esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Step High-Order Compact Corrected WENO Scheme
## 2 Numerical Methods
### Compact Corrected WENO (CCWENO)
This method applies a high-order correction without requiring a complete recalculation of the flux function at each point, thereby avoiding exponential increases in computational cost. Since the WENO-calculated values remain incorporated in the computation, this approach prevents excessive discontinuities in the flux function. To further mitigate abrupt changes in flux values, particularly in regions of strong discontinuity, a smoothness criterion (\(\beta\)) is introduced, ensuring that corrections are applied only where the numerical solution at grid point (\(x_{j}\)) is sufficiently smooth. In this study, we adopt the nonlinear weight of the middle stencil from WENO as the smoothness criterion, setting \(\beta=\omega_{1}\). The correction applied to the flux function at \(j+\frac{1}{2}\) is given by the following equation:  
\[\hat{f}_{j+\frac{1}{2}}= \begin{cases}\hat{f}^{cc}_{j+\frac{1}{2}}&\quad if\; \beta\geq\alpha\\ f^{WENO}_{j+\frac{1}{2}}&\quad if\;\beta<\alpha\end{cases} . \tag{13}\]  
In all numerical simulations conducted in this study, \(\alpha\) is set to 0.1, meaning that smoothness is weighted at 10% across all three stencils. We designate the method that applies corrections exclusively to the middle stencil as CCWENO (Compact Corrected WENO), while the approach that implements high-order compact corrections across all three stencils is referred to as CCWENO2. The parameter \(\alpha\) introduced here offers an optional choice, allowing for flexible tuning of accuracy and robustness to suit specific requirements.
## 3 Results and Discussion
### Effect of the Key Operating Conditions
#### 3.4.2 Effect of the Adsorption Time on Purity  
It has been observed in Figure 6 that when the adsorption time is longer than 300 s, the purity of CH\({}_{4}\) significantly decreases. In addition, if the adsorption time is too short, i.e., lower than 200 s, it would result in a negative effect with respect to the adsorbent limit. Therefore, this section's aim is to find the optimal adsorption time within the investigated range between 200 and 300 s. We compare the difference in purity and recovery time of methane with three adsorption step time of 200, 250 and 300 s, as shown in Table 6. It can be observed that the purity decreases and the recovery increases when increasing the adsorption time due to the interaction times of the CMS and CO\({}_{2}\). In Case 1 (200 s), the interaction between the CMS and biogas has high efficiency, as shown in Figure 6. The purity of CH\({}_{4}\) in Case 1 is higher than in Case 2 (250 s), but the difference in number of cycles in Case 1 is 23% higher than in Case 2. Consequently, the cycle time is higher and the purity is above the requirement. However, for Case 3 (300 s), the CMS was limited to absorbing only CO\({}_{2}\) and the purity was low. Therefore, the optimal condition is an adsorption step time of 250 s, which has met the minimum purity requirement and equals 92.08%. Moreover, it was very close to the real operating condition of 92.10% purity.
# Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems
## 1 Introduction
On the other side, there are some research works are dealt with enhancing the performance for MU-MIMO systems: [20] exploited the features of MU-MIMO such as high multiplexing gain and beamforming capabilities to improve the network throughput by proposing an efficient energy MAC scheme for LTE systems, the proposed scheme is worked to reduce the energy consumption by assigning the services for each user with highest SINR per beam. Another work [21] used a partial channel state information at the transmitter (CSIT) in order to have a good feedback knowledge about the network load, consequently a proposed multi-user MAC scheme is presented to enhance the system performance which it is measured in terms of system sum rate and network throughput. While the authors of [22] attempted to resolve the problem of LTE systems in which they used the TDD approach to share the same bandwidth for uplink and downlink, the adjacent cells in such systems with various time allocations in uplink and downlink practically suffer interference among terminals and base stations that deteriorate the system performance. Hence, the aim of this work is to determine the optimum amount of energy that should be assigned to each terminal in the system to diminish the interference impact and to provide the minimum QoS satisfaction to all users. In this paper, we measure the system performance gains in multi-cell massive MIMO and quantify the amount of array gain, diversity gain and spatial multiplexing gain and discuss the effect of non-orthogonal pilot sequence (i.e., imperfect CSI) on them. Energy Efficiency is calculated as a measurement for massive MIMO array gain while outage probability is measured for spatial diversity gain and spectral efficiency is calculated for spatial multiplexing gain, all the measurements are done under two main considerations; perfect CSI and imperfect CSI (the effect of pilot contamination). Then, the most important tradeoffs are discussed among these gains under the effect of pilot contamination, which includes the tradeoff between energy efficiency and spectral efficiency; and the tradeoff between multiplexing gain and diversity gain. This paper is organized as follows: Section 2 will describe the system model for massive MIMO multi-cell systems with uplink and downlink communication phases, Section 3 will measure the massive MIMO performance through analyzing the main three performance gains (spatial diversity gain, array gain and spatial multiplexing gain which are done also under perfect and imperfect CSI. Section 4 will discuss two main performance tradeoffs among these gains such as (array gain and spatial multiplexing gain tradeoff, spatial diversity gain and spatial multiplexing gain tradeoff). Section 5 will simulate the presently derived expressions for performance gains and tradeoffs analysis. Finally, Section 6 will give the research conclusions.
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed Conceptual Architecture
#### 3.1.4 ATG Model  
The PL of this model can be calculated as:  
\[\mathrm{P_{L}}=20\log\left(\frac{h_{t}-h_{r}}{\sin\theta}\right)+20\log\left(f \right)-27.55 \tag{16}\]  
where \(\mathrm{P_{L}}\) is Path Loss (dB), \(h_{t}\) is Transmitter Antenna Height (km), \(h_{r}\) is Receiver Antenna Height, \(\theta\) is Elevation Angle, and \(f\) is Carrier Frequency (GHz).
## 6 Experiments  
In this section, we first present the utilized data, evaluation metrics, and experimental setting. Then, we conduct experiments to answer the following questions:  
**RQ1:**_How does the performance of our proposed method compare to that of the state-of-the-art methods in the literature?_  
**RQ2:**_What are the influences of the two primary components of our proposed method, namely the diverse model stacking ensemble and self-training?_  
**RQ3:**_How does the performance of our proposed method compare to that of each base model trained using different learning paradigms in low-resource scenarios?_
## 3 Near-Infrared Silicon Absorption Physics
### Photoconductivity and Linear Absorption  
The term photoconductivity covers all the phenomena by which a change in conductivity--either an increase or decrease--follows absorption of light in the considered materials. Photoconductivity is not an elementary process. It includes several successive or simultaneous mechanisms: optical absorption, hot carrier relaxation, charge carrier transport and recombination. Photoconductivity offers a means of studying many physical properties of materials and, on the other hand, photoconductivity effects have obvious applications for the detection and measurement of light over the whole electromagnetic spectrum. The corresponding literature is very abundant and can be found in many scientific journals and books. Therefore, in this paragraph we don't intend to review all the published papers dealing with photoconductivity and related effects, but instead to provide a short introduction to the physical effects behind the working principles of recently proposed near-infrared all-silicon photodetectors [18].  
The observation of any photoconductivity phenomenon requires the presence of at least one type of mobile charge carrier. Photoconductivity is due to a change in carrier concentration, which results from the generation in the semiconductor of electronic excited states by absorption of light energy. The excited states have a finite lifetime: they lose energy through different processes of relaxation and recombination. Finally, we note that in homogeneous solids, photoconductivity is usually observed by applying an electric field to the material and measuring the resulting photocurrent.  
