esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 6 Discussion and Analysis of Results
### Non-Ideal Component Analysis
#### 6.1.1 Practical Limitations of System Components  
The simulation employs a 5 Hz sinusoidal input signal sampled at 1 kHz over 1 s duration, maintaining consistency with the experimental parameters used in Section 5.1.2. The 1 kHz sampling frequency provides adequate oversampling (200:1 ratio) to capture system dynamics while remaining computationally efficient. Non-ideal parameters are selected to represent realistic component tolerances: differentiator noise factor of 0.1 reflects typical operational amplifier noise characteristics; VCO phase noise standard deviation of 0.05 radians corresponds to moderate-quality oscillator specifications; and timing jitter values of 0.001 s (VCO) and 0.002 s (monostable) represent achievable precision with standard integrated circuits. The VCO center frequency of 20 Hz with 1 Hz/V sensitivity ensures adequate frequency deviation range while maintaining linear operation, and the 10 ms monostable pulse duration provides sufficient resolution for PPM pulse positioning analysis.
## 3 Results and Discussion
### Mechanical Properties Analysis
#### 3.5.2 Ductility Analysis  
Ductility is an important parameter in structural design to characterize deformation capacity. The ductility coefficient of reinforced specimen Z2-1 increased by 56.24% compared with unreinforced specimen Z1 under low eccentric compression. The UHPC reinforcement layer effectively restrains the core concrete in the compression zone, resulting in increased strength and deformation capacity. This significantly enhances the ductile properties of the RC eccentric column when the core concrete undergoes large lateral expansion and deformation. The ductility coefficients of reinforced specimen Z2-1 increased by 7.01% and 22.46% compared with reinforced specimens Z2-2 and Z2-3. The ductility of the material deteriorated as the eccentricity increased. Nevertheless, the fragility of the RC eccentric compression columns was much improved with the incorporation of UHPC reinforcement. This enhancement resulted in substantial anticipated damage and significantly enhanced the malleability of the specimens. The specimens exhibited a substantial improvement in ductility. The ductility coefficient of reinforced specimen Z2-3 increased by 2.60% compared with specimen Z3 under high eccentric compression. This shows that the steel fiber dosage affects the ductile properties. The ductile properties of reinforced specimen Z2-3 were increased by 9.08% compared with specimen Z4. The thickness of the UHPC reinforcement layer enhanced the ductile properties.
## 2 Materials and Methods
### Sampling and Testing
Out of the many slope stability analysis methods, the Fellenius analysis method is considered in this work as it gives the most optimum results. It also gives a smaller value for the slope factor of safety. The limit equilibrium software, namely Slide, offers all analysis methods such as Bishop simplified, Corps of Engineers # 1, Corps of Engineers # 2, GLE/Morgenstern-Price, Janbu simplified, Janbu corrected, Lowe-Karafiath, Ordinary/Fellenius and Spencer method. Out of all these methods, it is observed that the Fellenius method gives a lower slope factor of safety. Considering a lower value for slope factor of safety while designing a slope provides a higher factor of safety. Therefore, the Fellenius method is preferred in this analysis. Figure 5a,b presents the assumed slices and forces acting on each slice, respectively. The force applied to each slice is obtained by analyzing the mechanical balance of each component. Each slice is considered separate, and the interaction between the slices is ignored because the resultant strengths are the same as the base of each slice. However, [PERSON]'s third law is not satisfied in this method because, in general, the effects on the left and right of the slices are not of the same magnitude and are not collinear. In this method, the resultant horizontal and vertical equilibrium equations are as follows:  
\[\sum\mathrm{F_{v}}=\mathrm{W}\ \mathrm{-}\ \mathrm{Ncos}\ \alpha\ -\ \mathrm{Tsin}\ \alpha=0 \tag{2}\]  
\[\sum\mathrm{F_{h}}=\mathrm{kW}+\mathrm{Nsin}\ \alpha\ -\ \mathrm{Tcos}\ \alpha=0 \tag{3}\]  
Figure 5: (**a**). Assumed slices in the method of slices. (**b**) Forces acting on each slice. where k represents a factor that computes the horizontal force with depth. \[\text{N}=\text{Wcos}\ \alpha-\text{kWsin}\ \alpha \tag{4}\]  
Similarly, moment equation in this method is:  
