esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
AI-driven techniques have significantly advanced the field of GPR interpretation, but their deployment is not without complexity. A key strength of AI models (particularly deep learning) lies in their ability to generalize across various GPR scenarios, detecting patterns such as hyperbolas, stratigraphic layers, or structural voids that might elude manual inspection. These models excel at tasks such as segmentation and object detection, especially when applied to large datasets or complex 3D GPR volumes. Moreover, hybrid  
Figure 6: Best practice pipeline for developing an AI-GPR model (from data collection to validation).  
approaches that integrate physics-based constraints or multimodal sensor data represent promising strides towards more robust and interpretable models.  
Despite these advancements, there remains a noticeable gap between academic research and practical implementation. Most AI models are developed and tested under controlled environments using clean, well-labeled datasets--often synthetic or from limited field trials. In contrast, real-world GPR data are noisy, variable, and often lack ground truth. This disparity limits the direct transferability of many AI solutions to operational contexts such as construction sites, heritage conservation zones, or disaster response efforts. Bridging this gap will require not only improved model generalization but also stronger collaboration between AI researchers and GPR domain experts.  
Another important issue is interpretability. While deep learning offers impressive performance, it often functions as a \"black box,\" producing outputs without clear explanations. This lack of transparency can be problematic in high-stakes applications such as infrastructure monitoring or forensic investigations, where decisions may have legal or safety implications. XAI approaches, such as saliency maps, attention mechanisms, or rule-based post-processing, should be further explored and embedded into existing workflows to support user trust and decision-making.  
Lastly, the potential of AI in GPR is not just in automation, but in augmenting expert analysis. Instead of replacing human interpreters, AI should act as an intelligent assistant, highlighting regions of interest, suggesting classifications, or accelerating repetitive tasks. In this sense, the future of AI in GPR lies not in full autonomy, but in thoughtful human-AI collaboration that leverages the strengths of both parties.
## 4 3D BEM Algorithm and Simulation SET-UP  
This section is devoted to simulation set-up of the in-house 3D BEM code. An explanatory flow chart of the adopted algorithm is reported in Figure 3. By virtue of the general 3D shape of the VAWTs (and in the presence of wind shear), the numerical domain has to be discretised along three dimensions: along \(z\)-coordinate (vertical) and the azimuthal coordinate \(\theta\) laying on the \(x-y\) plane.  
A dedicated preliminary study was performed to identify the resolution required for obtaining predictions independent from the discretization. The analysis revealed that 80 streamtubes for each double actuator disc (\(\Delta\theta\approx 2.25^{\circ}\)) are required to solve the DMST algorithm at each blade section ensuring good accuracy, especially if dynamic stall is activated. Moreover, the VAWT semi-height \(H\) has to be split in 10 slices with a grading of 1.2, the minimum blade-element thickness being 0.164\(c\) at the blade tip.  
The convergence loop is based on the thrust coefficient difference evaluated by the blade element model and by the momentum theory. The residual tolerance is 0.01% of the thrust coefficient. When 100 iterations are exceeded, the induction factor is updated anyway. The algorithm is speeded-up by increasing or decreasing the induction factor according to thrust coefficient trend.  
By virtue of the code structure, the VAWT performance outputs are obtained for each blade slice, allowing in-depth investigation of the turbine behaviour as a result of tip effect, blade curvature and wind shear. Tip, strut, and pole effects are introduced once the induction factors reach convergence. Therefore, 2D converged velocity field is evaluated at each strut element to calculate its effect on the overall performance. The 3D BEM code calculates averaged and phase-resolved quantities with the aforementioned settings in 30 s for each wind velocity investigated. Runtime is doubled when neither symmetry of wind profile nor the rotor specular shape can be exploited, e.g., DeepWind5 MW study case, taking around 20 minutes to range the entire operating TSRs. The code has been written in MATLAB(r) and runs on one core Intel(R) Core(TM) CPU @ 1.30 GHz.  
Figure 3: Flow chart with the DMST algorithm implemented in 3D BEM code.
