esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 4 AiP Implementation and Measurements  
In this section, we present the measurement results for the proposed coupling structure, as well as the measurements of the sample PMF that was used.  
For both measurements, a Keysight N5244B VNA with a WR8-based frequency extender up to 140 GHz was used (Keysight, Rosebery, NSW, Australia). Additionally, a WR8-to-WR6.5 interface was used to connect the WR8 waveguide port of the extender with the WR6.5-to-PMF adaptors, while the mismatch was calibrated with the corresponding TRL calibration kit provided by Keysight. In Figure 19, the full measurement setup is illustrated. Due to the setup with the WR6.5 and the WR8 waveguides, only the overlapping frequency region could be measured, i.e., 110 GHz to 140 GHz.
###### Abstract  
(1) Background: In 90Y-TARE treatments, lung-absorbed doses should be calculated according to the manufacturer's instructions, using the MIRD-scheme. This scheme is derived from the assumption that 90Y-microspheres deliver the dose in a water-equivalent medium. Since the density of the lungs is quite different from that of the liver, the absorbed dose to the lungs could vary considerably, especially at the liver/lungs interface. The aim of this work is to compare the dosimetric results obtained by two dedicated software packages implementing a water-equivalent dose calculation and a Monte Carlo (MC) simulation, respectively. (2) Methods: An anthropomorphic IEC phantom and a retrospective selection of 24 patients with a diagnosis of HCC were taken into account. In the phantom study, starting from a 90Y-PET/CT acquisition, the liver cavity was manually fixed with a uniform activity concentration on PET series, while the lung compartment was manually expanded on a CT series to simulate a realistic situation in which the liver and lungs are adjacent. These steps were performed by using MIM 90Y SurePlan. Then, a first simulation was carried out with only the liver cavity filled, while a second one was carried out, in which the lung compartment was also manually fixed with a uniform activity concentration corresponding to 10% lung shunt fraction. MIM 90Y SurePlan was used to obtain Voxel S-Value (VSV) approach dose values; instead, Torch was used to obtain MC approach dose values for both the phantom and the patients. (3) Results: In the phantom study, the percentage mean dose differences (\(\Delta\)D%) between VSV and MC in the first and second simulation, respectively were found to be 1.2 and 0.5% (absolute dose variation, \(\Delta\)D, of 0.7 and 0.3 Gy) for the liver, \(-\)56 and 70% (\(\Delta\)D of \(-\)0.3 and \(-\)16.2 Gy) for the lungs, and \(-\)48 and \(-\)60% (\(\Delta\)D of \(-\)4.3 and \(-\)16.5 Gy) for the Liver/Lungs Edge region. The patient study reports similar results with \(\Delta\)D% between VSV and MC of 7.0%, 4.1% and 6.7% for the whole liver, healthy liver, and tumor, respectively, while the result was \(-\)61.2% for the left lung and \(-\)61.1% for both the right lung and lungs. (4) Conclusion: Both VSV and MC allowed accurate radiation dose estimation with small differences (<7%) in regions of uniform water-equivalent density (i.e., within the liver). Larger differences between the two methods (>50%) were observed for air-equivalent regions in the phantom simulation and the patient study.  
V 2024 14 1019. [[https://doi.org/10.3390/app1403109](https://doi.org/10.3390/app1403109)]([https://doi.org/10.3390/app1403109](https://doi.org/10.3390/app1403109))
# A High Capacity Watermarking Technique for the Printed Document
## 3 Proposed Model
### Watermark Extraction Process  
According to the watermark embedding process, the corresponding extraction process is as follows:  
1. Converting the watermarked image into a binary image, removing noise, and removing characters whose pixel points are less than a certain threshold \(T\), such as punctuation. It should be stressed that this paper takes \(T\) greater than half of the average pixels of all characters.  
