esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Breast-Volume Self-Measurement Method with Improved Convenient and Accuracy
## 2 Methodology
### Breast-Volume Mathematical Modeling  
Breast shape analysis is the most complex and critical aspect of breast-volume quantification. To achieve accurate breast-volume measurements, this study employed an  
Figure 1: Research process.  
approximate geometric method, constructing a mathematical model to represent the true shape of the breast. Geometric morphometrics, widely used in shape variation analysis for its flexibility and precision, was selected for breast shape modeling in this study [26]. Existing studies often use simple geometric forms, such as ellipsoids or cones, to approximate breast shapes [27; 28; 29]; however, these models struggle to accurately capture the complexity of breast morphology, particularly regarding the irregularity of the breast base and the fullness of the lower part. In this study, a combination of geometric assumptions was used to construct a mathematical model that closely approximates the true breast shape.  
As shown in Figure 2, 3D scans reveal irregularities in the breast's lower contour, and significant differences in curve length between the upper and lower sections. This indicates that simple cone or hemisphere assumptions are inadequate to describe the breast's true shape. Therefore, we modeled the breast shape as a composite of irregular geometric forms to more accurately reflect its structure.  
Figure 3 illustrates the composite geometric model of the breast developed in this study. In the model, the breast is divided into upper and lower sections along the Bi-Ai-Ci axis, with different geometric shapes representing the overall structure of the breast. To ensure the model's accuracy, a three-dimensional reference coordinate system was established, with the coordinate axes following anatomical directions: +X from back to front, +Y from right to left, and +Z from bottom to top [30; 31]. Within this coordinate system, the characteristic points of breast morphology were precisely marked, laying the foundation for further volume calculations.  
Figure 3: Breast approximation as irregular geometry.  
Figure 2: Verification of simple geometry.  
Given the sagging of the breast under gravity and the distinct shape differences between the upper and lower parts, the model incorporates curves and lines that represent the actual breast shape, such as AC, AB, DC, DB, AD, and straight lines like AA\({}^{\prime}\), CB, CG, and BF, with AA\({}^{\prime}\) perpendicular to CB. These geometric features serve as key references for the shape fitting and the development of the volume calculation formula.
# Towards a Catalogue of Self-Sovereign Identity Design Patterns
## 2 Background and Related Work
### Self-Sovereign Identity
#### 2.1.2 SSI Building Blocks
As the technology has evolved, digital wallets for the management of cryptocurrencies or cryptographic tokens [13] and, later, digital identity wallets, have emerged as a means for users to control and manage their digital identities. The _verifiable data registry (VDR)_ acts as the backbone of a decentralised public-key infrastructure (DPKI), and establishes trust between different entities within the SSI ecosystem. It functions as the single source of truth, eliminating dependencies on centralised registries, and can be implemented using blockchain, distributed ledger technology (DLT), or other decentralised systems. However, it should be noted that nothing prevents service or identity providers from proposing centralised systems (e.g., GitHub), as long as the entities (i.e., individuals or organisations) are willing to use such centralised registries within a decentralised-based ecosystem. In the context of SSI, VDR is utilised commonly for storing (i) public DIDs, (ii) VC schemas and definitions, (iii) VC state information, and (iv) evidence of interactions between entities (e.g., evidence of VC issuance and acceptance). The VDR can serve as a replacement for the centralised registration authority in traditional identity management systems, providing a DPKI [14] and storing the mapping between an identifier and an authentication method [15], as multiple DID methods rely on the VDR to resolve DID to DID documents [16].
# Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms
## 5 Results
### Evaluation
#### 5.2.4 Costs  
Creating and maintaining such a system will undoubtedly be expensive. In addition to the cost of purchasing drones, external devices are required, such as a Raspberry Pi running a Linux-based OS so that the drone can behave as an autonomous node in the blockchain while also being able to execute code according to the researcher's preferences, SSD disks as a storage medium for the data gathered by the sensors and the recording of the blockchain data, and a flight controller board that will run the drone's flight software (PX4, Axon, etc.). Also, flight-related sensors are required, including GPS, cameras, and lidars for obstacle avoidance. Depending on the nature of the operation, additional cargo may be needed (infrared cameras, night cameras, etc.).  
