esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### SZS Extract and Inhibitory Activities against MAO-A, MAO-B, AChE and BChE  
The SZS was extracted with the treatment of 70% ethanol and boiling. The yield was found to be 5.1% (_w/w_). Inhibitory activities of the extract against MAO-A, MAO-B, AChE, and BChE, relating to neurologic pathways, were evaluated along with a flavone C-glycos spinosin, which is a major constituent of SZS. The SZS extract showed 67.9% and 58.4% of the residual activities against MAO-B and AChE, respectively, at 50 \(\upmu\)g/mL (Table 1). The inhibitory activities against MAO-A and BChE were not effective. Spinosin exhibited moderate inhibitory activities against MAO-B and AChE, with IC\({}_{50}\) values of 63.3 and 39.8 \(\upmu\)M, respectively. However, the values were higher than those of reference inhibitor compounds.
# Acoustic Simulation for Performance Evaluation of Ultrasonic Ranging Systems
###### Abstract  
The recent growing interest in indoor positioning applications has paved the way for the development of new and more accurate positioning techniques. The envisioned applications, include people and asset tracking, indoor navigation, as well as other emerging market applications, require fast and precise positioning. To this end, the effectiveness and high accuracy and refresh rate of positioning systems based on ultrasonic signals have been already demonstrated. Typically, positioning is obtained by combining multiple ranging. In this work, it is shown that the performance of a given ultrasonic airborne ranging technique can be thoroughly analyzed using renowned academic acoustic simulation software, originally conceived for the simulation of echographic transducers and systems. Here, in order to show that the acoustic simulation software can be profitably applied to ranging systems in air, an example is provided. Simulations are performed for a typical ultrasonic chirp, from an ultrasound emitter, in a typical office room. The ranging performances are evaluated, including the effects of acoustic diffraction and air frequency dependent absorption, when the signal-to-noise ratio (SNR) decreases from 30 to \(-\)20 dB. The ranging error, computed over a point grid in the space, and the ranging cumulative error distribution is shown for different SNR levels. The proposed approach allowed us to estimate a ranging error of about 0.34 mm when the SNR is greater than 0 dB. For SNR levels down to \(-\)10 dB, the cumulative error distribution shows an error below 5 mm, while for lower SNR, the error can be unlimited.  
acoustic simulation; acoustic diffraction; acoustic attenuation; ultrasonic ranging; ranging error; SNR levels +
Footnote †: journal: Physics Letters  
0
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# Demonstration of Diode-Pumped Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) Lasers
## 4 Conclusions  
Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) lasers were successfully employed for CW operation, for the first time to our knowledge. Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) crystals with high optical quality were grown by the Bridgman method. They both had a suitable absorption spectrum, and their emission spectra were broad. Using a commercial LD as the pump source, the Yb:LaF\({}_{3}\) laser obtained a maximum average output power of 1.19 W with a slope efficiency of 37.1%. For the Tm,Ho:LaF\({}_{3}\) laser, the maximum average output power and corresponding slope efficiency were 574 mW and 18.5%, respectively. The laser wavelengths were 2043 nm and 2048 nm. In brief, our experiments proved that Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) crystals are effective laser gain materials, increasing the laser gain material selectivity.  
[PERSON] supervised and supported the experiments. [PERSON] designed and executed the experiments. [PERSON] and [PERSON] provided the Yb:LaF\({}_{3}\) and Tm,Ho:LaF\({}_{3}\) materials and tested their spectra.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB1603); the International S&T Cooperation Program of China (Grant No. 2016 YFE0119300); and the National Natural Science Foundation of China (NSFC) (Grant Nos. 61521093, 61505234). The authors declare no conflict of interest.
