esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### The PIN Process in the Case of Packet-by-Packet (PbP) Basis  
The PIN process on a packet-by-packet basis includes four steps. The first step determines, for the application, the information unit and the corresponding size. An information unit can be processed independently from the other packets, and the different processing phases (identified with numbers from 0 to n, where 0 means \"no processing has been performed\" and n means that processing has finished).  
The second step is to add an additional \"process phase\" field to each packet that is sent to the network. It is expected that all of the packets are sent with a zero in this field, which is not a restriction in any way. It could be possible that a source node performs some processing before sending the packet.  
The third step is that every intermediate node processes the packets (and updates the \"process phase\" field accordingly) instead of sending it to the transmission queue. The PIN function is continuously monitoring the queue length to determine the best moment to stop processing and putting the packet in the transmission queue.  
The fourth step is performed at the destination end node and finishes the processing that was not performed in the network.  
Figure 4: Moments in the queue for initiating the PIN on a packet-by-packet basis.  
There are two moments in the queue at which the PIN on a PbP basis could be triggered. One option (Figure 4(a)) initiates the PIN at the queue input depending on the queue length and other possible conditions (e.g., the processing phase). In this case, the PIN is performed only for the arriving packet. Another possibility (Figure 4(b)) is to perform the PIN for all of the packets in the queue. The PIN could be performed using either one or the other option or even combining both options.
# Insights into Native Fermentation Process of Apples (_Malus domestica_) in Low Sodium Conditions
## 2 Materials and Methods
### Physical and Chemical Analysis
#### 2.3.5 Reducing Sugar Concentration  
The quantitative analysis of sugars and uronic acids was performed using the 3.5-dinitrosalicylic acid method (measured absorbance at 540 nm) and the carbazole-borax method (measured absorbance at 530 nm), respectively [17]. Previously, approximately 10-15 g of finely comminuted and homogeneous samples were subjected to extraction for 30 min, then the extracts were clarified using potassium ferrocyanide 10% and zinc sulfate 15%. After being brought to a volume of 100 mL, the samples were centrifuged at 9000 rpm/10 min. For the quantification of total sugars and fermentable uronic acids, the extract was hydrolyzed as follows: 20 mL of clear supernatant was placed into a 50 mL volumetric flask and treated to hydrolysis on a water bath with 3 mL HCl density 1.19 g/mL at a temperature of 68-70 \({}^{\circ}\)C for 8 min. After cooling and neutralization with 30% NaCl solution, the volume was completed up to 50 mL. The evaluation of the amount of reducing sugars and reducing uronic acids was performed on the basis of the clarified and cleared samples, without their being subjected to acid hydrolysis. To quantify the reducing and total sugars, 0.25 mL of the clarified sample, 4-5 drops of 10% NaOH and 0.04 M DNS reagent were used. To evaluate uronic acids, 0.1 mL of the clarified samples was used, to which 0.9 mL of distilled water, 3 mL of 0.025 M sodium tetraborate in sulfuric acid and 0.2 mL of 0.125% carbazole reagent were added. After boiling for 10 min and 15 min, respectively, and cooling, the color intensity was measured at the wavelengths shown above.  
The reducing sugars and total sugars were expressed in g glucose/100 g sample, and the reducing uronic acids and fermentable uronic acids were expressed in g galacturonic acid/100 g sample.
## 1 Introduction
The related mechanisms of action involve the reduction of bile acid reabsorption, the modification of colonic microflora metabolism, and the reduction of cholesterol absorption, resulting in a reduction in total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) levels [28; 29]. Cereal \(\beta\)-glucan health claims, approved by the European Food Safety Authority (EFSA), involve their implication in the reduction of post-prandial glycemic responses as well as blood cholesterol levels [30; 31]. Barley is a cereal grain rich in dietary fiber that contains high levels of \(\beta\)-glucan compared to other cereal grains. It has been incorporated into breads and biscuits [32; 33; 34], pasta [35], noodles [36], and chips and tortillas [37; 38], as barley consumption on a regular basis may help to minimize blood sugar spikes and prevent insulin resistance due to its low glycemic index. Even though the beneficial action of \(\beta\)-glucan in reducing total and LDL-cholesterol levels is well documented, thus recommending a minimum intake of 3 g/day of soluble fiber from barley or oats [39], results on its effect on glucose levels are inconclusive [40]. Therefore, the aim of this study was to investigate the direct effect of crackers enriched with (i) 10% grape seed flour or (ii) 40% barley flour with added \(\beta\)-glucan along with a high-fat and high-carbohydrate meal on biomarkers of postprandial glycemia, lipidemia, and oxidative stress in a healthy cohort.
