esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fibre-Optic-Based Smart Geosynthetics
### Types of Fibre-Optic Sensors  
The focus of this discussion is mainly on strain, humidity, and temperature measurements--all of which are critical to geotechnical applications [27; 39; 40; 41; 42; 43]. However, OF sensors are employed in various other applications including electrical fields and chemical sensors for which comprehensive reviews may be found in the literature [44; 45; 46; 47; 48]. The benefits of applying OF sensors to geotechnical health monitoring stem from their low weights, small sizes, and non-electrical characteristics--which not only makes them impervious to electromagnetic interference and electrical noise but also enables them to operate with high-voltage electricity in explosive environments, mines, and train tunnels. Furthermore, in addition to topological classifications (e.g., intrinsic, extrinsic, local, or distributed), OF sensors can be fundamentally classified based on the optical parameters impacted by external factors such as phase, intensity, wavelength, and polarisation.
## 3 Materials and Methods
### Optimal Solution Selection Using M-ABC Algorithm for IoMT Clients
#### 3.2.5 Data Collection Using IoMT Clients  
The IoMT devices are initially used to collect patient health information, and the connected devices communicate with one another when sending patient data. IoMT devices capture medical information from the patient's body after they are implanted, including the heart rate, blood pressure, glucose level, cholesterol, and pulse rate. Using the proposed M-ABC technique, these details are locally optimized within an IoMT local healthcare site, after which the local model from each IoMT local healthcare site is transferred to the global cloud. Patient data from the UCI repository is also used to assess the efficacy of the proposed technique.
## 4 Solution Algorithm
### Move Operation  
In the proposed LNS, the move operation is chosen as the neighborhood search strategy and is applied to the optimal truck routing plan obtained after the \"Sequential insertion\" and \"Solution re-optimization\" operations. A certain number of request vertices are randomly selected based on the removal coefficient to undergo move operations. These request vertices are then sequentially removed from the current truck routing plan and reinserted into different positions. Specifically, for each request vertex to be inserted, any position within the current truck routing plan that complies with network flow constraints can be selected for insertion. When a request vertex is inserted into a specific position in the current routing plan, it is necessary to re-select the departure and return depots for each truck according to the nearest distance principle, under the constraints of the number of departures and returns of trucks at the depots and the empty container quantity at the depots. A new truck routing plan can be efficiently generated through this process.  
For the newly generated truck routing plan, it is necessary to check whether each truck meets the constraints of maximum working hours, request service time windows, and forbidden arcs. Additionally, the empty container quantity at each depot must be dynamically evaluated to ensure it is greater than zero, based on the specific time of empty container delivery to and pickup from each depot. If the new routing plan fails to satisfy all constraints, it is considered infeasible. Conversely, if it meets all constraints and has a better objective value than the current optimal solution, it is updated as the current optimal solution.  
Figure 4 illustrates two simple cases of move operations. The original truck routing plan (Figure 4a) employs three trucks while satisfying a series of constraints, including the truck's working time limit, capacity, request time windows, forbidden arcs, service priority, the number of departures and returns of trucks at depots, and the quantity of empty containers at each depot. By removing the first request vertex in the service sequence of the first truck (request 9) and inserting it into the first position of the service sequence of the second truck, a new truck routing plan (Figure 4b) can be generated. This new plan uses the same number of trucks, but the departure depots for the first and second trucks are modified due to the change in their first service vertex. Additionally, by removing the fourth request vertex of the service sequence of the first truck (request 10) and inserting it into the first position of the service sequence of a newly introduced fourth truck, another new routing plan (Figure 4c) is formed. In this case, the number of trucks increases by one, while the departure and return depots for the first, second, and third trucks remain unchanged.
