esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials  
In the present study, three different soil mixtures were used. The first (M1) was composed entirely of a clayey soil found in Pamplona (Spain). The second mixture (M2) consisted of a blend of this clayey soil with three differently sized gravel and sand by-products to enhance the dry density achieved by the mixture. The by-products employed were the following: a gravel (<12 mm) and a sandy gravel (<4 mm) calcium carbonate that came from the mining of magnesium carbonate rock for refractory material manufacturing, and a foundry-recycled sand (<1 mm). Based on a previous study [24], the mixture was composed of 17.9% gravel, 26.4% sandy gravel, 31.8% sand and 23.9% clayey soil. The third mixture (M3) was composed entirely of by-products, maintaining the same proportion of by-products as in mixture 2, but with the clayey soil replaced by another by-product, which had the role of fines. These fine by-products were composed of sludges from the cleaning process of the magnesium carbonate extracted in the magnesite mine. Figure 1 shows the PSD of the materials and Figure 2 shows the mineralogy obtained by XRD for the considered materials.  
Table 1 summarizes of the PSD (in accordance with UNE-EN 933-1:2012 [25]), bulk density (in accordance with UNE-EN 1097-3:1999 [26]), and mineralogy obtained by XRD for the materials considered. More detailed information regarding these materials can be found in articles [24; 27].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Gravel** & **Sandy Gravel** & **Sand** & **Sludge** & **Clayey Soil** \\ \hline PSD (\%) & & & & & \\ Gravel (x \(>\) 2 mm) (\%) & 92.8 & 76.9 & 0 & 0 & 0 \\ Sand (2 mm \(>\) x \(>\) 0.063 mm) & 6.9 & 22.8 & 95.5 & 1.8 & 2.3 \\ Fines (0.063 mm \(>\) x) & 0.3 & 0.3 & 4.5 & 98.2 & 97.7 \\ \hline Bulk Density (g/cm\({}^{3}\)) & 1.62 & 1.49 & 1.50 & 0.87 & 1.21 \\ \hline XRD mineralogy & Magnesite & Magnesite & Quartz & Magnesite & Calcite \\  & Dolomite & Dolomite & Dolomite & Quartz & Quartz \\  & & & & Quartz & Halloysite \\ \hline \end{tabular}
\end{table}
Table 1: Summary of the properties of the mixture constituents.  
Figure 1: PDS of the gravel, sandy gravel, sand, sludge and clay.  
Figure 2: XRD mineralogy of the considered materials.  
Table 2 shows the Liquid Limit (LL), Plastic Limit (PL), and Plasticity Index (PI) of the sludge and the clayey soil, as well as their Unified Soil Classification System (USCS) classifications.  
The cement (CEM) used in this study was produced in compliance with the UNE-EN 197-1:2011 standard [28] and marketed under the trade name CEM-II-BL-32.5-R. Table 3 provides the bulk density (in accordance with UNE-EN 459-2 [29]) and a summary of the chemical properties of the CEM.
## 3 Demand Response Models
### Incentive-Based Demand Response Model  
Incentive-based demand response offers rewards or incentives to motivate energy consumers to actively decrease their electricity usage during peak hours or critical situations, hence improving system efficiency [27]. The incentive cost is a measure of how effective load reduction participation is in scheduling. The cost of compensation for incentive-based response is expressed as follows:  
\[C_{\text{cut}}=c_{\text{cut}}\sum_{t=1}^{T}\Delta L_{\text{e}}^{t} \tag{9}\]  
where \(C_{\text{cut}}\) represents the compensation cost for user participation in incentive response; \(c_{\text{cut}}\) represents the incentive compensation cost per unit power; \(\Delta L_{\text{e}}^{t}\) represents the load reduction amount of user participation in incentive response during time interval \(t\); \(T\) represents one scheduling cycle, which is equivalent to 24 h.  
The adjusted electric load, considering the incentive-based demand response, is calculated as follows:  
\[\left\{\begin{array}{l}L_{\text{e}}^{t}=L_{\text{e},0}^{t}-\Delta L_{\text{ e}}^{t}\\ 0\leq\Delta L_{\text{e}}^{t}\leq\Delta L_{\text{e}}^{t,\max}\end{array}\right. \tag{10}\]  
where \(L_{\text{e}}^{t}\), \(L_{\text{e},0}^{t}\) and \(\Delta L_{\text{e}}^{t,\max}\) represent the user electricity load after incentive demand response at time \(t\), the electricity load before demand response at time \(t\), and the upper limit of electricity load change, respectively.
