esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mechanical Properties
#### 3.2.2 Peak Strain  
Brittle damage of rocks is characterized by small peak strains, an instantaneous release of elastic energy, and accompanying loud sounds. Ductile damage is the opposite, not only is the peak strain larger, but the elastic energy is also slowly released without instantaneous damage. In the uniaxial compression test, the strain corresponding to the uniaxial compressive strength of the specimen is the peak strain, and the value of the peak strain can be obtained by in the stress-strain curve. The peak strain can, to a certain extent, reflect the brittle-ductile characteristics of the specimen. Figure 6 shows the variation of limestone peak strain with temperature after thermal shock. In addition, the peak strain of the original rock is 1.194%.  
As shown in Figure 8, the peak strain of the limestone sample after thermal shock increases with the increase in heating temperature. It shows that the peak strain of the limestone sample increases from 0.918% at 100 \({}^{\circ}\)C to 1.751% at 600 \({}^{\circ}\)C after thermal shock at 20 \({}^{\circ}\)C, an increase of 90.7%. Peak strain increases from 1.32% at 100 \({}^{\circ}\)C to 2.422% at 600 \({}^{\circ}\)C after thermal shock at 60 \({}^{\circ}\)C, an increase of 83.5%. Peak strain increases from 1.156% at 100 \({}^{\circ}\)C to 1.715% at 600 \({}^{\circ}\)C after thermal shock at 100 \({}^{\circ}\)C, an increase of 48.4%. All of the above data show that the peak strain increases with increasing heating temperature when limestone is cooled by thermal shock at a constant temperature. The thermal shock promotes the transformation of limestone from brittle to ductile characteristics. It is noteworthy that the absolute value of the peak strain is always highest when the high temperature limestone is cooled at 60 \({}^{\circ}\)C. This indicates that peak strain and thermal shock temperature difference \(\Delta\)T do not have an obvious linear relationship. Although the peak temperature of heating and the cooling temperature jointly affect the evolution of peak strain, there is a very complex mechanism of action between the two for the effect of peak strain. We can then investigate this direction in depth.  
Figure 8: Trends of peak strain of limestone with temperature after thermal shock.
# Effects of Organic Acid Coagulants on the Textural and Physical-Chemical Properties of Tofu
## 3 Results and Discussion
The tofu yield is linked to the protein types present; the T1C5 (5% apple vinegar) treatment showed a higher curd yield despite taking longer to coagulate compared to the T6C3 (3% rice vinegar + white vinegar) treatment, which was considered the best treatment for its significantly reduced coagulation time. The T6C3 treatment achieved a pH of 4.7, promoting the precipitation of reserve proteins (globulins 11S and 7S) at their isoelectric pH (4.5 to 4.8), accelerating the coagulation but leaving protein remnants in the whey [21]. The T1C5 treatment obtained a pH of 4.9, delaying its coagulation time but resulting in higher protein accumulation and yield, with a more transparent whey, indicating better protein utilization. The pKa (negative logarithm of the acid dissociation constant (Ka) of an acid) of the acids influences the yield; lower pKa acids tend to ionize more, promoting protein flocculation. In the T6C3 treatment, acetic acid (pKa = 4.79) was prevalent, while the T1C5 treatment contained malic acid (pKa = 3.45). Stronger acids lead to better protein flocculation and higher yields. In another study [22], lower yield values were observed due to the longer agitation times required to dissolve the salts, which can destroy the gel structure, producing higher tofu yields and a thicker texture [23]. This study's results align with those of other authors, who reported tofu yields of 42.33%, 37.69%, and 39.70% for regular, germinated, and combined tofu, respectively. The variation in soybean varieties and treatment processes explains the differences in the tofu yield percentages [24]. Table 2 also shows the results of the different moisture, ash, and protein contents in the treatments. The highest protein content in the studied product is attributed to the lower precipitation of storage globulins caused by the coagulants, leading to higher retention of soybean proteins during the protein network formation in the tofu. This result is consistent with the recorded moisture, protein, and yield values for treatments T1C5 and T2C3, which had the lowest moisture, highest protein, and highest yield values.
