esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 4 Markov Decision Process
### Optimization Formulation  
The average data rate can be defined as  
\[\eta_{T}=\lim_{t\to\infty}\sup\frac{1}{t}\sum_{t^{\prime}}^{t}E[R(S_{t^{\prime }},A_{t^{\prime}})], \tag{9}\]  
where \(S_{t^{\prime}}\) and \(A_{t^{\prime}}\) are the global state and action at time \(t\), respectively. In addition, sup represents the least upper bound.  
Moreover, the average of the SINR for the D2D link is defined as  
\[\eta_{C}=\lim_{t\to\infty}\sup\frac{1}{t}\sum_{t^{\prime}}^{t}E[C(S_{t^{\prime }},A_{t^{\prime}})]. \tag{10}\]  
The MDP model can be defined as follows:  
\[\max_{\pi}\eta_{T} \tag{11}\]  
\[\text{s.t.}\quad\eta_{C}\leq\delta_{C} \tag{12}\]  
where \(\pi\) is the policy that represents the probability of taking an action in a certain state. In addition, \(\delta_{C}\) represents the threshold of the constraint. To acquire the optimal policy for the mobile node \(i\), we formulate an optimization problem that can be represented byLP. \(\theta(S_{i},A_{i})\) is the stationary probability of state \(S\) and action \(A\). The LP model can be expressed as  
\[\max_{\theta(S,A)}\sum_{S}\sum_{A}\theta(S,A)r(S,A) \tag{13}\]  
\[\text{s.t.}\quad\sum_{S}\sum_{A}\theta(S,A)C(S,A)\leq\delta_{max}, \tag{14}\]  
\[\sum_{A}\theta(S^{\prime},A)=\sum_{S}\sum_{A}\theta(S,A)P[S^{\prime}|S,A], \tag{15}\]  
\[\sum_{S}\sum_{A}\theta(S^{\prime},A)=1, \tag{16}\]  
and  
\[\theta(S^{\prime},A)\geq 0. \tag{17}\]  
The objective function in Equation (13) maximizes the sum data rate for the mobile node \(i\). The constraint in Equation (14) maintains the number of D2D links between the mobile node \(i\) and the relay node. The constraint in Equation (15) satisfies the Chapman-Kolmogorov equation. The constraints in Equations (16) and (17) are for the probability properties. When the LP problem is feasible, the optimal policy of the mobile node \(i\) can be obtained as  
\[\pi^{*}(S,A)=\frac{\theta^{*}(S,A)}{\sum_{A^{\prime}}\theta^{*}(S,A^{\prime})}. \tag{18}\]
## 3 Testbed Deployment
At the end of the second phase, which was finished by late June 2010, there were 15 nodes in place. The third phase consisted of filling Sites 2 and 3 with nodes, which were used for collecting environmental data. By late July of 2010, 25 additional nodes (12 in Site 2, 13 in Site 3) were in place. At this time the network consisted of a total of 40 motes, including both relay nodes and nodes with external sensors. Following the third phase, network modifications consisted of relay changes, adjusting both the number and location of relays, to increase the network connectivity. In late August 2010, many of the nodes in Site 1 were relocated to accommodate tree removal during a construction project taking place near the BFNR Nature Center. From August 2010 until March 2012 the network consisted of 40 motes, except for the three months in 2011 when there were only 39. In mid-March 2012, the network was increased to 42 motes. These two additional relay nodes were positioned near the boundary of Sites 1 and 2. At the end of March 2012, nodes in Site 1 were relocated once again due to the end of the construction taking place near the BFNR Nature Center. In May 2012, selected nodes, including the two new relays, were replaced with new IRIS motes. The purpose of this upgrade was to increase the connectivity between the sensor beds (_i.e._, Sites 2 and 3) and the base station. The WSN continued working with 42 motes until February 2013, when ten additional MicaZ motes were deployed forming Sites 4 and 5, as seen in Figure 3. Site 4 consists of four nodes with environmental sensors located in a copse of trees between Sites 1 and 2. Site 5 consists of six nodes with environmental sensors located south of Site 3. Figure 4 presents an overview of the network deployment timeline. The top curve shows the total number of nodes deployed in the network over the network's deployment time (solid line) and the monthly maximum number of connected nodes within the network (dotted line). The difference between the number of deployed nodes and connected nodes is due to maintenance-needed events (e.g., hardware failures and battery depletions) and the performance of the routing protocol. Along the bottom are color bars representing the time periods for the two different network gateways (_i.e._, Sluggo and Linux) and the two different WSN routing protocols (_i.e._, XMesh and CTP) employed by the WSN application, which are discussed in the following sections. As of the end of April 2014, the WSN consists of 52 nodes, 253 sensors (including 104 onboard sensors and 149 external sensors) and is running a CTP-based WSN application with a Linux WSN gateway.