\[\sum\text{M}=\sum(\text{W}_{\text{i}}\text{x}_{\text{i}}-\text{T}_{\text{i}} \text{R}_{\text{i}}-\text{N}_{\text{i}}\text{f}_{\text{i}}-\text{kW}_{\text{i}} \text{e}_{\text{i}})=0 \tag{5}\]  
where i is the slice index while x\({}_{\text{i}}\), R\({}_{\text{i}}\), f\({}_{\text{i}}\) and e\({}_{\text{i}}\) are the moment arms. Putting the value of normal force in Equation (5) results in:  
\[\sum_{\text{i}}\text{T}_{\text{i}}\ \text{R}_{\text{i}}=\sum\text{W}_{\text{i}} \text{x}_{\text{i}}-(\text{W}_{\text{i}}\cos\alpha_{\text{i}}-\text{kW}_{\text {i}}\sin\alpha_{\text{i}})\text{f}_{\text{i}}-\text{kW}_{\text{i}}\ \text{e}_{\text{i}}] \tag{6}\]  
Using [PERSON]'s theory, the equation could be written in moment form as:  
Figure 2:0  
The factor of safety equation in the method of slices is:  
Figure 2:1  
Numerical modeling provides a limited solution to problems that cannot be solved by conventional methods, e.g., complex geometry, material anisotropy, linear behavior and in situ pressures related models. Numerical analysis allows for soil fluctuations and failures, pore pressure modeling, unequal intensity, dynamic loading and testing parameters for variable parameters etc.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Task Broadcast Strategy Design
As described in the literature [35], the formula for calculating the maximum travel distance (\(MTD\)) that workers are willing to accept for a task is as follows:  
\[MTD=90\%\times MCD \tag{10}\]  
When the distance between the worker and the task exceeds the \(MTD\), the worker will not choose the task. Here, MAR denotes the maximum probability of a worker accepting the task. Additionally, \(AR\) represents the probability that the staff will accept and complete the task. \(AR\) is calculated as follows:  
\[AR=\max\{0,(1-\frac{dist}{MTD})\times MAR\} \tag{11}\]  
Here, \(dist\) represents the distance between the worker and the task. We establish a threshold, \(EU\). If the probability of completing the broadcast area surpasses this threshold, it implies that at least one worker can accept the task. Assuming uniform \(AR\) among workers within a single secondary grid, the distance between workers and tasks is the average distance between tasks and the four corners of the secondary grid. \(n_{c}\) represents the noise count of workers within grid \(c\). \(p_{c}\) represents the task acceptance rate of workers in grid \(c\). The utility of grid \(c\) is calculated as follows:  
\[U_{c}=1-(1-p_{c})^{n_{c}} \tag{12}\]  
Next, two calculation schemes of broadcast area will be introduced. Broadcast area calculation without considering the secondary grid division: Begin with the grid containing task \(t\) as the initial broadcast area. If the current broadcast area's utility is lower than \(EU\), the nearest neighbor to the task within the maximum travel range will be added to the broadcast area. The specific flow of the algorithm is outlined in Algorithm 2, while Table 2 provides the interpretation of the symbols used in Algorithm 2. Broadcast area calculation considering two-level grid continuous division: In order to reduce the cost as much as possible, Algorithm 2 can divide the final secondary grid and find the adjacent subregions that can reach the threshold. The following are the improvement steps of Algorithm 2 and special symbol notes.
## 2 Numerical Analysis Model
### Finite Element Model  
The symmetric part of a root connection model including a blade root, a pitch bearing, and a piece of hub is shown in Figure 5a,b, and the internal detail model of the bolt-insert joint and the bolt-nut joint can be seen in Figure 5c,d. The six components of loads are applied to the blade root joint in the direction of the blade root coordinate system, as shown in Figure 6.  
The finite element model consists of two types of elements in ABAQUS. The C3D8 element, used for bolt shank and nut geometries, is a general purpose linear brick element, fully integrated  
Figure 4: Schematic diagram of a relief cone parameter at the bolt thread.  
Figure 5: Schematic diagram of a blade root joint model.  
Figure 6: Blade coordinate system; XB in direction of the rotor axis, ZB in radial direction along the blade, and YB in direction of the cross product of ZB and XB [4].  
(\(2\times 2\times 2\) integration points), which uses linear interpolation in each direction and is often called first-order element, and every node has three degrees of freedom: translation in the nodal X, Y, and Z direction. The C3D10 element, used for threads and transition areas, is also a general-purpose tetrahedral element with four integration points [20].  