# Integrated Silicon Photonics for Enabling Next-Generation Space Systems
## 4 Space Radiation Effects
### System Integration Considerations  
One important factor to consider with silicon integrated systems that include both electronics and photonics is the radiation impact, or propagation, from the electronic to photonic domain. This monolithic, system integration aspect adds an entirely different set of radiation effects to silicon photonics, ones that come from the electronics domain. The good thing is that there have been over 40 years of radiation effects research in silicon electronics. However, the literature is fairly sparse when it comes to combining the radiation effects from electronics and photonics.  
One study attempts to describe how SETs that are generated within a SiGe bipolar-based driver propagate into the photonic domain [87]. An important result from this work is that the resulting optical transients are dependent on the magnitude of the electrical transient relative to the V\({}_{\pi}\) parameter of the modulator, as shown in Figure 13. An electrical transient with a peak magnitude of V\({}_{\pi}\) will result in an optical transient that fully depletes the optical signal, potentially causing bit errors and degradation in the system. Furthermore, since V\({}_{\pi}\) can be tuned based on doping and length of the phase shifters used in an MZM, a designer has the option to build a modulator that can be arbitrarily robust to SETs.  
This study suggested that there can be other design methodologies to harden silicon photonic systems. In other words, even electronics that can be highly vulnerable to various radiation effects can be compensated on the system level when photonics are brought on-board. Regardless, more SEE, TID, and DD investigations must be performed to obtain a complete picture of all of the design techniques that can be used to harden integrated silicon photonic systems.
## 3 Effect of Ca\({}^{2+}\) Source and Addition Method on Mineralized Products
### Experimental Method
#### 3.1.3 EDS, XRD, and SEM Analyses  
The testing method for EDS, XRD and SEM in this section is the same as that in Section 2.1. For details, please refer to Section 2.2.  
3.1.4. Effect of Ca\({}^{2+}\) Source and Addition Method on Mineralized Products  
Three testing groups were included in this section. Ca(NO\({}_{3}\))\({}_{2}\), CaCl\({}_{2}\), and Ca(CH\({}_{3}\)COO)\({}_{2}\) were selected as Ca\({}^{2+}\) sources, and the post-calcium-source method (add Ca\({}^{2+}\) sources after urea decompose) and pre-calcium-source method (add Ca\({}^{2+}\) sources before urea decompose) were selected as Ca\({}^{2+}\) sources addition methods. By means of weighing, multi-parameter analysis, EDS, XRD, and SEM, the production, productivity, and utilization rate of Ca\({}^{2+}\) source and the substance, phase, morphology, and structure of the mineralized products under different types of Ca\({}^{2+}\) sources and addition methods were tested in order to determine the most suitable Ca\({}^{2+}\) source and addition method.  
For Group A, 50 mL 1 mol/L urea solution was added to plastic bottles, while for Group B, 50 mL 1 mol/L repair fluid (the mixed solution containing the Ca\({}^{2+}\) source and urea) was added into plastic bottles. Then, 5 mL OD\({}_{600}\) = 1.0 _Sporosarcina pasteurii_ bacteria solution was added to the plastic bottles for each group. In order to compare the differences between the mineralized products of MICP and chemically synthesized minerals, a third group, Group C, was included. For Group C, 55 mL 1 mol/L mixed solution of CaCl\({}_{2}\) and Na\({}_{2}\)CO\({}_{3}\) was added into a plastic bottle. The initial pH values of the three groups were adjusted to 8.0, and the three groups of plastic bottles were placed in a shaking table for 48 h; the temperature was set to 30 \({}^{\circ}\)C and the rotating speed was set to 150 r/min. After removal from the shaking table, 1 mol/L Ca\({}^{2+}\) source solution was immediately added to the plastic bottles belonging to Group A, as shown in Figure 7. The production of mineralized products was measured via the weighing method described above. In addition, the concentration of Ca\({}^{2+}\) in the mixed solution before and after mineralization was measured using a multi-parameter analyzer, and the samples from each group were dried for further EDS, XRD, and SEM analyses.
