esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Rigid Robotic Exoskeletons for Hand Rehabilitation
### Interactive Experience Features
The literature analysis shows that, apart from force sensors, the force exerted by the user can be measured through bio-signals. These signals are employed as control inputs for the exoskeleton device. Current research indicates that the bio-signals commonly used for controlling rigid hand exoskeletons are electromyography (EMG) signals, electroencephalography (EEG) signals, and electrooculography (EOG) signals [22; 28; 29; 33; 37; 38; 42]. These signals can be obtained through non-invasive procedures, allowing clear interactions between the user and the device. The primary purpose of these technologies is to discern the patient's intentions. Studies have demonstrated that incorporating user interactions with training can enhance the recovery and promote cognitive function [25]. Furthermore, the current trend in this field is to integrate bio-signals with artificial intelligence techniques. * _Portability_. Portability is considered one of the emerging key features in rehabilitative hand devices and it turns out to be a relevant aspect in rigid hand exoskeletons. This feature allows end-users to independently wear and use the exoskeleton, possibly with limited support or without the support of specialists or assistants. The literature review shows that portability is an important feature to be considered: over time, in the field of rigid exoskeletons, there has been a shift in the design from grounded solutions that were challenging to wear and limited to specialized centers, like those presented in the work by [PERSON] et al. [53], to more easy-to-wear and friendly solutions, such as those proposed by [PERSON] et al. [52]. Currently, there is a growing emphasis on user-centered design solutions [62], where the user plays a central role in the development of the whole device. This approach gives the user a deeper impact on the design, as they are involved in all of the main design and prototyping phases. It is worth noting that the rehabilitative device, in this case, the rigid hand exoskeleton, ought to be accepted and adopted into everyday practice by the users. To accomplish this, it is crucial to understand the specific users' needs and expectations, including aspects such as quality of life, sense of control, dignity, and independence. Participatory design processes involving end-users might overcome the conflicting values and expectations of developers and end-users [63; 64] in terms of the device portability. In [32], a rigid hand exoskeleton designed for a specific user is discussed, allowing them to create their own rehabilitation program. Similarly, in [37], the authors prioritize the aspect of a lightweight design, aiming to enhance portability.
# Graphene Quantum Dots Doped PVDF(TBT)/PVP(TBT) Fiber Film with Enhanced Photocatalytic Performance
## 3 Results and Discussion
### Synthesis of PVDF(TBT)/PVP(TBT)-GQDs
Among them, in the XPS measurement, we used C1s (284.8 eV) for energy calibration. Figure 5A is a full-scan XPS spectrum of the PVDF(TBT)/PVP(TBT)-GQDs photocatalyst. As shown in Figure 5A, the peak at 284.7 eV can be assigned to C1s while the peak at 530.7 eV can be assigned to O1s [55]. In addition, there was a weak N1s peak at 397.7 eV and a weak F1s peak at 689.7 eV. This indicates that the prepared GQDs contained not only C and O elements, but also a small amount of N elements, which is in agreement with [PERSON] et.al [56]. Figure 5B is a C1s high-resolution XPS spectrum of the PVDF(TBT)/PVP(TBT)-GQDs photocatalyst [53; 57; 58; 59; 60]. Multimodal fitting of the XPS spectrum can obtain three peaks, located at 284.7 eV (red), 285.7 eV (pink), and 288.1 eV (blue), respectively. These three peak positions correspond to C=C (red), C-C/C-H (pink), and C-O (blue). Among them, the ratio of the C=C bond was the highest among all functional groups, indicating that the C=C bond is the most important functional group among the resultant GQDs. The low C-O content indicates that the prepared GQDs have a low oxygen content. Figure 4: XRD pattern of PVDF(TBT)/PVP(TBT)-GQDs photocatalyst. To further study the functional groups contained in the PVDF(TBT)/PVP(TBT)-GQDs photocatalysts, the infrared spectra of photocatalysts was also performed. Figure 6 shows the infrared spectrum of the PVDF(TBT)/PVP(TBT)-GQDs photocatalyst. As can be seen in Figure 6, the vibration peaks of C=C, C-H, C-C, and C-O of the GQDs corresponded to 1617.41 cm\({}^{-1}\), 1401.09 cm\({}^{-1}\), 1180.41 cm\({}^{-1}\), and 1068.91 cm\({}^{-1}\), respectively. As can be seen from Figures 5 and 6, the functional groups measured with the infrared spectrum matched well with the XPS spectra. Figure 5: (**A**) The XPS full spectrum of PVDF(TBT)/PVP(TBT)-GQDs (**B**) The C1s XPS spectrum of PVDF(TBT)/PVP(TBT)-GQDs.
