esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Cross-correlation, Phase Correlation and Their Modifications
After calculating the cross-correlation function, an important element is to precisely determine its maximum. These methods can be called post-processing. The main lobe of cross-correlation (peak) can be determined similarly to peaks determined for other types of signals as a centroid:  
\[\Delta\hat{\lambda}=\frac{\sum_{i=1}^{N}\lambda_{i}\text{-}C_{i}}{\sum_{i=1}^{N }C_{i}}. \tag{18}\]  
In the case of an asymmetric shape, the resonance peak does not have to coincide with the place of the lowest transmission value. Modifications of the basic centroid method have been proposed for spectroscopic measurements of surface plasmons [22; 23]. The cross-correlation function can be further processed by the Hilbert transform. It has the additional property of reducing noise in the case of a slowly changing signal [24]. The Hilbert transform of the signal \(x(t)\) can be written as follows:  
\[\varphi(k)=\frac{1}{|X_{1}(k)|\cdot|X_{2}(k)|}. \tag{10}\]0  
which corresponds to the convolution of the input signal with the formula \(1/(\pi\cdot t)\):  
\[\varphi(k)=\frac{1}{|X_{1}(k)|\cdot|X_{2}(k)|}. \tag{10}\]1  
The peaks of the \(x(t)\) signal correspond to the zeros of the Hilbert transform.
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 2 Materials and Methods
A classic Stage-Gate model is suitable for predictable contexts and an in-depth understanding of the requirements [19]. Therefore, the Stage-Gate process seems to be impractical for the AztechSat-1 project. Both students and professors from UPAEP had not previously implemented a similar case. To begin with, State-Gate processes require different groups to work on different subsystems, but some prior experience is needed. At the beginning of the project, the AztechSat-1 team, like teams from many other universities, lacked necessary expertise, making it impossible to design, manufacture, integrate, and test all components. Despite being an educational project, AztechSat-1 focused on the scientific and technological goal of providing both satellite and intersatellite communications.
# Energy Harvesting Methods for Transmission Lines: A Comprehensive Review
## 7 Conclusions  
Due to the continuous increase in energy consumption worldwide, power grids in general, and transmission lines in particular, face important challenges related to their optimal use, availability, and security aspects. Predictive maintenance plans that are based on network monitoring approaches are known to be useful in increasing the stability, reliability, availability, and security of power grids. Transmission lines are found everywhere, sometimes in remote and inaccessible areas, where routine inspections are complex and expensive. For this, monitoring strategies based on wireless sensors placed in strategic places that acquire and send data in rea time to a control center becomes essential. To maximize their lifetime, such sensors must incorporate an energy harvesting (EH) unit to supply all electronic components. Due to the specificities of transmission lines, this work has reviewed and analyzed the state-of-the-art of energy harvesting strategies for transmission lines in depth, an area with enormous potential for expansion. EH methods applicable to transmission lines include solar, thermoelectric, magnetic field (inductive, and different types of vibrations induced by the magnetic field), electric field (wound wire, dielectric layer, and corona), and RF. From the state-of-the-art, it has been shown that among the different existing energy harvesting methods, there is not a universal solution since the most suitable EH system depends on the characteristics of each particular application, characteristics such as the geographical location (it defines dust, sand, dirt, ice, or snow conditions, solar resource, or available ambient RF energy), the intensity of the current flowing in the line or the line voltage among others. This work has also identified the challenges and research that needs to be addressed for a successful application of this technology because, despite the importance of this topic, there is still a long way to go as this area is still in its early stages. There is a wide range of possible applications that could potentially benefit from the introduction of such energy harvesting systems,including power transformers, distribution switches, or medium- and low- voltage power lines, among others.  
Conceptualization, J.-R.R.; methodology, J.-R.R. and S.B.; formal analysis, J.-R.R., [PERSON] and S.B.; investigation, J.-R.R.; writing--original draft preparation, J.-R.R.; writing--review and editing, [PERSON] and [PERSON]; funding acquisition, [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Ciencia e Innovacion de Espana, grant number PID2020-114240 RB-I00 and by the Generalitat de Catalunya, grant number 2017 SGR 967.  
