esa-sceva/satcom-chunk-collection huggingface dataset:
# A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning
## 4 Analysis of Simulation Experiment Results
### Comparative Analysis of Performance with Other Algorithms  
To verify the overall performance of the FMMPA, six algorithms are selected for comparison, including the basic MPA, sine cosine algorithm (SCA) [28], whale optimization algorithm (WOA) [29], equilibrium optimizer (EO) [30], Archimedes' optimization algorithm (AOA) [31], and grey wolf optimization (GWO) [32]. The internal parameters of each basic algorithm are set as shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fun No.** & **Name** & **Dim** & **Range** & **Optimal Value** \\ \hline F1 & Sphere & 30/50/100 & \([-100,100]\) & 0 \\ F2 & Schwefel2.22 & 30/50/100 & \([-100,100]\) & 0 \\ F3 & Schwefel1.2 & 30/50/100 & \([-100,100]\) & 0 \\ F4 & Schwefel2.21 & 30/50/100 & \([-100,100]\) & 0 \\ F5 & Quartic & 30/50/100 & \([-1.28,1.28]\) & 0 \\ F6 & Zakharov & 30/50/100 & \([-5,10]\) & 0 \\ F7 & Rastrigin & 30/50/100 & \([-5.12,5.12]\) & 0 \\ F8 & Ackley & 30/50/100 & \([-32,32]\) & 0 \\ F9 & Griewank & 30/50/100 & \([-600,600]\) & 0 \\ F10 & Apline & 30/50/100 & \([-10,10]\) & 0 \\ F11 & Rastrigin & 30/50/100 & \([-100,100]\) & 0 \\ F12 & Sum spare & 30/50/100 & \([-5.12,5.12]\) & 0 \\ F13 & Six-Hump & 2 & \([-5,5]\) & \(-1.0316\) \\ F14 & Branin & 2 & \([-5,5]\) & 0.398 \\ F15 & Schaffer & 2 & \([-100,100]\) & 0 \\ F16 & Egcrate & 2 & \([-2\pi,2\pi]\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The benchmark test functions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameter** \\ \hline MPA & \(FADs=0.2\), \(p=0.5\), \(CF=1\) \\ SCA & \(a=2\) \\ WOA & \(b=1\) \\ EO & \(a1=2\), \(a2=1\), \(GP=0.5\), \(t=1\) \\ AOA & C1 = 2, C2 = \(6\), \(u=0.9\), \(l=0.1\) \\ C3 = 1, C4 = 2 \\ GWO & \(a=2\) \\ FMMPA & \(FADs=0.2\), \(CF=1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Algorithm parameter setting. To avoid performance comparison errors caused by the randomness of the algorithms, each test function is run 30 times independently, and the mean and standard deviation (Std) are used to judge the performance of the corresponding optimization algorithm. The average value reflects the convergence speed of the algorithm, and the standard deviation reflects the stability of the algorithm. The comparison results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fun No.
## 3 Background and Methods
### Hyper-Parameter Optimization in Machine Learning  
HPO is the process of finding optimal values of hyper-parameters. It is used for various purposes, where optimal values of parameters are not known in advance or they cannot be simply determined analytically. In ML, we want to use HPO to determine the values of hyper-parameters, which significantly contribute to improving the efficiency of the learning process, and especially to the predictive performance of the trained predictive model. Thus, the set of possible values of hyper-parameters represents the search space, within which, using the search algorithm or optimization method, we are looking for such a combination of value settings (hyper-parameter configuration), which will enable the selected ML algorithm to build the best possible prediction model according to the given evaluation function [11]. The search for optimal values is usually iterative, where the setting of hyper-parameter values is being constantly improved throughout iterations. The values can be continuous, discrete, binary, or categorical and have different constraints, so their optimization is often a complex constrained optimization problem [33].  
In supervised ML, such as classification, the goal is to build an optimal predictive model that minimizes the difference between actual ground-truth and predicted values [11]. If the mapping between input values and outputs in the predictive model is denoted as a model function \(f\) from a set of possible models \(F\), the optimal predictive model can be obtained by [34]:  
\[f^{*}=\arg\min_{f\in F}\frac{1}{n}\mathcal{L}(f(x_{i}),y_{i}), \tag{1}\]  
where \(n\) is the number of training instances, \(x_{i}\) is the feature vector of the \(i\)th instance, \(y_{i}\) is the corresponding actual output, and \(\mathcal{L}\) is the cost function value of each sample.  
