esa-sceva/satcom-chunk-collection huggingface dataset:
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Impact of Transmission Distortion  
It is generally accepted that it is lossy to send raw data like audio and video over the internet. At some point in their journey to the receiver, all transmissions suffer distortion. There has been a lot of theoretical work done on measuring source fidelity over fading channels as a means of coping with the lossy transmission. Distinct forms of channel coding and source coding have been designed to lessen the overall amount of distortion introduced during transmission. However, preferences for deformation, cost, and complexity might be at odds with one another depending on whether one uses source or channel coding diversity. The likelihood of an outage has an effect on both information capacity and distortion. Clearly, the maximum outage rate associated with a given outage probability may not result in the least amount of predicted distortion [158]. The greatest outage rate that can be achieved with tolerable distortion using a NOMA system can be investigated by conducting an inquiry to optimize the outage probability.
# Exploring the Feasibility of Autonomous Lighting Systems for Pedestrian Crossings in Off-Grid Areas
## 2 Materials and Methods
### Description of Mathematical and Simulation Models  
In order to perform an energy analysis of the autonomous lighting system, the following mathematical model is proposed.  
\[E_{pv}=\int P_{pv}(t)\ dt \tag{4}\]  
\[E_{led}=\int P_{led}(t)\ dt \tag{5}\]  
\[E_{batt}=\int P_{pv}(t)-P_{led}(t)\ dt \tag{6}\]  
where:  
\(E_{pv}\)--Energy generated by the installation PV [J];  
\(E_{led}\)--Energy consumed by the luminaire LED [J];  
\(E_{batt}\)--Energy stored in the battery [J].  
Formula (4) shows the calculation of the amount of electric energy produced by the photovoltaic installation. Formula (5) presents the amount of energy consumed by the LED luminaire, and Formula (6) indicates the amount of energy stored in the battery; Matlab-Simulink software (Matlab-Simulink R2022b) was used to perform the energy analysis. A view of the model is shown below (Figure 17).  
Figure 16: Calculated energy produced by a PV installation for a whole year and the value of the power produced by a 1.55 kWp PV installation every 15 min for a whole year.  
The primary variable of the simulation model is \"Time [min]\", which is the input signal for two blocks. The first block, \"ON-OFF Table\", contains a table with street lighting ON and OFF times in each day of the year. The second block, \"PV Power [W]\", contains a table with values of PV energy production described in a previous chapter of this article. The \"Control System\" block contains a set of security features to keep a minimum battery charge at the level of min. SOC = 50%. Such a requirement allows for the safe operation of the battery storage bank in harsh frost. In addition, in the case of Scenario 5, the block regulates the power supply to the luminaires depending on the presence of a pedestrian at the zebra crossing. The next step is to calculate the difference between the power produced by the PV system and that consumed by the luminaire. In the case of excess power during the day, the energy is stored in the battery, while at night, the energy is taken from the storage bank. For this purpose, \"Battery Bank\" block is determined to calculate the battery State Of Charge (SOC) level. The main tasks in the simulation model research were the analysis of key parameters of the lighting system. The relationship between power, daily energy consumption of the LED luminaires, required operating time, capacity of the batteries, PV power production and changes in light intensity were crucial for the research results.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Polymer Nanocomposites
Linearity was observed from 10.0 to 60.0 ng mL\({}^{-1}\) in spiked human serum with an LOD of 3.6 ng mL\({}^{-1}\). Yupintharakun's group developed an optosensor for the detection of ciprofloxacin, an antibiotic commonly used in the treatment of various ailments of the skin and kidneys, by employing a nanocomposite of CMWCNT, CdTe quantum dots, and molecularly imprinted APTES polymer [324]. The sensor showed high binding affinity and specificity, and exceptional sensitivity wherein the fluorescence intensity reduced in a linear fashion from 0.10 to 100.0 \(\upmu\)g L\({}^{-1}\) and the detection limit was found to be 66 ng L\({}^{-1}\). Soni et al. used PAni-MoS2 nanoflower composites for the detection of chronic myelogenous leukemia (CML), a leukemia biomarker [325]. The bioelectrode used to develop the impedimetric sensor consisted of ITO and plasmid DNA (pDNA) withstand the nanoflowers. EIS provided a very wide detection range of 10 aM to 1 \(\upmu\)M and the LOD was 3 aM. The genosensor was also tested with a variety of DNA sequences and real samples to exhibit its potential for healthcare diagnostics. Further research works, relying on nanoMIPs, nanomotors and polymeric nanocomposite-based sensors, are listed in Table 6.