# Low-Cost and Easily Fabricated Ultrasound-Guided Breast Phantom for Breast Biopsy Training
## 3 Results  
### Mechanical and Acoustic Property Characterization  
The mechanical and acoustic properties of the gelatin-based phantom and its simulated tumors were investigated. Gelatin tissue samples with wt. 16.7% and wt. 4.76% were fabricated to simulate glandular tissue and fat tissue, respectively. Force and displacement graphs for gelatin tissue samples were acquired from indentation tests performed at room temperature (24 \({}^{\circ}\)C). The Young's moduli of the glandular and fat gelatin tissue samples were 15.4 and 2.3 kPa, respectively, which fall reasonably in the ranges for real normal fibrolandular tissue and normal fat tissue, as listed in Table 2[9]. Moreover, the difference in stiffness between these two types of tissues could be distinguished. The speed of sound, attenuation coefficient and relative backscatter power of each sample were measured at a frequency of 5 MHz at room temperature. The range of acoustic properties discovered in the samples is displayed in Table 3. It should be explained that the ultrasound relative backscatter power value is a ratio function of the difference in acoustic characteristic impedance between the medium (water) and target tissue (sample) and the acoustic characteristic impedance is determined by the density of tissue multiplied by the speed of sound. Hence, the negative relative backscatter power value in the table indicates acoustic impedance of the sample is relatively low compared to the water.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Breast Tissue Composition**} & **Young’s Modulus** & **Speed of Sound** & **Attenuation Coefficient** \\  & **(kPa)** & **(m/s)** & **(dB/cm)** \\ \hline Normal fibrolandular tissue & 0.73–271.8 & 1553 \(\pm\) 35 & 2.0 \(\pm\) 0.7 at 7 MHz \\ Normal fat tissue & 0.69–24 & 1479 \(\pm\) 32 & 0.6 \(\pm\) 0.1 at 7 MHz \\ Malignant lesions & 6.41 \(\pm\) 2.86 & 1550 \(\pm\) 35 & 1.0 \(\pm\) 0.2 at 7 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical and acoustic properties for fat tissue, glandular tissue and tumor from literature review [9].
## 4 Discussion
#### Study Limitations  
Even though our IRt/c system is calibrated, one limitation of this study is the lack of a secondary temperature measurement system for confirming our results. We abstained from using a contact requiring temperature measurement method to avoid changing the thermal dissipation characteristics of the tissue and interfering with the laser exposure. Unfortunately, a high resolution thermal camera with a fast temperature response was not available at hand.
# Can the WiMAX IEEE 802.16 Standard Be Used to Resolve Last-Mile Connectivity Issues in Botswana?
## 4 Material and Methods
### Simulation Parameters  
The simulation bed used a WiMAX base station linked/connected with nodes that formed part of the network in real situations. The nodes included a WiMAX base station, application servers, internet connection (represented by cloud), routers, and computers or CPE (customer premises equipment).  
The applications relied on application configuration (Figure 1). Table 2 below summarizes various simulation scenarios; profile configuration created user profiles specified on different nodes to generate application traffic in the network, and used application configuration to configure profiles. The same configurations were applied in three scenarios; the only difference was the distance of each scenario from the WiMAX base station.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Percentage Change from Baseline**} & \multicolumn{3}{c}{**Percentage Change from Baseline**} \\  & **to within 1 Week after Surgery (\%)** & \multicolumn{3}{c}{**to 3 Months after Surgery (\%)**} \\  & **Delirium** & **Non-Delirium** & **_p-Value_** & **Delirium** & **Non-Delirium** & _p-Value_ \\ \hline Delta waves & \(-26.2\)\(\pm\) 33.0 & 31.5 \(\pm\) 71.7 & 0.082 & \(-20.2\)\(\pm\) 52.6 & 30.7 \(\pm\) 78.0 & 0.169 \\ \hline Theta waves & \(-23.2\)\(\pm\) 18.0 & 5.9 \(\pm\) 25.0 & 0.016 & \(-14.0\)\(\pm\) 30.5 & 5.5 \(\pm\) 26.1 & 0.144 \\ \hline Alpha waves & \(-6.2\)\(\pm\) 10.9 & 0.4 \(\pm\) 9.4 & 0.126 & 1.6 \(\pm\) 13.1 & 2.1 \(\pm\) 15.9 & 0.932 \\ \hline SMR waves & 15.5 \(\pm\) 19.6 & 0.1 \(\pm\) 10.8 & 0.155 & 19.5 \(\pm\) 24.1 & \(-0.9\)\(\pm\) 14.9 & 0.134 \\ \hline M-beta waves & 19.1 \(\pm\) 21.4 & \(-1.3\)\(\pm\) 12.3 & 0.097 & 20.4 \(\pm\) 24.7 & \(-0.1\)\(\pm\) 15.7 & 0.017 \\ \hline H-beta waves & 19.3 \(\pm\) 14.6 & \(-1.6\)\(\pm\) 13.5 & 0.003 & 14.7 \(\pm\) 22.8 & \(-0.6\)\(\pm\) 15.5 & 0.064 \\ \hline Gamma waves & 18.8 \(\pm\) 20.0 & \(-2.4\)\(\pm\) 14.3 & 0.006 & 12.8 \(\pm\) 24.1 & \(-1.7\)\(\pm\) 16.4 & 0.099 \\ \hline Tension index & 7.8 \(\pm\) 7.2 & \(-0.8\)\(\pm\) 6.9 & 0.011 & 3.3 \(\pm\) 10.8 & \(-0.9\)\(\pm\) 8.8 & 0.334 \\ \hline \end{tabular} Values are reported as the mean \(\pm\) standard deviation. SMR: sensorimotor rhythm.  
