section 6 provides conclusions and highlights on future works.
# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 3 Localization of Mobile Robot in Ongoing Scene
### Localization Based on Artificial Landmarks in an Ongoing Scene  
In order to reach the designated working location accurately, the paper adopts the three-sided measurement method based on reflectors [13] to ensure an initial position of mobile robot. The surface of the reflector is pasted with a high-reflectivity film, and its intensity information can be extracted by Lidar to achieve the localization. When mobile robot moves to a certain site of the ongoing scene, the robot adopts Lidar to extract the intensity value of the reflector to assist in localization. As shown in Figure 4, the extracted process to reflectors and localization for mobile robot are described. Firstly, a global coordinate and the local coordinate of Lidar are set to \(XOY\) and \(x_{1}\alpha_{1}y_{1}\), respectively. Then the coordinate of the center to the three reflectors are set separately to \(p_{1}(x_{r1},y_{r1})\), \(p_{2}(x_{r2},y_{r2})\) and \(p_{3}(x_{r3},y_{r3})\). The reflector \(p_{1}\) is set as the origin of the scene's global coordinate system, and its radius is expressed as \(D_{r}/2\). When the mobile robot arrives at station A, the Lidar to the global coordinate system is set as \((x^{\prime},y^{\prime},\mu)\). At this time, the distances of Lidar sensor to the center \([p_{1},p_{2},p_{3}]\) of three reflective cylinders are represented as \([d_{1},d_{2},d_{3}]\). The distance between the reflector and Lidar is different, the number of scanned points is also different. Just as shown in Figure 4, the red dots attached to reflectors are the scanned laser points. For the further research to the localization of mobile robot based on reflectors, the paper will describe in detail from the following two aspects: (1) Feature extraction of reflectors; (2) localization of scene-based reflectors.  
Figure 4: Extraction of reflectors and localization based on the three-sided measurement.  
Figure 3: Localization diagram of mobile robot in the ongoing scene.
## 6 Presentation and Analysis of Results
### Micro-Doppler Spectrograms for PRND Gear Shift Tests  
Spectrograms for the PRND Test of the GMC are shown in Figures 11 and 12. The vertical red lines indicate the gear shifts: ON in PARK, REVERSE, NEUTRAL, and DRIVE. During the recording, the driver was verbally and gesturally prompted to switch transmission modes, which means some impulses may appear slightly earlier or later at each 10 s interval.  
Figure 11: PRND micro-Doppler spectrograms for GMC Acadia from the back side. Vertical red lines represent the timing between transmission gear shifts: ON/Park (10 s), Reverse (20 s), Neutral (30 s), Drive (40 s), OFF (50 s).  
Figure 10: Micro-Doppler (dop) vs. acoustic accelerometer (ac) spectrogram comparison for GMC Acadia: (**a**) Back side, 4 persons, Doppler; (**b**) Rear license plate, 4 persons. The horizontal red lines at 30 Hz and 60 Hz represent the fundamental tone from a typical 6-cylinder 4-stroke engine.  
In Figure 11, high-intensity, impulse-like activity is visible at low frequencies at each 10 s mark. This pattern suggests the gearbox physically shifting to a different position, resulting in the observed transients. This phenomenon was also noted by other researchers, who observed a similar impulse when shifting from 'Park' to 'Drive' [6]. Spectral lines around 30 Hz and 60 Hz remain visible, and like the Multi-Passenger Tests, the lines are not perfectly straight, with amplitude variations throughout the recording. As observed during the recording process, the engine's RPM slightly fluctuates when changing gears, which likely explains the behavior seen in these spectrograms.  
Figure 12 displays the spectrograms for the PRND Tests on each side of the vehicle. The gear shifts can be detected from each side of the vehicle. It is important to note that only one PRND Test per side is shown. In the experiment, five PRND Tests were conducted for each side, and the results varied despite consistent test conditions. This variability may be due to an insufficient number of recordings to capture a more accurate profile of each side's behavior. However, the spectrograms provide detailed frequency content over time, suggesting that this method holds promise for creating a detailed profile of the vehicle's behavior. This information could potentially be used to train a machine learning model to recognize the impulse and spectral line characteristics associated with each transmission mode.  
