esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Cascade Approach
#### 5.4.4 Comparison  
The proposed approach is compared to a conventional non-cascade approach, which uses a non-hierarchical dataset containing the same categories of each individual dataset of the cascade approach. The global accuracy of the cascade approach (\(Accuracy_{Overall}\)) is obtained by multiplying the achieved accuracy at each stage, as defined in Equation (3). \[Accuracy_{Overall}=Accuracy_{Stage_{1}}\times Accuracy_{Stage_{2}}, \tag{3}\]  
where \(Accuracy_{Stage_{i}}\) is the accuracy achieved at stage \(i\in\){1, 2}. The measured accuracy and timing per classifier for the cascade approach is summarized in Table 10. As expected, the most accurate classifiers are the k-NN and the ANN classifiers. Notice that the Naive Bayes classifier outperforms these classifiers for the _Construction_ dataset but offers lower accuracy for most of the datasets. Moreover, due to its relatively low accuracy for the _Root_ dataset, this classifier is not a candidate for the initial stage. The overall accuracy of the cascade approach for the k-NN and ANN classifiers obtained by applying Equation (3) is summarized in Table 11. The results follow the achieved accuracy depicted in Figure 11. While the k-NN classifier achieves a slightly lower accuracy than the ANN classifier, it is approximately 30 times slower. Notice how \(Accuracy_{Stage\ 1}\) impacts the overall accuracy. The ANN classifier remains the most accurate one. However, classifiers can be combined to increase the overall accuracy or for speed. The last columns in Table 11 depict the accuracy and the timing when combining the most accurate and the fastest classifiers. For instance, to increase accuracy, the ANN can be used for most of the datasets while using k-NN for the _Airport_ and _Vehicles_ datasets. Such a combination achieves around 58% accuracy but doubles the timing of the ANN classifier standalone. On the other side, a faster sound classification can be obtained when combining the faster classifiers such as using the Decision Tree classifier for the _Root dataset_ while using the ANN classifier for the rest of the datasets. The execution time decreases to around 1 ms while the accuracy drastically decreases to around 39%. Both are good examples of one of the benefits of the cascade approach, which enables the combination of classifiers based on the priorities of the application performing the sound recognition. Table 12 shows the results of the classifiers' accuracy when all the categories are regrouped in one non-hierarchical dataset. The achieved accuracy of the cascade approach is similar to the traditional approach in all categories. The similarity in the accuracy of both approaches is due to the fact that the non-hierarchical dataset only considers 12 categories. The main advantage of the cascade approach is the additional flexibility due to combining different types of classifiers. Figure 12: Average execution time of the classifiers based on their configuration.
# On the Realistic Radio and Network Planning of IoT Sensor Networks
## 4 Proposed Approach  
### High Level Concept  
The proposed methodology creates a realistic snapshot of the Received Signal Strength (RSS) area coverage through TruNET which is then injected into Cooja, in order to generate a functional binary file that can be downloaded in real nodes for immediate implementation. In Figure 1, we present the flowchart of the proposed process for IoT sensor network deployment.  
The proposed process allows the 3D adjustment, visualisation and realistic simulation of wireless node deployment. The importance of accurately predicting the RSS coverage at the physical layer and combining it with the network layer is highlighted by the results of Figure 2.  
In this figure, the Packet Reception Rate (PRR) vs. the RSS is presented, based on a measurement campaign performed in [19], for the same radio chip CC2420 that was used in this demo. It is clear that correlation between PRR and RSS varies with an obvious transition. When the RSS is better than \(-87\) dBm, the PRR is always beyond \(90\%\), indicating a desirable link; when the RSS is less than \(-92\) dBm, the PRR is close to zero. In the transition zone, packets may also be lost due to instantaneous interference conditions from other co-channel systems or nodes.  
Figure 1: TruNET-Cooja interconnection: proposed methodology flowchart.  
The aforementioned bimodal behaviourof Wireless Sensor Nodes highlights that, if the RSS coverage estimation is not accurate enough, the simulation will most probably indicate a misleading RSS coverage and thus network performance, while, when deployed in a real environment, the network may under-perform or completely fail if radio propagation characteristics are not accurately predicted.
