esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision Support for Removing Fractured Endodontic Instruments: A Patient-Specific Approach
## 2 Case Report  
### Case Presentation  
A 26-year-old female patient was addressed to the department of endodontics of the Lyon University Hospital with a fractured instrument (FI) in the root canal of her right second maxillary premolar. The 8 mm-long instrument was fractured during an initial endodontic treatment of irreversible pulpitis one week previously. The patient reported no pain since the fracture occurred. Clinical examination of the premolar crown indicated the presence of four dental walls and a recent temporary restoration on the occlusal face. The tooth presented no cold response, no percussion or palpation tenderness, and physiological mobility. The intraoral periapical radiograph confirmed the transfixed position of the instrument, close to the sinus, and the absence of periapical radiolucency or local swelling of the sinus membrane (Figure 1a). The patient's tooth was scanned before any intervention to evaluate the instrument position using cone beam computed tomography (CBCT; Planmeca ProMax 3D, Helsinki, Finland) operating at 120 kV, 100 mAs, with a slice thickness of 0.75 mm. The data were recorded under the Digital Imaging and Communication in Medicine (DICOM) format and analyzed. Two non-surgical and surgical strategies emerged from the discussions of the healthcare team, but no consensus was defined on the treatment that could ensure the best outcome. A digital approach, combining VTP and FEA [19], was then implemented to visualize the planned treatment and predict the mechanical impact of the two removal strategies (Figure 1).
# Efficient Resource Allocation in Blockchain-Assisted Health Care Systems
## 3 System Model  
In this work, we consider the new data blocks that are required to become part of a consortium blockchain by attaining their nonce value. The health care information of patients has to be processed in a secure way by the central health care center. The patient healthcare data have different severity levels and have been divided into three different severity levels. To become part of the blockchain system, each data block requires a nonce value. Mining nodes are sparsely placed in different locations, and it is not possible to place computing machines such as mining machines in locations near to these healthcare patients. To obtain these healthcare data, load balancing machines (LBMs) are used to carry data and forward these data blocks to their nearby mining nodes, in such a way that no mining node is under- or overutilized.  
The system model comprises \(N\) number of health care data blocks that are generated continuously from the patients for which a nonce needs to be computed. These data blocks are categorized into 3 different levels of severity, such as highly sensitive (\(N_{h}\)), medium sensitivity (\(N_{m}\)), and low sensitivity (\(N_{l}\)) data blocks. The healthcare system is segregated into \(C\) clustering regions and each cluster comprises \(D\) load-balancing nodes within communication range of these patients' data. Each \(D_{j}\) load balancing machine (LBM) in a cluster is backwardly connected to the \(M\) mining machines, in such a way that they are a single hop location from the LBMs. The data forwarded from the LBMs in each cluster \(C_{c}\) are cached in the \(M\) mining machine, before processing to find the nonce value. Each \(M_{a}\) mining node has limited caching capacity and can store \(Y\) number of fixed-size data blocks. The mining node picks one data block from its cache and computes the nonce value to determine the valid hash value. Each \(D_{j}\) LBM in a cluster has live information on the caching capacities of all its associated mining machines. The total number of blocks of data that can be cached on all mining machines in a cluster \(N_{Cluster}\) is computed as  
\[N_{Cluster}=\sum_{j=1}^{D}\sum_{a=1}^{M}Y\times(N_{j}+N_{a}) \tag{1}\]  
The total caching capacity of data blocks (\(N_{Cache}\)) in \(C\) clusters are computed as:  
\[N_{Cache}=\sum_{c=1}^{C}\sum_{j=1}^{D}\sum_{a=1}^{M}Y\times(N_{c}+N_{a}+N_{j}) \tag{2}\]  
It is assumed that the data block transmission from the patients' sensors to the LBMs can be carried out concurrently, without any queuing. A system model of a complete network cluster for blockchain-based healthcare systems is shown in Figure 2.
## 3 Literature Review and Comparison of Interferometric Coherence and Intensity Correlation
### Summary of Achieved Accuracies of the Methods Reviewed in Sections 3.1-3.4  
In this section the accuracies of the aforementioned damage assessment methods (Sections 3.1-3.4) are briefly summarized (see also Section 6). The reviewed publications on interferometric coherence (Section 3.1) and SAR intensity correlation (Section 3.2) based damage assessment only presented qualitative validation. The validations are based on visual comparison of the results of the damage assessment with (I) high spatial resolution optical imagery [78,100,109,111,114]; (II) damage maps derived from optical data [74] or from (III) field survey information [7,8,73,94,99,100,107,109,111].  
