esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Composite Metric Routing Approach
### Composite Metric  
Let \(D(L)\) represent the total distance of any path \(L\) as follows  
\[D(L)=\sum_{\forall(m,n)\in L}d(m,n), \tag{9}\]  
where \(d(m,n)\) is the distance of link \((m,n)\) as provided by (2). Additionally, let \(E(L)\) represent the total energy cost of traversing path \(L\). Hence,  
\[E(L)=\sum_{\forall(m,n)\in L}e(m,n), \tag{10}\]  
where \(e(m,n)\) is the energy cost of link \((m,n)\) as provided by (8). Our proposed composite metric \(M(L)\) for path construction is defined as follows  
\[M(L)=D(L)\times E(L). \tag{11}\]  
The main idea here is to use a routing metric that combines both aspects of path distance and energy consumption. Thereby, we are hoping to be able to find routing paths that strike a better balance between the distance and energy consumption, as opposed to minimizing the energy consumption alone, or minimizing the distance alone. It is worth noticing that shortest path algorithms, such as Dijkstra or Bellman-Ford, are not guaranteed to converge unless the used routing metric is monotone [30; 31]. Moreover, convergence to an optimal path requires the routing metric to be also _isotonic_. In particular, monotonicity of a routing metric implies that the overall path metric does not improve when it is extended by a new link, while isotonicity of a routing metric implies that the weight-relationship between two paths remains unchanged if both paths are extended by a common link. It has been shown in [23] that the composite metric given by (11) is indeed monotone. Furthermore, it has been also shown in [23] that the composite metric (11) is _not_isotonic. The latter fact implies that convergence to an optimal path is not guaranteed if the metric (11) is minimized via a shortest path algorithm. However, we observed that an optimal path was indeed found in the vast majority of our numerical experiments, when the Dijkstra shortest path algorithm was used.
## 3 Summary of the Considered Mobility Models and Routing Protocols
### The Routing Protocols  
#### 3.2.1 Aodv  
The _ad hoc_ on-demand distance vector (AODV) protocol [7, 8] is a reactive routing protocol. When a source node wants to initiate transmission with a destination node in the network, it first initiates a route discovery by sending out a route request message (RREQ) to its neighbors, and those neighbors, in turn, forward the RREQ to their neighbors' nodes. Whenever the route to a destination node is located or an intermediate node has a route to destination, a route reply message (RREP) is generated and sent to the source node. Once a route is established, the data packet transmission starts between the source node and the destination node. However, when the data packet is being routed, it can happen that a next hop is unreachable (broken link) due to the mobility of the nodes. The intermediate node where this occurs sends out a route error (RERR) message to the source node and drops the data packet if local repair is not enabled. When local repair is enabled, the intermediate node holds on to the data packet while it tries to repair the route locally by sending out new RREQs in order to establish a new route to the destination.  
Figure 5: Trajectory of a UAV under the enhanced GM (EGM) model.
## 4 Numerical Simulation and Experimental Studies  
### Numerical Simulations  
We conducted some simulations in MATLAB2018a with sampling time of \(10^{-4}\) s and solver of ODE3. The manipulator dynamics is shown in Appendices A and B. The simulation structure is shown in Figure 2.  
The parameters of the hydraulic manipulator are shown in Table 1.  