Not applicable.  
The authors declare no conflict of interest.
# Altered Mechano-Electrochemical Behavior of Articular Cartilage in Populations with Obesity
## 3 Results and Discussion
### Alterations of Cartilage Tissue in People with Obesity
#### 3.2.2 Cation Fluxes  
To fully understand the alteration in the mechano-electrochemical events occurring in overloaded cartilage, ion fluxes have also been studied. A similar trend to that of water outflow is observed for cation fluxes. Cartilage samples with a weight corresponding to BMI = 22.262 kg/m\({}^{2}\) show a maximum cation flux of 8.19 \(\times\) 10\({}^{-4}\) mol/s in the upper surface. This flux is reduced to 6.06 \(\times\) 10\({}^{-4}\) mol/s in the bottom surface (Figure 5a.3). In obese cartilage simulations, these values are increased reaching a maximum value of 8.9 \(\times\) 10\({}^{-4}\) mol/s (Figure 5b.3).  
Cartilage degeneration due to osteoarthritis has been widely studied [6; 7; 8]. A maximum outgoing cation flux of J\({}^{+}\) = 2 \(\times\) 10\({}^{-4}\) mol/s was obtained for the early stage of osteoarthritis [20]. Interestingly, the cartilage of the population with obesity exhibits similar values to those obtained for early stages of osteoarthritis.  
These findings suggest that people with obesity have an increased risk of suffering cartilage degenerative diseases such as osteoarthritis. The use of preventive treatments to avoid cartilage degradation is thus recommended.  
Figure 5: **(1)**\(z\)-displacement (\(u_{z}\)); **(2)** water (J\({}^{w}\)); **(3)** cation (J\({}^{+}\)); and **(4)** anion (J\({}^{-}\)) fluxes obtained with the 3D computational model after 0.44 s of simulated human gait cycle for **(a)** healthy and **(b)** people with obesity. Note that BMI = 22.262 kg/m\({}^{2}\) corresponds to normal weight and BMI = 33.978 kg/m\({}^{2}\) to obesity. Positive fluxes refer to the emergence of the component from the cartilage sample to the external solution.
## 2 System Model  
The proposed digital speech communication system with semantic awareness is intended to align closely with speech transmission systems in practical use. It uses semantic advanced feature extraction, compression, and semantically aware reconstruction, distinguishing it from pure theoretical performance models. Our pipeline is compatible with conventional digital transmission techniques while allowing for an intermediate transition stage toward fully end-to-end semantic communication models found in the literature.  
The system comprises four key components:  
* Semantic Encoder: It extracts and compresses high-level speech representations, which ensures that the structure remains compatible with conventional transmission methods.
* Conventional Transmitter: It converts the compressed representations into a structured digital bitstream and prepares it for transmission through standard digital communication techniques.
* Conventional Receiver: It recovers the transmitted bitstream and reconstructs the semantic representation while maintaining alignment with existing digital decoding standards.
* Semantic Decoder: It expands and reconstructs the received tensor into an intelligible speech waveform, maintaining both linguistic content and perceptual quality.
# Automated Trucks' Impact on pavement Fatigue Damage
## 3 Results
### Alternative Payement Structures
#### 3.3.3 Comparative Analyses of Layer's Structure Effect on Fatigue DI Value  
To demonstrate the comparative influence of thickness and stiffness variations in the surface and base layers on individual fatigue DI values, we selected a lane width of 3.5 m with a market penetration rate of 100% as an example. Figure 11 depicts the changes in fatigue DI values resulting from alterations in the thickness and stiffness of the surface and base layers when adopting 100% ATs under zero- and uniform-wander modes.  
Upon a 100% increase in the thickness and stiffness of the layers, our findings reveal notable distinctions in the fatigue DI values. Under the zero-wander mode, the most substantial reduction in fatigue DI values occurs with increased base stiffness, followed by surface thickness, surface stiffness, and base thickness. Specifically, the fatigue DI values decreased by 77%, 43%, 24%, and 8%, respectively.  