Surface-to-surface contact formulation from the general contact option in ABAQUS/standard was used. The contact pairs, master surface (nut or insert thread surface), and slave surface (bolt surface) were defined with the tangential frictional coefficient of 0.1, which is a general value used for contact analysis between untreated steels and hard contact default in the normal direction to allow for the shear force and open/closed change during contact analysis according to the surface definition in ABAQUS manual. The seed size of 2.0 was used to minimize the incorrect contact behavior due to the very coarse meshes, that is, the gross penetration of the master surface into the slave surface. For the current analysis in ABAQUS/standard, the following SI unit system is used; mm for length, Newton for load, MPa (N/mm\({}^{2}\)) for stress, and kilogram for weight.  
For detailed investigation of SCF at the engagement threads, the FE model was focused on the bolt-nut joint and bolt-insert joint model with the preload of 400 kN applied at the bolt tip and the fixed condition of insert and nut as shown in Figure 7. Material properties of bolt and nut are listed in Table 2, and measured values of M42 bolts are listed in Table 3.
## 5 Results and Discussion
### Consolidated Trends
#### 5.1.4 Landsat 8 and Sentinel-2 Data Integration
The European Space Agency (ESA) funded two programs: Sen2 like [117] and Sentinel-2 for Agriculture (Sen2-Agri) [118], to develop open-source systems based on SITS analysis methods for crop mapping and monitoring, which can be globally applied. Providing harmonized L8/OLI and S2/MSI ARD, Sen2-Agri aims to address crop monitoring needs and to support national reports for the \"Sustainable Development Goal 2--Zero Hunger\" (SDG-2) and the GEOGLAM initiative [119]. Sen2 like aims to increase the acquisitions of this virtual constellation (95 products/year) by 30% to S2/MSI only acquisitions, promoting dense SITS [117]. [PERSON] et al. [120] developed a phenology-based algorithm integrating S2/MSI, Landsat 7, and L8/OLI NDVI SITS to classify winter crops in China. The approach filled gaps derived from cloud interference and reduced differences in winter crop phenologies, demonstrating that it is unnecessary to use images of the total length of the cycles. The OA was 89%. [PERSON] et al. [121] proposed a CNN for integrating multi-temporal and multi-sensor data to improve coastal LULC classifications. Results with an OA equal to 93.78% when mapping 11 LULC classes showed that including multi-temporal data improves accuracy by 6.85%, while multi-sensor data contribute to 3.24% of the increase. Methods to calibrate reflectance differences between L8/OLI and S2/MSI are in development [122; 118; 123; 22]. In addition, the processing system baselines and surface reflectance approach for both data sets, in particular, the more recent S2/MSI data [33], have frequently changed, and novel algorithms are being developed to mitigate these issues [43]. Projects such as HLS, FORCE, Sen2-Agri, and Sen2 like are in development, but they can already can provide robust medium-resolution data to be used in LULC and LULCC approaches, endorsing that, since both L8/OLI and S2/MSI provide precision information, they should coexist and be used jointly to increase data availability [36].
## 4 Application to Aggregate Computing
Consider the example of a _self-healing channel_[20]: an algorithm that yields, for each device, a Boolean value denoting whether it is part of the shortest path from a source to a destination group of devices, expressed in Protelis as shown in Figure 5.  
There, distanceTo(s) is a gradient (a field of minimum distances to source devices for which field s holds true) and distanceBetween(s,d) a function computing and propagating everywhere the distance between s and d. When this specification is executed in a decentralized, iterative way, it locally adapts the channel value after changes in topology, device-to-neighbor distances (obtained through a proper sensor used within distanceTo--not shown), and input fields source, destination, and tolerance. Notice how the specification is declarative: it describes the macro-level behavior without specifying _exactly_ how micro-level behavior is to be carried out. This enables pulverization of the system, first into devices (and their execution steps) and then into devices' components (and their execution steps). Pulverization does not depend on the specific program: the execution protocol is \"fixed\"; only communication payloads, which include vtrees yielded by local evaluations of the program, change. Each aggregate function call (such as distanceTo) yields a particular sub-tree including its very own relevant data.