One of the most important aspects is the generation of photocurrent, which is obtained by irradiating a sample with a homogeneous light flux. Inter-band generation, which is due to optical absorption by inter-band electronic transition, is characterized by high absorption coefficients. Intra-band generation refers to free-carrier optical absorption. In this case, the absorption coefficient is usually low and is proportional to the free-carrier concentration. Finally, we have extrinsic generation, which refers to photoionization of impurities and localized centers. There are two main differences with respect to the case of inter-band generation: only one excited carrier is generated, either electron or hole, and the absorption process is characterised by low absorption coefficients [19, 20]. Photoconductivity effects related to extrinsic generation have been recently investigated in order to realize NIR silicon photodetectors. Therefore, in the next two subsections, the photoconductivity effect due to mid-bandgap absorption (MBA), and due to surface state absorption (SSA), is reported. Finally, in the last subsection, internal photoemission absorption (IPA) is discussed.
# A Trusted Routing Scheme Using Blockchain and Reinforcement Learning for Wireless Sensor Networks
## 3 Approach
### RLBC Routing Algorithm
When the routing node \(R_{i}\) makes action \(a\), the number of nodes and packets it finally reaches is used to measure the reward. In a time slot, the value of \(r\) is \(V_{max}\) when the tokens are successfully delivered to the target node \(R_{t}\). When the tokens are delivered to the history node, it means that the address is in \(A_{i}\) and the loop routing problem happens, and we set \(r\) to \(V_{min}\). In other cases, the value of \(r\) is related to the amount of successfully delivered tokens, for example, if the amount of tokens actually transmitted is \(n_{i}\), the value of \(r\) is \(n_{i}\). Since the reinforcement learning reward value \(r\) is determined by the amount of tokens delivered, it also restricts the non-forwarding behavior of malicious nodes in the threat model. If a malicious node doesn't transmit any packet (token) in the routing process, the value of \(r\) will be small and it can greatly reduce the possibility that packets will pass through this malicious node. Thus, the problem of blocking routing link caused by malicious black hole nodes is greatly reduced. By dynamically extracting and learning relevant routing information from the blockchain, the learning model will eventually output a policy \(\pi\) for the routing node. The value \(\pi\) represents the routing policy which determines the next-hop of the packages targeted at \(R_{t}\), and it depends on the value of \(q_{t}(x,a)\cdot Q_{t}(x,a)\). Assuming that, when the state is \(x\) and the number of \(a\) in the action space is \(K\), the probability distribution of \(a_{k}\) is selected based on Boltzmann distribution. The specific equation of Boltzmann distribution is shown in Equation (2). The parameter \(\tau>0\) is called \"temperature\", and the lower \(\tau>0\) is, the higher the probability that the high reward action will be selected, and the packet is passed to the corresponding \(R_{\pi}\). In general, the higher the value of \(q_{t}(x,a_{k})\cdot Q_{t}(x,a_{k})\), the more likely it is to execute a policy \(\pi(x)=a_{k}\). \[P(x,a_{k})=\frac{e^{\frac{q_{t}(x,a_{k})\cdot Q_{t}(x,a_{k})}{\tau}}}{\sum\limits \limits_{k=1}^{K}e^{\frac{q_{t}(x,a_{k})\cdot Q_{t}(x,a_{k})}{\tau}}}. \tag{2}\]  
The reinforcement learning based routing algorithm helps the choice of routing by the nodes' dynamic learning. Each hop of the routing information is recorded on the blockchain, and if the hop is looped, or the link is untrusted, or the transmit rate is low, the algorithm greatly reduces the probability that the packets pass through the link. At the same time, the routing algorithm can dynamically discover the more reliable and efficient routing links, so as to help the routing nodes make better routing decisions.
