esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Comparison of AGBCPT and GBCPT  
In this section, we compare the proposed AGBCPT method with the previous GBCPT method [28]. Table 11 shows the datasets used in the experiments. They are: (1) the three-year training period (2016-2018), (2) the two-year training period (2017-2018), and (3) the one-year training period (2018). The testing period of these training periods is 2019.  
In Figure 14, the AGBCPT and GBCPT profits are positive in the training phase, with AGBCPT achieving higher profits than GBCPT. Based on the trained results, the profits on the testing datasets are shown in Figure 15.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Training Period** & **Testing Period** \\ \hline
2016–2018 & 2019 \\
2017–2018 & 2019 \\
2018 & 2019 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Training and testing periods for comparison.  
Figure 14: AGBCPT and GBCPT profits in training phase.  
Figure 15 shows that the AGBCPT and GBCPT profits using the three trained models are 3.15%, 0.91%, and 0%, and \(-\)2.33%, 2.46%, and \(-\)1.47%, respectively. In addition, the GBCPT profits are negative on the one-year and three-year datasets. However, the AGBCPT profits are positive. Thus, we conclude that the fitness function in the AGBCPT method, which accounts for risk, reduces the possibility of negative profits in the testing period. Next, to show the profitability of AGBCPT, experiments were conducted on the one-year training dataset (2015) and the one-year (2016), two-year (2016-2017), and three-year (2016-2018) testing datasets; the results on the training period are shown in Figure 16.  
In Figure 16, the 21.9% profit of AGBCPT is better than the 13.39% of CBCPT. Figure 17 compares the two approaches on the testing periods in terms of profit.  
Figure 16: AGBCPT and GBCPT profits in training period.  
Figure 15: AGBCPT and GBCPT profits in testing phase.  
Figure 17 shows that in the one-year testing period, neither method yields a profit. In the two-year testing period, the 16.34% profit of AGBCPT is better than the 1.35% of GBCPT. In the two-year testing period, the 20.26% profit of AGBCPT is better than the 8.26% of GBCPT. From the experimental results, we conclude that AGBCPT is effective and profitable for middle-long-term trading.
## 5 Evaluation of STAR COSMIC-2 Wet Profile Retrievals through Comparison with CrIS and ATMS  
Above in situ comparisons are mainly over lands and islands over oceans. To extend the validation globally (from 45\({}^{\circ}\)N to 45\({}^{\circ}\)S), we compare the STAR-derived COSMIC-2 temperature and water vapor profiles with NOAA CrIS and ATMS measurements in this section.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 5 Study Case: In-Vehicle BSM Data Services Platform
### Data Forecasting
#### 5.4.1 Data Extraction  
The SPM model deployment was conducted in Ann Arbor, Michigan. The field test includes 75 miles of instrumented roadway. Our dataset sample covered the amount of five days traffic activity that occurred over 6 h, during the period 6:00 a.m. to 12:00 p.m. (including congested and smooth traffic regimes); across a distance of 11.2 miles, as shown in Figure 14.  
Vehicles that participated in this study include light/passenger vehicles, heavy/commercial trucks, and busses. Based on DE_VehicleType data elements, vehicles were classified in three main categories: Passenger car, Bus, and Trailer.  
_Traffic volume can be determined by counting the number of vehicles that cross through a point on a road segment at a specific time and denoted by vehicles per hour (v/h)._  
We manually calculated the TV using the Position data frame by counting the number of vehicles traversing the road segment illustrated in Figure 14. It is noteworthy that we did not examine the Heading data element because we took into account both directions. Data extraction displayed in Table 12, was performed in 5 min. intervals.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{3}{*}{**Type of Vehicle**} & \multicolumn{6}{c}{**Both Directions**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Average Speed (mph)**} & \multicolumn{6}{c}{**Volume**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Minimum**} & \multicolumn{2}{c}{**Maximum**} & \multicolumn{2}{c}{**MEAN**} & \multicolumn{2}{c}{**SD**} & \multicolumn{2}{c}{**Minimum**} & \multicolumn{2}{c}{**Maximum**} & \multicolumn{2}{c}{**MEAN**} & \multicolumn{2}{c}{**SD**} \\ \hline Passenger car & 44.4280 & 48.4669 & 46.6463 & 1.1060 & 38.0000 & 60.0000 & 46.2540 & 4.3870 \\ \hline Bus & 30.7579 & 32.6220 & 31.7148 & 0.8326 & 4.0000 & 24.0000 & 10.5500 & 3.3250 \\ \hline Trailer & 18.3304 & 19.8839 & 18.7716 & 1.1744 & 3.0000 & 33.0000 & 11.4100 & 6.0900 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Five minutes of speed and traffic volume.  
