esa-sceva/satcom-chunk-collection huggingface dataset:
# The Quest for Low Loss High Refractive Index Dielectric Materials for UV Photonic Applications
## 4 Results
### Directionality Properties: The Full-Forward Condition
This makes it necessary to evaluate the _zero backward efficiency_, defined as \(Q_{ZB}=Q_{sca}\cdot I_{Fwd}\)/\(I_{Bck}\)  
Figure 3: **(a)** Photon energies where the zero-backward condition holds (\(E_{ZB}\)), **(b)** scattering efficiency \(Q_{sca}\) at the photon energy of the zero-backward condition, **(c)** forward to backward intensity ratio \(I_{Fwd}\)/\(I_{Bck}\) at the zero-backward condition and **(d)** zero backward efficiency, \(Q_{ZB}\), for spheres of radius \(R\) (between 30 and 90 nm) made of different materials. Figure 3d shows the values of \(Q_{ZB}\) at the photon energy of the zero-backward condition as a function of \(R\). Diamond, CeO\({}_{2}\) and AlN present the same constant value of \(Q_{ZB}\), approximately. The zero backward efficiency for AlP and TiO\({}_{2}\) takes the same values. However, in this case, \(Q_{ZB}\) becomes smaller as \(R\) decreases. Once again, Si is the material that shows the worst performance. Therefore, all the analyzed materials are better options than Si for scattering directionality applications, in both the VIS and UV ranges.
## 4 Methodology
### Detector Training Module  
In selecting a model for detecting motorcyclists at varying distances, weather, and lighting conditions, several options were initially considered, including Faster R-CNN and Mask R-CNN. YOLO was chosen for its versatility and ability to generalize to objects of varying sizes and backgrounds, making it well-suited to the task. Unlike the other models, YOLO is a single-shot detector that allows for multiple objects to be detected in a single pass as opposed to multiple stages. This makes it more efficient than other models. Moreover, YOLO models are more generalizable as objects of varying sizes and backgrounds may be detected. Lastly, YOLOv8 offers data augmentation tools such as mosaic that significantly aid in training the model to handle low-quality images. Thus, the YOLOv8 model architecture was used. Compared to previous iterations of YOLO such as YOLOv5, YOLOv8 includes several additional features that help address the issues identified in the previous section. Notably, YOLOv8 introduces focal loss as shown in Ref. [48], an improved version of cross-entropy loss, to address the class imbalance issue. This works by assigning more weights to difficult-to-detect objects and less weight to easy to detect objects. Thus, the balanced cross-entropy loss function shown in (7) is modified as shown in (8):  
\[CE(p_{t})=-\alpha\text{log}(p_{t}) \tag{7}\]  
\[FL(p_{t})=-(1-p_{t})^{\gamma}\text{log}(p_{t}) \tag{8}\]  
where \(p_{t}\) is the predicted probability of the ground truth class, \(\gamma\) is a class imbalance tuning parameter, and \(\alpha\) is a static class imbalance weighting factor. In addition to better-addressing class imbalance, YOLOv8 is also better suited to work in busy urban environments where objects may be occluded or differ in size. Unlike its previous iterations, YOLOv8 is an anchorless model which does not rely on predefined anchor boxes. Instead, it uses a series of learned key points which represent each object in the image. This anchorless architecture allows more flexibility in detecting objects of different sizes and aspect ratios, as well as better handling of object occlusion. An 80:10:10 split for the train/validation/test components was performed on the dataset, and Table 3 presents the hyperparameters used to train the YOLO models used (YOLOv5, YOLOv7, and YOLOv8) in our experiments.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions
### WAA Quantization  
WAA has been experimentally verified to have an important influence on the rainfall intensity retrieval, and many factors affect the WAA value, such as rainfall intensity, temperature, link length, radome material, and so on. The WAA value should be a dynamic function of time, and it is obviously inappropriate to give WAA a fixed constant to compensate the total attenuation. In the future, we should further understand the change pattern of WAA and then use appropriate time-frequency domain transformation, probability statistics, machine learning, and other methods to construct WAA model.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
## 4 Mechanism of Microbial Decontamination
### Photo-Chemical Effect  
Researches firmly claim the chemical effect of PL to be the most important factor for microbial inactivation. UV absorption by DNA is claimed to be a major factor of germicidal effect of PL [41]. UV light shows antibacterial action by virtue of the formation of pyrimidine dimers, mostly by forming thymine dimers [1; 41]. The UV light, particularly 253.7 nm, corresponds to the threshold, which leads to dimer formation in microbial DNA. The light radiation of wavelengths less than or equal to 253.7 nm have the energy to carry out DNA dimerization [14]. These thymine dimers hinder the bacterial DNA replication process, thus resulting in the inactivation. Other than DNA dimerization, [PERSON] et al. [41] also reported that UV-C treatment of spores forms \"spore photoproduct\" like 5-thyminyl-5, 6-dihydromine and cyclo-butane pyrimidine dimers. UV light also gets absorbed by conjugated systems like carbon-carbon double bonds in DNA and protein molecules, thus causing structural alterations [1; 23]. Wavelengths >295 nm have been demonstrated to have capacity to destroy viruses. In another study, wavelengths >400 nm were shown to destroy viruses due to the rupture of phage capsid [47].  