## 3 Method: Automated Target Detection
### Removal of Rivers and Coastal Data  
The aim of the automated target detection is to identify lakes and reservoirs for which a constant water level per observation epoch is assumed. Due to the slope of rivers, water level time series can be derived only for particular river sections and not for entire rivers. For the same reason, most satellite altimetry time series for river targets are based on a single mission, except for the rare cases of crossing tracks above a river. In consequence, rivers cannot be processed in the same way as lakes and reservoirs, for which the water level is assumed to be independent of the topography. Therefore, rivers are not part of this study and should not form part of the detected water targets.  
Since rivers are part of GSW--and also of any other water occurrence product based on satellite images--they have to be excluded from the water mask within the process of target detection. This is easy for smaller rivers, which are removed by the erosional step. However, some rivers might be wider than some narrow reservoirs as a result of river impoundments. Therefore, it is not possible to define a kernel size that removes all rivers but preserves all lakes and reservoirs at the same time. Thus, major rivers are removed manually from the binary dataset (resulting from Section 3.1) by setting all water pixels within the river polygons to land. All rivers sections removed manually from the binary dataset are indicated in orange in Figure 1.  
However, there are still river segments in the isolated water occurrence masks; this is caused by small water bodies near to and partly connected to a river. These masks have to be manually identified by visual inspection and removed from the dataset. The same holds for large, connected water systems at the coast, which cannot clearly be defined as a single water body or distinguished from the ocean. The primary reason for the manual interaction is to provide reliable statistics for this paper. Significantly less interaction is required for the target detection itself.
# Investigation of a Solar-Driven Organic Rankine Cycle with Reheating
## 2 Material and Methods
### The Configurations of the Solar-Power Units  
The two solar-driven ORCs of the present work are depicted in Figures 1 and 2, respectively. Figure 1 displays the conventional solar-driven ORC with a recuperator, while Figure 2 depicts a system with reheating, which is suggested in this work. The working medium is selected to be cyclopentane as an efficient choice according to other studies [19; 20]. In both examined cycles, the use of a recuperator is selected because it significantly increases efficiency, as has been stated in References [8; 9].  
Both systems are fed by evacuated tube collectors (ETC), while there is a sensible thermal storage tank in order to store thermal heat during the day. ETCs are a proper choice for applying this system in the building sector [21] because they do not have requirements for tracking the sun during the day, thus leading to lower complexity. The sensible storage tank is an insulated one that stores the produced thermal energy from the solar field and the temperature level inside it is increased. At this point, it is essential to say that the working medium in the solar collectors' field and the storage tank is selected to be thermal oil in order to achieve temperatures over 100 \({}^{\circ}\)C. More specifically, Therminol VP-1 is selected as a proper choice for the present solar system [22].  
The ORCs operate with small superheating of \(\Delta T_{sh}\) = 5 K [23] in order not to work in very high-temperature levels. This assumption is reasonable because the increase in superheating is not so beneficial for the ORCs [24]. It is useful to state that this superheating regards the high-pressure expander in the reheating system. Moreover, in this work, the minimum temperature difference on the recuperator is chosen at \(\Delta T_{rec}\) = 5 K [23] and the pinch point in the heat recovery system at \(PP\) = 5 K [23].  
The efficiency of the electrical generator is chosen at \(\eta_{gen}\) = 98%, the shaft mechanical efficiency \(\eta_{m}\) = 99%, the pump's motor efficiency at \(\eta_{motor}\) = 80%, and the pump's isentropic efficiency at \(\eta_{is,pump}\) = 80% [23]. For the case of Figure 2 with the reheating, the inlet temperature of both expanders is selected to be the same (\(T_{4}\) = \(T_{6}\)), a usual assumption that is done in reheating power cycles. Furthermore, the condenser temperature is selected to be at least 5 K higher than the ambient temperature for achieving a suitable heat rejection to the ambient. It is useful to state that the fluid from the condenser is a saturated liquid.  
