esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 The Case Study
### Kinetic Energy Harvesting Materials Assessment
#### 7.4.3. Cellulosic Materials
Therefore, a hybrid system combining triboelectric generation with additional sources (such as solar power) may be necessary to address greater energy needs. Although cellulose-based materials have improved durability, continuous high foot traffic could accelerate wear, especially in exposed areas. Regular maintenance, protective laminations, or strategic placement will be essential to prolong the nanogenerators' functional life. Additionally, given Saudi Arabia's harsh climate conditions--high temperatures, dust, and humidity--cellulosic TENGs may require extra environmental protection, such as waterproof or dust-resistant coatings, to ensure durability without compromising performance. Cellulosic nanogenerator technology aligns well with the Al-Haram Mosque's energy requirements and environmental values. By sustainably capturing mechanical energy from pedestrian traffic, these generators could support low-power needs such as lighting or sensors, contributing to a greener and more energy-efficient environment within the sacred space. In the context of the Al-Haram Mosque, cellulose-based materials represent a sustainable option for smart mats because of their eco-friendly and biodegradable nature. While cellulose itself is not inherently energy-harvesting (such as piezoelectric or triboelectric materials), recent advances have enabled the development of cellulose-based composites with energy-harvesting or sensing capabilities. Engineered into nanocellulose composites, cellulose can serve as:  
1. A flexible, breathable, and environmentally friendly base layer. 2. A substrate or matrix for embedding piezoelectric or triboelectric components. 3. An active material through chemical modification. Consequently, cellulose-based composites could function as:  
Substrates for embedded energy harvesters such as TENGs or PENGs. Motion or pressure sensors to monitor prayer movements (e.g., sujood, rukoo') for automation purposes. Eco-friendly smart flooring to replace synthetic polymers in mosque surfaces. Hybrid materials are integrated with nanomaterials (e.g., ZnO and PVDF) for enhanced energy generation and durability. Figure 18 illustrates a smart mat utilizing cellulose composite designed for motion sensing and low energy harvesting inside a mosque. The layered structure consists of a biodegradable cellulose base, an embedded sensor or nanogenerator layer, and an optional protective top cover to improve durability. Pressure applied during prayer positions (such as sujood or standing) activates sensing or minimal energy conversion, as indicated by directional arrows. A small graph demonstrates comparative energy or sensing outputs. Sustainability is emphasized visually with symbols such as green leaves and recycling icons, promoting eco-conscious design within mosque environments.
# The Potential of Focusing Acoustic Retroreflectors for Architectural Surface Treatment
## 4 Discussion
### Discussion of Results
This study did not include physically fabricated samples, but some previous studies of trihedral retroreflectors have found close correspondence between measurements and FDTD simulations [50]. Experimental verification of near-ideal focusing retroreflector performance could potentially be done using a fabricated reflective surface cut from a sphere, with the source and receiver mutually mirrored around the normal axis of symmetry. This allows space-reversal to be introduced by analysis, even though it does not occur physically, while focusing occurs physically. Another potential quasi-physical method for experimental verification of ideal performance is via an electroacoustic focusing space-reversal mirror (like a time-reversal mirror (e.g., [PERSON] et al. [67]), but with different signal processing). However, the performance of real passive retroreflector arrays is a more complex matter, and ideal performance provides a benchmark for real array performance. Being a conceptual study, there are limitations in translating this study's findings to physical architectural acoustics. The simulations assumed no surface absorption, lossless transmission through air, no scattering from the source or other objects (no other reflective surfaces), an omnidirectional source, time invariance, and so on. The relationship between retroreflected energy level and voice support may differ from that inferred by Figure 1 because of the spatial and spectral distributions of reflections, as well as the limitations of the voice support theoretical model.