In DL, there are two main types of hyper-parameters that could be optimized. While the model design hyper-parameters are used to improve the resulting NN architecture, the optimizer hyper-parameters are mainly used to fine-tune the learning process on the selected NN architecture. To achieve the best possible result, hyper-parameters from both two sets can be used simultaneously.  
There are different approaches to solve HPO tasks in ML, the most common being various grid and random search approaches [35; 36]. However, various metaheuristic algorithms have shown some very good results when optimizing hyper-parameters in deep neural networks recently [14; 37; 38].
## 3 Mapping of Land Cover and Physiological or Structural Variables
### Physiological Variables  
Specific modelling of structural and physiological changes in vegetation also serves as an important input parameter for biomass models, which can be substituted by adding leaf structure variables measured from remote sensing datasets and associated environmental and climate variables. Therefore, to avoid discrete LCLU classes, scientists have made efforts in developing alternative physiological variables, such as vegetation continuous field [85], life form [86], leaf type [59], phenology [51,87], and leaf longevity [78]. For example, a global product of MODIS-based VCF is produced at a 250 m resolution using an automated algorithm [88]. This product provides a percentage of tree cover, ranging from 0 to 100%, in a pixel and it is available at National Aeronautics and Space Administration (NASA)'s Land Processes Distributed Active Archive Center (LPDAAC). [PERSON] et al. [89] rescaled the MODIS 250 m VCF product by using Landsat and LiDAR-based data to produce a high-resolution global VCF at a 30 m resolution. VCF tree cover products have been widely used for biomass mapping across the tropics and globe [90, 91, 92, 93]. For example, VCF is used to develop the baseline map of carbon emission from deforestation and degradation in the tropical area at a spatial resolution of 18.5 km [92]. The study found 0.81 PgC emissions from 2000 to 2005 due to forest cover loss across the tropics. Another study estimated mean annual changes in carbon stock due to tropical deforestation and regeneration during the 1980s and 1990s [93], using VCF derived from National Oceanic and Atmospheric Administration (NOAA)-AVHRR data. This study reported a 10% increase in carbon emissions due to tropical forest clearing. Recently, a high-resolution map quantifying gains and losses in global tree cover was produced using vegetation tree cover percentage determined from Landsat imagery [67]. Global Land Cover (GLC)'s land monitoring services provide global land cover and vegetation physiological products (LAI, FCover, and FAPAR) derived from Project for On-Board Autonomy-Vegetation (PROBA-V) (2014 to present at 100 m and 300-m resolutions) and Satellite Pour I'Observation de la Terre (SPOT) vegetation (1991 to preset at a 1-km resolution) image time series [78]. The GLC is an initiative developed under the ESA's Copernicus program ([[https://land.copernicus.eu/global/](https://land.copernicus.eu/global/)]([https://land.copernicus.eu/global/](https://land.copernicus.eu/global/))), and the products have recently been integrated with Sentinel 1 and 2 time series for continuity [69,70,78].
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 4 Proposed Protocol
### Initialization Phase  
\(CS\) selects large prime numbers \(p,q\) and \(a,b\in F_{p}\). Then, \(CS\) selects a secure elliptic curve, \(E_{q}(a,b):y^{2}=x^{3}+ax+b\) (\(4a^{3}+27b^{2}\
eq 0\)), in a finite field, \(F_{p}\), and \(t=log_{2p}\) represents the security metrics. Moreover, \(G\) denotes a cyclic group with order \(q\) with a base point \(P\). Then, \(CS\) randomly selects an integer, \(s\in Z_{q}^{*}\), as a secret key and computes \(P_{pub}=sP\). The public system parameters are released as \((G,P,P_{pub})\), whereas the value of \(s\) remains confidential. Then, \(CS\) selects secure one-way hash functions \(h(.):\{0,1\}^{*}\rightarrow\{0,1\}^{tn}\), generating a 256-bit output and a fuzzy verifier, \(2^{4}\leq s_{0}\leq 2^{8}\). Finally, CS publishes system parameters \(\{P_{pub},E_{q},P,h(.)\}\) and keeps \(s\) secret.