\end{table} 
Table 1: Demographics and surgical characteristics of the enrolled patients.
## 4 Protocol Adaptation for Modbus and 5G
### Protocol Adaptation for Modbus RTU and 5G  
According to the basic process in Figure 4, the protocol adaptation between Modbus RTU and 5G further includes Modbus RTU frame structure judgment, message data resolution and storage, 5G data encapsulation and decapsulation, and Modbus RTU frame structure recovery.  
Figure 5 depicts the Modbus RTU data judgment process.  
The first step is to judge the Modbus RTU message frame. First of all, we must judge the correctness of the device address. If it is not correct, the message is discarded. Otherwise, we continue judging the function code. If the function code is unable to perform any function, we send an exception response with the exception code 01. Then, we confirm the data address. If the data address is not allowed by the slave device, we send an exception response with the exception code 02. Next, for the value of the data area which does not conform to the normal data range of the device, the exception code 03 is sent. If it is on the contrary, we continue judging the check code. Finally, the CRC check code in the message is consistent with the check code generated by logical operation; we try to execute different operation tasks according to the function code. If not, we send an  
Figure 5: The judgment process of Modbus RTU data.  
exception response with the exception code 04, meaning that the slave device makes an unrecoverable error when we execute various tasks.  
The second step is to resolve the valid message. First, we resolve the frame structure according to number of bytes each field occupies. After that, we can obtain the destination device address code, PDU data, and CRC check code in Modbus RTU. Then, we separately extract and store the PDU data.  
The third step is to encapsulate the resolved data by the 5G protocol stack and transmit it through the wireless channel. In the fourth step, we resolve the PDU data of the 5G data package in the receiver according to the reverse process of the 5G data encapsulation. In the fifth step, we add the missing Modbus RTU frame structure field. We need to add the destination device address to the PDU data first and then perform a logical operation on the device address and the PDU data to obtain the CRC check code of the last 2 bytes.
###### Abstract  
It is imperative to understand the changes of the levels of air pollutants in Northeast Asia with respect to the changes of the emissions of air pollutants. In this study, we analyzed long-term trends of both the chemical composition of fine particles and gaseous species in Seoul, a megacity, and Baengenyeong Island, a background area located in the Yellow Sea of Republic of Korea (South Korea) from 2012 to 2019. Long-term changes of the concentrations of air pollutants were analyzed using the Mann-Kendall trend test and Sen's slope. Since the SO\({}_{2}\) emissions in this region have been significantly reduced during last decade, NO\({}_{3}-\) has become the major species of fine particles instead of SO\({}_{4}\)\({}^{2-}\). Seoul and Baengenyeong Island are rich in NH\({}_{4}\)\({}^{+}\) in the atmosphere, and due to the SO\({}_{2}\) emission reduction, the balance of ammonia-nitric acid-sulfuric acid has been changed, and the concentration of NO\({}_{3}-\) has increased. This trend is more obvious in Baengenyeong Island than Seoul due to the lower local emissions in Baengenyeong Island. As a result of this study, it is possible to confirm that concentrations of air pollutants and the majority of aerosols affecting PM2.5 concentrations in Northeast Asia have changed according to the changes in emissions in this region.  
