esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 5 Conclusions and Future Work  
The conducted analysis can be practically applied in the design of MANETs to assess network parameters capable of providing information transmission within a specified service class at a given distance in different network deployment locations.  
The recommended maximum distances between nodes assume a high node density ranging from 15 m to 25 m indoors (depending on the frequency used) and 50 m to 70 m outdoors.  
By utilizing the 802.11 ac standard, network speed can be increased through channel expansion and reduced interference by utilizing the 5 GHz central frequency. Data transmission synchronization among different nodes can also improve throughput and reduce network load. Network scalability heavily depends on the quality of service requirements for routed packets, the number of senders, the throughput between participating nodes, network topology, and network operation scenarios. The maximum possible distance between the source and receiver with a packet delivery delay of 100 ms is approximately 5 km, 10 km for a delay of 400 ms, and 16 km for a delay of 1 s.  
The results of calculations of MANET parameters using the expressions obtained in the paper are confirmed with the modeling results given in [31; 38; 39].  
In further work, it is intended to focus on the creation, on the basis of the expressions obtained in the paper, of the methodology of the reasonable preliminary selection of MANET parameters for modeling or experimental studies of networks with routing OLSR, designed to solve specific problems in different environments.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The author declares no conflict of interest.
## 4 Results
### Performance Analysis
#### 4.2.2 Access Control Execution Time  
The execution time for access control is presented in Figure 8. The Y-axis represents the average execution time in milliseconds for ten permission operations. The permission operations are divided into five different functionalities: adding permission managers, removing permission managers, adding permissions, removing permissions, and updating permissions.  
For the operation of adding permission managers, the average execution time is 289.2 ms. On the other hand, the average time for removing permission managers is 313.7 ms. Adding permissions takes an average time of 315.4 ms, while removing permissions requires an average time of 437 ms. Lastly, updating permissions has an average execution time of 410.7 ms.  
Figure 8: Average execution time for permission operations.  
Figure 7: The duration of identity registration and verification.
## Materials and Methods
### Characterization  
Crystallinity and purity of the synthesized samples were studies using X-ray diffraction (XRD). The XRD patterns were acquired using a D8 Advance instrument (Bruker, Karlsruhe, Germany) (Cu K\(\alpha\) radiation). All the XRD patterns were collected at a scan rate of 2/min and step size of 0.02\({}^{\circ}\) between 5 and 40 (20) degrees. Fourier transform infrared (FT-IR) spectra were examined to confirm the formation of ZIF-8 as well as to compare the effects of various condition on the samples structure. FT-IR spectra were attained using a PerkinElmer-Spectrum RXI infrared spectrometer (PerkinElmer, Waltham, MA, USA). Scanning electron microscopy (SEM) was performed to observe the morphology and determine particle sizes of the synthesized ZIF-8. SEM micrographs were obtained using a CamScan Device MV 2300 (CamScan, Kingston, ON, Canada) at an acceleration voltage of 20 kV. In order to conduct SEM tests, the samples were mounted on conductive carbon double-sided sticky tape and a thin layer of gold was coated on the samples to decrease charging effects. Mean, standard deviation, and the particle size distribution of ZIF-8 was measured manually using Photoshop software from the SEM image. The diameter of about 100 particles in the SEM image were measured to determine these parameters.
# YOLOv8n-CBAM-EfficientNetV2 Model for Aircraft Wake Recognition
## 4 Aircraft Wake Recognition
### Performance of Different Recognition Models  
To provide a more accurate demonstration of the benefits of the YOLOv8n-CBAM-EfficientNetV2 model, this study undertakes a comparative analysis of the YOLOv8n-CBAM-EfficientNetV2 model with other machine learning and target detection algorithms, including KNN [11], SVM [12], ParNet [15], SSD, YOLOv5-lite, and YOLOv8n. Table 4 presents the results of the comparative experiments. SVM and KNN are traditional machine learning models, with evaluation criteria based on the precision rate, recall rate, and F1-score. The GFLOPs and number of parameters are not particularly informative when comparing these models.  
Table 4 shows that the YOLOv8n-CBAM-EfficientNetV2 model exhibits the highest precision rate, reaching 96.59%, in comparison with SVM and KNN. This notable improvement enables the accurate detection of aircraft wake. Furthermore, when benchmarked against ParNet, SSD, and the conventional YOLO algorithm, the YOLOv8n-CBAM-EfficientNetV2 model not only demonstrates an elevated precision rate but also exhibits the lowest GFLOPs and the minimal number of parameters, which helps to reduce the hardware performance requirements of the model and improve the real-time performance of wake detection. In summary, the YOLOv8n-CBAM-EfficientNetV2 model demonstrates satisfactory performance in terms of the precision rate, computational complexity, and real-time detection. This not only enhances the accuracy of wake detection but also reduces the computational cost and improves real-time detection performance.
