esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 1 Introduction
First, these approaches are inefficient. Most of these approaches first extract features, then measure them separately, and finally integrate them into a numerical value. In addition, the calculation method also faces various doubts because privacy is a virtual concept with no unifying principle, and any calculation is considered to be subjective and unconvincing. Second, this method relies too strongly on artificial feature extraction. In previous research on privacy metrics, feature extraction is a difficulty. Which features can be used for privacy measurement? Which features are more important? What associations exist between these features? These problems urgently need to be solved but have not been solved. Meanwhile, when considering the network environment of users [7], there may be tens of millions of links around a user. Previous research methods only obtained one user's privacy score after analysing the whole network environment, which is undoubtedly inefficient and a waste of resources. For the above problems, deep neural networks, which are currently a popular research topic, have inspired us. A deep neural network can effectively extract the hidden relationship between features without human intervention. A large number of studies show that the hidden relationship between these features has a very large effect on the final results. Among the many neural network models, graph convolution networks (GCNs) [8] can extract features from graph structures and combine the features of nodes, which is suitable for scenarios in social networks. However, deep learning frameworks require a large amount of labelled data for training, and the vast majority of people cannot accurately quantify privacy as a virtual concept, so data annotation is very difficult. Therefore, we label a small number of samples with the help of experts and use few-shot learning to solve this problem. The goal of this paper is to use GCNs to build a framework to measure the privacy of online social network users. Compared with the traditional method, this framework has the following advantages. First, this framework can eliminate the interference of human subjective consciousness to the greatest extent, it only needs to select features, and it does not participate in the calculation and measurement process. Second, due to the difficulty of sample labelling in privacy measurement, our method only needs a small number of labelled samples. Finally, our method can obtain the privacy measurement scores of all the users in the whole network at the same time.
## 4 Proposition of Signal Classification Algorithms  
Integrated EEG signals of the particular channels from Section 3 may be used by designed classification systems. Based on the literature review and provisions from Sections 1 and 2, the following elements will be used for this purpose: a classic expert system and algorithms in the field of artificial intelligence, i.e., the feed-forward neural network.  
Both systems have their advantages and disadvantages, of which a designer should be aware. A classic algorithm may not be able to foresee well enough all types of interference to properly classify a given type of a signal. A neural network, on the other hand, may be impervious to an unclear signal due to, for example, incorrect contact of electrodes with the head of the test participant.
## 4 Experimental Setup for Validating an NRS Algorithm and Its Outcomes
### Process and Data Collection  
During a period of ten days (21 April 2020-1 May 2020), the following datapoints (see Table 1) were collected for each user and per session (each time they used the designated browser to visit the website under study).  
Participants were fully informed that their activity was monitored in this manner; after providing their written consent to participate in the study, they were provided with an ad hoc created Chrome plugin pre-installed into a special browser to enable the collection of the aforementioned data. Users were asked to use this special browser any time they wanted to take part in this experiment (session). The plugin was transparently collecting and storing into a remote MongoDB database all the information necessary for analyzing the behavior of the news recommender algorithm implemented and applied by the medium. Data were only communicated and stored if users elected to press a specific button in their browser.  
Figure 5: Visualization of the experimental approach: Design and datasets.  
In addition to the aforementioned data, we collected information pertaining to the 'pool items' (the stories posted on the website) and also user information. Table 2 describes in detail the type of data collected. Having extracted the posts from the website, each entry was augmented with the user's information in order to associate users with content. User activity was captured by monitoring his/her click activity.  
