esa-sceva/satcom-chunk-collection huggingface dataset:
# Senior Living Lab: An Ecological Approach to Foster Social Innovation in an Ageing Society
## 6 Discussion: Role of ICT in the SLL and Participatory Design Practices
### Participatory Approach versus Older Adults' Expectations  
Beyond technologies, a key challenge during the creation of the Senior Living Lab has been its wish to be founded via a participatory approach. The goal of this approach was to ensure the functioning of the laboratory around the specific needs of the local target population having the older adults participating within the co-creation process of the laboratory. During the kick-off event and during a first world cafe, we asked older adults what they would expect from the Senior Living Lab, with a particular focus on the three action axes described in Section 3.2. The results of this participatory kick-off that sought to collaboratively define the Senior Living Lab's main actions were themselves controversial. Indeed, many attendees at the kick-off expected a concrete and clear action plan from the Senior Living Lab, aiming as soon as possible to obtain tangible results. In fact, at this time, the older adults did not understand their role and how they could belong to the laboratory even though an introductory presentation tried to explain their important role and the contributions that they could make as senior participants.  
The lesson learned from this experience is that, in the first period of life of a living lab, it is important to balance the participatory approach with more concrete actions. These actions should be established and discussed internally by the Senior Living Lab's constituting members, since the initial participatory process could be perceived as a lack of internal organization that could slow-up the results of the project from the older adults' perspective. Participatory governance must be jointly practiced and learned. Most participants are not familiar with grass-root processes that engender collaborative results.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 2 Chaos Oscillators, Discrete Models and Chaotic Synchronization
### Pecora-Carroll Chaotic Synchronization  
This work studies the performance of Pecora-Carroll chaotic synchronization [19] for synchronizing the analog chaos oscillator to its discrete model and vice versa. This synchronization technique is outlined in Figure 10. The synchronization principle is as follows: the state variable \(y_{1}\) of the drive chaos oscillator replaces the \(y_{2}\) state variable of the response chaos oscillator. As a result, the response system follows the behavior of the drive system--the \(x_{2}\) state variable repeats the behavior of \(x_{1}\), and \(z_{2}\) repeats \(z_{1}\). Figure 9(a) demonstrates that for the analog-discrete chaotic synchronization the analog-to-digital converter (ADC) is required to apply the synchronization signal of the drive analog chaos oscillator to the response discrete oscillator. The digital-to-analog converter (DAC) is required for the discrete-analog synchronization to apply the synchronization signal from the discrete chaos oscillator to its analog counterpart in Figure 9(b). For the Vilnius and RC chaos oscillators, the \(y\) state variable is used for synchronization, as it was found that synchronization can be achieved using only this state variable.  
The previous work [37] verified the possibility of synchronization between continuous and discrete time Vilnius chaos oscillators experimentally without analyzing the performance. An example in Figure 11 shows the time diagrams of the Vilnius chaos oscillator hardware circuit and discrete model after \(y_{1}\) of the hardware circuit was applied to the discrete model. The figure demonstrates that synchronization occurs even if the initial conditions of the two oscillators differ.  
Figure 10: Pecora–Carroll synchronization technique for achieving the analog-discrete (**a**) and discrete-analog (**b**) chaotic synchronization.  
Figure 9: Vilnius chaos oscillator’s hardware prototype’s attractor \(x\)-\(y\) projection (**a**), \(y\)-\(z\) projection (**b**) and \(x\)-\(z\) projection (**c**).
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### MDP Model for Expected Total Rewards
#### 3.2.1 MDP Basics  
A sequential decision-making method is the selection of one of several action strategies in each time slot during the operation of the system [25]. In the sequential decision process, if the transfer of the system state obeys the known probability law and is independent of the previous history, then this sequential problem is called an MDP problem [26]. An MDP model consists of a reward function, system states, actions, state transition probability and objective, each of which will be described in detail later.