# Electroless Copper Patterning on TiO\({}_{2}\)-Functionalized Mica for Flexible Electronics
###### Abstract
However, the most common impurity in electroless copper films is embedded hydrogen, typically originating from the gas released by the formaldehyde-based reducing agent used in the process [22; 23]. Dissolved oxygen in the plating bath might also be embedded, especially at lower deposition rates [26]. The main challenge with silicate materials, such as mica, is modifying the surface to improve adhesion and deposit a catalyst for the reaction [16]. Palladium (Pd) and platinum (Pt) are the most commonly used catalysts for electroless copper reactions with formaldehyde as the reducing agent [16; 27; 28]. However, the literature has shown that cost-effective alternative catalysts, such as highly dispersed silver (Ag) and copper (Cu) nanoparticles, can also effectively activate the electroless Cu deposition process [29; 30]. In recent studies, we demonstrated a method for selective photocatalytic activation and chemical metallization by functionalizing the surface with a thin TiO\({}_{2}\) layer. This approach was applied to anodic aluminum oxide (AAO) substrates [31] and glass substrates to fabricate functional humidity sensors based on the TiO\({}_{2}\) layer properties [32]. The method provides good adhesion to insulating substrates like glass and anodized aluminum oxide. It also allows the direct additive patterning of conductive images by UV irradiation through a mask in a solution of metal ions, such as Cu\({}^{2+}\) and Ag\({}^{+}\). However, this approach's limitation is the need for a high-temperature annealing step (450-500 \({}^{\circ}\)C) to form the TiO\({}_{2}\) layer by sol-gel coating, thus restricting its use to silicate or ceramic materials. Mica, with its excellent thermal, mechanical, and chemical stability, is a promising substrate to employ this functionalization route, while simultaneously offering mechanical flexibility. This study aims to explore the selective deposition of conductive copper patterns on functionalized mica substrates via electroless copper deposition (Cu-ELD). Unlike previous work focusing on the photocatalytic activation of the TiO\({}_{2}\)-functionalized substrate or its use as an active sensing material, this research investigates how the Cu-ELD process parameters affects the electrical properties of the copper layers. Understanding these effects could allow for the controlled modulation of copper layer conductivity, a feature with potential for the development of resistance temperature detectors (RTDs). Given copper'shigh temperature coefficient of resistance, this makes it well suited for RTD applications, despite the limitations of copper's low intrinsic resistivity. Demonstrating functional RTD devices based on Cu-ELD copper layers is also a key goal of this study.
## 2 Problem Description and Methodology  
In the Checa parish, as in other cantons in the metropolitan district of Quito, geographical conditions are influenced by factors such as vegetation, terrain relief, and population density, among others. A comparative analysis is performed between the predicted site coverage and the actual data collected in the field to assess the precision of the propagation model in relation to real conditions.  
When discrepancies arise between the results and the empirical data, the model undergoes adjustments to more accurately reflect the specific geographical conditions at the installation site in the Checa area. The subsequent steps are suggested for this analysis:  
* **Field Data Collection**: The signal power received at different distances from the emitting source is measured within the area of interest. In the case of Checa parish, these data can be obtained from the signal transmitted by a Wi-Fi network repeater from the Municipality of Quito using a Wi-Fi card and a GPS module embedded in a Raspberry Pi 3 controller.
* **Data Georeferencing and Extrapolation**: Using MATLAB, the collected data are georeferenced on a map. MATLAB's computational tools are then used to interpolate between the empirically obtained signal levels and the deterministic model SUI  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cite** & **Technology** & **Distance** & **Frequency** & **Equipment Used** \\ \hline
[19] & Wireless & \(\geq\)100 m & 1700 MHz & Experimental \\
[20] & Cellular Networks & \(\geq\)100 m & 3.5 GHz & Cellular \\
[21] & Wi-Fi & Up to 100 m & 2.4 GHz & Laptops and GPS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Research Contributions on the SUI Model.  
(based on linear or exponential regressions). The result is an adjusted curve that aligns with the signal reception levels, which is compared with empirical data to determine whether a new measurement should be taken or to proceed with the path loss calculation.
* **Statistical Analysis**: A statistical analysis is performed to calculate path loss and other attenuation factors, such as the standard deviation, from the collected data.
* **Determination of the Path Loss Exponent**: Based on the analyzed data, a regression is performed to determine the appropriate path loss exponent. The results are then compared with well-known theoretical models such as Free Space and Okumura-Hata, among others.
* **Hetatmap Generation**: Finally, with the resulting formulation, heat maps are generated to predict the signal levels in the study area.  
In Figure 1, the flowchart of the process to be followed in this document is shown.