ams: formation, solar-  
in 2011 and reported that organic matter and SO4\({}^{2-}\) were the major components of PM1 in Baengryeong Island. [PERSON] et al. [7] analyzed the ion component data measured by an Ambient Ion Monitor (AIM) from 2013 to 2014 on Baengryeong Island and also reported that SO4\({}^{2-}\) was the dominant species. [PERSON] et al. [8] analyzed the characteristics of ambient air pollutants on Baengryeong Island using WRF-Chem models and measurement data between 2014 and 2016. They reported that the major chemical species of fine particles changed from SO4\({}^{2-}\) to NO3\({}^{-}\) because of the changes in the emission trends in the region.  
As summarized, so far, a few studies on air quality have been conducted in Seoul and on Baengryeong Island. However, in most of them, the period was limited, and the measurements were not simultaneously carried out in both areas. In addition, there have been few studies simultaneously analyzing and comparing the air quality in metropolitan areas and background areas in the region. In this study, by analyzing long-term air pollutant measurement data measured in Seoul and on Baengryeong Island between 2012 and 2019, we studied (1) the trends of changes in the concentrations of major air pollutants including SO4\({}^{2-}\) and NO3\({}^{-}\) and their precursor gaseous species in Seoul and Baengryeong Island, (2) the similarities and differences between the two areas by comparing the results in both areas, and (3) whether the concentrations of air pollutants in both areas, the megacity and background area, were actually changing according to the changes of emissions in Northeast Asia.
## 2 The HOMER Micropower Optimization Model
### Assumptions within HOMER  
HOMER performs simulation calculations on an hourly basis, for each of the 8,760 h in a year. The calculations are based on energy balances and compare the thermal and electrical energy both provided and demanded by the system. Based on these calculations, HOMER determines the optimum system configuration as well as an analysis of the system cost. For systems that include batteries and/or fuel powered generators, HOMER determines for each hour whether to charge or discharge batteries and whether to run the generator.  
The most significant assumption made by HOMER relates to the round trip efficiency. The round trip efficiency of the battery is assumed to be constant over the load and power range and over the lifetime of the battery. In reality, the round trip efficiency (energy efficiency) of the cell will vary depending on the power drawn from the battery and the surrounding temperature, among other factors. Other assumed constants include the nominal voltage, the capacity curve, the lifetime curve and the minimum state of charge of the battery [9].  
In calculating the total net present costs and annualised costs of the system, HOMER assumes a project lifetime of 25 years with an interest rate of 6% per annum.  
For more detailed information on assumptions and algorithms within HOMER see the article entitled _Micropower System Modelling with HOMER_ by [PERSON] _et al._[9].
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 2 Related Work  
The optimization of Gaussian filtering algorithms has a long history, starting with the appearance of the earliest techniques for applying blurring in computer graphics. Various approaches have been developed, including algorithmic improvements, numerous filter approximations, and the utilization of specialized hardware to speed up the blurring process.  
In the early papers, the researchers proposed various algorithmic optimizations, like approximations to Gaussian filtering, that reduce the computational effort using fewer and simpler arithmetic operations. They considered Finite Impulse Response (FIR) [12; 13; 14] and Infinite Impulse Response (IIR) [15; 16; 17] digital filters. These approximations are described in detail below.  
Gaussian blur optimizations are still developing and incorporate various programming strategies, such as parallel computing, special hardware use, efficient device-specific algorithms, and use scenarios. Currently, the single-threaded solutions cannot keep up with the performance and speed required for image processing techniques. Orders of magnitude speedup can be achieved with the help of high parallelism. Researchers have suggested parallel solutions for fast blurring on central processing units (CPUs) and graphics processing units (GPUs) [18; 19].  
Solutions supporting thread-level parallelism and a data-level approach utilizing specialized vector instructions have also been proposed [20; 21]. Computer vision libraries are being developed to provide many practical high-performance algorithms for image processing. Some include optimized implementations of Gaussian blur using parallel solutions [22; 23].  
