esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Machine Learning Algorithms
### Classification Metrics Performance
#### 5.8.2 Combined Evaluation Metrics  
Apart from the fundamental metrics, combined assessments for the binary classification are also considered as alternative approaches to assess the models, based on the DRL and ML algorithms. These assessments are straightforward but effective. This study considers several measures, such as the F1 measure, G-mean, likelihood ratios, balanced accuracy, and MCC.  
The F1 measure is expressed as the harmonic mean among recall and precision, as illustrated in (16). The F1 measure has a range from 0 to 1, which means its high values reveal high classification performance.  
\[\text{F}_{1}=\frac{2\times(\text{Precision}\times\text{Recall})}{(\text{ Precision}+\text{Recall})} \tag{16}\]  
The G-mean is considered as the prediction accuracy outcome for both sensitivity and specificity, as illustrated in (17).  
\[\text{G}=\sqrt{\text{Sensitivity}\times\text{Specificity}} \tag{17}\]  
The Likelihood ratios are separated as a positive likelihood ratio and a negative likelihood ratio. The positive likelihood ratio is defined as the ratio sensitivity over one minus specificity, as shown in (18).  
\[\text{L}=\frac{\text{Sensitivity}}{1-\text{Specificity}} \tag{18}\]  
Conversely, the negative likelihood ratio is expressed in (19) as the ratio between one minus sensitivity and specificity.  
\[\lambda=\frac{1-\text{Sensitivity}}{\text{Specificity}} \tag{19}\]  
Based on the definition of the negative likelihood ratio and the positive likelihood ratio, a lower negative likelihood and a higher positive likelihood ratio will have correspondingly better performances on negative and positive classes. The threshold of positive likelihood is shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**L Value** & **Contribution of Model** \\ \hline \(>\)10 & Good \\
5-10 & Fair \\
1-5 & Poor \\
1 & Negligible \\ \hline \hline \end{tabular}
\end{table}
Table 3: Interpretation of Thresholds of Positive Likelihood.  
The balanced accuracy represents the average of sensitivity and specificity, as shown in (20).  
\[\text{Balanced Accuracy}=\frac{\text{Sensitivity}+\text{Specificity}}{2} \tag{20}\]  
MCC is not affected by the imbalanced dataset problem. MCC illustrated in (21) is defined as a contingency matrix approach of computing the Pearson product-moment correlation coefficient between the predicted and actual values.  
\[\text{MCC}=\frac{\text{TP}\times\text{TN}-\text{FP}\times\text{FN}}{\sqrt{( \text{TP}+\text{FP})(\text{TP}+\text{FN})(\text{TN}+\text{FP})(\text{TN}+ \text{FN})}} \tag{21}\]
## 3 Cognitive Radio for Smart City  
As stated before, wireless technologies are considered a promising solution for the communication infrastructure of a smart city despite several challenges, such as trade-offs between wireless coverage and capacity as well as limited spectral resources. For this reason, new communication paradigms are needed, and among these, CR networks (CRNs) are highly promising for providing timely wireless communications by utilizing all available spectrum resources.
## 1 Introduction
However, these datasets often contain highly correlated features, which can introduce noise and negatively impact the classification performance [31]. As a result, different feature selection (FS) methods have been created that simplify the model and consequently help to accelerate the classification. [PERSON] et al. [32] compared various FS methods for vegetation mapping using multitemporal Sentinel-1 and Sentinel-2 imagery and additional data derived from the RS imagery. As mentioned above, still most studies concentrate on allergenic pollen vegetation mapping on a large scale and the identification of individual allergenic tree species in urban green spaces is difficult due to their small size and patchy distribution. Hence, the primary goals of this study are to:  
1. Assess the feasibility of mapping allergenic tree species in the highly urbanized area using high-resolution PlanetScope imagery;
2. Compare and evaluate the effectiveness of the most important ML and FS methods for the accurate detection of individual allergenic tree species. The remainder of this paper is structured as follows: In Section 2, the research area and datasets utilized in this study are discussed. Section 3 explains the various classifiers and feature selection techniques used for identifying allergenic tree species. The results of the study and their analysis are presented in Section 4. Lastly, the key findings and conclusions are outlined in Section 5.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 4 Analytical Model and Performance Analysis
### Markov Chain for Safety-Related Message and WSA Message
Besides, our proposed analytical model considers the impact of bit error caused by fading channel which is an unfavorable characteristics of VANETs due to highly mobility of vehicles and constantly changing topology of vehicular network [44].