###### Abstract  
Numerical simulation has emerged as a powerful technique for landslide failure mechanism analysis and accurate stability assessment. However, due to the bias of simplified numerical models and the uncertainty of geomechanical parameters, simulation results often differ greatly from the actual situation. Therefore, in order to ensure the accuracy and rationality of numerical simulation results, and to improve landslide hazard warning capability, techniques and methods such as displacement back-analysis, machine learning, and numerical simulation are combined to create a novel landslide warning method based on DBA-LSTM (displacement back-analysis based on long short-term memory networks), and a numerical simulation algorithm is proposed, i.e., the DBA-LSTM algorithm is used to invert the equivalent physical and mechanical parameters of the numerical model, and the modified numerical model is used for stability analysis and failure simulation. Taking the Shangtan landslide as an example, the deformation mechanism of the landslide was analyzed based on the field monitoring data, and subsequently, the superiority of the DBA-LSTM algorithm was verified by comparing it with DBA-BPNN (displacement back-analysis based on back-propagation neural network); finally, the stability of the landslide was analyzed and evaluated a posteriori using the warning threshold calculated by the proposed method. The analytical results show that the displacement back-analysis based on the machine learning (DBA-ML) algorithm can achieve more than 95% accuracy, and the deep learning algorithm exemplified by LSTM had higher accuracy compared to the classical BPNN algorithm, meaning that it can be used to further improve the existing intelligent inversion theory and method. The proposed method calculates the landslide's factor of safety (FOS) before the accelerated deformation to be 1.38 and predicts that the landslide is in a metastable state after accelerated deformation rather than in failure. Compared to traditional empirical warning models, our method can avoid false warnings and can provide a new reference for research on landslide hazard warnings.  
deep learning; LSTM; displacement back-analysis; numerical simulation; warning thresholds analysis. Article
# Ensuring Energy Efficiency of Air Quality Monitoring Systems Based on Internet of Things Technology
## 4 Results
Putting a device to sleep between data sessions can significantly reduce its power consumption during periods of inactivity. Data compression also helps to reduce power consumption; reducing the size of the data packets reduces transmission time and also reduces the load on the transmitter. This is especially important for systems with a large number of sensors. The use of adaptive transmission algorithms allows the user to dynamically change the transmitter power depending on the conditions of radio signal propagation. This approach ensures the efficient use of energy and the maintenance of stable communication, even under difficult conditions. The combination of these strategies contributes to the creation of energy-efficient and sustainable IoT systems for air quality monitoring. \begin{table}
\begin{tabular}{c|c c c c c} \hline \hline
**Technology** & **Range of Action** & **Transmission Speed** & \begin{tabular}{c} **Energy** \\ **Consumption** \\ \end{tabular} & **Network Type** & **Capacity** \\ \hline Wi-Fi & \begin{tabular}{c} 30–50 m \\ (indoors) \\ \end{tabular} & Up to & \begin{tabular}{c} 100–200 Mbit/s \\ \end{tabular} & High & Point-to-point & High \\ Bluetooth & \(\sim\)10 m & Up to 1 Mbit/s & Average & One-to-one & Low \\ Bluetooth LE & 10–50 m & Up to 2 Mbit/s & Very low & \
\begin{tabular}{c} Star or mesh (BLE \\ 5.0) \\ \end{tabular} & Average \\ Zigbee & 10–100 m (mesh) & Up to 250 Kbit/s & Low & Mesh & Low \\ LoRaWAN & Up to 15 km &
\begin{tabular}{c} Up to 50 Kbit/s \\ (open space) \\ \end{tabular} & Very low & Star & Very low \\ NB-IoT & Up to 10 km & \(\sim\)50–100 Kbit/s & Average & Star (via operator) & Low \\ Thread & 10–100 m (mesh) & Up to 250 Kbit/s & Low & Mesh & Average \\ LPWAN (General \\ Category) & Up to 15 km & Up to 100 Kbit/s & Very low & Star & Very low \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison table of the available wireless technologies for the IoT.