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 1 Introduction
Here, \(\omega_{\rm pe}\) denotes the electron plasma frequency--which depends on the electron density--and \(\omega\) the angular frequency of the applied microwave field. Above this limit, the shift of the resonance frequency no longer scales linearly with the increase in the electron density and translating measurements into tangible physical parameters becomes difficult. However, this limit underestimates the range of plasma parameters that can be explored by MCRS. Especially for atmospheric-pressure plasmas--which do not fill the whole cavity volume and have a collision frequency larger than the angular microwave frequency--a new and more accurate limit would be beneficial for opening up an extension of the dynamic range of applicability of the diagnostic technique. A simpler approach is the usage of the (collisional) skin depth as an additional parameter to limit the range of plasmas, which can be probed by MCRS [18; 25], but this condition also does not capture all relevant physics involved. Several theoretical studies have been performed to investigate the perturbation of the microwave field by the presence of the plasma [27; 28; 29; 22]. Experiments demonstrated that overdense plasmas (\(\omega_{\rm pe}>\omega\)) can be probed when the plasma is located at a node of the applied resonant field [24]. Furthermore, the non-linear regime of the response of a cavity has been used to investigate discharges [28]. Besides sufficient spectral resolution, i.e., resolution in changes in permittivity, the resonant field needs to be able to respond to the changes in the probing volume [30]. This review article presents an overview of the opportunities and challenges in this new field for MCRS. The presented challenges needed to be overcome or taken into account during the preparation and execution of the experiments described in references [18; 19; 20]. Moreover, there are several complications in the analysis of experimental data related to atmospheric- and low-pressure plasmas. As these challenges could have a severe impact on the outcome of the analysis, it is necessary to propound them. These 'warnings' will often be accompanied by (possible) solutions. First, the general concepts of MCRS and the relevant physics are introduced in Section 2. The main messages of this article are related to the required improvement of the resolution, contributors to changes in resonant behavior, inhomogenous plasmas, determination of the effective plasma volume, lower limit for the permittivity, and the skin depth of the plasma. Each of the six main messages will be covered in one of the Sections 3.1- 3.6. This all is followed by a collection of recommendations to take (new) users of this diagnostic method by the hand as well as to suggest further improvements of the approach itself. The article is finalized with a conclusion.
## 2 Modelling  
The tuning fork probe can be seen as a mechanical oscillator in interaction with the surface by the tip. However, the output signal is current caused by the tuning fork's piezoelectric effect. Tuning fork probes can be described by electromechanical coupling models [8; 9; 10].  
A system's differential equation of motion is  
\[m\ddot{X}(t)+b\dot{X}(t)+kX(t)=F(t) \tag{1}\]  
A circuit's differential equation is  
\[\frac{I(t)}{C}+L\ddot{I}(t)+R\dot{I}(t)=\dot{U}(t) \tag{2}\]  
By comparing the Equations (1) and (2) after Laplace transformation as shown in Equation (3), the corresponding relationship of the electrical and mechanical parameters are shown in Table 1.  
\[\begin{array}{c}ms^{2}+bs+k=\frac{F(s)}{X(s)}=\frac{F(s)s}{v(s)}\\ Ls^{2}+Rs+\frac{1}{C}=\frac{U(s)s}{I(s)}\end{array} \tag{3}\]  
The coupling can be regarded as an ideal transformer. According to the principle of the ideal transformer as shown in Figure 2a, the equivalent impedance \(Z^{\prime}\) in the left part is:  
\[\begin{array}{c}\frac{U_{1}^{2}}{Z^{\prime}}=\frac{U_{2}^{2}}{Z}\\ Z^{\prime}=\frac{U_{1}^{2}}{U_{2}^{2}}Z=m^{2}Z\end{array} \tag{4}\]  
And the relation between \(Z\) and \(Z^{\prime}\) in Figure 2b is  
\[\begin{array}{c}\frac{2U_{1}^{2}}{Z^{\prime}}=\frac{U_{2}^{2}}{Z}\\ \frac{U_{1}}{U_{2}}=\frac{m}{Z}\\ Z^{\prime}=\frac{2U_{1}^{2}}{U_{2}^{2}}Z=\frac{m^{2}}{2}Z\end{array} \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Electrical** & **Mechanical** \\ \hline Inductance (\(L\)) & Mass (\(m\)) \\ Capacitance (\(C\)) & Reciprocal spring constant (\(1/k\)) \\ Resistance (\(R\)) & Damping (\(b\)) \\ Voltage (\(U\)) & Force (\(F\)) \\ Current (\(I\)) & Velocity (\(v\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The corresponding relationship of the electrical and mechanical parameters.  
Figure 2: The ideal transformers.
# Optimization Strategies in Quantum Machine Learning: A Performance Analysis
## 5 Comparative Analysis
### L_BFGS_B Optimizer  
We run L-BFGS-B, a gradient-based optimizer with parameter-shift gradients, and set the maximum iterations to 22. This optimizer dynamically adjusts the learning rate, removing the need for a fixed value. The resulting confusion matrix is shown below:  
\[\begin{bmatrix}24&5\\ 2&30\end{bmatrix} \tag{7}\]  
True positive (TP): Twenty-four instances were correctly predicted as positive.  
False positive (FP): Five instances were incorrectly predicted as positive.  
