esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Relations among Graph Parameters  
For the graph parameters described in the previous subsection, the following relations are known.  
* For each \(1\leq j\leq|V|\), \[\min_{j/2\leq i\leq j}b_{v}(i,G)-1\leq tw(G)\leq pw(G)\leq\min\{cutw(G),bany(G)\}. \text{ (see Lemma \ref{lem:2011} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})}\] (1)
* For each \(1\leq j\leq|V|\), \[\min_{j/2\leq i\leq j}b_{v}(i,G)\leq\min_{j/2\leq i\leq j}b_{e}(i,G)\leq carw(G) \leq cutw(G)\text{ (see Theorem \ref{lem:2011}{}{} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})},\] (2)
* \[rw(G)\leq\min\{tw(G)+1,cliw(G)\}\text{ (see Corollary \ref{lem:2011}{}{} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]}, Proposition \ref{lem:2011}{}{} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})},\] (3)
* \[lclw(G)\leq pw(G)+2\text{ (see Section \ref{lem:2011}{}{} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})},\] (4)
* \[\text{NLC}(G)\leq cliw(G)\leq 2\text{NLC}(G)\text{ (see \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})},\] (5)  
where \(\text{NLC}(G)\) denotes the NLC-width of \(G\) (see [38] for the definition of NLC-width and details).
* \[bow(G)\leq tw(G)+1\text{ (see Figure \ref{lem:2011}{} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})}.\] (6)
* \[mimw(G)\leq bow(G)\leq mimw(G)\log_{2}(|V(G)|)\text{ (see Theorem \ref{lem:2011}{}{} in \@@cite[cite]{[\@@bibref{}{Cos:2011}{}{}]})}.\] (7)  
Note that relation (5) implies that clique-width can be approximated within a constant factor if and only if NLC-width can be approximated within a constant factor.
# Towards a Catalogue of Self-Sovereign Identity Design Patterns
## 3 Self-Sovereign Identity Design Patterns and Their Classification
### Patterns and Their Classification
Last but not least, the _Wallet and Off-Chain Storage_ category encloses (6.1) _Hot and Cold Wallet Storage_, (6.2) _Local (Private) Storage_, and (6.3) _External (Remote) Cloud Storage_ patterns. Identity wallets provide a means for digital identification, authentication, and authorisation. They serve as a tool for creating and managing identifiers and associated cryptographic keys, as well as for obtaining, storing, and distributing credentials and other personal data, which have to be stored securely and under the complete control of the identity holder. Patterns (6.2) and (6.3) provide storage for identifiers, keys, credentials and other personal data completely off-chain, either locally on the identity holder's device (smartphone, tablet, computer, etc. ), or in remote locations in the cloud where they can be accessible to their holders or wallet controllers. Thereby, trusted cloud-based data storage can be used to avoid the availability and accessibility challenges of local storage. (6.1) _Hot and cold wallet storage_ provides an additional security layer, and can be used for recovery purposes. It allows each entity to maintain keys in two wallets, i.e., hot and cold wallets, providing online storage for frequently used keys and offline storage for infrequently used keys, respectively [14].
# An Explorative Model to Assess Individuals' Phubbing Risk
## 7 Discussion
Thus, it is not a surprise that the individuals more used to ICT could also be the ones that more often use smartphones and engage in phubbing. Whether their use can be read as an addition or a simple interruption of face-to-face communication, the \"confidence\" with ICTs appear to be a promoting factor for phubbing. We also registered how digital native subjects (i.e., individuals under the age of 26) were more likely to engage in phubbing than digital immigrants. Younger individuals were born and raised in the age of new ICTs [44]. Their use of smartphones is quite different from people who experience ICT revolution in adulthood. Indeed, digital natives' use of smartphones is pervasive in their lives. Not only their use is more frequent with obvious effects on both phubbing components, but it is also more socially connoted. Indeed, digital native individuals use smartphones also to signal their social affiliation as well as to build social relationships [45; 46], while digital immigrants, especially seniors, use smartphones mainly for their utility as phones [47]. Therefore, the difference between digital natives and digital immigrants in ICTs' social importance and pervasivity could be a possible explanation for age effect on phubbing. The social environment defined by dyadic-couple interactions also seemed to have a role in shaping phubbing behavior. Indeed, individual phubbing is associated with the perceived level of a partner's phubbing, which could mean that seeing a significant other engage in phubbing could influence the acceptability of such behavior and possibly reinforce phubbing dynamics [16]. Significant others' influence affects a broad variety of behaviors, among which addiction conducts [48]. Indeed, what others do (i.e., empirical expectations) influences the likely to engage in a behavior, thus defining a norm within a social system [49]. However, our measurement of partners' phubbing is based on a person's perception of what another person does and thus could be biased. Future works should investigate the possible role of phubbing as a shared and self-reinforcing norm. Moreover, future research should also consider the relationship between phubbing and other constructs, for instance selfishness, to better understand phubbing underlying motivations. Lastly, we verified the effects of gender and psychological and psychosocial observables on phubbing. Differently from the previous literature [11; 12], gender did not appear eeither to impact the phubbing level or mediate the relationship between the smartphone addiction, internet addiction and phubbing behavior. The relationship between self-efficacy and anxiety is well known in the scientific literature [20]. Nevertheless, the relation between self-efficacy and phubbing appears quite weak, probably indicating a more complex dynamic between the observables than a simple linear relation.
