esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 4 Materials and Methods for Offline Fire Detection
### YOLO Models
#### 4.2.2 YOLO-NAS  
Deci AI introduced YOLO-NAS in May 2023. YOLO-NAS was designed to address the detection of small objects, augment the localization accuracy, and improve the performance-computation ratio, thus rendering it suitable for real-time applications on edge devices. Its open-source framework is also accessible for research purposes. The distinctive elements of YOLO-NAS encompass the following.  
* Quantization aware modules named QSP and QCI, integrating re-parameterization for 8-bit quantization to minimize accuracy loss during post-training quantization.
* Automatic architecture design via AutoNAC, Deci's proprietary NAS technology.
* A hybrid quantization approach that selectively quantizes specific segments of a model to strike a balance between latency and accuracy, deviating from the conventional standard quantization affecting all layers.
* A pre-training regimen incorporating automatically labeled data, self-distillation, and extensive datasets.  
AutoNAC, which played a pivotal role in creating YOLO-NAS, is an adaptable system capable of tailoring itself to diverse tasks, data specifics, inference environments, and performance objectives. This technology assists users in identifying an optimal structure that offers a precise blend of accuracy and inference speed for their specific use cases. AutoNAC accounts for the data, hardware, and other factors influencing the inference process, such as compilers and quantization. During the NAS process, RepVGG blocks were integrated into the model architecture to ensure compatibility with post-training quantization (PTQ). The outcome was the generation of three architectures with varying depths and placements of the QSP and QCI blocks: YOLO-NASS, YOLO-NASM, and YOLO-NASL (denoting small, medium, and large). The model underwent pre-training on Objects365, encompassing two million images and 365 categories. Subsequently, pseudo-labels were generated using the COCO dataset, followed by training with the original 118 k training images from the COCO dataset. At present, three YOLO-NAS models have been released in FP32, FP16, and INT8 precisions. These models achieved an average precision (AP) of 52.2% on the MS COCO dataset using 16-bit precision [40].
## 3 Single-Hole Column Charge Blasting Crater Test for Coal and Rock
### Analysis of Blast Crater Test Results
#### 3.2.2 Analysis of Blasting Crater Test Outcomes

Table 3: Statistical results of coal and rock blasting crater experiments (part). large block rate, while the internal rock fragments became smaller. This indicated that with an increase in filling depth, more explosive energy acted on the internal rock, significantly increasing the upper rock mass's large block rate and enhancing the internal rock's crushing rate, adversely affecting the uniformity of the rock's fragmentation. (2) Analysis of Geometric Parameters of Blast Craters by Cartridges with Different Length-to-Diameter Ratios  
The statistical results in Table 3 were utilized to analyze the geometric parameters of the blast craters formed under six different length-to-diameter ratio \(\eta\) charge conditions for coal and rock. The relationships between crater statistical radius \(R\) and crater volume statistical radius \(V\) and fill height \(I_{  
Figure 5 illustrates the statistical analysis results of the geometric parameters of the blasting crater in coal benches under various length-to-diameter ratios. The results demonstrate that as the stemming length increases, the blasting crater's statistical radius and volume decrease. In addition, when the length-to-diameter ratio of the explosive cartridge is less than 4 (approximately equivalent to a spherical charge), the decrease in the radius and volume of the blasting crater is more pronounced than in craters with length-to-diameter ratios of 4.6 and 5.1. This analysis highlights the significance of selecting an appropriate charge length-to-diameter ratio and stemming to control the fragmentation range in coal blasting. The statistical analysis results of the geometric parameters of the sandstone bench blasting crater, presented in Figure 6, show a non-linear relationship between the stemming length and the radius and volume of the crater. As the stemming length increases, the rock crater's statistical radius and volume exhibit an overall trend of first increasing and then decreasing. A larger length-to-diameter ratio of the explosive corresponds to a greater average crater radius, with the peak radius gradually increasing within the stemming length range of 0.6 to 0.9 m. Similarly, the blasting crater's volume gradually increases as the explosive's length-to-diameter ratio rises. For a length-to-diameter ratio of 5.1 to 7.1, the crater volume reaches its peak within the stemming length range of 0.8 to 1.0 m, whereas for ratios exceeding 7.1, the crater volume continues to grow after the stemming length surpasses 1.0 m. These test results align with [PERSON]'s theory.
