esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Analysis of Parallel Corpora and In-Depth Analysis Using LIWC
## 1 Introduction
By analyzing various factors that can affect NMT performance, we proposed a method that can be applied in future research using the analysis results. LIWC is a text-analysis tool that automatically analyzes the number of words in a sentence and classifies words with similar meanings and sentimental characteristics. LIWC extracts various interpersonal variables related to clinical, social, physiological, cognitive, psychological, and developmental contexts that cannot be detected using previous text-analysis programs. Additionally, LIWC comprises a variety of features for analyzing text. LIWC is generally used to recognize linguistic markers for mental health study in such as detecting narcissism [22], schizophrenia [23], bipolar disorder [24]. However, LIWC provides various linguistic features, word count, gender bias and so on, so it can be used for various analyses. In this study, we use LIWC to analyze parallel corpora based on diverse properties. It is also first time analyzing a corpus using LIWC. In addition, we conduct baseline translation experiments by training transformer-base model structure [4] through all the parallel corpora given by AI Hub. By analyzing MT performance of corresponding models, we propose further research directions on MT for the Korean language. The contributions of this study are as follows:  
* For the first time, we conduct a deep data analysis on AI Hub data. To the best of our knowledge, this is the first time LIWC has been used to analyze corpora. This study acts as a milestone for further studies on NMT with respect to the Korean language. * We conduct baseline translation experiments on all the data in the AI Hub parallel corpus. Our experiments provide a foundation for further research on Korean-based NMT. * We discovered that many factors might cause decreasing model performance, and we provide the direction that those factors could be filtered through our correlation analysis between LIWC and model performance.
# Underwater Accompanying Robot Based on SSDLite Gesture Recognition
## 4 Algorithm and System Experiment
### Performance Evaluation
#### 4.2.2 Recognition Performance  
We trained different detection methods and compared their performance on the Diver test set. The test conditions were as follows: NVIDIA Jetson Xavier NX, Jetpack 4.6, and MODE_10W_DESKTOP. The results are shown in Table 3. As can be seen from the table, YOLOv3 achieved the highest mAP value in terms of detection accuracy. However, in terms of speed performance, except for SSDLite, the other methods had poor real-time performance and could not realize real-time detection on embedded devices. Considering the accuracy, speed, and model size, the SSDLite detection algorithm with MobileNetv3 as the backbone performed best.  
In addition, in order to prove the superiority of the Diver dataset, the SSDLite model trained using the PASCAL VOC 07 + 12 dataset to detect a person in an underwater environment, and the mAP obtained was only 0.4879, far lower than the 0.882 obtained by the Diver dataset training. A large number of images in the test set could not be correctly detected. As shown in Figure 10, the first row shows the test results using the air training set, and the second row shows the test results using the Diver dataset. In false detection rate, missed detection rate, and detection accuracy, the Diver dataset showed better performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **PSNR** & **SSIM** \\ \hline Retinex-based & 14.6280 & 0.3658 \\ Multi-scale fusion & 12.1468 & 0.2349 \\ WaterGAN & 16.7906 & 0.3649 \\ Proposed & **24.0392** & **0.6609** \\ \hline \hline \end{tabular} Bold indicates the best result.  
\end{table}
Table 2: PSNR and SSIM values between the output of different methods and the original image.  
Figure 9: Output results of different methods. (**a**) Original. (**b**) Generated underwater image. (**c**) Retinex-based. (**d**) Multi-scale fusion. (**e**) WaterGAN. (**f**) Proposed.  
Similarly, we tested the trained gesture recognition network model on the test set and compared the results underwater with different backbones, as shown in Table 4. The MobileNetV3 Small model we used achieved 99.94% mAP on Jetson Xavier NX, 23.5 FPS recognition speed and the model size was only 5.71 MB, which performed best.