Photosensitization is another facet of the photo-chemical effect. It involves the administration of certain photoactive compound (called 'photo-sensitizer') that gets accumulated in the target microorganism cells followed by light flashes [48]. The interaction of these compounds with light in excess of oxygen leads to the destruction of cells. Important advances have been made in photo-sensitized antimicrobial chemotherapy, like disinfection of the blood and blood products. [PERSON] et al. [49] carried out in vitro 5-aminolevulinic acid (ALA)-based photosensitization of_Bacillus cereus_ and on the surface of packaging material. As a consequence of the photosensitization, several 'cytotoxic reactions' were reported to occur in the cells. Disruption of cellular membrane, enzymes inactivation and damage of DNA took place as a result [48]. Photosensitization-based microbial inactivation by PL could be a new area of application for light-based technologies.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 2 Related Work
### Autonomous Driving Datasets  
For autonomous driving, early research primarily focused on single-vehicle perception, which utilizes on-board sensors to enable autonomous vehicle functionality. To support this research, several high-quality real-world datasets have been developed for training and evaluating algorithm performance. Notably, KITTI [1] is one of the pioneering datasets specifically designed for autonomous driving, providing extensive 2D and 3D annotated data for tasks such as object detection, tracking, and road segmentation. Larger-scale datasets, such as nuScenes [2] and Waymo Open Dataset [3], offer a significantly greater number of scenes, thereby enhancing the diversity and complexity of scenarios available for research.  
However, single-vehicle perception is inherently limited by factors such as blind spots and occlusion, which can hinder performance in complex traffic environments. In contrast, cooperative perception can substantially extend the perception range of vehicles by sharing information from infrastructure, thereby improving the robustness and reliability of the perception system, especially in situations involving occluded or distant objects.  
Currently, due to the challenges associated with data collection for cooperative perception, most datasets are generated through simulations. For instance, OPV2V [13] is the first dataset specifically focused on vehicle-to-vehicle cooperative perception, created using the CARLA simulator and OpenCDA tools. Similarly, V2 XSet [23] and V2X-Sim [24] are datasets generated with the CARLA simulator, concentrating on vehicle-to-infrastructure cooperative scenarios. However, simulated datasets often fall short of fully capturing the uncertainties and diversity of real-world conditions, which can affect the practical performance of the models trained on them.  
Real-world datasets for cooperative perception remain relatively scarce. DAIR-V2X [18] is the first real-world dataset dedicated to vehicle-to-infrastructure cooperation, providing authentic scenarios that are essential for addressing practical challenges. Nonetheless, most existing cooperative perception datasets primarily focus on the vehicle categories, failing to adequately represent the complexity of diverse traffic scenes. To address this limitation and offer a broader range of real-world scenarios, we have developed DZGSet, a multi-category real-world dataset that encompasses a wider variety of traffic participants.
