esa-sceva/satcom-chunk-collection huggingface dataset:
# High Performance Receiver Design for RX Carrier Aggregation
## 3 LNTA Topologies and Simulation Results
### Split-Cascode LNTA  
The split-cascode LNTA shown in Figure 8 realizes multi-output operation by signal splitting at the cascode node. Trans-conductance (\(g_{m}\)) is shared between CA paths. Therefore, gain is reduced by \(20\cdot\log_{10}(N)\) (dB) without current scaling. Even with current scaling by \(N\cdot x\) for N CA, gain reduction of around \(10\cdot\log_{10}(N)\) (dB) is inevitable due to square-root dependence of gm with respect to the current consumption in CMOS devices (short channel effects are ignored). Shared \(g_{m}\) dictates small variation in \(Z_{in}\) for different operation modes (non-CA and CA). NF is degraded due to significant noise contribution from the cascode devices and larger contribution from TIA due to smaller LNA gain.  
Significant noise contribution due to the cascode devices is coming from the small impedance looking into the another cascode device in intra-band CA operation. In non-CA mode, only the single cascode transistor is enabled and directs the current to either CA1 or CA2. Thus, cascode transistor noise is highly degenerated, giving rise to a small noise contribution. In CA mode, on the other hand, both cascode transistors are enabled, and the small impedance (\(\frac{1}{g_{m,cascode}}\)) looking into the source node is influencing each other. The noise current out of the cascode devices is expressed as Equations (5) and (6) for non-CA and CA mode, respectively. Table 2 summarizes the simulation results with split-cascode LNTA approach. High NF due to the cascode device is observed, and additional current consumption of the \(g_{m}\) device gives limited improvement in its NF.  
\[i_{\text{cascode,non-CA}}=\frac{i_{n}}{1+g_{m,cascode}\cdot r_{o}} \tag{5}\]  
\[i_{\text{cascode,CA}}=\frac{i_{n}}{1+g_{m,cascode}\cdot\left(r_{o}\parallel \frac{1}{g_{m,cascode}}\right)}\approx\frac{i_{n}}{2} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Gain (dB)** & **NF (dB)** & \(\mathbf{IIP_{3}}\) (dBm) & \(\mathbf{I_{d}}\) (mA)** \\ \hline Reference (SISO) & \(56.6\) & \(1.33\) & \(-6.2\) & \(7.4\) \\ \(2\)-stage, non-CA & \(55.4\) & \(1.57\) & \(-6.6\) & \(7.4\) \\ \(2\)-stage, CA1 path & \(56.1\) & \(1.52\) & \(-10.2\) & \(9.6\) \\ \(2\)-stage, CA2 path & \(57.5\) & \(1.5\) & \(-16\) & \(9.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation results with two-stage LNTA. SISO: single input, single output.  
Figure 7: Two-stage BALUN LNTA.
# Design of 4 \(\times\) 1 Power Beam Combiner Based on MultiCore Photonic Crystal Fiber
## 3 Results: The Designs of 4 \(\times\) 1 and 8 \(\times\) 1 Power Combiners  
Figures 2a-c and 3a-c shows the refractive index structure of the 4 \(\times\) 1 power combiner and 8 \(\times\) 1 power combiner, respectively. In these figures, the red color areas represent silica, and the purple color areas represent air.  
The optimal values of the 4 \(\times\) 1 power combiner multicore PCF structure are  
\[z\ =\ 2.6\ \mathrm{mm},\ d\ =\ 0.8277\ \mathrm{\upmu m},\ d^{\prime}\ =\ 0.2483\ \mathrm{\upmu m},\ \Lambda\ =\ 2.365\ \mathrm{\upmu m},\ \frac{d^{\prime}}{\Lambda}=0.105,\ \frac{d}{\Lambda}=0.35\]  
The 8 \(\times\) 1 power combiner is based on a cascade which includes two units of 4 \(\times\) 1 power combiners and one unit of 2 \(\times\) 1 power combiner, as shown in Figure 3a-c.  
Figure 3: _Cont._  
Figure 2: Refractive index profiles of the 4 \(\times\) 1 power combiner: (**a**) \(xz\) plane at \(y\) = 0; (**b**) \(yz\) plane at \(x\) = 0; (**c**) \(xy\) plane at \(z\) = 0.