# Combined Effect of Ferrous Ion and Biochar on Cadmium and Arsenic Accumulation in Rice
## 3 Results
### Comparison of Cd and As Translocation  
In this study, total As (27.3 mg\(\cdot\)kg\({}^{-1}\)) was much higher than Cd content (7.7 mg\(\cdot\)kg\({}^{-1}\)) in soil, but Cd accumulation in grain was much higher than As (Figure 4). BAF were calculated based on the ratio of the heavy metal uptake by different tissues to the total metal content in soil (Tables 3 and 4). In general, the amendment treatment which has poor BAF value could be suitable for immobilization of Cd or As to decrease their availability to the rice in the soil. The BAF value of Cd was the highest in 1 BC-1 Fe(II) treatment, which was the same as the pH reduction effect. This indicates that soil pH is the main affecting factor of BAF. In addition, the BAF value of Cd gradually decreased from root to grain migration. Combined with the analysis of TF, we found that it is easier to transfer Cd from root to stem than from stem to grain. The BAF of As have significantly higher of root and straw uptake ability than husk and grain, it ranged from 0.04 to 2.86 in root and straw, whereas, for husk and rice, it only ranged from 0.04 \(\times\) 10\({}^{-2}\) to 0.90 \(\times\) 10\({}^{-2}\), indicating that As in root and straw was much easier to be accumulated than in husk and grain. When Fe(II) was added, the accumulation of As in plants was lower than that in control and BC treatment, indicating that Fe(II) inhibited the migration of As to plants.  
Furthermore, the TF, representing the transfer capacity of metals in different tissues were calculated (Tables 3 and 4). The TF from root to straw (TFs) were in the range between 0.17 to 0.37 and 0.04 to 0.06 for Cd and As, respectively, and Cd and As from straw to grain (TFsg) ranged from 0.21 to 0.47 and from 0.02 to 0.13, respectively, indicating that the translocation of As from root to straw and from  
Figure 4: The Cd and As contents in rice tissues. The un-amendment soil (Control) inhibited the ear formation of rice (grain is null in control treatment). Different letters indicate that the differences are statistically significant (\(p<0.05\)). The data are mean \(\pm\) standard error (\(n=3\)), bars represent standard errors (\(n=3\)). Control, untreated soil; BC, applied 1% (w/w) biochar; 1 BC-1 Fe(II), applied 0.5% biochar and 0.5% ferrous sulfate; 3 BC-1 Fe(II), applied 0.75% biochar and 0.25% ferrous sulfate; 5 BC-1 Fe(II), applied 0.83% biochar, and 0.17% ferrous sulfate.  
straw to grain is more difficult than that of Cd. However, the application of BC did not alter (increased or decreased) the As BAF and TF.
## 5 Results and Discussion  
This section presents the comparison of results obtained from the DT, RF, and AdaBoost models. The results obtained from these models were subjected to several performance indicators: namely, accuracy, precision, recall, F1-score, and the area under ROC curve or AUC (area under curve) to determine which method was the most accurate and effective for slope stability classification. Here, the testing phase of the datasets was considered for the validation of each tree-based model. This is a common method of evaluation or model assessment to understand the level of accuracy during training/model development. On the other hand, the training stage results showed that the proposed AdaBoost model could be considered as perfect, and therefore, there is no need to discuss further about this stage and have any comparison between models. Table 7 shows the comparison of the testing stage results obtained by the indicators: i.e., accuracy, precision, recall, F1-score, and AUC of ROC. In addition, the ranking procedure proposed by [PERSON] et al. [77] was applied in this table. The ranking system is very easy to understand. In this system, the most accurate performance index receives the highest rank. According to Table 7, the model that showed the highest accuracy was AdaBoost as it obtained the highest rank value, which was 13. The second most accurate model was the RF, which obtained a total rank value of 10. The lowest accurate model was the DT model, with a total rank value of 7. Except for the AUC, AdaBoost achieved better accuracy and performance compared to the RF and DT models. It is important to note that the RF also received a high degree of accuracy, and it can be used for slope stability classification by the other researchers or engineers. For a better comparison, Figure 10 shows the classification results of the DT, RF, and AdaBoost models from the testing phase compared to the FOS results obtained with the GeoStudio software. As stated earlier, 175 data samples, which constituted 25% of the whole data, were used for each model in the testing phase. It is clear from Figure 10 that the AdaBoost technique was able to record an outstanding performance with the lowest number of unmatched answers (i.e., 11). The number of matched and unmatched for RF and DT were 160 and 15, and 156 and 19, respectively, confirming the RF model's superiority over the DT in slope stability classification. Overall, the error rate during the testing phase was very low, which reflected the high-performance level of the model development during the training phase. It was concluded that the best performing model for slope stability classification was the AdaBoost, and that it could be used in this field for the same purpose to minimize the associated risk.  