In the case of adopting the uniform-wander mode, the highest reductions in fatigue DI values are observed with an increase in base stiffness, surface stiffness, base thickness, and surface thickness, respectively. The corresponding reductions in fatigue DI values are 76%, 12%, 9%, and 6%, respectively.
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 5 Algorithm Design
### A Heuristic
#### 5.2.3 Computation of a Degree-Constrained Hierarchy Spanning a Star  
A polynomial-time algorithm computes a spanning hierarchy \(H_{c}\) in a star \(S\) with a central node \(c\) satisfying the degree constraints. The outline is as follows:  
1. If \(D(c)\geq d(c)\), the star itself satisfies the degree constraint; there is nothing to do.  
Figure 6: The decomposition of an MSTSL, the tree of stars and its spanning hierarchy.  
2. If \(D(c)<d(c)\) and there are more than two leaves with degree bound one in \(S\) and there is no node with a degree bound greater than two, there is no solution (cf. Theorem 1).
3. Otherwise, we propose to cover \(S\) by a set \(SC=\{Sc_{i},i=1\dots n_{S}\}\) of connected, degree constrained small stars.  
In the last case, multiple occurrences of \(c\) are needed, and some edges are duplicated. Trivially, edges going to nodes in \(V_{1}\) are not duplicated. For the duplication, the best cost edges are selected in ascending order. Each duplication of an edge \(\{c,w\}\) links two occurrences of \(c\). At most \(D(w)\) adjacent edges of \(w\) are possible. Each small star can cover at most \(D(c)-1\) leaves different from \(w\). Some adjacent edges of the occurrences of \(c\) must be reserved for the connection to the parent star. An example of these small stars in a star is illustrated in Figure 7. The dotted lines indicate the connections to the parent star.
# Use of CNN for Water Stress Identification in Rice Fields Using Thermal Imagery
## 2 Materials and Methods
### The Early Fusion Branch  
Early fusion usually refers to the fusion of data before the network input. In this study, leaf temperature and background temperature were considered as two types of data. Therefore, in this branch, we directly input each thermal image into the CNN, which can be regarded as a type of early fusion, and the leaf temperature and the background temperature are combined in the early stage. This branch plays a role when background pixels are much smaller than leaf pixels. When there are too few background pixels, the background branch of the later fusion will not obtain sufficient background temperature; therefore, this branch compensates for shortcomings in the late fusion branch.
## 1 Introduction
The analytical method can be used to analyze parameters as well as optimize design schemes. Therefore, an analytical model of the drainage scheme for a tunnel and parallel adit has a certain application value. Tunnel and parallel adit drainage schemes involve the analytical study of seepage fields for parallel-twin tunnels. At present, the research on tunnel seepage fields primarily focuses on single tunnel problems [13; 14; 15; 16; 17; 18; 19]. For seepage in twin tunnels, [PERSON] et al. [20; 21] solved the seepage field of a twin tunnel using conformal mapping and the superposition method considering the tunnel lining as isotropic porous material. In addition, [PERSON] [22] used the mirror image method to obtain the seepage field for twin tunnels. [PERSON] et al. [23] used conformal mapping and the Schwartz iteration method to obtain the seepage field of an underwater shallow-buried double parallel tunnel under the condition of equal water pressure at the tunnel boundary. The above studies are all based on a given fixed water pressure at the tunnel boundary. In the collaborative drainage scheme for a tunnel and parallel adit, the parallel adit actively controls the drainage volume through the drain borehole, reducing the external water pressure on the tunnel lining. Therefore, drainage volume is the appropriate boundary condition for a parallel adit. In addition, the influence of parallel adit drainage on different parts of the lining varies significantly, resulting in uneven water pressure at the tunnel boundary. Using equal water pressure or an equal total head boundary around the parallel adit and tunnel is not consistent with field conditions. Through the above introduction, previous studies on parallel adit drainage mainly focused on the influence of drainage on tunnel inflow and the stabilization of tunnel faces during construction. The analytical model of the drainage scheme for tunnel and parallel adit in the operation period has not been reported. In this paper, an analytical model of a collaborative drainage scheme is established for the actual drainage conditions of parallel adits and tunnels during operation. A semi-analytical algorithm is obtained using the complex variable method and the Schwartz method. The semi-analytical algorithm has a fast convergence speed. The boundary conditions can be accurately satisfied after a few iterations. In addition, the semi-analytical results are compared with numerical simulation results based on a case study, demonstrating the convergence and accuracy of the algorithm. This study also analyzes the relevant parameters of the drainage scheme. The tunnel crown pressure head fitting formula is also obtained using the orthogonal test and multivariate nonlinear regression method. This study provides a reference for the quantitative design of the operation drainage scheme of deeply buried tunnel in the presence of groundwater.