# A Failure Risk-Aware Multi-Hop Routing Protocol in LPWANs Using Deep Q-Network
## 2 Related Studies  
Over the past decades, numerous multi-hop routing protocols have been investigated, with a focus on relay selection strategies to optimize routing performance. In [8], link state information within two hops was considered when selecting relays to minimize delay and reduce packet loss. However, this two-hop dependency incurs high computational overhead in dynamic networks with frequent topology changes. In [9], the candidate relay with the highest reliability was selected to establish high-reliability and low-latency routes. Nevertheless, due to the dependence on predefined fuzzy rules, its adaptability to unmodeled network scenarios is limited. A method based on link quality prediction was proposed in [10], where a fuzzy logic system that incorporates distance, residual energy, and link quality (estimated via Kalman filtering) was adopted in relay decisions. However, this method is susceptible to model mismatch in event-driven networks, as bursty traffic violates the Markovian assumption underlying Kalman filtering-based prediction.  
Given the limitations of traditional approaches, RL-based methods have emerged as a promising solution. These methods enable agents to learn optimal routing policies through real-time interaction with the external environment and reward-driven optimization, eliminating dependence on predefined models [13, 14]. A Q-learning-based routing protocol was developed in [16], where energy consumption, bandwidth utilization, throughput, and data latency are jointly considered during relay selection. Similarly, ref. [17] proposed a Q-learning framework to reduce packet losses by depriitizing predicted faulty nodes within routing decisions. In [15], a DQN-based intelligent routing (DQIR) protocol that balances residual energy distribution while minimizing routing distance was introduced to select relays. To address challenges such as insufficient adaptability to network topology changes, high communication delays, and short network lifetime in multi-hop routing, a dueling double deep Q-network was employed in [14] to optimize routing decisions. In [18], a reinforcement learning framework that integrates different node centrality metrics was developed to optimize relay selection.  
A review of existing research reveals that while neighborhood state has been incorporated into relay selection decisions, these studies primarily focus on link quality without simultaneously considering the number of neighbors and their residual energy. This narrow focus prevents these methods from effectively excluding relays that introduce high routing failure risk, particularly in dynamic and complex networks. Geared toward this shortcoming, we propose FDRR in this article.
# Popularity-Aware Closeness Based Caching in NDN Edge Networks
## 3 PaC-Based Caching
### Popularity-Aware Closeness Metric  
The PaC metric of a potential cache \(v_{i}\) for content \(x_{n}\), \(PaC(v_{i},x_{n})\), considers the distance, in terms of hop count, between \(v_{i}\) and the ingress nodes connected to the consumers. Since, in NDN, \(v_{i}\) receives Interests for \(x_{n}\) from an ingress node \(v_{j}\) only if it is in the forwarding path between \(v_{j}\) and the origin source, \(PaC(v_{i},x_{n})\) takes into account only the set of ingress nodes \(\hat{I}_{i}(x_{n})\).  
In parallel, the metric considers the number of consumers that could be satisfied by the potential cache. The higher is the number of requests for \(x_{n}\) that cross \(v_{i}\), the higher should be the PaC metric.  
In mathematical terms, the PaC metric for a node \(v_{i}\in V\) and a content \(x_{n}\) can be expressed as:  
\[PaC(v_{i},x_{n})=\frac{R_{v_{j}}(x_{n})|\hat{I}_{i}(x_{n})|}{\sum_{j\in\hat{I} _{i}(x_{n})}(h_{(v_{i},v_{j})}+1)} \tag{5}\]  
where \(|\hat{I}_{i}(x_{n})|\) is the cardinality of the set of ingress nodes in \(\hat{I}_{i}(x_{n})\), used to normalize the metric, and \(h_{(v_{i},v_{j})}\) is the hop distance between \(v_{i}\) and the ingress node \(v_{j}\).  
It can be observed that \(PaC(v_{i},x_{n})\) increases with the request rate of \(x_{n}\) and it is equal to zero if \(v_{i}\) does not receive any request for \(x_{n}\). At the same time, the metric decreases if \(v_{i}\) is far from the consumers.
# Spatial Modes of Laser-Induced Mass Transfer in Micro-Gaps
## 1 Introduction  
We observed concentric deposition patterns (Figure 1) during our study of the laser-induced forward transfer (LIFT) process for biochemical compounds [1]. These patterns comprised an inner part and several outer parts whose presence changed with the process parameters. We studied this phenomenon in experiments varying the geometric conditions and developed a model that reproduced the main characteristics in numerical simulations. In this paper, we present our findings and possible explanations for these observations.  
