esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design
### Client
#### 3.3.2 Statistical Plots  
Besides displaying maps of climate variables, the system is able to return the statistical results as statistical plots to the client. These statistical results are derived from the initial statistics or interactive statistics through user operations. In the case of initial statics, static graphs are usually generated to provide the description of data. Dynamic statistical plots are suggested if data analysts want to manipulate elements on the plots during the analysis, for example, the analysis of Albedo, which is described in [42]. In the context of climate modeling, the model configurations (e.g., model inputs) are also taken into account as a part of statistical analysis. This will be further illustrated in Section 4. Due to the nature of interactive manipulation, dynamic statistical plots are directly linked to the knowledge discovery process.
## 3 Results and Discussion
### Prediction of TPMC Values Change  
The _TPMC_ values' 3D surface plots by the first- and second-degree models or Equations (3) and (4) are presented in Figure 8a,b. The \(x\)-axis and \(y\)-axis present the para rubber wood moisture content and burning time, respectively, while the \(z\)-axis shows _TPMC_ values. These results showed that the moisture content of para rubber wood affected _TPMC_ values when compared with the burning time. The \(p\) coefficient values in Table 3 were calculated by the least-squares method. As seen in Figure 8a,b, at the combustion period, the response of both models showed a decreasing trend of the _TPMC_ values as the combustion time progressed, synonymous with the _MMAD_ value. The response of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Degree in Model**} & \multicolumn{6}{c}{**Coefficient**} \\ \cline{2-7}  & \(\mathbf{P_{0}}\) & \(\mathbf{P_{1}}\) & \(\mathbf{P_{2}}\) & \(\mathbf{P_{3}}\) & \(\mathbf{P_{4}}\) & \(\mathbf{P_{5}}\) \\ \hline First & 0.9205 & \(-\)0.0025 & 0.0019 & - & - & - \\ \hline Second & 0.8661 & \(-\)0.0027 & 0.0036 & \(9.09\times 10^{-6}\) & \(-2.69\times 10^{-5}\) & \(4.12\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficient values of first- and second-degree models of _MMAD_ prediction.  
Figure 6: _MMAD_ values’ 3D surface plots by the first- and second-degree models: (**a**) First-degree model; (**b**) Second-degree model.  
Figure 7: Comparison of predicted and experimental _MMAD_ values: (**a**) First-degree model; (**b**) Second-degree model.  
second-degree model showed the effect of moisture content decreasing the _TPMC_ value as the combustion time progressed.  
Comparing the _TPMC_ data between the experimental results and prediction values, the second-degree model had a better fit in predicting than the first-degree model, similar to the _MMAD_ data comparison; this result was confirmed by the higher mean of the coefficient of determination (Figure 9a,b), respectively.  
Figure 8: _TPMC_ values’ 3D surface plots by the first- and second-degree models: (**a**) First-degree model; (**b**) Second-degree model.  
Figure 9: Comparison of predicted and experimental _TPMC_ values: (**a**) First-degree model; (**b**) Second-degree model.  
Besides wood moisture content and drying time, there may be other combustion rate variables that should be considered, such as air flow rate, pressure, atmosphere relative humidity, oxygen concentration in the atmosphere, and others. For example, water vapor, CO\({}_{2}\), N\({}_{2}\), and polyaromatic hydrocarbons (PAHs) are usually the result of a fuel-rich combustion at high temperatures (over 900 \({}^{\circ}\)C) [32; 33; 34]. Conversely, this is different from combustion at low temperatures (under 700 \({}^{\circ}\)C), resulting in CO, volatile organic compounds, smoke species, and soot coated in oxidized PAHs [32; 35].
## 3 Adaptive Control Method
### LMS-Based Adaptive Filter  
The LMS algorithm is used in an adaptive system to adjust the weight vector to change the system output timely in accordance with the variation in noise. An adaptive filter is a kind of linear combiner, and it is described in Figure 3.  