## 2 Optical Angle Nano-Positioning Mechanism Configuration  
The machine developed in this study is designed to have a simple and reliable structure, extensive range of movement adjustment, and high inherent frequency. To fulfill these criteria, a trapezoidal four-link is implemented for high-precision adjustment and positioning within a restricted range. The design features include a fixed platform, four flexible link rods, a moving platform, and a driver. The fixed platform is securely mounted to the base platform. Upon application of a driving force \(F_{\Sigma}\) by the driver, the moving platform undergoes a micrometric displacement, resulting in an angular displacement around the pivot point, as depicted in Figure 2.  
The optical angle nano-positioning mechanism incorporates two sets of symmetrically arranged flexible hinges, which effectively constrain translational movements along the \(z\)-axis and rotational movements around the \(x\)-axis. When the piezoelectric (PZT) actuator exerts a driving force \(F_{\Sigma}\) along the \(x\)-axis, the four sets of rigid rods pivot around the flexible hinges anchored to the fixed platform. This rotational motion is subsequently transmitted to the moving platform via the flexible hinges connected to it.
# Sensitivity Estimation for Differentially Private Query Processing
## 4 Sensitivity Estimation for Join Queries
### Sampling-Based Sensitivity Estimation  
As mentioned above, calculating residual sensitivity for a multi-way join query is expensive. A basic idea to reduce this cost is to leverage sampling methods to estimate the results of each residual query in the form of \(Q_{1}\) in example 1. Inspired by \"Wander-Join\" [28], we introduce a method to quickly estimate such a residual query based on the random walk in Section 4.2.1. Since the residual query result is only relevant to the largest group of the join result, we focus on the join paths for the largest group. To further reduce the cost, we propose an improved method to estimate all the residual queries using a set of join paths, which is described in Section 4.2.2.  
Figure 2: Impact of deleting a tuple on the join size.
## 3 Results
### Feature Inference and Extraction from Non-Etched Metallographic Microstructures
#### 3.2.3 Extraction of Various Measurements  
Correct extraction of dimensional and morphological measurements from metallographic images is complementary to the continuous characterization of nodular cast iron. These measurements, including nodule size, shape descriptors (for instance, roundness and circularity), and spatial distribution, provide essential inputs for quality assessment and process control.  
Figure 12: Example of nodule extraction, applying the methodology that will be included in Hawk-Eye software. (**a**) Starting image for nodule extraction and segmentation. (**b**) Image showing the segmentation of nodules by edges.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Nodule Type**} & \multirow{2}{*}{**Total Count**} & \multicolumn{2}{c}{**CV**} & \multirow{2}{*}{**CV Error (\%)**} & \multirow{2}{*}{**ML Classified**} & \multirow{2}{*}{**ML Error (\%)**} \\  & & **Classified** & & & \\ \hline Type III & 229 & 217 & 5.2 & 200 & 12.6 \\ Type V & 553 & 548 & 0.9 & 524 & 5.2 \\ Type VI & 3826 & 3788 & 1.0 & 3884 & 1.5 \\ \hline Total & 4608 & & 1.0 & & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Nodular classification results using CV and ML approaches [7].  
The methodology described by [PERSON] et al. [7] is also applied to feature extraction from the segmented nodules. Their approach includes preprocessing steps such as noise reduction, contrast enhancement, and adaptive thresholding to ensure robust segmentation under varying image conditions. From the segmented nodules, key morphological features are computed as explained in the aforementioned paper.  
The results reported in [7] demonstrate high accuracy in feature extraction, with mean absolute errors close to 1% when compared against expert manual measurements. For instance, the extracted nodule counts and size distributions showed a strong correlation with expert annotations, validating the reliability of the automated approach.  
Table 6 summarizes the accuracy metrics for the main features, evidencing that shape descriptors such as roundness and circularity provide effective differentiation of nodule types, which is critical for subsequent classification.
## 3 Results and Discussion
### OPLS-DA Analysis of Unroasted versus Roasted Red Mahlab Seeds  
OPLS-DA modeling of the volatile profile of unroasted RE _versus_ roasted red mahlab \"RRE\" seeds (Supplementary Figure S2) was attempted in order to assess the impact of roasting on their aroma as well as to identify roasting markers from the volatile dataset. The performance of the developed classification model was validated by the computed parameters \"R2 (0.9621)\" and \"Q2 (0.9102)\", which indicated the optimal fit and prediction power of the model, respectively, though with a \(p\) value > 0.05, suggestive of non-statistical significance.  
