esa-sceva/satcom-chunk-collection huggingface dataset:
characterize the spaceborne SAR observation network. In particular, airborne SAR systems guarantee a much quicker response time for the observation of sudden extreme events, such as flooding, earthquakes, volcanic eruptions and so on. Moreover, airborne SAR antennas are smaller than those used in spaceborne platforms due to the significantly lower operational altitude of the sensor. Therefore, for a given acquisition mode, the azimuth resolution achievable with airborne SAR systems is finer than that of the spaceborne SAR systems [1]. On the other hand, spaceborne SAR systems guarantee a much wider spatial coverage due to their much higher operational altitude [2]. Within this framework, an airborne SAR infrastructure is currently available at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR). This infrastructure, which was developed for scientific purposes, also plays a key role in supporting the emergency prevention and management activities of the Department of Civil Protection of the Italian Presidency of the Council of Ministers, for which IREA-CNR serves as the National Centre of Competence.  
In this work, the IREA-CNR airborne SAR infrastructure is presented. It consists of a flight segment and a ground segment. More specifically, the flight segment includes an airborne SAR system, namely, the Multiband Interferometric and Polarimetric SAR (MIPS) sensor [27]. This sensor is based on the Frequency Modulated Continuous Wave (FMCW) technology [28] and operates in two different frequency bands. In particular, it is equipped with a single-pass Interferometric SAR (InSAR) configuration at X-band or with a full polarimetric configuration at L-band. Thanks to its limited weight and size, the MIPS sensor can be mounted even on board small aircrafts. The ground segment of the infrastructure includes an Information Technology (IT) platform for data storage and processing, the SAR data processing chain [1; 29; 30] jointly developed by IREA-CNR and University \"Parthenope\". Beside, a series of supporting activities are carried out before and during the survey campaigns [21; 23] to guarantee their successful execution. In this work, the flight and ground segments of the infrastructure are described in detail in Section 2. In Section 3, a special focus is placed on to the generation of single-pass InSAR products, specifically, a Digital Elevation Model (DEM) of a selected test area, that is, the Stromboli volcano in the Sicily region, southern Italy [31; 32]. To show the monitoring capabilities of the infrastructure during crisis events, in Section 4, we discuss the results reported in Section 3 by focusing on the response times needed to generate the presented InSAR products. In the same section, we also present the short-term future perspectives of this airborne SAR infrastructure. Some concluding remarks are reported in Section 5.
## 6 Discussion
### Sensor Orientation of Deimos-2 Images
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Land Cover** & **Stereo Pair** & **Mean (m)** & **SD (m)** & **RMSE\({}_{\text{2}}\)** & **LE95 (m)** & **NMAD (m)** \\ \hline \multirow{4}{*}{Plastic covered greenhouse} & Deimos-2 & 0.52 & 1.44 \({}^{\text{a}}\) & \multirow{4}{*}{3.59 \({}^{\text{a}}\)} & \multirow{4}{*}{1.02 \({}^{\text{a}}\)} \\  & (30 July 2019) & (_1.60, -0.58_) & (_1.78, 1.21_) & & & \\ \cline{1-1}  & Deimos-2 & 0.30 & 1.19 \({}^{\text{a,b}}\) & & & \\ \cline{1-1}  & (5 August 2019) & (_1.60, -0.43_) & (_1.31, 1.07_) & & & \\ \cline{1-1}  & WV2 & 0.43 & 0.86 \({}^{\text{b}}\) & & & \\ \cline{1-1}  & (5 July 2015) & (_0.62, 0.19_) & (_0.65, 1.11_) & & & \\ \hline \multirow{4}{*}{Urban} & Deimos-2 & 0.04 & 3.16 \({}^{\text{a,b}}\) & \multirow{4}{*}{3.55 \({}^{\text{a,b}}\)} & \multirow{4}{*}{7.50 \({}^{\text{a,b}}\)} & \multirow{4}{*}{2.14 \({}^{\text{a}}\)} \\  & (30 July 2019) & (_2.24, -1.71_) & (_3.63, 2.91_) & & & \\ \cline{1-1}  & Deimos-2 & \(-\)0.14 & 3.93 \({}^{\text{a}}\) & & & \\ \cline{1-1}  & (5 August 2019) & (_0.43, -0.63_) & (_4.69, 3.02_) & & & \\ \cline{1-1}  & WV2 & 0.37 & 2.16 \({}^{\text{b}}\) & & & \\ \cline{1-1}  & (5 July 2015) & (_0.49, 0.30_) & (_3.05, 1.59_) & & & \\ \hline \multirow{4}{*}{Bare Soil} & Deimos-2 & 0.04 & 1.06 & \multirow{4}{*}{1.32} & \multirow{4}{*}{2.99} & \multirow{4}{*}{0.81 \({}^{\text{a}}\)} \\  & (30 July 2019) & (_0.76, -1.09_) & (_1.71, 0.60_) & & & \\ \cline{1-1}  & Deimos-2 & \(-\)0.27 & 1.06 & \multirow{4}{*}{1.41} & \multirow{4}{*}{3.20} & \multirow{4}{*}{0.73 \({}^{\text{a,b}}\)} \\ \cline{1-1}  & (5 August 2019) & (_0.97, -1.42_) & (_1.99, 0.42_) & & & \\ \cline{1-1}  & WV2 & 0.68 & 0.21 & \multirow{4}{*}{0.71} & \multirow{4}{*}{1.03} & \multirow{4}{*}{0.17 \({}^{\text{b}}\)} \\ \cline{1-1}  & (5 July 2015) & (_0.79, 0.61_) & (_0.24, 0.19_) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Vertical accuracy (mean, SD, RMSE\({}_{\text{2}}\), 95 th percentile linear error (LE95) and normalized median absolute deviation (NMAD)) computed at LiDAR reference ICPs. All the depicted values are mean values corresponding to three samples for each land cover. Minimum and maximum values for the three samples are depicted in brackets and italic font for mean and SD. For each land cover, different superscript letters between data along the same column indicate significant differences at a significance level \(p<0.05\) (\({}^{\text{a}}\) indicates differences at a significance level \(p<0.10\)). resolution of Deimos-2 images with an off-nadir angle of 35\({}^{\circ}\) would be 1.9 m. In this way, the update of the GSD could improve the expected RMSE\({}_{\rm 2D}\) to 0.66 pixels (1.255/1.9).
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 5 Simulations
### Discussion and Future Works  
The above results show that both the link connectivity and probability of SUs in UCANs heavily depend on acoustic signal frequency, underwater environment factors, and the activities of PUs. Therefore, when system parameters as well as underwater environment are given, our analysis (i.e., \(p_{con}\) and \(p_{cov}\)) can be used to find a proper signal frequency to acquire the optimal values for the link connectivity and the probability of coverage. Meanwhile, our results also offer an implication on optimizing the network throughput of SUs in UCANs. For example, if we assume SU pairs (i.e., both ST and SR) follow HPPP with intensity \(\lambda_{s}\) in our UCAN model, the spatial throughput of SUs can be expressed by \(p_{con}\lambda_{s}\log(1+\delta_{s})\) (the spatial throughput is defined in [33]. We observe that the throughput of SUs is proportional to \(p_{con}\).  
In addition to the analysis of QoS metrics of UCANs, there are many interesting topics in UCANs. For example, how to design effective Medium Access Control (MAC) protocols, considering spectrum constraint [4; 7; 11; 13]. Another issue is to design effective and efficient routing schemes [34] while considering the spectrum constraint. Our study presented in this paper has paved the way for the  
Figure 10: Coverage \(p_{cov}\) versus frequency \(f\) with different power of PUs \(P_{p}\). System parameters are set as \(R_{d}=12\) km, \(P_{s}=100\) dB, \(\lambda_{p}=0.003\), \(k=1\), \(w=10\) m/s, \(s=1\), \(\delta_{s}=20\) dB, and \(\delta_{d}=20\) dB.  
Figure 9: Coverage \(p_{cov}\) versus frequency \(f\) with different \(\lambda_{p}\). System parameters are set as \(R_{d}=12\) km, \(P_{p}=110\) dB, \(P_{s}=100\) dB, \(s=1\), \(k=1\), \(w=10\) m/s, \(\delta_{s}=20\) dB, and \(\delta_{d}=20\) dB.  
solutions to these research problems. For example, we can obtain the whole network topology once the link connectivity of each SU pair is obtained and we can then design the routing schemes based on the available topology.
# Optical Flow Estimation with Occlusion Detection
## 5 Experiments
### Experiments on Optical Flow  
In our final experiment we compare the performance of our novel optical flow approach to other methods from literature. After detailed comparison with the current optical flow estimation methods in Table 2, the results of our method that is applied to several training sequences are given in Figure 8.  