## 3 Results
### Transcriptional Analysis  
#### 3.2.1 Evaluation of Reference Gene Stability  
The comprehensive ranking generated by RefFinder for the total experimental conditions revealed that alpha-tubulin (_\(\alpha\)-Tub_) was the most stable gene followed by the actin gene (_Act_), while _18S_ RNA was the least stable gene (Supplementary Figure S1a). The analysis via GeNorm showed an expression stability value (M) less than 1.5 for all tested genes, which indicated that they were all accepted as reference gene candidates (Supplementary Figure S1b). Nonetheless, GeNorm classified _\(\alpha\)-Tub_ and _Act_ as the best stable reference genes since they had lower M values compared to those of 18S RNA.
## 1 Introduction
### The Difficulty of Estimating the Parameters of the Waves  
Scientists analysing the influence of the impact energy on the vibrations of the ground and objects use properties in the amplitude's form of the propagating wave, its frequency, or the speed of movement of soil particles. The Bornitz equation, describing the damping of vibrations at selected points of the ground, is commonly used [21; 22; 23; 24; 25; 26]:  
\[A_{2}=A_{1}\Big{(}r_{1}\cdot r_{2}^{-1}\Big{)}^{n}\cdot e^{-\alpha(r_{2}-r_{1})} \tag{1}\]  
where \(A_{1},\ A_{2}\)--vibration amplitudes at distances \(r_{1},\ r_{2}\) from the source of the vibration, \(\alpha\)--attenuation coefficient depending on the ground type and vibration frequency, and \(n\)--geometric attenuation coefficient, which depends on the type of wave and the position and size of the wave source.  
Exact analytical determination of wave or vibration properties under real conditions seems to be impossible. The two coefficients in the Bornitz equation handle this.  
The attenuation coefficient, \(\alpha\), handles the attenuation of the wave in the medium, such as the ground. Its precise determination would be possible only with homogeneous soil. This situation practically does not occur, because the soil is combined with many layers, such as sand, silt, clay, and rock [26; 27]. The components of the layers may have different granulation, the thickness of the layers may be varied, and there may be layers combining various components. As a result, the attenuation coefficient, \(\alpha\), for soil with known components and for selected frequencies is presented as ranges (e.g., for hard soil, for a frequency equal to 5 Hz, the value of the attenuation coefficient, \(\alpha\), is between 0.0003 and 0.003 m\({}^{-1}\)) [25].  
The geometric attenuation coefficient, \(n\), is the attenuation due to wave damping. Attenuation varies based on wave type, position, and source size [25]. With soil vibration analysis, there may be several parallel types of waves. With construction techniques, a common process is piling (pile driving) (Figure 1).  
During the pile setting, the hammer's energy is transferred to the shaft and tip of the pile or casing pipe. Driving a pile into the ground is accompanied by the displacement of soil particles and shear and compression waves' generation in the ground by the pile tip. The waves radiate in all directions. Another shear wave is generated by the moving soil particles due to friction between the pile and the soil particles. In practice, the wave front is assumed cylindrical. When the compression and shear waves reach the ground, some transform into a Rayleigh wave. The rest is reflected to the ground. Rayleigh waves propagate parallel to the ground surface [1; 21]. The influence of several types of waves on the geometric attenuation coefficient, \(n\), makes it difficult to determine the coefficient precisely. Researchers are trying to determine it experimentally for different values of the shapes and sizes of the wave source [29].  
Figure 1: Schematic illustration of the generation of different wave types during piling [28].