Damage assessment procedures, exclusively based on the coherence or the SAR intensity correlation, achieved overall accuracies of _ca._ 45%-49% or 41%-55%, respectively (Sections 3.3 and 3.4). Methods which combine the interferometric coherence and the SAR intensity correlation showed higher values of the overall accuracy of _ca._ 52%-60%. The Kappa coefficient for the damage assessment techniques based on the coherence, the intensity correlation and the combination of both approaches reached values of _ca._ 0.35, 0.38 and 0.46, respectively [7,8,78]. The increases of the Kappa coefficient and of the overall accuracy clearly demonstrate the advantage of damage assessment methods, which combine both coherence and intensity correlation.  
Adding optical imagery to the SAR damage assessment methodologies significantly increased the overall accuracy (and Kappa coefficient) values from _ca._ 48%-60% (0.35-0.46) (based on only SAR data) up to _ca._ 77%-88% (0.59-0.83) (based on data fusion) [7,78,115]. Moreover, the additional GIS data also increased the reported accuracy. For example the overall accuracy (user's accuracy) was increased from _ca._ 63% (19%-46%) (based on only SAR imagery) to _ca._ 81% (66%-78%) (additional use of GIS data) [2,94].
## 4 Special Ruled Surfaces and Their Characterizations
### The Ruled Surfaces with \(u_{1}=0\)  
Consider the (R-S) constructed by (14). At \(u_{1}=0,\ u_{2}^{2}+u_{3}^{2}=1,\) then the (R-S) takes the formula \[Q(s,v)=\gamma(s)+v(u_{2}N+u_{3}B). \tag{60}\]  
In this case, we have  
\[\delta^{2} =1-2u_{2}\kappa v+(u_{2}^{2}\kappa^{2}+\tau^{2})v^{2},\] \[\hat{a} =\kappa u_{3},\quad\hat{b}=\tau,\quad\hat{c}=u_{2}^{2}\kappa^{2}+ \tau^{2}.\]  
**Lemma 13**.: _The (GC), (MC), and the distribution parameter \(\lambda\) for the (R-S) at \(u_{1}=0\) are given by_  
\[K =-\frac{\tau^{2}}{(1-2u_{2}\kappa v+(u_{2}^{2}\kappa^{2}+\tau^{2} )v^{2})^{2}},\] \[H =-\frac{\kappa u_{3}}{2\sqrt{1-2u_{2}\kappa v+(u_{2}^{2}\kappa^{2} +\tau^{2})v^{2}}},\] \[\lambda =\frac{\tau}{u_{2}^{2}\kappa^{2}+\tau^{2}}.\]  
_Also, the (S-MC) and (S-GC) for the (R-S) at \(u_{1}=0\) are given by_  
\[H_{II} =-\frac{\sum_{i=0}^{4}A_{i}v^{i}}{2\tau^{2}\left(1-2u_{2}\kappa v +(u_{2}^{2}\kappa^{2}+\tau^{2})v^{2}\right)^{\frac{3}{2}}}\,,\] \[A_{0} =\kappa u_{3}\Big{(}2u_{2}^{2}\kappa^{2}+3\tau^{2}\,\Big{)},\] \[A_{1} =-2\kappa^{2}u_{2}u_{3}\Big{(}4\,u_{2}^{2}\kappa^{2}+5\tau^{2} \,\Big{)},\] \[A_{2} =\kappa u_{3}(u_{2}^{2}\kappa^{2}+\tau^{2})\Big{(}12\,u_{2}^{2} \kappa^{2}+5\tau^{2}\Big{)},\] \[A_{3} =-8\kappa^{2}u_{2}u_{3}\,(u_{2}^{2}\kappa^{2}+\tau^{2})^{2},\] \[A_{4} =2\kappa u_{3}\,(u_{2}^{2}\kappa^{2}+\tau^{2})^{3}.\]  
_And_  
\[K_{II} =-\frac{\sum_{i=0}^{4}B_{i}v^{i}}{2\tau^{2}\left(1-2u_{2}\kappa v +(u_{2}^{2}\kappa^{2}+\tau^{2})v^{2}\right)^{\frac{3}{2}}}\,,\] \[B_{0} =\kappa u_{3}(u_{2}^{2}\kappa^{2}+\tau^{2}),\] \[B_{1} =-4\kappa^{2}u_{2}u_{3}(u_{2}^{2}\kappa^{2}+\tau^{2}),\] \[B_{2} =2\kappa u_{3}(u_{2}^{2}\kappa^{2}+\tau^{2})\Big{(}3\,u_{2}^{2} \kappa^{2}+\tau^{2}\,\Big{)},\] \[B_{3} =-4\kappa u_{2}u_{3}\kappa^{2}(u_{2}^{2}\kappa^{2}+\tau^{2})^{2},\] \[B_{4} =\kappa u_{3}(u_{2}^{2}\kappa^{2}+\tau^{2})^{3}.\]  