False negative (FN): Two instances were incorrectly predicted as negative.  
True negative (TN): Thirty instances were correctly predicted as negative.  
Using the confusion matrix values, the performance metrics for the L-BFGS-B optimizer were calculated as follows:  
\[\text{Accuracy}=\frac{30+24}{30+5+24+2}=0.885=89\%\]  
\[\text{Precision}=\frac{30}{30+5}=0.857=86\%\]  
\[\text{Recall (Sensitivity)}=\frac{30}{30+2}=0.937=94\%\]  
\[\text{F1 score}=2\times\frac{0.857\times 0.937}{0.857+0.937}=0.896=90\%\]The model trained using the L-BFGS-B optimizer required 6 min, which is significantly longer than the time taken with COBYLA but still relatively efficient.
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Works** & **Heterogeneous Algorithm for Efficient Routing** & **Features and Strengths** & **Deficiencies and Vulnerabilities** \\ \hline \multirow{3}{*}{[PERSON] et al. [19]} & Heterogeneous multidepot cooperative vehicle-routing problem. & Improves vehicle routing by properly selecting transfer points for product transshipment between vehicles of different depots. & Increases cost savings among depots because the grand coalition is not stable in an urban environment. \\ \hline \multirow{3}{*}{[PERSON] et al. [20]} & Energy-aware clustering-based routing in wireless-sensor networks using the cuckoo-optimization algorithm. & Enhances the network lifetime and selects optimal cluster heads through the combination of clustering methods and cuckoo optimization. & The work is restricted to WSNs with stationary sensor nodes. In addition, it increases latency and delay. \\ \hline \multirow{3}{*}{[PERSON] et al. [21]} & & Testing the effectiveness and performance on NS-2, this algorithm outperforms many algorithms with a higher data-packet delivery ratio and a lower end-to-end delay. It also guarantees road-safety service quality and fulfills some QoS requirements. & This protocol is vulnerable to a number of security threats. \\ \hline \multirow{3}{*}{[PERSON] et al. [22]} & A hierarchical routing design and architecture in which nodes in the outer level forward data to inner-level nodes. & Increases energy saving, increases network lifespan, and reduces congestion. & With the use of ACO, certain cluster-head nodes become overburdened with data forwarding. \\ \hline \multirow{3}{*}{[PERSON] et al. [23]} & A framework that predicts pheromone values using quantum mechanics. & Generates the shortest and most optimal path faster than multiple algorithms. & Increases computation time. \\ \hline \multirow{3}{*}{[PERSON] et al. [24]} & AODV combined with ACO for data transmission. & Generates a path with the shortest distance, leads to improved throughput, and reduces lost and delayed packets. & Consumes large amounts of network bandwidth and increases network congestion. \\ \hline \multirow{3}{*}{[PERSON] et al. [25]} & Estimates statistical variables using a one-way road-network model. & Employs a route-planning algorithm for periodically constrained search regions that generate virtual borders with a lower confidence level. & The search process is reliable but utilizes more energy consumption for optimal path detection. \\ \hline \multirow{3}{*}{[PERSON] et al. [26]} & Plans an efficient path from the present to the future. & Proposes the shortest route for packet reduction, throughput increase, and latency reduction. & Addresses only congestion. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Contributions of existing techniques.
## 3 Results and Discussion
### Results and Discussion of Performance Test
#### 3.2.2 Regression Equation and Significance Test  
The data processing software Design Expert 8.0.6 was used to perform multiple regressions fitting the test data, in which the regression equation of each factor with ear damage rate was obtained:  
\[S=0.027+0.011X_{1}+0.004X_{2}+0.006125X_{3}+0.0005X_{1}X_{2}+0.00025X_{1}X_{3}+ 0.012X_{1}{}^{2}+0.005075X_{2}{}^{2}+0.019X_{3}{}^{2} \tag{20}\]  
The results of the variance analysis for the regression equation were shown in Table 5. The fitting degree of the regression model of ear damage rate was found to be extremely significant (\(p<0.01\)), while the \(p\) value of the lack-fit item was 0.9186, which was not significant, indicating a good fit with the actual test.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Serial Number**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{2}{c}{**Respond Values**} \\ \cline{2-5}  & **Rotational** & **Forward Speed** & **Spring Stiffness** & **Ear Damage** \\  & **Speed X1** & **X2** & **X3** & **Rate/\%** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 4: Test results.  
