esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Indicators for Model Performance  
Accuracy, F-measure, and the Matthews correlation coefficient (MCC) [39] are commonly used to explain the model's performance. The receiver operating characteristics (ROCs) curve is a two-dimensional graph presenting the relative tradeoff between true-positives on the \(Y\)-axis and false-positives on the \(X\)-axis [40]. The area under the curve (AUC) is a metric that summarizes the ROC curve [31]. The ROC curve and calculated AUC were used to visualize the predictive accuracy of the selected model. Furthermore, the criteria adopted in SDT [41; 42] were used in this paper. SDT is more commonly used in psychophysics and psychology studies and is applied to various tasks, including detection, identification, recognition, and classification. Regardless of the task, the initial focus in these studies is on how to effectively analyze decision-making under uncertainty and bias, i.e., the model's performance. The secondary focuses in SDT are determining how much information is obtained during decision-making and whether the obtained information is useful and applicable, i.e., the model's distinguishing ability.  
In SDT, \(\beta\) is defined as the ratio of neural activity produced by signal and noise; it is used to compute the response bias. \(\beta_{opt}\) is the adjustment of \(\beta\) used to detect changes in the signal and noise probabilities. \(\beta_{opt,payoff}\) is involved with additional information such as the reward for correct events (Value) and the penalty for incorrect events (Cost).  
\(X_{c}\) is the decision criterion serving as a useful indicator reflecting a criterion shift. An \(X_{c}\) value close to 0 indicates an unbiased decision. In other words, all the information was fully used during the decision-making process. A positive \(X_{c}\) represents a type of liberal decision-making, which is also interpreted as information is overused. A negative \(X_{c}\) indicates conservative decision-making, where information is filtered to reduce the risk of mistakes.  
\[\beta=\frac{p(X|S)}{p(X|N)}=C\times d^{\prime}\]  
\[\beta_{opt}=\frac{P(N)}{P(S)}=\frac{count\;\{FP+FN\}}{count\;\{TP+TN\}}\]  
\[\beta_{opt,\;payoff}=\beta_{opt}\times\frac{Value(TN)+Cost(FP)}{Value(TP)+Cost (FN)}\]  
\[X_{c}=-\frac{z(TP)+z(FP)}{2},\]  
where \(X\) is the decision variable of binary classification for either \(S\) (signal) or \(N\) (noise); \(C\) is the criterion that \(X\) is presented, \(d^{\prime}\) is the distance of the midways between the signal and noise distributions, _Value_ is a reward for events that are correctly classified, and _Cost_ is a penalty for events that are misclassified. True Negative (\(TN\)), False Positive (\(FP\)), True Positive (\(TP\)), and False Negative (\(FN\)) are the abbreviations of true-negative, false-positive, true-positive, and false-negative, respectively. \(z(TP)\) is the \(z\)-distribution transformed values of the true-positive events, and is the \(z\)-distribution transformed values of the false-positive events.
# Review of Photogrammetric and Lidar Applications of UAV
## 3 Classification of UAV
### Based on Wings and Rotors
#### 3.4.2 Single-Rotor UAV  
Single-rotor UAVs are solid and durable. Single-rotor UAVs resemble helicopters in terms of design and structure. Therefore, the launch of a single-rotor UAV is similar to that of a helicopter and requires special training. Single-rotor UAVs have just one rotor, which is like one large-diameter rotating wing or propeller, plus a tail rotor to control direction and stability. The aerodynamics of single-rotor UAVs allows the use of very long blades that resemble a rotating wing rather than a propeller, making single-rotor UAVs highly efficient. Single-rotor UAVs have an advantage over multi-rotor UAVs if the UAV is gas-powered, which guarantees a longer flight time and higher flight speed. Another advantage of single-rotor UAVs is the ability to fly with heavy payloads (e.g., an airborne lidar laser scanner). A single rotor is more efficient at lifting a heavy load and consumes less battery or fuel during flight. These single-rotor UAVs can also pose safety risks and must be operated only by a licensed pilot. The disadvantage of single-rotor UAVs is that they are mechanically complex, requiring much maintenance and care. Another disadvantage is their price, which ranges from EUR 25,000 to EUR 300,000. Single-rotor UAVs are mainly used by surveyors and civil engineers who want to lift heavier payloads of up to 40 kg into the air. However, today's most advanced technology in fixed-wing and multi-rotor UAVs gives surveyors more flexibility at the survey site [23; 24].