## 4 Conclusions  
Currently, it is of great importance to accurately locate the lateral position of a vehicle in the lane in order to facilitate the road test of these ADASs, such as LDW and LKAS. On the basis of the above, this study draws the following conclusions:  
1. An algorithm for image synthesis is proposed based on the Image Quilting by improving the metric of texture block matching and method of block selection, which can rapidly synthesize numerous high-quality lane and asphalt images.
2. A label automatic annotation algorithm is presented, wherein the distance between the edge of lane and asphalt was set as the label, and factors of light, road wear, and dirt were taken into account. The experiment shows that the label images obtained had a high quality and they could be used for the model training.
3. By constructing a deep CNN, LatDisLanes, the model can recognize the lateral distance with a sub-centimeter precision. Furthermore, a dynamic correction model is proposed to reduce the recognition error that is caused by the inclination angle of vehicle. The experiment was conducted in i-VISTA and the result showed that inclination angle greater than 3\({}^{\circ}\) would cause a bigger error, which can be reduced by the proposed correction model.  
In this study, a lateral lane distance prediction framework is constructed systematically, and the output reaches a sub-centimeter precision. However, the lane is determined by scanning the whole image with the deep neural network, the large computational amount slows down the recognition. With the rapid development of object detection algorithms, such as YOLO and SSD etc., we will further study the high-precision and high-speed lane detection algorithm using these object detection algorithms.  
[PERSON] and [PERSON] conceived and designed the method and performed experiments; [PERSON] reviewed and edited this manuscript; [PERSON] carried out part of the experimental verification.  
This research was funded by Chongqing Natural Science Foundation, grant number No. cstc2018 jcyjAX0468, National Natural Science Foundation of China, grant number No. 51705044, and Graduate Scientific Research and Innovation Foundation of Chongqing, China, grant number No. CXB226.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Light Condition** & **Average Error (cm)** \\ \hline Lateral supplementary light & 0.825 \\ Streetlights & 4.296 \\ No streetlights & 53.472 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection results of models at night.
# Resource Allocation for GBR Services in D2D-Enabled Communication
###### Abstract  
In device-to-device (D2D)-enabled communications, a channel can be matched to a cellular user (CU)-D2D user (DU) pair, where either two separate resources can be allocated in a dedicated mode, or a single resource is shared in a shared mode. It affects the channel capacity and the channel bandwidth needed to guarantee the target data rate. The transmit power also needs to be adaptively controlled according to channel bandwidth, as well as channel quality. In this paper, we propose a resource allocation scheme to minimize the total channel bandwidth while guaranteeing the target data rate of guaranteed bit rate services. The original problem is separated into two sub-problems; (1) the channel bandwidth calculation and channel sharing mode selection for arbitrary matching (2) the 3-D matching problem of the CU, DU and channel. To mitigate computational complexity, the 3-D matching problem is transformed into two 2-D matching problems, and a sub-optimal solution is derived using a sub-gradient algorithm. From numerical results, it was confirmed that the average channel bandwidth was almost the same even though the computational complexity of the sub-optimal algorithm was very low compared to the original algorithm. We also investigate the impact of various parameters, such as maximum D2D distance and the number of users.  
Keywords:device-to-device (D2D)-enabled communications; resource allocation; power control; channel assignment +
Footnote †: journal: Electronics
## 3 Materials and Methods
### Performance Evaluation  
The performance of classification models is determined using different metrics. These metrics are intended to assess how well the classifier uses both the predicted and actual class values. Among these, F1 score, precision, recall and accuracy are the most frequently utilized. The calculation of these metrics is performed using the confusion matrix. The confusion matrix compares the values predicted by a classification model with its actual values. This matrix is created using true positives \(\left(\mathrm{T}_{\mathrm{P}}\right)\), true negatives \(\left(\mathrm{T}_{\mathrm{N}}\right)\), false positives \(\left(\mathrm{F}_{\mathrm{P}}\right)\) and false negatives \(\left(\mathrm{F}_{\mathrm{N}}\right)\). Thanks to this matrix, the prediction success of the classification model according to different classes is obtained by calculating the precision, recall, F1 score and accuracy metrics. Accuracy is defined via Equation (2) as the percentage of samples accurately predicted by the model.  