The constant input signal \(X(n)\) is divided into a series of discrete signals with time delay, and the quality of discrete signals is equal to that of the weighting coefficient, as shown as follows:  
\[X(n)=[x(n),x(n-1),...,x(n-N+1)]^{T}, \tag{15}\]  
where \(T\) means transpose, is the time mark, and is the filter order. The weight vector is a significant component of the adaptive system, which can weigh the input signal \(X(n)\) and then form the output \(y(n)\) with a digital accumulator. The weight vector is given as:  
\[W(n)=[w_{0}(n),w_{1}(n),...,w_{N-1}(n)]^{T}, \tag{16}\]  
The output \(y(n)\) is obtained as follows:  
\[y(n)=W^{T}(n)X(n)=\sum_{i=0}^{N-1}w_{i}(n)x(n-i). \tag{17}\]  
The error signal \(e(n)\) is defined as the desired signal \(d(n)\) subtracted from the filter output \(y(n)\), which can be shown as:  
\[e(n)=d(n)-y(n). \tag{18}\]  
Figure 3: Adaptive filter.  
The core of the adaptive algorithm is the method to adjust the weight vector. In this study, an adaptive filter with the LMS algorithm is applied in the adjustment of the weight vector. The purpose is to minimize the mean squared error (MSE) _varepsilon_, which is given as:  
\[\begin{split} E(\varepsilon)&=E[e^{2}(n)]\\ &=E[(d(n)-y(n))^{2}]\\ &=E[d(n)^{2}]+W^{T}(n)E[x(n)x^{T}(n)]W(n)-2W^{T}(n)E[(d(n)x(n)] \end{split} \tag{19}\]  
From Equation (19), \(\varepsilon\) is a quadratic function about the weight coefficient vector. Therefore, Equation (19) has a unique minimum value, and the optimal weight vector can be gained at the minimum point. The approach to solving \(W\) is the steepest descent method, which makes the change in each step of the weight vector proportional to the negative value of the gradient vector and reaches the optimal solution along the shortest path. The iterative equation of the weight coefficient vector is described as follows:  
\[W(n+1)=W(n)-\mu\
abla(n), \tag{20}\]  
where \(n+1\) represents the next time of time \(n\), and the gradient vector is given as:  
\[\begin{split}\
abla(n)&=\frac{\partial E( \varepsilon)}{\partial W(n)}\\ &=\frac{\partial E[e(n)^{2}]}{\partial W(n)}\\ &=-2E[e(n)x(n)]\end{split} \tag{21}\]  
where \(\mu\) is the convergence coefficient.
## 5 System Model
### Geometric Propagation Models Common for UOWC  
In addition to the models for attenuation distribution and downwelling light, the geometric models that describe the optical signal propagation underwater need to be determined. This is dependent on the type of transmitter (Tx)-receiver (Rx) link configuration: a point-to-point line-of-sight (P2P-LOS) or diffused line-of-sight (D-LOS). In P2P-LOS connections, the Tx emits a focused beam at small beam divergence angles (\(\theta\) rad). However, despite such links achieving greater link distances, they are prone to misalignments due to the small beam area and require complex optical systems to maintain connectivity. In D-LOS links, on the other hand, the transmission beam covers a wide region, as for example, from a LED array. They are more suitable for mobile links due to less demanding alignment needs, but they suffer more losses due to geometric spread. The expressions for beam coverage (\(A_{s}\) m\({}^{2}\)) for P2P-LOS and D-LOS are given in Equation (26), where \(d\) is the distance of the link.  
\[A_{s}=\left\{\begin{array}{ll}\pi(d\tan\theta)^{2}&\left(\theta\ll\frac{\pi }{20}\right)\\ 2\pi d^{2}(1-\cos\theta)&\left(\theta>\frac{\pi}{20}\right)\end{array}\right. \tag{26}\]  
For horizontal links, the attenuation factor may be adopted directly from Equation (4). For downlinks experiencing stratification, the vertical link distance \(d\) becomes the difference in depth between the depth position of the Tx (\(z_{0}\)) and Rx (\(z\)), assuming the Tx is directly above the Rx to allow for a worst-case noise scenario at the Rx, expressed in Equation (27). The optical power received at the receiver \(P_{r}(S,\lambda,z)\) can then be determined for horizontal and vertical links with Equation (28), where \(P_{l}(\lambda)\) is the spectral emission power in wattsand \(\varphi\) is the sum of the angles of inclination (\(\alpha\)) and rotation (\(\beta\)) of the receiver with respect to the emitter's axis (29).  
