esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Zoning Algorithm to Optimize Overhead in Smart Mobile Communication
## 4 OLSR Protocol  
The optimized link state routing protocol [10] is one of the most used protocols in MANETs. It is part of the table-driven (proactive) routing protocols that build the routing tables by periodically exchanging topology control messages. The OLSR has adopted the MPR concept [29] to achieve good dissemination of TC messages. The MPR concept optimizes the overhead, firstly, by selecting the minimum number of nodes to act as forwarders of TC messages. Secondly, only links between MPRs and their selectors are shared to reduce the size of TC messages. Figure 2 shows the principle of the MPR concept. We model the OLSR network as a unit disk graph \(G=(V,E)\), where \(V\) represents the set of nodes and \(E\) the set of symmetric edges. \(R_{u}\) is the signal length of the node \(u\). For simplicity, we consider that signal range \(R\) is uniform for all nodes. Two nodes \(u\) and \(v\) form a symmetric link if and only if they are at distance \(d(u,v)<=min(R_{u},R_{v})\). The OLSR protocol maintains regularly  
Figure 1: Z-GFR: Two optimization strategies.  
local information and global information in each node. Neighborhood information is broadcasted via HELLO messages by each node \(u\) in the network. The HELLO messages are never forwarded and they serve to discover one-hop and two-hop neighbor sets denoted \(N1(u)\) and \(N2(u)\), respectively. Every node elects from its neighbors a set of nodes to act as MPRs to forward TC messages. We denote the set of MPRs of a node \(u\) as \(MPR(u)\) and the set of MPR selectors as \(MPR^{-1}(u)\). A node \(u\) updates its topology information information each time a HELLO or a TC message is received. The topology information on a node \(u\) is a directed subgraph of the graph \(G\) denoted \(G_{u}^{\prime}=(V(u),E(u))\).  
In this section, we describe the MPR concept of OLSR, especially the MPR selection and the default forwarding rules (DFR) of TC messages.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 3 Simulation Result
### Comparison and Discussion  
Table 3 provides a brief comparison between the L-slot MEMS frequency reconfigurable antenna proposed in this paper and previous antennas of the same type [15; 16; 17; 23]. The antenna designed in this paper has a small size, high frequency, and a large gain. With the same number of switches, it has more tunable states.
# Thickness-Stretch Vibration of an Infinite Piezoelectric Plate with Flexoelectricity
## 4 Conclusions  
This work investigated the flexoelectric effect on the thickness-stretch vibration of an infinite piezoelectric plate. The shift of the thickness-stretch vibration frequencies of piezoelectric plates was obtained from the present model with various flexoelectric coefficients. The results show that the thickness-stretch vibration of the micro plate is affected by the flexoelectric effect, which decreases the fundamental frequency. When the thickness of the plate increases, the influence of the flexoelectric effect decreases, implying that the flexoelectric effect has a strong size dependence. The value of frequency shift is about 14 ppm for the present flexoelectric coefficients. However, this represents a significant value for the design and application of accurate high-frequency devices. The precision requirement for high-frequency piezoelectric devices can be dozens of parts per million. Therefore, we believe flexoelectricity should be taken into consideration for the design of high-precision next-generation piezoelectric devices. These results also provide important theoretical guidance and help for the design and development of accurate high-frequency piezoelectric devices in the future.  
Methodology, software, and writing--original draft preparation, Y.G.; conceptualization, and writing--review and editing, B.H.; supervision, and funding acquisition, J.W. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (Grant no. 11702150), Natural Science Foundation of Zhejiang Province (Grant no. LY21A020003), Natural Science Foundation of Ningbo (Grant nos. 202003N4015 and 202003N4163), and the Technology Innovation 2025 Program of the Municipality of Ningbo (Grant no. 2019B10122).  
Not applicable.  
The authors declare no conflict of interest.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 4 HSTN Applications  
In this section, we discuss some of the typical applications of HSTNs, which may be exclusive to HSTNs due to incapabilities or limitations of conventional networks. Considering the capability of expanding network coverage, ensuring service continuity, and increasing network reliability, the following applications of HSTNs are discussed.