## 5 Blockchain-Based Supply Chain Security Solutions and Integration Models
### Data Privacy with Zero-Knowledge Proof (ZKP) and Confidential Transactions
#### 5.3.2 Architectural Model for Cross-Chain Supply Chains and Smart Contract Design
** & **Application Focus** & **Blockchain Role** & **Key Technologies** & **Technical Challenges** & **Security Feature** & **Traceability Feature** & **Data Integrity Feature** \\ \hline
[165] & Emergency dispatch with cold-chain & Route compliance and environmental control. & GPS, IoT temperature & Speilage prevention, route compliance. & Smart alerts on threshold violations. & Timestamped route and status logs. & Immutable status history with environmental data. \\
[166] & Emergency cold-chain medicine & Smart contracts for modeling and materials & IoT sensors, traditional alerts. & Real-time anomaly smart contracts. & Tamper-proof monitoring & Real-time sensor logs of vision smart contract rules. & Automated validation with immutable status. & \\
[167] & Transparency in PPE/medicine & Product compliance & QR codes, compliance & Counterfeit prevention, visibility gaps. & Anti-counterfeit validation using smart contract logic. & Unique QR-linked transaction chains. & On-chain event logging and proof of delivery. \\
[168] & Vaccine supply chains. & Blockchain logging of desktop & Hybrid logistics, optimization. & Route indistality, fluctuating demand. & Routing histories implemented. & Routing recorded across stakeholders. & Immutable proof of delivery events. \\
integrate blockchain with insurance-based fallback mechanisms to mitigate risks of fraud or system failure [174]. From a security perspective, these systems apply cryptographic proof, digital signature mechanisms (e.g., ECDSA), and consensus validation to prevent fund manipulation and unauthorized access [170; 172]. For traceability, smart contracts, QR codes, and public ledgers allow donors to monitor how their funds are utilized, including milestone-based disbursements and campaign progress [171; 175]. Finally, data integrity is ensured through immutable and decentralized storage, often complemented by off-chain systems like IPFS, to protect transaction records even under adverse conditions [64; 173; 175]. The technological diversity and implementation scope of blockchain-based donation platforms are provided in Table 14, highlighting their strengths in enhancing security, traceability, and data integrity across humanitarian fundraising ecosystems. #### 6.1.5 Food Security and Agricultural Aid

In conflict-affected and climate-vulnerable regions, ensuring the continuity and integrity of food supply chains has emerged as a top humanitarian priority. Blockchain-enabled food aid systems have been deployed to improve logistics efficiency, reduce food waste, and combat fraud in the distribution of scarce resources [177]. By leveraging smart contracts and real-time IoT integrations, such as RFID and QR-code tagging, these platforms monitor the conditions and movements of food items across the supply chain [177; 178]. A prominent case is the World Food Programme's Building Blocks initiative, where blockchain
## 3 FPHs--As a Pet Health Food Ingredi
### Cardiovascular and Hypertensive Health Issues  
Cardiovascular disease (CVD) affects over 10% of dogs, with its incidence increasing by 1.5-fold annually [107]. Large, ageing dogs are particularly susceptible to dilated cardiomyopathy (DCM), while smaller breeds often develop degenerative mitral valve disease (DMVD), both of which contribute to significant veterinary costs and reduced quality of life [108]. Nutritional interventions--including coenzyme Q10, taurine, arginine, vitamin E, L-carmitine, and omega-3 fatty acids--have been shown to reduce inflammation, improve appetite, enhance cardiac function, and extend lifespan in affected dogs [109; 110]. Fish-derived proteins and hydrolysates, rich in bioactive peptides and omega-3s, are increasingly recognised for their cardioprotective effects [111].  