Special mention needs to be made to the 'news category' variable mentioned in Table 2; this was an attribute assigned to each news item by the website, presumably the journalist responsible for writing the relevant article (i.e., these were not manually coded by the researchers). A total of 161 such categories were observed to be present on the news outlet under study; however, this included instances where mistakes had been made in tagging the article (e.g., the tags \"Greece and \"Greese\") and duplications (e.g., using the tag \"Greece\" and \"Ellada\", a phonetic spelling out of \"Greece\" in the Greek language). When cleaned and grouped appropriately, a total of 33 different news categories emerged. Since however, using such a large number of categories would make the results difficult if not impossible to assess, it was decided to group these 33 categories into broader categories (e.g., \"lifestyle news\" were grouped together with \"gossip\"), ending with the nine categories reported in the results below (see Table 3). It should be mentioned, however, that this only affects the visual aspects of the results, as all calculations were made using the 33 original news categories assigned by the medium. Finally, it ought to be noted that the plugin maintained the order of posts as they appeared on the website, thereby ensuring that the order of the entries appearing into the database reflected the order the articles were read by the users. Every time a user accessed the website, the plugin collected all content and user-related information.
## 5 Collection of KMNs
### Collection of KMNs Based on a Single Target  
We illustrate the collection of KMNs based on a single target by using the examples of _in-block_ and _off-block_ of an aircraft. _In-block_ means that an aircraft has arrived in the parking position and parking brakes are activated. Thus, _in-block_ of the aircraft is the transition of the aircraft's state from motion to rest. In contrast, _off-block_ is the moment when an aircraft starts to move from the parking position and prepares to taxi and take off. This implies that _off-block_ is the transition from the stationary to the moving state of the aircraft. Consequently, nodes of _in-block_ and _off-block_ can be detected based on the motion state estimation of a single target. The algorithm for _in-block_ and _off-block_ node detection is described in Algorithm 2. Without loss of generality, this algorithm can be used to collect other KMNs based on a single target.  
```
Inputs: For a sequence with _num_ frames, the bounding box of aircraft in each frame is \([x_{c}^{i},y_{c}^{i},w_{i}^{i},h^{i}]\), \(i=1,2,\dots\), \(num\). \(Th_{c}\) and \(Th_{IoU}\) of Equation (10) are initialized (manually determined) for\(t=r+2\):\(num\)-\(r\)1 Computing\(\sigma_{c}(t-1)\) and \(\sigma_{IoU}^{pc}(t-1)\), \(\sigma_{c}(t)\) and \(\sigma_{IoU}^{pc}(t)\), \(\sigma_{c}(t+1)\) and \(\sigma_{IoU}^{pc}(t+1)\). Computing\(C_{1}(t-1)\), \(C_{1}(t)\), and \(C_{1}(t+1)\) using Condition 1 of Equation (10). if\(C_{1}(t-1)=0\) and \(C_{1}(t)=1\) and \(C_{1}(t+1)=1\) \(ln\)-block node is \(t\). else if\(C_{1}(t-1)=1\) and \(C_{1}(t)=0\) and \(C_{1}(t+1)=0\)  Off-block node is \(t\). end if endfor Outputs: In-block and off-block nodes.
```  
**Algorithm 2**_In-block_ and _off-block_ node collection
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 2 Overview of ZigBee  
ZigBee is different from protocols such as IEEE 802.11, in that it specifies the use of a complete set of protocols specifically designed for device-to-device communication [1, 2].  
In its application layer, ZigBee defines application profiles that facilitate the communication between applications in the various devices and defines procedures for network discovery and connection establishment. ZigBee also defines the application support sub-layer (APS), which manages ongoing connections and has transport layer functionalities for end-to-end reliable data transfer with acknowledgments, retransmissions, and rejection of duplicate packets.  
In its network layer, ZigBee defines procedures to establish multihop communication routes between devices, defining procedures for route discovery and routing algorithms. Four routing algorithms are defined [4]: table-based routing (similar to AODV), hierarchical tree routing, multicast routing, and many-to-one source routing. In this paper, we focus on the many-to-one (M2O) source routing algorithm because many applications of sensor networks involve many sensors transmitting information to one concentrator node. Details of the M2O algorithm are present in Section 2.2.  
To support networking functions, ZigBee also defines that nodes periodically broadcast link status (LS) messages. In each LS message, a node broadcasts its current view of each neighboring link. More precisely, the LS message transmitted by a node contains a list of all neighbors of the node and contains the cost of the link from each of its neighbors to the node.  
It is important to highlight that  
* The link costs present in the LS message are quantized into three bits (see Section 3.4.8 of [13]).