## 2 Optimization Problem
### Gearbox Mass Calculation
#### 2.1.1 Gearbox Housing Mass Calculation  
The gearbox housing mass (\(m_{gh}\)) can be found by:  
\[m_{gh}=\rho_{gh}\cdot V_{gh} \tag{2}\]  
in which \(\rho_{gh}\) is the weight density of gearbox housing materials referred; as the material of the gearbox housing is gray cast iron (the most common material for the gearbox housing), \(\rho_{gh}=7300\) (kg/m\({}^{3}\)); \(V_{gh}\) is the gearbox housing volume (m\({}^{3}\)), which is determined by the following equation (see Figure 1):  
\[V_{gh}=2\cdot V_{A}+2\cdot V_{B}+2\cdot V_{C} \tag{3}\]  
where \(V_{A}\), \(V_{B}\), and \(V_{C}\) are the volumes of side A, B, and C (m\({}^{3}\)), respectively.  
\[V_{A}=L\cdot H\cdot S_{G} \tag{4}\]  
\[V_{B}=L\cdot B_{1}\cdot 1.5\cdot S_{G} \tag{5}\]  
\[V_{C}=B_{2}\cdot H\cdot S_{G}=(B_{1}-2\cdot S_{G})\cdot H\cdot S_{G} \tag{6}\]  
Figure 1: Calculated schema.  
coefficient of ith stage; and \(T_{1i}\) is the drive shaft torque of the ith stage (N.mm), which can be calculated by:  
\[T_{1i}=\frac{T_{r}}{\prod_{j=i}^{3}(u_{i}\cdot\eta_{hg}^{3-i}\cdot\eta_{be}^{4-i })} \tag{20}\]  
After calculating the gear parameters, the bending strength of the ith gear stage must be checked using the following formulas [23]:  
\[\sigma_{\Gamma 1i}=2\cdot T_{1i}\cdot K_{Fi}\cdot Y_{ei}\cdot Y_{gi}\cdot Y_{ Fi1i}/(b_{w1i}\cdot d_{w1i}\cdot m_{i})\leq[\sigma_{\Gamma 1}] \tag{21}\]  
\[\sigma_{\Gamma 2i}=\sigma_{\Gamma 1i}\cdot Y_{\Gamma 2i}/Y_{F1i}\leq[\sigma_{ \Gamma 2}] \tag{22}\]  
in which \(m_{i}\) is the module of the ith gear stage (mm); \(K_{Fi}\) is load factor; \(Y_{ei}=1/\varepsilon\) is the contact factor; \(\varepsilon\) is the contact ratio; \(Y_{gi}=1-\beta/140\) is factor taking into account the helix angle; and \(Y_{F1i}\) and \(Y_{F2i}\) are the geometry factor of the pinion and gear of ith gear stage.
# Technology-Economy-Policy: Biochar in the Low-Carbon Energy Transition--A Review
## 6 Economy: Application of Biochar
### Application of Biochar in the Renewable Energy Sector
#### 6.1.5 Biochar as a Building Material to Achieve Carbon Neutrality
The use of 5% of biochar in ultra-high-performance concrete contributed to the carbon sequestration of about 115 kg of CO\({}_{2}\) per m\({}^{3}\)[199].
# Performance Studies of High-Power Optical Parametric Oscillators Pumped by a Pulsed Fiber Laser
## 3 Experimental Results and Discussion  
### Characteristics of Pump Beam  
Before constructing the OPO, some basic information about the pump beam was investigated. The spectrum was measured by an optical spectrum analyzer (OSA205C, Thorlabs, Inc., Newton, NJ, USA). Figure 2a shows that the center wavelength of the pump beam is 1064.6 nm with FWHM bandwidths of \(\sim\)2.1 nm. To obtain the M\({}^{2}\) factors, we measured the beam diameters around the focus. The M\({}^{2}\) factors for two axes and beam-intensity profile are presented in Figure 2b. On the \(x\)-axis, the M\({}^{2}\) factor and diameter are \(\sim\)1.25 and 0.75 mm, respectively. On the other axis, the M\({}^{2}\) factor and diameter are \(\sim\)1.18 and 0.76 mm, respectively. Under the repetition rate of 200 kHz and the power of \(\sim\)81 W, we measured the temporal profile and long-term power stability using a photoconductive detector (PCI-4 TE-12-1x1, VIGO Photonics, Ozarow Mazowiecki, Poland) and a power meter (S425C, Thorlabs, Inc.), respectively. The measurement uncertainty of the power meter is \(\pm\)5% at the wavelength range of 250 nm to 17 \(\upmu\)m. Figure 2c,d shows that pump beam has a pulse width of \(\sim\)150 ns and power stability of 0.36% RMS over 6 h.