# A Study on the Conductivity and Infrared Spectroscopy of Elastic Polymer Composite Materials
## 5 Conclusions  
This study focuses on the research and development of highly conductive elastic polymer composite materials. These materials are created by doping electrically insulating elastic polymers with high concentrations of activated carbon or graphite particles, which are then dispersed and compounded to form elastic composite conductive polymers. Activated carbon and graphite, both composed of carbon atoms, interact with the rubbery polymeric material through physisorption, altering the molecular packing and reducing the distance between polymer molecules. This results in a high residual force within the polymer molecules.  
The performance of organic electronics has seen significant advancements over the past twenty years. Carbon powder allows molecules to stack tightly, reducing the energy gap and imparting semiconductor-like properties to polymers. When combined with solution-processed polymeric materials, such as those used in radio-frequency technologies, this development holds substantial practical value for electronic applications.  
However, the electrical conductivity of doped activated carbon and graphite differs significantly. The key distinction lies in how their doping concentration relates to resistivity: in activated carbon, the relationship is linear, while in graphite, it follows an exponential pattern. Consequently, the ratio of surface area to particle size in carbon powder influences its conductive properties. When the surface area is large relative to particle size, the doping concentration and resistivity show a linear relationship. Conversely, if the ratio is low, the relationship becomes exponential. This characteristic is worth further investigation.  
Additionally, this study revealed that the physisorption of elastic polymer composite materials is governed solely by van der Waals forces. Unlike covalent bonds, van der Waals forces are weaker and non-selective. While these forces do not form chemical bonds, they do produce a distinct infrared absorption spectrum.  
Conceptualization, T.-H.T. and P.-P.C.; methodology, T.-H.T.; software, P.-P.C.; validation, T.-H.T. and P.-P.C.; formal analysis, T.-H.T.; investigation, P.-P.C.; resources, T.-H.T.; data curation, T.-H.T.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 15: The flexible circuit boards in this study are made from conductive elastic polymer composite materials.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. All the data presented in this study are original.  
The authors declare no conflict of interest.
# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
## 5 Conclusions  
In this paper, the feasibility of the integrated hybrid network concept with pre-emption has been applied to C-RAN. FH traffic with time transparency, generated according to a CPRI standard, has been aggregated with pre-emptable BH traffic within the same optical Ethernet channel. Performance evaluations have been presented for different CPRI options, related to the amount of BH traffic which takes advantage of unused FH capacity. Scheduling BH packets transmission is first performed when gaps in the FH traffic are of suitable size as provided by the IHON mechanism. With this assumption, remarkable BH throughput is shown especially for CPRI option 1. CPRI option 6,  
Figure 10: Average number of segments (\(N_{\text{S}}\)) required to send a backhaul (BH) packet as a function of payload length \(L_{F}\) for different BH packet length \(L_{B}\) using CPRI opt. 1 and 6.  
Figure 9: Overhead for backhaul (BH) packets as a function of payload length \(L_{F}\) for different BH packet length \(L_{B}\) using CPRI opt. 6. Solid lines for the case with segmentation (S), dashed lines for the no-segmentation case (P).  
instead, limits BH throughput because of the smaller packet gaps in the FH traffic that makes the fitting of the BH traffic sometimes even not possible. For this reason, segmentation of backhaul packets has been introduced and analyzed, showing a remarkable improvement in BH throughput performance. The mechanism has been shown to enhance the basic performance of an Ethernet switch by guaranteed time-transparent aggregation of fronthaul traffic with efficient transfer of backhaul traffic. The introduction of such equipment in the 5G transport network will allow compatibility with the widely deployed Ethernet standard while enhancing significantly the scalability and the flexibility of the C-RAN optical infrastructure. This research can be further developed to include the introduction of delay bounded mechanisms to better adapt BH traffic to the unused bandwidth in the FH flow.  
[PERSON] has supervised and written the paper. [PERSON] has developed the simulator in C++ and obtained results and contributed to paper writing. [PERSON] has contributed to writing the paper, plotting figures and discussing results. [PERSON] and [PERSON] have reviewed the paper.  