The observed segregation in the derived score plot (Supplementary Figure S2a) was ascribed to the enrichment of unroasted RE in 1-hexanol (peak 25) and benzaldehyde (peak 36) _versus_ the abundance of lactones, e.g., \(\gamma\)-caprolactone (peak 114) and isomintlactone (peak 118); furans, e.g., 1-(2-furyl)-3-butene-1,2-diol (peak 99), and a polyaromatic, i.e., naphthalene (peak 68) in roasted mahlab seeds, likely produced due to the applied high temperature during roasting (as depicted in S-plot, Supplementary Figure S2b). The production of lactones, furans and polyaromatic compounds is expected at high temperatures [55] and could indeed reveal the roasting impact on mahlab seed aroma.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
Traffic rules, e.g., the speed limitation and the traffic light, were set according to the real world scenarios. By using the SUMO tool chain, all the geographic and traffic data were converted to the SUMO file format.
## 2 Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions
### Mapping Coastal Wetlands  
Wetlands are highly diverse ecosystems that can provide necessary ecological goods and services. However, wetlands are threatened by intensive anthropogenic activities, and wetland loss is a global public concern [159]. In the Parana River Delta, one-third of wetlands once covered by freshwater marshes have been modified into pastures and forestry in the past 14 years, as revealed by Landsat images [143]. In the Gulf of Mexico, many woody wetland regions were replaced by urban land between 2001 and 2006, as quantified by optical remote sensing satellite imagery [141]. Optical remote sensing has advantages of inventory and monitoring wetlands over large geographic areas. Landsat, SPOT, NOAA AVHRR, IRS-1B, and other major satellite systems can be used for wetland identification and discrimination [160]. Additionally, radar imagery can distinguish between flooded and non-flooded areas, and radar can work in all weather conditions. The integration of radar and optical remote sensing images provides a promising methodology for improving wetland identification and classification.
# Schiff Bases: Interesting Scaffolds with Promising Antitumoral Properties
## 1 Introduction  
Since their discovery by the German chemist [PERSON] [1], Schiff bases (imines), scaffolds with high chemical reactivity, and their metal complexes have been very well known for catalysis in various synthetic processes and for their biological properties. In therapy, Schiff bases and their metal complexes have been reported to manifest a wide range of biological activities [2, 3] such as antimicrobial [4], ureases inhibitory [5], anti-inflammatory [6, 7], anti-ulcerogenic [8], antioxidant [9, 10, 11], pesticidal, cytotoxic, and anticancer [12] including DNA damage [13, 14, 15]. Schiff bases have been also successfully used in scientific studies [16] as highly efficient and selective sensing materials for optical, electrochemical [17, 18], and membrane sensors [19]. Zinc-Schiff bases have been proposed as carrier vehicles for the delivery of zinc to prostate cells. Indeed, the use of the membrane-penetrating peptide Novicidin connected to zinc-Schiff base has been studied as a therapeutic approach for prostate cancer [20]. Schiff base ligands, as some other organic small molecules [21], have received great attention from researchers thanks to their easy preparation and ability to form complexes with almost all metals, due to the electron-donating nitrogen in their base structure [22, 23, 24]. Several metal complexes, in which the metal is coordinated to various ligands, are able not only to stabilize the metal but also to modify its chemical and pharmaceutical properties and are receiving attention in medicinal chemistry [25, 26, 27, 28, 29, 30]. The general structure of a Schiff base is shown in Figure 1, R\({}^{1}\), R\({}^{2}\) and R\({}^{3}\) being an alkyl or aryl moiety.  
Schiff bases are particularly interesting in the field of antitumor agents [31; 32; 33; 34] as many other small organic molecules (for instance, diarylureas [35], indoles [36; 37], carbazoles [38], phthalimides [39], and so on [40; 41]). The most salient and recent data on Schiff bases will be, herein, reviewed.  
For instance, in a recent study, the introduction of Schiff bases in the \(N\)-phenylcarbazole/triphenylamine modified half-sandwiched iridium(III) compounds determined an enhancement of antitumor activity of about 13 times that of the clinical cisplatin [42]. This review focused on studies of the last decades on mono- and bis-Schiff bases as antiproliferative agents, paying attention particularly on Schiff bases showing high activity (concentration which kills or inhibits cell viability by 50% (IC\({}_{50}\)) in the range of micromolar to nanomolar).