The processed binary image is divided into character line units, and then the effective characters of each line are segmented to obtain a valid character set \(a^{\prime}=\left\{a_{ij}^{\prime}\right|i=1,2,3,\cdots m;j=1,2,\cdots n\right\}\), where \(i\) is the row index and \(j\) is the column index. The corresponding black pixels set is \(b^{\prime}=\left\{b_{ij}^{\prime}\right|i=1,2,3,\cdots m;j=1,2,\cdots n\right\}\).
3. Calculating the average value \(M_{ps}\) of black pixels of all characters in the first row. Then calculating the ratio set \(X^{\prime}\) of the remaining character black pixels to \(M_{ps}\), as shown in Equations (11) and (12), respectively. \[M_{ps}=\frac{1}{N}\sum b_{1j}^{\prime}\] (11) \[X^{\prime}=\left\{x_{1}^{\prime},x_{2}^{\prime},\cdots,x_{s}^{ \prime}\right|x_{s}^{\prime}=b_{s}^{\prime}/M_{ps}\right\}\] (12)
4. Finding the watermark information carried by the character according to \(X^{\prime}\) and the quantization function Equation (6). If \(625\leq f(x_{s}^{\prime})<900\), then getting \([w_{2s-1},w_{2s}]=2^{\prime}b00\); else if \(225\leq f(x_{s}^{\prime})<625\); then getting \(\{w_{2s-1},w_{2s}\}=2^{\prime}b10\); else if \(25\leq f(x_{s}^{\prime})<625\); then getting \(\{w_{2s-1},w_{2s}\}=2^{\prime}b11\); else if \(0\leq f(x_{s}^{\prime})<25\); then getting \(\{w_{2s-1},w_{2s}\}=2^{\prime}b10\);
5. Generating a corresponding QR code based on the watermark information.
## 4 Discussion
Furthermore, the sample-based calibration method, which was employed for reflectance correction, may also bring some challenges when considering another optical property, i.e., the scattering phase function. Light backscattering at short source-detector separations is considerably influenced by the scattering phase function. In the spatial-frequency domain, as the horticultural product is illuminated by the structured lighting with high spatial frequency, part of the backscattered light would be referred to as sub-diffusive light, which largely depends on the scattering phase function. Different phase function parameters have been proposed for quantifying the influence of phase function on sub-diffusively backscattered light [39]. The influence of scattering phase function changed in the MC simulations on optical property estimations should be investigated. In recent years, deep learning algorithms (e.g., generative adversarial networks, random forest, etc.) have evolved rapidly, which provide a means for accelerating and also improving optical property estimations of turbid materials [40; 41; 42]. What makes such methods attractive is their capacity to perform particularly well in learning nonlinear mappings. Unlike the conventional nonlinear curve fitting based on diffusion model, a deep learning method could predict optical properties directly from the SFDI images, without a priori knowledge of the top-layer properties (e.g., thickness, absorption and reduced scattering coefficients), which, however, require a sufficiently large image dataset to train the networks. Hence, future research efforts should also be directed at efficient utilization of deep learning for rapid and accurate optical property estimations of horticultural products.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 3 Lab-in-a-Box (LiB) Architecture
### Encryption Algorithms  
#### 3.2.1 AES Implementation  
AES is a symmetric key encryption algorithm, and was originally called \"Rijndael Cipher\". AES encryption converts data into ciphertext and decryption converts the ciphertext back into plaintext. The decryption algorithm differs from the encryption, although similar steps are used to encrypt and decrypt data. The encryption process can be divided into four functional blocks. AES works with 128-bit data blocks, and if the data is longer than 128 bits, it is broken up into 16 byte blocks. Each block is encrypted independently. If the message is not divisible by 16, the remaining empty bits are padded with zeros. AES requires some initialization steps and recurring rounds as in Figure 3. Initially, key expansion and the initial round are performed followed by a series of encryption rounds, using expanded keys from the key expansion step. After defined number rounds, the ciphertext is passed through one final round. The number of rounds increase (10, 12, 14) along with the key size (128, 192, 256 bits). The keys used for each round is generated from the original key in the key expansion step. So different keys generated from the original key are used for every single round.  