Figure 1: The conventional solar-driven regenerative ORC.  
Figure 2: The suggested solar-driven regenerative ORC with reheating.
## 2 Numerical Model Development
### Kinetics and Mathematical Model of Catalyst Bed  
Kaiser 201 catalyst was used to activate the COS hydrolysis reaction, and its properties are listed in detail in Table 1. The catalyst was of spherical morphology and had a constant diameter of 5 mm. Its main constituent was Al\({}_{2}\)O\({}_{3}\) and its high catalyzing area enabled improved reaction efficiency.  
Numerical analysis of the COS hydrolysis reactor necessitated mathematical modeling of the catalyst layer. A porous medium morphology concept was introduced to simulate fluid behavior of the catalyst area. Such porous medium modeling was justified in view of the near-impossible modeling for the individual layer of packed catalysts and for cost-effectiveness, assuming completely spherical and consistent geometry of the catalyst layer to incorporate resistance to fluid flow. This modeling concept is schematically depicted in Figure 3, and the pertinent Ergun equation is written as Equation (7) to account for the pressure drop caused by fluid resistance.  
\[\frac{dP}{dL}=\frac{150\mu(1-\phi)^{2}}{\theta^{2}D_{cat}^{2}\phi^{3}}\overset{ \rightarrow}{u}+\frac{1.75\rho(1-\phi)}{\theta D_{cat}\phi^{3}}\overset{ \rightarrow}{u}^{2} \tag{7}\]  
Viscous and inertia losses are accounted for by the first and second terms of the Ergun equation as applied to a porous medium. Such fluid resistance was used to numerically model reactant gas mixture flow down the catalyst packed layer.  
Reaction advancement of the COS hydrolysis reaction is determined by empirically-derived kinetics. Experimentally substantiated kinetics was thus applied in the present study for numerical modeling of reaction kinetics [17]. The COS hydrolysis reaction and relevant kinetics are represented by Equations (8)-(11), respectively.  
Kinetics of COS hydrolysis reaction (Kaiser 201)  
\[COS+H_{2}O\to CO_{2}+H_{2}S \tag{8}\] \[-r_{\text{cos}}=k_{1}K_{3}\frac{P_{\text{cos}}P_{H_{2}O}}{1+K_{3}P _{H_{2}O}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Value** & **Unit** \\ \hline Shape & Sphere & - \\ Diameter & 4.5–5.0 & mm \\ Surface Area & 296 & m\({}^{2}\)/g \\ Al\({}_{2}\)O\({}_{3}\) & 93.60 & \% (by wt.) \\ SiO\({}_{2}\) & 0.20 & \% \\ Fe\({}_{2}\)O\({}_{3}\) & 0.02 & \% \\ TiO\({}_{2}\) & 0.02 & \% \\ Na\({}_{2}\)O & 0.30 & \% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material and chemical composition of Kaiser 201 catalyst.  
Figure 3: The concept of porous medium for catalyst bed.  
Here,  
\[k_{1}=\exp\biggl{(}0.835-\frac{3.039\times 10^{3}}{T}\biggr{)} \tag{10}\]  
\[K_{3}=\exp\biggl{(}-15.89+\frac{1.001\times 10^{4}}{T}\biggr{)} \tag{11}\]  
Reaction kinetics were simulated by the Eley-Rideal model, where individual modeling parameters were empirically determined. The significant parameters affecting the reaction rate were partial pressure of the gas reactant mixture and temperature, which determined reaction kinetics with utmost sensitivity.
# A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
## 3 General Framework of MOUTP Based on TSCEA  
The overall framework of MOUTP based on the TSCEA proposed in this study is shown in Figure 3 and consists of three main parts: (1) constructing the multi-objective mathematical model of MOUTP and establishing the UAV constraints and multiple objectives to be optimized; (2) using the mutation operator and selection operator of the differential evolutionary algorithm to obtain a set of offspring key trajectory points; and (3) performing the evolutionary process of the TSCEA. The main steps are described in detail below.