To verify the effectiveness of the proposed control, some simulations are implemented under the presence of the unknown variant payload, the unknown frictions and the unknown leakages in mechanical dynamics and hydraulic dynamics. They present not only the unmatched and matched uncertainties, but also the smooth and unsmooth uncertainties. Additionally, a backstepping sliding mode control (BSMC) and PI control are also carried out and their results are compared to the proposed control (ABSMC). The unknown frictions are expressed as:  
\[\tau_{f}=b_{v}\dot{\theta}+b_{c}sign\Big{(}\dot{\theta}\Big{)}\in R^{3\times 1} \tag{47}\]  
where \(b_{v}\in R^{3\times 3}\) is a vicious positive matrix with \(b_{v}=diag\Big{(}[4,4,4]^{T}\Big{)}\), and \(b_{c}\in R^{3\times 3}\) is a coulomb positive matrix with \(b_{c}=diag\Big{(}[4,4,4]^{T}\Big{)}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Symbol** & **Value** & **Unit** & **Symbol** & **Value** & **Unit** \\ \hline \(m_{i,i=1,\ldots,3}\) & 5 & kg & \(A_{ij(i,j=1,2)}\) & \(15.10^{-4}\) & m\({}^{2}\) \\ \(l_{1}\) & 0.1 & m & \(A_{13}\) & \(15.10^{-4}\) & m\({}^{2}\) \\ \(l_{2}\) & 0.5 & m & \(A_{23}\) & \(10^{-3}\) & m\({}^{2}\) \\ \(l_{3}\) & 0.2 & m & \(P_{s}\) & 100 & bar \\ \(g\) & 9.81 & m/s\({}^{2}\) & \(P_{r}\) & 3 & bar \\ \(d_{0}\) & 0.2453 & m & \(k_{qij(i,j=1,2)}\) & \(2.16\times 10^{-8}\) & m\({}^{3}/\sqrt{Pa.s}.V\) \\ \(d_{1}\) & 0.2471 & m & \(k_{q3(i=1,2)}\) & \(3.5\times 10^{-8}\) & m\({}^{3}/\sqrt{Pa.s}.V\) \\ \(d_{2}\) & 0.036 & m & \(\beta\) & \(1.25\times 10^{9}\) & \(Nm^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the hydraulic manipulator. Figure 2: Simulation structure. The leakages in hydraulic dynamics are derived by  
\[Q_{LLi}=v_{i}(P_{i1}-P_{i2}),(i=1,2,3) \tag{48}\]  
where \(v_{i}\) is leakage coefficients with \(v=1e^{-11}[20,15,1.5]^{T}\). The payload is alternated as shown in Figure 3.  
The reference trajectories of the hydraulic manipulator are selected as \(x_{d}=[30\sin[0.4\pi t\,,20[1+\sin[0.4\pi t+\pi/2\,50+30\sin 0.4\pi t+\pi]]^{T}\). Each approximation in the position control has three inputs, 10 nodes in the hidden layer, and one output, and each approximation in the torque control has two inputs, 10 nodes in the hidden layer, and one output. The parameters of the controllers are chosen by trial error method and shown in Table 2.  
**Remark 2**. : _In order to show the ability of the proposed control, the parameters of control are designed with payload 20 N and keep with other payloads.
## 3 Methods
### Sea Fog Identification
#### 3.1.1 Sample Selection of Sea Fog  
It is difficult to distinguish between sea fog and low clouds and stratus clouds through visual interpretations [32]. Therefore, a sea fog sample database is established using CALIPSO-VFM data to assist the selection of sea fog samples from AHI images [5; 33]. Figure 4 shows the selection method, where Figure 4a is an AHI image on 15 June 2018 at 05:10 UTC, and Figure 4b is the classification result when using CALIPSO-VFM data from 15 June 2018 at 05:17 UTC. In the area in the red box in Figure 4b, the CALIPSO-VFM data are used to identify places where clouds are in contact with the sea surface, which are considered as sea fog points. From these, we extract the spectral values from the corresponding positions in the AHI images (e.g., the red dots in Figure 4a). Then, the spectral values at the sea surface, low clouds, and medium-high level clouds are obtained using the same approach. A total of 9141 samples of sea fog, 10,071 samples of the sea surface, 8733 samples of low clouds, and 10,771 samples of middle-high level clouds were selected and used in this paper.
# Hardware-in-the-Loop Test of a Prosthetic Foot
## 3 Experimental Setup and Parameters
### Parameters  
This section summarizes the parameters used to conduct one stride with the presented HiL setup. The parameters are given in Table 1.  