Figure 12: GMC Acadia PRND micro-Doppler spectrograms per side: (**a**) Front side; (**b**) Back side; (**c**) Driver side; (**d**) Passenger side. Vertical red lines represent the timing between transmission gear shifts: ON/Park (10 s), Reverse (20 s), Neutral (30 s), Drive (40 s), OFF (50 s).  
We note that PRND gear shift impulses preferentially excite low-frequency structural vibration in the 0-10 Hz range. Low-frequency vibration is underdamped and is measurable for periods of greater than 5 s after gear shift. Frequencies extending up to 20 Hz are also measured immediately following gear shift, but experience more rapid decay over the first 1-2 s.
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 4 Experiment and Results
### Experimental Results and Analysis
#### 4.4.1 Proposed Algorithm Validation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithm** & **Evaluation** & **BaseLine** & **10\%** & **20\%** & **30\%** & **40\%** & **50\%** & **Average** \\ \hline \multirow{8}{*}{ALA} & AP & 0.6206 & 0.6290 & 0.6633 & 0.6558 & 0.6448 & 0.6354 & 0.6457 \\ \cline{2-8}  & HL & 0.0408 & 0.0373 & 0.0355 & 0.0374 & 0.0389 & 0.0396 & 0.0377 \\ \cline{2-8}  & OE & 0.4319 & 0.4327 & 0.3916 & 0.4005 & 0.4159 & 0.4197 & 0.4121 \\ \cline{2-8}  & CV & 5.4837 & 6.6824 & 5.6804 & 5.0189 & 4.8361 & 4.9038 & 5.4243 \\ \cline{2-8}  & _RL_ & 0.1218 & 0.1472 & 0.1231 & 0.1080 & 0.1039 & 0.1066 & 0.1178 \\ \hline \multirow{8}{*}{NLA} & AP & 0.6106 & 0.6357 & 0.6468 & 0.6306 & 0.6185 & 0.6134 & 0.6290 \\ \cline{2-8}  & HL & 0.0421 & 0.0380 & 0.0386 & 0.0405 & 0.0413 & 0.0417 & 0.0400 \\ \cline{2-8}  & OE & 0.4449 & 0.4211 & 0.4081 & 0.4265 & 0.4368 & 0.4427 & 0.4270 \\ \cline{2-8}  & CV & 5.2835 & 5.5427 & 5.0874 & 5.0331 & 5.1437 & 5.1691 & 5.1952 \\ \cline{2-8}  & _RL_ & 0.1199 & 0.1286 & 0.1142 & 0.1124 & 0.1155 & 0.1163 & 0.1174 \\ \hline \multirow{8}{*}{LLA} & AP & 0.6202 & 0.6308 & 0.6527 & 0.6503 & 0.6393 & 0.6344 & 0.6415 \\ \cline{2-8}  & HL & 0.0408 & 0.0371 & 0.0366 & 0.0381 & 0.0394 & 0.0401 & 0.0383 \\ \cline{2-8}  & OE & 0.4341 & 0.4226 & 0.4024 & 0.4099 & 0.4196 & 0.4262 & 0.4161 \\ \cline{2-8}  & CV & 5.4456 & 6.7910 & 5.8355 & 5.1061 & 4.9505 & 4.9109 & 5.5188 \\ \cline{2-8}  & _RL_ & 0.1204 & 0.1508 & 0.1261 & 0.1082 & 0.1069 & 0.1067 & 0.1197 \\ \hline \multirow{8}{*}{SLA} & AP & 0.6202 & 0.6322 & 0.6659 & 0.6576 & 0.6405 & 0.6345 & 0.6461 \\ \cline{2-8}  & HL & 0.0408 & 0.0372 & 0.0354 & 0.0373 & 0.0390 & 0.0395 & 0.0377 \\ \cline{1-1} \cline{2-8}  & OE & 0.4341 & 0.4301 & 0.3905 & 0.4012 & 0.4151 & 0.4219 & 0.4118 \\ \cline{1-1} \cline{2-8}  & CV & 5.4456 & 6.5895 & 5.5776 & 5.0150 & 5.0003 & 4.9911 & 5.4347 \\ \cline{1-1} \cline{2-8}  & _RL_ & 0.1204 & 0.1453 & 0.1206 & 0.1081 & 0.1090 & 0.1089 & 0.1184 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results on Computers data set. From Table 3 to Table 8, it can be seen that the feature importance method based on CC performed on ALA, NLA, LLA, and SLA all can effectively select the features with strong category discrimination ability. The performance of the classifications based on the feature space constructed by the proposed method is all superior to that on the original feature space, demonstrating that the proposed feature importance method is effective and universal.
# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP & Third Generation Partnership Project  
A3C & Asynchronous Advantage Actor-Critic  
AHP & Analytic Hierarchy Process  
A1 & Artificial Interlligence  
ANR & Automatic Neighbor Relations  
APBSO & Adaptive Particle-based Sailfish Optimizer  
B5G & Beyond 5G  
BS & Base Station  
CAPEX/OPEX & Capital expenditures/Operating expenses  
CATBoost & Categorical Boosting  
CBR & Cell Blocking Ratio  
CCO & Coverage and Capacity Optimization  
CDR & Cell Dropping Ratio  
CIO & Cell Individual Offset  
CoMP & Coordinated Multipoint  
CRE & Cell Range Expansion  
CS & Cuckoo Search  
D2D & Device-to-Device  
DC & Dual Connectivity  
DL & Deep Learning  
DT & Digital Twins  
DTSK-C & deep-Takagi-Sugeno-Kang fuzzy classifier  
eICIC & enhanced Inter-Cell Interference Coordination  
FLC & Fuzzy Logic Controller  
FWA & Fixed Wireless Access  
GA & Genetic Algorithm  
GWO & Grey Wolf Optimization  
H2 RDC & Heuristic Handover based on RCC-DTSK-C  
HCP & Handover Control Parameters  
HetNets & Heterogeneous Networks  
HIT & Handover Intrupption Time  
HO & Handover  
HOF & Handover Failures  
HOL & Handover Latency  
HOM & Handover Margin  
HOPP & Handover Ping Pong  
HOR & Handover Rate  
HOSR & Handover Success Ratio  
HSR & High Speed Railway  
ICI & Inter-Cell Interference  
ICIC & Inter-Cell Interference Coordination  
IMGA & Intelligent Mutation GA  
IoT & Internet of Things  
IT & Intrupption Time  
ITS & Intelligent Transportation Systems  
KPI & key performance indicators  
LBO & Load Balancing Optimization  
LSTM & Long Short-Term Memory  
MDIH-HD & Movement Direction Information History Handover  
MFO & May-Hy optimization  
MH & Media-Independent Handover  
ML & Machine Learning  
MLP & Multilayer Perceptron  
mmWave & millimeter wave
# Enhancement of English-Bengali Machine Translation Leveraging Back-Translation
## 2 Related Work
### Neural Machine Translation (NMT) Architecture  
In this section, we review the architectures used in NMT.  
NMT is the mainstream approach for MT tasks. Let \(x\) be the input sentence in the source language, \(y\) be the output sentence in the target language, and \(\theta\) be the parameters of the model; the objective of NMT is to find the Equation (1):  
\[y^{*}=\text{argmax}_{y\in target\_language}P(y|x,\theta) \tag{1}\]  
The basic architecture of NMT, which handles input and output of arbitrary length, is a Seq2 Seq model with pre- and post-processing. In this section, we introduce an example framework of NMT using LSTM as the encoder and decoder of a Seq2 Seq model, as illustrated in Figure 1. The selection of the Seq2 Seq model (including encoder and decoder) is essential in the architecture of NMT [70, 71]. A lot of network structures can be leveraged as Seq2 Seq models, including RNN-based models (basic RNN, LSTM, GRU, or other variants), RNN-based models with encoder-decoder attention mechanisms, Transformer, and so on. For these structures, the key idea is to store and leverage the information from the input sequence and the generated part of the output sequence.