In Figure 8, we visualized the results of the LDOF algorithm, the FlowFields algorithm and our algorithm. LDOF is a classical optical flow field estimation algorithm. Because of its better robustness and less computational cost, it is widely used in practice. FlowFields is more sophisticated in design and performs well in the entire datasets, but it may have'strange errors' in some cases due to its excessive dependence on neighborhood propagation. We use the red box to identify the contrast areas in Figure 8. We found that we can obtain better moving edges on either the clean data set (the first four  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **AEE\_Clean** & **AEE\_Final** \\ \hline FlownetS [8] & 4.50 & 5.45 \\ FlownetC [8] & 4.31 & 5.87 \\ SPyNet [10] & 4.12 & 5.57 \\ MR-Flow [33] & 1.83 & 3.59 \\ classic+NLP [28] & 4.13 & 5.90 \\ DeepFlow [5] & 2.66 & 3.57 \\ EpicFlow [36] & 2.27 & 3.56 \\ FlowFileds [6] & 2.13 & 3.82 \\ LDOF [7] & 4.32 & 6.21 \\ ours & 2.09 & 3.74 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative evaluation of our method.  
Figure 8: Optical flow estimation on the MPI-Sintel dataset. The first four rows are the result of clean version, and the last four rows are the result of final version. From left to right: overlaid input frames, ground truth, the results of LDOF, the results of FlowFields and the results of our method. For all methods, we use the red box to identify the contrast areas and the endpoint error(EPE) is printed in the image.  
rows) or the final data set (the last four rows). LDOF has a smooth phenomenon as a whole. Since the lack of occlusion detection, the FlowFields cannot reason accurate motion consistency areas. It is for this reason that the FlowFields algorithm in Figure 8 results in erroneous smoothing. For example, the foreground and background in the sixth row are not clearly distinguished, and the head in the seventh row is incorrectly divided into two areas.  
Compared with the mainstream optical flow estimation algorithms, such as LODF and FlowFields, our method can obtain more accurate results on the entire dataset. Our method also surpasses most of the methods based on deep learning. Compared with MR-Flow [33], which ranks first in data set, our method can achieve comparable results while greatly reducing the computational cost. Specifically, it refers to the huge training data and hardware requirements generated in the training process of convolutional neural networks.
# Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes
## 5 Conclusions  
The primary objective of this study was the exploration and possible applicability of the SAM algorithm for general image delineation to instance segmentation in XXL-CT volumetric data-sets.  
In conclusion, our study highlights the potential of SAM for instance segmentation in XXL-CT volumetric data-sets, while acknowledging that there is still significant room for improvement. Furthermore, our research contributes to the following areas: (1) the evaluation of SAM on data-sets from the field of non-destructive testing based on CT image data, (2) the exploration of various methods for integrating and fusing the output from image-based SAM with volumetric data-sets, (3) the introduction of a tile-based approach for segmenting objects of arbitrary size, and (4) the utilization of dense prompts for tile combination using an accumulator. Separately and in combination, these contributions provide novel insights to the community and hence establish a foundation for further advancements in this field.  
Conceptualization, R.G.; Data curation, R.G.; Investigation, R.G. and S.R.; Methodology, R.G.; Resources, R.G.; Software, R.G. and S.R.; Supervision, T.W.; Validation, R.G.; Visualization, R.G.; Writing--original draft, R.G.; Writing--review and editing, S.R. and T.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Bavarian Ministry of Economic Affairs, Regional Development and Energy through the centre for Analytics--Data--Applications (ADA-Center) within the framework of \"BAYERN DIGITAL II\" (20-3410-2-9-8).  
Not applicable.  
The Me 163 data-sets presented in this study are openly available in [7]. The bulk material data-sets will be made available by the authors on request.  