Figure 14: Road segment used for traffic volume prediction (from: Ypsilanti, Michigan 48197, USA to Ann Arbor, Michigan 48105, USA.
## 3 Results and Discussion
### Summary of Research Results
* Cylindrical protrusions obtained using a 50% fill rate have the smallest deviations in diameter dimension compared to other samples obtained using the same layer height. * All diameters of cylindrical protrusions and openings have a dimension below the nominal value, which indicates shrinkage of the obtained protrusion during the FFF process. * Cylindrical openings obtained using a 50% fill rate have the largest deviations in diameter dimension, averaging \(-\)0.061 mm compared to other samples obtained using the same layer height. * The angular deviation error for cylindrical openings decreases when using the higher layer, on average from 0.663\({}^{\circ}\) for 0.12 mm to 0.470\({}^{\circ}\) for 0.2 mm. * Samples manufactured with a 50% fill rate have a smaller cylinder diameter deviation relative to models obtained with the same layer height. * 8 out of 9 models are characterized by protrusions with dimensions below the nominal from \(-\)0.03 mm to \(-\)0.25 mm; the only sample with a dimension larger than nominal is sample S2. * The length of the created niches expands from the nominal by an average of 0.15 mm, due to shrinkage of the material dividing the cavities. The results of the described studies are valid only within the assumed range of variability of the process conditions, which also applies to the scale of the model. There is a large group of machine parts with similar dimensions, and in these cases the results of the described research may be applicable. The transfer of the obtained research results both to models of significantly larger and significantly smaller dimensions is not a trivial issue and cannot be implemented based on simple scaling, primarily due to the unique nature of additive manufacturing of models using layers (the range of variation in layer thickness determines the range of potential scaling possibilities).
## 2 Materials and Methods
### Data Extraction, Bias, and Outcomes  
The following data were extracted from each article: the name of the first author, publication year, country, type of study, number of patients, age and sex of patients, type of treatment, and outcome. The risk of bias was assessed independently by two auditors ([PERSON] and M.I.), and disagreements were resolved by a senior auditor. The quality of evidence was analyzed with the GRADE (grading of recommendations, assessment, development, and evaluation) system [18] which is a framework for evaluating and formulating recommendations in healthcare. It involves a systematic assessment of study quality, considering factors such as risk of bias, consistency of results, precision, and relevance. Recommendations are classified as \"strong\" or \"weak\" based on the robustness of the available evidence. The assessment of the risk of bias was carried out for NRSIs according to the ROBINS-I tool (risk of bias in non-randomized studies of interventions) [19] and Cochrane Risk of Bias Tool (RoB 2.0) [20] for the RCTs. ROBINS-I assesses the risk of bias across 7 domains, which correspond to different phases of the study. Each domain includes a series of questions to be answered to determine the risk of bias.  
Bias in any domain can distort findings, leading to overestimation or underestimation of intervention effects. For example, selection bias may arise from non-equivalent groups, while measurement bias can stem from inconsistent intervention documentation. These biases compromise the validity of conclusions and highlight the importance of meticulous, independent, and senior-level review to mitigate subjective interpretations and ensure robustness of the risk assessment process. The results were categorized using Endnote, and duplicates were excluded. The outcomes assessed were the recurrence of patellar dislocation and the score on the Kujala test [21].