## 3 Automated Operational Modal Analysis Based on Modal Uncertainty
### Removal of Outlier Modes  
The clusters of identical modes formed in this way may contain modes with varying characteristics other than frequency and MAC index, so these outlier modes should be removed.  
The damping ratio exhibits relatively greater variability than the frequency or the MAC index. If this damping ratio is considered as a condition for the construction of clusters of identical modes, it is likely that the number of clusters would be increased more than necessary. Consequently, the damping ratio is adopted as a criterion for the removal of outliers instead of a criterion for the construction of clusters of identical modes. Check table \(d_{ij}\), related to the modes within one cluster of identical modes is formulated as follows.  
\[\left|\mu_{\xi_{i}}-\mu_{\xi_{j}}\right|\leq z(p)\sigma_{\xi_{j}} \tag{12}\]  
The above equation means that the \(i\)-th mode related to the \(j\)-th mode will exhibit similar damping ratios if satisfied, and that the damping ratio will show different characteristics if not satisfied. A new check table \(c_{ij}=t_{ij}\wedge d_{ij}\) is constructed by the conjunction (AND) of the check table \(t_{ij}\) related to the frequency and MAC index and the check table \(d_{ij}\) for the damping ratio.  
In the case of a mode with a large standard deviation, a larger number of modes will be recognized as being identical to this mode than there is in reality. Such modes could be spurious modes that have been omitted at the pre-cleaning stage or be modes with other characteristics that should be removed. The number \(\text{num}(c_{i.})\) of modes regarded as identical to the \(i\)-th mode can be obtained by the number of _True_ columns in the \(i\)-th row of matrix \(c_{ij}\). The number \(\text{num}(c_{i.})\) of modes considered to be identical to the \(i\)-th mode can be obtained by the number of _True_ rows in the \(i\)-th column of matrix \(c_{ij}\). If we let \(\text{num}(c_{i.})\leq\text{num}(c_{i.})\) apply as the removal criterion and \(R\) be the resulting cluster, \(\widetilde{C}_{c}=C_{c}\cap R\) becomes the cluster of actually identical modes.  
For some measured data, modes with the same model order might be present in cluster \(\widetilde{C}_{c}\). In such cases, only one mode shall be selected by reckoning that these multiple candidate modes obtained through one execution of SSI correspond to one identical mode. The method adopted to select only one mode is to pick the mode with the smallest standard deviation related to the frequency.  
Each of the clusters of identical modes constructed through this process may contain a large or small number of modes. However, since a cluster with an excessively small number of modes might not be a real mode, clusters with a number of modes lower than \(n_{min}\) (>1) are excluded. Referring to [PERSON] et al. (2018), who suggested a range of \(\frac{N}{15}\sim\frac{N}{10}\) for the number \(N\) of SSI analyses, a range of 3-5 is applied.
## 2 Materials and Methods
### Method
#### 2.2.3 GSRPnet Network Structure  
In this paper, we proposed a deep neural network for UGS segmentation, named the Green Space Reverse Pixel Shuffle Network (GSRPnet). To expedite the model fitting speed and improve the accuracy of the UGS segmentation task, GSRPnet utilize the proposed RPS-ResNet as the feature extraction backbone. GSRPnet concatenates the feature map output by each residual convolutional layer to connect contextual information and high-low level features, enhancing the accuracy of UGS recognition. As shown in Figure 7, GSRPnet's network structure is straightforward, yet subsequent experiments indicate its effectiveness. GSRPnet can be divided into three components: the down-sampling module, the feature extraction module, and the feature compression filtering module.  
