esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances and Challenges in the Creation of Porous Metal Phosphonates
## 3 Applications
### Proton Conduction
We conclude this section with the illustration of the capabilities of the metal phosphonate frameworks in thin film applications, such as in sensors or devices Hybrid Co-Ca phosphonates consisting of both Co and Ca and 1,4,7-triazacyclononane-1,4,7-triyl-tris-(methylenephosphonic acid) have been synthesized in the form of powders and as single crystals for their use as proton conducting materials. For the powder samples, the proton conductivity was \(1.55\times 10^{-5}\) S/cm at 25 \({}^{\circ}\)C and 95% RH. However, the conductivity was reduced by five orders of magnitude, \(0.79\times 10^{-10}\) S/cm at 25 \({}^{\circ}\)C when the RH was 40%. Single-crystals have been grown for these hybrid framework along the (010), (202) and (201) directions to determine the conductivity along different planes. The single-crystal grown along the (010) direction displayed the highest conductivity of \(1.00\times 10^{-3}\) S/cm at 25 \({}^{\circ}\)C and 95% RH. The lowest conductivity was observed along the (201) direction with \(4.35\times 10^{-8}\) S/cm at 25 \({}^{\circ}\)C and 95% RH. The higher conductivity along the (010) direction was attributed to a continuouslattice of water molecules and the 'O' from the phosphonate present in the channel. The activation energy for the single-crystal hybrids along the (010), (202) and (20\(\overline{1}\)) directions were 0.90, 0.86 and 0.58 eV, respectively (Figure 10). These values were too large to assign the Grothuss mechanism for conduction, instead a vehicle mechanism could be in operation. It was highlighted that the presence of CIO4- anions, originating from an additive used during synthesis, stayed incorporated in the structure. These anions together with the phosphonate 'O' atoms played a key role in connecting the water molecules via a hydrogen bonded network during the conduction process [126].
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 5 Results and Discussion
### JADESCA Box Plot Analysis  
The box plots for the JADESCA algorithm, as seen in Figure 7, across functions F1-F12, provide insight into the distribution and variability of the best fitness scores achieved during the optimization process. For most functions, such as F1, F5, F9, and F11, the box plots show very tight interquartile ranges (IQRs) with minimal spread, indicating that the algorithm consistently finds solutions close to the best fitness score with little variability. The presence of outliers in some of these plots, especially in F1, F5, F6, and F11, suggests occasional deviations from this pattern, where the algorithm may have explored less optimal regions of the search space. However, these outliers are generally minor and do not significantly affect the overall performance. Functions like F2, F8, and F10 exhibit a broader range within their box plots, indicating greater variability in the fitness scores achieved, which may reflect
# Analysis and Verification Results of Manual Inspection of Pavement Condition Index
## 2 Materials and Methods  
### ASTM D6433-23  
ASTM D6433-23 [1] is an international standard published by the American Society for Testing and Materials (ASTM). It provides a systematic approach for evaluating the condition and deterioration of flexible and rigid pavements. This standard details methods for quantifying and documenting various types of pavement distresses, such as cracks, potholes, and deformations, and classifies each type of distress into three levels of severity: low (L), medium (M), and high (H). ASTM D6433-23 defines 20 distinct distress indicators for flexible pavements (asphalt concrete pavement) and another 20 for rigid pavements (Portland cement concrete pavement). Each indicator is accompanied by specific descriptions and scoring criteria, enabling inspectors to accurately assess pavement conditions.  
This study focuses on flexible pavements commonly observed in Taiwan, particularly urban roads with low traffic volume and stable surface conditions. To minimize the potential influence of pavement deterioration over time, the selected road segments were visually confirmed prior to each inspection round to ensure consistent surface conditions. The process for calculating the Pavement Condition Index (PCI) is illustrated in Figure 1a. The computation of deduct values (DVs) involves determining the distress type, severity level, and distress density. For instance, if the distress type is alligator cracking with low severity and a distress density of 10%, Figure 1b shows that the corresponding DV is approximately 33. After obtaining the DV values for all distresses, they are summed and adjusted using the corrected deduct value table. The corrected deduct values (CDVs) are compared, and the maximum value, referred to as the high corrected deduct value (HCDV), is subtracted from the perfect score of 100. This results in the PCI score for the flexible pavement, as depicted in Figure 1c.