[PERSON] et al. [112] reported that a fish-based diet enhanced cardiac and metabolic health in small-breed dogs with mitral valve disease. Similarly, [PERSON] et al. [113] found that collagen hydrolysate from Atlantic salmon skin exhibited antioxidant, anti-inflammatory, endothelial-protective, and anti-platelet properties--comparable to aspirin--in atherosclerosis models. Key peptides, including FAGPPGGDGQPGAK and IAGPAGPRGPSGPA, reduced plaque buildup and arterial thickening in high-fat-fed mice. [PERSON] et al. [114] further demonstrated that tuna protein hydrolysates (TPH) restored cardiovascular enzyme balance and reduced myocardial damage, oxidative stress, and inflammation in rats, yielding therapeutic effects comparable to metformin for managing obesity, hypertension, and hyperglycaemia.  
Hypertension is another growing health concern in companion animals, particularly in ageing dogs and cats, often associated with chronic kidney disease, obesity, and diabetes [115]. Frequently asymptomatic in early stages, it can lead to severe complications such as heart failure, renal damage, and vision loss. The condition's rising prevalence is closely tied to sedentary behaviour and poor diets, paralleling human trends [116]. Long-term treatment also imposes financial burdens on pet owners, prompting interest in natural, food-based interventions. Functional diets enriched with FPHs offer cardiovascular and antihypertensive benefits with minimal side effects [117].  
Research supports the efficacy of FPHs in managing hypertension. In obesity-related renal insufficiency models, herring and salmon FPH significantly inhibited angiotensin-converting enzyme (ACE) activity, improved renal biomarkers, and demonstrated potent neuroprotective effects. Herring and salmon hydrolysates yielded 81 and 49 ACE-inhibitory peptides, respectively. Obese Zucker rats on a 25% FPH diet exhibited decreased urinary protein, cystatin C, and glucose levels [118]. [PERSON] et al. [119] reported that red scorpi-onfish hydrolysates showed strong ACE-inhibitory and antioxidant activities. Likewise, [PERSON] and [PERSON] [120] found thatawakawa FPH, especially peptides within the 1-3 kDa range rich in hydrophobic amino acids, possessed potent antioxidant and antihypertensive properties.
# PEEK Retainers without CAD-CAM: Simple Solutions for Everyday Challenges
## 4 Discussion  
Currently, few studies have evaluated the efficiency of PEEK as a retainer material. According to the authors, PEEK represents a viable and alternative material in dentistry due to its biocompatibility, resistance to chemical and thermal agents, low plaque affinity, and good flexibility. Compared to other non-metallic and metallic aesthetic materials, PEEK has demonstrated good potential for creating customised fixed orthodontic retainers in these cases. The bonding procedure is consistent with traditional methods used for metal fixed retainers and is straightforward to perform. Since there are no differences in the bonding process compared to traditional metal retainers, no new adhesive materials or specialised expertise are required. Only the essential areas of the enamel are bonded, making the potential damage to the enamel similar that of conventional retainers. However, given that the PEEK has the same colour as teeth, the debonding process needs to be performed under magnification.  
As highlighted by [PERSON] [21; 33], PEEK is suitable for creating CAD-CAM retainer wires with the benefit of not causing interference during MRI scans or inducing allergies. Furthermore, [PERSON] emphasises the facility of reprinting or fabricating a new custom retainer in case of breakage or re-bonding a detached wire even years later.  
Other studies, such as that by [PERSON] et al. [26], report that the effectiveness of a PEEK retainer with a diameter of 0.8 mm is comparable to that of metal wires, especially if the PEEK wire is pre-treated with air abrasion. The analysis of [PERSON] et al. [27] also notes that an SS 0.036 retainer is stronger than a PEEK retainer. However, PEEK wires (1 mm \(\times\) 1.5 mm) were found to be less deformable under oral forces such as tongue pressure, which is important because less deformation ensures greater stability.  
Additionally, the authors believe that adapting the wire on the dental cast and shaping it with heat treatment allows to have a precise positioning and reduces chair time. This approach ensures the wire remains passive, avoiding any retainer-related pathologies.  
However, as highlighted in our latest in vivo research [34], PEEK retainers are subject to adhesion problems. Therefore, it is recommended to monitor the patient as long-term follow-up has not been thoroughly investigated.  
