esa-sceva/satcom-chunk-collection huggingface dataset:
# A Signaling-Free Underwater Code Division Multiple Access Scheme
## 6 Performance Evaluation
It proves that the channel access without signaling procedure works well, significantly reducing both the signaling overhead and the associated delay. An issue that we need to resolve is that as the offered load gets heavier, the throughput of the proposed scheme degrades rapidly, and even falls below CDMA of RIPT and MACA-U at over 0.25 offered load. This might be an extremely heavy traffic condition related to various monitoring-related applications [25]. The reason for this is that while the signaling-based schemes like CDMA of RIPT and MACA-U can prevent throughput from decreasing below a certain level using a channel reservation, as traffic load increases the proposed system, this shows excessive caution for transmission and keeps delaying it because the estimated traffic mostly exceeds capacity. Therefore, the probability of transmission \(p_{tr}\) needs to be adjusted for the special case of heavy traffic. This is not dealt with in this paper but remains as a potential future work. Regarding the fairness for channel occupation, in signaling-assisted protocols such as MACA-U and CDMA of RIPT, the nodes closer to the sink are more probably occupying the channel because the channel reservation control message such as RTS may arrive earlier at the sink than from the nodes far away from the sink. In contrast, the proposed scheme equally gives the opportunity for channel  
Figure 4: Success rate of traffic estimation. Figure 5: Throughput for proposed scheme with other MAC protocols. occupation independently of the nodes' location because any node finding an available channel is allowed to occupy the channel. Figure 6 shows the throughput of sensor nodes in relation to the location from the sink. As the node gets farther from the sink, the traffic estimation may be incorrect because the received power from the other nodes is probably not strong enough. Especially when other nodes are mostly located at the opposite side beyond the sink, the node is probably blind to their existence and accordingly, underestimates the number of nodes. Such a wrong estimation causes collisions at the sink and thus the throughput decreases. Meanwhile, the node closer to the sink is able to catch all the power transmitted from any location inside the cell and thus is able to estimate the traffic more precisely.
# Expanding Horizons: Taking Advantage of Graphene's Surface Area for Advanced Applications
## 3 Properties of Graphene with Enhanced Surface Area  
[PERSON] et al. [50] conducted a study with samples of graphene, rGO, and enhanced-surface-area graphene, named rGO_An, created by an annealing method. They compared the Brunauer-Emmett-Teller (BET) properties of the normal rGO with rGO_An. Using nitrogen adsorption-desorption isotherms, the particular surface area of the rGO and rGO_A materials was methodically investigated. The rGO and rGO_An samples' isotherm curves were classified as type IV isotherms because they displayed a hysteresis loop, indicating  
Figure 5: A schematic showing the formation of higher-surface-area graphene by buckling under tensile load.  
the existence of significant mesopores and macropores [51; 52]. Furthermore, the loops of hysteresis linked to the rGO_A sample were noticeably wider than those of the rGO sample, suggesting that the severely crumpled rGO sheets contained a substantial number of pores. The rGO and rGO_An samples were found to have specific BET surface areas of 130.2 and 672.1 m\({}^{2}\)/g, respectively. Additionally, the BJG method revealed that the rGO and rGO_An samples had mean pore size variations of 19.5 nm and 49.2 nm, respectively. Notably, the rGO_An sample's overall pore size ranges were higher than the rGO samples. The pore volumes of the rGO sample (0.635 cm\({}^{3}\)/g) and the rGO_A sample (0.826 cm\({}^{3}\)/g) were almost equal, according to another finding. Larger BET-specific surface areas, smaller pore diameters, and advantageous scattering states are known to make it easier for electrolyte ions to enter the electrodes' pore surfaces, which raises the specific capacitance [52]. Therefore, the rGO_An sample activated a greater number of electrodes than the rGO sample in supercapacitor applications. Graphene derivatives, such as GO and rGO, have undergone significant advancements, improving their surface area in the field of electronic structure and properties for diverse applications. The reduction of GO enhances electrical conductivity by restoring its graphene-like structure and reducing oxygen functional groups, resulting in smoother, thinner films with superior performance [53]. Additionally, their unique quantum properties, including Dirac electron interactions and the anomalous Hall effect, can be tuned to create advanced nanomaterials and metamaterials for electronic applications [54]. These substances are perfect for batteries, supercapacitors, and sensors because they also have outstanding electrochemical qualities, such as an increased surface area and improved electron mobility [55].