## 3 Results and Discussion
### cis- and trans-p-Mentl-2-en-1-ol Emissions from Graphium sp.  
The temporal pattern of emissions for _cis-_ and _trans-p_-menth-2-en-1-ol from the _Graphium_ sp. cultures is depicted in Figure 5. Based on our experimental design, peak emissions of both compounds were detected at day 7 (although highest levels may have occurred slightly before or after that timepoint since measurements were not recorded daily). Following that peak, emissions began to decline gradually over a period of 90 days. The decrease in emissions over time was best fit by regression with exponential decay models (_cis_: \(y=2.59e^{(-0.05\lambda)}\), \(R^{2}=0.924\); _trans_: \(y=1.34e^{(-0.04\lambda)}\), \(R^{2}=0.927\)). Emissions of _cis-p_-menth-2-en-1-ol were higher than _trans-p_-menth-2-en-1-ol for the first 30 days, but the difference was significant only on day 10 (\(t=11.34\), df = 2, \(p=0.004\)) and day 20 (\(t=3.24\), df = 2, \(p=0.042\)).
## 2 Materials and Methods
### Experimental Design  
Natural, uncontaminated soil with a grain size in the range of clayey silt was dried at 40 \({}^{\circ}\)C and homogenized. Laboratory samples were obtained by the quartering procedure. After that, 0.5 kg of soil sample was treated with 10.3 g of gypsum (CaSO\({}_{4}\) 2H\({}_{2}\)O), thus that the leachable sulfate content was equivalent to that in the contaminated soil from \"Old Zinc-works\" site. To individual aliquots of 0.5 kg of untreated and sulfate-treated natural soils, an aqueous solution of a mixture of nitrate salts (Cd(NO\({}_{3}\))\({}_{2}\), Pb(NO\({}_{3}\))\({}_{2}\), and Zn(NO\({}_{3}\))\({}_{2}\)) was added, thus that the contaminated soils contained approximately 10, 1000, and 10,000 mg/kg of Cd, Pb, and Zn, respectively. The contaminated soils were left for 48 h at 30 \({}^{\circ}\)C to allow equilibration of the added Cd, Pb, and Zn. Then, for the remediation, 0.25 kg aliquots of contaminated and sulfate-treated contaminated soils were amended with the paper-ash, thus that the soil-to-ash mass ratio was 75:25. This ratio was chosen based on the optimization of the paper ash remediation process from our previous research. After the addition of the paper-ash, water (wt. 30%) was added [18]. Samples were homogenized, transferred to plastic cylinders (diameter: 100 mm; height: 40 mm), and compacted manually with a rammer, simulating the field installation of geotechnical composites. The composites were then transferred to a climatic chamber at 20 \(\pm\) 2 \({}^{\circ}\)C and 98% humidity and kept under constant temperature and humidity conditions until the end of the experiment. To follow the partitioning of the Cd, Pb, and Zn in the prepared uncontaminated, contaminated, and remediated soils, the modified [PERSON]'s extraction procedure was applied in different time intervals over a time span of 365 days. Samples for the analysis were taken at 7, 28, 56, 90, 120, and 365 days after the remediation from the top and the bottom apertures of the cylinders. In uncontaminated, contaminated, and remediated soil, along with the Tessier extraction procedure, the XRD analyses were performed for each time interval. All the experiments were made using duplicate samples. A flow chart of the experimental design is presented in Figure 1.
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 6 Simulation Examples  
This section presents the performance analysis of a V2X communication system under different propagation scenarios employing binary phase-shift keying (BPSK) modulation. This framework can be adapted to any multicarrier system and modulation scheme, such as the ones specified by the IEEE 802.11p and 3 GPP LTE-V2X standards. In particular, we used the proposed simulator to evaluate the BER performance of a DSRC system based on the IEEE 802.11p standard in scenarios where road safety applications might be employed. We evaluated the performance of two channel estimation techniques that are considered for IEEE 802.11p transceivers, namely the LS estimator and the STA technique, which are described in the following subsections.