The stability of gait models is highly sensitive to the chosen parameters and initial conditions. Due to several hardware limitations of the Stewart Platform, such as limited work  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Value** & **Variable** & **Value** \\ \hline \(l_{0,\text{L}}\)\(l_{0,\text{R}}\) & \(1\,\text{m}\) & \(l_{0,\text{u}}\) & \(0.84\,\text{m}\) \\ \(m_{\text{VPP}}\) & \(30\,\text{kg}\) & \(l_{\text{f}}\) & \(0.16\,\text{m}\) \\ \(J_{\text{VPP}}\) & \(3\,\text{kg}\)\({}^{2}\) & \(m_{\text{IFFP}}\) & \(0.005\,\text{kg}\) \\ \(k_{\text{L}}\) & \(5.4\times 10^{3}\,\text{N/m}\) & \(k_{\text{u}}\) & \(1.1\times 10^{3}\,\text{N/m}\) \\ \(\alpha_{0}\) & \(75\,^{\circ}\) & \(d_{\text{u}}\) & \(4.69\,\text{kg/s}\) \\ \(r_{\text{VPP}}\) & \(0.25\,\text{m}\) & \(r_{\text{h}}\) & \(0.1\,\text{m}\) \\ \hline \(x_{0}\) & \(0\,\text{m}\) & \(\dot{x}_{0}\) & \(0.97633\,\text{m/s}\) \\ \(y_{0}\) & \(1.0709\,\text{m}\) & \(\dot{y}_{0}\) & \(0\,\text{m/s}\) \\ \(\phi_{\text{VPP}0}\) & \(0\,\text{rad}\) & \(\phi_{\text{VPP}0}\) & \(-0.0071\,\text{rad/s}\) \\ Solver & Euler & \(\Delta\dot{T}\) & \(0.0002\,\text{s}\) \\  & & \(\Delta T\) & \(0.001\,\text{s}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and initial conditions used in the HiL test.  
Figure 6: The transfer system and the experimental part are depicted. (**a**) The experimental setup consists of the prosthetic foot, the FTS, and the Stewart Platform. (**b**) The C-Walk foot 1C40 by Ottobock with silicone cosmesis (© by Ottobock).  
space (due to limited actuator stroke), limited maximum actuator velocity/acceleration, and limited maximum force, a scaled model of the amputee had to be found. Using an optimization algorithm, the model parameters and initial conditions (index \((\cdot)_{0}\)) as summarized in the table were found for the VPP model. With this set of parameters, the VPP model was able to walk continuously for \(>\)50 steps. However, this parameter set is biomechanically not meaningful, because this would be a very lightweight person on long legs. Additionally, the TD angle was quite steep compared to human walking.  
The numerical simulation needed to run with a small sample time such that the simulation was numerically stable. Furthermore, the sample time \(\Delta\hat{T}\) could not be too small because the model still had to be solved in real time. In the preliminary analysis, a sample time of \(\Delta\hat{T}=0.0002\) s proved successful. Furthermore, the sampling of the interface forces was performed with \(\Delta\hat{T}=0.0002\) s. The actuator control ran with a sample time of \(\Delta T=0.001\) s.
# Design and Experiment of Wideband Filters Based on Double-Layered Square-Loop Arrays in the F-Band
###### Abstract  
We present a wideband metamaterial (MM) filter based on a metal-dielectric-metal (MDM) structure. The proposed structure consists of a quartz dielectric substrate and two metallic square-loop arrays, which were deposited on both surfaces of the substrate. The performance of the proposed design was numerically simulated by considering the angle of incidence. The surface current and electric field distributions were calculated for the two resonant frequencies to examine the intrinsic mechanism of the proposed filter. The transmittance of the structure for various oblique angles of incidence was also analyzed. It is generally challenging to completely align the two metallic layers due to limitations in the manufacturing process. Hence, the influence of periodic misalignment on wave propagation was studied. Furthermore, a prototype of the proposed structure was fabricated and the transmission spectra were measured. Simulation and experimental results reveal that our proposed filter can achieve wideband transmission in the F-band. This work is valuable for the design of broadband filters in millimeter and terahertz waves.  
metamaterial; wideband filter; MDM structure; surface waves; square-loop arrays +
Footnote †: journal: Applied Sciences
# Multi-Fidelity Gradient-Based Strategy for Robust Optimization in Computational Fluid Dynamics
## 3 Uncertainty Quantification Methods
### Bayesian Kriging and Gradient-Enhanced Kriging  
Surface-response methods based on Bayesian kriging (BK) are implemented to achieve a non-intrusive estimate of the cost function statistics. In the following, we first introduce a BK formulation using only the observed values of the QoI \(\mathbf{J}\). Afterwards, we extend the formulation to account for gradient information, leading to Bayesian GEK.
