esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Kinematic Directional Index for Industrial Serial Manipulators
## 6 Conclusions  
A novel performance index called the KDI has been proposed. This index is not affected by the non-homogeneous Jacobian matrix and does not depend on the choice of the units of measurement and on the position of the absolute reference frame. The index was computed for a SCARA robot and an articulated robot to analyze the behavior of the manipulators for two main purposes: finding the direction of maximum velocity with respect to a point, and finding the area of maximum velocity with respect to a direction of interest in the workspace. Being a fully kinematic index (i.e., it does not consider the dynamics and the control of the robots), it gives a high-quality description of the robot's behavior, and the experimental tests demonstrate the effectiveness of the proposed index.  
Future works can be addressed by following some steps: first, the use of the index for redundant manipulators will be investigated; second, the same reasoning will be followed to synthesize a dynamic performance index that is able to consider the direction of motion; afterwards, the use of these indexes for other issues, such as the optimal design of manipulators and/or robot trajectory planning, will be investigated.  
This research received no external funding.  
The author declares no conflict of interest.
## 2 Digital Platform--DNS with SWAT Module
### Land Phase
#### 2.1.1 Surface Runoff  
Accurate determination of surface runoff is critical in estimating sediment transport in the land phase of the basin [90; 91]. The SWAT module helps to estimate surface runoff, taking into account the related transport of organic and inorganic pollutants [92]. In this tool, it is possible to choose one of two methods of estimating this phenomenon: the Green and Ampt infiltration method [93; 94], and the Soil Conservation Service Curve Number (SCS-CN) procedure [95]. The physical method of Green and Ampt assumes a sharp wetting front during infiltration with saturated soil above the unsaturated zone [96]. Nevertheless, the popularity of this method is much lower due to the required high time resolution of the input data on precipitation (time step less than 24 h) [97; 98]. The SCS-CN empirical model (2 and 3), which is mainly based on soil properties, land use, and hydrological conditions, is widely used for surface runoff estimation with daily precipitation as input.  
\[Q_{surf}=\frac{\left(R_{day}-0.25\right)^{2}}{\left(R_{day}+0.8S\right)} \tag{2}\]  
where: \(Q_{surf}\) is the daily surface runoff (mm), _Rday_ is the rainfall depth for the day (mm), and \(S\) is the retention parameter (mm). The retention parameter \(S\) and the prediction of lateral flow by SWAT model are defined in Equation (3):  
\[S=25.4\,\left(\frac{1000}{CN}-10\right) \tag{3}\]  
where: \(S\) is the drainable volume of soil water per unit area of saturated thickness (mm/day), and \(CN\) is the curve number.  
However, confidence in SCS-CN results should be limited [99; 100] because this method causes problems with underestimating peak flows in relation to monitoring data (e.g., [84; 101]). One of the causes is that the constant initial abstraction is used as an intrinsic parameter, which may result in inconsistency of the soil moisture accounting. In addition, the duration and intensity of rainfall are not considered; instead, the average daily rainfall is used as a SWAT input. In fact, high-intensity and even short-duration rainfall could generate more sediment than actualized in the model based on daily rainfall [86]. Therefore, after selecting this method, it is important to perform, in addition to the statistical evaluation of the model, a visual evaluation of the results, allowing a more intuitive way to characterize the model's ability to recreate the characteristics of the flow regime for a given basin area, especially floods and low flows. The few available studies [97] generally suggest that the Green and Ampt method works better for stream flow and avoids the limitations of the SCS method. The requirements for high-resolution precipitation data can be met by precipitation atlases containing accurate data from many years with a step for even every 10 min, which has already been prepared and described for the all territory of Poland by [102; 103].
# A Novel Impact Rotary-Linear Motor Based on Decomposed Screw-Type Motion of Piezoelectric Actuator
## 3 Fabrication of the Prototype Motor  
The overall structure diagram of the piezoelectric motor and the fabricated piezoelectric motor are shown in Figure 3. The prototype motor consists of a PA, a pedestal, a coupler, a snap ring, a spring, a special bolt, a shaft, and a pair of bearings. The PA is made from the lead zirconate titanate (PZT)-type piezoelectric tube (YT-SL, Baoding Yitian Ultrasonic Technology co., LTD, Baoding, China) The key material parameters of the PA are shown in Table 1.  