# A Millimeter-Wave 4 th-Harmonic Schottky Diodes Mixer with Integrated Local Oscillator
## 4 Conclusions  
In this paper we have described the design and evaluation of a fourth-harmonic mixer with an integrated MMIC-based LO. This cost effective solution allows the use of low-frequency LO sources based on COTS components, which can be developed on planar technology. The results show good performance, similar to the state-of-the-art for fourth-harmonic mixers (see Table 1) and a little worse than subharmonic mixers working in the same frequency range. The performance is limited for two main reasons. First, the available LO power; future steps would consider integrating an amplifier at the output of the second MMIC frequency doubler in order to increase the final available LO power. The second reason is the manual positioning of the diodes, which may introduce misalignments, besides the additional resistance caused by the silver epoxy used for fixing the diodes. This could be solved by using GaAs membrane or other substrateless technology that allows the integration of the diodes during the fabrication of the circuitry. Nevertheless, this prototype demonstrates the possibility of integration of a full THz receiver in the same single packaging metallic block, with the consequent reduction of mass and volume.  
Figure 5: Schematic of the Y-Factor setup used for mixer characterization.
## 2 Background and Related Work
### Explainable Intrusion Detection Systems  
The integration of explainable AI (XAI) into intrusion detection systems (IDSs) addresses the limitations of black-box models by enhancing transparency, accountability, and legal admissibility. Techniques such as SHAP and LIME have proven effective for interpreting feature contributions in classifiers [5], while rule-based models improve auditability [7]. Studies have shown their complementary strengths in explaining deep learning models [9], including CNNs and RNNs [10].
# High-Temporal-Resolution High-Spatial-Resolution Spaceborne SAR Based on Continuously Varying PRF
## 6 Simulation and Results  
Here, simulations are performed to verify the accuracy of the resampling processing and the performance of the high-order imaging algorithm. The simulation parameters are listed in Table 2. The simulation scene is shown in Figure 16, where the distances of different targets along the azimuth and range are 2.5 km.  
Figure 17 shows that all the targets are well focused after applying our proposed algorithm. To quantify the focusing performance, further results for the point targets are listed in Table 3, using the rectangular window. All these results indicate that the proposed imaging algorithm can effectively satisfy the imaging requirement of a spaceborne SAR system employing the CVPRF concept.  
Figure 16: Distribution of targets in the simulation scene.  
Figure 15: Block diagram of the high-order imaging algorithm.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 2 Materials and Methods
### Characterization of the Adsorbent  
The diameter of chitosan beads was found to be about 0.5-2 mm (judging from 200 beads as representative chitosan beads). The data obtained after weighing 200 hydrogel beads showed that the dry weight per chitosan bead was \(3.7\times 10^{-4}\) g, which suggests that the adsorbent contained 98% moisture. Several characterization methods have been used to determine the physicochemical properties of pristine and modified chitosan. FT-IR spectra of the samples were recorded in the range of 4000-500 cm\({}^{-1}\) with a JASCO Japan FTIR-4200 spectrophotometer using the KBr pellet method. The surface morphology and element distribution of the chitosan beads before and after the adsorption of Cr(VI) were obtained using SEM-EDS (JEOL Japan: JCM-6000 with JED-2300). The surface chemistry properties of SDS-modified chitosan beads were investigated by X-ray Photoelectron Spectroscopy (XPS, Thermo Scientific Center: K-Alpha).  
Figure 1: Procedure employed for the synthesis of sodium dodecyl sulfate (SDS)-chitosan beads.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### Multiple Access Techniques for BackCom
#### 5.3.2 Code Division Multiple Access  
In BackCom, multiple passive tags modulate the same carrier signals that cause overlapping. The overlapped backscattered signals can be separated using code division multiple access (CDMA). CDMA assigns different spreading codes to multiple users by using the characteristic of orthogonality spreading sequence. Different users can communicate with each other without any interference [118], Figure 26 delineates the comparison of TDMA and CDMA access techniques for BackCom and RFID systems. Figure 26b shows successful transmission by tags using CDMA. Figure 26a shows that tags operating in TDMA mode face collisions. A CDMA-based algorithm for EPC global generation 2 was also proposed to reduce collisions between tags [118]. Moreover, in practical scenarios, dominant signals (carrier component) leak into the receiver. Consequently, received signals lose antipodal modulation properties and corrupt the cross-correlation properties of CDMA codes. To address this issue, code shift modulation was proposed [102]. [PERSON] et al. suggested CDMA for RFID tags, which proved to be an adequate technique for predicting a large number of tags [103].