The AES encryption process can be divided into four functional blocks, namely key expansion, initial round, recurring rounds, and final round. For 128 bit key, 44-word keys are generated in the key generation step. In case of the 192-bit key and 256-bit key, 52 and 60 words would be required, respectively. The first four words are XORed with the input matrix before the round functions begin. The remaining 40 words are used for the 10 subsequent rounds, 4 words in each round. The 128-bit key is arranged in a 4 x 4 matrix in such a way that the first 4 bytes of the key are put into the first column of the matrix. Byte-oriented operations during the encryption and decryption process are optimum for 8-bit, 16-bit, and 32-bit microcontrollers, although going from 8-bit to 32-bit architecture does not improve timing performance as evident from the results obtained in Section 5. AES-256 provides adequate security but strains system resources such as timing, memory, and energy, hindering developers from using low-power edge devices in their respective IoT applications.
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 2 Mathematical Background  
The analysis part can be subdivided into three parts, i.e., the bundle adjustment, the reference point determination, and the analysis of the bias of the estimates. Section 2.1 summarizes the adjustment process of the photogrammetric measurements concerning the bundle adjustment as the initializing analysis step. The bundle adjustment yields coordinates of the observed points and the related fully populated dispersion matrix. These adjustment results are treated as observations for the reference point determination, which is addressed in Section 2.2.  
The collinearity equations of the bundle adjustment and the reference point determination model are nonlinear. By applying the first-order Taylor series approximation of the nonlinear problem, the results are generally biased, because the stochastic properties of the linear substitute problem cannot be passed to the nonlinear problem. In Section 2.3, the spherical simplex unscented transformation is introduced to evaluate if the truncated Taylor series expansion yields sufficient results.
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 3 Results and Discussion
### Scanning Electron Microscope (SEM) Analysis  
Scanning electron microscope (SEM) analysis was conducted to assess the surface morphology and structure of both the raw biowaste samples and their corresponding biochars. The SEM images (Figure 6) confirmed the heterogeneous nature of all analyzed biowaste types, each composed of particles differing in shape, size, and surface texture. For kitchen biowaste, the particles displayed diverse morphological features. Some had smooth surfaces with clearly defined sharp edges, while others exhibited rough or irregular textures with poorly distinguishable edges. This variation indicates the mixed composition of kitchen waste, likely containing materials of different origins and physical properties. In contrast, spring garden biowaste was characterized by the presence of large, elongated particles with visible edges and internal porous structures. These morphological traits are typical of fibrous plant materials, such as grass blades, and suggest a more uniform plant-based composition. The autumn garden biowaste consisted mainly of relatively small particles with rough, irregular surfaces with visible cavities.  
SEM analysis of the biochars derived from these biowaste types also revealed significant heterogeneity, with particle shapes and sizes varying within and between samples. For kitchen biochar, increasing the pyrolysis temperature led to the formation of larger and more porous particles. This enhanced porosity is a result of greater devolatilization at higher temperatures, which removes more volatile matter from the structure. In the case of spring garden biochar, the particle morphology remained consistent regardless of  
Figure 6: SEM images of: (**a**) kitchen; (**b**) spring garden; (**c**) autumn garden biowaste, before slow pyrolysis and biochar obtained from these biowastes at pyrolysis temperatures of 400 \({}^{\circ}\)C, 500 \({}^{\circ}\)C, and 600 \({}^{\circ}\)C.  