LIFT denotes a class of processes [2], where a laser beam induces mass transfer, mainly indirectly due to heat. The variants of LIFT reported in literature differ in the mechanisms that cause the mass transfer, such as ablation [3], droplet formation from a solid phase [4], droplet formation from a liquid phase [5], mechanical actuation due to the formation of blisters [6], and so forth. For our implementation of LIFT, we used a thick polymer layer (2 in Figure 0(a)), adhered to a glass substrate for absorbing a deflected, focused laser beam. Material was transferred from a second thin polymer layer, which embeds biomolecules (3 in Figure 0(a)) across a gap (4 in Figure 0(a)) onto a substrate that is able to bind the molecules (5 in Figure 0(a)). The gap between the two substrates lying on top of each other was caused by the waviness of the substrates and was on the order of tens of microns. The relevant physical effects leading to the mass transfer in our implementation of LIFT was previously unclear, and its determination was part of this study. We concluded that an aerosol was created and that it was subject to confinement conditions in the gap. This diverges from the known transfer mechanisms mentioned above. After chemical processing and fluorescent staining, images of the deposition patterns could be taken. Their intensity corresponded to the surface concentration of the bound molecules.
# Simulation and Experimental Analysis of Pressure Pulsation Characteristics of Pump Source Fluid
## 4 Simulation Analysis of Fluid Pressure Fluctuation Characteristics  
The plunger pump AMESim simplifies the parameter set of the system model, as shown in Table 1.  
When the speed was \(n=\) 1800 r/min, the load pressure was \(p_{1}=\) 4.375 MPa, \(p_{2}=\) 8.38 MPa, and \(p_{3}=\) 12.87 MPa, respectively, and the frequency domain waveform diagram of fluid pressure pulsation was obtained by simulation, as shown in Figures 7-9.  
At the same speed, the fluid pressure pulsation value and pressure pulsation rate under different load pressures are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Numerical Value** & **Parameter** & **Numerical Value** \\ \hline Plunger radius (m) & 0.05 & Cylinder inertia & 0.005 \\ Swash plate inclination (\({}^{\circ}\)) & 17 & Motor speed (r/min) & 1800 \\ Number of plungers & 7 & Plunger diameter clearance (mm) & 0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter setting of the plunger pump model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rotating Speed** & **Load Pressure (MPa)** & **Greatest Pressure (MPa)** & **Minimum Pressure (MPa)** & **Pressure Pulsation** \\ \hline
**(r/min)** & 4.375 & 4.50 & 4.25 & 5.71 \\
1800 & 8.38 & 8.578 & 8.182 & 4.73 \\  & 12.87 & 13.15 & 12.59 & 4.35 \\ \hline \end{tabular}
\end{table}
Table 2: Numerical simulation of fluid pressure fluctuation and pressure fluctuation rate under different load pressures.  
Figure 6: Overall model of the plunger pump.  
Under different load pressures, the first three frequency amplitudes were extracted as shown in Table 3.  
The above simulation results show that at a certain speed, as the load pressure of the hydraulic system increased, the time-domain waveform curve of the fluid pressure pulsation of the pump source changed, the pressure pulsation amplitude increased, and  
Figure 8: Load pressure (\(p_{2}\) = 8.38 MPa) waveform. (**a**) Time domain diagram; (**b**) frequency domain map.  
Figure 7: Load pressure (\(p_{1}\) = 4.375 MPa) waveform. (**a**) Time domain diagram; (**b**) frequency domain map.  
Figure 9: Load pressure (\(p_{3}\) = 12.87 MPa) waveform. (**a**) Time domain diagram; (**b**) frequency domain map.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Load Pressure** & **Triple Frequency** & **Double Frequency** & **Triple Frequency** \\
**(MPa)** & **Amplitude (MPa)** & **Amplitude (MPa)** & **Amplitude (MPa)** \\ \hline
4.375 & 0.068 & 0.039 & 0.017 \\ \hline
8.38 & 0.101 & 0.063 & 0.027 \\ \hline
12.87 & 0.132 & 0.088 & 0.035 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency doubling amplitude under different load pressures.  
the pressure pulsation rate decreased. From the frequency domain waveform curve, the fluid pressure pulsation contained multiple harmonic components in the wide frequency domain. Moreover, the fundamental frequency of the fluid pressure pulsation increased obviously with the increase of the load pressure, while the amplitude of other frequency doublings did not change significantly.