### Traffic Management System  
Smart cities use advanced traffic management systems that help to monitor, control, optimize and operate traffic in urban areas very effectively. The easiest way to manage and control traffic flows depends on sensing and monitoring road conditions. All these systems provide live data streaming to a traffic management center that, in turn, allows data transfer authorities and consumers to receive real-time updates on the city's transportation conditions and availability [31].  
Traffic control centers or management systems serve as control rooms for smart city transport. They enable data, such as traffic status, congestion, speed, accident, flow, and density, to be intervened. This can regulate the entire traffic system, such as responding to current demand, reducing traffic congestion, and increasing safety. In summary, these systems are controlling systems that enable cities to adjust the flow of traffic as required. A variety of commonly used technologies can be connected to the traffic management center [31; 32]. These can be control and monitoring units, such as road signs, classifiers, vehicle counters, traffic lights, camera sensors, etc. Figure 2 below shows an intersection plan that illustrates the sensors and vehicles of an intersection analyzed within the scope of this study.  
Many parameters are collected from the roads with loop detectors or camera sensors, such as the number of vehicles, weather conditions, density data, and momentary movements, leading to the dynamic management of traffic. With these developments, managers are particularly interested in accessing real-time data and using these data in decision support systems.
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
## 5 Acquisition of Fleet- and Depot-Specific Maintenance Knowledge
### The RailMain-KG Schema  
A Knowledge Graph for Rolling Stock maintenance tasks is regarded as a data source that organises the necessary information for the automation of scheduling maintenance  
Figure 2: Simplified abstract ontology.  
activities. This includes resources (such as tools, parts, and consumables) and priority of the tasks. This knowledge graph developed for this study, named RailMain-KG, forms a large part of the depot- and company-specific knowledge covered by the Virtual Depot's initial ontology Figure 2. To enable the collection of the knowledge graph, we selected a schema from the Virtual Depot's abstract ontology containing the concepts listed below, and shown visually in Figure 3.
* **Vehicle**: a distinct unit of a train (e.g., carriage); maintenance activities are applied on components of a unit.
* **Maintenance Task**: a set of procedures for preserving the operational integrity of a vehicle.
* **Activity type**: a classification of maintenance tasks that relies on their effects when applied on a component. Examples include inspection, replacement, and cleaning.
* **Component**: functional units of a vehicle. They range from individual elements (such as doors) to integrated subsystems such as air-conditioning units.
* **Requirement**: essential resources and their respective quantity that are necessary for delivering maintenance tasks.
* **Maintenance Resource**: a high-level class that expresses elements stored in maintenance facilities that are used to carry out maintenance tasks. They can be either tools or materials.
* **Material**: consumable elements or spare parts utilised during maintenance tasks, indicative examples include screws, filters, washers, cloth, or oil.
* **Tool**: items used to physically manipulate or assess the condition of a component during a maintenance task; their main difference compared to materials is that they are not expended during the process, for instance, screwdrivers, wrenches, or oil changing station.
* **Quantity**: the required count of resources needed for a maintenance task.
* **Priority**: a rating that describes the importance of a maintenance task in terms of its potential impact on the safe operation of a vehicle.
* **Safety warning**: messages detailing possible hazards that may arise while conducting a maintenance task.