\[\mathrm{Accuracy}= \frac{\mathrm{T}_{\mathrm{P}}+\mathrm{T}_{\mathrm{N}}}{\mathrm{T }_{\mathrm{P}}+\mathrm{T}_{\mathrm{N}}+\mathrm{F}_{\mathrm{P}}+\mathrm{F}_{ \mathrm{N}}} \tag{2}\]  
Precision indicates the proportion of samples that are genuinely positive and is measured via Equation (3).  
\[\mathrm{Precision}= \frac{\mathrm{T}_{\mathrm{P}}}{\mathrm{T}_{\mathrm{P}}+\mathrm{F}_{ \mathrm{P}}} \tag{3}\]  
Recall demonstrates the proportion of actual positive samples that are correctly predicted and is calculated via Equation (4).  
\[\mathrm{Recall}= \frac{\mathrm{T}_{\mathrm{P}}}{\mathrm{T}_{\mathrm{P}}+\mathrm{F}_{ \mathrm{N}}} \tag{4}\]  
The harmonic average of the precision and recall scores is denoted as the F1 score and is defined via Equation (5).  
\[\mathrm{F1-score}=2\times\frac{\mathrm{Precision}\ \times\ \mathrm{Recall}}{ \mathrm{Precision}+\mathrm{Recall}} \tag{5}\]
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 5 Conclusions
### Specific Conclusions  
1. Graphene, Chitosan, and Composites: Graphene demonstrates antibacterial properties and cellular stimulation, making it a valuable candidate. Chitosan, while needing improvement on its own, shows promise when combined with other materials like hydroxyapatite.
2. Bioprinting for Tailored Solutions: 3D bioprinting shows tremendous promise for creating patient-specific scaffolds, driving greater customization and success rates in dental tissue regeneration. This includes bioprinting of cells, matrix materials, and entire tooth structures.
3. Importance of Cell Source: Exploration of mesenchymal stem cells (MSCs) from different sources (dental pulp, bone marrow, adipose tissue) in conjunction with scaffolds is highly significant for determining optimal cell-material pairings for specific applications.
4. Newer Materials Emerge: Bioactive glasses, boron-doped biomaterials, and unique composites hold promise for enhanced bone and tooth regeneration.
# Analysis of the Impact of Multipath on Galileo System Measurements
## 2 Multipath for Galileo
It has to be noted that the cited analysis contains the expected multipath errors, determined based on multipath error envelopes and a generated rather than actual signal, the nature of which may be much more complex and impossible to model using deterministic models in the GNSS hardware simulator. The presented analyses do not include a comprehensive comparison of the multipath effect for Galileo and GPS, using identical analysis methods for identical measurement conditions and taking into account the impact of measurement noise and its time correlation. Available research on the multipath effect also does not include analyses of the impact of this phenomenon on positioning solution.
## 3 Hardware Implementation  
### Overall Architecture  
Figure 6 illustrates the overall architecture of the proposed processor, primarily comprising three modules: the ECG R-peak detection engine, the feature extraction engine, and the hybrid ECG classifier. The R-peak detection engine includes the process element (PE), the R-peak detection state machine, and the positive zero-crossing (PZC) detection engine. The hybrid ECG classifier includes a linear pre-classifier and a SVM classifier. In addition to these modules, the SPI are employed for loading parameters and ECG raw data, while the SRAM (static random access memory) serves to store raw ECG data, immediate data, filter coefficients, and SVM parameters.