**Lemma 14**.: _The geodesic curvature \(\kappa_{g}\), the normal curvature \(\kappa_{n}\), and the geodesic torsion \(\tau_{g}\) associated with the base curve \(\gamma(s)\) at \(u_{1}=0\), take the following formula:_  
\[\kappa_{g} =\frac{\kappa u_{2}(1-\kappa u_{2}v)}{\sqrt{1-2u_{2}\kappa v+(u_{ 2}^{2}\kappa^{2}+\tau^{2})v^{2}}},\] \[\kappa_{n} =\frac{-\kappa u_{3}(1-\kappa u_{2}v)}{\sqrt{1-2u_{2}\kappa v+(u_ {2}^{2}\kappa^{2}+\tau^{2})v^{2}}},\] \[\tau_{g} =\frac{\kappa u_{3}}{(1-2u_{2}\kappa v+(u_{2}^{2}\kappa^{2}+ \tau^{2})v^{2})}\Big{(}\kappa\,u_{2}(u_{2}^{2}\kappa^{2}+\tau^{2})v^{2}-(2u_{ 2}^{2}\kappa^{2}\,+\tau^{2})\,v+\kappa\,u_{2}\Big{)}.\]  
**Lemma 15**.: _There is no flat, minimal, II-minimal, and II-flat (R-S) with \(u_{1}=0\) at every point \((s,v)\) in \(\mathbb{E}^{3}\)._  
**Lemma 16**.: _The base curve for the (R-S) constructed by (60) with \(u_{1}=0\), \(u_{2}\
eq 0\), \(u_{3}\
eq 0\) is neither a geodesic curve nor an asymptotic line nor a principal line at every point \((s,v)\) in \(\mathbb{E}^{3}\)._  
**Lemma 17**.: _The base curve of the (R-S) constructed by (60) with \(u_{1}=0\) in \(\mathbb{E}^{3}\) has the following properties at the point \((s,0)\):_  
\[\kappa_{g}^{2}+\kappa_{n}^{2}=\kappa^{2},\ \ \kappa_{g}\kappa_{n}=-\tau_{g}.\]
## 3 Proposed Method
### Improve the Loss Function  
The YOLO series of models has derived various loss functions based on IoU for calculating losses. Both the YOLOv5 and YOLOv8 models employ the CIoU function, as shown  
Figure 8: Repeated Bidirectional Feature Pyramid Network (BiFPN) Structure for Advanced Feature Fusion. In the BiFPN layer, we can observe how features at different scales are fused through bidirectional paths. This structural design aims to maximize the effectiveness of feature fusion while maintaining computational efficiency, thereby enhancing the overall performance of object detection. The diagram also shows the classification network and bounding box regression network, which are parts of the network used to predict object classes and locate object bounding boxes after BiFPN feature fusion.  
in Equation (7). IoU is used to assess the overlap between the predicted bounding box (the target location predicted by the model) and the ground-truth box (the actual target location). This metric reflects the detection performance between the predicted and ground-truth boxes, where a higher overlap indicates better detection. However, this IoU measurement can be highly sensitive to minor positional offsets for small targets. For tiny objects, their pixel values are relatively small, and even slight shifts in the predicted bounding box may result in an IoU below the set threshold, causing them to be falsely classified as negative samples. This can hinder the effective convergence of the network model.  