Figure 9: The AdaBoost model results for FOS classification: (**A**) Training and (**B**) Testing.
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.3.2 Trust-Based Vehicle Selection Mechanism  
At each FL training round, the central server aggregates the trust scores from all vehicles and selects a subset for model updates. The selection aims to do the following:  
* The total trust of selected vehicles is maximised, enhancing global model reliability.
* Vehicles with low trust scores or flagged by IDS alerts are excluded to maintain security.
* The selected subset adapts to dynamic IoV conditions like network stability and vehicular reliability.  
We define a binary variable \(x_{i}\), indicating whether vehicle \(v_{i}\) is selected. We then solve the following optimisation problem:  
\[\text{maximise}\sum_{i=1}^{N}x_{i}\tau_{i}(t) \tag{5}\]  
subject to  
\[\sum_{i=1}^{N}|v_{i}|\leq K, \tag{6}\]  
\[\tau_{i}(t)\geq\tau_{\text{min}},\,\forall i, \tag{7}\]  
\[\tau_{i_{l}}(t)=1,\,\forall i, \tag{8}\]  
\[x_{i}\in\{0,1\},\,\forall i. \tag{9}\]  
Here, \(K\) is the maximum number of vehicles to be selected, \(\tau_{\text{min}}\) is the minimum acceptable trust threshold, and \(\tau_{i_{l}}(t)=1\) ensures that only vehicles without IDS alerts are considered. By solving this optimisation problem, the central server enforces a trust-based curation of participants, directly aligning selection criteria with the multi-factor trust evaluation.
# Valorisation of Star Wastewater by Anaerobic Fermentation
## 3 Biohydrogen Production  
Hydrogen is currently hyped as a future renewable energy carrier. Current hydrogen on the market is mainly produced from fossil resources. However, it is possible to  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Feedstock**} & \multirow{2}{*}{**Reactor**} & **OLR** & **Methane Yield** & **Literature** \\  & & (**kg COD m\({}^{-3}\) d\({}^{-1}\)) & (**L CH\({}_{4}\) kg COD\({}^{-1}\))** & **Literature** \\ \hline Potato juice & UASB & 5.1 & 210 & [16] \\ \hline Potato juice & EGSB & 3.2 & 332 & [16] \\ \hline Synthetic starch wastewater & UASB & 4 & 325 & [17] \\ \hline Modified tapioca starch & UASB & 7.5 & 160 & [18] \\ wastewater & UASB (with & 25 & 240 & [18] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Methane yields and organic loading rates from different starch wastewaters in UASB/EGSB lab-scale reactors.  
produce hydrogen from organic feedstocks by fermentation. In the case that biohydrogen is produced from wastewater, anaerobic dark fermentation is typically applied. This fermentation is part of the anaerobic digestion process, where methanogenesis is suppressed. Biohydrogen production is still in its infancy and is not applied at the industrial scale. The results of biohydrogen lab-scale experiments are shown in Table 2. [PERSON] et al. worked with two isolated strains and achieved 186.3 L H\({}_{2}\) kg COD\({}^{-1}\) with a mixture of corn gluten water and corn steep liquor [4]. They demonstrated that these starch wastewaters were excellent feedstocks for biohydrogen production. The hydrogen production rate of 165.89 mL L\({}^{-1}\) h\({}^{-1}\) was about 21 times higher than the reference using starch as a carbon source and peptone as a nitrogen source. [PERSON] et al. achieved even higher yields of 260 L H\({}_{2}\) kg COD\({}^{-1}\) when using wastewater from cassava processing in thermophilic fermentation [7].  