## 3 Results  
The optimized number of significant features associated with treatment failure was determined for each of the 22 models listed in Table 3 in the training dataset of 224 cases (N). The number of selected features was determined as shown, based on the best predictionperformance in each of the 22 models. The optimal number of significant features is shown in Table 3. In the binary images used to extract the \(T\) features, the treatment failure case had more cavities (b2), whereas the non-treatment failure case had almost a connected component (b0), as shown in Figure 1.  
Table 3 shows the optimal number of features, C-indices, balanced C-indices, and statistically significant differences of the treatment failure-free probability curves of the high- and low-risk groups, stratified based on the median Rad-score in the treatment failure prediction model. Statistically significant differences in the treatment failure-free probability curves appeared in the CT-\(R\), CT-\(L\), DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\) models. Figure 4 shows the treatment failure-free probability curves for the seven models, as mentioned above. The C-indices of training and test datasets were 0.726 and 0.641 for the CT-\(R\) model, 0.635 and 0.745 for the CT-\(L\) model, 0.670 and 0.641 for the DD-\(L\) model, 0.743 and 0.760 for the DD-\(T\) model, 0.730 and 0.747 for the DD-\(RT\) model, 0.679 and 0.756 for the DD-\(LT\) model, and 0.834 and 0.664 for the CD-\(RLT\) model. The DD-\(T\) model had the highest C-index of 0.760 for the test dataset. The balanced C-indices were 0.684, 0.690, 0.656, 0.752, 0.739, 0.718, and 0.739 for the CT-\(R\), CT-\(L\), DD-\(L\), DD-\(T\), DD-\(RT\), DD-\(LT\), and CD-\(RLT\) models, respectively. Considering the C-index of 0.760 for the test dataset, balanced C-index, and statistically significant differences in the treatment failure-free probability curves, the DD-\(T\) model demonstrated the best prediction performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\
\begin{tabular}{c} **Optimal Number of** \\ **Features** \\ \end{tabular} } & \multicolumn{2}{c}{**C-Index**} & \multirow{2}{*}{
\begin{tabular}{c} **Balanced** \\ **Training** \\ \end{tabular} } & \multicolumn{2}{c}{**Statistically Significant Differences**} \\ \cline{3-6}  & & & **Training** & & **Test** & **C-Index** & **Training** & **Test** \\ \hline CT-\(R\) & 3 & 0.726 & 0.641 & 0.684 & *** & * \\ CT-\(L\) & 3 & 0.635 & 0.745
# DIKWP-Driven Artificial Consciousness for IoT-Enabled Smart Healthcare Systems
## 6 Experimental Results
### Simulated Data Examples and Event Traces
This detailed trace exemplifies how the DIKWP-based artificial consciousness framework not only detects events but also contextually reasons \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & **Time** & **Heart Rate** & **Respiration** & \multirow{2}{*}{**Condition Label**} \\  & **(s)** & **(bpm)** & **(Breaths/min)** & \\ \hline \multirow{8}{*}{Normal} & 0 & 72 & 16 & Normal \\  & 1 & 75 & 16 & Normal \\  & 2 & 73 & 15 & Normal \\  & 3 & 74 & 16 & Normal \\  & 4 & 76 & 15 & Normal \\  &... &... &... &... \\  & 9 & 75 & 16 & Normal \\ \hline \multirow{8}{*}{Arrhythmia Episode} & 0 & 74 & 17 & Normal (pre-event) \\  & 1 & 89 & 19 & Arrhythmia onset \\  & 2 & 110 & 22 & Arrhythmia (irregular) \\  & 3 & 65 & 20 & Arrhythmia (irregular) \\  & 4 & 90 & 21 & Arrhythmia (peak) \\  & 5 & 78 & 18 & Arrhythmia (recovering) \\  & 6 & 80 & 18 & Normal (recovered) \\ \hline \multirow{4}{*}{Noise Artifact} & 0 & 75 & 16 & Normal \\  & 1 & 0 (error) & 16 & Noise artifact \\ \cline{1-1}  & 2 & 150 (spike) & 16 & Noise artifact \\ \cline{1-1}  & 3 & 74 & 16 & Normal (signal restored) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulated physiological signal data with condition labels. about them and takes appropriate actions (alerting or not alerting) in a manner similar to a conscious observer. In the above trace, the DIKWP agent demonstrates situation awareness: it ignores momentary normal fluctuations and noise, but when a sustained irregularity indicative of arrhythmia occurs, it quickly responds by raising an alert, and later clears the alert once the condition resolves. This aligns with the system's design goal of mimicking an attentive, purpose-driven artificial consciousness in healthcare monitoring.
# RF Energy Harvesting Wireless Communications:
## 4 Power Management in RF-EHWC
### Practical Issues in Power Management
#### 4.2.1 Nonlinear Battery Charging
Afterwards, according to the principle that the RF-EHWC system must use all harvested energy at the end of data transmission to maximize the throughput, it can be obtained that \(Q_{N+1}(E_{1}^{r})\!=\!0\). By solving this high order nonlinear equation, \(E_{1}^{r}\) is obtained. Eventually, substituting \(E_{1}^{r}\) into \(Q_{i}\), we can calculate the optimal amount of energy that should be retained in the battery when energy packets arrive. Thereafter, the RF-EHWC system can manage its transmission power efficiently based on the optimal residual energy.
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 4 Experiments
### Experimental Setup  
In order to benchmark our model and the estimated uncertainties, we decided in favor of person re-identification as the application. This application is a common choice in the state of the art on uncertainty estimation for feature vectors [22; 23; 28]. Person re-identification provides well-defined benchmarks while the task is very challenging. To enable a comparison of our results with multiple state-of-the-art approaches, we utilize the Market-1501 dataset [68] (see Section 4.1.1), which is the most frequently used within this context.  
We report the mean average precision (mAP) and the rank-1 statistic of the cumulative match characteristic (CMC) curve, which are the most commonly utilized measures of re-identification quality [68]. To get meaningful results, we repeated all experiments 20 times. Therefore, we report the mean and standard deviation for each quality measure. It is important to note that the rank-1 statistic is primarily designed to assess the re-identification ability to identify the best matches to each query in the gallery, which are easiest to find. In contrast, the mAP assesses the quality of the entire ranking. Given that uncertainties contribute to enhanced performance in challenging cases, we anticipate that the mAP will prove a more effective measure. Consequently, the mAP results will be the primary focus of discussion.  
In some cases, we need to select a specific model for visualization purposes. In these cases, out of the 20 trained models, we decided in favor of the model whose performance closely matches the mAP reported by [PERSON] et al. [25], which is the basis for our architecture. The mAP reported by [PERSON] et al. [25] is 87.0, while our selected model achieves an mAP of 87.03 when the re-identification is conducted identically to the UAL approach [25].
