esa-sceva/satcom-chunk-collection huggingface dataset:
# Envisat RA-2 Individual Echoes: A Unique Dataset for a Better Understanding of Inland Water
## 4 Selected Case Studies  
In this section, we describe real-world water bodies from three different locations, and we check their behavior with respect to our model expectations. Specular features are expected to be very common in unaveraged raw waveforms from inland altimetry, and most often associated with small size lakes and rivers. We examined \(\sim\)30 locations world-wide where the IE data base had multiple altimeter passes over water bodies, usually \(<\)300 m in extent. Three locations were chosen as representative cases for this paper, but most of the other cases were similarly specular. Case studies from very different contexts have been chosen, in order to show how this deduction can be generalized.  
The first case study is the 45-65 m wide Rio Nahuapa (Peru), which provides 3 crossings of the satellite track within the same IE packet, offering a simple and straightforward proof of specular water reflection in agreement with model calculations. The width of the RCS (radar cross-section) main lobe varies with the width of the river. The relative levels of side lobes with respect to the main lobe also change with the river width. Second is the 150 m wide River Semliki (Congo), useful to extend the proof of the specular model to a wider river. In addition, in this case, data are consistent with the model prediction. Third is the Prek Toal flood area (Cambodia), which approximates an \"infinitely\" wide reflecting surface. At maximum flood the water extent is \(>\)100 km, which for the purpose of the model calculation can be thought as \"infinite\". This is the opposite of point specular targets that produce the hyperbola radargram. In the \"infinite\" case, the theory predicts that all the echo power is in the first contact range bin. The data are consistent with this expectation.
## 4 Experimental Results  
### Movement Data Experiment  
The validation experiment successfully identified the touch input location with high accuracy (99%) using the cross-validated movement dataset, similar to the results reported by [9]. The confusion matrix is shown in Figure 9 (correct classification and misclassification indicated in percentage).  
To better understand the contribution of the various movement data to the performance of the model, the relative importance of each feature was identified. The weightage of these features contributing to touch input location classification are listed in Table 1, with rotation vector being the most important.  
Figure 8: Overhead view of mobile phone placement showing nine touch input locations with horizontal (**left**) and vertical (**right**) orientation lying on a flat surface.  
The rotation vector can be understood intuitively as the changes in pitch, roll and yaw of the device when a force is applied on different touch input location of the touchscreen. The rotation vector is represented by the angle of rotation around an axis which align the device's to the current orientation. A force applied near the edges of the device changes its orientation which is recorded as an angle along each axis relative to the reference device orientation. The rotation along all three axes is mapped to a specific region which can then be used alongside the other sensor features together, thus accurately predicting touch input location.
# A Conceptual Design Methodology for e-VTOL Aircraft for Urban Air Mobility
## 5 Preliminary Design of a Tilt-Wing Air Taxi with Box-Wing Architecture
### Potential TiltOne Configurations  
In this section, we discuss the main outcomes of the sensitivity analyses described in the previous paragraphs (Section 4.2 and Section 5.1). In particular, since this is a conceptual study aimed to identify the potential of an innovative aircraft such as the _TiltOne_, the main output is not represented by a detailed design of the aircraft. On the contrary, the main outcome is a critical analysis of the potential ways to best exploit this concept, thus providing the basis for the subsequent detailed development. It is worth highlighting that the results hereafter discussed are related to the technological level of the electrical powertrain envisaged within 2035.  
In Section 4.2 and Section 5.1, two conceptually different ways of setting the requirement for the _TiltOne_ have been outlined: the \"single mission\" case, based on a single design mission at the maximum payload-range combination, and the \"multi mission\" case, which is based on several rides covering shorter distances (without battery recharge). The sensitivity study performing varying _W/S_, _T/A_, and requirements, has led to the identification of several feasible _TiltOne_ configurations. In this section, only two configurations, developed from the two different sets of requirements, are selected, and their main performance and features are compared, highlighting their potential advantages and drawbacks.  