# A Method of Enhancing Silk Digital Printing Color Prediction through Pix2 Pix GAN-Based Approaches
## 2 Materials and Methods  
Figure 1 shows the overall workflow of color prediction method development for digital silk printing investigated in this study. A digital PANTONE TCX color card database was printed on silk crepe satin and silk twill fabrics. The DigiEye system is used to capture images of silk fabrics and match them with design color cards to train a supervised Pix2 Pix GAN model. After training the model, an independent test set of color cards and style categories are input into the model to generate predicted images of silk fabrics, which are then verified by the DigiEye system, and finally to calculate color differences and evaluate the prediction performance.
## 3 Path Loss Models
### Empirical Models
#### 3.1.1 Early Empirical Models  
These early models were based on simple empirical formulas that related path loss to distance, frequency, and other factors such as antenna height and terrain type. Some of the early models included the Okumura-Hata model (developed in 1968) [33], the COST 231 model (developed in 1991), and the Walfisch-Ikegami model (developed in 1992). These models were based on extensive measurements in urban, suburban, and rural environments and were widely used in the design of cellular networks [34]. Some of these early empirical models include the Okumura model [35], Okumura-Hata model [36], and COST-231 Hata model [37; 38]. They are discussed as follows:  
A. Okumura Model  
This widely adopted empirical radio propagation model serves as a valuable tool for predicting path loss in urban environments. The model's foundation lies in the collection of signal strength measurements across various frequencies and distances within urban environments [35]. The Okumura model leverages a series of empirical equations to estimate path loss, factoring in the signal's frequency, the transmitter-receiver separation distance, and environmental characteristics. These characteristics encompass terrain type, building height, and vegetation density. The model operates under the assumption that the radio signal follows a straight-line path from the transmitter to the receiver, encountering attenuation due to reflection, diffraction, and scattering in the process [39].  
The model can thus be written as follows [33; 37]:  
\[L_{50}(dB)=L_{F}+A_{mu}(f,d)-G(h_{te})-G(h_{re})-G_{AREA}, \tag{5}\]  
where \(A_{mu}\), \(G(h_{te})\), \(L_{F}\), \(G(h_{re})\), and \(G_{AREA}\) are the median attenuation relative to free space, base station antenna height gain factor, the free space propagation loss, and the mobile antenna height gain factor and gain due to the type of environment.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Target Prioritizing  
In this subsection, to manage the problem of unbalanced task allocation, we first prioritize the targets according to target properties and network status. We introduce four indicators as follows to evaluate the priority of targets:
# Self-Assembled Multinuclear Complexes for Cobalt(II/III) Mediated Sensitized Solar Cells
## 2 Experimental Section  
### Materials  
A total of 25% triethyl amine in H\({}_{2}\)O, 65 wt.% HPF\({}_{6}\), 99.995% lithium trifluoromethane-sulfonate (LiOTf), NH\({}_{4}\)PF\({}_{6}\)\(\geq\) 98%, 96% 4-tert-Butylpyridine (TBPy), Dichloro(p-cymene) ruthenium(II) dimer, Sephadex LH-20, 97% Ethylenedioxythiophene (EDOT), Titanium(IV) isopropoxide (Ti(OiP)\({}_{4}\)), Alconox, and solvents (anhydrous 99.8% Acetonitrile (ACN), ACS grade 2-propanol \(\geq\)99.8%, ACS grade absolute ethanol (EtOH), 99.9% 1-butanol, anhydrous 99.8% DMF (Dimethyl-formamide), 99.9 atom % DMSO (dimethylsulfoxide)-d\({}_{6}\), 99.9 atom % D acetone-d\({}_{6}\), 99.9 atom % D acetonitrile-d\({}_{3}\)) were bought from Sigma-Aldrich and used without further purification, LiTFSI \(\geq\) 98% was obtained from Alfa Aesar. LiClO\({}_{4}\)\(\geq\) 99% was purchased from Acros organics. FTO (Fluorine Tin Oxide) TEC-7 was bought from  
Figure 1: Schematic structure of: **(a) Z907**, **(b) 1** (Ru(TMAM)), **(c) 4** (2Z907-Ag\({}^{+}\)-(Ru(TMAM))); **(d)** energy transfer (red lines) and electron transfer (blue lines) processes involved in n-type sensitization.  