Comparably low hydrogen yields were obtained by [PERSON] et al. [24]. They worked with mixed-cultures and achieved hydrogen yields of 37.4 L H\({}_{2}\) kg COD\({}^{-1}\) with base-treated bacteria and 22.6 L H\({}_{2}\) kg COD\({}^{-1}\) with acid-treated bacteria. [PERSON] et al. achieved 16.9 L H\({}_{2}\) kg COD\({}^{-1}\) in starch wastewater co-digestion with buffalo dung [25].  
In the literature, combined hydrogen and methane production is also described, where yields of 81.5 L H\({}_{2}\) kg COD\({}^{-1}\) and 310.5 L CH\({}_{4}\) kg COD\({}^{-1}\)[26] as well as 141.9 L H\({}_{2}\) kg COD\({}^{-1}\) and 230 L CH\({}_{4}\) kg COD\({}^{-1}\)(removed) [27] were determined.
## 2 Materials and Methods
### GPP Estimation and Validation
#### 2.4.2 Cross-Validation  
To validate the reliability of the CASA model, cross-validation was conducted using the GOSIF GPP and MODIS GPP, yielding fitted equations. These datasets were processed by synthesizing the original 8-day resolution imagery into 16-day resolution imagery, and were clipped and averaged. These datasets were synthesized into 16-day resolution from the original 8-day resolution to match the temporal interval of Landsat-based estimations in this study. Linear regression was performed between the processed two products and the resampled Landsat-based estimates to evaluate the applicability of the estimated results. Although the temporal aggregation from 8-day to 16-day resolution may smooth some short-term variations, this approach maintains the integrity of the long-term dynamics and provides a reliable basis for comparison.
# Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
## 2 Power Requirements for Mobile Robots
### Walking Robots and Wheeled Mobile Robots
The Connor UVC Disinfection Robot [39] is designed for indoor virus prevention. It is equipped with cutting-edge technology including UV sterilization lamps and an automatic disinfect spray module. It is currently being used to fight the Coronavirus (COVID-19). In addition to the examples of mobile robots' power presented earlier, some companies (Fetch Robotics) use sealed lead acid batteries to power their Fetch and Freight AMRs [40]. The multi-tasking mobile robot presented in [41] used two 6 V 2800 mAh NiMh battery packs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Robots**} & \multicolumn{5}{c}{**Battery**} & \multirow{2}{*}{**Robot Description**} \\ \hline
**Name** & **Manufacturer** & **Mass [kg]** & **Voltage** & **Capacity** & **T\({}_{\text{rms}}\)** & **T\({}_{\text{charge}}\)** & **[h]** & **T\({}_{\text{charge}}\)** \\  & & & [VI] & **[Ah]** & **[h]** & **[h]** & **[h]** & **[h]** \\ \hline \multicolumn{3}{c}{LiFePO\({}_{4}\)/graphite} \\ \hline Swift & IAM Robotics & 272.2 & 55 & 100 & 10 & - & 3000 & Mobile picking and transport robot [29] \\ \hline Matthews AMR & Matthews Automation & 83 & 25.6 & 20 & 6-8 & 3 & - & AMR for specific tasks such as order picking and material transport [30] \\ \hline GR-1500 UPS & Kaze Robotics & 250 & 48 & 60 & 8 & - & - & Robot for automated material handling [31] \\ \hline RA660 Navi XL & CleanFix & 313 & 24 & 120 & 3–4 & 1 & - & Automated cleaning robot for industry [2] \\ \hline RB-Vulcano Base & Robotnik & 750 & 48 & 200 & 10 & - & - & Mobile manipulator for carrying out industrial tasks (payload 1750 kg) [33] \\ \hline \multicolumn{3}{c}{Li-ion} \\ \hline Jackal & Clearpath Robotics & 17 & 24 & 11.25 & 2–8 & 5 & - & Jackal is a small, fast, entry-level field robotics research platform [34] \\ \hline Star-L & Hansrobot & 200 & 48 & 46 & 8 & 2 & - & Mobile manipulation robot with 100 kg payload and locomotion speed of 1.5 m s/s [35] \\ \hline Star-H & Hansrobot & 900 & 48 & 125 & 12 & 2.5 & - & Mobile manipulation robot with payload 600 kg, speed 1.5 m/s [35] \\ \hline Caster & Liquotient Robotics & 40 & 24 & 40 & 5 & - & - & Caster is a diff-drive indoor unmanned ground vehicle, suitable for research and indoor service [36] \\ \hline AMB-UR5 & Seer & 130 & 48 & 52 & - & 2 & \textgreater{}500 & Collaborative Hybrid Robot UR5 based on Auto Mobile Base AGVs [37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features of selected mobile wheeled AMRs equipped with LiFePO\({}_{4}\)/graphite or Li-ion batteries. Successful solutions for a spherical solar robot are presented in [42], and those with wheels include Tertill [27] and Vitirover [28].
