esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Description
At present, the different operational modes cannot be used simultaneously; development of a synchronization solution aimed at circumventing this limitation is however straightforward and is matter of current activities. In order to achieve accurate flight information, necessary for the reliable processing of both Sounder and SAR data, the radar encompasses a navigation unit consisting of an Inertial NavigationSystem (INS) that embeds a Global Positioning System (GPS). In particular, the INS is a MTi model of Xsense Technologies B.V., which is an inertial measurement unit with integrated 3D magnetometers (3D compass), with an embedded processor capable of calculating roll, pitch and yaw in real time, as well as outputting calibrated 3D linear acceleration, rate of turn (gyro) and (Earth) magnetic field data. The INS device integrates a GPS receiver and the related information, position, velocity and time (PVT). The navigation unit is directly connected to the radar central unit by means of an USB interface; all the navigation data are synchronized with the radar pulses and embedded in its output data. Furthermore, two additional GPS devices have been added to allow differential processing of the positioning data. The two GPS receivers are based on MST chip series of U-blox, one installed onboard, close to the SAR antenna, and the other on the ground in the range of some kilometers from the flying radar. During the calibration campaigns of the radar, differential processing of the positioning data, carried out with the RTKLIB library, allowed a centimetric positioning accuracy. The overall system is easy to be transported and installed onboard relatively small airplanes or helicopters. Indeed, the radar module is stowed in a rack, which is quite compact and with dimensions of 50 cm \(\times\) 50 cm \(\times\) 65 cm, for a weight of about 30 kg. During the campaign, the radar system was installed on an Eurocopter AS-350 series helicopter (see Figure 1a). In particular, the radar electronics rack was accommodated in the helicopter cabin, in place of the two rear passenger seats (see Figure 1b). The sounder antenna is installed on the helicopter nose (see Figure 1c) through the mechanical framework normally used for handling the rear-view mirror; moreover, it is fixed to the front glass and to the fuselage by means of a standard adapter. The SAR antennas are installed through a certified framework normally used for side-looking cameras and by using a honeycomb panel and some brackets (see Figure 1d, where just one SAR antenna is shown). Table 1 reports the main electrical, mechanical and geometrical parameters of the system. Figure 1: (**a**) The system ready to flight, (**b**) radar electronics rack inside the cabin, (**c**) Sounder antenna, (**d**) SAR antenna.
## 4 Performance Analysis
### Simulation Model and Parameters  
The parameters in our simulation are shown in Table 1. Since the parameters in ACO greatly influenced the performance of the algorithms, we adopted the values of 1, 5 and 0.5 for \(\alpha\), \(\beta\) and \(\rho\) by default in accordance with the best practice from [40]. Furthermore, we set the constant \(\lambda\) in Equation (7) as 0.1 and control coefficient \(\xi\) in Equation (10) as 0.9 for the simulation. Furthermore, the parameter _try_number_ in the AFSA-based initial route discovery algorithm has an impact on the control overhead performance of the routing algorithm. A larger _try_number_ allows an AF individual to more fully explore a new node in its neighbourhood, as compared with a small _try_number_. However, a larger _try_number_ consumes more control packets. Here, we adopted the value 10 for _try_number_ by default according to the [41].  
For the series of simulations carried out, we use the network model as described in Section 3.1. A static network scenario was adopted to cover most, if not all, of the types of scenarios the algorithms might face, by varying the node density (number of nodes). The node density was varied in the range \([100,500]\) in steps of 50.