\[d=\left\{\begin{array}{l}d\quad\quad(\text{hor.})\\ z-z_{0}\quad(\text{ver.})\end{array}\right. \tag{27}\]  
\[P_{r}(S,\lambda,z)=\left\{\begin{array}{l}\frac{P_{r}(\lambda)A_{r}\cos p \epsilon^{-c(S,\lambda,z)d}}{A_{s}}\quad\quad(\text{hor.})\\ \frac{P_{r}(\lambda)A_{r}\cos p\epsilon^{-c(S,\lambda,z_{0})d}}{A_{s}}(\text{ ver.})\end{array}\right. \tag{28}\]  
\[\varphi=\alpha+\beta \tag{29}\]
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 2 UAV Routing with an Emphasis on the Problem Type, Transportation Mode, and Degree of Automation  
The basis of all routing literature is the VRP, which is a well-studied field and still very much applicable for the advancement of new technology [13; 14; 15]. VRPs have been applied to solve delivery problems [16], which could appear similar to the UAV routing as a VRP attempts to find the optimal routes for one or more vehicles to deliver commodities to a set of locations [17]. We identify three main dimensions that seem particularly relevant to apply when addressing the UAVRP:  
1. The problem type: When classifying routing literature, it can be segregated based on the problem type with an emphasis on the VRP, which has given the major research contributions in the domain of vehicle routing [16; 18] and is used as an input for all the routing problems in general.
2. The transportation mode: Routing literature can be partitioned according to the categories of transportation mode, as the characteristics of the modes (such as land, sea, and air) affect the routing methods. Transportation modes have different characteristics in terms of cost, transit time, accessibility, and environmental performance [19]. Compared to other transportation modes, UAVs can be a competitive alternative for delivery and pickup of time-sensitive items, regardless of the ground-level road conditions [20].
3. The degree of automation: The degree of automation in the transportation systems is a dimension as automated systems are discussed in dynamic vehicle routing [21], generalized vehicle routing [22], and real-time vehicle routing [23]. Technological advances in the area of UAV have been impressive, and this leads to an increased degree of automation of these systems [24]. In production systems, the majority of research has been focused on automated guided vehicles (AGVs) [25], mobile robot routing [26; 27; 28; 29; 30; 31; 32; 33; 34], or six degrees of freedom aerial robots [35], which are UAVs.
## 2 Related Works
### Packet Leashes  
In [9], [PERSON] _et al_. introduced a packet leashes method to restrict the time that packets can be transferred. They propose the TIK protocol based on _TESLA_[13] and use _temporal leashes_ to determine the wormhole attack by transmission time. Consequently, TIK requires precisely synchronized time among the nodes. In addition, TIK combines hash tree authentication to ensure the time information in the control packet is not modified. Therefore, the receiver can confirm if the packet transmission distance satisfies the restriction that sender has claimed. The TIK packet is transmitted by \(S\) as:  
\[S\to R\text{:}<\text{HMAC}_{K_{i}}(M)\text{, }M\text{, }T\text{, }K_{i}>\]  
Where:  
\[\text{HMAC}_{K_{i}}(M)\text{:}\text{HMAC for verifying the content;}\]  
\[M\text{:}\text{Plaintext;}\]  
\[T\text{:}\text{Values needed for authentications;}\]  
\[K_{i}\text{:}\text{The key for time interval }T_{i}\text{:}\text{\sim}T_{i}\text{.}\]Before the sender sends a packet \(P\), it estimates an upper bound \(t_{r}\) on the arrival time of the HMAC at the receiver. Then it picks a key \(K_{i}\) which will not expire before the receiver gets the packet's HMAC, i.e., \(T_{i}>t_{r}+\Delta\), where \(\Delta\) is the synchronized error between sender and receiver. Next, the sender computes the HMAC\({}_{K_{i}}\)(\(M\)) with \(K_{i}\) and attaches the HMAC to the packet. The sender then discloses the key \(K_{i}\) after the key has expired. After the receiver obtains the HMAC, it first checks if the key is expired. If the sender has not sent the corresponding key \(K_{i}\), the key is available. The receiver later uses the hash tree root \(m\) and the hash tree value \(T\) to verify the \(K_{i}\) at the end of the packet authentication, then it uses the authenticated \(K_{i}\) to verify the HMAC value in the packet. If all these verifications are correct, the packet is accepted as authenticated.  
However, the assumptions of TIK are impractical. It depends on precisely synchronized time between all nodes and assumes the packet sending and receiving delays are negligible. The wormhole is discovered because it passes packets more slowly than normal routes. Furthermore, knowledge of the positions of all nodes may be a prerequisite for correctly estimating transmission times.