## 6 Simulated Results
### Network Throughput  
Figure 2 illustrates the simulation experiment between the EGCIR protocol and existing solutions in terms of network throughput under a varying number of nodes. The network throughput can be defined as the transferring of sensors data towards the BS at a particular period. It is observed that the EGCIR protocol outperforms the network by an average of 9% and 19% than the existing work. This is due to the fact that it identifies the attackers in the observing area at the earliest position and avoids them in data routing. Furthermore, the cluster heads are arranged in the graph-based interconnectivity to achieve a reliable multi-hop transmission paradigm, which significantly increases data productivity. Unlike other solutions that impose extra overheads to achieve data security and drop more data packets, the EGCIR protocol using machine-learning techniques reduces the additional overheads on the part of sensors and increases the delivery rate between cluster heads and BS. Furthermore, overlooking constraint resources of sensors and frequent reselection of cluster heads increase the network burden and ultimately the existing solutions decreases the network throughput.
# A Reliability Scheduling Algorithm for the Static Segment of FlexRay on Vehicle Networks +
## 1 Introduction
In this paper, we describe the proposed FRSA in more detail to improve the reliability in the static segment of FlexRay communication, which increases the reliability of messages by re-transmission. In addition, the proposed FRSA makes a significant improvement in running time by using lookup table method compared to the previous algorithms, even in the case of a greater number of messages; this will be shown in the experimental results.
# Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems
## 2 Background  
### Equipment Overview  
The Digital Imagery and Remote Sensing (DIRS) Laboratory at the Rochester Institute of Technology uses a revolutionary multi-modal UAV payload for remote sensing applications. This includes the simultaneous observation of four different imaging modalities, which include Visible-Near Infrared (VNIR) hyperspectral and multispectral cameras, a Long-Wave Infrared (LWIR) camera and a Light Detection and Ranging (LIDAR) sensor (see Figure 1). This drone-based payload provides an indispensable resource for solving challenging remote sensing problems, including those which are the subject of this paper. Specifically, the hyperspectral drone mounted instrument under investigation was VNIR HSI systems. Raw imagery was collected and pre-processed (using the manufacturers standard processing software) to account for dark offsets, dark currents, and dark signal non-uniformity. Flat-fielding was performed after dark corrections. This includes photon response non-uniformity, irradiance fall-offs and signal non-linearity. The final pre-processing step includes a radiance correction (derived by the the manufacturer) that converts digital numbers (DNs) to spectral radiance. Lastly, all HS imagery was orthorectified and interpolated [11].  
As is standard practice within the DIRS Laboratory, all radiometric instruments are internally verified with a NIST-traceable Labsphere Inc., North Sutton, NH, USA 20 inch integrating sphere. The typical routine for validating radiometrically calibrated instruments  
Figure 1: The MX1 imaging payload, highlighted within the red box, is fixed mounted to a Matrice 600 PRO, DJI, Shenzhen, China.  
involves observing the NIST-traceable Quartz-Tungsten Halogen (QTH) lamp (\(\sim\)\(\pm\)1% (k = 2) in the VNIR) at four different light levels [12]. The radiometric validation demonstrates that the HSI system under investigation in this paper is well calibrated to spectral radiance within the expected uncertainty of the integrating sphere (see Figure 2). The HSI system under test was radiometrically calibrated by the manufacturer, where the absolute uncertainty and measurement errors are unknown. However, the knowledge of our integrating sphere can inform the accuracy of the spectral radiance measured with the HSI system to be within \(\pm\)5%. During all our outside experiments, we assumed that our HSI system holds this uncertainty and is consistent across the field-of-view (FOV).  
Other field equipment (e.g., an ASD field spectroradiometer) was also used during our experiments for predicting the at-aperture reaching spectral radiance of point targets. The ASD was also cross-validated using our integrating sphere and holds an absolute uncertainty of approximately \(\pm\)2% across the VNIR spectrum. The ASD instrument was used to measure the total downwelling spectral irradiance (utilizing an advanced cosine corrector) and provides atmospheric monitoring capabilities that ensure consistency of the point target predictions.