The two selected configurations are those indicated by the green and orange points in Figure 19, whose charts are taken from Figures 15 and 17. In particular:  
* the green point in Figure 19a is the solution selected for the \"single mission\" case. While fulfilling the constraints, it minimizes the disk loading, hence providing the lowest noise level. This criterion has been adopted as the main one in the selection of the configuration, since all the points on the lower frontier of the design space have similar MTOW values;
* the orange point in Figure 19b is the solution for the \"multi mission\" case. This configuration maximises the number of short rides and minimizes disk loading and noise.
# Development and Validation of a Capacitor-Current Circuit Model for Evaporation-Induced Electricity
## 4 Results and Discussion
### Model Fitting Results
(**a**) The time constant (\(\tau\)) increases with capacitance (\(C\)) for different internal resistance values (\(R_{\text{int}}\)). Higher capacitance and internal resistance result in longer time constants, indicating better charge retention. (**b**) The steady-state current (\(I_{\text{steady}}\)) decreases with increasing internal resistance (\(R_{\text{int}}\)). Lower resistance values lead to higher steady-state currents, demonstrating the importance of minimizing resistance for device performance. layer thickness to achieve balance between charge storage and efficient ion transport. And to decrease internal resistance, we need to use materials with high ionic conductivity, facilitating ion transport by having low resistant channel structures. One may notice the conflict about the materials structure to enable the high capacitance and efficient ion transport. We believe this is the field where composite and hybrid layered structures could make great contributions in the future to push forward the evaporation-induced electricity. The capacitor-current circuit model has significant potential for practical applications. In the field of energy harvesting, the model can guide the design of evaporation- and moisture-induced electricity generation devices by predicting performance metrics such as steady-state current and charge retention. These predictions help to optimize material properties, such as surface chemistry and microchannel dimensions, to achieve higher efficiency. In addition, the model can support the development of self-powered devices, including sensors and wearable electronics, where energy efficiency and stability are paramount. By simulating different configurations, the model provides valuable insights into material selection and device structure, enabling the creation of robust and efficient systems. Future research could extend the applicability of the model to other types of energy conversion mechanisms, further broadening its utility in practical applications.
## 2 Methodology
### GPS/BDS-Combined Single-Frequency KOD Model
The GPS or BDS satellite clock containing the satellite UCD \(d_{1}^{s}\) on L1 frequency can be expressed as follows:\[\overline{t}^{s}=t^{s}_{IGS}-d^{s}_{IGS} \tag{3}\]  
where \(\overline{t}^{s}\) is the satellite clock offset used to calculate \(\overline{p}^{s}_{r}\) and \(\overline{g}^{s}_{r}\); \(t^{s}_{IGS}\) is the known IGS precise satellite clock offset; \(d^{s}_{IGS}\) is the correction term, which can be calculated with the known IGS DCB product according to the formula. \[d^{s}_{IGS}=\frac{f_{2}^{2}}{f_{1}^{2}-f_{2}^{2}}(d^{s}_{1}-d^{s}_{2}) \tag{4}\]  
where \(f_{1}\) and \(f_{2}\) are L1 and L2 frequencies, respectively, \(d^{s}_{1}\) and \(d^{s}_{2}\) are the satellite UCD on L1 and L2 frequencies, and \(d^{s}_{1}-d^{s}_{2}\) represents the DCB between L1 and L2 frequencies.
## 3 Methodology
### Data Pre-Processing
#### 3.2.2 Data Normalization  
The unprocessed sensor data is subsequently normalized to a range of 0 to 1. This normalization procedure resolves the model learning concern by guaranteeing that all data values fall within a comparable scope. As a result, gradient descent algorithms can converge more efficiently [26].  
\[X_{i}^{norm}\ =\ \frac{X_{i}-X_{i}^{min}}{x_{i}^{max}-x_{i}^{min}},\ \ \ i=1,2,\ldots,n \tag{2}\]  
\(X_{i}^{norm}\) denotes the normalized data, \(n\) signifies the number of channels, while \(x_{i}^{max}\) and \(x_{i}^{min}\) denote the highest and lowest points of the \(i\)-th channel, respectively.