## 4 Results and Analysis of the Flight Test  
Flight test is an evaluation of the task completion ability of equipment with environmental adaptability working on a flight carrier after passing ground performance testing. The flight test was carried out on the premise of good performance of satellite communication equipment and orthographic stabilization platform by a heavy-duty twin rotor unmanned aerial vehicle-Camel 500, at the flight test site of unmanned aerial vehicle manufacturer Chongqing Camel Navigation Technology Co., Ltd. and on Nanpingba Island in Chongqing, China. The flight test site is relatively small, with a length of \(\sim\)300 m and a width of \(\sim\)300 m, so the test mainly focuses on hovering and small-scale flight for debugging of aircraft, platform equipment, and communication link. Nanpingba Island is located in the middle of the Yangtze River, with dense gullies on both sides. The narrow river channel--where the river flows rapidly and flood disasters, landslides, and mudslides are prone to occur--makes it a typical site for natural disasters. Therefore, it is used as the observation site for this flight test. Due to airspace control restrictions, this flight test site is limited to an island area of \(\sim\)2000 m length and 1000 m width for turnaround flights to assess the reliability, stability, and real-time performance of the system.
# Adaptive Clustering of Users in Power Domain NOMA
## 4 User Clustering Algorithm
\(w_{new}=1.0\) is the weight of the user \(i\) that will be added to the cluster \(j\), and \(w_{j,i}\) is the weight of the user \(i\) present in the cluster \(j\). If \(W_{j}\) exceeds a threshold \(\kappa\), the o-cluster \(j\) becomes a p-cluster with \(\kappa\) as the parameter that determines the outlier threshold. The smaller the \(\kappa\), the more sensitive the cluster is to the arrival of new users; it only takes a few users for an o-cluster to become a p-cluster. ```
Attempt to join user \(i\) to the nearest p-cluster \(C_{p}\); if(\(\eta\leq\sigma_{j}<\varphi\) and Equation (8) is True)then  Merge \(i\) into C\({}_{p}\); else  Attempt to join user \(i\) to o-cluster \(C_{o}\); if(\(\eta\leq\sigma_{o}<\varphi\) and Equation (8) is True)then  Merge \(i\) into C\({}_{o}\); if\(w_{o}\) (the new weight of C\({}_{o}\))\(>\kappa\)then  Remove C\({}_{o}\) from the outlier clusters and create a new p-cluster \(C_{o}\);  end if else  Create a new o-cluster and insert \(i\) into it;  end if  end while
```  
**Algorithm 1**Merging  
Every \(T\) seconds, each user in a cluster is analyzed for changes in their channel state. If the difference between the current channel gain of the \(i\)-th user, \(Lg_{i}(t)\), and their old one, \(Lg_{i}(t)(t-T)\), exceeds a threshold \(\zeta\), given in percentage, then the \(i\)-th user is removed from their cluster and added to the system's input queue. At the same time, \(T\), the elements in o-clusters \(C_{o}\) are added to the system's input queue as can be seen from Algorithm 2, where \(T\) can be defined by:  
\[T=\left\lceil\frac{1}{\lambda}\log\left(\frac{\kappa}{\kappa-1}\right)\right\rceil, \tag{10}\]  
and when the BS requires, in the offline step, the algorithm returns the final clusters formed by the cluster index of each group. ```
CalculateT by Equation (10); Get the next user \(i\) at the current time \(t\) from the input data; Merging(\(i\)); if(\(t\) mod\(T=0\))then foreach\(p\)-clusters\(C_{p}\)do foreachuser \(i\) in \(C_{p}\)do if\(\|Lg_{m}(t)-Lg_{m}(t)(t-T)\|/Lg_{m}(t)(t-T)<\zeta\)then  Delete \(i\); Insert \(i\) into the input queue. end if  end foreach\(o\)-clusters\(C_{o}\)do foreachuser \(i\) in \(C_{o}\)do  Delete \(i\); Insert \(i\) into the input queue. end foreach end if ifA cluster formation request arrivesthen  Generate the final clusters;  end foreach
```  
**Algorithm 2**Modified DenStream
# Multi-Gateway-Based Energy Holes Avoidance Routing Protocol for WSN
## 2 Related Work
### Other Clustering-Based Approaches  
Authors in [23] have done an analysis of different cluster-based protocols for the energy holes problem. They have computed the energy consumption of nodes in different regions at different times and found out that the node density has a small influence on power utilization of cluster-based networks. [PERSON] _et al._[24] have considered heterogeneous clustering schemes where different ringscontain sensor nodes with dissimilar initial energy. The cluster heads in inner rings can transmit data directly towards the sink, while cluster heads (CHs) in outer ring forward data to the cluster heads of inner rings.  
