esa-sceva/satcom-chunk-collection huggingface dataset:
# A Methodology for Exploiting Smart Prosumers' Flexibility in a Bottom-Up Aggregation Process
## 4 Case Studies
### Case Study 2--Active User (with PV System and Batteries)  
The scenario of the second case study differs from the first one for the presence of a local PV system with a battery storage system. In particular, the production profile forecasted for the day-after is shown in Figure 13, while le characteristics of the storage system [31] are reported in Table 3.  
Figure 12: Updated flexibility diagrams after the first DR action.  
Figure 13: Forecasted PV production profile.  
In this case study, the storage system is assumed to be used to provide flexibility by injecting energy into the grid, causing the same effect as a local reduction in electricity demand. The estimated hourly dissatisfaction cost for the storage system is chosen by the prosumer during the configuration phase and can be taken as a multiple of the hourly cost of electricity based on perceived discomfort. In this case, it was set to the minimum value (equal to the cost of electricity: 0.188 \(\epsilon\)/kWh from 8:00 to 19:00 and 0.182 \(\epsilon\)/kWh in the rest of the day) because it was considered that using the storage system to provide flexibility causes low discomfort for the prosumer.  
In order to calculate the price of bids involving the battery storage system, it is required to calculate the degradation cost using Equation (6) with \(\mathrm{N_{d}}=1\):  
\[\mathrm{C_{BESS}}=\frac{\mathrm{N_{d}}}{\mathrm{N_{max}}}\cdot\mathrm{C_{0}}= \frac{1}{5000}\cdot 7000=1.40\ \epsilon \tag{10}\]  
Figure 14: Comparison between predicted load diagram and predicted PV production.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Capacity** & **13.5 kWh** \\ \hline Efficiency & 90\% \\ \hline Power & 7 kW \\ \hline Number of charge/discharge cycles & 5000 \\ \hline Total cost & 7000 \(\epsilon\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Battery Storage Characteristics [31].  
The simulation is performed considering a request for a decrease in energy consumption in the time interval 12:00-12:15. The prosumer is assumed to be selected for the winning cluster with the quantity/price tuple in Table 4.  
In this case, the price of the winning bid is higher than in case 1, where the storage system is not involved. In fact, although the dissatisfaction cost is lower than the other cases, the contribution of the degradation cost is predominant.
# Resource Allocation in 5G Cellular IoT Systems with Early Transmissions at the Random Access Phase
## 4 Performance Evaluation Framework
### Stability and Maximum Throughput  
One of the critical measures for the system of interest is the maximum throughput that can be attained. However, systems with random access and unlimited population of EDs are not always stable. Thus, to determine the maximum throughput, we first need to determine the stability conditions of the introduced system model.  
We note that the Markov chain \(R_{n}\) that describes the number of active EDs on the RA phase is not ergodic in the strict mathematical sense. This is because the expected number of EDs \(B(L,r)\) that successfully complete the RA phase in a single frame tends to zero as the number of active EDs increases. Therefore, if there is a constant average arrival rate, the Markov chain cannot be considered stable.  
On the other hand, it can be derived from expression (4) that \(B(L,r)\) has a maximum. Indeed, following [12], we see  
\[\frac{B(L,r+1)}{B(L,r)}=\begin{cases}>1,&r<L-1,\\ =1,&r=L-1,\\ <1,&r>L-1.\end{cases} \tag{6}\]  
From (6), we immediately see that the mean number of EDs passing the RA phase reaches a maximum at \(r=L-1\) and \(r=L\). Thus, when the mean number of new ED arrivals to the RA phase is less than the maximum \(B(L,L)\), the considered system may be called metastable, as it spends quite a long time in states with the number of active EDs near \(L\) before going to infinity. Using this argument, we establish the following necessary (but not sufficient) conditions for metastability:  
\[\lambda_{r}\Delta<B(L,L). \tag{7}\]  
Figure 4: Approximation of (3) by binomial distribution.  