# Improving Very High Plastic Clays with the Combined Effect of Sand, Line, and Polypropylene Fibers
###### Abstract  
Improving the mechanical properties of low-strength soils (e.g., high plasticity clays) is one of the main branches of geotechnical engineering. The adoption of stabilization techniques for ensuring that structures will be founded on an adequately strong soil base is a common practice. Stabilization techniques for clay soils may include inert materials (cohesionless soils), chemical substances (cement, lime, or industrial additives), or the use of randomly distributed fibers. While all of these additives are added to low-strength soils by mixing, the question remains whether an optimal combination of stabilization techniques can be achieved for maximizing soil strength. Besides, each one of these additives contributes to an increase in soil strength in a different manner (soil replacement, chemical bonding of soil particles, and soil reinforcement respectively), while, according to the literature, each technique has its limitations. The latter refers to a limited effect on strength improvement and a maximum possible percentage, beyond which an additive has an adverse effect on strength; it also refers to other factors, such as brittleness failure, material availability, overall cost, and environment-related issues. Hence, in the present study, the efficiency of improving the basic geotechnical properties of a very high plasticity clay (liquid limit \(\omega_{1}=86\%\)) with a coupled effect between dune sand, lime, and polypropylene (PP) fibers has been investigated. The samples prepared by combining the three aforementioned soil improvement techniques were compared in terms of plasticity, compaction characteristics, unconfined compressive strength (UCS), and California Bearing Ratio (CBR) index. The experimental results show that the combination of these additives may lead to a considerable improvement in the strength and ductility of soils, even with a small amount of lime additive. Also, it was observed that 20% of sand, 3.4% of lime and 0.9% of fibers (by wt%) offers the best performance in terms of strength improvement for the clay tested (i.e., 12.75 times improvement compared to the untreated clay).  
stabilization; reinforcement; high plastic clays; lime; PP fibers +
Footnote †: journal: Applied Sciences
## 8 Conclusions and Future Work
### Transitioning Towards Post-Quantum Cryptography  
In light of the threat scenarios outlined in Section 8.1, the integration of post-quantum cryptographic techniques constitutes a key aspect of our future work [41; 42; 43; 44]. In particular, we aim to explore hybrid key encapsulation mechanisms, such as HQC [45] and CRYSTALS-Kyber [38], which have been selected by the National Institute of Standards and Technology (NIST) as part of its fourth-round and third-round post-quantum cryptography standardisation process, respectively. These mechanisms offer a practical pathway towards the implementation of fully post-quantum schemes once standardisation is complete and widespread adoption is achieved. This dual-layer encryption provides immediate protection against emerging quantum threats while preserving interoperability with existing systems.  
As the standardisation of post-quantum cryptographic algorithms continues to progress under the guidance of the NIST, the ability to dynamically integrate or coexist with post-quantum cryptography components becomes increasingly critical for large-scale and long-term deployments. However, post-quantum schemes are often associated with increased computational and communication overhead [46]. Consequently, future research will investigate lightweight post-quantum cryptographic variants and explore offloading strategies to edge or fog nodes, thereby enhancing the practicality and sustainability of hybrid mechanisms in resource-constrained IoT devices.
# Towards Detection of Interest Using Physiological Sensors
## 4 Effects of the Experimental Design on Experiment Output
### Task and Stimulation  
The stimulation is a major component of the experiment because if it fails to induce the respective emotion in the participant with the required intensity, the results and findings will be inaccurate or non-significant. As seen in the previous sections, the stimuli differed widely in length, type, and intensity depending on the experimental setting and the purpose of the study. To validate the stimuli, some authors used a pre-selected stimulus that was well-categorized [86]. Alternatively, the authors used pre-tests [34], wherein they used multiple comparisons analyzed using Bonferroni \(t\)-tests to show that clips A and B were rated significantly more interesting that clips C and D. These data were later tested to determine whether they were in agreement with the outcomes of the physiological sensors used in this study to differentiate between interesting and neutral or less-interesting clips.  
Moreover, the studies included here did not use standard stimuli, which makes it difficult to perform valid comparisons between the methods used and the findings. The majority of the studies used paradigms to compare interesting vs. boring stimulations. These studies indicate clearly that interesting information is processed differently than boring information, although further investigation is required. This allows us to gain better insights into the phenomenon of interest itself rather than considering interest and lack of interest as two different states, whereby the presence of one state is necessary for the absence of the other one. It is difficult to separate the feelings of an individual when viewing certain stimulations, but one can control the stimulation.  