# Molecular Science of Lubricant Additives +
## 1 Introduction
### Category of Lubricant Additives
#### 1.6.1 Working Function Category  
This is an upgraded categorization of existing methods, grouping \"substances by their role\" into large groups. All additives belong to one of the following groups.  
* Tribo-improvers (tribology-improving additives) directly contribute to improve the tribological performances of the lubricant. In short, they are responsible for the primary role of lubricants (see Section 1.2). Friction modifiers (FM), anti-wear agents (AW), extreme pressure additives (EP) are representative ones. They stand at a central position in the technology of lubricant additives.
* Rheo-improvers (rheology-improving additives) concern the fluidity of the base oil. Viscosity modifiers (VM) are the main additives in this group. Pour point depressants (PPD) make the base oil applicable in a chilly environment. They indirectly contribute to the lubrication performances, mainly in hydrodynamic regime.
* Maintainers help to keep the substances (both lubricant and materials of machine elements) in good condition through preventing the degradation of substances participating in the lubrication system. They mainly contribute to prolonging the lifetime of the lubrication system and partly contribute to the lubrication performances in some cases. Antioxidants (AO) play a decisive role in preventing the ageing process of lubricants. Detergents and dispersants can mitigate the negative influences of contaminants on lubrication. Corrosion inhibitors (rust preventives) can protect the tribo-materials from corrosion. Air bubbles may be incorporated in lubricants during machine operation. They cause lubricant starvation at the contact and promote the autoxidation processes. Anti-foam agents (foam breaking agents, defoamers) can break bubbles. Water is a ubiquitous contaminant in most applications. It drops the viscosity of the lubricant and causes ageing of both lubricants and materials. Demulsifiers (emulsion breaking agents) are beneficial for separating contaminated water in a lubricant.
* Auxiliaries are sometimes incorporated with specific purpose in addition to above additives.
## 5 Simulation Experiments
### Simulation Results
#### 5.2.2 SD-EAR versus SD-WSN1, SD-WSN2 and SD-WSN3  
SD-WSN1 applies either a proactive or reactive routing policy depending upon the total number of nodes in the network. If the number of nodes is low, then proactive routing is applied, where the identifiers of successors in optimum paths to various destinations are stored in routing tables. This does not require route discovery before each communication session, and saves energy by eliminating the broadcast operation during route discovery when the number of nodes is low [12]. However, when the number of nodes becomes high, routing table storage becomes inefficient; also, keeping track of stale routes is difficult. In that case, it applies reactive routing and the corresponding protocol is ad hoc on demand distance vector routing, or AODV. Among the various paths through which the route-request arrives at its destination from the source, the path with the least number of hops is elected as optimal. No energy efficiency is applied during optimum route selection. Also, there is no provision of giving rest to exhausted nodes. SD-WSN1 is capable of avoiding the broadcasting of route-request only when number of nodes is low enough.  
Figure 20: PALC vs. number of nodes.  
SD-WSN2 [20] is concerned with only the optimum placement of energy transmitters. Energy transmitters provide energy to the nodes when they suffer from a shortage of charge. Please note that the recharging of nodes is less frequent than the initiation of new communication sessions. SD-WSN3 [21] selects a few control nodes among all of the nodes in the network. These nodes assign task to others, depending upon the residual energy. However, here, the authors want to humbly state that residual energy is not the only criteria. A node \(n_{i}\) may have higher residual energy (say for example, 50 mj) than another node \(n_{j}\) (30 mj), but this does not mean that \(n_{i}\) will live longer. It depends on the energy depletion rate or packet-forwarding load. If we assume that the energy depletion rate of \(n_{i}\) is 10 mj/s and that of \(n_{j}\) is 2 mj/s, then the residual lifetime of \(n_{i}\) and \(n_{j}\) are 5 s and 15 s, respectively.  
SD-EAR considers different aspects of energy efficiency, such as residual energy, the energy depletion rate, the distance between consecutive nodes in a route, the number of sleeping nodes, and their alternatives. All of these criteria enable the establishment of long-lasting routes and the requirement of route rediscovery reduces up to a greater extent than SD-WSN1, SD-WSN2, and SD-WSN3. SD-EAR converts the broadcasting of a route-request to multicasting opt-route-select to only the peripheral node group whose member cardinality is generally much smaller than the total number of nodes in a zone. Therefore, SD-EAR suffers from much less message cost than its state-of-the-art software defined competitors, as seen in Figure 16.