## 4 Results
### Analysis of Costs  
Ultra-deep drilling operations in the pre-salt fields can reach costs of around USD 1.3 million per day [20; 61]. Considering the daily operating cost presented, Table 4 shows the cost and time to drill zone 2 (inefficient zone; exhibited in Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Weight**} & \multicolumn{3}{c}{**110 [rev/min]**} & \multicolumn{3}{c}{**120 [rev/min]**} \\ \hline  & **ROP** & **MSE** & **Desirability (D)** & **WOB** & **ROP** & **MSE** & **Desirability (D)** & **WOB** & **ROP** & **MSE** \\  & **-** & **-** & **-** & **[klb]** & **[ft/h]** & **[kpsi]** & **-** & **[klb]** & **[ft/h]** & **[kpsi]** \\ \hline
01 & 1.00 & 0.00 & 0.99 & 22.79 & 21.53  
The inefficient zone has an extension of 99.49 [ft]. For this depth range, a total operating time of 25.42 [h] was registered, which may reflect to a total cost of approximately USD 1,387,335. This result shows that the ROP for this zone was very low, with mean values of approximately 3.42 [ft/h] or 1.04 [m/h].  
Tables 5-7 show the cost and time required to drill zone 2 (inefficient zone) after carrying out the drill rate test (pre-operational test) and multi-objective optimization, using the parameters obtained and presented in Tables 1-3, respectively.  
Tables 5-7 show the time, cost, time savings, and cost savings to drilling the inefficient zone through the WOB's grouping of 3, 5, and 7 [klb] and 110 and 120 [rev/min]. The cost and time to drill this inefficient zone using the parameters obtained from the pre-operational tests ranged from 226,437 to 322,154 [USD] and 4.18 to 5.95 [h], respectively. In addition, when comparing the values shown in Tables 5-7 with the values in Table 4, the cost-saving and time-saving values range from USD 1,056,180 to 1,151,898 and 19.50 to 21.27 [h], respectively.
# Tabu Search Aided Multi-Track Detection Scheme for Bit-Patterned Media Recording
## 2 Proposed Detection Scheme
### TS Detector  
The low-complexity TS detector is applied in the proposed detection scheme to improve the reliability for bits with low reliability on {\(-1\), \(+1\)} tracks. Assuming \(a_{+1,k}\) is the bit with low reliability and the received intensities are \(r_{+1,k-1}\), \(r_{+1,k}\), and \(r_{+1,k+1}\), the detector for \(a_{+1,k}\) should consider 15 bits, \(a_{i,k}\), where \(i\) = {\(0\), \(+1\), \(+2\)} and \(k\) = {\(k-2\), \(k-1\), \(k\), \(k+1\), \(k+2\)}. Therefore, the total number of candidate vectors to be considered  
**Step (1)**: Initialization: An initial solution vector \(\textbf{x}^{(0)}\) is set to a vector having the detected data \(\hat{a}_{i,k}\), where \(i\) = [0, +1, +2] and \(k\) = [\(k-2\), \(k-1\), \(k\), \(k+1\), \(k+2\)] from the single-track detector. In addition, the tabu list is initialized while the iteration number and the tabu period are set to \(M\) and \(P\), respectively.
**Step (2)**: Neighbor Vector Enumeration: The \(N\) neighbor vectors at each iteration are derived from \(\textbf{x}^{(l)}\) with the length of \(N\) by changing the opposite bit for each bit position. We take a simple example with the initial solution vector \(\textbf{x}^{(0)}\) = [\(-1\), +1, \(-1\), +1]. According to the definition of neighborhood, the four neighbor vectors (\(\textbf{n}_{1}\) = [+1, +1, -1, +1], \(\textbf{n}_{2}\) = [\(-1\), \(-1\), \(-1\), \(+1\)], \(\textbf{n}_{3}\) = [\(-1\), +1, +1], and \(\textbf{n}_{4}\) = [\(-1\), +1, \(-1\), \(-1\)]) are \(\textbf{x}^{(0)}\)'s neighbors.
**Step (3)**: Tabu Move: A new solution vector \(\textbf{x}^{(l+1)}\) with the lowest metric is selected among the neighbor vectors excluding the solution vectors in the tabu list. The TS measures the distance metric between \(r_{i,k}\) and \(\bar{r}_{i,k}^{j}\), where \(k\) = {\(k-1\), \(k,k+1\)} and \(j\) = {\(1\) \(\cdot\) \(\cdot\) \(\cdot\) \(M\)}, where \(\bar{r}_{i,k}^{j}\) is the reconstructed signal with the \(j\)-th neighbor vector. The selected solution vector \(\textbf{x}^{(l+1)}\) is used as the initial solution vector in the next iteration.