The length of the piezoelectric tube is 15 mm, the outer radius is 3 mm, and the inner radius is 2.5 mm. The effective length of the surface helical interdigitated electrodes of the PA is 9 mm, the angle of electrodes is 30\({}^{\circ}\), and the intensity of the polarizing electric field is 2 kV/mm. The PA can produce screw-type motion under saw-shaped driving voltage. One end of the PA is glued to the pedestal by epoxy resin. The inner surface of the coupler is in contact with the shaft, the cone-shaped end of the coupler is tangent to the inner surface of the PA, and the outer cylindrical surface of the coupler is fixed by the snap ring. The coupler is made up of a pair of bushings with alumina ceramic. The snap ring can be changed to different dimensions so that the radial prepressure between the coupler and shaft can be adjusted according to different motions. By adjusting the position of the special bolt, the length of the spring will be changed, thus the axial prepressure between the coupler and PA can be changed. The two different kinds of prepressure will provide different friction force or friction torque between the contact surfaces of the contact objects when there is a trend of relative movement among them. A pair of bearings is used to support and guide the motions of the shaft. The shaft has a diameter of 2 mm with a length of 100 mm and it is made by zirconia ceramic. The fabricated prototype motor has a maximum diameter of 15 mm with a length of 100 mm.
# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
###### Abstract  
Due to the limitations of drones and satellites to obtain aerial images of the crops in real time, the time to flight delay, the problems caused by adverse weather conditions and other issues, the use of fixed cameras placed on the regions of interest is essential to get closer, periodic and on-demand images. Water management in agriculture is one of the most important applications of these images. Top view images of a crop can be processed for determining the percentage of green cover (PGC), and 2D images from different viewing angles can be applied for obtaining 3D models of the crops. In both cases, the obtained data can be managed for calculating several parameters such as crop evapotranspiration, water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant. For this purpose, a remote image capture system has been developed for an application in lettuce crops. The system consists of several capture nodes and a local processing base station which includes image processing algorithms to obtain key features for decision-making in irrigation and harvesting strategies. Placing multiple image capture nodes allows obtaining different observation zones that are representative of the entire crop. The nodes have been designed to have autonomous power supply and wireless connection with the base station. This station carries out irrigation and harvesting decisions using the results of the processing of the images captured by the nodes and the information of other local sensors. The wireless connection is made using the ZigBee communication architecture, supported by XBee hardware. The two main benefits of this choice are its low energy consumption and the long range of the connection.  
image capture system; lettuce; irrigation management; wireless; ZigBee and XBee +
Footnote †: journal:
# Embedding Security Awareness in IoT Systems: A Framework for Providing Change Impact Insights
## 4 Experiment and Result
The survey emphasizes that a single mitigation strategy is unlikely to be effective across all categories--nor is a fixed strategy consistently effective even within a specific category. For instance, volumetric attacks may be mitigated using techniques such as flow filtering, rate limiting, or request prioritization. Flow filtering aims to block traffic identified as malicious, rate limiting imposes caps on traffic volume, and request prioritization allows service only to requests from highly trusted sources. Figure 10: LIME explanation of DDoS_HTTP attack scenario (**top**) and corresponding generated report (partial) in JSON format (**bottom**). Consider the previously discussed _DDoS_UDP_ attack, which floods the network with UDP packets to exhaust bandwidth. In this scenario, both flow filtering and rate limiting can be effective, while request prioritization is less suitable due to the difficulty and overhead of assessing the reliability of sensor-generated UDP packets, potentially introducing new vulnerabilities. Between the two effective techniques, choosing flow filtering could risk disrupting critical system operations by inadvertently blocking essential data from certain components. In contrast, rate limiting allows continued operation, albeit with possible exposure to noisy or malicious traffic. On the other hand, flow filtering provides better data integrity by eliminating unwanted traffic more precisely. Therefore, selecting an appropriate mitigation strategy depends heavily on contextual awareness and the specific security and operational requirements of the system. Understanding these contextual and security factors is essential for strategizing effective mitigation. Our approach to extracting such insights plays a valuable role in supporting this strategy development--whether implemented autonomously or used by a human system administrator.