NSG, and 18 NR-T TiO\({}_{2}\) paste was purchased from Greatcell Solar. Surlyn 25 was supplied by Dyepower Consortium. ACS grade Silver nitrate was bought from Rield- de Haen. 4-4'-bis(bromomethyl)-2-2'-bipyridine was already available from previous works and it was prepared according to the procedure in the literature [14,15]. [Co(bpy)\({}_{3}\)](OTf)\({}_{2}\) (bpy = 2,2'-bipyridine and OTf = trifluoromenthanesulfonate), [Co(bpy)\({}_{3}\)](PF\({}_{6}\))\({}_{3}\), were prepared according to previous works [16]. [Ru(dcbH\({}_{2}\))(dnbpy)(NCS)\({}_{2}\)] ((dcbH\({}_{2}\) = (4,4'-dicarboxy-2,2'-bipyridine) and dnbpy = (4,4'-dinonyl-2,2'-bipyridine)) were synthesized according to previous works [17]. ZrO\({}_{2}\) colloidal paste was also prepared following the literature [18].
# PINE: Post-Quantum Based Incentive Technique for Non-Cooperating Nodes in Internet of Everything
## 3 Proposed Approach--PINE Protocol
### Protocol Design
#### 3.3.1 Initialization Phase
Moreover, in the _Initialization Phase_, all forwarding nodes \(F_{j}\) are considered to be cooperative nodes \(C_{j}\), as the network tends to behave non-cooperatively after a time and the protocol encounters non-cooperative nodes \(NC_{j}\) mostly in the _Operational Phase_ (refer to Section 3.3.2). Here, exponential decay functions are employed to minimize the incentive value by a consistent rate depicting lower energy of the nodes over a time. * This greedy approach is performed to stabilize and socialize with peer nodes. During the initial part of this communication, \(NetInfo\) is gathered for selecting nodes based on historical information. Thus, this phase is implemented in order to build the historical information for future decisions. Future decisions are determined based on \(NetInfo\) and incentives generated by node \(S\). Here, _Network Stabilization_ (refer to Figure 3) denotes that each and every node has been involved in any part of the communication cycle or visited at least once in order to avoid null entries in the table. Wireless networks are often prone to node bias, and hence it is crucial to address this case in order to promoting fairness among the nodes. * When the network stabilizes, i.e., if no null entries are found, node \(S\) can make a clear judgement as to how to identify the next-hop nodes in subsequent communication cycles by considering the direct energy status, incentive, and overheard \(NetInfo\) (refer to Section 3.3.2) rather than relying on the energy status and distance range. Finally, if there are more packets to send in the upcoming communication cycles, then a new session \(Session_{Initialization}\) starts again to transmit a new \(Enc_{R}\) to node \(R\). This selection and nomination procedure continues until the encrypted text reaches node \(R\), which is elaborated in Algorithm 1. \(E_{S\to R}\ =E_{Transmit}*K_{Bit-packet}*N_{Relay-nodes}*D^{Path-lossfactor}+E_{Receive}*K_{Bit- packet}*L_{Overhering-nodes}\) (1)  
\[E_{S\to F_{1,2,3,\to R}}\ =E_{Transmit}*K_{Bit-packet}*D^{Path-lossfactor}+N_{Overhering-nodes}*E_{Receive}*K_{Bit-packet} \tag{2}\]  
\[\mathit{Incentive}_{Current}\ =\ \mathit{Incentive}_{Previous}+0.5^{(ForwardCount-DropCount)}* log_{2}(EnergyStatus) \tag{3}\]  
\[\mathit{Incentive}_{Current}\ =\ \mathit{Incentive}_{Previous}-0.5^{(DropCount-ForwardCount)}* log_{2}(EnergyStatus) \tag{4}\]
## 3 Ground Landing Station Design
### Battery-UAV Coupling System  
Although a magnetic coupling system provides an efficient solution for battery replacement, the decoupling requires an external force that complicates the system design. In this study, a battery-carriage receiver and a battery holder are designed for UAV coupling, as shown in Figure 2.  
Figure 1: Robotic ground-station structure.  