**Step (4)**: Tabu List Updating: The new solution vector obtained in Step 3 is recorded in order on the tabu list. When the tabu list with a length of \(P\) is full, the new  
Figure 2: Flow chart of the TS algorithm.  
solution vector will push the first solution vector out of the list, and the new solution vector is added in the tabu list.  
Then, the detected bit \(a_{+1,k}\) from the final solution vector of the TS detector is transferred to the simplified multi-track detector.
# The Synergy of Renewable Energy and Desalination:
## 3 Application of Renewable Energy Sources in Desalination
### Solar Energy
PV systems alone may not deliver the instantaneous peak power that is necessary for motor-driven variable torque load. Therefore, these systems require substantial starting power, necessitating batteries (or supercapacitors) to meet the initial surge demand. In [49; 50], the authors investigate a developed simulation approach to optimize the operation of PV powered pumping systems. The primary parts of a photovoltaic pump system are the motor-pump set, battery storage, power converter unit, and PV array. The two most popular motor-pump sets in photovoltaic pump applications are three-phase alternative current (AC) electric motors with centrifugal pumps or direct current (DC) motors with positive displacement pumps. In addition to a maximum power point tracking (MPPT) and energy storage system, a power electronics converter unit for these motor-pump sets needs to feature a DC/AC inverter or a DC/DC converter. Figure 4 illustrates the basic structure of a solar water-pumping system [51]. Using solar PV power generation to power the high-pressure pumping system helps to combine solar energy and conventional seawater desalination technologies. As a result, a promising technology involves using solar PV power generation to power RO systems. In [52], the authors conducted a study focusing on both economic and environmental factors. The study investigated the possibility of using solar-powered desalination instead of grid-imported electricity as an affordable solution to water scarcity. By creating a model that took into account desalination capacity, input power prices, and specific energy usage, they explored the economic feasibility of PV-powered desalination. They showed that the integration of photovoltaic generating systems to provide the electricity needed by the RO desalination process could decrease the unit production costs of desalinated water by approximately 33%, according to case studies conducted in Portugal.
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery  
In this section, we examine how an LTE/5G-based PLMN can fulfil the requirements stated in Section 2 on communications reliability, latency and throughput for the autarkic microgrid black start use case. PLMN addresses limitations of the current communications technologies used for DG management: Internet communications generally require power to be available at the DER sites, so are vulnerable to blackouts. Using wireless technologies such as LoRa requires that the wireless network be set up to be reliable and available during blackouts. The communication range of broadband PLC is limited by its sensitivity to noise, making it unsuitable for this application.  
Mobile networks fulfil the requirement for having wide-area network, while using PLMN avoids the cost of having to roll out a separate network for grid management purposes. Crucially, they also offer a high degree of reliability [3]. However, using a public network means that microgrid management traffic shares the same public mobile network with H2H traffic and MTC traffic from other applications, becoming just another application to be supported by the network, with a particular QoS requirement.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 2 Literature Review
### Fault Current Limitation Method  
There are various methods used to earth the neutral point of the secondary transformers in power systems, and each of these methods plays a critical role in the design and performance of substation earth grids. The commonly used earthing methods include solid earthing and impedance earthing [2]. In the solid earthing method, the secondary transformer neutral point is directly connected to the earth, whereas in the impedance earthing method, which is further divided into resistive or reactance earthing, the neutral point is connected to the earth via resistive or reactive elements [2,3]. In some rare cases, the secondary transformer's neutral is left unearthed, such as in systems using the IT earthing system [2].  
Even though there are various earthing methods to choose from, there are advantages and disadvantages presented by each method that should be considered before choosing and implementing an earthing method. Table 1 compares the advantages and disadvantages of each method obtained from findings by Prev\(\acute{\text{e}}\), Cressal Resistors, and Electrical Desk [2,4,5].  
From the information in Table 1, it is observed that the resistive neutral earthing method is the most beneficial in terms of safety, application, reliability, and cost. For this study, the resistive neutral earthing method will be modeled and analyzed in Section 3: Earth Grid Design Criteria. In practice, the resistive earthing method is applied using devices called neutral earthing resistors (NERs), which are commonly used in medium-voltage installations to limit fault current that flows through the neutral point of a transformer or generator to safe, predetermined maximum values when under transient conditions to limit the damage to the transformer and other critical power system equipment [6]. NERs are application-based devices that are selected and sized to suit their intended purpose as per end-user needs. Their sizing is based on the anticipated heat dissipation from the fault current on the resistor elements [3,7].  