This research was partially funded by 5G-PICTURE supported by the European Horizon 2020 initiative.  
The authors declare no conflict of interest.
# Construction and Evaluation of a Control Mechanism for Fuzzy Fractional-Order PID
## 5 Controller Design
### Fuzzy Fractional-Order PID (FFOPID) Controller  
The Fuzzy Fractional-Order PID (FFOPID) regulator is a blend of rule-based fluffy framework with fragmentary request PID (FOPID) control. Figure 3 demonstrates the structure of the FFOPID control scheme. In this control structure, the rule-base fuzzy system takes the error and change-in-error input parameters at individual sampling and generates three output signals corresponding to KP, KI, and KD of FOPID controller. In fact, the fuzzy system fine-tunes gain values of KP, KI, and KD online based on the system dynamic. This online tuning technique makes the system stabilize quickly at the desired level.  
During load variations and changes in set-point circumstances, the online gain amendment type control scheme will guarantee that the system's static and dynamic characteristics are maintained. In the FFOPID controller, the ultimate amount of controller gains may be stated as  
\[\begin{array}{l}\text{k}_{\text{P}}\text{=K}_{\text{P}}+\Delta\text{K}_{ \text{P}}\\ \text{k}_{\text{I}}=\text{K}_{\text{I}}+\Delta\text{K}_{\text{I}}\\ \text{k}_{\text{d}}=\text{K}_{\text{D}}+\Delta\text{K}_{\text{D}}\\ \end{array} \tag{15}\]  
For inputs and outputs, a triangle shape membership function is used. Three membership functions are chosen for inputs, and five membership functions are picked for each output to generate an accurate output. Figure 4 demonstrates the membership functions which are the language variables NB (negative large), NS (negative small), N (negative), Z (zero), P (positive), PS (positive small), and PB (positive large). The formula to normalize values is in Figure 4a,b.  
\[x^{\pi}=2\times\left\{\left(x^{\prime}-min(x)\right)/(max(x)-min(x))\right\}-1\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**KP** & **KI** & \(\lambda\) & **KD** & \(\mu\) \\ \hline
0.34 & 0.19 & 0.91 & 0.28 & 0.87 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for FOPID controller.  
Figure 4: Fuzzy membership functions.  
Figure 3: FFOPID control structure block diagram.  
Further, the Mamdani type fuzzy inference system is used while designing the proposed fuzzy system. [PERSON] and [PERSON] [42] suggested the techniques for framing rule base via intuitive logic. The rule-base used in the proposed fuzzy system to produce suitable gain factor for the three outputs is shown in Table 2.
# Noise Reduction Mechanisms of an Airfoil with Trailing Edge Serrations at Low Mach Number
## 2 Computational Methodology
### Numerical Method for Weak Compressibility  
Through introducing the concept of C-CUP method ([PERSON] and [PERSON], 1980), the time marching of the compressible continuity and momentum equations is divided into two steps by the fractional step method and treated as the usual incompressible scheme. The time advancement approach of Equation (2) is divided as:  
\[\frac{(\hat{p}\hat{u})^{F}-(\hat{p}\hat{u})^{n}}{\Delta t}=\
abla\cdot[-(\hat{p }\hat{u}\hat{u})+\tau]^{n}, \tag{14}\]  
\[\frac{(\hat{p}\hat{u})^{n+1}-(\hat{p}\hat{u})^{F}}{\Delta t}=-\
abla\hat{p}^{n+ 1}, \tag{15}\]in which \(F\) represents the fraction step partially marched without the pressure gradient, \(n\) is the time step count, \(\Delta t\) is the time increment and \(\tau\) is the viscous stress. The time marching method of Equation (1) is given as:  
\[\frac{\tilde{p}^{n+1}-\tilde{p}^{n}}{\Delta t}+\
abla\cdot(\tilde{p}\tilde{u})^ {n+1}=0. \tag{16}\]  
Taking the divergence of Equation (15) and substituting that into Equation (16) derives the elliptic equation for \(\tilde{p}^{n+1}\):  
\[\frac{1}{\Delta t}\left[-\frac{\tilde{p}^{n+1}-\tilde{p}^{n}}{\Delta t}- \
abla\cdot(\tilde{p}\tilde{u})^{F}\right]=-\
abla^{2}\tilde{p}^{n+1}. \tag{17}\]  