Modeled as follows:  
\[l_{\mathit{CIoU}}=1-IoU+\frac{\rho^{2}\big{(}b,b^{\mathit{gt}}\big{)}}{c^{2}}+ \alpha v \tag{7}\]  
\(IoU\) is the intersection ratio, \(b\) is the center point of the rectangular frame, \(\rho\) is the Euclidean distance between the two rectangular frames, \(c\) is the diagonal distance of the closed areas of the two rectangular frames, \(v\) is used to measure the consistency of the relative proportions of the two rectangular frames, and \(\alpha\) is the weight coefficient.  
In a practical industrial product packaging bag assembly line, defects such as packaging damage and black spots caused by upstream equipment only account for a small portion of the surface of the packaging, and their characteristics can easily be confused with the information on the packaging surface. The existence of small target defects like packaging damage and black spots increases the difficulty of extracting network features. In such scenarios, the IoU loss fails to optimize the loss effectively, resulting in small gradients of the function and poor network convergence. Therefore, this paper introduces the NWD loss function as a measurement method for small targets, which can effectively reduce the sensitivity to positional deviations of small target defects.
## 2 FMCW LiDAR
### FMCW LiDAR Key Parameters
#### 2.3.7 Narrow Linewidth of Light Source
The use of passive optical components for frequency selection and feedback facilitates lower phase noise and improved temperature stability. Recently, there have been an increasing number of reports on the design of external cavity feedback lasers using SOI passive optical waveguides. The design and fabrication of high-refractive-index-difference nano-waveguides based on the SOI platform is more advanced in terms of device simulation methods and process preparation, and various passive devices such as couplers, multimode interference beam splitter, filters, and AWG array wave-guide gratings have been designed. Using the low-loss silicon waveguide design and the preparation of high-Q passive external cavity structure, combined with the III-V gain materials, narrow linewidth heterodyne integrated semiconductor lasers can be designed on a silicon substrate. Using silicon heterogeneous integration technology, III-V materials or other incompatible materials can be transferred to silicon wafers, which can meet the optimized design requirements of practicality and scalability while ensuring the higher performance of III-V and the advantages of SOI waveguide platforms. A comparison of common semiconductor narrow-linewidth lasers is shown in Table 2.  
In addition, in the LiDAR field, narrow-linewidth lasers are tested for linewidth indirectly using the auto-differential method and frequency monitors. The inability to use a spectrometer for direct linewidth testing is primarily because the spectrometer is based on a grating for testing and its limits the measurement accuracy to linewidths on the order of pm, which translates to a linewidth in the GHz range, and is therefore insufficiently precise for direct linewidth testing. Figure 9 illustrates the system architecture of the autocorrelation linewidth test. The two key components are the fiber delay line (FDL) and the acousto-optical modulator (AOM). The FDL provides the amount of delay, while the AOM is used for frequency shifting, shifting the center frequency after coherence from zero-frequency to high-frequency to avoid interference from low-frequency signals. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modulation Type** & **Lasers** & **Static Linewidth** & \begin{tabular}{c} **Dynamic Linewidth** \\ **Expansion** \\ \end{tabular} & \begin{tabular}{c} **FM Power Ups** \\ **and Downs** \\ \end{tabular} & **Maturity and Cost** \\ \hline \multirow{3}{*}{Internal cavity optical feedback} & DFB & 0.6–5 MHz & Moderate & \
\begin{tabular}{c} Severe \\ Fluctuations \\ \end{tabular} & Excellence \\ \cline{2-5}  & DBR & 200–500 kHz & Poor & Moderate &
\begin{tabular}{c} Complex design, \\ low yield \\ \end{tabular} \\ \hline \multirow{2}{*}{External cavity optical feedback} & External cavity laser & 1 kHz & Excellence & Excellence & Expensive \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Common semiconductor narrow-linewidth lasers.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 5 Performance Evaluation  
To evaluate performance of our proposed scheme, we used the NDN daemon implemented in the NS-3 simulator (ndnSIM) considering the IEEE 802.11p standard. The theoretical communication range of UAVs is assumed to be 400 m radius of the sphere created around their positions. As for the mobility model, UAVs are moving following the 3D random Waypoint Mobility Model with roughly 30 m altitude. In addition, since the current NDN implementation is over the routing layer, obtained results are not affected by the used mobility model. The topology region is considered to be 5 km\({}^{2}\) in the simulations. Furthermore, the results are average of 10 simulation runs, where in each run 10 requesting UAVs or consumers generate interests at the rate of 20 interests per second. In addition to that, 4 data producers and 10% malicious UAVs are considered in each scenario. Those malicious UAVs inject unauthenticated data when they receive the interest packet. The consumer and producer UAVs are randomly chosen in every run. The total simulation duration is 600 s and the consumers generate interests during the whole simulation duration.