## Materials and Methods
### Generation of the Physical Model  
The physical modelling step regards assigning the physical tissue and material properties and distribution to the anatomical and technical model. In this study, it is necessary to assign the dielectric tissue and material properties, i.e., electric conductivity \(\sigma\) and relative permittivity \(\varepsilon_{r}\), to the respective model domains to simulate the electric field distribution resulting from electric stimulation of the mandible. These quantities are highly frequency-dependent and their values at the stimulation frequency of \(f=20\) Hz have been taken from the literature [50; 51] in the case of the biological tissues (a practical online tool can be found at [52]). As described earlier, the model takes into account two types of bone: cortical bone representing the mandible and cancellous bone inside the mandible and filling the defect domain.  
As for the electro-stimulating implant, the assigned biocompatible material commonly used in orthopedics and maxillofacial surgery are Ti6 Al4V for the electrodes and PEEK (polyether ether ketone)for the insulator. The dielectric properties of these materials have been taken from technical data sheets [53; 54]. The assigned dielectric tissue and material properties are summarized in Table 1.  
In this study, the individual tissues and materials are modelled macroscopically. Furthermore, they are simplified to be linear, isotropic, and locally homogeneous. In that case, the constitutive relations \(\mathbf{J}=\sigma\mathbf{E}\) and \(\mathbf{D}=\varepsilon_{0}\varepsilon_{r}\mathbf{E}\) apply, with the current density \(\mathbf{J}\), the electric field strength \(\mathbf{E}\), the electric flux density \(\mathbf{D}\), and the dielectric constant of vacuum \(\varepsilon_{0}\). Still, it must be noted that bone is in general a highly anisotropic tissue with a hierarchic microscopic substructure [55]. Nonetheless, to employ a homogenization approach is feasible in our case. This holds especially true since only macroscopic dielectric tissue properties are available in the literature [50].
## 2 Materials and Methods
### Evaluation Metrics  
We evaluated the detection and the segmentation accuracy of our proposed method. First, we defined the criteria for evaluation as follows.  
The detection sensitivity was considered correct when an overlap was observed between the gastric cancer area obtained by this method and the ground truth created by a gastrointestinal specialist. In many lesions, the presence thereof is often confirmed in a plurality of images. Some of them include subtle patterns that are difficult to identify. Therefore, a method of simply counting the number of images of detected gastric cancer (image-based sensitivity) and a method of judging that one lesion  
Figure 2: Network architecture.  
was correctly detected if it was detected in at least one image (lesion-based sensitivity) were introduced. For false positives (FPs), the average number of FPs per image was calculated by counting the number of regions detected in images and dividing it by the total number of images in healthy subjects.  
The invasive region of gastric cancer can be obtained using the proposed method. The Dice index (Di) and the Jaccard index (Ji) were calculated using the following formulas to evaluate the similarity between the detected region and the ground truth created by a gastrointestinal specialist.  
\[\text{Di}=\frac{2|\text{A}\cap\text{B}|}{|\text{A}|+|\text{B}|}\times 100\ [ \%] \tag{1}\]  
\[\text{Ji}=\frac{|\text{A}\cap\text{B}|}{|\text{A}\cup\text{B}|}\times 100\ [ \%] \tag{2}\]  
where A indicates the ground truth region and B indicates the detected region of gastric cancer.  
These evaluations were conducted using a cross-validation method [28]. This method evaluates the model by dividing the original dataset into k subsets (k-fold cross-validation). Among the k subsets, one subset was used as the test data, and the remaining k-1 subsets were used as training data. The cross-validation process was repeated k times. Each of the k subsets was used once as the test data. The model accuracy was obtained by summarizing the test results. In this study, we employed a five-fold cross-validation (k = 5). During this process, 42 healthy subjects and 95 gastric cancer cases were randomly divided into five groups. Here, images from the same case were divided to ensure that they were not present in both the training and test data.  
To compare the segmentation ability of this method with the performance of other segmentation methods, we also performed gastric cancer region extraction using U-Net. Various studies have shown that U-Net has excellent performance in medical image segmentation [17]. We introduced a U-Net model with five encoder and decoder layers, as described in the original paper.  
Statement of Ethics: This study was approved by the Ethical Review Committee of Fujita Health University (HM17-226) and carried out in accordance with the World Medical Association's Declaration of Helsinki. Patients' informed consent was obtained in the form of opt-out in the endoscopic center of Fujita Health University Hospital, and all data were anonymized.