The battery holder consists of a rectangular housing of 4 mm thick walls, and it can contain a \(48\times 44\times 164\) mm battery or any other type with smaller dimensions. The main feature of the battery holder is an EC3 connector on its outside right wall. Another characteristic is the rack design covering all the lengths of its bottom surface of 2 mm depth and 1 mm spacing. This design interfaces the pinion placed on the base of the battery swapping mechanism. When actuated, it moves the holder into or out of the carriage receiver. The receiver is placed underneath the UAV. It is composed of a payload box with dimensions of \(138\times 141\times 56\) mm and the battery carriage. An EC3 connector, attached on its right side, interacts with the connector on the battery holder. When the connectors are plugged in, the carriage is securely held inside the receiver.
## 2 Motivation and Background Analysis  
### Edge Computing  
Edge computing is a model where the important data are processed by bringing the computational resources and data storages closer to the nearby required location of a network to reduce the response time, reduce the traffic volume in the core network and optimize the consumed local and global bandwidth. EC pushes the computational resources and data storages to the network edges instead of the centralized cloud management system. The important and larger volume of IoT data is preprocessed before sending to the global cloud data center and storing locally to access the important data within a very short time.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods  
### Study Area  
This research was carried out in Tarom City (Zanjan Province), Iran, which has a wide range of climates (Figure 2). The semiarid cold climate, which occupies about 34% of the city area and, unlike the cold and humid climate, has the lowest location, is the driest. The region's lowest point is 300 m above sea level, and the region's highest point is 2700 m in the northeastern mountainous areas. Tarom receives 450 mm of annual rainfall on average, ranging from 200 mm in the lowlands to 1050 mm in the northern highlands. The average annual temperature is 17.3 degrees Celsius, with lows of 11 degrees Celsius and highs of 45 degrees Celsius. Autumn and spring are the rainiest seasons.
## 4 Conclusions  
Upon close examination of the results of this study, it is clear that waste materials from Kuwait can be repurposed to yield viable construction materials that can replace conventional construction materials to produce high-strength SCC concrete. CWCA was found to be a viable replacement of 4.75 mm used in a benchmark SCC concrete, though its introduction may have subjected the SCC to a slight increase in risk of segregation and viscosity. CWFA used at either a high (20%) or low (5%) dose was shown to greatly improve the SCC's viscosity to make it more desirable. The CWP investigations were useful, showing that its introduction had no negative impact on the SCC's rheological properties. However, it was found that CWP's introduction at low doses (5% and 10%) can negatively impact the SCC's compressive strength but its introduction at higher doses (20% and 30%) as a replacement of cement in mortar cubes shows that it can match and exceed the control sample, indicating its feasibility as a sustainable cement replacement. Introducing EAFS to a benchmark SCC mix at the doses put forward in this study had no adverse effects on its rheological properties or its compressive strength whatsoever. The findings for SCC with EAFS stand in disagreement with the outcomes of previous studies. Based on these results, three hybrid mixes were devised using optimum doses of each replacement material, and it was shown that a high-strength SCC concrete can be produced using these waste materials as a partial replacement of conventional materials that can at least match a benchmark SCC concrete mix's properties, if not improve them.  
Conceptualization, S.M.S.; methodology, S.M.S., A.R.A., A.J. and A.M.; formal analysis, S.M.S., and A.R.A.; investigation, A.J. and A.M.; resources, S.M.S. and A.R.A.; writing--original draft preparation, A.R.A.; writing--review and editing, A.R.A. and S.M.S.; supervision, S.M.S.; project administration, A.R.A.; funding acquisition, S.M.S. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Compressive strength variation of hybrid SCC samples.  
**Funding:** This project was funded \"partially\" by Kuwait Foundation for the Advancement of Sciences under project code: PN18-15 EV-01.  
**Acknowledgments:** The authors would like to acknowledge [PERSON], [PERSON], [PERSON], and [PERSON] for their contributions and hard work and dedication while preparing the concrete mixes for this study. The authors would also like to express their gratitude to United Steel Industrial Company for providing their waste electric-arc furnace slag and to Gulf Shores Company for Sanitary Wares and Construction Building Materials for providing their waste glazed ceramics for use in this project. The authors are grateful to Combined Group Contracting Co. for providing the team with the concrete materials and to Sika for providing the team with the superplasticizer used in the study.  
**Conflicts of Interest:** The authors declare no conflict of interest.