## 6 Discussion on Clinical Relationship Extraction  
RE is a specialized task of collecting meaningful structured information from unstructured text. In clinical and biomedical domains, RE has been applied to drug-gene relationships [68], disease-gene relationships [69], semantic classes for radiology report text identification [70, 71], relation extraction for biological pathway construction [72], relationship between lexical contexts and category of medical concepts [73], and disease-mutation relationship from biomedical literature [74]. Temporal relationship extraction from clinical texts is another important RE task [75]. In all these of different tasks, the NLP-based methods that are used to extract the relations between different entities are very much specific to the particular dataset, i.e.,the particular combination of feature representation and learning algorithm is very distinct from any other case. Due to this fact, the methods that are used to extract relations such as an ML problem are not very generalizable. However, RE from clinical texts has also been performed by using a domain invariant convolutional neural network (CNN) [76]. Most RE tasks are based on finding the relationship between entities inside the same sentence but there are some instances of RE tasks across sentences as well [77, 78, 79]. Since, in most cases, RE is treated as a classification problem, both multi-labelclassification [80] and multi-class classification [76; 81] were proposed to extract clinical relations. An RE task consists of syntactic processing modules, which deals with the process of text representation and feature generation such as tokenization, word embeddings, etc., and semantic processing modules, which deals with meaningful information collection such as relationship identification and classification, in this case. In clinical texts, a variety of feature generation techniques are used to extract relations from various data, which can range from contextualized word embeddings, part-of-speech (POS) tagging, etc. The next or the final step is to select a learning algorithm such as supervised, unsupervised, or even rule-based methods on the features in order to identify the relations. The various feature representation and learning methods used in the clinical and biomedical text are discussed next. A pictorial representation of the different learning methods used to learn the different relations from clinical texts is shown in Figure 6.
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 2 Materials and Methods
### Preparation of the Bacterial Consortium  
Endophytic bacteria were isolated from the roots of hemp seedlings grown on soil characterized by significant arsenic contamination (about 1000 mg kg\({}^{-1}\)[52]). The protocol described in [49] was followed to isolate the endophytes. In short, after removing the soil by repeatedly rinsing with tap water, the roots of the plants were sterilized by treatment with 70% EtOH for 5 min, with 0.1% NaClO for 2 min and again with 70% EtOH for another 5 min, and then washed with sterile water. The roots were then minced with a sterile scalpel, placed in sterile flasks containing TYEG (Trypticase Yeast Extract Glucose) and incubated overnight at 30 \({}^{\circ}\)C.  
Serial dilutions (10\({}^{-4}\), 10\({}^{-6}\), 10\({}^{-8}\)) were then prepared with the suspension, and quantities of 100 \(\upmu\)L of each dilution (in triplicate) were spread on R2A agar plates (Merck\({}^{\text{\textregistered}}\), DA, DE, Darmstadt, Germany). Then, 100 \(\upmu\)L of the third-rinse water was plated to confirm the efficiency of external root sterilization. Colonies of the isolates appeared after approximately five days. About 50 colonies were randomly selected and propagated to obtain pure cultures.