## 3 Methodology
### Testing and Relaxing the Classical Assumptions
#### 3.3.1 Diagnostics of the Multicollinearity Assumption
By contrast, if the \(j\)th singular value is really a small positive, then the vector \(\overset{\rightarrow}{u}_{j}^{nonzero}=\mathbf{X}\overset{\rightarrow}{v}_{j}/s_ {j}\) will be an estimate of the unit vector \(\overset{\rightarrow}{u}_{j}\) and the two of them will be close both in direction and in magnitude. If the approximation (26) is used in Formulas (13), (18) and (21), the numerical problems resulting from violation of the perfect multicollinearity assumption will disappear. It can be proven that using (13), (25), and (26) we can obtain a better point estimate \(\overset{\rightarrow}{b}=\big{(}b_{1},b_{2},\ldots,b_{p}\big{)}^{T}\) of the parameter vector:  
\[\overset{\rightarrow}{b}=\sum_{ \begin{array}{c}j=1\\ s_{j}^{cor}>0\end{array} }^{p}\frac{\overset{\rightarrow}{u}_{j}^{T}\overset{ \rightarrow}{y}\overset{\rightarrow}{y}}{s_{j}^{cor}}\overset{\rightarrow} {v}_{j} \tag{27}\]  
The estimate (27) is computationally more expensive than (13) because of the SVD decomposition. However, the robustness of (27) is superior to (13) as it will work reliably even if the design matrix, \(\boldsymbol{X}\), is ill-conditioned or singular.
###### Abstract  
The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and the Czech Republic. This study aims to develop a practical approach for land cover classification in the Czech Republic, with the goal of efficiently acquiring spatial distribution information regarding its forest resources. This approach is based on multi-level feature extraction and selection, integrated with advanced machine learning or deep learning models. To accomplish this goal, the study concentrated on two typical experimental regions in the Czech Republic and conducted a series of classification experiments, using Sentinel-2 and DEM data in 2018 as the main data sources. Initially, this study extracted various features, including spectral, vegetation, and terrain features, from the study area, then assessed and selected key features based on their importance. Additionally, this study also explored multi-level spatial contextual features to improve classification performance. The extracted features include texture and morphological features, as well as deep semantic information learned by utilizing a deep learning model, 3D CNN. Finally, an AdaBoost ensemble learning model with the random forest as the base classifier is designed to produce land cover classification maps, thus obtaining the spatial distribution of forest resources. The experimental results demonstrate that feature optimization significantly enhances the extraction of high-quality features of surface objects, thereby improving classification performance. Specifically, morphological and texture features can effectively enhance the discriminability between different features of surface objects, thereby improving classification accuracy. Utilizing deep learning networks enables more efficient extraction of deep feature information, further enhancing classification accuracy. Moreover, employing an ensemble learning model effectively boosts the accuracy of the original classification results from different individual classifiers. Ultimately, the classification accuracy of the two experimental areas reaches 92.84% and 93.83%, respectively. The user accuracies for forests are 92.24% and 93.14%, while the producer accuracies are 97.71% and 97.02%. This study applies the proposed approach for nationwide classification in the Czech Republic, resulting in an overall classification accuracy of 90.98%, with forest user accuracy at 91.97% and producer accuracy at 96.2%. The results in this study demonstrate the feasibility of combining feature optimization with the 3D Convolutional Neural Network (3 DCNN) model for land cover classification. This study can serve as a reference for research methods in deep learning for land cover classification, utilizing optimized features.  
Keywords:land cover classification; feature optimization; 3 DCNN; ensemble learning +
Footnote †: journal: Applied sciences
# Evaluating the Effectiveness of Designs for Low-Cost Digital Manufacturing Systems
## 4 Development of the Evaluation Testbed
### Implementation of the Low-Cost Digital Design Scenarios  
To create the testbed for the structured implementation comparison, the three selected low-cost digital solutions are used to implement the different design scenarios for each candidate reference architecture. For the implementation, a range of different low-cost (hardware and software) components are incorporated into the designs. Using a different set of low-cost components for the solution designs may only have a small effect on the evaluation results, since to be considered 'low-cost', selected components should satisfy the requirements derived from the various design challenges described in Section 2.3. For example, low-cost components should provide a commonly used interface, such that in case of a malfunction the effort of replacing them is low. An overview of the implemented design scenarios is shown in Table 2. Overall, there are 18 different designs. While the first three design scenarios are applied to vibration monitoring, the integration is performed for job tracking and digital job cards. The latter has been choosen to solve the needs of a timber doorset manufacturer in the UK as a result from an in-company requirements workshop:  
1. _Add a feature_: a threshold function is added for analysing the sensor data to detect the status of the machine. Shoestring captures this functionality with an additional architecture element, while PROSA/Erlang and WoT incorporate the function into the sensor component.