For large numbers of preambles \(L\), this condition can be approximated by  
\[\lambda_{r}\Delta<Le^{-1}, \tag{8}\]  
which is in agreement with previously known asymptotics, e.g., [5].  
Note that (7) is related to the RA phase. For the overall system to be metastable, in addition to (7), we also need to ensure that the mean arrival rate to the DT phase is less than the maximum number of transmitted packets \(W\), resulting in the following second metastability condition:  
\[(\lambda_{r}+\lambda_{d}\bar{l})\Delta<W. \tag{9}\]
## 2 Materials and Methods
### Methods
#### 2.2.8 Ex Vivo Skin Permeation Experiments  
Skin permeation experiments were performed with a handheld Franz Cell diffusion apparatus (Perme Gear, Hellertown, PA, USA) using vertical diffusion Franz cells with an effective permeation diameter of 0.636 cm. Porcine skin samples were obtained from a local abattoir. Subcutaneous fat and hair were removed from the skin. Skin fragments measuring 1.1 cm \(\times\) 1.1 cm were washed with a 0.9% NaCl solution until an absorbance of A \(<\) 0.02 was obtained and then mounted in diffusion cells (temperature 37.0 \(\pm\) 0.2 \({}^{\circ}\)C). Next, a 0.2g hydrogel sample was placed on the skin, facing the stratum corneum into the donor compartment. A mixture of ethanol (96% v/o) and PBS in a ratio of 40:60 _v/o_ was used as acceptor fluid in a volume of 5 mL [61]. At predetermined times (after 1, 2, 4, 8, 12, and 24 h), acceptor solution in a volume of 2 mL was taken through the sampling port of the Franz diffusion cell, and the same volume of fresh acceptor solution was added. The samples were analyzed for absorbance at a wavelength of 373 nm using the Cecil CE  
Figure 1: Representative picture of data of texture analysis of quercetin hydrogels.  
3021 UV-VIS spectrophotometer (Cecil Instruments Limited, Cambridge, UK), and the concentration of quercetin was calculated from the standard curve. Each formulation was tested six times.  
To determine the amount of API remaining in the skin, residual hydrogels were removed from the skin, washed a few times with 3 mL PBS, and then wiped 4 times with paper soaked in ethanol and dried with filter paper. After washing, the skin was cut into small pieces, flooded with 5 mL ethanol, and shaken for 4 h on the Compact Shaker KS15 (Edmund Buhler GmbH, Bodelshausen, Germany) to extract the API residue. A blank of untreated skin was prepared in the same way as treated skin. In control experiments carried out previously, in which the quercetin hydrogel preparations were removed immediately after application and the skin was extracted with ethanol, the validity of such a cleansing procedure was confirmed.  
The concentrations of extracted quercetin were determined spectrophotometrically, and the data were expressed as the amount of quercetin per area (\(\mu\)g/cm\({}^{2}\)).
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 3 Methodology and Data Processing  
### Ambient Vibration Data Acquisition  
Ambient vibrations were recorded using triangular configuration arrays, with 7 short-period triaxial velocity stations (1 Hz natural frequency, and 100 Hz sampling frequency). The vibrations were recorded for 40 min, at each of the 15 recording sites. The recordings were made in accordance with the recommendations of the SESAME project [30]. The array was arranged as an equilateral triangle, with each side measuring 30 m. The sensors were placed at each vertex, halfway along each side, and an additional sensor was positioned in the center (Figure 3a). In Figure 3b, the theoretical response of the array, together with the aliasing (kmax) and the maximum resolution (kmin/2) wavenumber limits, are presented. This arrangement, as noted by [31], provides a better coverage. The measurements recorded at each site have been used to obtain the HVSR curve, the dispersion curve, and also for directionality analysis.  
Figure 3: (a) Array geometry. Blue dots are seismic stations. (b) Theoretical response. The large and small black circles correspond to Kmax and Kmin/2 values, respectively.