## 2 Methodology and Data
### Satellite Data
#### 2.1.1 Sentinel-5P Satellite TROPOMI Sensor Data  
Launched in October 2017, Sentinel-5P is equipped with TROPOMI, a single payload that functions as a push-broom high-spectral imaging spectrometer with nadir observations, covering wavelengths from the ultraviolet (UV) to the shortwave infrared (SWIR). With the exception of the UV1 band, all spectral bands have a typical pixel size of 7 \(\times\) 3.5 square kilometers at the nadir. Operated by the European Space Agency (ESA), TROPOMI is dedicated to monitoring global atmospheric composition using passive remote sensing technology, measuring solar radiation reflected and emitted by the Earth at the top of the atmosphere (TOA). The instrument's swath width is approximately 2600 km in a sun-synchronous orbit, enabling daily global mapping of atmospheric gases related to air quality (such as aerosols, sulfur dioxide, nitrogen dioxide, and carbon monoxide) and climate (methane). In this study, we obtained five years' worth of TROPOMI sensor XCH4 product data from 2019 to 2023. These data were sourced from the Copernicus Data Space Ecosystem's data center as TROPOMI Level 2 XCH4 products.
# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 2 Related Research  
### Research on the Causes of Defects in OLED Cells  
In the field of OLED displays, continuous research is being performed to extend the panel lifespan and improve yield. The presence of defects in OLED cells directly influences the panel lifespan, which is determined by the operational conditions and production process of the OLED panels. When investigating the formation process of dark spots,defects can arise because the organic materials are not stable. These materials can break down gradually over time, causing degradation that results in the appearance of dark spots within the cells. In addition, particles or impurities might get trapped in the display while it is being manufactured, leading to the creation of defects that could show up as dark spots. OLED panels are susceptible to moisture and oxygen; dark spots emerge if these substances penetrate the display surface. Dark spots are defects that gradually emerge while OLED panels are being used, usually as a result of electric current flowing through the panel over time. The review article by [PERSON] et al., discusses the relationship between dark spots and current in OLED cells, as well as the impact of current on OLED cells [7]. The environmental instability of OLED components and resultant dark spot formation are significant concerns, influenced by factors like external impurities, pinholes, and high current density. These elements trigger gas formation within OLED cells, alongside processes like electrical migration and crystallization, shortening OLED lifespan and performance. Another study by [PERSON] et al. highlights the significance of employing rigorous encapsulation technology in OLED manufacturing [8]. This is because exposure to oxygen or humidity results in the formation of dark spots, which has a detrimental impact on OLED performance. Therefore, applying rigorous encapsulation technology during manufacturing is crucial for preventing the infiltration of various impurities from the surrounding environment. In addition, the formation of dark spots can be minimized, and growth can be inhibited by heating the substrate to high temperatures during cathode deposition in the OLED organic layer. However, this approach has certain limitations.  
Based on these studies, it is evident that dark spots are formed in OLED panels owing to various factors, and the impact of these dark spots on OLED displays is understood. Focusing on dark spots as one of the primary defects in OLEDs, this study uses artificial intelligence to differentiate OLED cell defects by analyzing the size and distribution of dark spots that develop within cells. Through this approach, the number of defective products can be reduced, thereby improving the yield and suggesting potential cost savings in production.
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
## 2 Materials and Methods
### Vibrational Spectral Analyses of Lenses  
After all irradiation tests, we conducted vibrational spectral analyses to investigate possible effects of the radiation tests that could lead to alterations in the internal structure of the optical lenses. For that, we have carried out absorption spectroscopy in the ultraviolet (UV)-visible light wavelength range and also Raman spectroscopy, on four lenses that were previously irradiated. Two of the lenses are made of borosilicate BK-7 glass and the other two are made of silica glass.  
Figure 7: Scheme showing: (**a**) positions where measurements with radiometer were carried out. Inside each circle, the value indicates the measured flux, in W:cm\({}^{-2}\); (**b**) zones used to calculate the total power; using the attenuator no. 5, estimated average values of power (in W) for each of the zones are mentioned in the figure, as well as the total power (607 W).