## 3 Methods
### Preprocessing  
There are several significant challenges related to the quality and structure of the dataset. The nature of these challenges was diverse, ranging from technical scanning errors to contextual inaccuracies in word usage. Understanding the specific characteristics of these challenges is crucial for developing an effective preprocessing method to prepare the dataset for our GEC pipeline.  
1. The Presence of Meaningless Symbols: The dataset contained numerous instances of meaningless symbols within the documents, likely artifacts of the text conversion or scanning process. These extraneous symbols posed a significant hurdle in accurately interpreting and processing the text data.
2. Scanning Errors: A notable problem was the misrecognition of letters by the scanning system. A common error observed was the scanner mistaking 'tt' for 'H'. Such errors could lead to substantial misinterpretations of the text, affecting the integrity of our text complexity analysis.
3. Contextual Inaccuracies in Word Usage: Words were incorrectly used in context. For example, the dataset used \"horten\" (to hoard) instead of \"horten\" (heard), as seen in the sentence: \"Aber da horten sie hinter sich schon das gefahrliche Knurren von Stormellas Wolfen\". These inaccuracies could potentially skew the assessment of text complexity and readability.
4. Redundant Information: The dataset retained headers, footers, and tables of contents within the documents. These elements, while essential in a book's layout, are extraneous for the purpose of analyzing text complexity and thus need to be removed.  
To enhance the document's conciseness and relevance, a pattern-matching algorithm was developed. This algorithm is particularly tailored to search through documents and methodically identify as well as remove sections deemed non-essential, thereby streamlining the textual content. The current results of the algorithm are illustrated in Figure 1.  
The algorithm demonstrates a high rate of accuracy, identifying and removing superfluous lines at a rate that exceeds the one at which it inappropriately discards necessary lines by more than sixteenfold, and correctly identifies and purges 95.7% of lines that are classified as non-critical, a testament to its accuracy.  
Further, Figure 2 provides an illustrative example of the algorithm in action. It takes a segment of a document and demonstrates the transformative effect of removing redundant parts. This visual representation exhibits the enhancement in the readability and clarity of the document post-processing. The elimination of superfluous elements declutters the text and significantly improves the ease with which the reader can assimilate and comprehend the text.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Document** & **Number of Sentences** & **Number of Words** \\ \hline Document 1 & 50 & 507 \\ Document 2 & 21 & 209 \\ Document 3 & 47 & 472 \\ Document 4 & 44 & 533 \\ Document 5 & 26 & 380 \\ Document 6 & 72 & 443 \\ Document 7 & 37 & 505 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentence and word counts for each document.
# Reliable Internet of Things: Challenges and Future Trends
## 6 Future Opportunities and Challenges
### Blockchain-Enabled Security  
Robust security mechanisms need to be developed for IoT applications. Some IoT applications are critical such as health monitoring, vehicle safety data sharing, etc. Hence, attacks on these applications can cause safety concerns to humans. Blockchain is an efficient technique that can ensure secure data communications due to its distributed record-keeping and proof-of-work mechanism [148; 149].  
While blockchain technology has many use cases in IoT, major challenges such as mining task computation and consensus algorithm selection need further research. Moreover, the energy required to mine the blockchain transactions is a critical challenge for which energy-efficient algorithms need to be designed. IoT can also benefit from blockchain-based smart contracts, which have applications in the supply chain, healthcare, and industrial automation. However, further studies are required to analyze the performance and security of a blockchain-enabled smart contract [150; 151].
###### Abstract
The traveling distance of the mobile anchor can be reduced if the unknown nodes whose positions have been determined are able to act as reference points to help the mobile anchor dynamically change its subsequent movement direction, decreasing the traveling distance required compared to the SCAN trajectory. Therefore, this paper proposes an iterative positioning algorithm (ISP) that uses the positioned nodes as reference points in cooperation with the mobile anchor to locate the unknown nodes. A mobile anchor route scheduling method using the ISP algorithm, named MARS, is also proposed to reduce the traveling distance of the mobile anchor. The rest of this article is structured as follows. Section 2 presents related works that use the mobile anchor to locate the unknown nodes. Section 3 describes in detail the mechanism by which the ISP algorithm and the MARS method dynamically determine the mobile anchor's next movement direction to reduce the traveling distance of the mobile anchor. Section 4 presents the results of the evaluation of the proposed method in comparison with existing methods. Finally, Section 5 presents the conclusions.