## 4 Discussion
Phytoremediation, on the other hand, is a process that takes a very long time (even decades) to achieve satisfactory results, and is limited to the depth to which the applied plants' root systems penetrate [113]. The effectiveness of phytoremediation depends on climatic conditions, the type of plants and the concentration of contaminants [114]. At too high a concentration, phytotoxic effects may limit the application of this remediation method [37]. Additionally, the management of contaminated biomass can pose a risk of contaminating trophic networks [113]. Bioremediation allows environmental toxins to be neutralised through the metabolic activity of microorganisms and their enzymes. However, it too has its limitations [115]. The main issues include sensitivity to weather and climatic conditions, a slow degradation rate (of several years) [116], a risk of secondary contamination, limited suitability for certain pollutants and highly contaminated soils [37] and a high cost. The process also requires continuous monitoring of biotic parameters, pH values, temperature, moisture and nutrient availability in order to maintain consistent performance [16]. Therefore, the in situ stabilisation method proposed in this study, which uses bentonite, calcium oxide and compost, represents an optimal compromise between the efficiency, implementation time and costs associated with the remediation of contaminated soils. In situ stabilisation is an effective, economical and sustainable method of remediating soils contaminated with petroleum derivatives, particularly in areas where other methods would be difficult or impractical to implement. This study therefore expands our knowledge of the suitability and effectiveness of using bentonite, calcium oxide and compost to treat oil-contaminated soils. The controlled conditions of the experiment ensure that the resulting data can be used to inform the design of full-scale remediation interventions under real environmental conditions.
# Service Interface Translation. An Interoperability Approach
## 3 Service Interface Translation
### System Architecture  
The Interface Generator communicates with the other systems via services. One service is provided (_GenerateInterface_), and three services are consumed (_GetInterfaceDescription_, _SecurityConfiguration_ and _ServiceRegistry_) to achieve code generation.  
Figure 3: Block description of the internal parts of the dynamically generated interface.  
Figure 2: An example of an Arrowhead local cloud where consumer interface generation systems are used to generate a new interface between a service provider and a service consumer.  
* _GenerateInterface._ This service is the trigger for code generation. It is consumed by the orchestration system. The orchestration system posts a request providing the service identifiers of the requesting consumer and the selected provider. This information is used by the consumer interface generator system to request the interface descriptions of both systems for the service to be delivered.
* _GetInterfaceDescription._ The interface generator consumes this service from the interface description database to request the interface descriptions. The preliminary location of the interface description database is the system registry. The system sends a get request using the system name.
* _SecurityConfiguration._ The security configuration for the new interface is requested via the _SecurityConfiguration service_, which the authorization system must provide.
* _ServiceRegistry._ This service is consumed to ensure that the interface generator system becomes accessible to other systems.
## 5 Results and Discussion
### Link Level Assessment of Bit Error Probability
In order to stretch the extension of the coverage zone pursuing its smoothness at the same time, a possible option is the reduction of data rate requirements for the communication link. In fact, with a smaller data-rate the noise bandwidth in the receiver scales down as well, whereas the resistance R\({}_{L}\) can be scaled up because of the reduced receiver bandwidth requirements. In this way, the noise power level decreases and the receiver sensitivity increases. The BEP map in Figure 11 refers to the same situation considered in Figure 10a with data-rate R\({}_{b}\) = 1 Gbit/s and R\({}_{L}\) = 10 k\(\Omega\). The coverage zone uniformly extends to a maximum distance of nearly 600 \(\upmu\)m, provided that the links spacing is large enough to keep interference under control. This result also highlights the existence of a further trade-off between wireless communication distance and data-rate, which might be taken into account in the design of the wireless network. Preserving reliable communications with data-rate of the order of 10 Gbit/s even at longer distances might be possible, provided that a greater design efficiency is pursued, e.g., improving the coupling with the off-chip laser, or increasing the gain of the antennas and/or the responsivity of the photodiode. The scenario with two interfering links is finally addressed in Figure 12, where \(\Delta\) = \(\Delta^{\prime}\) is considered for the sake of simplicity. As for Figure 10a, 5-element Vivaldi antenna arrays and thickness of the SiO\({}_{2}\) layer equal to 6 \(\upmu\)m are assumed. Although noise is still the heaviest limiting factor, interference is doubled and its effect is therefore slightly increased, hampering the optical communication when \(\Delta\)/d \(<\) 1/8 approximately. Hence, in the presence of more interfering links, the inter-link distance \(\Delta\) has to be increased to keep the same protection level against interference. It should be noted that the value of \(\Delta\)/d (or the values of \(\Delta\)/d,..., \(\Delta\)/d, in a more general setting) that limits the coverage zones depends on the antenna radiation diagram, the number of the interferers and their power, which has to be evaluated for each specific application scenario. Figure 10: BEP map in the space (d,\(\Delta\)) for the scenario in Figure 6a with 5-elements Vivaldi antenna and t = 6 \(\upmu\)m (**a**) and t = 4 \(\upmu\)m (**b**).