## 5 Results and Discussion
### Cluster Analysis
#### 5.1.1 Latent Profile Cluster Identification
According to the observed variable scores and the value of latent variables, we named the three clusters as follows:  
Cluster 1 (4.7% of the sample): \"Negative individuals\". The smallest cluster had the lowest average of all five latent variables in comparison to the other two clusters, indicating little understanding of CAVs, suspicion of CAV performance, and the most passive attitude towards sharing the same road as CAVs. In addition, this cluster had poor risk awareness and confidence when driving on a road of HDVs mixed with CAVs. Cluster 2 (58.1% of the sample): \"Neutral individuals\". This cluster can be regarded as neutral-minded and conservative. Even though they had a neutral outlook in relation to familiarity, trust, attitude and perceived behavior control, they had relatively strong risk awareness when sharing the same road as CAVs. Cluster 3 (37.2% of the sample): \"Positive individuals\". This cluster had the highest familiarity and trust in CAVs, and had the most optimistic attitude towards sharing the same road as CAVs. In addition, this cluster had the strongest risk awareness and confidence when driving on the road mixed with CAVs. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clusters**} & \multirow{2}{*}{**AIC**} & \multirow{2}{*}{**BIC**} & \multirow{2}{*}{**aBIC**} & \multirow{2}{*}{**Entropy**} & \multirow{2}{*}{**LMR**} & \multicolumn{2}{c}{**BLRT**} & \multicolumn{2}{c}{**Proportion of**} \\  & & & & & & & & **the Respective Cluster \%** \\ \hline
1 & 15,911.66 & 16,034.098 & 15,932.589 & & & & 100 \\
2 & 14,742.964 & 14,930.438 & 14,775.001 & 0.892 & 0.0003 & 2 \(>\) 1 & 0 & 54.6/45.4 \\
3 & 14,166.234 & 14,418.75 & 14,209.387 & 0.925 & 0.0444 & 3 \(>\) 2 & 0 & 4.7/58.1/37.2 \\
4 & 13,920.392 & 14,237.95 & 13,974.66 & 0.9 & 0.1806 & 4 \(<\) 3 & 0 & 4.4/36.3/43.7/15.6 \\
5 & 13,752.607 & 14,135.207 & 13,817.99 & 0.915 & 0.1389 & 5 \(<\) 4 & 0 & 3.8/43.4/11.8/34.2/6.8 \\
6 & 13,608.63 & 14,056.272 & 13,685.128 & 0.928 & 0.1758 & 6 \(<\) 5 & 0 & 2.6/2.4/42.8/33.9/11.5/6.8 \\ \hline \hline \end{tabular} Note. AIC = Akaike Information Criterion, BIC = Bayesian Information Criterion, LMR = Lo-Mendell-Rubin likelihood ratio test, BLRT = Bootstrapped Likelihood Ratio Test. Entropy, LMR, B and LRT are not available for models with only one cluster.  
\end{table} 
Table 6: Model fit based on the number of clusters. Figure 1: Observed variable scores of different latent classes.
## 2 Research Methodology
### Autoencoder Architecture  
An AE is an unsupervised feedforward neural network designed to match its output values with its input values through repeated application of backpropagation to minimize  
Figure 1: Damage detection methodology.  
the reconstruction error between the input and output [10]. A typical AE consists of three parts: an input layer, one or more hidden layers, and an output (reconstructed input) layer. The hidden layer is composed of two key components: the encoder and the decoder. The encoder at the hidden layer compresses the input data into a lower-dimensional space by extracting and encoding its most significant features. The decoder then reconstructs the original input from this compressed representation using various reconstruction techniques, ensuring the generated outputs closely resemble the inputs. To facilitate accurate data reconstruction, the output layer is the same size as the input layer. This ensures that the algorithm learns the compressed representations used to approximate input data and not just the identity function which remembers the original input.  
Figure 2 presents the typical architecture of an undercomplete AE, that features a single hidden layer with fewer neurons than the input layer (bottleneck), forcing the AE to learn a compressed representation of the input data. The encoder \(f(x)\), transforming an \(n\)-dimensional input \(x\in\Re^{n}\) to an \(r\)-dimensional representation \(h\in\Re^{r}\) (\(r<n\)), at the hidden layer, capturing the most important features of the input data, can be expressed as follows:  
\[f(x)=h=\Phi(Wx+b) \tag{2}\]  
where \(\Phi\) denotes the activation function; \(W\in\Re^{r\times n}\) is the weight matrix of the encoder; and \(b\in\Re^{r}\) is the bias vector for the input. The activation function is usually selected as a nonlinear function in the form of a sigmoid or hyperbolic tangent function.  