## 2 Materials and Methods
### Study Selection Process and Data Analysis  
The main author performed the search. A database was created in a computer programme (Excel, Microsoft, San Francisco, CA, USA) in which each article found in the databases (PubMed, ScienceDirect and Web of Science) was included. This database had the name of the database, the article title, the authors, the journal name in which it was published, and the year of publication. Subsequently, duplicate articles were eliminated, and the title and abstract of the remaining documents were read. The full text was read to verify that the proposed eligibility criteria were met to judge the relevance of the article. From a methodological standpoint, studies were analysed using the _PEDro Scale_ with items described as low risk of bias, unclear risk of bias, or high risk of bias (see Figure 2). Two authors independently assessed the risk of bias. Figure 3 shows the evolution of publications of recent literature related to this review; it is noteworthy that there has been an increase in publications in the last few years.  
Figure 2: Methodological classification of studies used in the systematic review. A rating of 9–11 is considered excellent, a rating of 6–8 is good, a rating of 4–5 is acceptable, and a rating of \(\leq\)3 shows a lack of methodological quality [6, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32].
# Nonlinear Optimal Control Law of Autonomous Unmanned Surface Vessels
## 2 Mathematical Model and Design Objective
### Nonlinear Optimal Trajectory- and Waypoint-Tracking Problem of AUSVs  
Considering the nonlinear tracking-error dynamics in Equation (8), and given weighting matrices \(\mathbf{Q}_{2}\) and \(\mathbf{W}_{2}\), the design objective of this investigation was to find closed-form nonlinear optimal control law \(\mathbf{\tau}_{\eta 2}\) such that the following optimal performance (optimal performance) could be analytically achieved.  
\[J(\mathbf{u}_{2}{}^{*})=\underset{u_{2}}{\text{min}}J(\mathbf{u}_{2})= \underset{u_{2}}{\text{min}}\Big{[}\mathbf{e}^{T}(t_{f})\mathbf{Q}_{2} \mathbf{e}(t_{f})+\int_{0}^{t_{f}}\Big{[}\mathbf{e}^{T}(t)\mathbf{Q}_{2} \mathbf{e}(t)+\mathbf{u}_{2}^{T}(t)\mathbf{W}_{2}\mathbf{u}_{2}(t)\Big{]}dt \Big{]}=\mathbf{e}^{T}(0)\mathbf{P}_{2}(\mathbf{e}(0),0)\mathbf{e}(0) \tag{11}\]for all \(t_{f}\in[0,\infty]\) and \(\mathbf{Q}_{2f}=\mathbf{Q}_{2f}{}^{T}>0\).
# Robust and Accurate Recognition of Carriage Linear Array Images for Train Fault Detection
## 3 Methodology  
To tackle the recognition challenges of carriage linear array images, we propose a template matching-based image identification method. By exploiting the distinct correlation between the spatial arrangement of key carriage components and their categories, the high-resolution image recognition challenge was reframed as a sparse point set template matching task. Considering the nonlinear scale distortions in the horizontal dimension of these images, a weighted radial basis function was utilized for coordinate transformations during point set registration. This approach effectively addresses the template matching challenges, enhancing both accuracy and robustness. Before detailing the algorithm, it is essential to categorize the unique features of linear array carriage images.