It is not an easy process for anyone to install, integrate, and maintain these technologies; hence, some money should be invested in staff training. Naturally, such an endeavor would involve a sizable time commitment; therefore, new positions may be required to hire experts with specific knowledge of these systems. On the one hand, this increases the total budget's expenses, but on the other hand, it undoubtedly opens up new career opportunities for scientists who pursue studies in these domains of technology.
# Sol-Gel Silica Nanoparticles in Medicine: A Natural Choice. Design, Synthesis and Products
## 1 Introduction
Sol Gel Gateway [43] lists the _on business_ sol-gel companies, according to which CeraMem Corporation (Waltham, MA, USA), Quantum Dot Corporation (Hayward, CA, USA), General Engineering and Research (San Diego, CA, USA) and CD Creative Diagnostics (Shirley, NY, USA) stand out for their global market shares in sol-gel silica NPs. As to pharmaceutical and medical applications, the Canadian company PreveCeutical (Vancouver, BC, Canada) has developed (and patented) a sol-gel delivery platform for Nose-to-Brain Delivery of Therapeutic Compounds(r), the first FDA approved CBD-based nose-to-brain delivery systems. Sol-gels are taken via nasal (systemic) administration and rapidly gel upon contact with mucosal tissue allowing a direct nose-to-brain delivery by slow releasing of drug/therapeutic molecules. By bypassing the stomach and intestines bioavailability may be improved (even when compared to nasal sprays and other newer delivery systems). Fluorescent ultra-small sol-gel SiO\({}_{2}\) NPs (Cornell dots(r)) have emerged as a particularly fascinating fluorescent probe, which demonstrated excellent outcomes in the first human clinical trials (for cancer imaging) in patients with metastatic melanoma, being commercialized by Hybrid Silica Technologies (HST), a Cornell business startup (Ithaca, NY, USA) [44; 45; 46; 47]. Magnetic (core-shell) sol-gel silica NPs (confering multifunctionality to the carrier) are particularly useful for externally magnetic guided systems in diagnosis, and hyperthermia treatments [48]. USA Cd Creative Diagnostics (Shirley, NY, USA) has commercialized a wide range of plain and core-shell (iron oxide magnetic core) sol-gel silica NPs while the French company NH TherAguix (Crolles, France) provides ultra-small sol-gel silica-based bismuth/gadolinium contrast agents. AuroLase(r) Therapy (from Nanospectra Biosciences, Houston, TX, USA) utilizes _optically tunable_ sol-gel nanoshells, able to convert light into heat and thermally destroy solid tumors without damaging adjacent healthy tissue. MRI/US Fusion Imaging and Biopsy in Combination with Nanoparticle Directed Focal Therapy for Ablation of Prostate Tissue(r) and MR/US Fusion Guided Ultra-Focal Gold Nanoparticle Directed Photothermal Ablation of Prostate Gland Tumors(r) are products in clinical trials. The Israeli (clinical-stage) company Sol Gel Technologies (Ness Ziona, Israel) focuses on developing and commercializing topical dermatological drug products, based on its proprietary sol-gel microencapsulation delivery system. Presently Vered(r), Twin(r) and Sirs-T(r) are in phase II trials, while a generic candidate is in phase III (Table 2). In contrast to the limited number of sol-gel silica NPs currently under clinical tests a considerable number of sol-gel SiO\({}_{2}\) NPs products/processes aiming medical applications are under investigation and some have been recently patented [49] (as proof of concept) and await clinical tests for national agency approval (Table 3).