# Valorization of Insulation Cellulose Waste as Solid Biomass Fuel
###### Abstract  
This paper investigates the ability of insulation cellulose fiber powder (CFP) to be pelletized for its valorization as biomass fuel. CFP is a waste originating from insulation cellulose manufacturing that lacks any method of valorization because of its boron salts content. A sugar byproduct and lignosulfonate (LS) were considered as binders for the pellet manufacturing process. Physical tests were carried out to characterize the pellets' performance. Chemical and combustion tests were considered to state the pellets' potential as a green energy source. Raw CFP showed good ability in its pelletization and durability in the range of 15-30% of moisture content. The pellet's density decreased as water content increased. Binders increased the pellet's length before and after the durability test. Binders also increased the CFP pellet's water absorption, demonstrating a potential decrease in durability against environmental factors. Binders also decreased the lower heating value. Ultimate analysis showed a slight Nitrogen increase in both binder combinations that could potentially raise the pollutant NO\({}_{\text{x}}\) combustion emissions. All the combinations showed adequate combustion characteristics, but binders increased ash production. Additives decreased the CFP volatile matter content and increased the fixed carbon, which could facilitate a more stable combustion. DTA curves showed a mass loss rate decrease in the volatile stage for the binder combinations, which also could be considered as an indicator of a more stable combustion. The ashes' chemical compositions when analyzed by XPS showed boron contents oscillating between 10.03% and 16.42%, demonstrating the possibility of recovering them from the combustion ashes.  
cellulose fiber powder; biomass fuel; pelletization; sugar; lignosulfonate +
Footnote †: journal: Applied Sciences
# Effect of Micro-Silica Addition into Electric Arc Furnace Steel Slag Eco-Efficient Concrete
## 3 Results and Discussion
### Hardened Properties
#### 3.2.1 Compressive Strength
Figure 7: Density of concrete containing different percentages of micro-silica (**left**) and optimal density of concrete for each of the previous parts (**right**). Figure 8: Crack patterns of the different mixes after the compressive strength test. As shown in Figure 12 below, by increasing the percentage of micro-silica additive to 8% wt. of cement, we are faced with an increase in strength. In this descending process, compressive strength decreases, due to increased chemical reaction between micro-silica and cement consumption. The noteworthy point in the diagram is that all samples display significant differences to the strength of the control sample except CF25M6, also at the age of 7 days, where there is a slight difference. This shows that at this age, micro silica only plays the role of filler. Moreover, samples containing 8% micro-silica have a higher strength after 90 days than other samples. In other samples, with the increase in micro-silica, no  
Figure 10: Compressive strength of replaced EAFS coarse aggregates concrete (**left**) and EAFS fine aggregates concrete (**right**). Figure 9: Compressive strength of basic concrete. further increase in compressive strength is observed. CF25M8 and CF25M10 have increased strength at 90 days of age compared to the control sample. However, in the rest of the samples, the increase in strength was less. With the addition of micro-silica, a large increase in strength is observed in the samples. Another noteworthy point is that the compressive strength of CF25M8 and CF25M10 after 7 days is 3 and 6% higher than the control strength, respectively. This point also emphasizes that until the age of 7 days, micro-silica has only a fine filler role and performs most of its activities between the ages of 7 and 90 days. The best-case scenario is to use 8% micro-silica. It can also be concluded from Figure 12 that micro-silica in all percentages and in all proportions does not increase the strength in an acceptable way. The reason for this is that the micro-silica at this age of 7 days has still not fully developed its function.
# PET/CT Radiomics in Lung Cancer: An Overview
###### Abstract  
Quantitative extraction of imaging features from medical scans ('radiomics') has attracted a lot of research attention in the last few years. The literature has consistently emphasized the potential use of radiomics for computer-assisted diagnosis, as well as for predicting survival and response to treatment. Radiomics is appealing in that it enables full-field analysis of the lesion, provides nearly real-time results, and is non-invasive. Still, a lot of studies suffer from a series of drawbacks such as lack of standardization and repeatability. Such limitations, along with the unmet demand for large enough image datasets for training the algorithms, are major hurdles that still limit the application of radiomics on a large scale. In this paper, we review the current developments, potential applications, limitations, and perspectives of PET/CT radiomics with specific focus on the management of patients with lung cancer.  