## 3 Results and Discussions
When the excess air ratio increased, the reduction in the fuel and the increase in the oxygen concentration in the engine contributed to a sharp decrease in CO emissions. It can be seen from the figure that, when the excess air ratio was greater than 1.4, the CO emissions in the engine were almost zero. Moreover, the different hydrogen injection modes showed notable effects on the engine CO emissions under the equivalent ratio conditions, while the effects decreased rapidly with the increase in the excess air ratio. Since the gasoline was reduced by 20%, the CO emissions with hydrogen addition were 24.72% lower than with the NHMD. Figure 10 shows the effects of the HMD on the hydrocarbon (HC) emissions. It was noted that the HC emissions showed a trend of decreasing first and then increasing. The oxygen concentration increased with the increase in the excess air ratio, resulting in lower HC emissions. However, with the further increase in the excess air ratio, especially when it was close to the lean burn limit, the HC emissions of the engine increased sharply because of the extreme deterioration of combustion and misfire. With the SHMD, the lowest HC emissions appeared at the excess air ratio of 1.6. In terms of different HMDs, the HC emissions with the NHMD were the greatest and those with the PHMD were the lowest. The engine combustion was more complete with hydrogen addition because of the decrease in the wall quenching distance, which meant that the HC emissions could be effectively reduced. The hydrogen concentration affected the HC emissions significantly. The HC emissions with the PHMD were, on average, 4.01% lower than with the PPHMD, 14.07% lower than with the SHMD and 51.96% lower than with the NHMD. Figure 10: Effects of the HMD on the HC emissions. Figure 9: Effects of the HMD on the CO emissions. Figure 11 illustrates the effects of the HMD on the NOx emissions. The NOx emissions increased first and then decreased with the increase in the excess air ratio. The conditions for the generation of NOx emissions were high temperature and high oxygen concentration. When the excess air ratio increased, the temperature in the engine was reduced, while the oxygen concentration in the engine kept increasing; therefore, the NOx emissions first increased. With the further increase in the excess air ratio, the combustion deteriorated and the temperature gradually decreased, resulting in a sharp drop in the NOx emissions. For the NHMD, the highest NOx emissions occurred with the excess air ratio of 1.1, and they increased by 10.60% when the excess air ratio was 1.0 and by 446.09% when the excess air ratio was 1.5. With an excess air ratio greater than 2.0, there were almost no NOx emissions because of the low temperature. The NOx emissions were the lowest with the NHMD. The stratified hydrogen in the SHMD enhanced the ignition and combustion, thus resulting in the highest NOx emissions. The NOx emissions with the NHMD were 42.22% lower than with the PHMD, 45.08% lower than with the PPHMD and 47.21% lower than with the SHMD.
# Variants of the Low-Energy Adaptive Clustering Hierarchy Protocol: Survey, Issues and Challenges
## 3 Comparative Analysis
FT-LEACH utilizes fault tolerance in considering local and global re-clustering. Selecting the best CH node and cluster formation exert a significant impact on the performance of the overall network in terms of network lifetime, reliability, energy consumption, and other criteria. In LEACH, the randomness of the selection of CH and the direct communication between the CH and BS are the main factors that affect network lifetime. However, existing CH-based and cluster formation-based protocols in WSNs suffer from many problems based on several metrics. Therefore, designing a CH selection protocol that selects an efficient and reliable CH is important. Designing a cluster formation protocol that efficiently forms clusters to minimize energy consumption, minimize complexity, and ensure packet delivery is also crucial. A comparison of this survey with existing surveys [30; 31; 32; 33] is presented in Table 12.