## 3 Results and Discussion
### Selection of Fungal Strain  
The highest decolorization percentages were approximately 50% in 10\(\times\) diluted ADPE for NU-3 and NU-27 on day 5 at the end of the experiment, followed by <10% for NU-8 (Figure 3a). Algal growths of NCU-39 were not completely inhibited in the ADPEs pretreated by NU-3 and NU-27, with the growth curves almost consistent with trajectories in the pretreated ADPEs (Figure 3b). However, algal growth was completely inhibited in the untreated and NU-8-pretreated ADPEs, corresponding to the decolorization percentage of <10% (Figure 3). These results indicated that the inhibition of algal growth may be related to low decolorization efficiency. To determine the effect of the brown color (turbidity) on algal inhibition, it may be needed to run an independent experiment with an inert source of turbidity in further study.  
The highest decolorization percentages were achieved in the NU-27-pretreated ADPE by day 3 and NU-3-pretreated ADPE by day 4 in the airlift bioreactor (Figure 3a). Appropriate decolorization time may depend on various culture conditions such as fungal species and wastewater type [25]. [PERSON] et al. [30] reported that the white-rot fungi _Trametes hirsitu_ could decolorize textile dye indigo carmine almost 100% within three days. [PERSON] et al. showed that _Phareochete chrysosporium_ could achieve decolorization of 80% from ADPE by day five [15]. In this study, the isolated strain NU-27 with three-day decolorization was considered a potential candidate for ADPE pretreatment and subsequent experiments.
# Extending Cooling Rate Performance of Fast Scanning Chip Calorimetry by Liquid Droplet Cooling
## 3 Results
### PBN Vitrification  
The power of the droplet cooling is further demonstrated by reheating at 1000 K/s a relatively large PBN sample (ca. 150 ng) on the large Mettler Toledo UFS1 sensor. After liquid quenching, the remaining liquid was removed from the sample and the sensor by purging the sensor with dry nitrogen for 30 s. Unlike HDPE, the critical cooling rate for suppression of crystallization in PBN is only ca. 6000 K/s [35]. PBN is a thermoplastic material with a relatively high glass transition temperature of about 360 K. This gives the possibility of room-temperature studies of PBN by X-ray diffraction (XRD) [36], atomic force microscopy (AFM) [26] and optical microscopy [35]. With the Mettler Toledo optical sensor (XEN 39476-50 and -100, which are based on the UFS1 sensor, but have an optically transparent window of 50 and 100 \(\upmu\)m diameter, respectively, in the center [37], the amount of material on the sensor is large enough to perform XRD and even FTIR measurements [38] after or during rapid thermal treatments. However, the cooling of this sensor in a room temperature environment, such as that usually used in combined devices, is limited by ballistic cooling and reaches a maximum of 3500 K/s at the glass transition temperature of PBN. Since the critical cooling rate is about 6000 K/s, obtaining an amorphous material for reference measurements and following crystallization at deep undercooling is impossible.  
The cold-spray allows for quenching a relatively large sample on the Mettler Toledo Flash DSC UFS1 sensor, loaded with a large (150 ng) sample with a rate of at least 50,000 K/s in the region of the glass transition temperature, even at room temperature environment (\(T_{\rm t}\) = 300 K). The reheating of the cold-spray-cooled PBN (red curve) compared to PBN quenched under a liquid nitrogen cooled environment is shown in Figure 10b and is discussed in the next section.  
Figure 9: HDPE (**a**) and indium (**b**) solidification in air and liquid cooling (ethanol) on the XI395 sensor at room conditions. Crystallization of HDPE at high cooling rates occurs very close to room temperature.