## 4 Elaboration of the Optimization Method
### Weight and Cost Objective Functions
#### 4.1.2 Cost Objective Function  
The total cost of the structure is the sum of the cost of the two face sheets and the cost of the core:  
\[C_{t}=C_{f}+C_{c}, \tag{4}\]  
where \(C_{t}\) is the total cost of the sandwich structure, and \(C_{f}\) and \(C_{c}\) are the costs of the face sheet's layers and cost of the core. In order to estimate the costs of the constituent materials in a sandwich structure, a deep survey was conducted as part of this study. In the interest of generalization, the unit price of each material was normalized to the price of GFRP. The cost of CFRP, the core, and aluminum were reported as 1.5, 0.5, and 0.31 times the cost of GFRP, respectively.
# Bayesian Optimization for Instruction Generation
## 2 Related Works  
In [16], a prompt (\(\mathbf{p}\in V^{l}\)) is defined as a sequence of \(l\) n-grams. The tokens of the original model's vocabulary are merged in n-grams based on their Pointwise Mutual Information (PMI) and considered as prompt candidates. This ensures that only n-grams of tokens that frequently appear together are used to form the actual vocabulary (V).  
The final goal is to find the prompt (\(\mathbf{p^{*}}\)) that maximizes a scoring function (\(h\)). The scoring function (\(h\)) utilized in the prompt optimization framework is defined as the classification score between the predicted label and the ground-truth label. The algorithm proposed in [16] is analyzed is Section 4; it is based on vanilla BO over a continuous relaxation of the combinatorial space and a successive rounding of the continuous solution to the closest integer-valued solution.  
On the contrary, InstructZero [14] deals with discrete instructions by optimizing over a low-dimensional continuous latent space and using a linear projection to map the result back to the original discrete space. The soft prompt in the continuous latent space is given as input to an open-source LLM whose output is an instruction which, in turn, is given a black-box LLM as input. BO is used to identify new soft prompts aimed at improving the zero-shot performance.  
Another approach using two LLMs is named Instinct [15], which moves from the observation that gaussian processes (GPs), which are usually adopted as probabilistic surrogate models in BO, perform poorly for sophisticated or high-dimensional objective functions. This can become a serious drawback attributed to the representational power of GPs, which are usually unsuitable when a soft prompt is the argument of the mapping. Owing to this drawback, growing attention is being paid to transformer architectures, which have empirically shown not only a better representation capacity but also effectiveness in balancing exploration and exploitation, in alignment with GPs.  
The method proposed in this paper is called BOInG (Bayesian Optimization for Instruction Generation), and it is also based on two LLMs: one used as an instruction generator and the other used as a solver of the target task. In contrast to InstructZero and Instruct, in which one LLM must be a white-box model, in BOING, both LLMs can be black-box models.
# DisA Limits RecG Activities at Stalled or Reversed Replication Forks
## 2 Materials and Methods
### ATPase Activity and c-di-AMP Synthesis  
The DNA-dependent ATP hydrolysis activity of the RecG protein was assayed by monitoring the disappearance of absorbance at 340 nm, due to NADH conversion to NAD, using a Shimadzu CPS-20A (Tokyo, Japan) dual-beam spectrophotometer via an ATP/NADH-coupled spectrophotometric enzymatic assay. The DNA effectors were a 3199-nucleotides (nt) circular pGEM3 Zf(+) ssDNA (cssDNA) or HJ-J3 DNA (10 \(\mu\)M in nt). Assays were done in buffer D (50 mM Tris-HCl pH 7.5, 80 mM NaCl, 10 mM MgOOAc, 0.05 mg/mL BSA BSA, 1 mM DTT, 5% glycerol) containing 5 mM ATP and an ATP regeneration system (620 \(\mu\)M NADH, 100 U/mL of lactate dehydrogenase, 500 U/mL pyruvate kinase, and 2.5 mM phosphoenolpyruvate) for 30 min at 37 \({}^{\circ}\)C. The concentration and order of addition of the purified proteins are indicated in the text. Data obtained from A\({}_{340}\) absorbance were converted to ADP produced, and plotted as a function of time. In some reactions, the curves reached a plateau, due to a consumption of the NADH pool, and the rate of ATP hydrolysis was derived from the slope of the linear part of the curves as described [48]. T-tests were applied to analyze the statistical significance of the data.  