In regard to the regression equation of the ear damage rate, the \(p\) values of \(X_{1}X_{2}\), \(X_{1}X_{3}\), and \(X_{2}X_{3}\) were all found to be greater than 0.05. Hence, the effect of these factors was not noted to be significant, whereas the effect of the other factors was significant. According to the regression coefficient test of Equation (20), the primary and secondary order of the factors affecting the ear damage rate was concluded to be the rotational speed of the stalk roller, spring stiffness and forward speed.
## 2 Mathematical Models of the Induction Motor and the PQ-MRAS Estimator
### Mathematical Model of the Induction Motor  
The model of the three-phase squirrel cage induction motor can be expressed in the stationary reference frame (\(\alpha\)-\(\beta\)) by the following equations [1]:  
\[\frac{\mathrm{d}\psi_{\mathbf{s}}}{\mathrm{d}t}=\mathbf{u_{s}}-R_{s}\mathbf{i_ {s}}, \tag{1}\]  
\[\frac{\mathrm{d}\psi_{\mathbf{r}}}{\mathrm{d}t} =-R_{s}\mathbf{i_{r}}+\mathrm{j}p_{b}\omega_{m}\psi_{\mathbf{r}}, \tag{2}\] \[\mathbf{i_{s}} =\frac{L_{r}}{w}\psi_{\mathbf{s}}-\frac{L_{m}}{w}\psi_{\mathbf{r}}\] (3) \[\mathbf{i_{r}} =\frac{L_{s}}{w}\psi_{\mathbf{r}}-\frac{L_{m}}{w}\psi_{\mathbf{s}}.\] (4) \[t_{e} =\frac{3}{2}p_{b}\mathrm{Im}\{\psi_{\mathbf{s}}^{*}\cdot\mathbf{i_ {s}}\}\] (5) \[\frac{\mathrm{d}\omega_{m}}{\mathrm{d}t} =\frac{1}{j}(t_{e}-t_{l}) \tag{6}\]  
where \(\mathbf{u_{s}}=u_{s\alpha}+\mathrm{j}u_{s\beta}\)--stator voltage vector, \(\mathbf{i_{s}}=i_{s\alpha}+\mathrm{j}i_{s\beta}\)--stator current vector, \(\mathbf{i_{r}}=i_{r\alpha}+\mathrm{j}i_{r\beta}\)--rotor current vector, \(\psi_{\mathbf{r}}=\psi_{r\alpha}+\mathrm{j}\psi_{r\beta}\)--stator electromagnetic flux vector, \(\psi_{\mathbf{s}}=\psi_{s\alpha}+\mathrm{j}\psi_{r\beta}\)--stator electromagnetic flux vector, \(\omega_{m}\)--rotational shaft speed, \(t_{e}\)--electromagnetic torque, \(t_{l}\)--load torque. \(R_{s}\), \(R_{r}\)--stator, rotor resistances respectively; \(L_{s},L_{r},L_{m}\)--stator, rotor, magnetizing inductances, respectively; \(p_{b}\)--number of pole pairs, \(J\)--inertia of the shaft, \(w=L_{s}L_{r}-L_{m}{}^{2}\).  
It is necessary to point out that in the presented model, such phenomena as motor losses and magnetic saturation are not considered.
## 3 Results and Discussions
### FT-IR Analysis  
The functional groups present in the NDG are characterized by FTIR spectroscopy. The comparative data of FTIR spectra for GO and NDG are plotted in Figure 2. Since the GO contains plenty of oxygen groups attached to the graphene layer, numerous signals are produced in the FTIR spectrum, especially in the wavenumber range of 1000-1800 cm\({}^{-1}\). The oxygen-to-carbon bonding in GO varies from carbonyl (C=O), hydroxyl (-OH), etheric (C-O-C), and alcoholic (C-O), which produce various vibration signals at 1735 cm\({}^{-1}\) (stretching), 1400 cm\({}^{-1}\) (bending), 1224 cm\({}^{-1}\) (stretching), and 1053 cm\({}^{-1}\) (stretching), respectively [20]. In addition to the vibration peaks produced by carbon-bonded oxygen atoms, a broad strong band is displayed at 3428 cm\({}^{-1}\) in the FTIR spectrum of GO, which is produced by the -OH moiety from the absorbed moisture content. During simultaneous reduction and nitrogen doping, most of the oxygen species were removed, which is observed from the reduced intensity of most of the peaks produced by the oxygen-containing groups, especially the oxygen present on the edges. However, a small peak present at 1053 cm\({}^{-1}\) indicates the presence of traces of oxygen in the NDG. The doping of nitrogen in the graphene sheet is confirmed by the peak at 1154 cm\({}^{-1}\), which resonated from the vibrations of the C-N bond [21]. Moreover, the C=C unsaturation of graphene is reverted in the NDG evident from the peak at 1558 cm\({}^{-1}\)[22]. The wide high intense peak present at 3436 cm\({}^{-1}\) is possibly due to the N-H stretching vibrations or by the O-H bond as in GO [23].  