The authors gratefully acknowledge the scientific support and HPC resources provided by the Erlangen National High-Performance Computing Center (NHR@FAU) of the Friedrich--Alexander Universitat Erlangen--Nurnberg (FAU) under the NHR project b179 dc. NHR funding is provided by federal and Bavarian state authorities. NHR@FAU hardware is partially funded by the German Research Foundation (DFG)--440719683. Additionally, the authors also extend their appreciation to [PERSON] for his valuable assistance during the manuscript revision process.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Novel Context-Aware Reliable Routing Protocol and SVM Implementation in Vehicular Area Networks
###### Abstract
Using the shortest route for data communication between network nodes without considering route reliability may be expensive. This occurred because these paths could become unacceptable shortly, interrupting data transmission frequently [8]. In VANET, there are two types of reliability, which are mentioned below:  
* Link Reliability: The probability of a connection remaining uninterrupted for a definite period of time is known as link reliability. Assumed a prediction time \(T_{P}\) for constant accessibility of a dedicated link \(l\) among the interconnection of the nodes at time \(t\), whereas \(r(l)\) is representing the link reliability. \[r(l)=P\{\text{until }t+T_{P}|\}\] (1)  
Figure 1: Types of available wireless links. * Route Reliability: In VANETs, various possible paths could occur between the sender node \(s_{r}\) and the receiver nodes \(d_{e}\), where each path is the connection between various links in the dedicated route. For every provided path, the number of its established links by \(\Omega:l_{1}=(s_{1},n_{1}),l_{2}=(n_{1},n_{2}),\ldots l_{\Omega}=(n_{\Omega},d_{e})\). The route reliability \(R(P((s_{r},d_{e}))\) for path \(P\) is described as follows: \[R(P((s_{r},d_{e}))=\prod_{\omega=1}^{\Omega}r_{t}(l_{\omega})\] (2) where \(r_{t}(l_{\omega})\) is the link reliability as calculated in Equation (1). In past years, with the rapid development of bio-inspired techniques and machine learning techniques, routing protocols based on particle swarm optimization (PSO) [9], artificial bee colony (ABC) [10], ant colony optimization (ACO) [11], genetic algorithm (GA) [12], harmony search (HS) [13], support vector machine (SVM) [14], reinforcement learning (RL) [15], and researchers have extensively adopted k-means [16] to recognize and route packets among nodes in an improved way [17; 18]. Machine learning is a collection of predictive mathematical models that can be used to make predictions and decisions based on a large amount of data. This ability to predict and make decisions may be critical in the VANET [19; 20]. However, in route selection, background details such as communication type, E2E link dependency, and packet load size can boost the performance of the VANET system. All these observations encourage the adoption of machine learning techniques to mitigate the various challenges and issues in routing between vehicles. Thus, a context-aware reliable routing protocol has been proposed that incorporates k-means and SVM approaches in an attempt to provide a better quality of service (QoS) in VANET.
## 4 Application of HSI and AI in the Food Industry
### Solid Products
The capability of Vis/NIR-HSI for the determination of physicochemical characteristics of dairy powders, which involve tapped density, moisture content, surface free fat, and bulk density, was investigated using different techniques, namely portable, bench-top devices, and HSI systems. PLSR models successfully predicted the various quality attributes with accuracy ranging from moderate to good, proving the flexibility of the Vis-NIR technology for its employment online in dairy powder quality control. Each of these scanning techniques had their own relative advantages for different applications in industries and, therefore, provided flexibility for choosing an appropriate system for fast, non-invasive analysis [159]. The assessment of physicochemical characteristics of dairy powders demonstrating the accurate prediction of attributes like tapped density, moisture content, surface free fat, and bulk density by Vis/Nir-HSI provided moderate to good accuracy, which underscores its potential for online quality control in dairy powder production. This method emphasizes the practical applications and benefits of Vis/NIR-HSI in enhancing dairy powder quality assessment processes. [159]. For further exploration, investigating the use of deep learning models, and extending this approach to predict other complex attributes like shelf life or nutritional content, can be considered. In another study, determining the end date of the cheese-ripening process using NIR-HSI by using a PLSR model to predict E-index associated with maturation time showed an accuracy of 69.6% [160], demonstrating the good potential of HSI in the detection of cheese ripeness. However, further refinement should be carried out with larger datasets, and repetitive scans, in order to view improvements in predictive capacity at commercial use. It has been stated that two HSI techniques for egg quality assessment were developed, namely a Hyperspectral Egg Defect Inspection Technique (HEDIT) for factory use and a Hyperspectral Egg Freshness Inspection Technique (HEFIT) for consumers. Both obtained a high accuracy from the CNN architecture, with 99% on freshness and 100% on defects [161], while another approach based on VNIR with XGBoost reached a high precision of egg freshness, R2P = 0.91, with a yolk detection of 97.33% and a crack detection of 93.33% [162]. It has been recommended to optimize AI-based HSI methods for real-time automation, especially for defects like cracks and scattered yolks, in order to develop a rapid and non-destructive method for an efficient egg processing.
## 3 Results and Discussion
### E-Tongue's Analysis  
The E-tongue can successfully characterize the nonvolatile flavor components in food and has higher taste sensitivity than that in the trained group [23; 24]. They reflect different tastes between different samples, ranging from sourness, bitterness, saltiness, umami, asringent, after-bitterness, and after-astringency. In this test, for the reference solution, the tasteless point of sourness was \(-13\), the tasteless point of saltiness was \(-6\), and the tasteless point of other tastes was \(0\). The test results are shown in Figure 2 and Table 2.  