* ZigBee differentiates between outgoing and incoming link costs; however, for M2O routing, the ZigBee specifications define that path costs be based on the maximum between them (see second paragraph of page 347 of [13]). To simplify our discussion, link costs in this paper refer to the maximum between the incoming and outgoing costs of a link.  
For the medium access control (MAC) and physical layers, ZigBee specifies the use of the IEEE 802.15.4 MAC and physical layers, which are described next.
# A Novel Error Correction Approach to Improve Standard Point Positioning of Integrated BDS/GPS
## 2 Standard Point Positioning of Integrated BDS/GPS  
### Unification of Time and Space Benchmarks  
Both the BDS and GPS time systems use an atomic time base with consistent seconds, both of which are counted in weeks and seconds. The time reference of the BDS is Bei Dou navigation satellite system time (BDT), whose starting epoch is the coordinated universal time (UTC) of 00:00:00 on 1 January 2006. The time reference of the GPS is GPS time (GPST), whose starting epoch is the UTC of 00:00:00 on 6 January 1980. Their different time bases determine the time of each other, but both can be linked to UTC. The conversion relationship between BDT and GPST can be defined as [26]:  
\[\begin{split} BDT\text{ week}&=GPST\text{ week}+1356\\ BDT\text{ sec}&=GPST\text{ sec}+14\end{split} \tag{1}\]  
BDS adopts China geodetic coordinate system 2000 (CGCS2000), while GPS adopts world geodetic coordinate system 1984 (WGS-84). The coordinate frames of both systems are the International Terrestrial Reference Frame (ITRF) defined by the International Earth Rotation Service (IERS) organization. In terms of coordinate system accuracy, the difference between the CGCS2000 and ITRF is about 5 cm, while the accuracy of the WGS-84 has reached 2 cm, which is basically consistent with the accuracy of ITRF. The study has proved that the coordinate and gravity changes caused by the flatness difference of the two coordinate systems are negligible compared with the current high-precision measurement level. The WGS84 coordinate system and the CGCS2000 coordinate system are compatible at the level of accuracy for standard point positioning.
## 3 Datasets and Evaluation Metrics
### HDR-VDP3  
The latest iteration, HDR-VDP3, builds upon HDR-VDP2, incorporating more advanced HVS models, including color difference perception, improved temporal modeling (for video), and better handling of different display characteristics. It aims to provide more accurate predictions of visible differences for both images and videos across various viewing conditions. The detailed architecture and validation are in [36].
## 3 Materials and Methods
### Design and Manufacturing
#### 3.1.1 Transducer: Magnetorheological Elastomer (MRE)
Because the signal-conditioning stage for the MRE is still in the development phase, the signal-conditioning circuits were tested as a single subsystem and without the signals generated by the MRE, but with an input signal coming from a strain-gauge array, as explained in Section 5.2. With the obtained outcomes, the signal-conditioning circuits were placed and welded onto a perforated phenolic plate and inside a protective case designed using computer-aided-design (CAD) software. The protective case consisted of two parts--base and cover--which were attached and secured by holes filled with M3 screws. Moreover, fixed headers were added to the case to connect the conditioning circuit to the CPU module and the strain-gauge terminals. The connection with the CPU module was a 6-pin cable, providing \(-\)5 V, electrical ground, and +5 V inputs. The last 3 pins were analog outputs, of which only 1 was used, while the others remained just for symmetry with the CPU case. The case was modelled in 3D with Autodesk Inventor. To manufacture the case, it was printed with polylactic acid (PLA) filament, and all components were stored inside it. The performed phenolic plate was attached with double-sided foam to the bottom of the case, and the data cables were fixed with glue at the sides of the case. The CAD model and the manufactured prototype are shown in Figure 3.1  
Figure 3.2 Sequence of experiments during the manufacture of the MRE.