The decoder \(g(h)\), transforming the compressed latent representation back into reconstructed vector \(z\in\Re^{n}\), can be expressed as follows:  
\[g(h)=g(f(x))=z=\Phi\Big{(}\hat{W}h+\hat{b}\Big{)} \tag{3}\]  
where \(\hat{W}\in\Re^{n\times r}\) is the weight matrix of the decoder and \(\hat{b}\in\Re^{n}\) is the bias vector for the hidden layer.  
During training, the AE is designed to learn a compact representation in the hidden layer by minimizing the difference between the original input and its reconstructed version using a mean squared error (MSE) loss function:  
\[L(x,z)=\left\|x-z\right\|_{2}^{2} \tag{4}\]  
Figure 2: Architecture of a traditional undercomplete AE.  
If the input is completely random, the compression task becomes challenging, requiring a large number of hidden units. To address this, a sparsity constraint is often imposed on the activations in the hidden layer. This encourages the AE to produce a more compact and sparse representation of the input data.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
### Background Work and Limitations  
Several studies propose to distribute state information by utilizing extended versions of existing routing protocols, such as Open Shortest Path First (OSPF) [27; 28]. However,these studies overlook the challenges and costs of acquiring accurate state information [29]. The precision of state information depends on the frequency of updates, and frequent updates by numerous nodes as the network scales can result in substantial overhead [12].  
The distributed multicast routing algorithm can be categorized into two distinct types: source-driven and receiver-driven path discovery. Numerous algorithms conduct indiscriminate exploration operations when selecting joining nodes for multicast services. Source-driven approaches may explore all nodes along the multicast tree to calculate appropriate joining nodes. Similarly, receiver-driven methodologies may explore a plethora of paths from the receivers to multicast tree nodes. Such practices result in intricate message processing overhead, consequently elevating the complexity of multicast tree construction.  
Many algorithms assume that the set of group members is deterministic at the beginning of the algorithm. However, this aligns differently from many real-world scenarios, where group members often generate and join multicast groups randomly, making it impossible to pre-determine the set of group member nodes. In addition, some algorithms may not handle dynamic changes in group membership effectively. Although some algorithms support dynamic member joining and leaving, they may incur significant message processing overhead when handling requests.
# Study on the Ozone Gas Sensing Properties of rf-Sputtered Al-Doped NiO Films
## 2 Materials and Methods
### Characterization  
The structure of the Al-doped NiO films was examined by the X-ray Diffraction (XRD) technique, using a PANalyticalEmpyrean diffractometer equipped with Cu-LFF as an X-Ray source, at \(\uplambda\) = 0.15406 nm. The measurement mode was 20/\(\theta\) varying from 20\({}^{\circ}\) to 90\({}^{\circ}\), with a step of 0.013\({}^{\circ}\)/s. Using the XRD pattern the crystallite size (D) of the films was calculated according to Scherrer's Equation (1)  
\[\text{D(nm)}=(0.9\uplambda)/(\text{B-cos}\theta) \tag{1}\]  
where \(\uplambda\) is the X-ray wavelength which equals to 0.154 nm. B is the Full Width at Half Maximum (FWHM) of the corresponding peak at an angle 2\(\theta\). Moreover, the lattice constant (a\({}_{0}\)) for the cubic structure of NiO was calculated according to Equation (2)  
\[\text{a}=(\text{h}^{2}+\text{k}^{2}+\ell^{2})^{1/2}/\text{d} \tag{2}\]  
where d is the distance between the adjacent planes in the set (hk\(\ell\)). Field Emission Surface Electron Microscopy (FESEM) was used to investigate the surface morphology of the films using a Hitachi S570 microscope, equipped with an Energy Dispersive X-ray (EDX) spectrometry system, which was employed to determine the Al at% of the NiO:Al films. Optical properties of the films were studied with a Perkin Elmer Lambda 950 UV/Vis/NIR spectrophotometer in the wavelength range of 250-2500 nm. The transmittance spectra and the corresponding Tauc plot were used in order to calculate the optical energy bandgap of the films assuming that direct transitions are permitted according to Equation (3)  
\[(\upalpha\text{hv})=\text{A}\ (\text{hv}-\text{E}_{\text{g}})^{1/2} \tag{3}\]where \(\alpha\) is the absorption coefficient, hv the photon energy, A is a constant and \(\rm{E_{g}}\) the optical energy bandgap. The thickness of the films was measured by using a Veeco Dektak 150 stylus profilometer.