# A Hybrid Trust Model against Insider Packet Drop Attacks in Wireless Sensor Networks
## 5 Performance Evaluation
### Goals, Metrics, and Methodology
#### 5.1.5 OPNET Simulation Setup  
The parameters in Table 3 are used in our simulations. An ad hoc WSN with 50 sensor nodes is built in a 1 km \(\times\) 1 km area as shown in Figure 5. We place a single base station (BS) at one corner and every other sensor will generate and send data packets to the BS. We use the default OPNET setting of data packet size (1024 bits) and transmission power to 0.001 W. For the channel access model, we use CSMA/CA (carrier sense multiple access/collision avoidance) with a maximum of seven retransmissions (default value in OPNET). Each node generates a data packet randomly within each 10 s period as the simulation time of 100 s elapse, and forwards it toward the BS based on its routing algorithm, such as the pure GRP, the beta GRP, and our hybrid GRP. For trust models, we use three different trust thresholds of 0.6, 0.7, and 0.8. Considering a practical assumption that attackers develop a high trust value before launching attacks [31], we set the initial trust value of each node to be approximately 0.99. We place single or multiple attackers near the BS so that attackers can receive many data packets from nodes. Attackers start launching pre-defined attacks (blackhole attack or grayhole attack) once they receive data packets. By considering our simulation PC's performance (Pentium CPU 3 GHz and RAM 2 GB), we set the maximum simulation time to be three hours (10,800 s) to find the detection completion time _DCT_.
## 3 Results and Discussion
### License Plate Detection Comparison with Other Methods
Precision for all datasets is average precision, recall is precise for each dataset, and speed is average for all datasets. Results from the CaltechCars dataset showed that the proposed algorithm has better recall (99.19%) compared to [18; 19], with recall and precision of 91.3% and 93.8%, and 96.83% and 98.39%, respectively. However, recall and precision were worse compared to [32], although the proposed algorithm was significantly faster. A two-step license plate detection presented in [23] takes a similar approach, where the candidate license plate is extracted from the original image and then verified. Results show a high accuracy of 98.1% with 452 ms speed. Study [24] also used a different approach and achieved high precision. However, the current detection algorithm performs faster, although comparisons in these situations should be performed for the same datasets. The proposed neural network performs poorly on very small objects. When the input data is minimized to 256 \(\times\) 256, these objects are not visible to the human eye, which is likely why the model has difficulty detecting them. Images larger than this, such as 2500 \(\times\) 2500 \(\times\) 3, are downscaled to fit the input size, therefore, any objects that are approximately 50 pixels in diameter will disappear and appear as \"clouds\". However, increasing the input size to 512 \(\times\) 512 would improve the system's performance in detecting these small objects at the cost of decreased overall system performance. This would likely result in a faster and more precise system than those analyzed in existing papers.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 4 Applications of the LiFi Network
### Educational System  
LiFi has performed in similar outstanding standards as other technologies used in a broad range of applications, such as education, eventhough LiFi is among the latest of technology. The most advanced Internet access technology offers one of the fastest Internet connectivity [70; 143]. Thus, LiFi can be considered the most modern Internet networking infrastructure that provides the best connection to the Internet. It can be an excellent alternative to Wi-Fi in educational and corporate organizations. Therefore, itcan be considered among the ideal frameworks to be used by colleges, lecture rooms, conference rooms, testing centres, research centres, and laboratories [156; 157].