# Applying Deep Learning to Medical Imaging: A Review
## 6 Ethical Considerations for Using DL Methods
### Bias and Fairness  
Another important ethical consideration in the use of DL in medical imaging is the risk of bias and unfairness. DL models are trained on large datasets, and if these datasets are biased or unrepresentative, the resulting models can perpetuate or amplify these biases, leading to unfair or inaccurate predictions [168]. Several studies have highlighted the issue of bias in medical imaging datasets, such as disparities in the representation of certain demographic groups [169]. To address these issues, researchers have proposed various approaches, such as data augmentation, data balancing, and fairness-aware training [77]. These methods can help to mitigate bias and improve the fairness of DL models.
## 2 General Notations and Model Definition
### AUV Kinematic and Dynamic Model
#### 2.2.2 Dynamic Model  
The dynamic model of the vehicle is based on the general Fossen model for underwater vehicles [49; 50; 51]. The Newton-Euler dynamic equations of motion are given by  
\[{}^{b}\mathbf{v}_{r}={}^{b}\mathbf{v}-{}^{b}\mathbf{v}_{c}, \tag{4}\]  
\[(\mathbf{M}_{rb}+\mathbf{M}_{A}){}^{b}\hat{\mathbf{v}}_{r}+\Big{(}\mathbf{C}_{rb}\Big{(}{}^{b} \mathbf{v}_{r}\Big{)}+\mathbf{C}_{A}\Big{(}{}^{b}\mathbf{v}_{r}\Big{)}\Big{)}{}^{b}\mathbf{v}_ {r}+{}^{b}\mathbf{\tau}_{rf}={}^{b}\mathbf{\tau}_{v}+{}^{b}\mathbf{\tau}_{L}+{}^{b}\mathbf{ \tau}_{E}, \tag{5}\]  
where \({}^{b}\mathbf{v}_{r}\in\mathbb{R}^{6\times 1}\) is the generalized vector of body relative velocities, \({}^{b}\mathbf{v}_{c}\in\mathbb{R}^{6\times 1}\) is the generalized vector of ocean current velocity, \((\mathbf{M}_{rb}+\mathbf{M}_{A})\) is the system inertia matrix (including added mass), \(\Big{(}\mathbf{C}_{rb}\Big{(}{}^{b}\mathbf{v}_{r}\Big{)}+\mathbf{C}_{A}\Big{(}{}^{b}\mathbf{v} _{r}\Big{)}\Big{)}\) represents the Coriolis and centripetal matrix (including added mass), \(\mathbf{D}\Big{(}{}^{b}\mathbf{v}_{r}\Big{)}\) is the drag matrix, \({}^{b}\mathbf{\tau}_{rf}\) is the generalized vector of restoring forces (gravitational and buoyancy), \({}^{b}\mathbf{\tau}_{v}\) represents the generalized vector of propulsion forces, \({}^{b}\mathbf{\tau}_{L}\) is the generalized vector of lifting forces, and \({}^{b}\mathbf{\tau}_{E}\) is the generalized vector of environment forces on a rigid body. As the fluid flow is expected to be traversal/longitudinal w.r.t. the length of the vehicle during typical operating conditions such as hovering or forward motion characterized by slow velocities and accelerations (thus small angles of attack), the lift forces and moments are expected to be negligible w.r.t. drag forces and moments and can be neglected. The drag matrix \(\mathbf{D}\Big{(}{}^{b}\mathbf{v}_{r}\Big{)}\) is modeled as the sum of the linear and quadratic damping terms with diagonal structure as  
\[\mathbf{D}\Big{(}{}^{b}\mathbf{v}_{r}\Big{)}=\Big{(}\mathbf{D}_{I}+\mathbf{D}_{q}\Big{(}{}^{b }\mathbf{v}_{r}\Big{)}\Big{)}, \tag{6}\]  
The generalized vector of propulsion forces produced by the thrusters, is given by  
\[{}^{b}\mathbf{\tau}_{v}=\mathbf{B}\big{(}{}^{b}\mathbf{v}_{r}\big{)}\mathbf{u}, \tag{7}\]  
where \(\mathbf{u}\in\mathbb{R}^{5\times 1}\) is the vector of control inputs in which each component is the force produced by the respective thruster and \(\mathbf{B}\) is the control allocation or mapping matrix that allows computing the resulting body wrench \({}^{b}\mathbf{\tau}_{v}\), given a certain \(\mathbf{u}\). The relation between the force of a given thruster and its velocity is highly nonlinear and depends on the thruster characteristics, thruster motor velocity, and the fluid velocity passing through the thruster. Note that the specific model parameters will be revisited in the Section 4.1 where we will discuss more on the vehicle at hand.