###### Abstract
The optical system proposed for THz applications can have various focal lengths while maintaining the same angle of view by setting a fixed angle of view. A single optical system can support various sensor sizes, thereby reducing optical system design and manufacturing costs. The first lens group was designed with a positive power such that consistent brightness could be maintained at the wide-angle and telephoto ends. 2. The focus was adjusted while maintaining the overall optical field by moving a specific internal lens group. This reduced the weight of the driving lens group, broadened the range of motor choices, and improved inspection speed. The remainder of this paper is organized as follows. Section 2 describes the design and analysis of a zoomed-in optical system used in THz applications. Section 3 describes the performance analysis of the designed zoom optical system, including spot diagrams, distortion, and illumination analysis. Finally, Section 4 concludes the paper and summarizes the designed optical system.
## 2 Materials and Methods
### Application of the Ranking Algorithms on In-Silico and Real Data  
#### 2.4.1 Assessment of the Ranking Algorithms on In-Silico Data  
The ability of the proposed algorithm to perform an accurate variable ranking was assessed on the simulated data described in Section 2.2. On each simulated dataset, the variables were ranked considering two approaches:  
* Approach 1: standard RFE-Borda count method without considering the correlation between features;
* Approach 2: the proposed algorithm that considers the correlation between features. For both the approaches, we considered B = 100 bootstrap resamplings of the training set. For the proposed algorithm, a threshold of 0.7 on the Pearson correlation coefficient was used to identify the groups of highly correlated features.  
For both the ranking methods, the accuracy of the ranking is assessed by calculating the root mean square error (RMSE) between the estimated ranking and the true ranking. This metric was chosen because it has a very intuitive interpretation and it quantifies the average number of positions mistaken in the ranking, regardless of the direction of the mistake (overestimation or underestimation) and the relative position in the ranked list.  
For the simulated scenarios 1-12, in particular, we assessed the RMSE value for each of the N = 50 training sets and then compared the distribution of RMSE between the two ranking algorithms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Name** & **Description** & **Values** \\ \hline sex & Sex & 0 = females \\ age & Age & Continuous [years] \\  & & 0 = married, living as married \\ mar\_stat & Marital status & 1 = separated, widowed \\  & & 2 = never married \\ depriv & Level of economic deprivation & Integers, range 1–5 \\  & & 0 = never smoked \\ smoke & Smoking status & 1 = past smoker \\  & & 2 = current smoker \\  & & 0 = hardly ever or never \\ phys\_act & Frequency of moderate or vigorous physical activity & 1 = 1–3 times per month \\  & & 2 = once/week \\  & & 3 = >once/week \\ bmi & Body mass index & Continuous [kg/m\({}^{2}\)] \\ waist & Waist circumference & Continuous [cm] \\ sys\_bp & Systolic blood pressure & Continuous [mmHg] \\ depress & CESD-8 depression score & Integers, range 1–8 \\ life\_exp & Life expectation & Integers, range 1–100 \\ health & Self-reported poor health & Integers, range 1–5 \\ htn & Ever had hypertension & 0 = no, 1 = yes \\ hchol & Ever had high cholesterol & 0 = no, 1 = yes \\ hdl & HDL cholesterol & Continuous [mg/dL] \\ tot\_chol & Total cholesterol & Continuous [mg/dL] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Baseline variables selected as candidate predictors for T2D model development.
## 3 Results
### Cytotoxicity Assessment and NO Inhibition in LPS-Stimulated RAW 264.7 Cells  
To evaluate RLs\({}^{\prime}\) (RL1, RL2, and RL3) cytotoxicity, RAW 264.7 cells were treated with various RL (RL1, RL2, and RL3) concentrations (25, 50, 100, 200, and 400 \(\upmu\)g/mL) and incubated for 24 h. Cell viability was assessed, with the control set at 100%. RLs did not display cytotoxicity at the concentrations, respectively (Figure 5A). The anti-inflammatory effect of the extracts (RL1, RL2, and RL3) was tested by measuring the NO production in LPS-stimulated RAW 264.7 cells using the Griess assay (Figure 5B). Cells were pre-treated with RLs (RL1, RL2, and RL3) for 1 h, followed by LPS (100 ng/mL) stimulation. Control group cells showed low NO production levels, while the LPS group increased the NO production to 99.1 \(\upmu\)M, confirming activation. Following the pre-treatment with 100 and 200 \(\upmu\)g/mL of extracts (RL1-3), RL1 did not show an inhibition of NO production, whereas RL2 at 200 \(\upmu\)g/mL reduced the NO production to 25.2 \(\upmu\)M, and RL3 at 100 and 200 \(\upmu\)g/mL significantly reduced the NO production to 30.5 and 48 \(\upmu\)M, respectively, in a dose-dependent manner.  