# Energy Management Scheme for an EV Smart
## 4 EMS for a Smart Charger
### EMS Control Flow Process  
For a better understanding, this section states the control flow process of the EMS. The EMS consists of two primary sub-control operations that work in parallel: the control for the smart charger inverter and the DC-DC converter of EVs. The inverter control further comprises two parallel processes for active and reactive power. The sub-control mechanisms coordinate with each other within the progression of the control structure. The following steps describe the multi-stage control flow of the EMS:  
1. At each time step, the EMS updates the values of voltages, currents and phase angles from the grid and PCC. The information regarding the EV SoC is updated from the EVs. Since the rated battery capacity is a constant during the whole control process, it is determined from the EV upon the initial connection with the smart charger.
2. In the second stage, the smart charger active and reactive power references are calculated from Equations (19) and (37). All the required values to be utilized in these equations are adopted from the first step.
3. The \(P_{(inv)}^{*}\) reference calculated is final, while the other references \(Q_{(inv)}^{*}\) and \(P_{EV(i)}^{*}\) coordinate with it to calculate their conclusive values. The reactive power block receives the reference \(P_{(inv)}^{*}\) to calculate the maximum reactive power capacity \(Q_{sc(max)}\). Further, it checks that the calculated reactive power reference is within reactive capability. If this condition is breached, the reactive reference is set as \(Q_{sc(max)}\). Simultaneously, the EV control block checks the \(P_{(inv)}^{*}\) reference and the SoC constraints for the EVs and calculates the power references for individual EVs based on the proposed proportional methodology.
4. The computed references \(P_{(inv)}^{*}\) and \(Q_{(inv)}^{*}\) are fed to the inverter controller, while \(P_{EV(i)}^{*}\) is fed to the controller of the DC-DC converter of the \(i\)-th EV. The control advances to the next time step.  
The EMS sets the charge/discharge rate of each EV during steady state, as well as under dynamic grid conditions. It updates the available charging capacity of the smart charger upon entry or exit of each EV from its DC-bus. The number of EVs connected to the charger depends on the charging power \(P_{EV(i)}^{*}\) set for all the connected EVs and \(S_{n}\), as stated below:  
\[\sum_{i=1}^{n}P_{EV(i)}^{*}\leq S_{n} \tag{40}\]  
The complete workflow within the EMS is shown in Figure 11. Differential DG power is supplied by the EVs to the grid, while the remaining charger capacity can be utilized for reactive power operation. The smart charger reactive power capability \(Q_{sc}\) depends on the maximum reactive power capacity \(Q_{sc(max)}\), which is calculated from the active power reference given to the inverter \(P_{(inv)}^{*}\). Therefore, active power has priority over reactive power compensation.
# A New Lightweight Watchdog-Based Algorithm for Detecting Sybil Nodes in Mobile WSNs
## 5 Simulation Results and Performance Evaluation
### Simulation Results
#### 5.3.1 Evaluation
This is somewhat expected since in wireless networks it is the routing that is mostly susceptible to scalability which is not a concern herein. Below we give a summary of the test results of the proposed algorithm. Figure 11: Scalability evaluation of the proposed algorithm. Figure 10: The effect of WD population on the variation of TDR (**a**) and FDR (**b**) versus the number of Sybil IDs. Figure 9: Time variations of TDR (**a**) and FDR (**b**) for different malicious node populations. * Observing Figures 7 and 9, TDRs and FDRs surely reach their steady-state values (upon sufficient information collection) and interestingly almost at the same time (at movement step greater than or equal to 120) regardless of the values of the network parameters. * Comparing Figures 7 and 8, the convergence rates of TDR and FDR are more sensitive to the number of Sybil IDs per malicious node and the number of normal nodes respectively, before reaching their steady states. This is expected because of the very definition of TDR and FDR in Section 5.1. * Somewhat related to the previous observation, Figures 7a and 9a show that