2. _Add a component_: vibration monitoring is augmented by a noise filter and a graph visualisation. For each candidate reference architecture, these functions are added at the component level and do not yield new architecture elements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline Shoestring & VM & VM & VM & VM & VM & JT \& DJC \\ \hline WoT & VM & VM & VM & VM & VM & JT \& DJC \\ \hline PROSA/Erlang & VM & VM & VM & VM & VM & JT \& DJC \\ \hline \hline \end{tabular}
\end{table}
Table 2: Implementation of the low-cost digital design scenarios. The first three scenarios are applied to vibration monitoring (VM), while the integration is performed for job tracking (JT) and digital job cards (DJC).  
3. _Replace a resource_: the vibration sensor is replaced by a temperature sensor (physical) and a GUI is used instead of the dashboard (virtual). These replacements only require minor modifications of the interfaces and architecture elements for the three candidates.
4. _Integration_: both job tracking and digital job cards share the same barcode scanner. The integration strategy relies on coordinators in combination with a common service bus and virtual data integration [37]: Shoestring designs these coordinators based on service wrappers. WoT only uses one client to manage the resources of both solutions. For PROSA/Erlang, order holons are added, which form a holarchy with the holons of the individual solutions.
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 5 Analysis of Factors Influencing Wellbore Stability
### Effect of Permeability Capacity of Weak Interlayer on Wellbore Stability  
To investigate the impact of varying the permeability capacity of the weak interlayer on wellbore instability, three sets of parameters with different permeability and porosity ratios (5 mD, 0.18; 50 mD, 0.2; 500 mD, 0.22) were selected, and the results are depicted in Figure 11. As the permeability of the weak interlayer increases, the risk of collapse is subsequently raised. However, the relative change is not significant, indicating that the permeability of the weak interlayer has a limited effect on the risk of wellbore collapse.  
The plastic strain results under different permeabilities of the weak interlayer are illustrated in Figure 12. It is observed that as the permeability of the weak interlayer increases, the peak plastic strain also rises, indicating a higher risk of instability. However, the difference between the peak plastic strain and the longest extension distance for different  
Figure 11: Variation of the maximum and minimum principal stress difference with well the circumference angle.  
Figure 10: Variation of plastic strain with extension length under different strength parameters of weak interlayer.  
permeabilities of the weak interlayer is relatively small. Increasing the permeability of the weak interlayer amplifies the degree of plastic deformation, which is detrimental to the stability of the wellbore in igneous formation. Nevertheless, the change in permeability of the weak interlayer has a relatively minor impact on plastic deformation.
###### Abstract  
Rural regions face significant barriers to adopting IoT technologies, due to limited connectivity, energy constraints, and poor technical infrastructure. While urban environments benefit from advanced digital systems and cloud services, rural areas often lack the necessary conditions to deploy and evaluate secure and autonomous IoT solutions. To help overcome this gap, this paper presents the Smart Rural IoT Lab, a modular and reproducible testbed designed to replicate the deployment conditions in rural areas using open-source tools and affordable hardware. The laboratory integrates long-range and short-range communication technologies in six experimental scenarios, implementing protocols such as MQTT, HTTP, UDP, and CoAP. These scenarios simulate realistic rural use cases, including environmental monitoring, livestock tracking, infrastructure access control, and heritage site protection. Local data processing is achieved through containerized services like Node-RED, InfluxDB, MongoDB, and Grafana, ensuring complete autonomy, without dependence on cloud services. A key contribution of the laboratory is the generation of structured datasets from real network traffic captured with Tcpdump and preprocessed using Zeek. Unlike simulated datasets, the collected data reflect communication patterns generated from real devices. Although the current dataset only includes benign traffic, the platform is prepared for future incorporation of adversarial scenarios (spoofing, DoS) to support AI-based cybersecurity research. While experiments were conducted in an indoor controlled environment, the testbed architecture is portable and suitable for future outdoor deployment. The Smart Rural IoT Lab addresses a critical gap in current research infrastructure, providing a realistic and flexible foundation for developing secure, cloud-independent IoT solutions, contributing to the digital transformation of rural regions.  