To account for the compressibility for the pressure equation, assuming that flow field is isothermal and the change of \(\Delta\tilde{p}\) and \(\Delta\tilde{p}\) is small in \(\Delta t\), Equation (3) can be written as  
\[\tilde{p}^{n+1}-\tilde{p}^{n}=(\tilde{p}^{n+1}-\tilde{p}^{n})RT. \tag{18}\]  
Substitution of Equation (18) into Equation (17) leads to a pressure equation considering the compressibility given as follows:  
\[\
abla^{2}\tilde{p}^{n+1}-\frac{\tilde{p}^{n+1}}{(\Delta t)^{2}RT}=\frac{ \
abla\cdot(\tilde{p}\tilde{u})^{F}}{\Delta t}-\frac{\tilde{p}^{n}}{(\Delta t )^{2}RT}. \tag{19}\]  
Thus, the compressibility effect is represented by the second term of both sides of Equation (19). In short, \(\tilde{p}^{n+1}\) is calculated by Equation (19) and then \((\tilde{p}\tilde{u})^{n+1}\) is obtained from Equation (14), and finally \(\tilde{p}^{n+1}\) is solved by Equation (1). As mentioned above, our method is suitable for treating the density variation in low Mach number flows without using any artificial compressibility parameters since the assumption of small change of \(\Delta\tilde{p}\) and \(\Delta\tilde{p}\) is valid in low Mach number flows, rather than in a high Mach number flow.
## 3 Materials and Methods
### Reconstruction of Relative Sea-Level Change  
Marine stratigraphic architecture is fundamentally governed by tectonic subsidence, relative sea-level fluctuations, and sediment supply flux, where tectonic subsidence and sea-level changes jointly regulate accommodation space while sediment influx dictates depositional volume [30]. The Upper Zhijiang Member in the Pearl River Mouth Basin developed atop inherited lacustrine rift basins, maintaining pre-existing basin geometry with relative sea-level oscillations and sediment flux emerging as dominant controls on depositional system evolution [31]. The DYNOT model quantifies non-orbital noise in climate and sea-level proxies, calculates the ratio of the non-orbital signal variance to the total signal variance, extracts astronomical orbital cycle-related signals from sedimentary records, and reveals long-term sea-level fluctuation patterns. Simultaneously, it dynamically tunes cyclic components in sedimentary records to clarify the relationship between climatic cycles and sea-level changes. The \(\rho\)1 model, based on the lag-1 autocorrelation coefficient of time series, characterizes the noise properties in climate change and uses noise variance as an indicator of relative sea-level variations when noise and other influencing factors are minimal [32]. A total of 5000 Monte Carlo simulations were applied to reconstruct sea-level change curves, uncovering potential long-term, million-year-scale astronomical orbital cycles within sedimentary noise (and sea-level fluctuations). By employing the Dynamic Noise after Orbital Tuning (DYNOT) model coupled with Lag-1 autocorrelation coefficient (\(\rho\)1) analysis, this study reconstructs relative sea-level variations, enabling the systematic investigation of sequence stratigraphic patterns, internal depositional architectures, and their linkages to paleoclimate dynamics.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 8 Thoughts on Economics and Sustainability  
Microalgae cultivation requires several energy- and cost-intensive steps, especially during downstream processing. If digestate is used as a nutrient source, additional environmentally and economically costly operations might be necessary to make it suitable as a cultivation medium. At the same time, the need for the production and acquisition of inorganic nutrients can be omitted by using a nutrient-rich waste stream instead [99; 100]. Using nutrients, water, and CO\({}_{2}\) from such cheap sources can reduce production costs by 50% and simultaneously improve sustainability [100; 101]. The required treatment depends on the digestate, microalgae species, and product. Therefore, it must be evaluated for each individual case if the usage and the associated treatment of digestate are suitable. If elaborate processing steps or the partial addition of inorganic nutrients were necessary, drawbacks would outweigh the benefits [98].  