c-di-AMP synthesis by DisA was analyzed using thin-layer chromatography (TLC) and [\(\alpha^{32}\)P]-ATP as described [21; 27]. Reactions were performed at 37 \({}^{\circ}\)C using a fixed DisA concentration (25 nM) and increasing RecG concentrations (7 to 30 nM) in buffer E (50 mM Tris-HCl pH 7.5, 50 mM NaCl, 1 mM DTT, 10 mM MgCl2, 0.05 mg/mL BSA, 0.1% Triton, 5% glycerol) containing 100 \(\mu\)M ATP (at a ratio of 1:2000 [\(\alpha^{32}\)P]-ATP:ATP). After 30 min incubation, the reaction was stopped by adding 50 mM EDTA, 2 \(\mu\)L of each reaction were spotted onto 20 \(\times\) 20 cm TLC polyethyleneimine cellulose plates and run for about 2 h in a TLC chamber containing running buffer F [1:1 (\(v/v\)) 1.5 M KH\({}_{2}\)PO\({}_{4}\) (pH 3.6) and 70% ammonium sulfate]. Dried TLC plates were analyzed by phosphor imaging and spots were quantified using ImageJ. T-tests were applied to analyze the statistical significance of the data.
## 3 Results and Discussion
### Assessment of Uncertainty
#### 3.8.3 Calculation of Combined Relative Uncertainty by NORDTEST  
Combining the uncertainties, u\({}_{\text{bias,N}}\) and u(R\({}_{\text{w}}\)), calculated above, Equation (1) gives the uncertainty according to NORDTEST [14]:  
\[\text{u}_{\text{c}}\text{=7.9\% }\text{  
statistical tests were performed. The impurities in the Ouzo matrix did not affect the determination of DEHP using the validated method. The lower limit of quantification was determined to be 0.06 mg/L DEHP. The precision study involved duplicate measurements on eight different days, and RSD values for repeatability and intermediate precision were calculated using ANOVA analysis. The resulting RSD values met acceptable standards based on the expected DEHP concentration level. A fractional Taguchi experimental design was applied to assess ruggedness, and the relative standard uncertainty of DEHP determination in Ouzo was found to be \(\pm\)8%. These results confirm that the laboratory's in-house developed method is reliable and suitable for its intended purpose. Future research will focus on expanding the method to other phthalates and assessing the uncertainty of DEHP determination using the NORDTEST technique. Additionally, the method's performance will be periodically assessed through interlaboratory tests.  
Conceptualization, E.G.G.; methodology, E.G.G. and N.A.A.; validation, E.G.G.; formal analysis, E.G.G. and N.A.A.; investigation, E.G.G.; resources, N.A.A.; data curation, E.G.G.; writing--original draft preparation, E.G.G.; writing--review and editing, N.A.A.; visualization, E.G.G.; supervision, N.A.A.; project administration, N.A.A. All authors have read and agreed to the published version of the manuscript.  
This project did not receive funding.  
Not applicable.  
All data has been provided in the manuscript.  
The authors declare no conflict of interest.
# Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign
## 2 Outdoor Experiment Concept, Design, and Simulations
We now analyze the clutter contribution (the first term in Equation (4)), assuming sufficient stability of the atmosphere in the time interval of one acquisition. This assumption was verified by analyzing the Doppler spectrum of the acquired clutter echoes (see Section 4). The result of the focusing operation in (7) is a stochastic process with variance depending on the clutter spatial and temporal covariance matrix. This will depend on the scenario (e.g., rocks or vegetation) and on its stability (e.g., due to wind). To show the principle of the outdoor test, we analytically derive the case of clutter perfectly correlated in time (frozen scene). In this case, the clutter \(a_{c}\) is a constant along time, so its contribution to the focused signal \(y_{F}\) is given by:  
\[\sigma_{c}=\frac{\rho_{g}\lambda R}{L_{a}}\sigma_{T}^{0} \tag{2}\]0  
where \(L\) is the total length of the target motion. The relation above shows that the contribution of the clutter to the focused moving target is a cardinal sinc, which depends only on the total motion extent \(L\). With a motion extent multiple of \(\frac{\lambda}{2}\), the clutter power is (ideally) completely cancelled, as the total signal-to-noise ratio is only thermal-noise limited. In this ideal case, the velocity of the target is completely irrelevant (provided that the radar Pulse repetition Frequency (PRF) is sufficient to sample the sinusoid at \(f=\frac{2}{\lambda}v\), which, for typical PRFs in the order of KHz, is practically always true).
## 2 Materials and Methods
### Intensity Processing Procedures
#### 2.5.1 N3 Inhomogeneity Correction  
Nonparametric Nonuniform Intensity Normalization (N3), from [PERSON] et al. [30], is a widely used technique for addressing intensity inhomogeneity in MRI images. N3 has been extensively employed in various neuroimaging studies and is often considered a standard preprocessing step in many MRI analysis pipelines. For instance, N3 is the default inhomogeneity correction method in popular neuroimaging software packages such as FreeSurfer [37] and is commonly used in large-scale, multi-site neuroimaging studies like the Alzheimer's Disease Neuroimaging Initiative (ADNI) [38]. Moreover, N3 has been shown to effectively reduce intensity inhomogeneity in MRI images of various patient populations, including those with multiple sclerosis [39], Alzheimer's disease [40], and brain tumors [41].  
N3 addresses intensity inhomogeneity using a nonparametric approach without pre-supposing specific tissue distribution models or necessitating initial segmentation. Central to this method is the principle that a uniform tissue's intensity histogram, unaffected by inhomogeneity, should ideally manifest as unimodal. Through iterative refinement, N3 enhances the sharpness of this histogram, targeting the elimination of the bias field distorting the true signal. Initially, N3 operates under the presumption that deviations from the expected unimodal distribution stem primarily from an underlying, smoothly varying bias field, attributing intensity variations to this inhomogeneity. The correction process unfolds iteratively, alternating between estimating the true tissue intensity distribution, assuming that the bias field is known, and estimating the bias field, assuming that the true tissue intensity distribution is known. Key to N3 is the deconvolution step, which separates the true signal from the bias field by conceptualizing the observed image intensities as the multiplication of the true signal by the bias field and then solving for the bias field in the frequency domain, where convolution simplifies to multiplication. This process is accompanied by the histogram sharpening of the image, aiming to counteract the bias field's blurring effect by iteratively adjusting the image to achieve a more pronounced unimodal histogram. Notably, the N3 method is designed to be independent of the MRI pulse sequence and is robust to pathological data that might otherwise compromise model-based assumptions. The iterative process is designed to continue until subsequent adjustments yield negligible changes in histogram sharpness, indicating successful compensation for the bias field and the restoration of the true tissue intensities. This automated, minimal-assumption approach renders N3 a versatile and effective tool for enhancing tissue appearance uniformity across an MRI image.
## 2 Experimental Details  
Figure 1 shows a diagram of the laser irradiation system. The sapphire pulse laser had a central wavelength of 800 nm, a repetition rate of 1 kHz, and a pulse length of 120 fs. The duration of the laser was controlled using an electronic shutter. A neutral-density filter in the light path was utilized to adjust the laser radiation energy. The laser beam was focused on a circular spot with a diameter of \(\approx\)5 \(\upmu\)m and perpendicular to the sample's surface. The samples were placed on a precise XY translation stage. The range of the pulse energies used in this experiment was set from 20 to 50 nJ, corresponding to laser fluences from 1.02 to 2.55 J/cm\({}^{2}\). All the laser ablation experiments were performed in an air environment under normal incidence. A crystal <100> n-type silicon wafer was selected as the semiconductor material for the laser-induced experiment for the reason that n-type crystalline silicon with patterned surface structures used in crystalline silicon solar cells show higher bulk quality and greater stability [30; 31]. A Bi-In film with a thickness of 195 nm was deposited on silicon using electron beam evaporation (EBE). After laser ablation, the irradiated regions were characterized by scanning electron microscopy (SEM) and atomic force microscopy (AFM).  