## 8 Discussion
### Challenges
#### 8.1.5 Distributed Security Mechanisms  
The concept so far outlined envisions a massive exchange of data to enable widespread adoption of machine learning techniques. Clearly, this poses a significant problem in terms of security, privacy, and trust, which 6G networks must address appropriately. To establish an effective compromise between ML and privacy, innovative cryptographic techniques should be applied. A mobile user, for example, may utilise homomorphic encryption [390] to execute a distant ML algorithm on its data. Instead of transmitting raw data, the user may send encrypted data, run the remote ML algorithm on the encrypted data, and still get the desired result. Existing techniques are now impractical due to their enormous computational complexity, but we can expect this to change in the next decade or two. Another important issue, particularly in the IoT situation, is decentralised authentication. Using blockchain-like methods, distributed ledger technologies are likely to play a crucial role in distributed authentication [391].
## 3 Results and Discussion
### Coffee Farms  
RF models were also created to assess whether the characteristic fingerprint and/or profile of the corresponding coffee farm can be derived from coffee batches of the same variety. However, this hypothesis has been tested only in catuai rojo (CR) and bourbon (BO), since among the seven varieties collected, only these two are produced by more than one farm (see Table 1). RF models have been built to distinguish CR batches of farm 1 and farm 3 and BO batches of farm 1 and farm 2. All the four RF models built to distinguish farms of CR show optimal predictive accuracies (pred. acc%. 94 \(\pm\) 3.15) both for green and roasted coffee (Supplementary Materials Figure S7a-d). Consistently with the summaries of the most important variables (Supplementary Materials Figure S7a1-d1), univariate analysis on metabolites shows significant higher content of quinic acid, alanine, trigonelline, caffeine, and lower amounts of theophylline, 5-CQA, citric and chlorogenic acid in green coffee beans of the catuai rojo variety of farm 1 when compared to farm 3 (Figure 4a). Higher levels of choline, sucrose, xanthine, and lower levels of 5-hydroxymethyl furural (5-HMF), fumaric acid, hydroxyacetone and formic acid can be observed in CR roasted coffee of farm 1 (Figure 4b). Even the four RF models built to classify BO coffee batches according to the farm of origin (farm 1 vs. farm 2) show optimal classification accuracies (pred. acc%. 98.1 \(\pm\) 2.4, Supplementary Materials Figure S7e-h). The summary of variables importance (Supplementary Materials Figure S7f1) and univariate analysis on metabolite levels (Figure 4c) report higher amounts of alanine, 5-CQA, malic and chlorogenic acid, and lower levels of theophylline, quinic acid, GABA and sucrose in green beans of farm 1 when compared to farm 2. The trends of theophylline, 5-CQA and chlorogenic acid are conserved even after roasting (see Figure 4d). Moreover, in roasted BO coffee beans of farm 1 compared to farm 2, lower amounts of 5-HMF, lactic acid, hydroxyacetone, formic and acetic acid and myo-inositol are detected. Taken together, these results suggest that farm 1 is characterized by higher levels of theophylline when compared with the other farms. Theophylline is a xanthine alkaloid and it is usually detected in higher amount in Robust than in Arabica beans [29; 30]. It has already been demonstrated by [PERSON] et al., that the concentrations of xanthine alkaloids (such as theophylline, theobromine, trigonelline and caffeine) could change significantly in coffee according to geographical origin [31]. Higher levels of theophylline could derive from different caffeine metabolisms of the plant, but also from caffeine degradation performed by natural occurring microorganisms during bean fermentation [32; 33].
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 2 Metal Sulfide Thin Films
### Manganese Sulfide Films  
The deposition of MnS films on a glass slide was reported by [PERSON] and co-workers [71]. The XRD patterns confirmed the polycrystalline phase and SEM images indicated that grains well-covered well the surfaces of the substrates. The band gap (3.39 eV to 2.92 eV) and resistivity (11.84 \(\times\) 10\({}^{6}\) to 2.21 \(\times\) 10\({}^{6}\)\(\Omega\cdot\)cm) reduced with the film thickness increase (180 to 350 nm). Sodium sulfide and manganese (II) acetate (Mn(CH\({}_{3}\)CO\({}_{2}\))2) were used to produce thin films as reported by [PERSON] and co-workers [72] in Equations (22)-(24).  