## 4 Bounds on Excess Noise
### Threshold of Excess Noise  
In the previous section, we took advantage of the BS wavelength-dependent coupling ratio defect to correct the excess noise changes caused by Trojan horse attacks and the imperfection of BS. Assuming that the excess noise in the two methods are \(\epsilon_{A}\) (in shot noise units) and \(\epsilon_{B}\) respectively. It can be seen from Section 2 that the shared key between [PERSON] and [PERSON] must satisfy:  
\[\epsilon_{A}+\epsilon_{B}\leq\epsilon_{max}, \tag{28}\]  
where  
\[\widetilde{N_{0}}=N_{0}=0.5I_{lo}=0.5\times 10^{8}. \tag{29}\]  
According to Equation (26), the excess noise caused by the imperfection of BS can be expressed as:  
\[\epsilon_{B}=\left[(2+\epsilon_{0})N_{0}+(r_{1}+r_{2}-2)D^{2}/\eta T_{ch}+571. 9(r_{1}+r_{2})D/\eta T_{ch}\right]/\widetilde{N_{0}}. \tag{30}\]  
Bring specific values into the Equation (30), \(\epsilon_{B}\) is arbitrarily close to 0. Therefore, the bounds on excess noise caused by Trojan horse attacks are as follows:  
\[\epsilon_{A}\leq\epsilon_{max}. \tag{31}\]  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \(\lambda\) & 1545 & 1545.4 & 1546.2 & 1547 & 1547.6 & 1548.4 & 1548.6 & 1549.2 & 1546.4 & 1550 \\ \(T\) & 0.4936 & 0.4941 & 0.4951 & 0.4962 &  
Combined with the analysis in Section 3, when the Trojan horse pulses wavelength are equal to the original, the total excess noise should satisfy:  
\[\epsilon\leq\epsilon_{max}+\epsilon_{0}=0.0245. \tag{32}\]  
According to Table 1, the crosstalk level is set to \(\tau\leq 0.3\). When the Trojan horse pulses wavelengths are different from the original, the total excess noise should also satisfy Equation (32). Assuming fiber length \(L\) = 50 km, the bounds on input power \(P_{in}\) of Trojan horse pulses are as follows:  
\[P_{in}\leq 0.7 mw. \tag{33}\]  
We take this number as an example to quantify the boundary of excess noise caused by [PERSON]'s Trojan horse attacks. However, we can adopt a more conservative threshold for \(\epsilon_{max}\) to arrange different uses for applications that require stronger bound.
# Context-Based Energy Disaggregation in Smart Homes
## 5 Experimental Results
### Context-Based Disaggregation  
The experimentation has been composed of several phases. Firstly, each context conditioning has been singularly applied to the algorithm and the obtained results have been compared to those obtained in the work by [PERSON] and [PERSON] [9]. Secondly, disaggregation results have been evaluated considering both context information items. Each test has been performed by providing the system with the full-knowledge regarding each appliance that could compose the aggregated consumption trace (Table 4), _i.e._, including even those turned off. As mentioned above, the aggregate consumption trace has been composed synthetically by summing the daily traces of each single appliance. In order to create the test set, we combined each daily trace of a given appliance for a given day with all the daily traces of the other appliances. First, we describe how each single context-based conditioning approach operates.
## 2 Results and Discussion
### Profile of Volatile Compounds
Aldehydes in fermented dairy products derive from transamination and decarboxylation of amino acids, by Strecker degradation, or by lipid metabolism by microorganisms [15]. Similarly to diacetyl and acetoin, acetaldehyde is an important flavor compound in fermented milk beverages, resulting from pyruvate decarboxylation or via formation of the intermediate acetyl co-enzyme A by pyruvate formate lyase, or pyruvate dehydrogenase [12; 16; 29; 43]. Acetaldehyde may bring unique flavor of fermented beverages, when present in sufficient quantity [15]. Like in other reports, acetaldehyde content was scarce in both types of kefir, maybe due to its conversion to ethanol by alcohol dehydrogenase [12; 13; 29]. Among the four identified aldehydes, the relative abundance of 3-methylbutanal in the traditional kefir proved two times higher than in lactose-free kefir (Table 2). High content of 3-methylbutanal in kefir samples was also reported in another study [12]. This branched aldehyde is probably the product of the Strecker degradation of leucine [12]. 3-Methylbutanal may be related to malty and cheesy flavor of kefir [14] being, however, irrelevant because of its low relative abundance in both types of kefir (Table 2). Terpenes were represented by six compounds, the contents of some of them being significantly (\(p<0.05\)) higher in the traditional vs. lactose-free kefir; however, this was irrelevant regarding aroma profile. D-limonene, a monoterpene with citrus flavor, quantitatively dominated in both types of kefir, like in other studies on traditional kefir [14; 40]. The presence of \(\alpha\)-pinene and of \(\beta\)-pinene should be mentioned as these were also detected in regional cheeses [46; 47]. The terpen in kefir may originate from degradation and biosynthesis of monoterpenes and sesquiterpenes by lactic acid bacteria [14]. Terpenes are detected in milk and milk products because of their wide occurrence in plants [40]. According to [PERSON] et al. [47], the abundance of terpen in cheese may indicate that milk was derived from pasture-fed animals. Among the three unclassified volatile compounds, the presence of toluene may be mentioned, as its low content in kefir was reported by [PERSON] et al. [15]. Substantial amounts of toluene were detected in cheese samples, especially those derived from milk of summer season, as a product of caroten degradation in fresh grass [45; 46; 47]. Hydrocarbons may serve as precursors of other aromatic compounds, but their contribution to the aroma is negligible due to high threshold values [47]. Although obtained results are interesting and indicated significant differences in volatile profile of two types of kefir, it should be noted that this is a preliminary experiment and further work is needed. In particular, there is the need to replicate the trial on a larger scale, with different batches of milk, and to explore the multiple effects of the technological variables and the microbial strains.