## 3 Methodology and Implementation
### Architecture and Principle of Operation of Victim Identification Module
#### 3.1.2 Metric Learning
The above learning process is facilitated by the triplet loss function [51], described by the following equation:  
\[L\big{(}x,x^{+},x^{-}\big{)}=max(0,\big{|}\big{|}h(x)-h\big{(}x^{+}\big{)} \big{|}\big{|}-\big{|}\big{|}h(x)-h\big{(}x^{-}\big{)}\big{|}\big{|}+m, \tag{3}\]  
\(x\), \(x^{+}\), \(x^{-}\)--anchor, positive element, negative element,  
\(h(x)\), \(h(x^{+})\), \(h(x^{-})\)--embeddings determined for the anchor, positive and negative element, \(m\)--margin, the desired value of the difference in distance between the anchor and positive and between the anchor and negative. The standard approach to machine learning and deep learning assumes a random selection of learning sets. In this case, it usually results in the negative element being significantly more different from the anchor than the positive element, as expressed by the relation:  
\[\big{|}\big{|}h(x)-h\big{(}x^{+}\big{)}\big{|}\big{|}<<\big{|}\big{|}h(x)-h \big{(}x^{-}\big{)}\big{|}\big{|} \tag{4}\]  
This results in a zero value of the loss function and thus no update of the weight coefficients, i.e., stagnation of the learning process. In order to solve this problem, the hard example mining technique described by [PERSON] [52] was applied, which allows us to search for the most difficult learning triplets, i.e., such that the negative example is as similar as possible to the anchor (hard negative mining) and the positive example is as little similar as possible to the base element (hard positive mining). This results in a deviation from the correct distribution of distances between embeddings in the learning triplet, conditioned by the relation [49]:  
\[d(x,y)=||x-y||=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}, \tag{2}\]0  
but this allows the neural network learning process to be triggered and the weighting coefficients to be updated to achieve the correct distribution.
## 2 Circuit Topology of RF-Energy-Harvesting Integrated Circuit
### Matching Network  
Because of an antenna impedance of 50 \(\Omega\), the input impedance of the matching network must be matched to 50 \(\Omega\). This matching circuit is used to not only guarantee maximum power transfer but also obtain maximum conversion efficiency. If the matching network includes capacitor C and inductor L, maximum power transformation is obtained from an antenna to a load. Source impedance  
Figure 1: Proposed RF-energy-harvesting system.  
Figure 2: Functional diagram of RF-energy-harvesting IC.  
\(R_{a}\) must be equal to the conjugate input impedance \(Z_{in}\) of the matching network [20]. The maximum output power \(P_{L,max}\) can be expressed as follows:  
\[P_{L,\text{max}}=\left|I_{a}^{2}\right|\times\Re e[Z_{in}]=\frac{V_{a}^{2}}{4R_ {a}} \tag{1}\]  
where \(V_{a}\) is the peak voltage of the antenna. \(I_{a}\) and \(Z_{in}\) are the input current and input impedance of the matching network, respectively.  
In this study, the impedance of the antenna and the input impedance of the RF-DC rectifier were 50 \(\Omega\) and 240.4 - j473.9 \(\Omega\), respectively. The impedance of the matching network must be equal to 50 \(\Omega\)[21]. Simulations were performed with ADS software and component datasheet provided from MuRata Company, and inductor L, labeled as LQG15 HS_02, and capacitor C, labeled as GRM15, were used to complete the matching network. Moreover, authors paid more attention toward self-resonant frequencies (SRFs) for selecting a suitable inductor. The operating frequency of the matching network must be lower than the SRF of the selected inductor. The larger the inductor is, the smaller the SRF is. For example, the minimum SRFs were 1000 and 800 MHz for L \(\leq\) 47 \(\mu\)H and L \(\geq\) 56 \(\mu\)H, respectively. If input power was set to \(-\)10 dBm at an operating frequency of 915 MHz, a suitable inductor lower than 47 \(\mu\)H was selected.