PET; CT; radiomics; lung cancer +
Footnote †: journal:  
analysis of one lesion but also of more lesions within the examined area, and, depending on the protocol used, of the whole body too [9].  
Fluorine 18 (18F) fluorodeoxy-glucose Positron Emission Tomography-Computed Tomography (PET/CT henceforth) is nowadays the mainstay in the management of lung cancer, having greatly improved patient diagnosis, staging, and follow-up [10]. The role of radiomics in this context has therefore attracted widespread interest during the last few years [11; 12; 13; 14; 15; 16]. In this work, we discuss methods, potential applications, and perspectives of radiomics analysis at PET/CT in the management of patients with lung cancer. In the remainder of this paper, we describe the overall work-flow in radiomics (Section 2), present four specific applications in lung cancer (Section 3), discuss issues and limitations (Section 4), and conclude with final considerations and a look at the perspectives (Section 5).
## 4 Experiments and Results
### Hydra Heads Performance
#### 4.2.2 Lung Segmentation

Table 3: Performance of _Hydra_ segmentation head and baseline model on Montgomery dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Hydra Scale** & **Train** & **Test** & **Accuracy** & **Loss** & **IoU** \\ \hline \multirow{3}{*}{2} & Shenzhen & Shenzhen & 0.9762 & 0.0125 & 0.9521 \\  & Shenzhen & JSRT & 0.9832 & 0.0351 & 0.9265 \\  & Montgomery \& JSRT & Shenzhen & 0.9768 & 0.0561 & 0.9294 \\ \hline \multirow{3}{*}{3} & Shenzhen & Shenzhen & 0.9647 & 0.0297 & 0.8986 \\  & Shenzhen & JSRT & 0.9824 & 0.0132 & 0.9461 \\  & Montgomery \& JSRT & Shenzhen & 0.9727 & 0.0444 & 0.9178 \\ \hline \multirow{3}{*}{4} & Shenzhen & Shenzhen & 0.9719 & 0.0454 & 0.9163 \\  & Shenzhen & JSRT & 0.9663 & 0.0536 & 0.9175 \\  & Montgomery \& JSRT & Shenzhen & 0.9742 & 0.0711 & 0.9116 \\ \hline \multirow{3}{*}{8} & Shenzhen & Shenzhen & 0.9612 & 0.0663 & 0.9132 \\  & Shenzhen & JSRT & 0.9364 & 0.0653 & 0.8947 \\ \cline{1-1}  & Montgomery \& JSRT & Shenzhen & 0.9641 & 0.0753 & 0.8816 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of _Hydra_ head for CXR lung segmentation on different training and testing sets. Figure 4: Example of lung segmentation. 1 st column: ground truth red contour and the superimposition on the original image. 2 nd column: green contour obtained by _Hydra_ segmentation head and the superimposition on the original image. 3 rd column: purple contour obtained by baseline (traditional approach) and the superimposition on the original image. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **IoU** & **Computation Time (per image)** \\ \hline Anatomical Atlas [40] & 0.9500 & 20–25 s \\ Efficient U-Net [41] & 0.9200 & 0.08 s \\ _Hydra_ Head (Scale 2) & **0.9742** & **0.03 s** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of _Hydra_ head and existing methods for CXR lung segmentation on JSRT dataset. Bold numerical values denote superior performance.
# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 3 Research Methodology
### Lexicon Construction  
Previous approaches suffer from the high cost of developing a sentiment dictionary \"from scratch\", we have decided to take advantage of the lexicons that are already available and make improvements to them in order to establish our sentiment dictionary. To this end, we present a semi-automatic technique that makes use of two Arabic sentiment lexicons, the Arabic senti-lexicon [3] and the NileULex [21]. In addition, the approach includes automatic extraction of sentiment words from an annotated Arabic COVID-19 dataset called SenWave, as well as manual checking of all words in the proposed lexicon. Our strategy is organized into three stages, which include both automated and manual steps.  