Figure 1: Schematic diagram of the experimental setup for the generation of laser-induced periodic surface structures (LIPSSs). CCD: charge-coupled device.
## 7 Experimental Results
### Performance Comparison of EAR2 and EAR2M
#### 7.3.2 Impact of End-to-End Distance  
Figure 8a shows the effect of end-to-end distance on DSR with the different number of flooding points. As the end-to-end distance from the source to the sink increases, the probability of failure to maintain the desired data delivery speeds increases, and thus the data loss ratio also increases. Data dissemination through multiple flooding points can reduce these issues because the data are copied and forwarded in the separated routing path. Therefore, the higher number of flooding points in EAR2M leads to a higher DSR.  
Figure 8b shows the EC according to the end-to-end distance with the different number of flooding points. As the end-to-end distance increases, the cost for flooding is fixed but the energy consumption for data transmission from the source to the flooding points increases. Therefore, the energy consumption for data dissemination through more flooding points in EAR2M increases much more rapidly as the end-to-end distance increases.
## 5 Conclusions  
This systematic review analyzed literature on image processing, particularly medical images, focusing on brain tumor detection using magnetic resonance imaging (MRI). Fifty-three articles from the past ten years were reviewed and selected for their relevance to brain tumor detection. The analysis showed that 43 of the 53 studies employed brain tumor segmentation techniques, and 47 included practical applications of their proposed methods. Most studies relied on convolutional neural networks (CNNs) and machine learning (ML) models. Neural networks are recognized for their strong learning ability and accuracy, but require large datasets. Fuzzy logic offers interpretability and handles uncertainty well, but may lack precision in complex patterns. Genetic algorithms are effective in feature selection and optimization, but are computationally intensive. By comparing these techniques, the review provides insights into their suitability for different tumor classification tasks.  
However, several limitations were identified, including long training times for datasets, particularly in cases involving hybrid models or complex ML algorithms. Additionally, some studies had insufficient dataset sizes, limiting their ability to achieve optimal accuracy. These findings emphasize the need for further in-depth research to enhance accuracy, as the results directly impact patient outcomes. Therefore, several approaches can be adopted to enhance the model's accuracy when dealing with limited medical image data, like (data augmentation, transfer learning, synthetic data generation, and others).  
Prioritizing tumor segmentation, localization, and classification remains critical to improving the precision and reliability of brain tumor detection methods. Future studies should explore larger, more diverse datasets and develop optimized computational strategies to mitigate processing inefficiencies. Additionally, integrating multi-modal imaging techniques and leveraging advanced AI models could drive significant breakthroughs in early tumor detection, ultimately leading to improved clinical decision-making and patient survival rates.
## 5 Test on Aircraft Longitudinal Flight Control Problem
The Kriging model is established via two methods, and the modeling time (excluding the time for simulation estimation) is recorded as the first-time value. In each iteration process, a sample point is added, and the corresponding expensive estimate is obtained through simulation, and the modeling time at this time (excluding the time of the simulation estimate) is recorded as a time value. The iterative process is repeated until the final number of sample points is \(110\), and then the iterative process is stopped. The following two conclusions can be drawn from Figure 14. (a) It can be seen from Figure 14 that the time required for KMDS to establish Kriging and to directly establish the Kriging model gradually increases as the number of sample points increases. However, the time required to directly establish the Kriging model is more and more than the time required for KMDS modeling with the increase in the number of sampling points, and it  
Figure 14: (**a**) Trends of modeling time. (**b**) Trends of RMSE. Figure 13: Aircraft longitudinal flight control simulation diagram. finally reaches a result where the time difference is more than ten times. (b) The figure shows that the model error is gradually decreasing as the number of sample points increases. However, the modeling error of KMDS to establish Kriging has always been smaller than that of directly establishing Kriging, which shows that the KMDS method has higher modeling accuracy. In summary, for the simulation of an aircraft longitudinal flight control system, the KMDS method can improve the modeling efficiency and modeling accuracy of the Kriging model.