###### Abstract  
Robust channel estimation is required for coherent demodulation in multipath fading wireless communication systems which are often deteriorated by non-Gaussian noises. Our research is motivated by the fact that classical sparse least mean square error (LMS) algorithms are very sensitive to impulsive noise while standard SLMS algorithm does not take into account the inherent sparsity information of wireless channels. This paper proposes a sign function based sparse adaptive filtering algorithm for developing robust channel estimation techniques. Specifically, sign function based least mean square error (SLMS) algorithms to remove the non-Gaussian noise that is described by a symmetric \(\alpha\)-stable noise model. By exploiting channel sparsity, sparse SLMS algorithms are proposed by introducing several effective sparse-promoting functions into the standard SLMS algorithm. The convergence analysis of the proposed sparse SLMS algorithms indicates that they outperform the standard SLMS algorithm for robust sparse channel estimation, which can be also verified by simulation results.  
robust sparse channel estimation; sign function based least mean square error (SLMS); sparsity-promoting function; non-Gaussian noise; convergence analysis +
Footnote †: journal:  
[PERSON] 1,2,* and [PERSON] 3
## 4 Results
### Evaluation of the Individual Retrofit Strategies
#### 4.2.1 External Wall Retrofit Strategies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy Code** & **Strategy Description** & **Cooling Energy [kWh]** & **Reduction [\%]** & **Sig. Value** \\ \hline S-WA-1 & VF with ACP cladding & 401,673 & 15.8 & 0.002 * \\ \hline S-WA-2 & VF with FC cladding & 424,315 & 11.1 & 0.004 * \\ \hline S-WA-3 & ETICS & 415,182 & 13.0 & 0.002 * \\ \hline S-WA-4 & Prefabricated wood & 391,024 & 18.0 & 0.003 * \\ \hline S-WA-5 & VF with limestone cladding & 423,534 & 11.2 & 0.003 * \\ \hline S-WA-6 & VF with sandstone cladding & 423,490 & 11.2 & 0.003 * \\ \hline S-WA-7 & VF with AAC brick cladding & 411,651 & 13.7 & 0.003 * \\ \hline S-WA-8 & VF with WPC cladding & 403,729 & 15.4 & 0.005 * \\ \hline \hline \end{tabular}  
* AAC = Autolcaved Aerated Concrete, ACP = Aluminum Composite Panel, ETICS = External Thermal Insulation Composite Systems, FC = Fiber Cement Board, VF = ventilated Facade, WPC = Wood Plastic Composite.
* Observed reduction is statistically significant at \(\alpha\leq 0.05\).  
\end{table} 
Table 5: The annual cooling energy reduction in the examined wall retrofit strategies (compared to 477,074 kWh cooling energy in the base case). The assessment of facade retrofit strategies must consider the relationship between the investment cost and the payback period. Table 6 outlines the payback period for each retrofit strategy, considering its estimated cost in the Saudi market, along with the financial recovery periods based on the savings in cooling electricity costs. Notably, external insulation strategies tend to have a longer financial recovery period due to their high initial cost. Among the strategies, ETICS had the shortest payback period at 15.39 years, attributed to its affordability and widespread availability in the Saudi market. This was followed by the ventilated facade with AAC brick cladding (23.3 years) and the ventilated facade with FC cladding (24.1 years). The remaining strategies demonstrated longer recovery periods, often exceeding thirty years.
## 3 Cyclic Depressurization Simulation Study
### Results of Production Time Case during Primary Depressurization Stage  
The production time during the primary depressurization stage ranged from 2 to 8 days. As represented in Figure 11a, more gas was produced in all cyclic depressurization cases than the non-cyclic case during the primary depressurization stage, and the gas production rate of all cases was kept at a certain level. At the initial production time, the gas production rate during the secondary depressurization stage increased in all cases. The maximum gas production rate was about 193 m\({}^{3}\)/day in the case of 2 days. However, the gas production rate reduced after 100 days during the secondary depressurization stage. As production days of primary depressurization increased, cumulative gas production also increased from 1.43 \(\times\) 10\({}^{5}\) m\({}^{3}\) (in the case of 2 days) to 2.00 \(\times\) 10\({}^{5}\) m\({}^{3}\) (in the case of 8 days) (Figure 11b). The cumulative gas production of the non-cyclic case was higher than that of the cyclic cases. In the case of 2 days, cumulative gas production was less than half that of the non-cyclic case.  
During the secondary depressurization stage, the vertical subsidence of the cyclic cases decreased more than the non-cyclic case, due to the increased pore pressure, for example, from \(-\)2.29 m (in the case of non-cyclic) to between \(-\)1.56 m (in the case of 2 days) and \(-\)1.91 m (in the case of 8 days) after 400 days (Figure 12). Moreover, our simulation results showed that short-production-time cases (i.e., 2-day case) provided good stability, showing the low vertical subsidence due to the low gas production.  
Figure 10: Results of vertical displacement by use of different bottomhole pressure during primary depressurization stage.