Many of the studies that addressed learning content did not consider the variability of the learning content or the impact of this content on the subjects. It is obvious that the subjects had individual differences in learning strategies. Differences in the learning materials should not be overlooked when studying interest. To control for such variability, researchers may wish to control for the age of participants, examine intelligence quotients (IQs) or emotional quotients (EQs) of the subjects, assess the individual and situational interest of the participants, and investigate pre vs. post knowledge. Using these strategies, one can obtain a deeper understanding of the relationship or association between the \"interestingness\" of the stimulation and the physiological results obtained. Furthermore, the length of the stimulation was not addressed in these studies. A common stimulus length was 20 min. However, the shortest stimulus lasted for 15 s, and the longest stimulus lasted for several sessions over 4 to 5 days. Thus, the information provided here is not sufficient for the discussion of this factor.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 4 Experimental Results  
In order to verify the applicability of the model, we first tested the robustness of the model in the synthetic datasets, and then carried out simulation experiments in the semiconductor manufacturing datasets.
# Volumetric Analysis of Aortic Changes after TEVAR Using Three-Dimensional Virtual Modeling
## 4 Discussion
In this scenario, this parameter could be used as a marker for patients at higher risk for re-TEVAR who may undergo a stricter CTA follow-up and an early multidisciplinary re-evaluation. Even though this was out of the scope of our study, global descending thoracic aortic volume analysis also has another distinct feature that could represent an attractive opportunity for the follow-up of patients after TEVAR. Since the volume in segmentation analysis of the descending thoracic aorta is defined as the volume between two anatomic landmarks that are the origin of the left subclavian artery and the origin of the celiac trunk, a chest CT without the use of iodinated contrast media should be enough to generate volumetric analysis. As these patients need life-time imaging follow-up, this could have a favorable impact for reducing both ionizing radiation exposure and iodine contrast media administration, but further studies are needed to confirm our results. Our study has several limitations. The major limitation is the small sample size and its retrospective nature, which underpowers our analysis, even though both the diameter and volume analysis were performed blinded to the outcomes and presence of complications. The proposed volumetric analysis, which represents a preliminary evaluation, will soon be expanded by adding a greater number of cases and taking into account some patient-specific characteristics, such as the length of the aortic regions, age, body surface area, and body mass index, against which the results can be normalized. Another limitation is related to the slice thickness of the CTA imaging of 2 mm, which is routinely used at our institution for the CTA acquisition protocol in aortic pathology, being at the upper limit of the recommended range of slice thickness according to the guidelines of our National Radiology Society. CTA slice thicknesses smaller than 2 mm would allow for more accurate anatomical 3D reconstructions and conventional diameter evaluations. Moreover, in this study, only the discharge and 12-month follow-up CTAs were evaluated, in contrast to a long clinical follow-up (average 4.5 years) available for these patients. Also, a pre-TEVAR CTA was not included in the analysis. Finally, a possible limitation of the proposed volumetric analysis could be the total time required for the preparation of the 3D anatomical models. In our experience, a mean time of three hours to segment and validate the 3D reconstructions of the aortic vasculature at both discharge and the 12-month follow-up with the radiologist is required.