Generally, for the protection of the power system, earth fault relays are employed and set at a specific sensitivity that will trigger the relay to operate, but, in cases where stringent earth fault protection is required of the MV/HV system, the NERs are added not only for the protection of equipment but also for that of personnel working in the vicinity of the substation [8].
## 2 Related Work
### Bloom Filter  
As a kind of probabilistic data structure, a Bloom filter consists of an array of \(n\) bits. Together with \(k\) independently and randomly selected hash functions, \(h_{1}\), \(\cdots\), \(h_{k}\), with range \([0,n-1]\), it is used to represent a set of elements with the support of membership query. Assume that a Bloom filter \(B\) is used to represent a set \(S=\{s_{1},\ldots,s_{m}\}\) of \(m\) elements. To insert an element \(s_{i}\), the bits \(B[h_{j}(s_{i})]\) for \(1\leq j\leq k\) are set to \(1\). Note that the bit remains unchanged when being already set to \(1\). To check whether an element \(x\) is in the set \(S\), we can check whether the bits \(B[h_{j}(x)]\) for \(1\leq j\leq k\) are all \(1\)'s. If and only if they are all equal to \(1\), \(x\) is deemed to be an element of \(S\). The size \(n\) of Bloom filter is  
Figure 1: In a UWSN, the itinerant sink roams around the sensing region and collects the data sensed by sensors.  
independent of the size of elements and can be constant, which is very memory-efficient. Nevertheless, the membership query on Bloom filter has false positive but has no false negative. In other words, it is probable to falsely consider an element that actually does not belong to \(S\) as an element of \(S\). In [7], such false positive probability can be obtained as \((1-(1-\frac{1}{n})^{km})^{k}\approx(1-e^{\frac{-km}{n}})^{k}\). The optimization between the performance efficiency e.g., array length or hash functions required) of Bloom filter and the false positive probability can be obtained but is beyond the scope of this paper. Note that one of the characteristics of Bloom filter we use in the design of our proposed scheme is that the query result is always correct if the content to be queried is indeed stored in the Bloom filter.
# Recent Advances in the Synthesis of Graphene-Based Nanomaterials for Controlled Drug Delivery
## 2 Ways of Controlled Drug Delivery  
In contrast to traditional chemotherapy, in which drugs diffuse freely in the bloodstream and are evenly distributed in the entire body, taking advantage of tumor microenvironments and cell characteristics, and using nanomaterials as drug carriers via chemical and physical manners, enable the controlled delivery of drugs through exogenous (temperature, magnetic fields, ultrasound, and light and electric fields) or endogenous stimuli (pH, enzyme concentration, and redox). These approaches not only increase drug bioavailability and capacity to cross physical barriers, but also reduce the dosage to be used for treatment, consequently reducing the toxic side effects of the delivered drugs [33; 34]. As shown in Figure 1, current methods for controlled drug delivery mainly include exogenous stimuli-responsive, endogenous stimuli-responsive, and multi-stimuli-responsive drug delivery. To make it more clear, the specific stimuli patterns was listed and illustrated in the next sections. Moreover, the corresponding examples of graphene-based nanomaterials for controlled drug delivery were also listed and showed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Stimulus** & **Nanocarrier** & **Stabilizer** & **Drug** & **Targets** & **Reference** \\ \hline Temperature & GO/PNP & - & ADR & MC3T3-E1 cells & [35] \\ \hline Magnetic & Graphene/Fe\({}_{3}\)O\({}_{4}\) & - & Dox, PTX & HeLa cells & [36] \\ \hline \multirow{4}{*}{Light} & Au@GO & - & Dox & HeLa cells & [37] \\  & GO@Ag & - & Dox, NGR & MCF-7 cells & [38] \\  & GODs/SiO\({}_{2}\)/Fe\({}_{3}\)O\({}_{4}\) & - & Dox & 4T1 cells & [39] \\  & GO/CuS & PEG & Dox & HeLa cells & [40] \\  & rGO & PEG & Resveratrol & 4T1 cells & [41] \\ \hline Electric & GO & PPy & DEX & Astrocytes & [42] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stimuli-responsive drug-delivery systems of graphene-based materials 1.