inter-of things; IoT; smart rural; IoT laboratory; sensor networks; dataset generation +
Footnote †: journal:  
_Article_
## 4 Discussion
### Comparative Analysis of Damping Mechanisms in a Sine Sweep Test: NiTi WMD versus SS 304 WMD  
As shown in Figure 25a, the WMDs under forced vibration can be described by a second-order system, which can be expressed by the following differential equations,  
\[m\frac{\mathrm{d}^{2}y}{\mathrm{d}x^{2}}+c\frac{\mathrm{d}y}{\mathrm{d}t}+ky+ z(t)=mu_{0}\omega^{2}\sin(\omega t), \tag{14}\]  
where the \(u_{0}\) is the prescribed motion amplitude of the base, \(x(t)\) is the displacement of the mass, \(y(t)\) is the relative displacement of the spring and dashpot's endpoints. As for the system equipped with NiTi WMDs, a term \(\zeta\) (\(\Gamma\), \(\tau\), \(t\)) is utilized to describe the nonlinear stiffness and damping force contributed by the phase transformation. Therefore, the second-order SDOF system of NiTi WMD shown in Figure 25b now becomes,  
\[m\frac{\mathrm{d}^{2}y}{\mathrm{d}x^{2}}+c^{\prime}\frac{\mathrm{d}y}{\mathrm{d }t}+k^{\prime}y+z^{\prime}(t)+\zeta(\Gamma,\tau,t)=mu_{0}\omega^{2}\sin( \omega t) \tag{15}\]  
Although the phase transformation damping takes effect in forced vibration, the advantage of vibration isolation depends upon the input acceleration. Only when the input acceleration is lower than 1.5 g can the NiTi WMD achieve higher damping capacity than its SS 304 counterpart.  
The SS 304 and NiTi WMD exhibit higher resonant frequencies and lower vibration amplitudes once the input acceleration is lower than 0.5 g. The load and deformation of the WMD change too fast to activate the relative movement in the friction joints, which in turn  
Figure 25: Second-order SDOF system of WMD isolator under forced vibration. (**a**) SS 304 WMD; (**b**) SE NiTi WMD.  
depresses the friction dissipation. However, the stress-induced martensite transformation within the NiTi WMD brings about extra energy dissipation, resulting in lower acceleration transmissibility and a higher loss factor than the SS 304 WMD.  
When the input acceleration increases from 0.5 g to 1.5 g, the two types of WMDs demonstrate lower resonance frequencies. Thus, the friction dissipation can be retrieved, which brings about a simultaneous increase to the maximum in the loss factor of both types of WMDs. Note that the NiTi wire is able to demonstrate stronger damping capacity as the excitation frequency decreases [22], which contributes to a larger increase in NiTi WMD's loss factor during this stage.  
When the input acceleration increases from 1.5 g to 2 g, the resonant frequency decreases, and the motion amplitude of the dummy mass increases. As a result, the porous structure of the WMD is highly compacted, which makes it difficult for the WMDs to attenuate the vibration transmitted to the dummy mass. In addition, the increased motion amplitude also causes greater pseudo-elastic strain in the NiTi WMD, which finally reduces the loss factor of NiTi WMD to the same level as that of the SS 304 WMD.
## 4 Results
### Terrain Spatial Perception
Figure 4: Two-order confirmatory factor analysis of structural equation model. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Indictors** & **CMIN/DF** & **GFI** & **CFI** & **NFI** & **TLI** & **IFI** & **RMSEA** & **RMR** & **SRMR** \\ \hline Criteria & 1–5 & \textgreater{}0.8 & \textgreater{}0.8 & \textgreater{}0.8 & \textgreater{}0.8 & \textgreater{}0.8 & \textless{}0.08 & \textless{}0.05 & \textless{}0.05 \\ Actual & 3.267 & 0.934 & 0.962 & 0.946 & 0.950 & 0.962 & 0.071 & 0.026 & 0.038 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Goodness of fit for structural equation models. Model 1 only incorporates personal socioeconomic attributes and travel characteristics into the model to analyze motorists' P&R usage behavior. Model 2 covers the objective built environment and subjective terrain spatial perception of river-valley-type cities based on Model 1. Model 3 considers the moderating effect of terrain spatial perception on other factors influencing motorists' P&R behavior. The final results are presented in Table 7. According to the model results, the significance values of the likelihood ratio test are all less than 0.001, indicating that the three established models are significant [48]. The goodness of fit of Model 1, Model 2, and Model 3 increases successively, and the AIC value of the models decreases successively. This suggests that Model 3 has more explanatory power for motorists' P&R usage behavior in river-valley-type cities [49]. In Model 1, travelers' gender, drive age, degree of freedom in working time, the number of private cars owned, the payment method of traffic cost, the experience of using P&R, departure time, time saved, and cost saved by using P&R all have significant effects on P&R use. Among them, gender, driver age, degree of freedom in working time, the payment method of traffic cost, and the experience of using P&R have negative effects on the use of P&R. The number of cars owned, departure time, time saved, and cost saved by P&R have positive effects on P&R usage. After including the subjective and objective built-environment attributes in Model 2, gender becomes relatively insignificant. However, the significance and influence direction of other factors are consistent with Model 1. Additionally, the objective built environment