## 5 Research Challenges and Future Directions
### Communication Security  
Since there are numerous possible risks from the neighboring RF frequencies, such as pseudo-BSs and eavesdroppers, figuring out how to enhance communication security is crucial for the next generation of HetNets. Consideration of secure communication in advance RA problem models might make current RA algorithms useless [156]. Therefore, a study of communication security in HetNets is essential.  
Among the various 6G applications, human-centric communications stand out as the most crucial. Therefore, 6G networks must prioritize secrecy, privacy, and security. The physical security of a 6G network cannot be ensured using the same approaches that have been used up to this point in regulation and privacy and security procedures. Security and secrecy in 5G networks are provided by accepted encryption methods based on the Rivest-Shamir-Adleman (RSA) public-key cryptosystems. Nonetheless, big data and artificial intelligence have made RSA cryptosystems insecure [157]. Therefore, a novel physical-layer privacy approach must be created for future 6G communication based on big data and AI. The 6G standard requires the use of a physical-layer security architecture and encrypted interactions with upper layers. Solutions for providing security in 6G networks include physical security technologies and the distribution of quantum keys using visible light communication (VLC) [158]. Because of the vast growth of edge and cloud infrastructures, it is anticipated that large-scale quantum computing will expand, which might raise asymmetric cryptography's study interest in this field. Network virtualization and software-defined networking might greatly benefit from using machine learning in automated security in order to identify better and prevent attacks.
## 2 Review
SDPS further splits \(X(\alpha)\) into connected components ((**f**), yellow and black subsets). Let us detail the SDPS algorithm within the framework of classical cluster analysis: SDPS-density algorithm of direct, free, parameter-dependent classification that does not require human involvement and does not depend on the order of space scanning [16; 17]. The SDPS algorithm, like the well-known modern algorithms DBSCAN, OPTICS, and RSC [18; 19; 20], represents a new stage in cluster analysis, since it not only breaks the original space into homogeneous parts but also pre-clears it of noise (filters), passing to the maximum \(\alpha\)-perfect subset. The use of the construction of \(\alpha\)-perfect sets is an essential difference of the SDPS algorithm. For example, the SDPS and DBSCAN algorithms have the same initial parameters: the radius \(r\) of the view and the density level \(\alpha\) (the minimum number of points that must be in a ball of radius \(r\)). Then, they act in different ways. As mentioned above, SDPS cuts out the maximum \(\alpha\)-perfect set from the original space, parses it into connected components and considers them to be clusters. The DBSCAN algorithm uses an asymmetric reachability ratio, searches for regions of such reachability with centers at dense points, combines under the condition of \(r\)-proximity and considers them to be clusters. Figure 3 shows examples of clustering a complex array (Figure 3a) using algorithms: MDPS (Figure 3b), DBSCAN (Figure 3c) and OPTICS (Figure 3d). Removing noise from space and then partitioning the balance part into connected components is possible based on a large and most important class of local monotone densities. This scenario is called a DPS-scheme, and its specific implementations at a particular density are called DPS-series algorithms (DPS-algorithms). They represent the state-of-the-art DMA clustering. Previous DMA clustering algorithms Rodin, Crystal and Monolith [21; 22; 23] were based on non-monotone densities, and, despite successful applications, theoretically, they had drawbacks that are characteristic of density-based cluster analysis algorithms: dependence on the order of space scanning and, as a consequence, ambiguity of results, issues with convergence, etc. Figure 3: Examples of clustering a complex array (**a**) using algorithms: MDPS (**b**), DBSCAN (**c**) and OPTICS (**d**). In conclusion of the general description of DMA clustering, we note that the DPS-series algorithms are extremely versatile: they are capable of working with any kind of similarity in cluster analysis (distance, correlation and associativity factors). The point is that DMA has effective procedures in place that construct monotone densities. Let us go back to the SDPS algorithm. The resulting clusters do not have any special geometry: they are simply \"continuous\" and contain \"much space\" locally. It seems that the natural extension of research should be the search for clusters with a particular geometry. Doubtless, the first step in that process is deemed to be linear structures.