Figure 4: Antioxidant capacity of R. sativus cultivars were measured by DPPH assay, and gallic acid was used as positive control. (**A**) Absorbance compared to the blank. (**B**) Percentage of radical scavenging activity of DPPH by extracts. Values expressed are means \(\pm\) SD (%) of triplicates. ***\(p<0.0001\) vs. blank group.  
Figure 5: Inhibitory action of RL1-3 extracts on NO production in LPS-stimulated RAW264.7 cells. (**A**) Cell viability of the RAW 264.7 cells. (**B**) Effect of various concentration of RL1-3 on LPS-induced NO production. Con: control. ***\(p<0.0001\) vs. control; ***\(\#\#\)\(p<0.0001\) vs. RL1.
# Adaptive Multi-Path Routing Protocol in Autonomous Vehicular Networks
## 2 Related Work  
Many MAC protocols have been proposed to utilize the multi-rate functionality. The auto rate fallback (ARF) [20; 21] is typically implemented in commercial 802.11 products. ARF chooses to raise or lower its transmission rate according to consecutive transmission successes or failures, respectively. In the receiver-based auto rate (RBAR) [22; 23], the receiver selects an adequate transmission rate according to the channel quality measured from the received request-to-transmit (RTS) frame. The opportunistic auto rate (OAR) [24; 25] protocol is based on  
Figure 1: Source node selects the path to destination using the min-hop metric in a vehicular ad hoc network example.  
RBAR but provides a temporal fairness among the nodes. In effect, the network throughput increases since the high-data-rate links get more opportunities. We employed RBAR for the multi-rate utilization in consideration.  
Several multi-rate-aware routing protocols have been proposed to exploit the high throughput routing paths. multi-rate-aware sub-layer (MAS) [19] takes the reciprocal of the estimated raw data rate as the routing metric. To estimate the channel conditions, MAS employs HELLO packets for proactive routing protocols while overhearing RTS/CTS packets for on-demand routing protocols. However, raw data rates are actually different from the effective throughput due to the PHY/MAC overheads as considered in this paper. Exchanging HELLO packets will result in high overhead, while obtaining the channel quality only through RTS/CTS overhearing will not provide enough information.  
Medium time metric (MTM) [26] selects the paths with the lowest medium time. The medium time metric is measured by the reciprocal of the effective throughput, which takes into account the PHY/MAC overheads. However, MTM uses DSDV to trade the channel quality information. Since DSDV is a proactive routing protocol, it will cost constant high overhead. MTM also assumes that the data packet size is fixed at 1500 bytes, which is very unrealistic.  
The authors in [27] propose a VANET routing protocol that considers real-time road vehicle density as its routing metric. This enables to acquire the route that offers minimum delay. However, this scheme aims to provide high packet-delivery ratio and does not consider throughput.  
W-GeoR [28] introduces innovation by incorporating key routing metrics, including node mobility, link expiration time, channel signal-to-noise ratio (SNR), and destination proximity information, to make distinct routing decisions when selecting the next-hop vehicle during the ad hoc route discovery process. Nevertheless, W-GeoR is custom-tailored for health monitoring applications.  
The authors in [29] exploit blockchain features to implement a secure VANET routing protocol. They modified the existing AODV routing protocol to compute the trust scores of neighbor nodes. However, this proposal mainly focuses on security rather than throughput.  
We summarize the above related work in Table 1.