[PERSON] _et al._[PERSON] _et al._ (2015) have proposed an energy-efficient clustering algorithm with load balancing to maximize network lifetime. Initially they have proposed a density-based dynamic clustering (DDC) algorithm for clustering the sensors in the network area. The DDC algorithm uses the subtractive clustering to generate the cluster seeds. The subtractive clustering computes the sufficient number of clusters (seeds) and their position in the network area. The seed node provides a unique cluster id which is used to identify a cluster among all the clusters in the network. The seed node generates a packet with its unique id and broadcasts this packet to all the neighboring nodes. A receiving node examines the packet and accepts it, if it is not already clustered and then broadcasts it to the neighboring nodes. However, if the receiving node is already clustered, it simply discards the packet. This process is repeated until all of the nodes in the network are clustered. They have also designed a distributed independence set discovery (DISD) method for cluster head selection. The independence set provides a maximum possible coverage of area by the selected nodes. They have vigorously set up an optimal sensor cover to control maximum sensors inside a cluster and consequently making an in-dependence set (IDS). In the CH selection, only the members of Cluster Heads are permitted to take part. A mandatory energy level for becoming a cluster head is defined. A node which satisfies this energy level becomes the cluster head.  
Authors in [PERSON] _et al._ (2015) have divided the network area into circular regions. The density of nodes in the inner circular region is more than the density of nodes in the outer circular region. Initially CHs are chosen from inner region. When the total residual energy of inner region becomes lower than the outer region, CHs are selected from outer region. The energy of both the regions is calculated at every round. The cluster heads are always selected from the region of higher energy. In this way the energy of the entire area is consumed uniformly, so the chance of energy holes in the area becomes minimal. We have used a LEACH-based clustering method in our protocol.
# Adaptive Deployable Thin Spherical Shell Reflectors
## 3 Control of a Spherical Shell  
So far, our work has been focused on the shape control with piezoelectric unimorph strain actuators of a thin spherical shell, ignoring its thermal response. The demonstrators consist of a PET substrate covered with a thin layer of PVDF-TrFE with Aluminum electrodes (Figure 2a); the material properties are listed in Table 2 (notice the large value of the CTE of the PVDF-TrFE). The sphere has a radius of 2.5 m; two mirrors have been built, one with a diameter of 100 mm, provided with a set of 7 independent electrodes, and one of 200 mm with 25 electrodes arranged in a keystone layout (Figure 4). The demonstrators were manufactured by MateriaNova according to the procedure described in [7]. The experimental determination of the influence functions of the electrodes is described in [6]. For illustration purpose, Figure 5 compares the cross sections of the measured influence functions of 6 electrodes of the same row (rotated successively by 60\({}^{\circ}\)) with a numerical simulation. More comprehensive results and details on the experimental setup are provided in [7].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Deposition Technique** & **Spin Coating** & **Spray Coating** \\ \hline Dielectric constant & \(11.86\) & \(9.6\) \\ \(e_{T}\) [/] & & \\ \hline Piezoelectric constant & \(13.54\) & \(12.54\) \\ \(d_{31}\) [pC/N] (\(E_{B}=0\)) & & \\ \hline Electrostrictive constant & \(-12.65\) & \(-13.81\) \\ \(Q_{33}\) [m\({}^{4}\)/C\({}^{2}\)] & & \\ \hline Spontaneous polarization & \(0.0152\) & \(0.0157\) \\ \cline{2-3} \(P_{s}\) [C/m\({}^{2}\)] & \(0.0163\) & \(0.0144\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimation of material parameters of PVDF-TrFE.  
Figure 3: Piezoelectric coefficient \(d_{31}\) as a function of the bias electric field.  