## 2 Numerical Methods
### Heterodyne Detection Processes with ISAL
The random variation in \(C_{0,0}^{S}\) directly affects the heterodyne detection efficiency, the absolute \(\left|C_{0,0}^{S}\right|\) affects the amplitude of the coherent signal, and the angle \(\phi_{0,0}^{S}\) affects the phase history data of the coherent signal. If \(\phi_{0,0}^{S}\) hardly changes during the ISAL imaging time, then the imaging resolution will be high; otherwise, the image quality will be reduced. For the linear frequency modulation (LFM) signal, the local oscillator signal is obtained after a certain time delay \(\tau_{ref}\):  
\[S_{L}(t)=S\Big{(}t-\tau_{ref}\Big{)}=rect\left(\frac{t-\tau_{ref}}{T_{P}} \right)\exp\left\{j\Big{[}2\pi f_{c}\Big{(}t-\tau_{ref}\Big{)}+\pi K\Big{(}t -\tau_{ref}\Big{)}^{2}\Big{]}\right\} \tag{12}\]  
where \(f_{c}\) and \(K\) represent the center frequency and chirp rate, respectively. Therefore, the received echo signal with a time delay \(\tau\) can be expressed as  
\[S_{r}(t)=S(t-\tau)=rect\left(\frac{t-\tau}{T_{P}}\right)\exp\left\{j\Big{[}2 \pi f_{c}(t-\tau)+\pi K(t-\tau)^{2}\Big{]}\right\} \tag{13}\]  
The raw ISAL data are obtained by laser heterodyne detection, which can be expressed as [29]  
\[S_{n}(t)=Arect\left(\frac{t-\tau}{T_{P}}\right)\exp\left\{ \begin{array}{c}- 2\pi K(\tau-\tau_{ref})(t-\tau_{ref})\\ +\eta K(\tau-\tau_{ref})^{2}-2\pi f_{c}(\tau-\tau_{ref})\end{array} \right\} \tag{14}\]  
where \(\tau=2R/c\), \(\tau_{ref}=2R_{ref}/c\), and \(R\) and \(R_{ref}\) represent the distance from ladar to the target and the distance of the reference signal from ladar to the target, respectively. Let \(R_{\triangle}=R-R_{ref}\), \(t^{\prime}=t-\tau_{ref}\). Then, Equation (14) can be written as  
\[S_{n}(t)=Arect\left(\frac{t^{\prime}-\frac{2R_{\triangle}}{T_{P}}}{\exp} \right)\exp\left(-j\pi x\frac{2R_{\triangle}}{c}t^{\prime}\right)\exp\left(-j \frac{4\pi f_{c}}{c}R_{\triangle}\right)\exp\left(j\frac{4\pi K}{c^{2}}R_{ \triangle}^{2}\right) \tag{15}\]  
Thus, the heterodyne signal affected by atmospheric turbulence can be written as  
\[S_{n}(t)=Arect\left(\frac{t^{\prime}-\frac{2R_{\triangle}}{c}}{T_{P}}\right) \exp\left(-j\pi x\frac{2R_{\triangle}}{c}t^{\prime}\right)\exp\left(-j\frac{4 \pi f_{c}}{c}R_{\triangle}\right)\exp\left(j\frac{4\pi K}{c^{2}}R_{\triangle} ^{2}\right)\exp\left(i\phi_{00}\right) \tag{16}\]  
Finally, the motion compensation algorithm is used to achieve target imaging.