## 4 Discussion  
### Analysis of ISW Interactions and Hydration Phase Quantification  
The synergistic interplay between SS, GGBFS, PS, and CS in hydration reactions critically governs the mechanical performance of stabilized aeolian sand. As evidenced by  
Figure 10: Particle morphology of sample with low strength: (**a**) specimen No. 4; (**b**) specimen No. 8 and (**c**) specimen No. 26.  
the chemical compositions in Table 1, GGBFS contributes high SiO\({}_{2}\) (30.07%) and CaO (38.22%), serving as the primary source of reactive silicates for C-(A)-S-H gel formation. PS, with its elevated SiO\({}_{2}\) (44.89%) and CaO (43.97%), complements GGBFS by supplying additional amorphous silica, while CS (70.35% CaO) acts as a pH regulator to sustain alkaline conditions for sustained pozzolanic activity. SS, despite its lower reactivity, introduces Fe\({}_{2}\)O\({}_{3}\) (29.1%), which facilitates the formation of ferrite phases (e.g., C\({}_{4}\)AF) that enhance microstructural strength.  
Quantitative TG/DSC analysis (Figure 8) and XRD results (Figure 7) highlight distinct hydration behaviors across ISW proportions. Specimens with optimal ISW proportions (No. 11) exhibited markedly higher mass loss in the 60-120 \({}^{\circ}\)C range compared to suboptimal mixtures (e.g., No. 4), correlating with enhanced C-(A)-S-H and ettringite formation. The stoichiometric synergy between GGBFS-derived silicates and CS-provided Ca\({}^{2+}\) ensures balanced C/S ratios in hydration products, maximizing gel polymerization and pore-filling efficiency. Conversely, excessive CS disrupts this equilibrium, promoting Ca(OH)\({}_{2}\) precipitation and calcite overgrowth, as observed in SEM micrographs (Figure 10c), which weaken interparticle bonding through crystalline expansion.
## 2 Materials and Methods
### Dataset
#### 2.1.1 Dataset Preparation  
For the creation of ground truth labels, computation of NDVI and MNDWI values were performed using the Sentinel-2 images, followed by the application of predefined expert-defined thresholds: 0.2 for NDVI and 0.3 for MNDWI. These thresholds served to identify flooded and non-flooded pixels. Furthermore, analysts were granted access to the images and the initial labels obtained from Sentinel-2 data. They meticulously reviewed these labels and adjusted areas where water classifications required modification to non-water or vice versa. This meticulous review process enriched the quality of the labels, thus enhancing their utility in model training and evaluation.  
To ensure the fidelity of the dataset, measures were implemented to remove cloud shadows that can exhibit a spectral signature similar to that of floodwater, potentially  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Flood Event** & **S1 Date** & **S2 Date** & **Orbit** & **Rel. Orbit** \\ \hline Bolivia & 15 February 2018 & 15 February 2018 & Descending & 156 \\ Ghana & 18 September 2018 & 19 September 2018 & Ascending & 147 \\ India & 12 August 2016 & 12 August 2016 & Descending & 77 \\ Cambodia & 5 August 2018 & 4 August 2018 & Ascending & 26 \\ Nigeria & 21 September 2018 & 20 September 2018 & Ascending & 103 \\ Pakistan & 28 June 2017 & 28 June 2017 & Descending & 5 \\ Paraguay & 31 October 2018 & 31 October 2018 & Ascending & 68 \\ Somalia & 7 May 2018 & 5 May 2018 & Ascending & 116 \\ Spain & 17 September 2019 & 18 September 2019 & Descending & 110 \\ Sri Lanka & 30 May 2017 & 28 May 2017 & Descending & 19 \\ USA & 22 May 2019 & 22 May 2019 & Ascending & 136 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The dates, orbit direction and relative orbit value for each flood event.  
Figure 2: Visualization of the two polarizations from a Sentinel-1 image, and the 13 spectral bands from a Sentinel-2 image taken from the flood event in India. Here the speckle in the Sentinel-1 images and the cloud coverage in the Sentinel-2 image can be clearly observed, highlighting the differences between the images from these two satellites.  
impeding the accurate classification of flooded pixels. This process involved identifying clouds by applying a threshold to the blue band, setting the threshold at 0.2, with values falling below this threshold being designated as indicative of clouds. Subsequently, the removal of cloud shadows was executed by projecting the cloud shadow based on factors such as potential cloud height, solar azimuth angle, and solar zenith angle.