# Advances in Improving Energy Efficiency of Fiber-Wireless Access Networks: A Comprehensive Overview
## 3 Energy Efficiency Analyses of Radio-and-Fiber Networks
### Power-Saving Techniques in the Optical Domain of FiWi Networks  
The most popular optical back-end technology currently used in the realization of the FiWi network is the passive optical network (PON). The PON is a cost-effective point-to-multipoint network access architecture. In the PON network, OLT is allocated in the CO and via a passive optical splitter connected with numerous ONUs which are allocated near users (Figure 2) [21]. Due to the constant growth in the number of end users, the largest part of energy consumption in telecommunications falls on access networks [22]. Additionally, in optical access networks, 90% of the energy is consumed for powering ONUs and, therefore, the energy-conservation techniques for ONU devices are of great importance [23]. Moreover, in the PONs, the OLT is continuously occupied with transmitting and receiving data, making it unsuitable for energy-saving strategies. On the other hand, the ONU's transmitter is idle for the majority of the time and it is appropriate for the implementation of energy-saving strategies [16]. Reducing ONU device power consumption can be achieved primarily through different PS operation modes prescribed by the ITU-T standard [24] (Figure 3). These PS modes of operation are characterized according to the operational state of the ONU receiver and transmitter. Accordingly, the ONU operation state can be in one of the following energy-saving modes: ONU power shedding, ONU dozing or ONU (fast and deep) sleeping modes (Figure 3). The power-shedding mode conserves energy by switching off only unnecessary ONU features, while keeping the receiver and transmitter fully operational. Thus, in the power-shedding mode, minimal power savings are ensured (Figure 3), while the best possible performance is maintained. Figure 3: ONU power consumption under different active and energy-saving states.
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 4 V-GRADIENT Design
### Dynamic Replication Mechanism
In this sense, the radius of these regions is obtained as the product between the corresponding ROIs' radiuses and a variable, \(Flood_{ratio}\), computed as a function of the penetration rate and the messages' initial TTL as shown in Equation (7). It should be noted that messages are disseminated within the flooding region near the ROI center even when they run out of tickets. \[Flood_{ratio}=1-\frac{e^{k(TTL-40[min])}}{1+e^{k(TTL-40[min])}}\big{(}1-\sqrt{ \sigma_{rate}}\big{)}. \tag{7}\]  
The inclusion of the TTL parameter in the expression of the \(Flood_{ratio}\) relates to the volatility of messages with low values of TTL, meaning that such messages are dropped shortly after they are created and, for that reason, should be disseminated quickly as they only constrain the available resources in the network for brief periods of time. A threshold of 40 minutes for the TTL was considered, as messages with shorter TTL values expire very soon. Figure 5 shows the resulting graph regarding the \(Flood_{ratio}\) outcome for distinct penetration rates and different initial TTL values (with a logistic function similar to Equation (6), but with \(k=0.25\)). Figure 4: Average geocast group penetration rate measured by nodes. For very dense networks, there are many contact opportunities, which suggests that nodes can select the relays nodes more carefully, leading to a more even geographic dissemination of messages. Thus, the ticket-based spraying heuristic is modified for very dense networks, handling a message copy to another node and spending a ticket, if the following conditions apply: (i) the network density level measured by the sender node exceeds a minimum threshold \(\rho_{min}\); (ii) the sender node resides within the flooding region of the ROI; (iii) the receiver node is not an eligible receiver of that particular bundle; (iv) the absolute difference between the moving direction of the sender and receiver is larger than 45 degrees, as proposed in [16]; and (v) there are unspent tickets. A threshold value \(\rho_{min}\) = 2.8 was used, which only applies to more than 300 nodes in the Helsinki scenario (Figure 1).