## 2 Materials and Methods  
### Laboratory Studies  
Uniaxial compression strength, or UCS, is the standardized universal test that can be performed on all types of rock specimens that is used by the American Society for Testing and Materials, ASTM [7], and the International Society for Rock Mechanics, ISRM [29]. The principle of UCS involves placing cylindrical samples or prepared drill cores under axial loading until the failure moment in a uniaxial compression test (UCT) device. Figure 1 illustrates a conventional rock UCT machine [30]. The UCT is used for the determination of the ultimate UCS values. The presented study used regular cylindrical specimens for testing via the UCT in a geotechnical laboratory. The rock samples in this study were obtained from limestone from Maragheh County, located in East Azerbaijan province, northwest Iran. In total, 120 samples were taken from the studied region, where the main component of the rock is calcium carbonate and is categorized as Argillaceous lime to limestone with a 75% to 100% carbonate content according to the Pettijohn classification [31]. After sampling the limestone from the site, the specimens were prepared according to the ASTM instructions and tested via the UCT [7]. In order to prepare the test specimens, the taken samples were transferred into the geotechnical laboratory and drilled with a 50 mm diameter (5 cm) and a 100 mm height (10 cm) with an overall cylindrical shape. These specimens were cut and polished to produce UCS samples with smooth and flat surfaces and no evident fissures, as per the ASTM requirements. After preparation, all samples were divided and tested regarding the UCS compression test, and the results were recorded. During the tests, the water content of the samples was natural, and the tests were conducted at room temperature. The results of these tests were prepared and inputted into the primary database for implementing the SVM-based predictive modeling.  
Figure 2 illustrates several limestone images in the laboratory during the UCS test, which was used for preparing the SVM primary dataset. According to the ASTM [7] instructions, providing a proper sample is one of the key steps in UCS testing, as this leads to a reduction in measurement errors. UCS tests are typically performed on intact rocks with standard lengths of 100 mm (prepared) or with NQ, HQ, or PQ diamond drill cores, and the maximum axial loading is recorded at the failure point [1]. The correct preparation of samples leads to the obtaining of more reliable UCS values and classified accuracy of a rocks' strength. The International Society for Rock Mechanics (ISRM) standard classification for UCS variation in rock is very low (<5 MPa), low (5-25 MPa), moderate (25-50 MPa),  
medium (50-100 MPa), high (100-250 MPa), and very high (250\(<\) MPa) [32]. Sedimentary rock is generally classified as being in the very low to medium classes [11]. Nevertheless, the UCS corresponds to axial loading, where peak stress is responsible for the failure of a rock sample. Using displacement transducers and strain gages, the stress-strain data were continuously logged and recorded.
# Lacase in Biorefinery of Lignocellulosic Biomass
## 3 Significance of Lacase in Biorefinery
### Lacase Sources
#### 3.1.2 Fungal Sources of Lacase  
Fungi are the most widely studied and reported sources of lacase, with several fungal species from different taxonomic groups known to produce lacase. Fungal lacase production is influenced by several factors, including the type of fungus, culture conditions, and substrate availability. Some of the commonly reported fungal sources of lacase include white-rot fungi, brown-rot fungi, and ascomycete fungi [40]. White-rot fungi are the most extensively studied group of fungi known to produce lacase. White-rot fungi lacase production is influenced by several factors, including the type of fungus, culture conditions, and substrate availability. Some of the commonly reported white-rot fungal sources of lacase include the _Planerochaete chrysosporium_, _Trametes versicolor_, and _Pleurotus ostreatus_ species [41]. Brown-rot fungi are another group of fungi known to produce lacase. Brown-rot fungi lacase production is influenced by several factors, including the type of fungus, culture conditions, and substrate availability. Some of the commonly reported brown-rot fungal sources of lacase include the _Gloeophyllum trabeum_, _Fomitopsis pinicola_, and _Laetiporus sulphureus_ species [42]. Ascomycete fungi are a group of fungi that produce ascospores during sexual reproduction. Several ascomycete fungi have been reported to produce lacases, including _Nectriella pironii_, _Aspergillus_, and _Penicillium_. Ascomycete fungi lacases have potential applications in the production of aromatic compounds and the degradation of lignocellulosic [43].  
Figure 3: Structure and components of lignin.