## 2 Materials and Methods
### Extraction and Determination of Bioactive Compounds  
One gram of bell pepper powder was placed in plastic tubes and 10 mL methanol:H\({}_{2}\)O (80:20, \(v/v\)) was added. The extraction was carried out in an orbital shaker (Stuart, Stone, UK), where the samples were vigorously shaken for 1 h at 4 \({}^{\circ}\)C in darkness. The extracts were centrifuged at 3220\(\times\)\(g\) for 10 min at 5 \({}^{\circ}\)C and the supernatant was collected and kept at \(-80\)\({}^{\circ}\)C until further analysis of total phenolic content (TPC), total flavonoid content (TFC), and total antioxidant capacity (TAC). Extractions were carried out on each sampling day in triplicate.  
The TPC was determined as previously described by [PERSON] et al. (2018). For that, 19 \(\mu\)L sample extract were mixed with 29 \(\mu\)L of 1 mol L\({}^{-1}\) Folin-Ciocalteu reagent. Three minutes later, 192 \(\mu\)L of 0.4% Na\({}_{2}\)CO\({}_{3}\) and 2% NaOH were added. After 1 h incubation in darkness, the absorbance was measured at 750 nm using a microplate reader (Tecan Infinite M200, Mannedorf, Switzerland). The TPC was expressed as g gallic acid equivalents (GAE) kg\({}^{-1}\) fresh weight (fw). Each sample was analysed in triplicate.  
The TFC was determined following the method described by [PERSON] et al. (2018). Briefly, 0.030 mL of extract were mixed with 80 \(\mu\)L of aluminium chloride (20 g L\({}^{-1}\)). The sampleswere shaken for 30 s and then incubated in darkness for 1 h. After that, the reaction absorbance was measured at 415 nm. TFC was expressed as g rutin equivalents (RE) kg\({}^{-1}\) of fw. Each sample was analysed in triplicate on each sampling day.  
The TAC was analysed following the DPPH procedure [29]. DPPH assay was performed by adding 194 uL of DPPH (0.7 mM) solution to 21 uL of bell pepper extract. After 30 min incubation in darkness, absorbance was measured at 515 nm. Obtained data were expressed as g of trolox equivalents (TE) kg\({}^{-1}\) fw. Each sample was analysed in triplicate on each sampling day.
## 3 Proposed Methodology
### Estimation of Link Lifetime
Hence, note that, as evident from Figure 5, the input layer consists of 104 (13 \(\times\) 8) neurons, which correspond to 1 st-8 th-order terms for difference in \(x\) coordinates (\(\delta_{x}\)), difference in \(y\) coordinates (\(\delta_{y}\)), difference in \(x\) direction velocities (\(\delta v_{x}\)), difference in \(y\) direction velocities (\(\delta v_{y}\)), difference of accelerations in \(x\) direction (\(\delta a_{x}\)), difference of accelerations in \(y\) direction (\(\delta a_{y}\)), remaining distance to maximum transmission distance (\(\delta D=\sqrt{(D^{max}_{ij})^{2}-\delta x^{2}-\delta y^{2}}\)), product of \(x\) direction differential acceleration and velocity (\(\delta a_{x}\delta v_{x}\)), product of \(y\) direction differential acceleration and velocity (\(\delta a_{y}\delta v_{y}\)), product of \(x\) direction differential acceleration and displacement (\(\delta a_{x}\delta v_{x}\)), product of \(y\) direction differential acceleration and displacement (\(\delta a_{y}\delta y\)), the product of \(x\) direction differential displacement and velocity (\(\delta x\delta v_{x}\)), and, lastly, the product of \(y\) direction differential displacement and velocity (\(\delta y\delta v_{y}\)) between the nodes  
Figure 5: The structure of the deep neural network for predicting wireless link lifetime. \(i\) and \(j\). The output of the neural network is the wireless link lifetime. The proposed DNN consists of two hidden layers of size 1024 per layer, as evident from Figure 5. By providing the preceding set of input features, the neural network is able to predict wireless link lifetime once it is fitted to real data, such that it can replace the optimization-based approach. Computation of the Link Lifetime Matrix  
Thus, using the DNN to predict the link lifetime in the wireless communication channel (\(t_{ij}\)) for each link, we can compute the link lifetime matrix of the vehicular network (\(T[N,N,2]\)), containing the link lifetimes of each and every link of the network in both communication channels. These are required for the routing algorithm, as shown in Equation (5):  
\[T[i,j,k]\ =\ \left\{ \begin{array}{l}t_{ij}\ ;\ \text{if}\ link\ i-j\ \text{is }wireless\ (k=0)\\ large\ ;\ \text{if}\ link\ i-j\ \text{is }wired\ (k=1)\\ 0\ ;\ \text{if}\ i=j\\ 0\ ;\ \text{if}\ link\ i-j\ \text{in}\ k^{th}\ \text{communication channel does not exist}\end{array} \right. \tag{5}\]  
Note that, as evident from Equation (5), the link lifetime for its own link \(T[i,i,k]\) and non-existing links is zero. Note that Ethernet links are stationary, so their link lifetime is much larger; hence, such links' lifetimes are set to be large. However, for existing wireless links, the previously presented DNN approach is used to compute link lifetimes, as shown in Equation (5).