Figure 1: UV-Visible spectra of graphene oxide (GO) and nitrogen-doped graphene (NDG).
## 4 Experiments and Analysis
### Ablation Experiment
In the backbone and neck, SC-Conv first calibrates features--expanding receptive fields and bolstering context--then passes them to FRFN for refinement. FRFN's output is subsequently recalibrated by SC-Conv, forming an iterative \"Calibration-Refinement Loop\". This loop executes multiple times, progressively reducing redundancy and enhancing discriminative features layer by layer. Figure 8: [EMAIL_ADDRESS] Gains for Method Combinations on SfdDD and YawDD-E Datasets. This iterative process yields multiscale features (P3, P4, P5) with varying receptive fields and semantic strengths: P3 (smallest field, shallowest semantics) offers high localization precision; P4 balances semantics and localization; and P5 (largest field, richest semantics) is ideal for global context and large-scale targets. Subsequently, in the head, the SEAM module unifies these optimized P3, P4, and P5 features into a common contextual space. This ensures consistent prediction logic across all target sizes, significantly mitigating cross-scale conflicts and occlusion-based misdetections. Together, FRFN, SC-Conv, and SEAM create a hierarchically complementary, information-enhancing framework, which is fundamental to achieving YOLO-AFR's characteristic high precision and robustness. In conclusion, each of the proposed modules contributes significantly to the overall performance, and their joint integration yields a synergistic effect. The final YOLO-AFR model (A + B + C) consistently outperforms the baseline YOLOv12 on both datasets, validating the effectiveness and generalizability of the proposed architecture.
# Alluvial Fan Scree Deposits: Formation Characteristics and Erosion Mitigation Strategies
###### Abstract  
Alluvial fan scree deposits (AFSDs) in arid/semi-arid regions are highly susceptible to rainfall-induced erosion, posing significant risks to infrastructure like oil pipelines. This study evaluates the efficacy of SH polymer materials in enhancing AFSD erosion resistance through three experimental approaches: film characterization, rainfall erosion simulation, and environmental compatibility assessment. Tensile tests demonstrated that SH polymer films (0.16-0.56 mm thick) retained >80% mass after prolonged immersion, exhibiting prolonged ductility (250 mm elongation) and stable post-immersion softening, ideal for enduring cyclic erosion. Rainfall simulations (200 mm/h intensity) revealed that SH application rates \(\geq\) 1.5 kg/m\({}^{2}\) reduced soil loss by >90%, with 2.0 kg/m\({}^{2}\) ensuring near-complete slope integrity across planar/curved morphologies. Ecological tests confirmed SH's environmental friendliness, as treated soils supported robust tall fescue growth without permeability inhibition. The findings advocate SH polymers as a sustainable solution for AFSD stabilization, combining mechanical resilience, terrain adaptability, and eco-compatibility.  
alluvial fan scree deposits; SH polymer; rainfall erosion; soil stabilization 2025  
24 June 2025  
22 June 2025  
202 Contemporary soil conservation research predominantly focuses on pedogenic processes in loesic and lateritic terrains [11; 12; 13; 14; 15; 16], with limited attention to AFSDs erosion dynamics. Conventional mitigation approaches emphasize infiltration suppression to preserve geomechanical properties, though this strategy adversely impacts xerophytic vegetation in northwest China's water-limited ecosystems [17; 18; 19; 20; 21]. Alternative chemical stabilization techniques demonstrate enhanced efficacy through matrix cementation and shear strength augmentation [22; 23; 24; 25], notably improving hydro-structural stability and erosion resistance [26; 27]. Additionally, in arid and semi-arid regions of northwest China, the establishment conditions for different AFSDs oil and gas pipeline layouts and prevention areas mainly consider geographical, geological, topographical, geomorphic (including micro-topography), climatic, hydrological, soil, and vegetation conditions [28; 29; 30]. Based on contemporary soil conservation research, it is concluded that adopting environmentally friendly chemical methods to solidify AFSDs is a priority consideration.  
This study systematically evaluates three stabilization strategies: SH polymer membranes, bioengineered AFSDs with native grass integration, and surface-applied SH composites. Experimental methodologies included membrane durability assessments, controlled rainfall simulations, and ecotoxicological profiling. This research quantifies long-term performance in erosion mitigation while assessing environmental sustainability.