## 3 Methods
### Accuracy Evaluation  
To evaluate the accuracy of remote sensing in identifying apple orchard distribution, this study calculated the _OA_, kappa coefficient, user accuracy (_UA_), and producer accuracy (_PA_) of the classification results based on the confusion matrix and conducted an accuracy evaluation. The overall accuracy and kappa coefficient were used to compare the overall accuracy, and the _UA_ and _PA_ were used to evaluate the classification accuracy of a particular target within a multi-classification target, as shown in the following equations:  
\[Overall\ Accuracy=\frac{TP+TN}{FN+TP+FP+TN} \tag{2}\]  
\[kappa=\frac{(TP+FP)\times(TP+FN)\times(FP+TN)\times(FN+TN)}{\left(FN+TP+FP+ TN\right)^{2}} \tag{3}\]  
\[UA=\frac{TP}{TP+FP} \tag{4}\]  
\[PA=\frac{TP}{TP+FN} \tag{5}\]  
where _TP_ is the number of pixels for which positive categories were judged as positive, _FP_ is the number of pixels for which negative categories were judged as positive, _FN_ is the number of pixels for which positive categories were judged as negative, and _TN_ is the number of pixels for which negative categories were judged as negative.  
To evaluate the recognition accuracy of apple orchards individually, this study used the combined _PA_ and _UA_[34; 35] to evaluate the apple orchard categories in the classification results, which are defined by the following formula:  
\[F1\ Score=\frac{(\beta^{2}+1)\times PA\times UA}{\beta^{2}\times PA+UA} \tag{6}\]  
where \(\beta\) is the weight of _PA_ and _UA_. When \(\beta>1\), _UA_ is more important than _PA_. Conversely, when \(\beta<1\), _PA_ is more important than _UA_. In this study, we used \(\beta=1\) to equalize the weights of _UA_ and _PA_.  
Figure 8: Optimal number selection for the number of trees based on RGB bands.
###### Abstract  
_Gracilaria fisheri_ (GF) is a red seaweed that is widely found in Southeast Asia and has gained attention for its potential bioactive compounds and versatile applications in food products. This study explored the potential of GF as a natural gelling agent in the development of sustainable strawberry-based drinking jelly. By utilizing GF at varying concentrations (0.2 (S1), 0.4 (S2), 0.6 (S3), 0.8 (S4), and 1.0% (S5)), the impact on the physicochemical, textural, phytochemical, and flavor profiles of the strawberry concentrate-based drinking jelly was examined. The results demonstrated that GF concentration significantly affected the color characteristics, structural development, and flavor profiles of the drinking jelly samples. Increasing GF levels progressively enhanced the lightness (L*) and redness (a*) values while reducing the yellowness (b*), with optimal visual appeal achieved in the S4 samples compared to others. Microscopical observations revealed that gel matrix development improved with GF concentrations up to 0.8% (S4), transitioning from a sparse, liquid-like structure at lower levels to a compact, over-gelated network at 1.0% (S5). Physicochemical parameters, including pH, total soluble solid (TSS), and TSS/titratable acidity (TA) ratios, increased with GF levels, contributing to a sweeter, less acidic product, while water activity (a\({}_{\text{w}}\)) decreased, enhancing jelly stability. Viscosity and sulfate content increased significantly with GF concentration, indicating improved gel strength but reduced fluidity. phytochemical analysis revealed that ascorbic acid (AsA) and total phenolic content (TPC) decreased progressively with higher GF levels, leading to a reduction in antioxidant activity (DPPH and ABTS). Volatile compound analysis identified alcohols, esters, and aldehydes as dominant contributors to the flavor profile. 1-Octanol (waxy, citrus-like) and methyl anthranilate (grape-like, sweet) increased substantially, while minor groups such as terpenoids and phenolic compounds contributed floral and woody notes. The findings suggest that S4 samples strike the optimal balance between texture, color, flavor, and antioxidant properties, achieving a cohesive, visually appealing, and flavorful drinking jelly suitable for commercial applications.  
**Keywords:**_Gracilaria fisher;_ gelling agent; drinking jelly; strawberry; physicochemical quality; flavor