# Development of Potato Mass Estimation System Based on Deep Learning
## 2 Materials and Methods
### Potato Mass Estimation System
#### 2.1.2 Classification  
The potatoes that are dug using a harvester go through the conveying unit and the sorting unit and are finally put into a ton bag attached to the collection unit. The sorting unit, in which a camera is installed, contains foreign substances such as soil, stems, stones, etc., in addition to potatoes. Therefore, potatoes must be sorted out from the foreign substances to determine the accurate yield.  
In the present study, YOLOv5, one of the deep learning algorithms, is used to classify potatoes and foreign substances. In essence, YOLO divides the image into n \(\times\) n grids, estimates the bounding box and confidence score of each grid, and then adjusts the position of the bounding box by performing NMS (non-maximum suppression) in the process of merging each grid before finally performing object classification [16]. The YOLOv5 algorithm is divided into several models depending on the network size and image size and, in the present study, the YOLOv5n model--which has a relatively lower classification accuracy but shows a high processing speed--is used while taking into account the sizes of the potatoes in the harvester, conveying speed, and the load of image processing and follow-up works applied to the computer. In this study, the model was developed using 227,838 images (original 9906 and augmentation 217,932) labeled (potatoes, stems, soil, and others) using PyTorch, and the batch-size and epoch were set to 64,300, respectively.
## 3 Results
### Ratio Assignment Techniques
#### 3.1.2 Simple Multi Attribute Rating Technique (SMART)
Using the least important attribute (i.e., Gas Mileage) as the reference attribute with a reference score of 50 points, point scores could be assigned to the remaining attributes as shown in Table 4.  
Step 4: Calculate Weights  
Using the point scores assigned to each of the attributes in Step 3, the final step of the SMART process is to calculate attribute weights. This is done by normalizing each attribute score against the total score among all attributes as shown in Equation (3). In the car buying example, the total points distributed among all five preference attributes are \(50+100+150+300+400=1000\) points. The corresponding weights for each attribute are calculated as shown in Table 5. Note that this method can generate precisely the same weights as the DAT method (assuming the correct points are used in both). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Abbreviation** & **Criteria** & **Points** & **Total Points** \\ \hline (G) & Gas Mileage & 50 & =50 \\ (A) & Attractiveness & 50 & =100 \\ (S) & Safety & 100 & =150 \\ (R) & Reliability & 250 & =300 \\ (P) & Purchase Price & 350 & =400 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Total points for decision criteria. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviation** & **Criteria** & **Formula** \\ \hline (G) & Gas Mileage & 1 \\ (A) & Attractiveness & 2 \\ (S) & Safety & 3 \\ (R) & Reliability & 4 \\ (P) & Purchase Price & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Rank ordering of decision criteria. Strengths of This Approach  
SMART does not need to be repeated if old attributes are removed or new attributes are added, unless the one being removed is also the one that is least important or the one being added assumes the role of being the least important. The number of questions needed to assign weights using the SMART technique is equal to one less than the number of preference attributes. Thus, the effort required to obtain attribute weights scales linearly with the number of attributes. Limitations of This Approach  
The choice of the score for the lowest- (or highest-) weighted attribute may affect the resulting attribute weights if the scores for other attributes are not chosen based on relative comparisons. For example, if the least important attribute is given a value of 10 and some other attribute is given a value of 30, this latter value should increase to 60 if the baseline score given to the least important attribute is raised to 20.