the pyrolysis temperature. The elongated, porous structures observed in the raw spring garden biowaste were largely preserved during pyrolysis, indicating the structural stability of the plant fibers under thermal treatment. For autumn garden biochar, the porosity of the particles increased with the rising pyrolysis temperature. Despite this change, both spring and autumn garden biochars shared a similar porous morphology, which was different from that of kitchen biochar. This suggests that the structural characteristics of garden waste-derived biochar are primarily influenced by the original biomass structure rather than the pyrolysis conditions alone.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 4 Design and Deployment
### Node Technology
#### 4.2.1 Fixed Nodes (Soil)
The boxes were internally packed out with polystyrene to prevent movement of the components inside, and dessicant sachets were included in each node to counter the build-up of condensation that could damage the electronics. For each external sensor, holes were drilled in the enclosure through which to pass the cables; these holes were then sealed with adhesive. Each node included a 4\"antenna (RN-SMA4-RP, Microchip Technology, Chandler, AZ, USA) mounted externally using the RPSMA connector's (MC000800, Multicomp by Newark, Chicago, IL, USA) nut and a sealing ring. The individual Grove sensors, being generally designed for use in protected environments, were modified for field deployment. All were fitted with 50-cm cables to the Arduino. Grove temperature sensors (Seeed Studio, Shenzhen, China, 101020015) were fully enclosed in adhesive-lined heat-shrinkable tubing to include the cable termination. Hot melt glue was injected into the top of the tube prior to shrinking around the cable, creating a rigid section and protecting the connection. Three sensors per node were installed as soil-temperature replicates at a 5-cm depth. Grove moisture sensors (Seeed Studio, 101020008) and Grove water sensors (Seeed Studio 101020018) were similarly enclosed but left the probes bare. Three soil moisture sensors per node were installed in the soil surface. Each node also had one Grove water sensor, which was used to prepare a prototype overland-flow detector. A plastic box was used to exclude rain from the sensor, with a wide cutout on the base of the upslope side and a drain on the other. Inside the box, a V-shaped funnel was constructed in plastic to direct any overland flow over the sensor and out of the drain. Water flowing through the bottom of the V-shape would pass over the water sensor, completing the electrical connection to send a signal to the Arduino indicating over-land water flow. The Grove temperature and humidity sensors (Seeed Studio, 101020011) were shielded from rain under an upturned plastic cup. For each node, two sensors were installed at 35 cm above the soil surface and one sensor was installed at 5 cm above the soil surface. The entire soil node assembly was placed inside a steel mesh dome to protect it from curious animals--these domes were already at the field deployment site and had been well-tested by the environmental science teams in their pre-IoT data logger deployments.
## 4 Discussion
### The Effect of Combining SC and FG on Thiols and Esters Concentrations  
The combination of SC and FG1 or FG2 in one treatment (T7 and T8) resulted in greater levels of thiols and most esters compared to the wines in which either SC (T2) or FG (T4 and T5) alone were investigated. As previously discussed, the addition of \(\beta\)-lyase did not affect the levels of thiols and esters; therefore, the wines T7, T8, T11, and T12 presented similar levels of these compounds (Figure 4, Table S12). A nearly 2-fold increase in 3 SH levels was obtained in T7-8 and T11-12 compared to T2 and T4-5. The acetate 3 SHA, and, to a lower extent, 4 MSP, also resulted in greater levels of thiols with the SCxFG combinations (Figure 3, Table 4). This outcome was expected as SC extracted thiol precursors from grape skins into the must, and FG accelerated fermentation onset and increased yeast enzymatic activity, thus optimizing the release of free volatile thiols to the wines. Several ethyl and acetate esters were increased in the SCxFG wines. Similar to volatile thiols, all acetate and isoamyl esters increased with T7-8 and T11-12, possibly due to the enrichment of the must with specific amino acids during SC, as previously discussed. While most short-chain FAEEs (ethyl acetate, ethyl propanoate, ethyl butanoate, ethyl propanoate, and ethyl pentanoate) were more strongly associated with T4 and T9, the medium-chain fatty acid (MCFA) ethyl esters (ethyl hexanoate, ethyl octanoate, ethyl nonanoate, and ethyl dodecanoate) were correlated with the T7-8 and T11-12 wines (Figure 4). It is possible that the MCFA ethyl esters in SCxFG wines are higher due to an increase of biosynthetic intermediates for lipids (such as fatty acids) in SC musts, as seen in [PERSON] et al. [37]. When combined with SC, FG altered yeast metabolism and yeast membrane cell structure, thus facilitating the excretion of longer ester molecules.