## 4 Results of Probabilistic Stability and Performance Assessment
### The Impact of PLL Bandwidth on System Stability and Performance
#### 4.3.1 Damping Factor Analysis  
As shown in Figure 10 and Table 5, for \(f_{PLL}\) = 1 Hz, PDFs become very narrow, and it could be concluded that the damping factor was less sensitive to grid inductance variations. However, the stability margin was also insufficient. It could also be deduced from participation factor analysis that the PLL remarkably contributed to the critical mode. In addition, it seemed that the maximum damping factor for lower PLL bandwidths could be estimated by \(\sigma_{\max}=-\omega_{B,PLL}=-2\pi f_{PLL}\).  
Figure 10: Probability density functions (PDFs) of stability indices for different grid SCRs and PLL bandwidths. (**a**) PDFs of the maximum damping factor; (**b**) PDFs of the minimum damping ratio. Colors correspond to different grid SCRs in Figure 6.  
Therefore, it could be expected that the damping factor could be improved by increasing the PLL bandwidth. By increasing the PLL bandwidth to 8 Hz, the stability margin was improved; however, the PDFs became wider, which meant more sensitivity to grid inductance variations and higher coupling and control-loop interactions. Finally, increasing the PLL bandwidth further under weak grid conditions might cause stability problems.
## 4 Experiments and Result Analyses
### Implementation Details  
We use the PyTorch [34] framework to carry out the following experiments. The backbone network used is HRNet-W32, where 32 represents the channel factor of the branch in HRNet. For HRNet-W32, the channel numbers of the four branches are 32, 64, 128, and 256. When training the network, SGD is used to update the network parameters, with a weight decay of 0.0001, a momentum of 0.9, an epoch of 160, and an initial learning rate of 0.01 and using the poly learning rate strategy. The resolution of the input image is 512 \(\times\) 512. All the experiments were performed on a single Nvidia GeForce GTX 2080 Ti.  
Figure 11: Images and the ground truth of weld defect dataset.
# Opto-Electronic Oscillators for Micro- and Millimeter Wave Signal Generation
## 3 Recent Development of the OEO's Application
### Photonic Integrated Chip  
The traditional OEO uses an optical fiber to form the optical delay line. This makes the OEO very large and in some cases, bulky because it contains additional components for the improvement of short-term and long-term stabilization, multimode operation, etc. (an additional delay line, additional optical and/or electrical devices, etc.). To reduce the size of the OEO, a compact OEO was proposed. In the compact OEO, the optical fiber was replaced by a whispering-gallery-mode resonator (WGMR) to reduce the size of the OEO [31; 32; 33].  
The fully integrated OEO based on an InP substrate was first presented by [PERSON] et al. at the Microwave Photonics Conference 2017 [38; 84]. All the components were assembled in a photonic integrated chip (PIC). An optical waveguide in a spiral shape was used as an optical resonator. When a microwave frequency was generated with such an integrated OEO, one of the main handicaps was the length of the delay line. Due to the small size of the spiral-shaped delay line, which was a few centimeters, the Q-factor of the OEO was very low, and thus the phase noise characteristics were worse than those of a non-integrated OEO. Therefore, for the integrated OEO, an SSB phase noise of \(-91\) dBc/Hz at a 1-MHz offset from the 7.3 GHz carrier was obtained [38]. The integrated OEO and its tunability and short-term performance are given in Figure 9.  
Figure 8: Simple configuration of an OEO with a micro-ring resonator (MRR).  
Figure 9: The integrated OEO. The frequency tunability of the OEO is around 20 MHz, with a central oscillation frequency of 7.3 GHz [38]. Reprinted with permission from Reference [38]. Copyright 2017 IEEE.  
In addition to the integrated OEO on the InP substrate, an integrated OEO based on a silicon substrate was introduced to the literature by [PERSON] and [PERSON] in 2018 [85]. A high-speed phase modulator, a tunable micro-disk resonator and a high-speed photodetector were integrated on the silicon-substrate chip. The proposed silicon photonic OEO was tunable in the frequency range between 3 GHz and 8 GHz. The measured SSB phase noise was -81 dBc/Hz at a 10 kHz offset from the 4.74 GHz RF carrier signal. On the other hand, a photonic and electronic hybrid OEO was presented in 2019 [86]. In this configuration, the OEO consisted of photonic and electronic chips that were fabricated separately. An SSB phase noise of \(-103\) dBc/Hz at a 100 kHz offset from the carrier was measured.  