Figure 1: LDA analysis chart (**A**) and radar chart (**B**) of aroma at different times using the E-nose test. Figure 2A shows that the umami taste of the fermented pork lion ham was more pronounced. With the increase in time, umami was consistently the highest relative to other taste receptors in the E-tongue, which might be related to the higher content of umami amino acids. The saltiness was caused by salt. However, umami also contributed to it. [PERSON] showed that the umami components could be used as substitutes for reducing the sodium level and retaining saltiness [25]. Bitterness was a well-known common sensory defect in dry cured ham. The bitterness of fermented pork loin ham was only second to umami in the E-tongue score. However, the bitterness in the other groups was significantly lower relative to the 0-day group. According to [PERSON] et al., this could be due to the use of a starter which reduced the defect flavor of ham [26]. However, the bitterness of the fermented pork loin ham was significantly increased on day 42, which might be due to excessive degradation of myosin and troponin by endogenous enzymes (cathepsin B, L and dipeptidase) [26]. The difference in sourness might be caused by _L. plantarum_ SJ4, which had an acid-producing ability. The sourness values were all lower than the tasteless point \(-13\), indicating no uncomfortableness in taste perception during the actual taste analysis. Richness could be used to characterize the residual umami taste, also known as aftertaste-umami. In the present study, the richness reached a maximum in 35 days. Some reports suggest that the increase in richness may be due to the formation of various taste substances, such as FAA, from proteolysis [27]. Overall, fermented pork loin ham consistently had the highest E-tongue umami values from 0 to 42 days relative to
# Route Optimization for Open Vehicle Routing Problem (OVRP): A Mathematical and Solution Approach
## 3 Materials and Methods
### Experiment Design
#### 3.1.2 Data Analysis  
For data analysis, symbols and descriptions of the required elements are specified (see Table 1). Allowing the identification and comparison of the results, as well as the evaluation of the time efficiency of each method in the context of the OVRP.
## 2 Theoretical Background  
### Theory of ABH Circular Plate  
A circular plate embedded with a two-dimensional acoustic black hole is shown in Figure 1. The equation between the thickness of the circular plate and the radius is given by:  
\[h(x)=\left\{\begin{array}{ll}\varepsilon x^{m}-b,&\text{for ABH region}\\ h_{p},&\text{for uniform region}\end{array}\right. \tag{1}\]  
where \(\varepsilon\) is a constant number; \(m\) is the taper power, \(m\geq 2\); \(b\) is the distance between the center of the ABH and the origin of the coordinate in the \(z\)-direction; \(h_{p}\) is the thickness of the uniform region of the plate. The propagation velocity of flexural wave in a thin isotropic plate is:  
\[c_{B}=\left(B\alpha^{2}/m\right)^{1/4} \tag{2}\]  
where \(B\) is the bending stiffness, \(B=Eh(x)^{3}/[12(1-\
u^{2})]\); \(E\) is the [PERSON]'s modulus; \(\
u\) is [PERSON]'s ratio; \(\omega\) is the angular frequency; \(m\) is the areal density of the plate, \(m=\rho_{m}h(x)\); \(\rho_{m}\) is the density of the plate material. When the flexural wave propagates from the uniform region to the ABH region, the wave velocity will be gradually reduced, the wavelength will be compressed, and the amplitude of the fluctuation will also be gradually increased. As the thickness gradually decreases to zero, the flexural wave propagates here where its cumulative phase reaches infinity, as a result it cannot be reflected back from the center of the ABH region. However, an ideal acoustic black hole cannot be realized in the actual manufacturing due to manufacturing process limitations so that the actual ABH structure must have a truncation. The truncation thickness of the model in Figure 1 can be expressed as:  
\[h_{t}=\frac{h_{p}}{2}-b \tag{3}\]  
The existence of truncation makes the reflection coefficient of the flexural wave increase significantly, which will seriously affect the ABH effect [19]. Attaching a damping layer to the ABH region can significantly reduce the reflection of flexural wave and improve the efficiency of energy dissipation [20]. The ABH structures have exhibited obvious broadband characteristics for the aggregation effect of the flexural wave. These above characteristics of the acoustic black hole play an important role in applications such as vibration reduction [21; 22], noise reduction, and energy recovery [23; 24].