esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Solution
### Attack Assumptions  
In order to provide uninterrupted network operation, core network protocols (aggregation, routing and time synchronization) have to be secured. Regarding the attacks on the aggregation protocol [10], we assume that they demonstrate themselves as skewed aggregated values, which can be the result of either a number of skewed sensed values, or a compromised aggregated node. The assumption is very reasonable, bearing in mind that the main objective of these attacks is to provide a wrong picture of the observed phenomenon, or wrong context information in context aware systems. On the other hand, in time critical systems it is mandatory to receive information within a certain time window. If the attacker manages to introduce delays or desynchronize clock signal in various nodes, the received critical information will not be up to date, which can destabilize the system. Also, if the received information is not up to date, the aggregated value will be skewed, as it will also be out of date. For these reasons, and given the existing redundancy in WSNs, we believe that these attacks can be detected as temporal and/or spatial inconsistencies of sensed values.  
Regarding attacks on routing protocols [10], we assume that they will introduce new and different paths than those that have been seen before. Here we have attacks whose main objective is to compromise the routing protocol, so the data do not reach the sink, or the sink is reached with a considerable delay, which makes the data useless. These attacks usually spoof or alter the data stored in the routing tables of the nodes. Thus, the resulting routing paths will be different from those used in a normal situation. The other group of attacks, which are more dangerous, has the objective of tampering with the data, and they usually do it in two steps: in the first step they establish themselves as an attractive routing hop (e.g., by falsely presenting lower cost of its routes), so the majority of the data goes through that node, and in the second step they tamper with the data. In the case of wormholes for example, two nodes that are not within each other's radio range result in consecutive routing hops in routing paths, which is not possible in a normal situation. From these examples we can see that the assumption about the attacks resulting in routing paths different from those that appear in normal  
Figure 1: Envisioned WSN Model.  
situation is reasonable. Thus, in this case we want to detect temporal inconsistencies in paths used by each node.  
The attacks can be either mote-based or laptop-based, but can be either inside or outside based. Another assumption is that the attack always starts after the initialization of the network, _i.e._, the network functions normally for some time, which is a very reasonable assumption.
# Grapevine Branch Recognition and Pruning Point Localization Technology Based on Image Processing
## 3 Results
### Results of Semantic Segmentation
#### 3.1.2 Comparison of Results from Different Semantic Segmentation Models.  
In order to better segment the branches and trunks, we chose three semantic segmentation models PSP-net, U-Net and DeepLab v3+ [33] to test the same dataset. The evaluation indexes of each algorithm are obtained as shown in Table 4 and the data are compared and analyzed to select the most suitable segmentation model for this experiment.  
U-net shows high mPA and Accuracy for semantic segmentation, but in actual training, U-net consumes the most computational resources and achieves the longest training time, high computational complexity and slower processing of images. PSP-net achieved the highest mIoU of 83.73%, while PSP-net achieved the highest FPS value of 152.72; PSP-net achieved higher mIoU than the deeplabv3+ network in all the metrics. Considering the segmentation performance and segmentation speed of the model, this paper chooses PSP  
Figure 9: The branch is inclined to the right with respect to the vertical position. The red dots represent the positions of bud points and pruning points on the branches, while the black dots represent auxiliary points used for drawing guide lines. (**a**) The case when the bud is on the left side of the branch. (**b**) The case when the bud is on the right side of the branch.  
net as the semantic segmentation model for grape branch and main vine ROI images. The results of this algorithm acting on different lighting conditions are shown in Figure 10.
# Ground Displacement in East Azerbaijan Province, Iran, Revealed by L-band and C-band InSAR Analyses
## 2 Study Area and Datasets  
### Study Area Description  
The present study mainly focuses on two major plains in East Azerbaijan Province of northwestern Iran, Shabestar and Tabriz basins, with an area of approximately 1000 km\({}^{2}\). As shown in Figure 1a, the study area has a harsh topography, and accordingly, there is a concentration of agricultural activities in these flat basins due to their fertility and suitability for different activities. Shabestar and Tabriz basins have been known for intensive agricultural and industrial practices for several decades [45; 46; 47; 48]. The substrate of these basins contains various formations such as gypsiferous marl and Quaternary units [13]. The basins contain abundant unconsolidated Quaternary deposits and a lower erosion level (Figure 1b). As shown in Figure 1b, the level of erosion and soil loss are related to the topography, in which steep positions present a higher erosion level [49]. According to the geological map of the study area provided by the Geological Survey of Iran (Figure 1c), the area of Quaternary deposits inside the PALSAR-2 footprint is higher than in other units. Approximately 2500 km\({}^{2}\) inside the PALSAR-2 footprint contains unconsolidated Quaternary units, whereas the Quaternary units near Lake Urmia changed to salt flats. The second largest unit in terms of area is around 900 km\({}^{2}\) for pyroclastic and claystone units over the Sahand volcano. Figure 1c shows that major roads and railways are mainly extended over Quaternary deposits in the Shabestar and Tabriz basins, which generally implies a high risk of damage to infrastructure due to ground subsidence. The Tabriz basin is a hotspot of agricultural and industrial activities in NW Iran, with many legal and illegal pumping wells. Some of the legal piezometric wells are shown in Figure 1c, indicated by blue pushpins.  