## 3 Results and Discussion
### The Extraction Yield
#### 3.2.3 Sterols  
Two \(\Delta^{5}\)-sterols, namely campesterol (**57**) and \(\beta\)-sitosterol (**58**), and a \(\Delta^{4}\)-3-keto-steroid, named stigmast-4-en-3-one (**59**), were found, at lower abundances, in the _P. pinaster_ and _P. pinea_ bark lipophilic extracts (Table 1). Among these, \(\beta\)-sitosterol was found to be the major component in the studied _Pinus_ bark samples. To the best of our knowledge, these sterols were also identified here for the first time in _P. pinaster_ bark. On the other hand, campesterol (**57**) and \(\beta\)-sitosterol (**58**) have already been described as lipophilic components of _P. pinea_ bark and wood [23].
## 4 Results
### Comparison of Time Series Forecasting Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Batch Size**} & \multirow{2}{*}{**Epoch**} & \multirow{2}{*}{**Neuron**} & \multirow{2}{*}{**Dropout Rate**} & \multirow{2}{*}{**RMSE (10\({}^{4}\) gha)**} & \multirow{2}{*}{**MAPE (\%)**} \\  & & & & & & \\ \hline LSTM\({}_{1}\) & 1 & 100 & 50 & 0.4 & 503,752 & 24.95 \\ LSTM\({}_{2}\) & 4 & 50 & 50 & 0.2 & 322,527 & 15.96 \\ LSTM\({}_{3}\) & 4 & 150 & 150 & 0.4 & 518,833 & 25.70 \\ LSTM\({}_{4}\) & 8 & 50 & 128 & 0.0 & 118,784 & 5.85 \\ LSTM\({}_{5}\) & 8 & 250 & 128 & 0.2 & 236,088 & 11.67 \\ LSTM\({}_{6}\) & 16 & 200 & 128 & 0.0 & 97,094 & 4.85 \\ LSTM\({}_{7}\) & 16 & 150 & 100 & 0.2 & 322,728 & 15.97 \\ LSTM\({}_{8}\) & 16 & 200 & 100 & 0.4 & 484,008 & 23.97 \\ LSTM\({}_{9}\) & 32 & 100 & 100 & 0.2 & 373,708 & 18.50 \\ LSTM\({}_{10}\) & 32 & 150 & 150 & 0.4 & 495,785 & 24.55 \\ LSTM\({}_{11}\) & 32 & 150 & 150 & 0.0 & 139,483 & 6.88 \\
**LSTM\({}_{12}\)** & **32** & **250** & **128** & **0.0** & **64,030** & **3.19** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The RMSE and MAPE results of LSTM models with different hyperparameters. According to the results in Table 7, the ARIMA model generally has lower RMSE and MAPE values compared to other models. The average RMSE value is 42,452 \(\times\) 10\({}^{4}\) gha, and the average MAPE value is 2.12% for the test set. These results indicate that the ARIMA (1,1,0) model performs better on the test set compared to other models. However, a significant decrease in the performance of all models is observed in the year 2020. The reason for this is the difficulty in predicting the sudden decrease in EF due to lockdowns caused by the COVID-19 pandemic. In response to this unexpected drop, the ARIMA model has shown better adaptation with a 5.93% MAPE compared to other models. There are several reasons why the ARIMA model outperforms other models. Firstly, the ARIMA model effectively captures significant trends and seasonal patterns in time series data, enabling accurate forecasts. Its simplicity and limited number of parameters reduce the risk of overfitting, contributing to overall model performance. The fewer parameters compared to complex models further minimize the risk of overfitting, positively impacting the model's effectiveness. Additionally, the differencing process of ARIMA applied to the data over a specific time period, helps stabilize non-stationary series, enhancing the model's ability to capture abrupt changes.