## 1 Introduction
### Related Work
The following is a comparative summary of these regularization methods:  
* Traditional TV regularization: Strengths: Good at preserving edges by promoting sparsity in the first derivative of the image. It is well-studied and understood within the community [21]. Weaknesses: Can lead to the'staircasing' effect, where smooth transitions are turned into piecewise constant regions. This effect is undesirable in EIT, where smooth conductivity changes are common [22]. * Higher-order TV regularization (HOTV): Strengths: Addresses some limitations of traditional TV, such as staircasing, by considering higher-order derivatives [23]. Weaknesses: May not be as effective at preserving fine details as second-order TV, and the selection of regularization parameters becomes more complex [19]. Strengths: Specifically designed to overcome staircasing by incorporating second-order derivatives. Balances the preservation of edges and smooth regions better than traditional TV, particularly in EIT, where conductivity distributions can have complex structures [24]. Can be more robust to noise and data inconsistencies due to its higher-order nature. So, we wanted to propose a method designed to overcome staircasing by incorporating second-order derivatives and that balances the preservation of edges and smooth regions better than traditional TV, particularly in EIT, where conductivity distributions can have complex structures.
# Interference Management in Femtocells by the Adaptive Network Sensing Power Control Technique
## 4 Power Control Techniques  
### FUE-Assisted Power Control Technique  
In a real scenario, the HeNB instructs all the FUE to measure the received signal power from the neighboring interferer BS and send a feedback report [25]. The femtocell uses the information  
Figure 1: Interference scenario in the Primary and Secondary System.  
received from the FUE and makes some necessary adjustments in downlink transmission power. The FUE-assisted power can be expressed as:  
\[P_{FUE_{Assist.}}=\max(P_{min},\ \min(P_{0},P_{max})) \tag{4}\]  
In (4), \(P_{min}\) is the minimum transmission power and \(P_{max}\) is the maximum transmission power of the femtocell.  
\[P_{0}=\max(I_{FUE_{i}}+PL_{FBS\to FUE_{i}})+\varepsilon_{0} \tag{5}\]  
where \(\varepsilon_{0}\) is set to maintain the necessary QoS of femto users in (5).
## 3 Transient Voltage Stability Boundary
### Stability Boundary Based on Voltage Magnitude and Sag Duration  
As can be seen from Figure 3b, if the slip \(s\) exceeds \(s_{crit}\) during the voltage sag, the induction motor will stall. Assuming that the critical sag duration is \(t_{crit}\) when the voltage drops to \(U_{sqg}\), the induction motor will remain stable if the sag duration \(t_{sqg}\) is less than \(t_{crit}\), and the motor will stall if \(t_{sqg}\) is larger than \(t_{crit}\). The expression of \(t_{crit}\) is Equation (14).  
\[t_{crit}=\int_{s_{0}}^{s_{crit}}\frac{2H}{T_{l}(s)-T_{e}(U_{sqg},s)}ds \tag{14}\]  
On the \(U\)-\(t\) plane, the transient voltage stability boundary is drawn, as Figure 4 shows. The stability boundary is the curve of \(t_{crit}\) versus \(U_{sqg}\). The voltage magnitude \(U_{sqg}\) and the sag duration \(t_{sqg}\) can be used to determine a point (\(t_{sqg}\), \(U_{sqg}\)) on the \(U\)-\(t\) plane. If the point is located in the region above the stability boundary, the induction motor can operate stably. On the contrary, if the point is located in the region below the stability boundary, the induction motor will stall. As Figure 4 shows, the stability boundary starts from point (\(t_{safe}\), 0) and approaches \(U_{safe}\) infinitely with the increase of \(t_{sqg}\).