## 3 Prediction Model
### Bead Height Prediction Model  
The prediction algorithm begins by creating a dataset to store training examples \((x_{i},y_{i})\), \(i=1\) to \(m,m=\) final layer. \(x_{i}\) is the input variable (number of layers) known as input features, and \(y_{i}\) is the output variable (layer height) that needs to be predicted, also called the target variable.  
The output of the regression is a prediction function \(F(x)\). The function relates the evolution of the bead height over layers. The algorithm is implemented in Python and is composed of the following steps: First, a mapping function is created to represent a decreasing concave up function trend: \(a*x^{b}+c\); \(a\) and \(c>0\); \(b<0\) ; \(a\) is the vertical stretch of the function; \(b\) is the rate of decay of the function and \(c\) the horizontal asymptote of the function. The mapping function displays the bead height evolution over layers. Then, the training set is plotted and the data linearized with a natural logarithm. After that, a linear regression algorithm is applied to fit the least-squares line to the natural logarithm plot (linearized model). Finally, the power decay function parameters are derived with which to compose the layer prediction function. The process of the bead height prediction is shown in Figure 4.  
The linear model is represented with the following linear equation,  
\[h(x)=\theta_{0}+\theta_{1}x \tag{1}\]  
where \(\{\theta_{j};j=0,1\}\) are unknown variables. Then, a cost function \(J(\theta)\) is defined as follows:  
\[J(\theta)=\frac{1}{2}\sum_{i=1}^{m}(h(x_{i})-y_{i})^{2} \tag{2}\]  
where \(\{x_{i},y_{i};i=1,\dots,m\}\). After that, \(\theta\) is chosen so that the cost function \(J(\theta)\) is minimized. A search algorithm called _gradient descent_ is applied.  
\[\theta_{j}:=\theta_{j}-\alpha\frac{\partial}{\partial\theta_{j}}J(\theta) \tag{3}\]  
Note that this update is performed simultaneously for all values of \(j=0,\dots,n\), and \(n\) is the number of unknown variables. In this work, the _learning rate_ (\(\alpha\)) technique is used; such that when the learning rate is decreased to zero, the regression algorithm converges to the global minimum rather than oscillating around the minimum. Tuning experiments  
Figure 4: Bead height prediction process.  
will help to detect a reasonable learning-rate value. The partial derivative of \(J(\theta)\) gives the least-mean-squares (LMS) update, which is also known as the Widrow-Hoff learning rule, and it is defined as follows:  
\[\theta_{j}:=\theta_{j}+\alpha\sum_{i=1}^{m}(y_{i}-h_{\theta}(x_{i}))x_{i,j}\ \ \forall j \tag{4}\]  
Finally, a number of iteration and learning rates were selected to converge to the best values of gradient descents. If the prediction output is not good, either the learning rate and the number of iterations are modified or the mapping function parameters are changed. The prediction process is summarized in the flowchart shown in Figure 5. In this work, the number of iterations ranges from 1500 to 2000, with a 0.03 learning rate.
## 3 Numerical Examples
### Effect of Different Reference Temperatures on the Propagation Characteristics  
To investigate the effect of temperature on the propagation properties of longitudinally guided waves in hollow cylinders, based on the above theory, the longitudinal mode dispersion curves at different temperatures are numerically solved by taking the temperature gradient as 50 K. In this section, the dispersion curves of the first three modes are selected for ease of analysis.  
Figure 8a-c are the local magnification diagrams of the three modes. It can be seen that under the given temperature range, the phase velocities of the three modes show a corresponding change rule. That is, with the gradual increase in temperature, the phase velocity values of the three modes show a decreasing trend. Therefore, by studying the effect of temperature on the propagation characteristics of guided waves, it can provide a certain theoretical basis and guidance for the non-destructive testing of hollow cylindrical pipes at high temperatures.  
Figure 8: Phase velocity dispersion curves at different temperatures: (**a**) dispersion curves for the first three modes; (**b**) localized enlarged view of region a; (**c**) localized enlarged view of region b; (**d**) localized enlarged view of region c.