## 4 Data Fusion
### Second Type of Exclusion  
The second type of exclusion exploits the knowledge of the normal vectors \(\hat{n}_{i}\) in each 3D point \(P_{i}\) and requires that all the points for which Equation (5) holds true are excluded:  
\[(O-P_{i})\cdot\hat{n}_{i}<0 \tag{5}\]  
where \(O\) is the optical center.  
This condition derives from the fact that, if the dot product in Equation (5) is negative, the angle between the vector (\(O\)-\(P_{i}\)) and the normal vector \(\hat{n}_{i}\) is greater than 90 degrees, and so the point is not seen by the camera. This condition on its own is theoretically suitable for excluding all the inappropriate points in convex surfaces, but it fails when dealing with surfaces that present concavities. Conversely, even if the first exclusion appears to be able to handle a generic shape of the point cloud, it fails in some particular scenarios in which the point cloud lacks some parts, because in these cases some temperatures might be wrongly assigned to the opposite part of the point cloud. For these reasons, the two types of exclusion were combined, in accordance with the experimental results as well. Figure 5 shows two examples of applications of the two exclusions in succession with two different values of \(\xi\), on a region of a hypothetical surface, which is represented in the points cloud by 11 points. The zones between each pair of dotted lines (converging to the optical center) represent the band corresponding to each pixel. Inside each band, the part at the right of the purple segments are excluded according to the first type of exclusion. The points in green are the points to be maintained, the points in blue are the points correctly excluded with the first type of exclusion, the points in yellow the ones correctly excluded only with the second type of exclusion and the points in red the ones not correctly excluded after applying both the exclusions, and to which consequently a wrong temperature is assigned. In Figure 5, better results are obtained if the value of \(\xi\) is doubled, because in this way all the points belonging to a part of the object surface not seen by the camera are correctly excluded. Indeed, the value of the parameter \(\xi\) has to be chosen so that in each cell the number of reprojected points is higher than one, so that they form an actual cluster to which we can properly apply Equation (2). Finally, it is worth noticing that the order of the two exclusions is important: in fact, by inverting the exclusions order, it is easy to see that the point \(P_{11}\) is never excluded.
# Federated Reinforcement Learning in IoT: Applications, Opportunities and Open Challenges
## 3 Methodology
### FRL Applications of IoT in the Vehicular Industry  
The authors of [124] present an online federated deep Q-learning-based offloading technique for Vehicular fog computing (FedDOVe). In an urban environment, connected autonomous vehicles (CAVs) offload processing jobs to RSUs with restricted power, computational abilities, and storage powered by renewable energy. In this context, although vehicular fog computing can perform computation-intensive tasks, defining which RSUs can be associated with fog servers is challenging since offloading demands robust power consumption and varying offloading rates across uneven computation loads. To tackle these issues, the authors optimize RSUs' energy consumption and perform load balancing across fog servers using a model-free RL approach based on global information to identify appropriate connections among fog servers and RSUs. The experiments demonstrate that this strategy reduces energy consumption and enhances load balancing compared to existing offloading methods. In reference [125], the authors face the ultra reliable low-latency communications (URLLC) resource slicing and scheduling challenge focusing on trustworthy 6G vehicular services. New technologies aim to connect vehicles to roadside units (RSUs), which can lead to security problems. The authors target mitigating malicious attacks by unauthorized edge access points using a reputation score based on a personal logic sample. In this scenario, offloading is conducted based on such a reputation, which is supported by a federated asynchronous RL algorithm. In addition to that, the authors of [126] present a federated multi-agent DRL method to optimize task-offloading decisions at local and global scales. Focusing on vehicular fog computing, this effort focuses on fast convergence by fostering a local learning approach with limited information sharing, reducing the communication overhead and improving overall privacy.