The authors also suggest using this material only in those who must undergo MRI or have periodontal problems, for which PEEK represents a good solution.  
Further research is required to fully explore and enhance PEEK's potential applications in orthodontics and highlight its related problems.
# Cascaded Nanorod Arrays for Ultrabroadband, Omnidirectional and Polarization-Insensitive Absorption
## 1 Introduction
The CNA-based absorber requires only several metal/dielectric stacks, and an average absorptivity of 98.2% with a relative absorption bandwidth (RAB) of 149.8% can be achieved in the 0.38-2.65 \(\upmu\)m wavelength range. The physical mechanism for ultrabroadband light absorption of the CNA-based absorber results from localized surface plasmon (LSP) mode, plasmonic resonant cavity (PRC) mode, and their hybrid modes, which is fundamentally different from the tapered metal/dielectric multilayer-based absorbers associated with the slow-light mode. In addition, the absorption characteristics of the CNA-based absorber are robust to the variation of the structural parameters and the metal/dielectric materials, and omnidirectional absorption performance can be realized due to high angular tolerance for both TM and TE polarizations in the range of 0-60\({}^{\circ}\).
## 2 Methodology
### \"Minimum Output Sum of Squared Error\" Tracking Algorithm  
[PERSON] et al. [18] proposed the use of the MOSSE (Minimum Output Sum of Squared Error) filter for target tracking. This tracking algorithm demonstrates significant robustness in handling rotation, occlusion, and illumination changes while maintaining a high speed throughout the tracking process. The FPS (frames per second) comparison between the MOSSE tracker and other filter algorithms across a dataset of 50 videos is illustrated in Table 1.  
The core of the MOSSE tracker is the minimization of the output sum of squared error across all training images. The mathematical model of the optimal filter \(H\) can be expressed as:  
\[H=argmin{\sum_{i}}{\left\|{{F_{i}}\odot{H^{*}}-{G_{i}}}\right\|^{2}} \tag{1}\]  
where \(H^{*}\) represents the complex conjugate of \(H\). \({F_{i}}\) represents the Fourier transform of the pre-processed training image \({f_{i}}\), \({G_{i}}\) is the Fourier transform of the desired output (a Gaussian function centered on the target), \(H^{*}\) denotes the complex conjugate of the filter \(H\), and \(\odot\) indicates element-wise multiplication.  
Equation (1) minimizes the squared difference between the filtered input image \({F_{i}}\odot{H^{*}}\) and the desired Gaussian output \({G_{i}}\), leading to an optimal filter that best matches the target appearance.  
The MOSSE filter is updated iteratively based on the current frame to maintain tracking accuracy. The updated position of the target is determined by calculating the correlation graph \({C_{g}}\) using the inverse Fourier transform:  
\[{C_{g}}=\mathcal{F}\ ^{-1}({F\odot{H^{*}}}) \tag{2}\]  
where \(\mathcal{F}\ ^{-1}\) represents the inverse Fourier transform, \({C_{g}}\) is the correlation graph indicating the location of the tracked object, and \(F\) is the Fourier transform of the current frame.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tracking Algorithm** & **FPS** \\ \hline MOSSE & 615 \\ TLD & 28 \\ Struck & 20 \\ MIL & 38 \\ ORIA & 9 \\ CT & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the comparison of correlation filter tracking algorithms [19].  
Figure 1: Structure of the C2f module. This diagram shows the ConvBNSiLU module handling convolution, batch normalization, and SiLU activation functions, split into three parallel ‘BottleNeck’ pathways to enhance detailed feature extraction.  
The position of the target in the current frame is identified as the peak of the correlation graph \(C_{g}\), given by:  
\[(x_{new},y_{new})=argmax\big{(}C_{g}\big{)} \tag{3}\]  
Here, \((x_{new},y_{new})\) represents the coordinates of the target in the current frame, and \(argmax\big{(}C_{g}\big{)}\) finds the coordinates where the graph \(C_{g}\) is maximized, indicating the target's location.