## 2 Materials and Methods
### Deep Learning Block
The proposed deep architecture is composed of the following elements:  
* Input layer;
* LSTM layer (HiddenCellNumber);
* FullyConnected Layer (NumberClasses);
* SoftMaxLayer
* ClassificationLayer  
In the proposed pipeline the variable HiddenCellNumber defining the number of cell for each LSTM layer is 300 while NumberClasses is 3 i.e. : (0): representing SHORT trend; (1) representing LONG trend; (2) representing no trend as the currency is in 'trading range', as is commonly said in financial jargon, to indicate that the financial instrument does not have a well-defined trend. In the proposed pipeline, the input time-series for each currency cross is divided in the following way: a well-defined part of length will be used as input sequence for the deep pipeline described above while the subsequent quotations (of a length equal to that used for the input sequence) will be used to establish the financial trend of the currency which one intends to predict. In the present case, we will consider the length of the financial time-series segment, a number of quotations equal to 100 candlesticks for each incoming currency. Considering that we are interested in HFT trading algorithms, we used a one-minute (1M) timeframe for each currency cross. The prices of the quotations refer to the closing prices of each financial candlestick. The currency of which we intend to predict the trend is the EUR/USD while, as mentioned, the three triangular arbitrage currencies are EUR/USD, EUR/GBP, and GBP/USD. Therefore, for every 100 quotations of the closing prices of the three currencies mentioned above, the deep learning system described above will process these data providing a predictive estimation of the trend of the next 100 quotations (closing price) relative to the currency you are chosen to trade i.e., EUR/USD. Figure 6 shows the described learning process. Figure 6: The proposed LSTM based mid-term trend prediction. The input currency time-series is being fed into LSTM input at blocks of close price quotations with a length of 100 (blue/green/red part of the input financial time-series). The output of the deep framework is a trend prediction (0: SHORT; 1: LONG; 2: Trading Range) related to next 100 close price quotations of the EUR/USD. The predicted trend made by LSTM based system will be fed to next block, i.e., the RL correction block.
## Materials and Methods  
### Participant Characteristics  
A 78-year-old male was recruited for this study who had previously (12 years prior) undergone a unilateral, right vocal fold hemilaryngectomy for the treatment of laryngeal cancer (T2 bN0M0 squamous cell carcinoma). An adipose tissue implant, taken from the periumbilical region, was injected to medialize the right vocal fold and facilitate glottal closure as the left vocal fold oscillated during phonation. The individual follows up regularly with his treating laryngologist for cancer surveillance and to assess vocal function, which includes laryngeal videoendoscopic examinations with stroboscopy. Figure 1 displays endoscopic images of the individual's larynx in an abducted and adducted state. Video S1 (Supplementary Materials) displays a segment from the videostroboscopic examination as the individual produces voice at a comfortable and higher-than-comfortable pitch level. According to clinical notes, the individual exhibited normal pliability and mucosal wave of the left vocal fold and diminished pliability and mucosal wave of the right vocal fold, consistent with expectations following the hemilaryngectomy and reconstruction. Complete glottal closure was observed during phonation with entrained vocal fold oscillation, mild phase asymmetry, and significantly reduced amplitude of the right vocal fold. There was no evidence of recurrent laryngeal disease.  
Figure 1: Endoscopic images of the larynx from the videostroboscopy examination of the participant who had previously undergone a unilateral (right vocal fold) hemilaryngectomy to treat laryngeal cancer. Shown are snapshots of the vocal folds in states of (**a**) abduction and (**b**) adduction.
## 3 Results
### Time-of-Addition and Time-Course Assay of Saucerneol in Virus-Infected Cells  
To identify the mechanism underlying the antiviral activity of saucerneol, we conducted time-of-addition and time-course assays for EV71 and CVA16. The time-of-addition assay showed that the antiviral effect of saucerneol decreased depending on the time  
Figure 2: Antiviral effect of saucerneol confirmed using immunofluorescence. (**A**) Representative image of VP3 staining in EV71-infected and non-infected Vero cells treated with 10 \(\upmu\)g/mL saucerneol for 24 h. The image was observed under a confocal microscope. Red signals represent VP3; blue signals represent DAPI-stained nuclei. Scale bar, 50 \(\upmu\)m. (**B**) MFI values of immunofluorescence images were quantified using ZEN software. The data were obtained through two independent experiments for validation assessment. The results are shown as mean \(\pm\) SEM. ***\(p<\) 0.001 for comparison with the EV71-infected vehicle-treated group (Veh) based on one-way ANOVA with [PERSON]’s multiple comparison test.  