increasing the total number of Sybil IDs by increasing \(S\) and \(M\) have opposite impacts on TDR. In fact, increasing \(S\) makes the Sybil pattern more noticeable and in favor of TDR. As illustrated by Figures 7b and 9b increasing the total number of Sybil IDs does not have much effect on FDR. * The results in Figure 10a suggest that, in steady state, there is a turning point of WD population above which not much TDR gain is achieved. Regarding the three remaining measures of performance, in the proposed algorithm, the memory overhead is equal to the amount of memory needed for storing upper-triangle matrices \(A^{k}_{co-prs}\) and \(C^{k}_{co-prs}\) which is equal to \(O(H^{2})\) (refer to Equation (1)). It should be noted that this memory consumption occurs only in WDs. To do their jobs, WDs overhear the Hello Packets which are already in use in mobile sensor networks. After forming and updating \(C^{k}_{co-prs}\), each WD sends its final \(C^{k}_{co-prs}\) to the designated WD just once, in the detection phase. So the communicational overhead is \(O(H^{2})\). It is very important to note that in calculating the communicational overhead, we do not consider Hello-Packets related overhead (and its consumed energy) since Hello-Packets broadcasting mechanism is embedded in any mobile wireless routing and is not additionally imposed by our proposed detection algorithm. The algorithm includes arithmetic and comparison operations on upper-triangle matrices (with \(H\times H\) dimensions) in WDs and the designated WD leading to a computational complexity of \(O(H^{2})\).
## 2 Related Work and Technologies
### Nb-IoT  
Narrowband IoT (NB-IoT) technology is also a radio technology deployed in mobile networks that is especially suitable for indoor coverage, low cost and long battery life for a large number of devices. NB-IoT limits bandwidth to a single narrow band of 200 kHz, offering maximum downlink speeds of 26 kbs in version 13 of the 3 GPP standard. Version 14 will see this increase to 127 kbps. Both LTE-M technology and NB-IoT operate over a mobile network, requiring coverage with a sufficient signal [43]. All Global System for Mobile communications (GSM) cells that work with LTE can also support NB-IoT, but this requires new protocol installation and licensee fees, so not all operators provide it by default. It is crucial to check if the local GSM operator offers NB-IoT. Moreover, the Subscriber Identity Module (SIM) card must have this protocol enabled. SIMs with LTE may or may not work with NB-IoT--this depends on the GSM operator [44].  
In [45], the authors present a comparative study of the different technology applications in the health care area, namely SigFox, LoRaWAN and NB-IoT.  
In [46], the authors present a study related to health care, particularly the remote development of rural regions and the application of IoT in these regions for remote health monitoring based on NB-IoT technology. They feature an intelligent IoT-based edge system for remote health monitoring, in which vital wearable sensors transmit data and alerts to an IoT system. The collected data and alerts are then sent to doctors based on a risk-stratified push/pull protocol using the best combination of cellular/mobile/NB-IoT networks. Clinical validation through implantation at the hospital where the system was tested and remote telemedicine location demonstrated that the NB-IoT-based system can be a low-cost, yet feature-rich alternative and that it adds value to devices for remote patient monitoring.
## 3 Mathematical Modeling
### Energy Analysis  
According to the hypotheses mentioned above and the energy conservation principles, a mathematical model is established for each component.  
Compressor input power rate during the energy charge process is defined as:  
\[\dot{W}_{\text{C}}=\dot{m}_{\text{CO}_{2}}(h_{2}-h_{1}) \tag{1}\]  
Compressor isentropic efficiency is defined as:  
\[\eta_{\text{C}}=\frac{h_{\text{2s}}-h_{1}}{h_{2}-h_{1}} \tag{2}\]  
Turbine output power rate during the energy discharge process is defined as:  
\[\dot{W}_{\text{T}}=\dot{m}_{\text{CO}_{2}}(h_{10}-h_{11}) \tag{3}\]  
Turbine isentropic efficiency is defined as:  
\[\eta_{\text{T}}=\frac{h_{10}-h_{11}}{h_{10}-h_{11\text{s}}} \tag{4}\]  
Figure 2: \(T\)-s plot of the proposed system.  