## 3 Results
### Results of Pearson Correlation Analysis  
In this investigation, Pearson correlation analysis was carried out using IBM SPSS Statistics 26 software to establish relationships among Softscape, Hardscape, NDVI, and CN, as detailed in Table 8. This analysis aimed to validate the earlier conclusions. The results of the correlation analysis revealed the following relationships:  
* NDVI and CN exhibited a strong positive correlation, with a coefficient of 0.965 and \(p\) < 0.01, implying that CN increases in tandem with NDVI growth.
* NDVI and Hardscape were inversely correlated, with a coefficient of \(-\)0.88 and \(p\) < 0.05, indicating that Hardscape proportion decreases as NDVI rises.
* NDVI and Softscape were highly correlated, with a coefficient of 0.994 and \(p\) < 0.05, revealing that Softscape proportion increases with NDVI enhancement.
* CN and Hardscape were inversely related, with a coefficient of \(-\)0.903 and \(p\) < 0.05, illustrating that Hardscape proportion negatively influences CN.
* CN and Softscape were positively correlated, with a coefficient of 0.967 and \(p\) < 0.01, signifying that CN is positively associated with Softscape.
* Hardscape and Softscape were inversely correlated, with a coefficient of \(-\)0.912 and \(p\) < 0.05, indicating that higher Softscape percentages lead to reduced Hardscape proportions.
# Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter
## 1 Introduction
The idea behind the use of the EKF to train the ANN is that other training algorithms, such as gradient descent, recursive least squares and backpropagation, are particular cases of the Kalman filter; for this reason, the EKF is suitable for training [12; 13]. When using the EKF for training, the weights of the neural network are the states that the filter estimates, and the output of the neural network is the measurement used by the Kalman filter. Then, the training of the ANN can be seen as an optimal filtering problem. The EKF has been successfully applied to estimate parameters of an ANN in [14; 15; 16]. Although several PID tuning algorithms have been proposed, those approaches are mostly offline applications [17; 18] or simulation-only experiments [19]. The main contribution of this work is the control of both position and orientation of a quadrotor in real-time experimental tests using direct control; i.e., the output of the neural network is the control action for the UAV. The neural networks are trained online in order to adapt to changes in the dynamics and uncertainties. The objective is to find a robust solution to real applications [20] in which UAVs are capable of grabbing or deliver objects. This approach also uses a solution for the SLAM problem, to control the position using only onboard sensors, making it able to fly in unknown environments. The remainder of this paper is organized as follows: in Section 2, some previous and related works are presented. The dynamic model of the platform used is described in Section 3. Section 4 presents the architecture of the neural network trained with EKF. In Section 5, the algorithm of localization is described. The quadrotor control scheme is shown in Section 6. Finally, simulation and experimental results are shown in Section 7. The conclusions of this work are discussed in Section 8.
# Constraints of Using Conductive Screen-Printing for Chipless RFID Tags with Enhanced RCS Response
## 4 Conductive Screen-Printing of the Tags with Enhanced RCS Response  
As mentioned above, all tags were re-designed for a Folanorm polyester film dielectric substrate with relative permitivity \(\varepsilon_{\mathrm{r}}\) = 4, tan \(\delta\) = 0.005, and with the thickness of 125 \(\upmu\)m. The same geometry without size optimization was printed on a standard 200 g/m\({}^{2}\) quarto paper substrate with thickness 100 \(\upmu\)m, relative permitivity \(\varepsilon_{\mathrm{r}}\) = 2.2, and tan \(\delta\) = 0.03. A comparison of the measured results can be seen in Section 5.  
The screen-printing of the conductive parts was performed by ESL Electroscience polymer silver conductor paste ESL 1901-S with thickness 30 \(\upmu\)m. An Aurel C880 (Aurel s.p.a., Modigliana, Italy), a semi-automatic printer, was used. After screen-printing and curing, the silver conductor remains reasonably flexible and the resistance of the conductor remains constant over time. The paste resistivity is lower than 20 m\(\Omega\)/sq measured on a 100 mm \(\times\) 0.25 mm conductor track. In order to ensure a sufficient quality of the metallic surfaces, screen-printing of the conductive paste was repeated three times. The paste was then cured at a temperature of 80 \({}^{\circ}\)C for 30 min.