interval between infection and treatment. A potent antiviral effect was observed during the early stages of viral infection (0-2 h), with a marginal antiviral effect extending for up to 4-6 h (Figure 3A,B). However, when the drug was administered before the viral infection, no antiviral effects were observed. Additionally, using a time-course assay, we confirmed that saucerneol continuously inhibited EV71 and CVA16 infections. Similar to rupintrivir, a 3C protease inhibitor, saucerneol, strongly inhibited the expression of viral RNA beginning at 8 h, when viral replication increased (Figure 3C,D). Our results suggest that saucerneol exhibits antiviral activity against EV71 and CVA16 primarily during the early stages of infection and shares similarities with rupintrivir in potentially inhibiting viral replication and translation.  
Figure 3: Saucerneol inhibited EV71 and covascadevirus A16 (CVA16) replication at the early stage of viral infection. **(A)** Vero cells were treated with 10 \(\upmu\)g/mL saucerneol during or after infection with EV71 at the indicated time points and viral mRNA was analyzed 14 h post infection. **(B)** Vero cells were treated with 10 \(\upmu\)g/mL saucerneol during or after infection with CVA16 at the indicated time points and viral mRNA was analyzed 14 h post infection. **(C)** Vero cells were infected with EV71 and harvested at the indicated time points, including at 4, 6, 8, 10, and 12 h after treatment with 10 \(\upmu\)g/mL saucerneol. **(D)** Vero cells were infected with 1 \(\times\) 10\({}^{3}\) PFU CVA16 and harvested at the indicated time points, including at 4, 6, 8, 10, and 12 h after treatment with 10 \(\upmu\)g/mL saucerneol. Rupintrivir was used as a positive control. The data were obtained through two independent experiments for validation assessment. Total RNA was isolated and viral RNA was analyzed using reverse transcription–real-time PCR.
# Prediction of Process Quality Performance Using Statistical Analysis and Long Short-Term Memory
## 4 Results
### Result of Composite Statistical Analysis Using PPD  
In the proposed system, the user enters a start date and an end date to analyze PPD, and collects all PPD within the period. With the data, we describe the composite statistical process analysis, such as SPC type X and R charts with PCCA result, WCt analysis, and PTAD in this section.  
Figure 8 shows the results of the X and R charts for a certain period of time in the real-world manufacturing process. The centerline is the average of all production process ranges. The red points indicate processes that fail at least one of the tests and are not in control. If the same point fails multiple tests, the point is labeled with the lowest test number, to avoid cluttering the graph. The out-of-control points can lead the production process to produce many defective products; therefore, if the chart shows out-of-control points, the manufacturers need to investigate those points to solve the problem as soon as possible. As can be observed from the figure, both charts are stable; no points are out of control in the charts.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Division** & **Specification** \\ \hline Ram & 64 GB \\ OS & Ubuntu 16.0 \\ CPU & Intel(R) Xeon(R) Silver 4114 CPC @ 2.20 GHz \\ HDD & 2T,2T,1T,1T \\ \hline \hline \end{tabular}
\end{table}
Table 7: Server specification for data storage with Hadoop ecosystem.  
Figure 8: Result of various charts in real-world manufacturing processes; (**a**) result of X chart and (**b**) result of R chart.  
For one workpiece, Figure 9 shows the WCt results with the other three sub WCt, such as PrCt, PInCt, and PCt. Cycle time is the duration that it takes to complete a production run divided by the number of acceptable workpieces produced.  
Figure 9: Results of manufacturing cycle time calculation in real-world manufacturing process; (a) WCt, (b) PInCt, (c) PrCt and (d) PCt.  
Figure 10 shows the process trajectory of abnormal detection with Gaussian distribution. The histogram combines all three processes into one histogram plot. The Gaussian is a distribution that is parameterized by the average and standard deviation. With the Gaussian, we can know whether a piece of data is an anomaly based on a threshold value. If the probability of the data under the Gaussian is higher or lower than the threshold, it is probably an anomaly. We can define the threshold by looking at the plot of the Gaussian, above. Anomaly data would be data that fall under the low probability areas of the Gaussian, because being in the low probability area, that data is highly unlikely to be distributed in the distribution. Those low probability areas are the left and the right tails of the Gaussian.