## 3 Results and Discussion  
### Preliminary Results  
The taste, after-taste, odor, color, texture and overall acceptability of the canned fish burgers were evaluated. Results are shown in Table 1. In general, the hedonic results showed better scores for carp and barbel burgers enriched with 0.5 and 1% (_w/v_) of _Spirulina_. Statistically, formulations with the highest _Spirulina_ concentration (1.5%) possessed inferior sensory acceptability for the majority of the organoleptic characteristics compared to formulations with the lower _Spirulina_ concentrations (\(p<0.05\)) and controls (\(p<0.05\)) (Table 1). The inappropriate taste caused by the supplementation of high concentration of _Spirulina_ is related to the obtained compounds from the lipid oxidation and to the minerals that not only operate as pro-oxidant molecules but also might produce undesirable metallic off-flavors [33]. Yet, taste scores of carp and barbel burgers formulated with 0.5 and 1% of _Spirulina_ were similar to control fish burgers (\(p>0.05\)) (Table 1). The microalga addition modified the color of carp and barbel burgers from yellowish white to green, depending on the added concentration of microalga. This characteristic was considered by panelists as an inappropriate sensory feature (color appearance) (\(p<0.05\)). The decrease in color pleasantness might be explained by the fact that batches rich in _Spirulina_ showed an increase in the green color as the amount of chlorophylls increased, which results in a color very different from that expected in a control fish burger. In the case of mouth-texture, some panel members declared that the burger texture was ameliorated by increasing the percentage of _Spirulina_; however, these differences were significant (\(p<0.05\)) compared to controls only for carp formulations (Table 1). Overall, treatments with 1.5% of _Spirulina_ minimized the sensorial quality of fish burgers, especially from the point of view of taste and after taste. According to these results, it seems that 0.5% _Spirulina_
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
## 1 Introduction
We present the network model in Section 2 and give a brief introduction to the underwater channel model. We give a brief review of network coding and introduce a practical design of network coding based on fountain codes in Section 3. Then, we elaborate the process of our proposed protocol NCHARQ in Section 4, which is the main body of our paper. After that, we conduct the simulations and analyze the performance of different protocols in Section 5. Finally, we give a conclusion and suggest some future works in Section 6.
# Design and Analysis of a Planar Six-Bar Crank-Driven Leg Mechanism for Walking Robots
## 7 Comparison of the New Leg Mechanism with Existing Designs  
Walking leg mechanisms, such as the Klann, [PERSON]'s, and others, have been practically adopted in walking machines, each showcasing distinct geometry and behavior. Figures 10 and 11 illustrate the foot trajectories, for one complete crank rotation, of the proposed walking leg mechanism, [PERSON]'s mechanism, and the eight-link leg mechanism proposed by [PERSON] et al. [7]. The data on [PERSON]'s mechanism have been sourced from [PERSON] et al. [22], and the data on the eight-link mechanism have been sourced from [PERSON] et al. [7]. Compared to the new design, [PERSON]'s linkage foot trajectory features a smaller lift but a larger stride. In comparison with the eight-link linkage, the new mechanism has a very similar stride length but a higher lift. Another notable difference with [PERSON]'s linkage is the position of the crank, which is nearly in the mid-portion of the mechanism when measured vertically, whereas in the proposed mechanism the crankshaft is positioned at the top. The proposed mechanism can be designed solely with binary links; however, it is also possible to construct it using ternary links. Its major differences compared to [PERSON]'s mechanism and the eight-link leg are summarized in Table 3.  
Figure 9: Input torque obtained using MATLAB and GIM software.  
Figure 11: Foot trajectories of the newly proposed, [PERSON]’s, and Eight-Link Leg Mechanisms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Comparison** & **Jansen’s Leg** & **Eight-Link Leg** & **New Proposed** \\ \hline Number of links & 8 & 8 & 6 \\ Number of fixed joints & 2 & 3 & 2 \\ Stride length to size of the & & & \\ mechanism & Large & Compact & Compact \\ Velocity of the foot & Nearly constant & Fluctuating & Fluctuating \\ Maximum velocity in the X & NA & 0.22 & 0.18 \\ direction (m/s) & NA & 0.1 & 0.2 \\ Maximum acceleration in the X & NA & 0.1 & 0.2 \\ direction (m/s\({}^{2}\)) & 0.122 & 0.122 & 0.175 \\ \(B_{p}\) (m) & 0.363 & 0.279 & 0.266 \\ Crank rotation & CCW & Both directions & Both directions \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the new leg mechanism with existing designs.  
Figure 10: Comparison of the foot profiles of the (a) new, (b) Jansen’s [22], and (c) Eight-Link Leg Mechanisms [7].