## 7 Conclusions  
We have presented new analytical results on the PER performances of optically pre-amplified PPM receivers in OWCs. The results enable the evaluation of the OWC system performance under fading, and we have focused on the general Malaga-\(\mathcal{M}\) fading distribution. We have considered the impacts of increased modulation orders and diversity reception, and it was shown that high order PPM is better suited for transmission over weak fading, while diversity reception becomes more effective as fading becomes more intense. The presented analysis is extended in a straightforward fashion to evaluate the performance under different fading distributions, which is of interest for the design of OWC links in a variety of atmospheric conditions.  
All authors contributed equally to the work presented in this manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} EDFA & Erbium doped fiber amplifier \\ EGC & Equal-gain combiner \\ OWC & Optical wireless communication \\ PER & Probability of error \\ PPM & Pulse-position modulation \\ RV & Random variable \\ SEP & Symbol error probability \\ SOA & Semiconductor optical amplifier \\ \end{tabular}  
Figure 9: Diversity reception of optically pre-amplified PPM under negative exponential fading. The noise modes are equal to \(M=2\) and \(M=200\) in **(a)** and **(b)**, respectively.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 5 Results and Discussion
### Accuracy  
Figure 7 shows the correlation between the number of samples versus the accuracy. The accuracy rose as the number of samples increased for all the algorithms. In an FFNN up to 1000 samples, the accuracy increased exponentially. Additionally, after 1000 samples, accuracy became sort of saturated. However, the FFNN provides the lowest accuracy compared to the other two algorithms. The smaller number of data points could be a reason for it showing the lowest accuracy in the FFNN. In the SVM and kNN, up to 200 samples, we could see a significant increment of accuracy. However, after 200 samples, we saw less of an increment. The reduction of model overfitting can be used to explain the accuracy versus the training dataset size curve. In general, as the amount of the training dataset increases, model overfitting considerably decreases.  
Figure 6: Training time vs. various machine learning algorithms.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 3 System Model
### Optimization Problem Formulation  
Based on the aforementioned models, a stochastic optimization problem is formulated whose goal is to maximize the time-average user utility of BANs. \(U_{n}(c_{n}(t))\) is a concave function over the interval \(0\leq c_{n}(t)\leq c^{max}\). We let \(O_{n}(t)=U_{n}(c_{n}(t))\). The time-average user utility can be written as  
\[\overline{O}=\lim_{T\rightarrow\infty}\frac{1}{T}\sum_{t=0}^{T}\sum_{n\in N}O_ {n}(t). \tag{8}\]  
We use \(c(t)\), \(h(t)\) and \(p(t)\) to denote collecting rate \(c_{n}(t)\), energy harvesting rate \(h_{n}(t)\) and transmission power \(p_{n}(t)\). We define \(Y(t)=(c(t),\,h(t),\,p(t))\) to represent the set of variables. To maximize the time-average user utility, we formulate a stochastic optimization problem as follows, subject to the constraints Equations (1) to (7).  
\[\begin{split}(\text{UEDP})\max_{Y(t)}\overline{O}\\ s.t.\,\,\,\text{Equations}\,\,(\ref{eq:1})\,\,\text{to}\,\,(\ref{eq:2}). \end{split} \tag{9}\]  
By optimizing the set of variables \(Y(t)\), we maximize the stochastic optimization problem. In the following section, we use Lyapunov optimization approach to decompose UEDP into a series of deterministic subproblems in each time slot.