\[\text{Mn}(\text{CH}_{3}\text{COO})_{2}\rightarrow\text{Mn}^{2+}+2\text{CH}_{3 }\text{COO}^{-} \tag{22}\]  
\[\text{Na}_{2}\text{S}\rightarrow\text{2 Na}^{+}+\text{S}^{2-} \tag{23}\]  
\[\text{Mn}^{2+}+\text{S}^{2-}\rightarrow\text{MnS} \tag{24}\]  
The film thickness and water angle contact were 170 nm and 34\({}^{\circ}\) (hydrophilic nature). The synthesis of films onto stainless steel substrates was highlighted by [PERSON] and co-workers [73]. The band gap and crystallite size were 2.6 eV and 0.26 nm, respectively. Experimental results showed the highest specific capacitance was 632.9 F/g, under 100 mV/s (scan rate), indicating these films could be used in supercapacitor applications.
## 3 Dataset  
The dataset used in this study, collected as part of the 2023 AICity ([[https://www.aicitychallenge.org/](https://www.aicitychallenge.org/)]([https://www.aicitychallenge.org/](https://www.aicitychallenge.org/))) (accessed on 20 March 2023), comprises of 100 videos from India, each with a resolution of 1920 \(\times\) 1080 at a length of 20 seconds with 10 frames per second. Bounding box labels were included in the dataset for each of the classes. The dataset poses a number of difficulties due to the numerous visual complications brought on by the weather, glare, and time of day, as observed in Figure 1. Additionally, the photos' objects provide additional challenges including pixelation and occlusion, which are very common issues experienced by practitioners when analyzing images from CCTV cameras. Eight classes of interest are included in the dataset and their respective frequencies are listed in Table 1. Another major challenge associated with this dataset is that the majority of scenes suffer from moderate to extreme mislabeling. This mislabeling was predominately omission, where a large number of objects in a frame were ignored. In many cases, timestamps were labelled as motorcycles, among other erroneous labels, which would have severely affected any model's quality. This establishes the first objective of developing a methodology that considers the potential for incorrect ground truth and establishes a remedial measure. In addition to the challenges associated with the lack of accurate labels, the raw dataset consisted of images that were low resolution, coupled with additional challenges such as fog and low lighting. Moreover, the configuration of the cameras resulted in a need to track motorcycles at a distance where the image size is extremely small. To address this issue, substantial augmentation and variation in environmental conditions should be observed in the training dataset. Lastly, as is evident from Table 1, the dataset suffers from unbalanced classes which requires novel minority oversampling techniques to be used to allow for these under-represented classes to be identified accurately.  
Figure 1: Sample of the collected data with visual complexities such as dark, blurry, and occlusion conditions.
## 1 Introduction
A significant improvement to the calibration step, using the full spectrum, is possible based on a suitable algorithm [12]. These methods can be categorized as one wavelength or interval wavelength selection, such as interval PLS (iPLS) and synergy interval PLS (siPLS) [13]. Artificial neural networks (ANNs) are defined as non-parametric regression models that take any phenomenon to any accuracy degree without previous data on the phenomena. ANNs are especially useful for classification and function approximation/mapping problems, which are tolerant of some imprecision and have many training data available, but to which hard and fast rules cannot easily be applied [14]. A neural network is an adaptable system that learns relationships from input and output data sets and then can predict previously unseen experimental results with similar characteristics to the input set. ANNs accurately fit nonlinear variables, which is an advantage compared to multivariate linear analysis [14]. The quality analysis methods used in the food industry are time-consuming and highly expensive, as they require specific equipment and specialized labor. For this reason, the main goal of this study was to develop different models based on machine learning algorithms, such as iPLS, siPLS, and back-propagation ANN, combined with NIR spectroscopy to examine the rice pasting properties BD, FV, PV, TR, and ST, with each model characterized by a specific spectral region that presents a significative influence in terms of these pasting parameters. This strategy represents an important impact on the rice value chain (breeding programs, industry, and consumers), focusing on a non-destructive technique for the evaluation of rice quality.