Figure 4: Demonstrators built and tested in the earlier part of the project: D = 100 mm with 7 independent electrodes [6] and D = 200 mm with 25 independent electrodes [7].  
Figure 5: Comparison between experimental 30\({}^{\circ}\) cross sections of the influence functions of the six electrodes of the same row and a numerical simulation. The voltage applied is 100 V.
## 3 The DELTA Platform
### Gnss  
The dataset encompasses comprehensive Global Navigation Satellite System (GNSS) measurements from multiple satellite constellations, namely GPS, GLONASS, Galileo, and BeiDou. These measurements are essential for high-precision navigation and geospatial  
Figure 4: Data formats found in our stereocamera dataset: (**a**) RGB, (**b**) depth map, and (**c**) point cloud.  
Figure 3: Data formats found in our LiDAR dataset: (**a**) point cloud, (**b**) range, and (**c**) reflectivity channel.  
analysis, providing an accuracy of approximately 1.1 m. The dataset comprises three distinct data types, pseudorange, carrier phase, and Doppler shift, each offering different levels of insight into distance and velocity measurements, crucial for applications that require enhanced accuracy, such as differential GNSS. Each satellite data point within the dataset is characterized by unique identifiers (svId), signal quality metrics (carrier-to-noise ratio (CnO), lock time), and detailed navigation outputs (UBV-NAV-PVT message). These navigation outputs report position, velocity, and time information, rendering the dataset valuable for real-time applications and urban planning endeavors. Furthermore, the dataset facilitates post-processed kinematics (PPK), enabling centimeter-level accuracy improvements. Figure 5 shows the spatial coverage and the trajectory of our data collection.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation
### Test Data  
The test data consist of two traces generated in the MobEmu simulator [14] using the mobility model described in Section 3.5, customized with different parameters (as presented in the rest of this section).
# Experimental Study of LoRa Transmission over Seawater
###### Abstract  
Low Power Wide Area Networks (LPWANs) are gaining attention in both academia and industry by offering the possibility of connecting a large number of nodes over extended distances. LoRa is one of the technologies used as a physical layer in such networks. This paper investigates the LoRa links over seawater in two typical scenarios: clear Line-of-Sight (LOS) and obstructed path in two different Industrial, Scientific and Medical (ISM) radio bands: 868 MHz and 434 MHz. We used three different LoRa devices in the experiments: the Own Developed LoRa Transceiver (ODT) and two commercial transceivers. Firstly we investigated transceivers' Receive Signal Strength Indicator (RSSI) and Signal-to-Noise (SNR) measurement chain linearity and provided correction factors for RSSI to correlate it with actual signal levels received at transceivers' inputs. Next, we carried out field experiments for three different LoRa Spreading Factors, \(SF\in[7,10,12]\), within a bandwidth of \(BW=125\,\mathrm{kHz}\) and Coding Rate \(CR=4/6\). The experiments showed that LoRa links are fully feasible over seawater at distances at least 22 km long, using only low-cost off-the-shelf rubber duck antennas in LOS path condition in both ISM bands. In addition, we showed that LoRa links can be established over 28 km obstructed LOS oversea path in ISM 434 MHz band, but using costly, higher gain antennas. Furthermore, the laboratory experiments revealed that RSSI is linear in a wide range, up to \(-50\,\mathrm{dBm}\), whereas the SNR measurement chain goes into saturation for Received Signal Strength (RSS) values higher than \(-100\,\mathrm{dBm}\). These findings enabled accurate interpretation of the results obtained in field experiments.  