# Cognitive Radio Networks for Internet of Things and Wireless Sensor Networks
## 2 5G and Beyond-5G IoT Based on Cognitive Radio Networks
Each technique is employed according to the QoS requirements of IoT applications. These strategies vary in the way of the SUs' channel access in relation to the PUs. The steps involved in the CR process present many challenges in network design. The first step is to collect spectral, geographic, and topological details. The status of the network resources is then determined along with the network protocols and policies. The required communication technique is implemented, based on this knowledge. There are various problems facing the CRNs in terms of both network architecture and hardware. For IoT terminals, the required features include interoperability with various technologies, such as radio environment context awareness, communication pattern learning intelligence, low-power self-optimization, and spectrum usage robustness. The CR networks are needed to be effective both in terms of spectrum utilization and network capacity. There are also challenges in terms of interoperability and co-existence between multiple communication standards. The support for various techniques should be provided for seamless operation of many connected CRUs. The CRNs should be able to accommodate a large amount of data generated by a large number of devices in real-time,i.e., with low latency. To boost network performance, therefore, a CRN cloud architecture has been implemented. However, this raises a new set of research challenges. Future research areas are listed as follow:  
* Interoperability challenges
* CRN based device-to-device communications
* CRN based device-to-network communications
* CRN based machine-to-machine communications
* CRN based service-to-service communications
* Co-existence network technologies challenges
* CRN based radio access
* CRN based medium access control (MAC)
* CRN based handover between multiple communication standards
* CRN based channel hopping
* Resource management challenges
* CRN based resource allocation for the heterogenous CRUs
* CRN based radio resource management (RRM) for spectrum sensing and interference avoidance
* CRN based dynamic RRM systems for centralized and non-centralized sharing
* Intelligent CRN challenges
* Machine learning (ML)/ deep learning (DL)/ reinforcement learning (RL) based MAC for CRN
* ML/DL/RL based routing protocols for CRN
* ML/DL/RL based Handover between the CR Base Stations
* ML/DL/RL based offloading in the CR enabled cloud services
* ML/DL/RL based spectrum sensing and spectrum sharing strategies
* CRN based cloud services challenges
* Offloading techniques
* Co-operative sensing
* Geo-location identification
* CRN based hardware challenges
* Interoperability between multiple radios
* Antenna design for massive multiple-input multiple-output (MIMO)
* Solutions for mmWave and THz channels and miniaturization of radios
* CRN based energy efficient approaches
* Low power CR node hardware design
* Duty-cycling for SUs
* Low power spectrum sensing
* Security and Privacy challenges for CRN
## 1 Introduction
A duration of 35 days and 21 days of soaking were carried out on specimens cured (in dry and moist conditions) for 7 and 21 days, respectively. Results indicated that specimens cured in dry conditions prior to soaking had the best strength improvement but with the greatest strength produced for the mix having the lowest lime-to-GGBS ratio. Hence, strength was affected more by the conditions of curing rather than soaking. [PERSON] et al. [6] investigated the effect of MgO waste and GGBS combinations (maintaining a total of 10% by dry weight of soil) on the unconfined compressive strength of a naturally occurring sulphate soil (containing 22% gypsum) stabilised by varying proportions (6%, 8%, and 10%) of Portland cement. The samples were compacted at optimal conditions and moist-cured for 7, 28, and 56 days. The compressive strength of the MgO/GGBS-stabilised sulphate soil was significantly improved in general but with the highest strength produced by using 10% of Portland cement. The foregoing studies examined the unconfined compressive strength (mostly the peak stress at failure) of sulphate soils stabilised by agents rich in calcium oxide (or hydroxide) with a further incorporation of GGBS to prevent the harmful effects of sulphates. However, these studies may be quite insufficient to provide design guidelines for successful implementation of a stabilised sulphate soil project because of their limited and in some cases, their inclusive nature of outcomes. The results of unconfined compressive strength utilised to establish the general design parameters were presented in terms of the final yielding condition or peak strength from these studies. Hence, no account was taken of the sequence of stress changes throughout the test or the design problems for which the parameters are needed. Hence, in order to carry out design of complex problems, specific parameters with details of changes in stresses, volumetric strains, pore pressures, etc. are required. Therefore, the present research aims to investigate in further detail, the consolidated undrained shear strength (with an examination of pore pressure developments and stress paths) of a stabilised sulphate soil system consisting of gypsum, lime, and GGBS. Moreover, soils with very high sulphate contents have rarely been considered by previous studies. Hence, this research shall uniquely examine the effect of GGBS on the undrained strength capacity of lime-treated soils containing up to 30% of sulphates.