As seen in Figure 1, these stages include developing keyword and opinion rules lexicon, developing a lexicon using training data, and reforming and revising the lexicon.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 3 Horizontal Surface Plane Modelling--Directivity Analysis of Rayleigh Waves
### Experimental Validations  
In Figure 15, based on the analytical simulations, the distribution of the Rayleigh waves at various depths is presented. In this part, the measured results at \(z=0\) are picked to compare with the simulation results. The experimental setup was the same as the one in Section 2.4. The measured beam directivity at \(z=0\) is obtained by placing the receiver along the scanning path, as shown in Figure 16. Figure 17 shows the beam directivity results at \(z=0\) from simulations and experiments. Thirty-three measuring points on the scanning path were picked with a moving step of \(2.5^{\circ}\). The measured beam directivity at \(z=0\) agrees well with the simulated beam directivity, which validates the proposed method. There are some non-overlapping points between these two curves due to several factors, which include the experimental noise and the tolerance of the receiver's position, etc.  
Figure 16: The scanning path of the receiver.  
Figure 17: The comparison between the simulated beam directivity and the experimental beam directivity at \(z=0\). The red curve is the beam directivity from simulations, while the blue curve is the beam directivity from experiments.  
Figure 15: The simulated beam directivity of Rayleigh waves at various depths.
## 4 Results
### Evaluation of Conditioning Factors  
#### 4.2.1 Application of Conventional Algorithms  
Bivariate methods were used to establish relationships between the conditioning factors and the occurrence of the landslides. From Table 4, as for elevation, the percentages of landslide area for 3700-4700 m and 4700-5700 m were 43.80% and 28.63%, respectively, which means that over 70% of landslide areas were distributed among the two classes but the IV value of these two classes were both negative, while the highest IV of elevation is 1.098 for the class of \(>\)5700. For slope, the IV values increased as the slope increased, as well as the factor MED, which indicated that landslides are more likely to occur in steep areas. The IV of slope ranged from \(-\)1.106-1.709, in which the classes of \(>\)20 were all positive. In terms of TWI, the class of 6-7 had the highest IV of 0.371 and the IV were all negative at the class of \(>\)8. \begin{table}
\begin{tabular}{c c c c c} \hline
**Conditioning Factors** & **Zone** & **Ni/N** & **Si/S** & **IV** \\ \hline \multirow{6}{*}{Elevation (m)} & \(<\)2700 & 1.21\% & 1.09\% & 0.100 \\  & 2700–3700 & 21.98\% & 8.26\% & 0.978 \\  &  
For curvature, the highest IV of the plan curvature was 0.538, located at the class of \(<\)\(-\)0.4, and 1.106 of profile curvature at the class of \(>\)0.3. It was found that landslides were concentrated in convex terrain. On the other hand, landslides are highly concentrated near the faults, streams, and roads as the IV reached the maximum value at first class. It indicated that the development of faults, streams, and human engineering activities was conducive to the occurrence of landslides. As for rainfall, the highest probability of landslides occurring appeared in the class of \(>\)780 mm (IV reached 1.107). Meanwhile, the IV did not increase with increasing rainfall and it indicated that landslide occurring is complex. In the case of NDVI, the class of 0.6\(-\)0.75 has the highest IV of 0.804. The IV changed erratically for NDVI although high vegetation cover helped enhance the stability of the landslide. As for the LR model, the final regression equation is as follows:  
\[ \begin{array}{l}y=(1.756~{}MED)+(0.395~{}profile.curvature)+(1.799\mbox{rainfall})+(-1.641~{}DTR)+\\ (0.684~{}DTS)+0.28\end{array}  \tag{6}\]  
It can be found that MED, rainfall, and DTR were essential to landslide occurring as the coefficients were relatively large. While profile curvature and DTS were considered as secondary factors. The coefficient of DTR was negative, which indicated a negative effect on landslide occurring. Conventional algorithms such as IV and LR methods were used to establish the relationships between the conditioning factors and the occurrence of the landslides in this study. In summary, the conditioning factors have an effect on landslides occurring and the impact of different factors on landslides varies in different intervals.