# Application of Nanofluids in CO\({}_{2}\) Absorption: A Review
## 3 Effective Factors in the CO\({}_{2}\) Absorption by Nanofluid  
Nanofluids' properties have opened a new research field in new technologies. Researchers started investigations on various nanofluids to be used as absorbents in CO\({}_{2}\) absorption processes. In this respect, different metal oxide, metallic, and nonmetallic nanoparticles were examined for CO\({}_{2}\) absorption enhancement; some of them are TiO\({}_{2}\), MgO, SiO\({}_{2}\), Cu, CuO, Al\({}_{2}\)O\({}_{3}\), and carbon nanotubes [84]. The dominant parameters were determined according to the results of CO\({}_{2}\) absorption using nanoparticles. These factors are nanoparticle type, morphology, size, and concentration in the base fluid, flow rate of gas, concentration of CO\({}_{2}\) in the feed stream, and base fluid type, flow rate, pressure, temperature, and hydrodynamics as presented in Figure 4 [53].  
Figure 3: The schematic of (**a**) Bubble breaking, (**b**) Brownian motion, and (**c**) Grazing effect mechanisms in the improvement of CO\({}_{2}\) absorption.
## 4 Results
### Routing Performance Evaluation
#### 4.4.2 Impact of Routing Frequency
Furthermore, at lower frequencies, the routing reliability of the proposed method approaches that of AODV, as evident from overlapping 95% confidence intervals and a lower gap between the PDR of the proposed method and AODV (shown in Figure 17d). Furthermore, the performance gap for reliability between AODV and Dijkstra decreases with increasing routing frequency, as, with increasing routing frequency, the PDR of AODV degrades, while that of routing using Dijkstra improves. On the other hand, the PDR gap between the proposed routing and AODV widens, as the routing frequency increment enhances the proposed routing and deteriorates AODV. Overall, as evident from Figure 17, all three of cost, channel utilization, and latency increase with routing frequency for the proposed routing method, which can be considered a disadvantage at high routing frequencies. However, the previous disadvantage can be traded off with the gain in PDR for the proposed method at high frequencies.
## 3 Results and Discussion
### Impact of Feeding Strategy
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy \({}^{*}\)** & **I** & **II** \\ \hline Time (h) & 144 & 120 \\
1,3-PD (g/L) & 47.48 \(\pm\) 0.76 & 53.44 \(\pm\) 1.19 \\ Glycerol utilization (\%) & 82.05 \(\pm\) 0.78 & 95.15 \(\pm\) 1.15 \\ Yield (\%) & 38.91 \(\pm\) 1.30 & 40.96 \(\pm\) 1.82 \\ Volumetric productivity & 0.33 \(\pm\) 0.01 & 0.44 \(\pm\) 0.01 \\ (g/(L-h) & 24.29 \(\pm\) 2.54 & 16.41 \(\pm\) 2.37 \\ Acetic acid (g/L) & 9.62 \(\pm\) 0.40 & 12.32 \(\pm\) 0.45 \\ Succinic acid (g/L) & 5.95 \(\pm\) 0.16 & 6.87 \(\pm\) 0.13 \\ Ethanol (g/L) & 3.06 \(\pm\) 0.49 & 3.92 \(\pm\) 0.06 \\ Biomass (g/L) & 2.82 \(\pm\) 0.21 & 3.02 \(\pm\) 0.30 \\ \hline \hline \end{tabular} \({}^{*}\)Strategy I: maintaining a relatively high glycerol concentration. Strategy II: maintaining a relatively low glycerol concentration after 27 h of the process.  
\end{table} 
Table 4: Comparison of glycerol fermentation with _C. freundii_ AD119 using different feeding strategies. Figure 10: Time course of dry cell weight (DCW) and metabolite production (1,3-propanediol (1,3-PD), acetic acid, lactic acid, succinic acid, ethanol) with _C. freundii_ AD119 in a fed-batch culture using different feeding strategies: (**a**) maintaining a relatively high glycerol concentration and (**b**) maintaining a relatively low glycerol concentration after 27 h of the process. regularly containing reports on the bioconversion of glycerol to this diol with different microorganisms, including _C. freundii_[42; 43], _K. pneumoniae_[44; 45], _C. butyricum_[46; 47].