## 4 Formulation of an Operation and Maintenance Strategy for Equipment with a High Failure Rate
### Verification of Equipment Value Improvement
#### 4.2.3 Value Coefficient Calculation  
Assuming that the occurrence probability of each state of the camera in the toll square reaches a stable state after 3 years of service, the annual cost of not implementing the maintenance strategy can be calculated as \(C_{n}=130,812.83\), and the annual maintenance cost of implementing the maintenance strategy as \(C_{S}=18,725.39\). Then, the value coefficient of video surveillance equipment without the maintenance strategy is \(V_{n}=0.4356\), and the value coefficient when implementing the maintenance strategy is \(V_{S}=3.0429\). It can be seen that the value coefficient \(V_{S}\) after adopting the maintenance strategy is much higher than that without the maintenance strategy \(V_{n}\). This shows that the maintenance strategy proposed in this study can effectively control the wear and fatigue of key components, slow down the speed of performance degradation, and significantly improve the charging square camera after its scheduled service life expires; the value coefficient of continuing service in the electromechanical system and its overall maintenance cost is kept at a relatively low level.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Serial**} & \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Unit**} & \multicolumn{6}{c}{**Video Surveillance Equipment**} \\ \cline{3-8}  & & & **Toll Booth** & **Toll Island** & **Toll Plaza** & **Tunnel** & **Indoor** \\ \cline{3-8}  & & & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & Fees for labor & Working day & 3.8 & 4.8 & 9 & 3.6 & 3.6 \\
2 & Bolts & Kg & — & — & 2.9 & — & — \\
3 & Expansion bolts & Set group & 4.1 & 10.2 & 10.2 & 8.2 & 8.2 \\
4 & Other materials & CNY & 1.9 & 2.5 & 4.1 & 1.9 & 1.9 \\
5 & Trucks less than 4 t & Shift group & — & — & 0.88 & 0.22 & 0.18 \\
6 & Hydraulic lifts within 300 kg & Shift group & — & — & 0.16 & — & — \\
7 & Small tools & CNY & 12.2 & 23.6 & 62.7 & 13.1 & 13.1 \\
8 & Basic pricing & CNY & 353 & 585 & 1529 & 540 & 522 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Resource consumption and basic pricing table for installation and debugging of each set of video surveillance equipment.
## 3 Results and Discussion  
### Characterization of Nanocom  
#### 3.1.1 FESEM and Morphology of AC/CFO  
The FESEM images of AC/CFO shown in (Figure 1) depicts the formation of AC/CFO nanocomposite with an average particle size of about 30 nm. The surface morphology of nanocomposite shows the generation of cobalt ferrite particles on the surface of activated carbon; small aggregates were observed.
# Smart Cities of the Future as Cyber Physical Systems: Challenges and Enabling Technologies
###### Abstract  
A smart city represents an improvement of today's cities, both functionally and structurally, that strategically utilizes several smart factors, capitalizing on Information and Communications Technology (ICT) to increase the city's sustainable growth and strengthen the city's functions, while ensuring the citizens' enhanced quality of life and health. Cities can be viewed as a microcosm of interconnected \"objects\" with which citizens interact daily, which represents an extremely interesting example of a cyber physical system (CPS), where the continuous monitoring of a city's status occurs through sensors and processors applied within the real-world infrastructure. Each object in a city can be both the collector and distributor of information regarding mobility, energy consumption, air pollution as well as potentially offering cultural and tourist information. As a consequence, the cyber and real worlds are strongly linked and interdependent in a smart city. New services can be deployed when needed, and evaluation mechanisms can be set up to assess the health and success of a smart city. In particular, the objectives of creating ICT-enabled smart city environments target (but are not limited to) improved city services; optimized decision-making; the creation of smart urban infrastructures; the orchestration of cyber and physical resources; addressing challenging urban issues, such as environmental pollution, transportation management, energy usage and public health; the optimization of the use and benefits of next generation (5G and beyond) communication; the capitalization of social networks and their analysis; support for tactile internet applications; and the inspiration of urban citizens to improve their quality of life. However, the large scale deployment of cyber-physical-social systems faces a series of challenges and issues (e.g., energy efficiency requirements, architecture, protocol stack design, implementation, and security), which requires more smart sensing and computing methods as well as advanced networking and communications technologies to provide more pervasive cyber-physical-social services. In this paper, we discuss the challenges, the state-of-the-art, and the solutions to a set of currently unresolved key questions related to CPSs and smart cities.  