## 2 Review of Low Frequency Ultrasonic Communication-based Audio Markers  
Figure 2 shows a block diagram of a conventional LFU communication-based audio marker system [14]. As shown in the figure, a binary text data message as an audio marker is encoded in the audio marker transmitter using a forward error correction (FEC) coding algorithm, followed by DPSK modulation and gain normalization. In the audio marker receiver, the received signal is processed by gain normalization, DPSK demodulation, and FEC decoding.
# Data Collection Based on Opportunistic Node Connections in Wireless Sensor Networks
## 5 Performance Evaluation
### Evaluation Results
#### 5.2.1 Packet Delivery Ratio
In the next scenario, we conduct a simulation to evaluate the performance of these four algorithms in PDR with a large enough data collection scope, in which the mobile sink keeps stationary in the center of network and the long radio range \(R_{l}\) of the mobile sink is set to 283 m so that it can cover the whole network. Figure 11 shows that as the network density increases, the PDR in the proposed scheme first decreases and then increases. The main reason is that increasing the number of nodes not only improves the network connectivity, but also increases the number of packets sent by source nodes, when the number of nodes in network increases from 600 to 1100, the increased packets sent by the source nodes decreases its PDR, however, as the increased number of nodes is greater than 1100, the improved network connectivity increases its PDR. The PDR in POFA is always higher than that in the proposed scheme, ExOR and RPRDC, which can be explained by that, in POFA, this large data collection scope increases the number of possible paths for a source node to send its sensing data to the mobile sink, which improves the success rate of data delivering greatly. However, in the proposed scheme, ExOR and RPRDC, this large scope increases the number of hops the nodes that near the edge of the data collection scope takes to forward their sensing data, which decreases the success rate of data delivering greatly. In addition, the proposed scheme achieves a better performance in PDR than that in ExOR and RPRDC. Since the node adopts the intermittent communication mode, the delay of data delivering in each hop will affect the success rate of this forwarding, especially when the node is far away from the mobile sink. In ExOR, a node needs to run a protocol to explore the neighbors that received the data and from which to select forwarders, therefore it delays the data forwarding and then decreases the success rate of data delivering. During the reliable path exploration phase of RPRDC, a node needs to calculate the reliability of its neighbors to select the optimal forwarder, thus causing additional delay. Compared with ExOR and RPRDC, in the proposed scheme, the path for a source node to forward its sensing data is calculated in advance, so it has a shorter delay in each hop of data forwarding, which is the  
Figure 10: The PDR varies with the network density, in addition, \(R_{l}=110\) m.  
main reason that it can achieve almost five times and four times improvement in PDR over ExOR and RPRDC, respectively. In addition, our proposed routing protocol can change the current path adaptively when it is failed, thus blocks the decline of the PDR.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 5 Results and Analysis
### Scenario 3: DER Penetration Level Effect  
Figure 14a-c presents the performance for the proposed method using the IEDs information and using the DER electrical model.  
For this scenario, a clear difference is observed in the performance of the method. When IEDs information is used, the method exhibits an excellent performance with errors below 0.02% (approximately 10 m in a feeder with 58 km of length). Also, its performance is quasi-invariant for different DER penetration levels. When the DER electrical model for INIDER is used, the performance of the method decreases significantly in relation to the performance obtained with IEDs information. For this case, a clear tendency to overestimate the fault distance as DER penetration level increases are observed; reaching an error of 0.5% for a penetration level of 30%. This behavior is due to the increasing penetration, which increases the error in the estimation of the current supplied to the fault point from the DER. On the other hand, the method presents errors lower than 0.06% when using the DER electrical model for IIDER. A tendency to underestimate the fault distance when the DER penetration level increases, is observed. However, its performance is better than when the INIDER model is used. This occurs because the contribution of the current from the IIDER to the fault point is lower than in the case of the INIDER, and this current is estimated by an iterative process.