# Effects of Monochromatic and Composite Light Withering on Black Tea Aroma
## 3 Results
### Effects of Different Light Withering Conditions on Major Quality Components of Black Tea  
To elucidate the effects of different monochromatic and composite light withering conditions on black tea flavor, the contents of major quality components were detected. As depicted in Figure 1, different monochromatic and composite light withering conditions did not affect the water extract content of black tea (Figure 1A). Compared to natural light withering (CK), yellow light (YL) and red-blue light (RB) withering significantly decreased the content of TP (Figure 1B). In addition, the combination of red and blue light significantly reduced the TP content compared to withering with red and blue monochromatic light (Figure 1B). Compared to CK, different light withering conditions showed no significant effects on the content of free amino acid (Figure 1C). On the contrary, yellow (YL) and red-blue light (RB) withering increased soluble sugar content, while other monochromatic light and red-yellow composite light showed no statistical effect on it when compared to the CK group (Figure 1D). This indicates that appropriate LED light withering is effective in improving the flavor of black tea, and composite light treatment is more effective to some extent.  
Figure 1: Effects of different light quality withering on major quality components of black tea. Content of water extract (**A**), tea polyphenols (**B**), free amino acid (**C**) and soluble sugar (**D**) in black tea withered with different lights. Data are means \(\pm\) SD. Bars with different letters indicate significant difference (\(p\) < 0.05).
# Design and Implementation of a Multi-Function Gripper for Grasping General Objects
## 4 Experimental Evaluation and Discussion
### Discussion
Furthermore, because the two-finger parallel grasping has only two contact areas/points, grasping points would also influence the stability of the two-finger parallel grasping. For example, it might be unstable to grasp a relatively long object by selecting the corresponding grasp contact points shown in Figure 23a. This is because the gravity center of the object is far from the grasping zone. The gravity force will generate some rotation torque, which is a leading cause of the unstable two-finger parallel grasping. Experiments of grasping the same object at different contact points were performed as shown in Figure 23b. We note that the grasping motor was actuated by using almost the same current in these two experiments. The left panel shown in Figure 23b demonstrates the success of a stable grasping by selecting the grasping points/area near the gravity center. The right panel shown in Figure 24b demonstrates one case of unstable grasping. As far as the objects with irregular shapes are concerned, if the two contact points are not well selected, the static force equilibrium might not be achieved. One example is shown in Figure 24, where the two contact points A and B are relatively far from each other. During applying the contact forces, the configuration of the object might be changed (Initial configuration of the object is indicated by yellow color; the new configuration is indicated by transparent green color) because the two contact forces do not coincide, a winding moment will be produced on the object. After continually applying the grasping force, it is not sure whether the static equilibrium between two fingers can be achieved or not. In this case, a multi-contact grasping generally is required for achieving stable grasping. For certain objects to be grasped, the grasping mode and grasp contact points should be carefully selected. It is also possible to increase the grasping stability by adding an appropriate compliant structure to the fingertip because the compliance increases the ability of a gripper to conform to the shape of the  
Figure 22: Planar grasp model of a two-finger gripper. object being grasped, and also increases the area of the contact patches, increasing the grasp wrench space [37].
## 1 Introduction
have been applied in geological borehole surveys. At the same time, geological radars, seismic waves, electromagnetic waves and other geophysical techniques have also made rapid development in geological borehole research, greatly facilitating the exploration and application of engineering geology [16; 17]. In addition, ultrasonic testing technology has been applied to the integrity, stability and internal defects of rock and soil structure. Many research achievements have been made in fluid media, fine structure of rock and soil and acoustic emission characteristics, but there are still no good methods for imaging detection and visual analysis of soft structures in rock and soil. Through the use of borehole digital imaging technology, the geological information in the borehole is more intuitive and clearer [18; 19; 20]. The digital imaging results can truly reflect the damage of the rock mass in the hole under the influence of structure, karst and sliding, and make up for the defect that the borehole method is not suitable for exposing the long-standing sliding surface in the rock mass and the thickness of the sliding zone is very thin. Although the borehole digital imaging technology makes up for the shortcomings of the conventional geological borehole and provides more intuitive and clear geological information in the borehole. Since the borehole imaging technology only reflects the image characteristics of the borehole rock wall surface and does not consider the differences and characteristics of the internal structure of the borehole rock mass, it is often difficult to identify the type of structural plane and provide more rich data sources for the identification and analysis of the structure surface [21; 22; 23]. Therefore, in view of the technical difficulties in judging the structure surface by image recognition in the geological structure, this paper proposes a method for identifying the weak interlayer based on the borehole photo-acoustic combination measurement, taking the weak intercalation of landslide as an example. Through the combination of optical imaging technology and borehole acoustic technology, the organic integration of borehole image and acoustic scanning data is realized. The borehole photo-acoustic combined measurement technology can complement and confirm the advantages of both optical image and acoustic scanning technology, which can compensate for the shortage of single data sources in the process of change information extraction, and combine the complementary characteristics of different data analysis methods to ensure the correctness and reliability of weak interlayer identification, which can provide a scientific basis for geologic hazard control.