Molten salt and water are two system heat storage media, and the calculation for the heat exchanger is also divided into two types. The heat transfer rate of the CO\({}_{2}\)-molten salt heat exchanger is calculated as:  
\[\dot{Q}=\dot{m}_{\text{CO}_{2}}\cdot\Delta h_{\text{CO}_{2}}=\dot{m}_{\text{ salt}}\cdot c_{\text{salt}}\cdot\Delta T_{\text{salt}} \tag{5}\]  
The heat transfer rate of the CO\({}_{2}\)-water heat exchanger is defined as:  
\[\dot{Q}=\dot{m}_{\text{CO}_{2}}\cdot\Delta h_{\text{CO}_{2}}=\dot{m}_{\text{ water}}\cdot\Delta h_{\text{water}} \tag{6}\]  
where \(\dot{m}_{\text{salt}}\) represents molten salt mass flow rate, \(c_{\text{salt}}\) represents molten salt specific heat capacity, \(\dot{m}_{\text{water}}\) represents water mass flow.  
The definition of the system energy storage efficiency is shown in Equation (7), equal to the total power output during the energy discharge period versus the total power input during the energy charge period.  
\[\eta_{\text{stor}}=\frac{\dot{W}_{\text{T}}\cdot t_{\text{r}}}{\dot{W}_{\text {C}}\cdot t_{\text{s}}} \tag{7}\]  
where \(t_{\text{r}}\) denotes the energy release duration and \(t_{\text{s}}\) denotes the energy storage duration.  
### Energy Analysis  
Exergy represents the part of energy that could be converted into useful power to the greatest extent. Exergy analysis is an important indicator to assess the system's irreversible loss and the energy utilization. In this system, chemical exergy is ignored [42], and the exergy of each state point can be expressed as physical exergy [43]:  
\[\dot{E}=\dot{m}[(h-h_{0})-T_{0}(s-s_{0})] \tag{8}\]  
where the subscript 0 signifies a normal state.  
The exergy destruction (\(\dot{E}_{D}\)) is defined as the energy that cannot be used for the inefficiency of each component, which can be represented by:  
\[\dot{E}_{\text{D,k}}=\dot{E}_{\text{F,k}}-\dot{E}_{\text{P,k}} \tag{9}\]  
where the subscript k signifies the component, and subscripts F and P denote the fuel exergy and the product exergy of components.  
The system exergy efficiency is as follows:  
\[\eta_{\text{ex}}=1-\frac{\sum\dot{E}_{\text{D,k}}}{\dot{W}_{\text{C}}} \tag{10}\]
# Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics
## 3 Thermodynamic Formalism in Neuroscience
### The Hammersley-Clifford Theorem
#### 3.3.1 Finite Memory, Markov Chains and Gibbs Distributions  
We now explicitly show how to build a Gibbs measure from a finite set of experimental averages as constraints of the maximum entropy variational problem. We assume that these constraints involve events (monomials) over a memory depth \(R\). We build the corresponding Markov chain while using the material of Section 2.3.2. We associate to each spike block \(x_{n,n+R-1}\) an integer \(w_{n}\),  
\[w_{n}=\sum_{r=0}^{R-1}\sum_{k=1}^{N}2^{k-1+Nr}x_{n+r}^{k}, \tag{23}\]  
we write \(w_{n}\sim x_{n,n+R-1}\). In this way a sequence of spike patterns (spike block) can be encoded as sequences of integers, that define the alphabet. Next, we define the incidence matrix (\"grammar\") between symbols of the alphabet. Not all transitions between symbols are legal or allowed. A transition between the two symbols denoted by \(w_{n}\to w_{n+1}\) or \(w_{n},w_{n+1}\) is legal if the corresponding blocks overlap according to this pattern, i.e., they have the block \(x_{n+1}\ldots x_{n+R-1}\) in common (see Figure 3).  
This defines an incidence matrix \(I(w^{\prime},w)=1\) if the transition between symbols \(w^{\prime}\) and \(w\) is legal and 0 otherwise. This incidence matrix defines the grammar of allowed and forbidden words or sequences of symbols. From this incidence matrix, we define the Perron-Frobenius transfer matrix \(\mathcal{L}_{\psi}\) in the same way, as in (15). In order to obtain the unique Markov transition matrix of maximum entropy, we follow the procedure that is explained in Section 2.3.2.  