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 4 Validation
### Data Obtainment  
The waveform of the wheel hub bearing signal is shown in Figure 3. At the last moment of the vibration signal stream, the experimental instrument activated an alarm; the actual fault was occurred after 17 h and 55 min. After the fault occurred, we checked the damaged hub bearing and found that the fault occurred in the inner hub bearing. The rolling contact angle \(\alpha\) is 70.12\({}^{\lx@math@degree}\). The hub bearing speed \(r\) is 300 rev/min. The bearing outer diameter \(D_{r}\) is 52 mm. The bearing diameter \(D_{i}\) is 25 mm. The number of balls \(n\) is 15. The diameter of the rolling element \(d\) is 25.4 mm. The bearing pitch diameter \(D\) can be calculated from Equation (32). The actual frequencies of the theoretical values are \(f_{bpfo}=21.3\) Hz and \(f_{bpfi}=45.91\) Hz, calculated with Equations (1)-(8).  
\[D=\frac{D_{r}+D_{i}}{2}=38.5\,\mathrm{mm} \tag{32}\]  
The Fast Fourier Transformation (FFT) spectrum of the wheel hub bearing signal is shown in Figure 4. The theoretical fault frequency values \(f_{bpfo}=21.3\) Hz and \(f_{bpfi}=45.91\) Hz can be detected in the FFT spectrum. Due to the influence of various fault noise signals described in Equation (13), the fault frequency \(f_{bpfo}=21.3\) Hz is submerged in the noise signals. However, the fault frequency \(f_{bpfi}=45.91\) Hz is still obvious in the FFT spectrum.  
Figure 4: The FFT spectrum of the wheel hub bearing signal.  
Figure 3: The waveform of wheel hub bearing signal.
## 4 Test Results
### Output Current Distortion During Steady-State Operation at MPP
#### 4.1.1 SOLIS S5-EH1P3.6K-I Steady-State Operation  
The test results of the SOLIS S5-EH1P3.6K-I inverter, with rated current that is 15.6A, are shown in Table 4. The values of the TRD factor, DC component of the output current, fundamental, and higher harmonics are shown. The results are shown for 5%, 25%, 50%, 75%, and 100% of the nominal power of the PV array (10%, 20%, and 30% are omitted due to lack of space). The DC component and harmonics values are given in the percentage of the rated current.
# Relational Positioning Method for 2D and 3D Ad Hoc Sensor Networks in Industry 4.0
## 1 Introduction
We present a location-searching method on a discretized space defined by available anchor nodes (ANs). The method used sole distance relationships between ANs and the node of interest, rather than mean square optimization, to develop likelihood indicators for vertices in the searching space according to those distance relationships. Since the process occurred in location systems, observations were prone to impairments due to errors in distance estimation. In addition, the absence of direct connectivity from nodes to ANs is observed in ad hoc routes that depart from a line-of-sight trajectory. The proposed methodology is adequate for 2D and 3D localization, and its performance was assessed throughout simulations assuming different node densities, transmission ranges, and the number of available ANs. Results under different scenarios show a remarkable performance of the method reaching a small distance error (less than 1 m) for some scenarios. Simulation results show that, compared with the 3D positioning algorithm introduced in [7], the proposed method achieves a significant improvement (approximately 50% improvement when combined with finer discretization). The rest of the paper is as follows: In Section 2, a discussion on related research about localization in 2D and 3D situations is provided. In Section 3, we introduce the relational location-searching method proposed in the paper, starting with a 2D setup and extending it to the 3D scenario. In Section 4, results are presented with their corresponding discussion. Lastly, Section 5 provides the conclusions.