###### Abstract  
The welding quality of industrial pipelines directly impacts structural safety. X-ray non-destructive testing (NDT), known for its non-invasive and efficient characteristics, is widely used for weld defect detection. However, challenges such as low contrast between defects and background, as well as large variations in defect scales, reduce the accuracy of existing object detection models. To address these, an optimized detection model based on You Only Look Once (YOLO) v5 is proposed. Firstly, the Efficient Multi-Scale Attention (EMA) attention mechanism is integrated into the first Cross Stage Partial (C3) module of the backbone to enhance the model's receptive field and the initial feature extraction. Secondly, the Efficient Channel Attention (ECA) attention mechanism is embedded before the Spatial Pyramaid Pooling Fast (SPPF) layer to enhance the model's ability to extract small targets and key features. Finally, the Complete Intersection over Union (CIoU) loss is replaced with Wise Intersection over Union (WIoU) to improve localization accuracy and multi-scale detection performance. The experimental results show that the optimized model achieves a precision of 94.1%, a recall of 89.2%, and an [EMAIL_ADDRESS] of 94.6%, representing improvements by 11.5%, 5.4%, and 6.9%, respectively, over the original YOLOv5. The optimized model also outperforms several mainstream object detection models in weld defect detection. In terms of [EMAIL_ADDRESS], the optimized YOLOv5 model shows improvements of 14.89%, 13.02%, 6.1%, 19.37%, 7.1%, 7.5%, and 10.7% compared with the Faster-RCNN, SSD, RT-DETR, YOLOv3, YOLOv8, YOLOv9, and YOLOv10 models, respectively. This optimized model significantly enhances X-ray weld defect detection accuracy, meeting industrial application requirements and offering another high-precision solution for weld defect detection.  
X-ray nondestructive testing; weld defects; attention mechanism; loss function; YOLO 2025
## 2 Calculation Methods
### Tracking Error  
In this paper, we assume that tracking errors result from errors of SPA entirely and calculate the tracking error of trough solar system by comparing with NREL's SPA. For a system with different tracking methods, the tracking angle and incidence angle can be calculated as follows [6]:  
For north-south axis tracking mode, the tracking angle \(\theta_{\textit{Tracking}}=arctan(\sin\gamma/\tan z)\), which \(\gamma\) is solar azimuth; For east-west axis tracking mode, the tracking angle \(\theta_{\textit{Tracking}}=arctan(\cos\gamma/\tan z)\), which \(z\) is solar zenith; For polar axis tracking mode, the tracking angle is equal to the hour angle \(\omega\).  
In the past, when calculating the influence of tracking error on the performance of the trough solar system, we intended to add it to the total optical error. The calculation formula is:  
\[\sigma^{2}_{\textit{optical}}=4\sigma^{2}_{\textit{contour}_{\bot}}+\sigma^{2 }_{\textit{specular}_{\bot}}+\lambda(4\sigma^{2}_{\textit{contour}_{\|}}+ \sigma^{2}_{\textit{specular}_{\|}})+\sigma^{2}_{\textit{displacement}}+\sigma^{2 }_{\textit{tracking}} \tag{5}\]  
When the tracking error is much smaller than the optical error, the error of this formula is small. However, while the tracking error is larger, especially larger than the optical error, the calculation error is also larger. Some simplified SPAs take fewer factors into consideration, having simple formulae and low accuracy. These methods will introduce larger tracking errors. When the tracking error \(\sigma_{\textit{tracking}}\) is considered, the ray from sun center is reflected \(\sigma_{\textit{tracking}}\) away from the ray to the focus point, then the up and low limit of integration in Formula (1) is from \(-(\theta_{0}+\sigma_{\textit{tracking}})\) to \((\theta_{0}-\sigma_{\textit{tracking}})\)[12].