LoRa; IoT; LPWAN; sea; transmission +
Footnote †: journal: Sensors
## 2 Methods
### Data Analysis
For individuals, locations were determined to be within or outside the EEZ bounds of The Bahamas based on the GPS coordinates of satellite detections overlaid with the 'Natural-Earth' shape file in R. The percentage of time spent within the EEZ was calculated as the number of daily location estimates within the EEZ bounds, divided by the total number of days the satellite tag was transmitting. Further, we used the 'MOVE' package [44] to perform a dynamic Brownian bridge movement model (dBBMM, [45]) to calculate utilization distributions (UDs) at the individual level. To avoid error overestimation by the model when tiger sharks were not detected, the variance wasconstrained (using the brownian.motion.variance.dyn function) each time an individual was absent for \(>\)24 h. The sizes of the 50% and 95% (core and extended areas, respectively) UDs were calculated using the volumeUD() function in the 'fishtrack3d' library [46]. Subsequently, all individual rasters were summed into one global raster to calculate group-level UDs. The obtained global raster was scaled so that the raster cells summed to 1 to allow for UD calculations (similarly to [PERSON] et al., [7]). The contours of the group-level UDs were then plotted in relation to the EEZ. All statistical analyses were performed in the RStudio (2021.09.1; R Studio Team, Vienna, Austria) integrated development environment for R version 4.0.3 (R Core Team). Statistical significance was determined at an \(\alpha\) = 0.05, and where appropriate, the mean and standard deviation are reported.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 3 Experiments and Results  
In this section, the efficacy of adversarial attacks (FGSM, BIM, PGD, and CW) against four popular DNN models (VGG-16, ResNet-101, Inception V3, and DenseNet-121) for plant disease identification is investigated. The effectiveness of adversarial sample detection methods against adversarial attacks is also studied. Without loss of generality, we present the results of apple leaf disease identification for which several DL models have been developed [30; 35]. Although we only report the results of apple leaf disease identification, our unreported experiments obtained similar results from other leaf disease datasets.
# Adaptive Detection and Classification of Brain Tumour Images Based on Photoacoustic Imaging
## 2 Materials and Methods
### Deep Learning Algorithms for Brain Tumour Classification  
A convolutional neural network (CNN) as an image classification algorithm has the ability to abstract and generalise image features with efficient computational performance, which can effectively extract features from photoacoustic brain tumour images, retain spatial information, and use large-scale data for end-to-end learning to achieve accurate classification tasks. The CNN classification algorithm [15; 23; 33] used in this paper shows the best results in classifying photoacoustic brain tumour images, and its network structure consists of a convolutional layer, a pooling layer, and a fully connected layer [34]. The convolutional layer is the core component of a CNN which extracts the local features of an image by sliding a convolutional kernel over the input image to perform convolutional operations. The spatial invariance of the features can be extracted using a maximum pooling layer. After multiple convolutional and pooling layers, the resulting feature map is spread into a one-dimensional vector, which is then classified by a fully connected layer. After the final fully connected layer, the output is usually transformed into a probability distribution representing the probability of each category using the softmax function.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 7 Experimental Results
### Runtime Analysis  
Figure 6 shows the average run time of experiments that super-resolved video sequences that lasted one second and were encoded in high definition, i.e., 720p and 1080p. The quality of these super-resolved sequences was assessed by the VMAF metric. The proposed model, i.e., VSRGAN+, had a middle-ground computational cost. It was higher than the cost of the SRCNN and ESPCN models but lower than that of the SRResNet and SRGAN models. This middle-ground performance paid off by enabling the VSRGAN+ to achieve the highest perceptual quality assessed by the VMAF metric.  
Figure 6: The average running time versus the VMAF assessed video quality. (**a**) Video sequence with 720p. (**b**) Video sequence with 1080p.  
The proposed video distribution architecture supports VoD applications; see Section 3. In this scenario, any publisher committed to high-standards of perceptual quality would agree to postpone the content release time to have the best output for the publishing step. This reasoning reinforces the importance of balancing the computational cost and the perceptual quality of super-resolved video clips, which is the case of the VSRGAN+ model.
## 4 Case Study: NSS Data from a UK University
### Phase 2: Teaching Intervention and Evaluation
#### 4.2.3 Evaluation: Discussion on Questionnaire Results
The results of the Level 5 modules show an average rating of 74%, and three questions achieved 90%+. As they were part of the NSS questionnaire, for Questions 2, 3, 4, 6, and 7, we listed the department's previous year's NSS scores (an average rating of 74%) to provide a direct comparison: the results for the Level 6 modules show substantial improvement (all responses were rated higher), while Level 5 yielded mixed results. Since the actions were designed for Level 6 students, the survey results indicate a significant improvement in students' experience of teaching and learning in the department: on average, 22% more satisfaction. The Level 5 results are for comparison and will be discussed further in a later section.