## 3 Experimental Procedure and Analysis Process
### Preprocessing of the Data into Performance Index
#### 3.2.2 Analysis of CVibs  
As shown in Figure 2, the discontinuity of the velocity profile in the corner can cause a large acceleration and jerk. The vibrations in corner 3 in the x direction in Figure 9 validate the observation. The parameters of \(A_{max}\), \(T_{a}\), \(T2\), \(V_{c}\), and \(f_{max}\) used in the experiment were 2700 mm/s\({}^{2}\), 35 ms, 0 ms, 580 mm/min, and 1500 mm/min, respectively. To further analyze the vibration behavior in the corner, four periods were marked as A, B, C, and D. As indicated in Figure 2, period A corresponds to the motion of the x axis moving in a  
Figure 8: Maximum contour error (MCoE) and machining time (MT) for one experiment. (The period 1, 2 and 3 were marked as the corner 1, 2 and 3 in Figure 7, respectively.)  
Figure 7: Testing trajectory. (The corner 1, 2 and 3 were described in Section 3.2.1 and the period A, B, C and D were described in Section 3.2.2).  
state of constant velocity. Period C is the state right after the corner as shown in Figure 7. Due to the discontinuity velocity profile as shown in Figure 2, excessive vibration might occur as shown in Figure 9. Period D corresponds to the motion for the y axis moving at a constant velocity. Because the vibration of period B corresponds to the designed Acc/Dec, the residual vibration at period C could cause machining marks in the corner. The vibration in period C was selected as the third performance index.  
To further observe the vibration behavior around the corner, the fast Fourier transform (FFT) for the periods A, C, and D was calculated (Figure 10). Four major peaks were observed at 30, 40, 116, and 426 Hz. The first natural frequency was revealed to be 30 Hz, which affects the machining process more dramatically compared with the other modes. Furthermore, the magnitude of the 30 Hz mode was highly dependent upon the interpolator parameters. The analysis of 1024 experiments revealed that the 30 Hz vibration mode can be effectively reduced by tuning the interpolation parameters, whereas the other frequency modes were less susceptible to change. Therefore, the vibration magnitude of 30 Hz was selected as the third performance index.  
Figure 10: Fast Fourier transform (FFT) for vibrations of periods A, C, and D in Figure 9.  
Figure 9: Raw vibrations of the high Acc/Dec experiment. (The period A, B, C and D were marked as in Figure 7, respectively.)
# Fusion of Different Image Sources for Improved Monitoring of Agricultural Plots
## 5 Conclusions  
This article describes a simple method for fusing images of high spectral quality, low spatial resolution, and high temporal resolution, from the Sentinel 2 mission, with images of lower spectral quality, low temporal resolution and higher spatial resolution, such as those from orthophotogrammetry and drone flights.  
After homogenization of the observed reflectances, synthetic images can be generated from the time series of the Sentinel 2 mission, in which the values of the gray levels of the higher resolution images are reassigned in a differentiated way can be obtained. In addition, the evolution of various spectral indices on the ground and on vegetation can be estimated in a coherent way.  
Although this work shows an application example of the proposed fusion algorithm, it can be further generalized to allow further exploitation of Sentinel 2 data to characterize the agronomic conditions of plots producing woody annual crops.  
Finally, the robustness of the procedure has been validated by using different image similarity measurements to analyze the effects of employing different drone images and to compare against the similarity of images in the Sentinel 2 series.  
**Funding:** This work has been partially financed by the project \"Engineering developments for the assurance of a profitable, sustainable and competitive agriculture from field to table\" (internal reference number 51918) co-financed by the IVIA and the European Regional Development Fund (ERDF).  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** Authors thank Asdron Spain for the transfer of the drone images.  
**Conflicts of Interest:** The author declares no conflict of interest.