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 3 Proposed Method  
### Model Architecture  
The entire system structure consists of three sub-systems (i.e., data preprocessing, byte tokenizing, and multi-task classification) and is shown in Figure 2. Data preprocessing is the process of converting raw traffic data into an input format before applying it to DistilBERT model, resulting in byte-separated data as the output. Byte tokenizing takes the data from the previous module as the input and performs tokenization for each byte. Multi-task classification takes the tokenized data as the input, performs embedding, runs it through the DistilBERT model, and predicts a label for each task.
# Structured Data REST Protocol for End to End Data Mashup
## Structured Data Rest Protocol
### Composition of SDMB and MDRs  
#### 5.5.1 SDMB Composition  
Each DSC receives Structured Data Module (SDM) through mashup data service identifier from MSP/DSP and uses SDMB to store and/or forward mashed up data. Structured Data Module (SDM) is the collection of Structured Data Records (SDRs) in the standard format like XML, JSON or CSV etc. Each SDR is filtered and transformed into DSC's defined Mashup Data Record (MDR) and is stored in SDMB using the proposed algorithms (See Section 5.15). Thus, SDMB is the collection of unique SDmb#Id, Sdm#Id, Msp#Id/Dsp#Id, Mds#Id and one or more MDRs.  
\[\text{SDMB}=(\text{Sdmb}\#\text{Id},\text{Sdm}\#\text{Id},\text{Msp}\#\text{Id} /\text{Dsp}\#\text{Id},\text{Mds}\#\text{Id},\text{MDRs})\]  
Sdmb#Id is generated when DSC creates new SDMB. Mds#Id is contained in MCAttributes received via mashup configuration service identifier. Msp#Id and Dsp#Id are nothing but URLs of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DMD\#Id** & \multicolumn{3}{c}{**DMDAttributes**} \\ \hline Dmd\#1 & x11 & x12 &... & x1k \\ Dmd\#2 & x21 & x22 &... & x2l \\... &... &... &... &... \\ Dmd\#i & x11 & x2 &... & xij \\... &... &... &... &... \\ Dmd\#n & xn1 & xn2 &... & xns \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data Mashup Definition (DMD).  
MSP and DSP respectively. Mds#Id is the unique id of mashup data service identifier through which structured data module would be made available at MSP's/DSP's end.
## 4 Conclusions and Perspectives  
In this work, the great effect of small additives in sodium-zinc-alumina glass composition on the spectral and optical properties of silver nanostructures grown on the surface or subsurface layers of Na\({}^{+}\)-Ag\({}^{+}\) ion-exchanged glasses was demonstrated. Silver ions introduced in the PTR glass matrix containing no polyvalent ions can interact with the atmosphere water molecules during heat treatment to form SERS-active silver nanoisland films on the glass surface. Trivalent cerium and antimony ions typically used as photosensitive additives in PTR glasses were shown to act as reducing agents for silver ions in Na\({}^{+}\)-Ag\({}^{+}\) ion-exchanged glasses and to promote the formation of luminescent silver molecular clusters. The concentration of polyvalent ions, as well as the ion exchange and subsequent heat treatment parameters, can be used to tune and optimize the emission of silver molecular clusters in a wide range.  
Na\({}^{+}\)-Ag\({}^{+}\) ion exchange of Br(Cl)-containing PTR glass followed by heat treatment above the glass transition temperature led to the growth of silver bromide nanocrystals. The obtained nano-glass ceramic possesses photochromic and thermochromic properties. The presented experimental findings indicate that small additives contained in glass play a key role in the chemical reactions with silver ions introduced by Na\({}^{+}\)-Ag\({}^{+}\) ion exchange and determine the properties of the obtained silver nanostructures and functionality of nano-glass ceramics. We believe that the results of this work can be useful for developing biochemical sensors based on the SERS phenomenon, white LEDs, spectral converters, and temperature sensors.  