Figure 1: **(a)** Topographic map of study area from Shuttle Radar Topography Mission (SRTM 1 arc-second). **(b)** Erosion map of study area provided by Geological Survey of Iran (GSI) on shaded relief SRTM 1 arc-second. **(c)** Study area with geological units provided by GSI on shaded relief SRTM 1 arc-second. White polygons are cities, red stars and numbers are locations of field observations, blue pushpins are locations of piezometers, dashed square indicates ascending footprint of L-band PALSAR-2 images. Green and thick yellow lines are main railways and roads, respectively. Qsl, Quaternary (unconsolidated sediment); Md.av, dacitic to andecitic subvolcanic rocks; Murm, red to brown marl with gypsyiferous marl with sandstone; Plmb, pyroclastic and claystone with vertebrate fauna remains; pck, dull green-gray salty shales; Plmb, ash-flows; Egb, gabbro rock.
# Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets +
## 4 Demand Scenario Generation
### Dataset Description
#### 4.2.2 Traffic Dataset  
Within the framework of our case study, we used travel demand matrices provided by the municipality of Magdeburg. They contain trips for four different modes of transport, including foot, bike, car, and public transport, for a time period of one day. The matrices are structured in a way that the rows contain all trips generated from each origin and the columns contain all trips attracted from each destination. Therefore, they are often referred to as origin-destination matrices (OD-matrices). Within the matrices, a total of 182 districts evenly distributed throughout the city of Magdeburg serve as origins and destinations. In order to access the geographic location and geometry information of those districts, we used an ESRI shapefile which was also provided by the municipality of Magdeburg. A map of the city of Magdeburg as well as its districts is shown in Figure 5a.  
Figure 4: Methodological approach.  
Additionally, the inner-city OD-flows for the transport modes foot and bike are shown in Figure 5b and Figure 5c, respectively.  
In the transport sector, since travel demand matrices contain passenger or vehicle trips related to a certain period of time [60], they commonly serve as input for the development of travel demand models which are designed to model movements of road users within a transport network [61]. According to [PERSON] [63], traffic models for a period of one day are usually related to so-called normal working days or statistical traffic days (i.e., Tuesday or Thursday), since they reflect traffic events most representatively. This also applies for the traffic demand matrices used for this study.
## 3 Classification of Optical Waveguide Sensors
### Microring Structured Optical Waveguide Sensor
#### 3.1.1 Silicon-Based Microring Optical Waveguide Sensor
Compared with medium-refractive-index contrast material systems (such as \(\mathrm{Si_{3}N_{4}/SiO_{2}}\)), the use of high-refractive-index contrast material systems can reduce the size of photonic devices and increase the electric field strength at the slot waveguide interface, which improves the sensor sensitivity.
# BEAC-Net: Boundary-Enhanced Adaptive Context Network for Optic Disk and Optic Cup Segmentation
###### Abstract  
Accurately segmenting the optic disk (OD) and optic cup (OC) on retinal fundus images is important for treating glaucoma. With the development of deep learning, some CNN-based methods have been implemented to segment OD and OC, but it is difficult to accurately segment OD and OC boundaries affected by blood vessels and the lesion area. To this end, we propose a novel boundary-enhanced adaptive context network (BEAC-Net) for OD and OC segmentation. Firstly, a newly designed efficient boundary pixel attention (EBPA) module enhances pixel-by-pixel feature capture to collect the boundary contextual information of OD and OC in the horizontal and vertical directions. In addition, background noise makes segmenting boundary pixels difficult. To this end, an adaptive context module (ACM) was designed, which simultaneously learns local-range and long-range information to capture richer context. Finally, BEAC-Net adaptively integrates the feature maps from different levels using the attentional feature fusion (AFF) module. In addition, we provide a high-quality retinal fundus image dataset named the 66 Vision-Tech dataset, which advances the field of diagnostic glaucoma. Our proposed BEAC-Net was used to perform extensive experiments on the RIM-ONE-v3, DRISHTI-GS, and 66 Vision-Tech datasets. In particular, BEAC-Net achieved a Dice coefficient of 0.8267 and an IoU of 0.8138 for OD segmentation and a Dice coefficient of 0.8057 and an IoU value of 0.7858 for OC segmentation on the 66 Vision-Tech dataset, achieving state-of-the-art segmentation results.  