## 1 Introduction
[33] observed that the shear force between the skin and the ice layer under electro-impulse loading dominated the ice-shedding process and developed a preliminary prediction of the ice-shedding region on the skin surface by comparing the shear stress at the interface between the ice layer and the skin. [PERSON] et al. [34] suggested a better de-icing criterion that emphasizes the de-icing influence of shear stress, and they employed an explicit central difference algorithm to describe the leading edge structure's electro-impulse de-icing. These studies highlight ice shedding when the shear stress between the ice and skin reaches a threshold value, but they do not look at how ice shedding is affected by normal skin-ice interaction. [PERSON] et al. [35] investigated the effect of interface normal tensile stress on ice shedding using shear stress analysis. They presented an ice interface shedding failure criterion based on coupled shear stress and normal tensile stress at the ice interface. [PERSON] et al. [36] also investigated the application of the coupled tensile-shear shedding failure criterion, the single tensile shedding failure criterion, and the single shear shedding failure criterion for electro-impulse de-icing simulations. The tensile shear linked failure criterion is more accurate than the single shedding failure criterion. It has also been argued that one of the reasons for the low accuracy of ice-shedding prediction is that the current model only considers ice interface failure [37]. This paper first introduces the theory of CZM and the Embedding Method of cohesive elements, with an aim to improving the current ice shedding model, which primarily focuses on the effect of ice-skin interface debonding on ice shedding and generally ignores the role of ice fracture on the de-icing effect. Then, it is proven that the cohesive element model can simulate the ice fracture and also characterize the debonding of the ice and the base material using the ice beam three-point bending experiment, single cantilever beam (SCB) experiment, and zero-degree cone angle push-out (push-out) experiment. Ultimately,an accurate finite element model of wing electro-impulse de-icing is established, taking ice fracture and the ice-skin interface debonding into complete account. The modeling's reasonableness and accuracy are evaluated by comparing skin deflection curves and de-icing rates once the doubler is loaded, both experimentally and numerically. This paper examines the impacts of various ice-skin interface shear adhesion strengths, doubler loading positions, and impulse sequences on the de-icing effect to support the design and implementation of the current wing electric-impulse de-icing system.
## 2 Precise Calculation of the Aluminum Extraction Flow Rate  
### Aluminum Output Speed Model Establishment  
In the aluminum electrolysis process, the PLC calculates the speed of aluminum extraction based on weight data from the electronic scale. By dividing the difference in the net aluminum weight between two consecutive measurements by the sampling interval, the real-time extraction speed is determined. However, in this process, due to the measurement errors caused by the swinging of the electronic scale and the real-time changes in the aluminum tapping speed, nonlinearities arise, leading to inevitable errors in the velocity calculation. First, the following about the electrolysis system is assumed:  
It is assumed that the system parameters (such as electrolyte conductivity and temperature gradient) are relatively stable over short periods.  
It is assumed that electrochemical reactions follow linear dynamics, ignoring the effects of complex reaction rate variations.  
It is assumed that the noise in aluminum output follows a Gaussian distribution.  
This study used the UKF method to filter extraction speed data. The simulations assumed a PLC sampling frequency of 8 Hz, with the initial speed set to 10 kg.s\({}^{-1}\) and this transitioning to 15 kg.s\({}^{-1}\) after 6.25 s. The initial extraction weight is 0 kg, with Gaussian white noise (1 dBW intensity) being used to model the scale observation noise. Using MATLAB R2021b software, the wgn function is used to generate Gaussian white noise, and simulations are performed on an Intel Core i7-12700H processor (Intel, Santa Clara, CA, USA).