Thus, for a given choice of monomials, we associate a potential of the form (22), where the \(\lambda_{I}\)s that do not correspond to a chosen monomial are set to 0. Subsequently, one computes the empirical average of the chosen monomials from data. From the Perron-Frobenius theorem, there is a unique Gibbs measure, the Markov measure \(\mu_{\lambda}\) of the Markov chain, which solves the variational problem (14), giving a statistical model of data, minimizing the KL divergence between the empirical measure and \(\mu_{\lambda}\). Here, \(\lambda\) is the set of parameters \(\lambda_{I}\) that achieve the variational principle. These parameters can be numerically computed, either by using the explicit form of the measure (17) [49] or by while using the MonteCarlo methods [89].  
Figure 3: Spike blocks transition. Example of legal transition \(w_{n}\to w_{n+1}\) between blocks of range four (\(R=4\)). The two blocks are \(w_{n}\sim x_{n,n+3}\) and \(w_{n+1}\sim x_{n+1,n+4}\) and have the block \(x_{n+1}x_{n+2}x_{n+3}\) in light blue in common.  
The software PRANAS allows for the handling of spike train statistics by numerically computing the Gibbs distribution, solving (14) for up to 100 neurons [99].
## 8 Conclusions  
In conclusion, the combination of sixth-generation (6G) wireless networks and artificial intelligence (AI) is a major advancement in the field of communication technologies, with the potential to transform a wide range of industries, including online gaming, robotics, agriculture, autonomous vehicles, smart cities, robotics, and UAV communication. While  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Challenge** & **Description** & **Potential Solutions** \\ \hline \hline Data Privacy and Security & Ensuring the protection of sensitive data in IoT systems & Advanced encryption, blockchain technology \\ \hline Scalability & Managing the vast number of IoT devices and data generated & Edge computing, hierarchical IoT architecture \\ \hline Interoperability & Ensuring seamless communication between diverse IoT devices & Standardization, open protocols \\ \hline Energy Efficiency & Reducing the energy consumption of IoT devices and networks & Energy-efficient hardware, AI-driven optimization \\ \hline Regulatory and Ethical Issues & Navigating the legal and ethical implications of AI and IoT & Clear regulations, ethical guidelines \\ \hline \hline \end{tabular}
\end{table}
Table 3: Challenges in AI-enabled 6G IoT.  
AI presents significant problems including scalability, security, and ethical issues, it also has the unmatched potential to optimize network performance, improve user experiences, and spur novel applications. Strong and proactive solutions that include developments in big data processing, real-time analytics, hardware development, energy management, and computing efficiency are needed to meet these problems. AI and 6G have a dynamic and mutually beneficial interaction, which emphasizes the need for an infrastructure that is well-supported and cooperative efforts between researchers, industry stakeholders, and legislators. The full revolutionary potential of AI-enabled 6G networks may be fully realized by carefully managing these difficulties, opening the door to an era of never-before-seen connectedness, efficiency, and creativity.  
Conceptualization, M.W.P.M. and V.T.; methodology, M.W.P.M.; software, R.M.M.R.R.; formal analysis, V.T.; investigation, C.S.; resources, C.S.; data curation, M.W.P.M.; writing--original draft preparation, M.W.P.M.; writing--review and editing, C.S.; visualization, M.W.P.M.; supervision, V.T.; project administration, V.T.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study.  
The authors declare no conflict of interest.
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods
### Experimental Setup
#### 2.6.3 Receiver  
The receiver uses a different microcontroller, also an Arduino Uno, connected to the main computer. This board function is to read the pH levels in the water through the pH probe, as shown in Figure 5. We used a 5 V pH probe, featuring, as most pH probes, a glass bulb filled with strong electrolytes and a silver wire inside. This allows the pH electrode to measure the difference in potentials between the two sides of the glass electrode. This signal is then converted into an electric potential which allows reading the pH value. The pH probe is connected to a pH meter, which is connected to the Arduino Uno microcontroller, that digitizes the pH reading through a 10-bit analog to digital converter (ADC). This probe can introduce a pH measurements error of \(\pm\)0.1 at 25 \({}^{\circ}\)C.  
Figure 5: (a) Schematic diagram of the experimental implementation. (b) Photo of the experiment.