* **Maintenance Instructions**: a step-wise guide that lists the actions needed, regarding the use of resources, to successfully perform a maintenance task, while avoiding any potential hazard.
# The Relationship between Gait Velocity and Walking Pattern in Hemiplegic Patients
## 2 Materials and Methods
### Assessment
In order to perform the gait assessment (spatio-temporal and kinematic parameters) and identify the main compensatory gait strategies, three gait analysis acquisitions per patient were recorded, applying IOR GAIT experimental protocol [30] using a stereophotogrammetric system for movement acquisition (Oxford Metrics VICON 612). The video record was also checked. Compensatory gait patterns were identified using the kinematic data for the lower limbs and checked via video recording by two expert gait analysts ([PERSON] and [PERSON]). This system tracks several 10-mm-diameter spherical markers applied in specific anatomical landmarks as follows: the anterior and posterior margin of the iliac spines, the prominence of the great trochanter, the lateral femorus epicondyle, the head of the fibula, the anterior border of the tibial tuberosity, the lateral malleolus, the Achilles tendon insertion on the calcaneous, the dorsal margins of the first and fifth metatarsal heads [30]. According to various international recommendations [31; 32] and previous work by [PERSON] et al. [33], anatomical reference frames for each body segment are defined. For each joint a standard coordinate system was adopted to define their movements as flexion/extension, intra/extra-rotation and ab/adduction, except for the ankle, where a special terminology is needed to define the movements as dorsi/plantarflexion, adduction/abduction and inversion/evers [30].
# Emergent Search of UAV Swarm Guided by the Target Probability Map
## 2 Mathematical Model of Cooperative Search
### Target Information Model  
In the cooperative searching process, UAV swarm usually faces complex unknown environments. Therefore, the goals of the UAV swarm are to perform the overlaying search of the whole mission region as soon as possible for the full area coverage, target detection and reduction in environmental uncertainty. Due to a lack of accurate prior information, the targets cannot be located accurately at the initial time.  
Every grid \((m,n)\) in the mission area owns its target existing probability \(P_{mn}\) in the TPM. The target existing probability represents the uncertainty of the region grid, and it can be updated in the cooperative searching process. The mixed two-dimensional Gaussian distribution function (MTGDF) is suitable for describing the state uncertainty of the targets. Assuming that the initial coordination of a target is \((x,y)\), the two-dimensional Gaussian distribution of the target's existing probability density is shown in Equation (1).  
\[f(x,y)=\frac{1}{2\pi\sigma_{1}\sigma_{2}\sqrt{1-\rho^{2}}}\cdot\exp\left(- \frac{1}{2(1-\rho^{2})}\left\{\frac{(x-\mu_{1})^{2}}{{\sigma_{1}}^{2}}-\frac{ 2\rho(x-\mu_{1})(y-\mu_{2})}{\sigma_{1}\sigma_{2}}-\frac{(y-\mu_{2})^{2}}{{ \sigma_{2}}^{2}}\right\}\right), \tag{1}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are the mean values of \(x\) and \(y\), respectively. \(\sigma_{1}\) and \(\sigma_{2}\) are the standard deviation of \(x\) and \(y\), respectively. \(\rho\) is the covariance of \(x\) and \(y\), \(\rho=\text{cov}\left(x,y\right)\).  
There may be multiple targets in the mission area, and the target existing probability density distribution of each target is different. Thus, the target existing probability density in the mission area is defined as  
\[f(x,y)=\sum_{i=1}^{N}\alpha_{i}f_{i}(x,y), \tag{2}\]  
where \(\alpha_{i}\) is the scale factor presenting the important degree of the target \(i\), \(f_{i}(x,y)\) is the probability density function of the target \(i\), and \(N\) is the target number.  
The target existing probability in grid \((m,n)\) is \(P_{mn}=\iint sf(\cdot)dxdy\), where \(s\) represents the area corresponding to the \(f(\cdot)\). Assuming that the existing probability distribution of the targets is independent in the \(x\) and \(y\) directions, thus \(\sigma_{1}=\sigma_{2}=\sigma_{0}\), \(\sigma_{0}\) is a constant.