Conceptualization, Y.S. and N.N.; formal analysis, Y.S.; funding acquisition, N.N.; investigation, Y.S.; methodology, Y.S. and A.I.; project administration, N.N.; validation, Y.S., N.N. and A.I.; writing--original draft, Y.S.; writing--review and editing, N.N. and A.I. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Russian Science Foundation (Project # 20-19-00559).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors are thankful to Oreshkina K.S. for XRD measurements and Klyukin D.A. for SEM images.  
The authors declare no conflict of interest.  
Figure 17: Dependence of photoluminescence peak location of PTR-Sb2 glass samples on the time of Na\({}^{+}\)–Ag\({}^{+}\) IE and subsequent heat treatment at 450 \({}^{\circ}\)C.
# Energy-Efficient Power Allocation in Non-Linear Energy Harvesting Multiple Relay Systems
## 3 Joint Optimization Algorithm for the Relay Transmit Power and PS Ratio
\begin{array}{ll}(\text{P5})\text{ max}&y_{2}(P_{i},t_{i})=\frac{1}{2} \log_{2}\Bigl{(}1+\psi_{1}+\frac{t_{i}P_{5}h_{i}}{(t_{i}+1)\sigma^{2}}\Bigr{)} \\ &\qquad\qquad-x^{*}\cdot\bigl{(}P_{5}+2P_{C}+\phi_{i}+P_{i}+P_{Q}-a_{s_{i}} \cdot(1-t_{i})P_{5}h_{i}-b_{s_{i}}\bigr{)}\\ s.t.C1:&t_{\text{min}}\leq t_{i}\leq t_{\text{max}}\\ C2:&0\leq P_{i}\leq a_{s_{i}}\cdot(1-t_{i})P_{5}h_{i}+b_{s_{i}}-P_{Q}\\ C3:&\frac{t_{i}P_{5}h_{i}}{(t_{i}+1)\sigma^{2}}\geq\gamma_{\text{min}}-\psi_{ i}\\ C4:&P_{i}\geq\frac{1}{(t_{i}+1)g_{i}}\\ C5:&P_{i}\leq P_{\text{max}}-\varphi_{i}\end{array} \] (14)  
Due to the uncertainty of \(P_{RF}^{i}\), according to the EH model in Equation (3), (P4) and (P5) are divided into three cases: Case 1, \(P_{RF}^{i}(t_{i})<P_{th}^{1}\); Case 2, \(P_{RF}^{i}(t_{i})>P_{th}^{1}\); and Case 3, \(P_{RF}^{i}(t_{i})\in\left[P_{th}^{l},P_{th}^{l+1}\right]J=1,2,\ldots,L-1\).
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 5 Evaluating LEO Satellite Internet with SatScope: Case Studies
### Single-Shell Satellite Network Topology Similarity Experiment  
We consider the satellites from the sixth shell listed in Table 1, along with all V1.0 version satellites, as examples of a single-shell satellite network. For this analysis, all ground stations and terminal user nodes obtained from network measurements are treated as ground nodes and input into SatScope. We conducted an 8 min continuous simulation using SatScope to calculate the minimum edit distance between adjacent topological points and edges, as illustrated in Figure 12.  
The minimum edit distance is computed by determining the least number of operations required to transform one graph into another, serving as a quantitative measure of structural differences between two graphs. To standardize this measure, we divide the minimum edit distances for nodes and edges by the maximum number of nodes and edges in the two topologies, respectively. Our findings indicate that, on average, 38.94% of nodes and 83.66% of links change during each satellite network connection switch. By calculating the minimum edit distance, SatScope effectively quantifies the stability of satellite networks under real-time topological conditions. This capability enhances our ability to accurately assess network stability and reliability, providing valuable data support for network optimization and fault management.  
Given the dynamic nature of these networks, it is essential that they possess robust adaptive capabilities to handle frequent topological changes. Additionally, networks must be capable of rapidly adjusting routing and resource allocation to maintain continuous and stable communication.