# Compact High-Diectivity Contra-Directional Coupler
## 3 Results  
The proposed coupler was designed and fabricated with Taconic RF-35 HT 20 mil PCB. Figure 9 shows the fabricated and assembled PCB with the external coaxial connectors. The insertion loss, coupling, return loss and isolation are measured with Agilent ENA E5071C after coaxial calibration. A comparison of the measurement and simulation results is shown in Figure 10. The coupling is about 20 dB, and well-matched with the simulation result. The directivity is better than 20 dB in bandwidth from 12.8 GHz to 14.8 GHz, and the peak directivity is better than 40 dB. The worst return loss is about 17 dB within the band.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension Designation** & **Dimension (mm)** & **Position** \\ \hline \(l_{c}\) & 2.0 & Coupled-line length \\ \(w_{c}\) & 1.1 & Coupled-line width \\ \(l_{e}\) & 0.5 & Main-line stub length \\ \(l_{e}\) & 0.7 & Coupled-line stub length \\ \(l_{o}\) & 0.425 & Inner stub length \\ \(w_{s}\) & 1.0 & Stub width \\ \(s_{c}\) & 1.1 & Coupled-line space \\ \(g_{i}\) & 0.25 & The gap between inner stubs \\ \(w_{0}\) & 1.15 & 50 Ohm line width \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed dimensions of the coupler in Figure 8b.  
Figure 8: The PCB layout of the coupler: (**a**) final PCB layout (substrate 30 \(\times\) 30 mm, coupler 4 \(\times\) 5.5 mm); (**b**) Detailed coupler dimension designation.  
Table 3 shows a comparison of the performance of this work with others. Most of the previous studies were conducted in relatively lower frequency ranges. Therefore, a figure of merit is introduced in this paper, for the size comparison between the couplers in different operating frequencies, as \(S/\lambda\). The parallel coupled-line length decreases inversely in proportion with the increase in frequency; however, the height change with frequency change is negligible, because the change of the 50 Ohm line width and the coupling gap is negligible. The active coupler area \(S\) was divided by the wavelength \(\lambda\) to compensate for the coupler length variation in the different frequencies. Additionally, weak structures vulnerable to high power, such as a thin interdigital capacitor, were identified. The 20 dB directivity frequency ranges are shown, except when the 20 dB directivity bandwidth is much wider than the 3 dB bandwidth, or when the 20 dB bandwidth is not identifiable in the paper.  
From a comparison of the figure of merits, this work shows the most compact size among the structures without weak components which are vulnerable to high power, and it is still very compact compared with all the other works. It also shows high peak directivity of 45 dB.  
Figure 10: Comparison of the measurement results: (**a**) S-parameter; (**b**) Directivity.  
Figure 9: The fabrication of the proposed coupler.
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Reactive
#### 5.3.1 Enhancing Routing Performance in Vehicular Networks Using UAV-Assisted Approach  
[PERSON] et al. [101] propose a reactive routing scheme that enables UAVs to establish routes among themselves and with vehicular nodes on land when network partitions occur among the vehicular nodes. This means that, during normal operation, vehicular nodes communicate among themselves; however, when network partitions occur, some vehicular nodes are unable to communicate with each other. Therefore, the vehicular nodes establish routes with UAVs; specifically, a route consists of several parts, including links between vehicles, between vehicles and UAVs, and between UAVs. The main objectives are to enhance the route stability (O.1) as well as to enhance routing performance and QoS (O.3) in FANETs. The routing scheme addresses two challenges, namely high dynamicity (X.1) of network topology and cost (X.2), particularly routing overhead. The routing scheme is designed for source routing (C.1.2).  