## 3 Results and Discussion
### Antimicrobial Activity
's study indicated more pronounced inhibition, particularly for the _S. aureus_ bacterial isolate, surpassing the values obtained in our study for other extracts from various by-products and the selected grape varieties. In the study conducted by [PERSON] et al., 2019 [33], the antimicrobial activity of grape pulp, specifically the Othello variety, was evaluated alongside other extracts and against various bacterial isolates, including _E. coli_ and _S. aureus_. When comparing their results with the present study, notable similarities and differences emerge. For the _S. aureus_ bacterial isolate, [PERSON] et al. (2019) achieved inhibition halos ranging from 8 to 16 mm (excluding the 5 mm disc diameter used in their study), surpassing the values presented in Table 6 of our research. This indicates that grape pulp exhibits greater inhibitions on bacterial growth than the stem and pruning firewood extracts. Conversely, for the _E. coli_ isolate, [PERSON] et al. (2019) achieved halos of inhibition with values between 2 to 10 mm (excluding the 5 mm disc diameter used in their study), which represent better results compared to our study, where no inhibition of bacterial growth was observed for the same isolate. Historically, many studies have portrayed the stem, in comparison with other by-products, as possessing the strongest antimicrobial potency against Gram + bacteria, making it a potential option for combatting such pathogens. However, the present study, which also examined pruning firewood, reveals that the pruning firewood exhibits bacterial growth inhibition values superior to those obtained for the stem, even for the same isolates. This suggests that pruning firewood is also a viable option for combating these pathogens, thereby expanding the spectrum of biological alternatives for replacing antibiotics that have lost their efficacy. Nevertheless, it is crucial to acknowledge that the antimicrobial potential of each extract can be influenced by the extraction method used and the specific microorganism tested, as highlighted by [PERSON] et al. [33]. Additionally, the polyphenic content of the extracts plays a pivotal role. For instance, compounds belonging to the flavonoids, flavanols, and tannins class exhibit higher microbial activity in comparison to other polyphenols, as documented by [PERSON] et al., 2018 [34]. This underscores the multifaceted nature of antimicrobial activity, where factors such as extraction methodology and polyphenolic composition contribute to the observed outcomes.
# Non-Terrestrial Networks with UAVs: A Projection on Flying Ad-Hoc Networks
## 4 State-of-the-Art FANET  
In this section, we provide the state-of-the-art and key latest findings of FANETs in terms of (i) communications standards, (ii) physical layer aspects, (iii) role-based connectivity and trajectory management, and (iv) routing protocols.
## 4 Conclusions
* Knowing the quality of the fluid \(X\) and the internal energies of each phase (\(u_{g},\ u_{l}\) again directly determined by the current pressure), it is possible to compute the homogeneous fluid's overall internal energy (\(u\)) according to Equation (A2). * Adding an arbitrary pressure increase to the tank, the overall density will not change, since it is a closed system. The pressure increase, however, will change the properties of the liquid and gaseous phases (state 2), leading to a variation in the quality \(X\) and internal energy \(u\), which can be computed for the new state, again using Equations (A1) and (A2). * The part of Equation (28) corresponding to \(du/dP\) can be approximated by Equation (A3). \(du/dP\) is computed at constant overall density, as required by Equation (28), due to the closed tank assumption. * \(\Phi\) can be finally computed by Equation (28) considering the state 1 density \[X=\frac{v-v_{l}}{v_{g}-v_{l}}\] (A1) \[u=u_{l}+X\big{(}u_{g}-u_{l}\big{)}\] (A2) \[\frac{\Delta u}{\Delta P}=\frac{u_{2}-u_{1}}{P_{2}-P_{1}}\] (A3)  
Figure A1 shows the comparison of the estimation of the energy derivative computed with this method with the graphs reported in [31].
## 4 Experimental and Computational Results
### Computational Results Using CNN  
Implementing a GoogleNet convolutional neural network for classifying material samples based on intrinsic magnetic field exploration was shown to be efficient and robust. The model was tested on 15% of all samples, with inputs presented randomly to the network. This ensured that the classification results were consistent and minimally affected by data structure or sample sequence. The classification results achieved excellent accuracy with minimal errors (Figure 10). Figure 11 shows the confusion matrix created for 39 random maps from all classes. Each class has a different representation. Further, it can be seen that the use of SGDM proved crucial for stable and efficient network training (Figure 12). This optimization significantly reduced the risk of overtraining. It ensured a smooth progression to the global minimum during learning.  