# Practical I-Voting on Stellar Blockchain
## 2 Blockchain Technology for I-Voting
### Stellar Consensus Protocol
Most of the consensus protocols choose this property as one of the three properties. The decision has to be made between Liveness and Safety. Safety (also called Consistency) prevents a distributed system from partitioning, meaning that, if nodes do not agree on a transaction, they will not split into two different ledgers with different states; instead, they will rather wait until the conflict is resolved. Liveness, on the other hand, guarantees that the system will never stop; it will always respond and accept new transactions. When the conflict occurs, the system is split into two different states, and hopefully, at some point, one of them will be rejected, bringing back the consistency. Most of the Nakamoto Consensus protocols favor Liveness. Proof-of-Work (PoW) used in both Bitcoin and Ethereum, allows a temporary split of the blockchain. The main issue comes with the confirmation period. Although modifying and recalculating a large number of blocks is very hard, it is possible, and the attack can be successfully performed [35]. Be absolutely sure if the chain in which our transaction was included will not be rejected in favor of a longer forked chain. In practice, many systems require some fixed number of confirmations (the number of confirmations is counted in a length of blocks appended on top of a block including our transaction) to be made to trust a transaction. Typically, for Bitcoin, 2-3 confirmations are required, where each confirmation takes on average 10 min (20-30 in total). In Ethereum, the number of confirmations is higher, about 15-30, but the confirmation takes on average 15 s (3-7.5 min in total). SCP and other vote-based protocols choose Safety. Once the quorum of the network agrees on the state, the ledger is closed and can not be reverted. Furthermore, the confirmation speed is much faster. In Stellar, the ledger closes in about 3-5 s.  
In contrast to Nakamoto Consensus, vote-based algorithms are asymptotic resistant. A unit consisting of large computational resources does not gain any advantages in the network, especially can not proceed 51% attack, which is possible in PoW protocols.
## 4 Validation and Comparison
### Verification of the Numerical Predictions with the Theoretical Bounds  
To illustrate the correctness of the proposed computational method, the effective elastic moduli of the benchmark model containing differently-sized pores in the last subsection are taken as examples. Since the radii of pores in porous titanium ranges typically from 300 \(\upmu\)m to 600 \(\upmu\)m and those in porous tantalum are usually between 200 \(\upmu\)m and 400 \(\upmu\)m [12; 45], the model parameters in Equation (11) are specified as \(a=b=c\in[300,600]\upmu\)m and \(n=2\) for the porous titanium and \(a=b=c\in[200,400]\upmu\)m and \(n=2\) for the porous tantalum. Besides, the cubic model of side length 1000 \(\upmu\)m is applied. In this study, the 3D domain of each instance is first discretized into \(\zeta\times\zeta\times\zeta\) small cubes and each cube is further discretized into five tetrahedrons. The mesh sensitivity is tested by gradually increasing the \(\zeta\) value, and a fine enough mesh is adopted to conduct for the following simulations. For the examples in this subsection, each cubic domain in question is meshed by \(59\times 59\times 59\times 5\) tetrahedral elements. Figure 9 shows the resulting numerical predictions of the isotropic moduli together with the theoretical bounds by Equations (28)-(31). Note that the effective elastic moduli are normalized with respect to the moduli of the solid phase in this study so as to facilitate the visualization of the comparisons.  
Figure 8: A simplified porous model for verifying our computational approach.  
It is obvious that the numerical predictions are in good agreement with the two bounds when \(f\) is small. Particularly, \(f\) changes in the range of \([0,0.2]\), the numerical predication nearly coincides with the upper bounds for both the porous tantalum and the porous titanium. With the increment of \(f\), the range between the upper and lower bounds gets larger and our predictions are strictly in the upper and lower bounds and closer to the upper bounds, which highlights the validity of the numerical predictions by our method. To be noted, the lower bounds by Equations (30) and (31) decrease in a nearly linear manner as \(f\) increases and ultimately becomes a negative value, which is infeasible for actual materials.
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 3 Chitosan-Based Nanocomposites
### Chitosan-Carbon Materials  
Carbon-based nanomaterials, such as CNT, graphene, and graphene oxide have been well-studied. Incorporation of graphene oxide into chitosan improves the mechanical properties of chitosan in addition to enhancing its antimicrobial and pollutant removal abilities [88; 89]. Chitosan being a biocompatible polymer has been applied to improve the solubility of CNT and reduce its toxicity to facilitate practical applications (Table 1) [90].