During the neighbor and route discoveries stage, each node broadcasts hello messages to form a neighbor set comprised of two-hop neighboring nodes. The hello message contains the geographical location (M.3), node identity (M.5), and one-hop neighboring nodes. Subsequently, the source node broadcasts RREQ messages that accumulate the link expiration time (M.2) along potential routes. Shorter distance between a pair of UAVs tends to increase the link expiration time and, hence, provides a longer link connectivity. During the route selection stage, the destination nodes receive RREQ messages via different routes and select a route with the highest link expiration time among the routes based on the minimum link expiration time of each route in order to prolong route lifetime. The destination node sends a RREP message to the source node using the same route that the RREQ message traverses.  
The routing scheme is compared with a state-of-the-art scheme, which is a conventional routing scheme that establishes routes to destinations in an on-demand manner and supports both unicast and multicast routing. Random walk mobility model (A.1) is used in simulation. The routing scheme has been shown to increase QoS (P.1) (i.e., higher packet delivery rate and lower end-to-end delay) and route setup success rate (P.4).
# Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
## 2 Theoretical Background: Predictive Models  
### Witczak Model: Sigmoid Function  
Research conducted at the University of Maryland [17] demonstrated that the master curve of asphalt can be represented by a sigmoid function. This method, established by [PERSON] and [PERSON] [17], involves constructing the master curve by adjusting the parameters of the sigmoid function based on laboratory measurements of the dynamic modulus measured using a nonlinear regression method.  
\[Log\Big{(}\Big{|}E^{*}\Big{|}\Big{)}=\delta+\frac{\alpha}{1+e^{\beta-\gamma Log (f_{r})}}, \tag{2}\]  
\(|E^{*}|\) is the dynamic modulus (MPa); \(\alpha,\ \beta,\ \gamma\), and \(\delta\) are the regression parameters; and \(f_{r}\) is the reduced frequency (Hz) defined by the following relation.  
\[f_{r}=a_{T}\times f\Longleftrightarrow Log(f_{r})=Log(a_{T})+Log(f), \tag{3}\]  
\(a_{T}\) is the shift factor, \(f\) is the tested frequency at the temperature \(T\) (Hz), and \(T\) is the tested temperature (\({}^{\circ}\)C).  
This sigmoid model varies between \(Log(|E^{*}|)_{min}\) (\(\delta\)) and \(Log(|E^{*}|)_{max}\) (\(\delta+\alpha\)) of the mix, based on two shape parameters (\(\beta,\ \gamma\)), depending on the reduced frequency \(f_{r}\), as determined according to the principle of time-temperature superposition. In this equation, the parameter \(\gamma\) influences the slope of the function (the rate of variation between the maximum and the minimum), while the parameter \(\beta\) influences the horizontal position of the point of rotation (Figure 3). In addition, the coefficients \(\alpha\) and \(\delta\) depend on the particle size composition of the mixture, the asphalt binder content, and the air void content. The coefficients \(\beta\) and \(\gamma\) depend on the properties of the binder and the magnitude of \(\delta\) and \(\alpha\). The determination of these parameters is simply performed using the SOLVER function of Microsoft Excel 2016, which involves varying the parameter values in order to fit the measured data.  
Many methods existing in the literature help define the shift factor, such as the Williams-Landel-Ferry (WLF) equation, the Arrhenius equation, Generalized Time-Temperature Superposition (GTTS), and empirical or semi-empirical methods. In the case of this study, graphic determination was used. The isotherms were translated horizontally until they fit an adjacent isotherm relative to a fixed temperature (in this study, the reference temperature was set to 20 \({}^{\circ}\)C), based on the principle of time-temperature superposition. With this method, the translation factors were dependent on the adjustment criteria.