# A Novel Magnetic Coupling for Miniaturized Bandpass Filters in Embedded Coaxial SIW
## 3 Results
### Discussion  
To sum up, the designed three-pole filter achieves a MF of 96.8% while providing a quite wide passband with FBW = 10%, very wide spurious-free band with a resonator \(Q_{u}\) greater than 160. The latter values confirm the optimal trade-off between remarkable miniaturization design, and wideband filtering response of the proposed magnetic coupling mechanism for embedded CSIW components. In this context, the filter performance in terms of FBW, MF, \(Q_{u}\) and spurious-free band of this work are summarized and compared to related references in Table 4.  
Wideband filtering responses are extremely complicated to implement in miniaturized SIW BPFs, especially if the MF is strongly increased. For instance, such characteristics have been latterly achieved  
Figure 11: (**a**) Photograph of some layers of the LTCC stack-up (i.e., from top to bottom, via holes punching and after printing the coupling and capacitive patch, respectively). The resonator layout is also visible at the sides of the photograph; (**b**) Photograph of three-pole BPF prototype.  
Figure 12: Measured and simulated results of the three-pole BPF: (**a**) wideband response and (**b**) filter passband.  
in [8] by using a combination of eight-mode SIW resonators with CRME structures. However, such an approach enables the MF to be higher than 98% at the cost of a low \(Q_{u}\), which has been extracted to be just 90.  
As a comparison, in [5], the authors proposed a three-pole embedded CSIW filter centered at 2.14 GHz with FBW\(=2.8\%\) whose resonators have an area of 36 mm\({}^{2}\), which means that the MF is around 96%. In [18], the designed two-pole and three-pole BPFs based on HMSIW and QMSIW resonators showed passband FBWs of 5.9% and 4.2%, respectively, at UHF band. However, in the case of the trisection filters, the achieved MF was below 80%.  
Finally, some recent works proposed the use of conventional multi-layer PCB technology to implement miniaturized SIW filters based on coaxial topologies. As has been presented in [13], the FBW of the two four-pole CSIW BPFs centered at UHF band is 9.1% if MF = 92.4%, but is reduced to 4.6% when the MF is increased up to 96.4% because standard magnetic post-wall irises were used to couple adjacent CSIW resonators. On the contrary, in [19], third-order BPFs were designed and tested by arranging side-by-side three vertical coaxial stepped-impedance resonators (SIRs), showing an extremely high MF, good \(Q_{u}\), but limited spurious-free band.
## 4 Results and Discussion
### Condensation Heat Transfer Coefficient  
Figure 5 shows the relationship of R1234 ze(E) between the condensation heat transfer coefficient \(\alpha\), and the wetness \(1-x\) at the mass velocities of 400, 200, and 100 kg m\({}^{-2}\)s\({}^{-1}\). From the results, the heat transfer coefficient of R1234 ze(E) is lower than that of R152a, as well as that of R32, R410A, and R152a. The heat transfer coefficient decreased as wetness increased, because the heat transfer resistance of the liquid film increased as condensation proceeded. Additionally, the forced convection condensation decreased as the refrigerant vapor velocity decreased. A comparison of the results for different refrigerants shows that the heat transfer coefficient of R152a is higher than those of other refrigerants, under most conditions. Moreover, the heat transfer coefficient of R1234 ze(E) is lower than that of R152a, as well as that of R32, R410A, and R152a.  
Figure 6a-d show the comparison of experimental values, including R1234 ze(E), to values estimated from previous correlations, [PERSON] et al. [2], [PERSON] [3], [PERSON] et al. [4] and [PERSON] [7], respectively. The [PERSON] et al. correlation, shown in Figure 6a, overestimates the experimental values by up to 80%. However, the predicted trends agree with the measured values. The [PERSON] and [PERSON] correlation, shown in Figure 6b, overestimates the experimental values by up to 80%. However, the predicted trends agree with the measured values. Figure 6c shows that the experimental values and the values predicted by the [PERSON] et al. correlation can be correlated within \(\pm\)30%. Figure 6d shows that the [PERSON] and [PERSON] correlation overestimates the experimental values by up to 150%.  
Figure 4: Comparison of pressure drop between that predicted by [PERSON] et al. correlation and R1234 ze(E).  
Figure 5: Relationship between condensation heat transfer and wetness for the small-diameter smooth tube.