The data type also affects the efficiency of computation. For this reason, quantization is a widespread technique for reducing the runtime of various image processing methods [24; 25; 26] by translating the calculations from a floating-point data type to a fixed-point one stored in integers. For instance, regarding digital filtering, the authors in [27] proposed fast and accurate blurring by the Look-Up Table technique and using coefficients rounded to integers. Methods for digital image filtering using the residue number system were described in [28; 29], where the idea was to perform all the computations on fixed-point numbers by replacing the division with the multiplication of all the fractions by a power of two and rounding to integers.  
All these optimizations led to substantial improvements in the computational performance, making it a valuable tool for real-time image processing, computer vision, and graphics applications.
## 3 Results and Discussion
### Effect of SA Seed Priming on the Phenolics Composition
Alternatively, [PERSON] et al. [5] supposed that the allocation of additional biomass from shoot to root organ via the reduction of lignin production by hydroxycinnamic acids is affected by Fe uptake under bicarbonate induced Fe deficiency. Phenolic compounds as stress markers are in general stimulated under stressful environment. It is believed that soluble phenolic compounds act as scavengers of ROS and membrane stabilizers during abiotic stress [1; 35]. Considered together, the stimulation of phenolic compounds in Fe deficient plants primed with SA could be considered as a strategy adopted by _S. carnos_ to counteract the oxidative stress and indirectly facilitate root Fe assimilation (described in Table 3), which is an input function in the adaptation of strategy I plants to Fe deficiency. In this context, [PERSON] et al. [35] proposed that phenolic metabolites might boost the apoplastic Fe exploitation and consequently improve Fe nutrition in shoots. In fact, phenolics induced by Fe deficiency play a crucial function in the regulation of Fe assimilation through improved accumulation in the root apoplast. [PERSON] and [PERSON] [36] later confirmed that phenolic compounds function as antioxidants by chelating metal or scavenging ROS for defense against oxidative damage. In this context, SA applied as foliar spray in peppermint plants under water stress increased the total phenolic contents, compared to control plants [29], with this accumulation being dependent on the severity of the stress and the SA concentration. Moreover, in the same plant species, [PERSON] et al. [37] reported that total levels of flavonoids were increased in relation to control by approximately 93%, 100%, and 56%, when treated with 0.5, 1, and 2 mM SA, respectively. The same effect was reported in finger plants as well, with an increment of TPC of approximately 20% being registered after treatment with 10 \(\upmu\)M SA [38]. In our study, the priming with SA for control or deficiency conditions did not change the contents of TP and TPC in KA cultivar. Yet, SK primed seeds showed an improvement in the contents of such compounds, under control or Fe shortage conditions (Tables 3 and 5). It is possible that this effect in SK cultivar may be related to the decrease of lignin biosynthesis, since phenolic compounds, such as hydroxycinnamic acids, are considered as lignin precursors, whereas lignification reduction has been recommended to be an adjustment reaction to Fe deficiency [5]. On the other hand, hydroxycinnamic acids are considered as
# From Dormancy to Eradication: Strategies for Controlling Bacterial Persisters in Food Settings
## 5 Mechanisms of Persister Cell Formation
### SOS Response Connected with Both TA Systems and Efflux Pumps  
The SOS response, in conjunction with TA systems or efflux pumps, plays a pivotal role in the formation of persister cells, thereby enhancing bacterial population tolerance to antibiotics. This DNA damage repair system, regulated via LexA and RecA, is essential  
Figure 1: Schematic representation of the modes of action type I and type II TA systems. Figure adaptation from [PERSON] et al. (2014) review.  
for bacterial adaptation and the development of antibiotic resistance. Notably, in _E. coli_, the TiB/IstR module is the only known toxin-antitoxin system regulated via the SOS response that directly contributes to persister formation. Additionally, the SOS response is crucial for the formation of antibiotic-resistant biofilms, particularly in bacteria such as _E. coli_, _P. aeruginosa_, _Staphylococcus aureus_, and _Mycobacterium tuberculosis_[35]. Biofilms in dynamic environments generate compounds that induce DNA damage, thereby promoting adaptation and resistance.