# A Vibration Based Automatic Fault Detection Scheme for Drilling Process Using Type-2 Fuzzy Logic
###### Abstract  
The fault detection system using automated concepts is a crucial aspect of the industrial process. The automated system can contribute efficiently in minimizing equipment downtime therefore improving the production process cost. This paper highlights a novel model based fault detection (FD) approach combined with an interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy system for fault detection in the drilling process. The system uncertainty is considered prevailing during the process, and type-2 fuzzy methodology is utilized to deal with these uncertainties in an effective way. Two theorems are developed; Theorem 1, which proves the stability of the fuzzy modeling, and Theorem 2, which establishes the fault detector algorithm stability. A Lyapunov stability analysis is implemented for validating the stability criterion for Theorem 1 and Theorem 2. In order to validate the effective implementation of the complex theoretical approach, a numerical analysis is carried out at the end. The proposed methodology can be implemented in real time to detect faults in the drilling tool maintaining the stability of the proposed fault detection estimator. This is critical for increasing the productivity and quality of the machining process, and it also helps improve the surface finish of the work piece satisfying the customer needs and expectations.  
fault detection; fuzzy logic; stability analysis; drilling operation; predictive maintenance
## 5 Future Developments
Decision-making at the managerial level is also evolving: decision makers need to be able to interpret simulation results, assess the operational and environmental implications of hypothesized strategies, and take timely action. Artificial intelligence must be integrated as decision support, without replacing human judgment. In fact, the management of intelligent systems requires continuous supervision that can recognize when it is necessary to adapt to complex or unexpected contexts. In such situations, human intuition, experience and critical capacity remain indispensable elements.
# Region Adjacency Graph Approach for Acral Melanocytic Lesion Segmentation
## 2 Methods
### Region Adjacency Graph  
In the previous step, we intentionally oversegmented the acral melanoma image. The SLIC algorithm (as most of the segmentation algorithms) tends to favor small regions of similar color and properties. Medical images tend to be complex by nature and often contain several regions with different shades of the same color that need to be differentiated. Accurate skin lesion segmentation on dermoscopy images is difficult due to the following two main challenges: (i) the appearance patterns vary a lot around the lesion boundary across patients and (ii) the intensity distributions highly vary across different lesion types. An efficient merging algorithm is required to connect adjacent and similar regions into correctly segmented objects. A region adjacency graph (RAG) has been chosen to analyze relationships between those regions. Figure 4a presents the outcome of the SLIC algorithm which generates an over-segmentation, on which the RAG graph is built. Each superpixel region has a yellow edge. Each superpixel region is considered a vertex in a graph (Figure 4b). The edges between the first and the second superpixels have been coloured depending on their weight. The edges between similar regions are dark, whereas edges between dissimilar regions are purple. Each region is connected to all its immediate neighbors and the weight of such a connection is proportional to the similarity between the two regions, as has been proposed by [PERSON] in [27]. Each region \(R_{i}\) is represented by two color distribution metrics: \(\mu_{i}\) and \(\sigma_{i}\), where \(\mu_{i}\) is the mean color value of the set of pixels in the region while \(\sigma_{i}\) is the standard deviation:  
\[\sigma=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\mu)^{2}},\ \ \text{where}\ \ \mu=\frac{1}{N}\sum_{i=1}^{N}x_{i}. \tag{6}\]  
The weight of an edge \(e_{ij}\) which connects two regions \(R_{i}\) and \(R_{j}\) is calculated using the Fisher distance:  
\[d^{2}(R_{i},R_{j})=d^{2}(\vec{\mu}_{i},\vec{\mu}_{j})=\begin{cases}\frac{(N_{ R_{i}}+N_{R_{j}})\|\vec{\mu}_{i}-\vec{\mu}_{j}\|^{2}}{N_{R_{i}}\sigma_{i}^{2}+N_{R _{j}}\sigma_{j}^{2}},&\text{if }\sigma_{i},\sigma_{j}\
eq 0,\\ \|\vec{\mu}_{i}-\vec{\mu}_{j}\|^{2},&\text{if }\sigma_{i},\sigma_{j}=0,\end{cases} \tag{7}\]  
Figure 3: Applying SLIC segmentation to generate oversegmented regions: (**a**) 50 superpixels; (**b**) 200 superpixels; (**c**) 400 superpixels; and (**d**) 800 superpixels.  
where \(N_{R_{i}},N_{R_{j}}\) refers to the set of pixels in each region.  
After applying the algorithm, a graph similar to the one shown in Figure 5 is constructed.