Keywords:glaucoma; boundary enhanced; adaptive context; OD and OC segmentation +
Footnote †: journal:
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 3 Satellite Development Lessons
### Software Development  
We suggest assessing early in the project if the team shall create a new software suite instead of risking the software redesign at mid-development. In our case, at the beginning of the project, we decided to create our hardware abstraction layer (HAL), because the available HAL of the manufacturers was not deemed efficient.  
When the ESTCube-2 project started, the core team members were convinced we needed to develop a HAL called ECHAL. It stemmed from the software development problems mentioned by [PERSON] et al. [18]. At the beginning of the ESTCube-2 project, the ECHAL development was deemed necessary because STM HAL did not meet our firmware development requirements. It was unclear if ST Microelectronics would improve its software suite and what the timeline would be for it. The goal of our team was to develop a functional and well-thought-out HAL that would be lightweight on memory, have proper abstraction, and make good use of the general-purpose input/output (GPIO) definitions. This led to an opportunity for the students to get a chance to understand how multiple-layer firmware development is performed. Nevertheless, having our own HAL created high entry requirements for new members, and it became more complex as more of it was developed. Currently, there is not enough time to polish everything in ECHAL. As a result, the team has to finish a stable version of ECHAL, properly document it and publish it, which we plan to do under an open-source license.
## 2 Data and Methods
### Evaluation Methodology  
In India, daily rainfall is computed by accumulating rainfall observations ending at 0300 UTC. For a fair comparison, daily rainfall from half-hourly IMERG products has been computed using the same time convention. As spatial resolution of the IMD gridded gauge-based rainfall data is 0.25\({}^{\circ}\), daily IMERG datasets have been re-sampled at the same spatial resolution of 0.25\({}^{\circ}\) for the study period, and daily precipitation across India is extracted. As India receives precipitation in the form of rainfall alone during the southwest monsoon season, the terms \"rainfall\" and \"precipitation\" were interchangeably used in this manuscript.  
In order to quantify the error characteristics of IMERG products against the IMD rain-gauge-based dataset, several continuous and categorical error metrics were used. Table 1 outlines the definitions/formulae of continuous error metrics used in this study to quantify the accuracy of IMERG in precipitation estimation. Mean, bias, correlation coefficient, and RMSE are the most commonly used continuous error metrics for the evaluation of any satellite-based precipitation product. Correlation coefficient has no unit, whereas mean, bias, and RMSE have been expressed in mm day\({}^{-1}\). To express bias and RMSE in percentage, these quantities have to be normalized against the observed mean (e.g., rain-gauge-based data) and multiplied by 100. Coefficient of variation (CV) is used to compare the temporal variability of monsoon precipitation from IMERG and IMD datasets. Moreover, modified Kling-Gupta efficiency (KGE) has been used to assess the reliability of IMERG products for hydrological applications. This KGE score combines correlation coefficient, bias, and variability and has proven to be very useful statistical metric in hydrology (47). Furthermore, the errors in the satellite-based precipitation estimates can be decomposed into systematic and random components, which is vital for algorithm development and improvement (42; 48). In this study, both systematic and random components of errors have been computed using Equations (1) and (2), respectively.  
\[\left(Error\right)_{\text{\footnotesize{\footnotesize{\footnotesize{ \footnotesize{\footnotesize{\footnotesize{\footnotesize{\footnotesize{\footnotesize{\footnotesize {\footnotesize{\footnotesize{\footnotesize{\footnotesize{\footnotesize{ \footnotesize{\footnotesize{\
u}}}}}}}}}}}}}}}}}}\right. \tag{1}\]\[\left(Error\right)_{Rand}=\frac{\sum_{i=1}^{N}\left(S_{i}-S_{i}^{*}\right)^{2}}{N} \tag{2}\]  
where \(S_{i}^{*}=a\times G_{i}+b\) with a and b be the slope and intercept of a linear regression error model, respectively [48].  
For the quantification of skill of IMERG products in the detection of monsoon rainy days, four categorical skill metrics defined in Table 2 have been used. Daily monsoon precipitation of less than 2.5 mm is considered as non-rainy days for India as per the IMD convention.