# Empirical Evaluation of TLS-Enhanced MQTT on IoT Devices for V2X Use Cases
## 1 Introduction
This provides an empirical edge-device testbed, moving beyond prior VM- or simulation-based studies and enabling direct measurement of resource demands under realistic hardware constraints. 2. Comprehensive profiling of TLS-secured MQTT: We systematically benchmark the impact of TLS 1.3 on MQTT performance, quantifying CPU cycles, execution time, energy consumption, memory usage, and goodput for each MQTT role and each phase of the TLS protocol, over both Ethernet and WiFi. We provide a direct, side-by-side comparison of the ChaCha20_Poly1305 and AES_256_GCM cipher suites, highlighting their suitability for constrained V2X and IoT deployment. 3. Standard-driven analysis of message packaging strategies: We evaluate the effect of five distinct message size/count configurations, mapped to V2X-relevant standards, on system performance and resource consumption. This analysis provides actionable insights into the trade-offs among batching, latency, goodput, and energy efficiency, guiding application-specific protocol configuration. 4. Empirical foundation for real-world deployment: Our results synthesize practical recommendations for selecting secure communication parameters, optimizing for both performance and energy efficiency in real-world, resource-constrained vehicular and IoT environments. The remainder of the paper is organized as follows: Section 2 reviews related work, while Section 3 provides a concise overview of the MQTT protocol and highlights representative V2X use cases that motivate the need for robust security. Section 4 details our system implementation and experimental methodology. Section 5 presents and analyzes the results of our performance evaluation. Section 6 synthesizes the key findings, discusses their implications, and outlines future work directions. Finally, Section 7 offers concluding remarks.
# Fault Diagnosis of Rolling Bearings Based on Improved Kurtogram in Varying Speed Conditions
## 3 Simulations  
In this section, several simulations are used to demonstrate the effectiveness of the proposed method. Considering the complexity of the rotating system, the synthetic signals usually include three terms: deterministic components, including the fundamental frequency and harmonics of the shaft, which are caused by factors such as misalignment, eccentricity or imbalance. Random components, which represent a series of impulses excited by a fault, and measurement noise. The simulated signal is defined as:  
\[x(t)=\underbrace{\sum_{m}A_{m}\cos(2\pi mf(t)t+\phi_{m})}_{\text{the deterministic components}}+\underbrace{[1+\lambda M(t)]*\sum_{n}B_{n}s(t-t_{n}-\tau_{n})}_{\text{the random components}}+\underbrace{n(t)}_{\text{the noise}} \tag{13}\]  
where \(A_{m}\) and \(\phi_{m}\) are the amplitude and initial phase of the \(m\)th harmonic frequency of the shaft, respectively; \(f(t)\) is the instantaneous rotating frequency of the shaft; \(1+\lambda M(t)\) denotes the amplitude modulation term,  
\[1+\lambda M(t)=\left\{\begin{array}{ll}1,\lambda=0&if\;bearing\;outer\; race\;fault\\ 1+\lambda\cos(2\pi ft),\lambda\
eq 0&if\;bearing\;inner\;race\;fault\\ 1+\lambda\cos(2\pi f_{\text{cage}}t),\lambda\
eq 0&if\;bearing\;rolling\; element\;fault\\ 0&normal\end{array}\right. \tag{14}\]  
where \(f_{cage}\) is the cage speed; \(B_{n}\) and \(t_{n}\) are the amplitude and occurrence time of the nth impulse, and the occurrence time \(t_{n}\) is determined according to the instantaneous rotating frequency \(f(t)\) and the fault order frequency \(f_{o}\); \(\tau_{n}\) is the coefficient used to calculate slippage time, which varies from 1% to 2% of the time period of the fault impulse; \(s(t)\) is the impulse response function of the system; and \(n(t)\) is the Gaussian white noise that is uncorrelated with other components. The impulse response function can be written as  
\[s(t)=\left\{\begin{array}{ll}e^{-\beta(t-t_{n}-\tau_{n})}\sum\limits_{i} \sin\{2\pi f_{r}(t-t_{n}-\tau_{n})\},&if\;t-t_{n}>0\\ 0&otherwise\end{array}\right. \tag{15}\]  
where \(\beta\) is the structural damping coefficient, and \(f_{r}\) is the resonance frequency.