# Indoor Localization for Augmented Reality Devices Using BIM, Point Clouds, and Template Matching
## 3 Methodology
### Ground Estimation  
In the case of matching point clouds of a single floor, a simple ground estimation will suffice for this application. Along the \(z\)-axis, points cluster at the floor and ceiling levels. When filtering out points with downward-facing normals, only clusters on the floor are left. The \(z\) values of the points are then divided into bins along the axis. The bin with the most points can be established as the ground plane with height \(z_{\text{max}}\). The final transformation \(T\) can then be calculated:  
\[T=\begin{pmatrix}\cos\left(\phi_{\text{max}}\right)&\sin\left(\phi_{\text{max} }\right)&0&x_{\text{max}}\\ -\sin\left(\phi_{\text{max}}\right)&\cos\left(\phi_{\text{max}}\right)&0&y_{ \text{max}}\\ 0&0&1&z_{\text{max}}\\ 0&0&0&1\end{pmatrix} \tag{8}\]
## 3 Proposed Controller Design and Stability Analysis  
In this section, a novel adaptive ADRC (AADRC) approach is established for the time-delayed SBW plant (22), in which the AADRC is constituted from an adaptive full state error feedback control (AFSEFBC) and adaptive extended state observer (AESO). The overall configuration of our designed control scheme is shown in Figure 2, including the AESO and the AFSEFBC, where the adaptive gains of the AESO and the AFSEFBC are, respectively, driven by the estimation, and the tracking error of the angular position of SBW dynamics in (8) are constructed to develop the control performance according to [28].
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
## 2 Materials and Methods
### Vibrotactile Sensory Feedback System  
For vibrotactile stimulation, we used a non-invasive, microprocessor-controlled, gait-synchronized, vibrotactile feedback system ([PERSON](r), Saphenus Medical Technology GmbH, Baden, Austria). The system features a set of four sensors implemented into a sleeve to be worn over the fitted prosthetic foot. The transmitter unit and the actuator unit of the system are compatible with any commercially available prosthetic setup. The sensors detect pressure at the heel, the lateral forefoot area, the medial forefoot area and at the great toe (see Figure 1). These sensors can record information about the weightbearing of the different foot parts as well as the rolling behavior of the prosthetic foot and can wirelessly transmit this information to the actuator unit through the transmitter unit (Bluetooth technology).  
The actuator unit comprises a receiver and an elastic leg cuff containing four embedded vibration elements, with one element allocated to each sensor area of the sock (see Figure 1). The vibration elements oscillate with amplitudes of 60 to 120 Hz, and the latency between sensor ground contact and triggering of vibration on the stump is less than 100 ms.  
Figure 1: System components of the herein used vibrotactile feedback system (VTFS): (1) actuator unit, (2) transmitter unit, (3) sensor sock, (4) four pressure sensors at the heel, lateral midfoot area, medial midfoot area and big toe, (5) elastic leg cuff with the embedded vibration actuators (marked in red), (6) single vibration actuator (Copyright Saphenus Medical Technology GmbH).  
Figure 2: User interface: An App displays the ranges of intensity (**a**), duration (**b**), and threshold (**c**) of the vibrotactile stimulation, which can be adjusted (**d**). The accompanying visual representations provide insight into the parameter settings of the vibrotactile feedback system (Copyright Saphenus Medical Technology GmbH).
## 3 Results and discussion
### Discussion
Combined with the actual operation situation, the damage index of rice seedlings was defined artificially, and the influence trend of various  
Figure 14: Stress distribution diagram: (**a–d**) represent stress distribution diagram at different times. factors on the damage force of rice seedlings was analyzed to determine the critical force value that can not only control weeds but also mitigate damage to rice seedlings. Barnyardgrass is the main weed type in paddy field, and the growth of this kind of grass mainly depends on the photosynthesis of leaves. When the paddy field weeding wheel disturbs the mud layer at the bottom of the paddy field, burying weeds and preventing photosynthesis can achieve the effect of weeding. According to the soil characteristics of paddy field, the method of buried weeding is only suitable for paddy field. The weeding wheel designed in this study can not only bury weeds, but also has good tillage effect through simulation test. At present, the paddy field weeder developed by researchers only focused on the weeding effect within one week after weeding [21; 33]. In order to show the feasibility in the actual production process, a field management comparative test shall be carried out in the later stage. A comparative analysis between the non-use of weeders, the use of conventional weeders and the use of paddy field self-propelled inter row weeders shall be carried out. The growth of rice after weeding, and comprehensively verifying the weeding effect and tillage effect of weeders from the aspects of the weeding rate and rice harvest shall be tracked, so as to form a systematic system for the development and production management of weeding devices. This study provides a theoretical basis for the accuracy and efficiency of weeding and provides a reference for the intelligent development and reliable application of paddy field weeding equipment.