As can be clearly seen from the SSB phase noise measurements of the integrated OEO [84; 85; 86], although the integrated OEOs provide the opportunity to reduce the size and enable mass production of the OEO in the chip, they unfortunately have poor short-term stability in the microwave range. However, due to their small size, the long-term stability of the OEO can be easily improved by temperature stabilization of the chip.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 4 Planning Models in Penetration Path Planning
### Sequential Decision Model  
The sequential decision model is a mathematical model for describing multi-stage decision-making problems in uncertain environments [50; 51]. It focuses on determining the optimal action at each decision step based on the current state to optimize cumulative rewards or costs. The classical sequential decision models encompass the Markov Decision Process (MDP) [52] and the Partially Observable Markov Decision Process (POMDP) [53].  
The MDP and POMDP frame the problem of penetration path planning within an optimization problem. The MDP is defined by a quintuple \((S,A,P,R,\gamma)\), where the following characteristics are present:  
* \(S\) denotes the set of all possible states of the environment;
* \(A\) denotes the set of all possible actions an agent can take;
* \(P\) denotes the transition probabilities between states given an action is selected;
* \(R\) denotes the reward function assigning a reward for each state-action pair;  
Figure 3: Attack graph generated with MulVAL.  
* \(\gamma\) denotes the discount factor that weighs future reward relative to immediate reward.  
Additionally, a policy \(\pi\) is defined, which is a mapping from states to actions, indicating the optimal course of action in each state to maximize the expected cumulative reward.  
In MDPs, the agent can fully observe the state of the environment, enabling decision-making based on complete information. Conversely, in POMDPs, agents lack direct access to the true environmental state and must rely on observations to infer it. To manage this uncertainty, the agent updates its belief state--a probability distribution over possible states--based on received observations. This belief state is essential for evaluating the expected utility of actions and selecting those projected to yield the highest long-term rewards, despite the inherent uncertainty in the observations.  
The complexity of POMDPs stems from the continuous need to maintain and revise the belief state as new observations are made. This computational challenge escalates with the expansion of the state and observation spaces, highlighting the increased demands on computational resources. Formally, a POMDP is encapsulated by a heptameric tuple \((S,\ A,\ P,\ R,\ \Omega,\ O,\ \gamma)\), where \(\Omega\) is the set of all possible observations, and \(O\) is the observation function linking states to observations.
## 2 Materials and Methods
### Nanotoxicological Bioassays
#### 2.3.4 Measurement of Nitrite Production by Macrophages  
The Griess assay was employed to quantify macrophage nitrite production following exposure to different concentrations of Fe\({}_{3}\)O\({}_{4}\) or Fe\({}_{3}\)O\({}_{4}\)-Ag nanoparticles. In macrophages, nitrites result from the oxidation of nitric oxide in the modulation of inflammation and other physiological processes. RAW 264.7 (TIB-71, ATCC) macrophages were seeded in a 96-well plate at a density of 10,000 cells per well and cultured for 24 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Different concentrations of Fe\({}_{3}\)O\({}_{4}\) or Fe\({}_{3}\)O\({}_{4}\)-Ag nanoparticles 0.1, 1, 5, 10, 25, 50, and 100 \(\upmu\)g/mL were added to the wells and further incubated for 24 h under the same conditions. Subsequently, 20 \(\upmu\)L of cell culture supernatant from each well was combined with 80 \(\upmu\)L of 5 mM sodium nitropruside in a new well, followed by incubation for 1 h in darkness at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Next, the reaction mixture was treated with 100 \(\upmu\)L of [PERSON] reagent solution (0.1% sulfanilamide and 0.1% N-(1-naphthylethylenediamine)) and incubated at 25 \({}^{\circ}\)C for 15 min in darkness. Absorbance was measured at 540 nm, and the values were compared against a standard curve generated using sodium nitrite concentrations ranging from 1.67 to 100 \(\upmu\)M as reference reagents for nitrite production. Experiments were conducted independently in a triplicate manner.