cloud; IoT; smart cities; embedded systems; wireless systems; cyber physical systems; online social networks; software-defined networks +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Thermal Conductivity  
As explained in Section 2.3, thermal conductivity of CNT-cement composites was measured by applying a certain amount of electricity. Figure 8 shows the test results for thermal conductivity for paste composites at the ages of 28 days. A constant 200 volts was applied, even as currents inside the samples differed.  
The more CNT added in the mixtures, the higher the increase in temperature was observed. The ambient temperature started in the experiment was 20 degree Celsius, and the peak temperature for MC00 was just 38.8 \({}^{\circ}\)C. However, the highest temperature peaks MC01 and MC02 were 42.5 \({}^{\circ}\)C and 46.3 \({}^{\circ}\)C after 60 min elapsed, which was earlier than MC00.  
The slopes for the first 20 min for MC00, MC01, and MC02 were similar, but differences in peak temperature were relatively large. The highest temperature elevated during the experiment was observed on MC05, which contained largest amount of CNT added. MC04 and MC03 developed 64.5 \({}^{\circ}\)C and 54.6 \({}^{\circ}\)C at 47.2 and 51 min, respectively.  
As shown in Table 7, as CNT additions increased in the mixtures, heat production inside the specimens increased, but the time to reach highest temperature for each specimen decreased. That is, CNT played an important role in enhancement of heat transfer capacity caused by electrical conductivity, low resistance, and high current migration [29].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dot Number**} & \multicolumn{6}{c}{**Chemical Compositions (Mass\%)**} \\ \cline{2-7}  & **Ca** & **Si** & **Al** & **O** & **C** & **Others** \\ \hline
1 & 22.7 & 9.3 & 1.8 & 50.6 & 11.4 & 4.2 \\
2 & 40.3 & 1.2 & 0.9 & 50.9 & 4.4 & 2.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Electron Dispersive Spectroscopy results from SEM image for PC05.  
Figure 8: Thermal conductivity plot for CNT-cement composites with peak temperatures with various CNT additions.
# Fleet Resilience: Evaluating Maintenance Strategies in Critical Equipment
## 3 Proposed Methodology
This concept has been extensively discussed in the literature, without reaching a consensus on how to quantify it, therefore, this may constitute a difficulty for its application in real cases. According to the same authors [12], there is no real steady state availability, nevertheless, they defined steady state availability as a value whose variation does not exceed \(10^{-5}\) within a given continuous five-periods interval. They called such an instant as the steady state time. Another relevant aspect of the aforementioned model is the lack of thresholds establishing the availability values, before and after availability recovery. This paper proposes the adaptation and use of the resilience index proposed by [12] by considering that the main objective of maintenance organizations and actions are pointed to the maximization of the system availability, that is, 100%. Therefore, we propose such a value as the reference or initial availability value. In relation to the second issue, we propose the following application principle: any availability loss at the system level corresponds to one or more availability losses in the elements of that system. How these losses impact the system will depend on the criticality levels of each piece of equipment, the topological structure of the system, and the duration of each of these losses. Therefore, and considering that the systemic availability is only the result of the behavior of the individual availabilities, considering the topologies of the system, the availability of the system throughout the time may be represented as shown in Figure 6. In this paper, we consider each loss and recovery in systemic availability as an individual disruptive event. Each of these events is the combined result of one or more disruptive events in one or more pieces of equipment within the system. Therefore, once the time series of availability at the system level is obtained, the sequence of local minima and local maxima in that series is identified (Figure 7). Then, all the data required to use the model of Equation (2) in a given time interval are available.