## 3 Results and Discussion
### Substrate Specificity of L-Arabinose Isomerase from K. pneumoniae
The reactions were performed in 50 mM phosphate buffer (pH 8.0) containing 100 g/L d-galactose at 50 \({}^{\circ}\)C with varying concentrations of cells from 0.5 to 5 g/L. (**b**) Time-course reactions for the production of d-tagatose (black circle) from d-galactose (white circle) by _E. coli_ cells expressing l-arabinose isomerase from _K. pneumoniae_. The reactions were performed in 50 mM phosphate buffer (pH 8.0) containing 100 g/L d-galactose and 5 g/L cells at 50 \({}^{\circ}\)C. Data represent the mean of values from three experiments, and error bars represent standard deviation. Production of d-Tagatose from d-Galactose Using E. coli Cells Expressing l-Arabinose Isomerase from K. pneumoniae  
The optimal reaction conditions for producing d-tagatose from d-galactose were pH 8.0, 50 \({}^{\circ}\)C, and 60 g/L recombinant _E. coli_ cells using 100 g/L d-galactose with 1 mM Mn\({}^{2+}\). Under these conditions, the time-course reaction for producing d-tagatose from d-galactose was performed by _E. coli_ cells expressing l-arabinose isomerase from _K. pneumoniae_ (Figure 5b). The recombinant _E. coli_ cells produced 33.5 g/L d-tagatose for 30 min with a molar yield of 33.5% and a productivity of 67 g/L/h. l-Arabinose isomerases from _G. stearothermophilus_ and _T. neapolitana_ have been applied to recombinant _E. coli_ cells and immobilized for better d-tagatose production [45, 46]. In addition, d-tagatose has been produced from milk whey powder using recombinant _E. coli_ cells expressing l-arabinose isomerase from _Lactobacillus plantarum_[47] and from maltodextrin by multienzyme coexpression system of _E. coli_ cells containing l-arabinose isomerase [48]. Therefore, further studies on applying immobilization and multienzyme system to _E. coli_ cells expressing l-arabinose isomerase from _K. pneumoniae_ will enhance its industrial applicability.
## 4 Simulation and Result Analysis  
A real vehicle model is constructed in the software CarSim, and a control model is constructed in MATLAB/Simulink. The simulation analysis is performed through cosimulation in MATLAB/Simulink and CarSim. Passive suspension without any control (abbreviated as NC) and passive suspension with ESP (abbreviated as P-ESP) are compared with the multi-objective fuzzy continuous damping control strategy with ESP (abbreviated as MFCDC-ESP) to illustrate the effect. The main parameters of vehicle model and control model are shown in Tables 5 and 6, respectively.  
The real vehicle selected for the funding project is an A0 class high centroid vehicle. Although it has a high centroid, its mass is not large, and it is more prone to sideslip than to rollover in the absence of an ESC system when driving. Therefore, the selection of its parameters is the critical condition obtained via repeated simulations, and it can show the effect of vehicle control under the limited state.  
To verify the MFCDC-ESP effect of stability and antirollover, this work compares and simulates three test conditions, namely, sine wave steer input test, DLC test, and fishhook test.
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 2 Materials and Methods
### Ensemble-Based Feature Selection
#### 2.3.2 Classification Models
Similar to RF, it combines different machine learning algorithms to create a strong predictive classifier [40]. In contrast to gradient boosting decision trees, which is based on the gradient-powered decision trees exploiting linear combination of an ensemble of weak learning models where the trees are produced sequentially, correcting the error of prior weak learners, XGBoost builds its trees in parallel elevating its performance considerably with high computational speed. [PERSON] and [PERSON] [41], for this latter reason, chose XGBoost as the GA iterative optimization fitness assessment classifier. * Cubist regression models (CB): Another model based on decision trees is the Cubist rule-based model [cubist]. A potent algorithm developing sequentially a series of trees. It induces accurate yet simple rules built and selected repeatedly at each iteration. Unlike RF, CB returns a set of rules linked to sets of multivariate models rather than a single final model. Then, depending on the rule that best matches the predictors, a particular collection of predictor variables will choose an actual prediction model [42; 43]. To some extent, in order to interpret the complicated links between the influential features the Cubist model was seen to be very promising compared to tree methods counterparts [44]. * Trees-based models can produce a more thorough predictive accuracy when compared to traditional linear machine learning techniques. Their main benefits for classification tasks include (a) a reduced risk of overfitting, (b) a small number of effective tuning hyperparameters, (c) the ability to automatically determine variable importance in order to interpret the variable contribution mechanism in the final prediction model. To recapitulate, in order to capture the linear/non-linear relationships between the VIS-NIR hyperspectral bands, various predictors, random forests, Cubist and XGBoost-based tree ensemble models in addition to LR and SVMs algorithms are used in this context. A 3-fold cross-validation set has been exploited during the training process searching for the best hyper-parameters values of our respective supervised learning models. We use Savitzky-Golay filter (SG), a polynomial interpolation, for smoothing the input data as a pre-processing step with a polynomial order 2, and a passing window equal to 13. The K-fold cross-validation resulting scores in the training stage use the forward selection as in [PERSON] et al. [29].