Nonetheless, in spite of potentially higher upstream costs, the proposed concept can be beneficial if microalgae production is incorporated into an existing process [102]. As mentioned, the water content of digestate must be reduced for its application as fertilizer in agriculture. Biofertilizer production from digestate often includes processes that would also be needed for making the raw digestate suitable for microalgae cultivation (e.g., solid-liquid separation, ammonia stripping, struvite precipitation). Consequently, it may yield a waste fraction that is exactly suitable for microalgae production [102]. This nutrient-rich liquid fraction is usually processed in a wastewater treatment plant and disposed, which requires large amounts of chemicals and energy [99]. As stated by [PERSON] et al., wastewater treatment costs could be reduced by 17% compared to activated sludge treatment by applying microalgae technology with the additional production of biofertilizer and biomethane [98]. Thus, obtaining the nutrient-rich waste fraction for microalgae production would be beneficial for both processes.  
However, large amounts of energy are needed for downstream processing, as described above [99]. Methods such as gravity sedimentation and solar drying should be used whenever necessary to minimize the costs [98]. Moreover, it is essential that the whole process not only serves as effluent treatment. The produced biomass should be valorized and multiple products obtained by applying a biorefinery concept [98; 99]. A positive energy balance can be achieved if the biomass is used for the production of bioenergy [98].
# Laser Beam Welding of CubeSat 1U Structure Parts Obtained by Powder Bed Fusion
## 3 Results and Discussions  
### Characterization of the AM Connectors  
Initially, connectors obtained by additive manufacturing should be characterized from a metrological point of view. Figure 2a,b shows the 3D mapping of the connectors obtained by SLM (a) and EBM (b), respectively. Considering the vector moduli, the SLM real dimensions deviate from the electronic model in the range from \(-\)0.242 mm (minimum) to \(+\)0.145 mm (maximum). The EBM measurements indicated a deviation of \(-\)0.179 mm (minimum) to \(+\)0.130 mm (maximum). As the tolerances are quite large, with an approximately 0.5 mm tube-to-flange gap, the solids reproduced the idealized connector from the CAD quite well, independently of the heat source. Figure 2c,d presents the actual connectors. As can be seen, both the SLM and the EBM presented the required design for the purpose. The surface aspect is granular, and the SLM aspect seems to be more rounded than that of the EBM. This feature was observed in all pieces and could be linked to a higher heat input from the electron beam source, as reported by [PERSON] et al. (2016).  
Figure 2: Shapes and dimensions of the connectors: Isometric view of the connector pieces: (**a**) SLM and (**b**) EBM. Scale at the right. Actual pieces: (**c**) SLM and (**d**) EBM connectors.  
The measured average surface roughness (Ra) values in the SLM and EBM parts were 32 \(\pm\) 5 \(\upmu\)m and 42 \(\pm\) 6 \(\upmu\)m, respectively. The EBM was slightly rougher than the SLM, as is also visible in Figure 2, perhaps as an indication of the different AM strategies. These values are also below the powder mean diameter (45-60 \(\upmu\)m), indicating fusion and consolidation of the impingent added material. The measured roughness values were similar to the layer thickness, indicating that this parameter could influence the resolution and quality of the final product, corroborating the literature [17].  
Although slightly different in shape (Figure 2c,d), the samples produced by SLM and EBM showed densifications of 87 \(\pm\) 3% and 83 \(\pm\) 5%, respectively. The density values were similar, indicating that the number of inner pores in the two processes was similar. These densification levels are below those reported for the SLM of pure Ti by [PERSON] et al. (density of 97%) [18] and Ti4 Al6V by [PERSON] and [PERSON] (density of 99.7%) [19]. A possible explanation for the low relative densification in both cases may be the methodology used to prepare the connectors in the AM equipment for prostheses. As the mechanical properties of TiAl6V are much better than those of bone, the internal porosity does not affect the performance of the implant. Currently, it is not possible to determine whether the same applies to the CubeSat structure.  
Figure 3 shows the real mounted CubeSat structure that is ready to be joined. Independently of the AM route, the entire CubeSat frame weighed 122 \(\pm\) 4 g, which is approximately the same mass as the aluminum versions [20]. However, it has superior thermal and mechanical resistance to titanium.
## 4 Validation and Comparison
### Data Pre-Processing  
We further divided the measured farmland into four sub-scenarios: crops with dry earth, crops with wet earth, paddy fields, and dense crops (without exposed earth), as shown in Figure 5. After the channel measurement, we corrected the electromagnetic  
Figure 4: Channel measurement campaigns in the farmland scenario.  
Figure 3: Overview of the **(a)** TX and **(b)** RX of the A2G channel sounder.  
parameters of the ground materials for RT simulations based on some of the measurement data. The modified electromagnetic parameters of different materials are shown in Table 2.