## 4 Discussion  
In this paper, the temperature dependence, displacement dependence and frequency dependence of asphalt damping properties are studied. A shaking table test was conducted to validate the effectiveness of asphalt damping. The results show the following:  
1. According to the results of cyclic simple shear tests, the storage stiffness, the loss factor, equivalent stiffness and the equivalent damping ratio decrease with increasing displacement and temperature. Meanwhile, the above damping parameters increase with increasing frequency. These results demonstrate that when the ambient temperature is low, the displacement is small, the loading frequency is high, and the damping of the structure is larger.
2. Damping tests are qualitative tests, so the results are not the real parameters in practice because the test used scaled and simplified test specimens. However, the results can reveal the variation law of the damping characteristics of isolation bearings with this kind of asphalt.
3. The results of the modal analysis of the shaking table indicate that the damping effect is pronounced. Compared with non-asphalt tests, the duration and peak value of the acceleration responses in the with-asphalt tests are reduced by varying degrees, and the displacement is reduced to about 50% of that in the non-asphalt test.
4. A vertical earthquake will not change the frequency response function of the structure, but a rocking earthquake will markedly change it. Therefore, a modal analysis method for multi-dimensional seismic input is proposed. It is concluded that the analytical value agrees well with the experimental results.
5. The damping characteristics of the structure change with time, and a modal analysis method that is suitable for an invariant structure may cause errors. Therefore, the time-varying characteristics of the structure should be further studied in the future.
# UAV Formation Trajectory Planning Algorithms: A Review
## 3 Traditional Algorithm
### Dijkstra Algorithm  
The Dijkstra algorithm is the classical shortest trajectory method in the geometric graph method, in which the vertex represents trajectory points, the edge represents a feasible trajectory, the line between nodes is called an edge, and each edge has a corresponding weight, which is the distance or cost of the journey; it is suitable for two-dimensional static obstacle avoidance scenes with a non-negative side weight. The key to using this algorithm is to select effective trajectory points, shorten the planning time, expand from the starting point, find the shortest trajectory for a node in each step, select the node with the smallest distance from the node that has never been visited to register, then traverse the adjacent nodes of the node after the node is included, and then update the distance. The cost diagram of a Dijkstra algorithm is shown in Figure 5.  
Aiming at the uncertain region search problem, [PERSON] and [PERSON] [13] proposed a search algorithm based on the K-shortest trajectory algorithm for UAV to search targets in an unknown environment. It satisfies the requirements of endurance time of each UAV and the location of the base station of UAV operation, and it enables each UAV to search in the area of maximum uncertainty so as to maximize the search benefit.  
In order to meet the needs of searching an unknown environment and tracking moving targets in a balanced way, [PERSON] [14] improved on [PERSON]'s algorithm and proposed a robust shortest algorithm (ARSP) to deal with arc uncertainty. The influence of information uncertainty and environmental change on the trajectory planning algorithm is overcome, and the trajectory is quickly replanned at the same time.  
[PERSON] and [PERSON] [15] applied the Dijkstra algorithm to the minimum time reconstruction of UAV formation in order to meet the requirements of optimality and short computing time, and the trajectory generated within the shortest time is close to the optimal trajectory.  
[PERSON] et al. [16] proposed an approximate trajectory generation method to generate an approximate trajectory length under the condition of meeting the safety constraints of a UAV. This method is based on the total cost of the Lagrange relaxation (LARAC) algorithm, and it iteratively uses the Dijkstra algorithm (iDijkstra) to modify the edge cost, which solves the safety constraints and flight energy consumption of UAV caused by extreme high temperature.