## 3 Results and Discussion
### Evaluation of Similarities Among Classification Methods  
#### 3.3.1 Similarity Evaluation Based on NB  
A confusion matrix was generated based on the pixel count for the reclassification results by grading the final suitability map using NB, and the results were compared with those from other classification methods. Based on this, the overall accuracy, kappa coefficient, and reclassification accuracy for the same grade were compared and analyzed. The overall accuracy and kappa coefficient between NB and EI were 77.40% and 0.57, respectively, indicating the highest similarity among the classification methods. However, the average value of the reclassification accuracy for the same grade between the different classification methods was the highest (72.7%) between NB and GI. This means that based on NB, GI reclassified the same areas (locations) more similarly for all four grades compared to Qu or EI. When the primary objective was to explore the SS, the reclassification accuracy of the SS with EI was very low at 3.5%. In contrast, the reclassification accuracy for the SS was 100.0% for Qu and 64.3% for GI (Table 5).
# ENDURUNS: An Integrated and Flexible Approach for Seabed Survey Through Autonomous Mobile Vehicles
## 3 System Functionalities
### Data Communication  
The AUV is designed to perform a double-link communication with the USV through acoustic modems, during underwater operations and through wireless communication at sea-surface level, close the USV, as shown in the Figure 1.  
Furthermore, the USV is capable to perform real time communication with the RMCC [48]. As already introduced, the USV can receive mission specification and navigation instructions (e.g., changes of the mission parameters) from the RMCC. The USV can transmit quasi real-time USV- and AUV latitude/longitude coordinates, respective functioning status (e.g., battery level, sensors status, AUV real time depth, USV/AUV real time speed and direction), as well as geo-referenced data from the AUV due to the USBL system installed on both vehicles.  
The AUV-USV connection via acoustic signals is bandwidth-limited in the range of kbps, with possible long connection gaps [48]. An L-band antenna for the USV communication was chosen [85]. These family of antennas do not have any moving parts, use electronic beam-steering to optimize gain in a certain direction, are reasonably priced, small and light. The L-band satellite link (500 kbps) connects the USV to the RMCC to relay the AUV data and receive commands, as reported graphically in Figure 1. The USV also uses a modem to ping the RMCC command queue at certain intervals of time and determine if powering up the main antenna would be needed. This can be used to concentrate transmissions to timeframes where energy is abundantly available, or in general make information transmission more efficient.
# Multi-Span Tension Control for Printing Systems in Gravure Printed Electronic Equipment
## 4 Performance Study of the ADRC Decoupling Controller for the Printing Tension System
### Simulations  
The printing tension system operates in a stable state, maintaining a steady-state value of 50 N for each span of web tension. At 8 s, 10 s, and 12 s, respectively, three printing spans undergo 10 N step changes. The web tension change curves for each span, when using a PID controller, are depicted in Figure 7, while the corresponding curves for each span utilizing an ADRC decoupling controller are shown in Figure 8. Figure 7a-c show the web tension curves for printing span 1, printing span 2, and printing span 3, respectively.  
In PID control, the step change in web tension of the previous span will affect the web tension of the subsequent span, but the step change in web tension of the subsequent span has no effect on the web tension of the previous span. The tension step change of printing span 1 in Figure 7 resulted in a tension pulse fluctuation of \(-8.1\) N in printing span 2, as well as tension pulse fluctuations of \(4\) N and \(-5.6\) N in printing span 3. The tension step change of printing span 2 resulted in a tension pulse fluctuation of \(-8.1\) N in printing span 3. The reason for this phenomenon is that the actual control quantity of each span controller is the discharge roller speed of that span. This roller speed, in turn, serves as the input quantity for the tension system of the subsequent span, indicating that the printing roller speed between the two spans acts as a coupling quantity. PID control cannot suppress the influence of this coupling relationship on the web tension. From Figure 8, it can be seen that when using the ADRC decoupling control method, tension step changes in one span  
Figure 7: Performance simulations of the PID controller for the printing tension system.  
do not affect the tension in others, indicating that the proposed ADRC decoupling control method can effectively achieve tension decoupling control of multi-span printing systems.  
In addition, when comparing PID control with ADRC decoupling control, it can be observed that the step-response adjustment time of each span is longer when using ADRC decoupling control, reaching 0.18 s, which is higher than the 0.05 s achieved with PID control. This indicates a decrease in dynamic performance. Nonetheless, this value still fully meets the requirements for tension systems.