In general, classification success rates range from 0.95 to 1, indicating high accuracy in recognizing samples across all classes. This result speaks for the correct classification of the samples based on their magnetic properties. Some samples, such as sample 31, show slightly higher variability in the classification results, which may  
Figure 11: Computational results: confusion matrix for random maps.  
Figure 10: Computational results: classification success rate box diagram.  
differences in the magnetic characteristics of this sample. However, these differences did not affect the model's overall performance, as the median success rates for most samples range from 0.975 to 0.99, indicating classification accuracy for most samples. This finding demonstrates the high reliability of the model in analysing and classifying materials based on experimentally obtained data, which provides evidence of the effectiveness of the proposed approach and its potential in practical applications.  
Overall, the results confirm that CNNs represent an efficient and reliable method for classifying materials based on their hardness using giant maps of their magnetic profiles. This technique, which exploits the potential of advanced machine learning algorithms, was proven useful, especially in extracting relevant patterns and features from complex datasets that represent the magnetic properties of materials. Based on thorough analysis and testing, it can be concluded that CNNs can identify patterns that correlate with different levels of hardness of materials with high accuracy and robustness, thus providing a very effective tool for materials analysis. This output demonstrates the potential of CNNs for automated materials analysis and classification. This can lead to significant improvements in materials engineering and manufacturing, where precision and efficiency are critical.
## 4 Evaluation and Results
### Results
ROC curves provide a graphical representation of the trade-off between true-positive and false-positive rates across various thresholds, and the Area Under the Curve (AUC) quantifies the model's ability to distinguish between classes. Figure 5(a),b illustrate the ROC performance for YOLO11x (binary) and YOLO11n (multi-class), respectively. Both configurations achieve AUC values exceeding 0.999, indicating excellent classification performance. Additionally, micro- and macro-averaged AUCs are reported for the multi-class scenario, demonstrating consistent performance across all malware categories. Figure 5: Confusion matrices for the best-performing YOLO11 variants: YOLO11x (binary) and YOLO11n (multi-class). Figure 6: ROC analysis of YoloMal-XAI in (**a**) binary and (**b**) multi-class classification settings, showing near-perfect separability across classes.
# Magnetically Deployable Robots Using Layered Lamina Emergent Mechanism
## 1 Introduction
### Biomimetic Locomotion
The inchworm motion relies on the linear contraction of longitudinal muscles while using friction at the head and the tail of the inchworm for directional motion. Studies made use of SMA actuators [6], double balloons [26], magnetic attraction [27], and electromagnetic oscillatory actuators [28] to contract the body of worm-inspired robots. Despite the differences in actuation methods, they primarily aim to mimic the bending of the mechanism body. These mechanisms also implement different ways to anchor either the structure's head or tail to allow the other to slide. For example, [PERSON] [27] relies on using materials that possess a higher friction coefficient in the rear \"foot\" of the magnetic-actuated structure to induce forward motion. [PERSON] [28] designed claws that reduce the coefficient of friction in the forward direction relative to the backward direction. Mechanisms that implement electromagnetic actuators rely on crawling locomotion created by impact-driven force or stick-slip mechanism. [PERSON] et al. [29] generate a magnetic field using a coil surrounding a permanent magnet to achieve impulse-driven motion. [PERSON] et al. [30] achieved similar locomotion by moving a permanent magnet between two solenoids. [PERSON] et al. [17] used a series of solenoids and a combination of attraction and repulsion from magnet plungers to achieve bending. Other electromagnetic control systems use a combination of Helmholtz and Maxwell coils to provide precise control [18; 31]. These mechanisms are typically fabricated with a non-uniform magnetization profile that enables the mechanisms to bend and fold in certain directions when a magnetic field is applied, which causes the mechanisms to experience magnetic torques and forces proportional to the magnetic field strength. In the case of the tapered feet structure by [PERSON] et al. [32], the magnetic torque rotates and aligns the structure in the direction of the magnetization field while the magnetic force acts as a driving force.