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 9 IGF-1 and Mechanical Unloading  
Studies during spaceflight and in bone unloading experiments on Earth have shown that bone unloading suppresses IGF-1 production (see Figure 4). [PERSON] et al. found that spaceflight decreased mRNA levels of IGF-1 in cultures of rat osteoblasts as compared to ground controls; they also found that microgravity completely suppressed mRNA expression of the insulin receptor substrate-1, a post-receptor signaling molecule of IGF-1 [53]. [PERSON] and associates found that skeletal unloading induces resistance to IGF-1 by inhibiting the activation of IGF-signaling pathways including the expression of alphaVbeta3 integrin [54]. And [PERSON] et al. found that mechanical loading by fluid shear stress enhanced IGF-1R signaling in osteoblasts whereas mechanical unloading inhibited its signaling [51]. [PERSON] and associates found that skeletal unloading caused resistance of osteoprogenitor cells to parathyroid hormone due to the development of resistance to IGF-1 [48].  
Hind limb suspension in rats has been shown to inhibit IGF1R signaling pathways with consequent reduction in the number and differentiation of osteoblast progenitors and reduced bone formation; responsiveness to IGF1R could be restored with reloading [54; 55]. In contrast, unloading of IGF-1R knockout mice caused a permanent loss of bone synthesis, indicating that IGF1 signaling is necessary for bone formation following dis  
Figure 4: Bone loading and unloading experiments demonstrate the key role IGF-1 and its receptor IGF1R play in bone formation. Bone unloading increases IGF-1 resistance and decreases IGF-1 production, signaling, and IGF-1-mediated bone formation by osteoblasts and enhances IGF-1 and PTH resistance in osteoblast mesenchymal stem cell precursors. In contrast, bone loading increases IGF-1 production and IGF1R efficiency and decreases TNF-\(\alpha\)-mediated apoptosis in osteoblasts. Deletion of IGF-1 or IGF1R in rodents abrogates osteoblast responses to bone loading and unloading (not shown).
# Alternative Categorization of Radio Frequency Power Amplifier for Generalized Design Insights
## 4 Generalized Design Approach  
Designing RFFAs must always be guided by system requirements and the specific needs of the application, while also considering several inherent trade-offs. Thus, one can imagine a structured modeling procedure that begins with system requirements and encompasses design, analysis, verification and, finally, validation [51]. Figure 8 illustrates this systematic approach, incorporating checkpoints at various stages, which is crucial for ensuring accuracy and efficiency.  
Categorizing these various techniques and architectures serves as a stepping stone for the subsequent design phases. The main objective in systems engineering [52; 53] is to identify the critical system requirements, namely the frequency and power, along with the time domain description of the signal. For instance, considering whether the signal will  
Figure 8: Generalized flow for RFPA design, where \(f\) is frequency, \(P\) represents power, and EM stands for ‘electromagnetic’.  
have modulation, like in communication systems, or if it will be a pure tone, such as when driving scientific instruments [54; 55], is crucial from a systems engineering perspective.  
An abstract approach, driven by system requirements, becomes necessary to avoid the unnecessary optimization of design parameters which may not ultimately prove useful or lead to impractical solutions. To simplify the design process of an RFPA, it can be divided into two modules: the horizontal flow, which involves a deep and thorough mathematical analysis followed by simulations, and the vertical flow, where technological tools play a critical role. The flows encompass steps that are purely analytical in nature, requiring the iterative improvement of the topology, and decision-making steps, leading to the final implementation of the RFPA.
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 5 Experimental Results, Analysis, and Discussion
### Number of Clusters  
Optimizing the clustering number would result in more efficient energy usage as it will automatically minimize energy consumption. Moreover, if the number of clusters is below the optimum value, more nodes are further away from CHs, causing more rapid network energy consumption. If cluster numbers exceed the optimum number, this will result in a high number of CHs required to send data to the BS covering long distances. In Figure 6, all three protocols are compared, and the relationship between several cluster and node placements can also be seen in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Default Value** \\ \hline Simulation area & \(100\times 100\) \\ \hline Number of nodes & \(100\) \\ \hline Minimum distance between nodes & \(2\) m \\ \hline Simulation runs & \(10\) \\ \hline Simulation time & \(120\) s \\ \hline BS position & \((50,50)\) \\ \hline Initial energy & \(0.5\) J \\ \hline Probability of becoming a node as CH & \(0.1\) \\ \hline Energy for transferring of each bit & \(50\times 0.000000001\) \\ \hline Energy for receiving & \(50\times 0.000000001\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
Figure 5: Distribution of network nodes. \(\blacktriangle\) represents the BS and \(\bullet\) is considered as ordinary sensor nodes.