Figure 6: Comparison of different feature extraction backbones: (**a**) the original ResNet-50 and (**b**) the proposed RPS-ResNet.  
Figure 7: Overall architecture of the proposed GSRPnet.  
In the down-sampling module, the input remote sensing images undergo a half-resolution reduction using RPS to conserve computational resources, along with a convolutional layer to expand the feature map dimension. In the feature extraction module, features are extracted through R2, R3, and R4, and the resulting feature maps (designated as F2, F3, and F4) have their resolution increased to half of the original resolution using bilinear interpolation. Subsequently, these feature maps are concatenated along the channel dimension to form a global feature block named the Feature Concatenated Block. In the feature compression filtering module, the global feature block undergoes compression and filtering of green space features through an MLP layer. Finally, the Pixel Shuffle method [37] is used to raise the feature map resolution to the target space, obtaining the binary image of a UGS.
## 2 The Transmitting and Receiving System of the Composite Signal
### Signal Model  
The composite BOC signal has two parts: a BOC signal and a BPSK signal. The main idea of BOC modulation is to modulate the square wave subcarrier on the spread spectrum modulation so that the power spectrum of the signal is moved to both sides of the central frequency point [27; 28]. In addition, we need to insert the BPSK signal at the central frequency to achieve data transmission. Mathematically, the composite BOC signal can be expressed as Equation (1):  
\[s_{t}(t)=s_{\text{BOC}}(t)+s_{\text{BPSK}}(t)=\alpha d(t)c(t)sign[\cos(2\pi f_ {s}t)]+\beta b(t) \tag{1}\]  
where \(d(t)\) is the navigation data, \(c(t)\) represents the PN sequence, \(sign(\cdot)\) is the symbolic function, and \(f_{s}\) is the frequency of the square wave subcarrier. \(b(t)\) is the communication data. In addition, \(\alpha\) and \(\beta\) are the signal power of the BOC signal and BPSK signal, respectively. These values indicate the proportion of BOC and BPSK signals in the whole transmitted signal. The chosen \(\alpha\) and \(\beta\) are explained in Section 4. \(c(t)\) and \(b(t)\) can be expressed as Equations (2) and (3):  
\[c(t)=\sum_{k=-\infty}^{\infty}\alpha_{k}p_{T_{c}}\left(t-k\frac{1}{f_{s}}\right) \tag{2}\]  
\[b(t)=\sum_{l=-\infty}^{\infty}\alpha_{l}p_{T_{b}}\left(t-k\frac{1}{f_{s}}\right) \tag{3}\]  
\(\alpha_{k}=\pm 1\) and \(\alpha_{l}\pm 1\). \(p_{T_{c}}\) is the PN sequence, and \(p_{T_{b}}\) is the communication data. \(f_{s}\) is the bandwidth, which is equal to the frequency of the subcarrier.  
The received analog intermediate frequency (IF) composite signal at the wideband ADC input can be expressed as Equation (4):  
\[\begin{array}{l}s_{r}(t)=s_{\text{BOC}}(t)+s_{\text{BPSK}}(t)+n(t)\\ =\alpha d(t-\tau)c(t-\tau)sign[\cos(2\pi f_{s}^{\prime}(t-\tau))]e^{[-/2\pi(f_ {\text{IF}}+f_{d})(t-\tau)]}\ (\alpha+\beta=1)\\ +\beta b(t-\tau)e^{[-/2\pi(f_{\text{IF}}+f_{d})(t-\tau)]}+n(t)\end{array} \tag{4}\]  
where \(f_{\text{IF}}\) is the IF carrier frequency, \(f_{s}^{\prime}\) represents the subcarrier frequency in the receiving end, and \(\tau\) is the time delay.  
The paper uses BOCos(5,5) and BPSK(5) to form the composite signal. The power spectrum with \(f_{\text{IF}}=20\) MHz is shown in Figure 1. The bandwidth of BPSK and PN and the frequency of the subcarrier in the system are 5.115 MHz. The signal power spectra of the BOCos(5,5) signal, BPSK(5) signal, and composite signal are shown in Figure 2. When the multifunctional signal is buried in noise, such as the GNSS signals, the communication data cannot be demodulated. However, in inter-spacecraft or inter-UAV communication, the signals are well above the noise floor. Thus, we ignore the case when the signal is buried in noise.  
Figure 1: The transmitter of the composite signal.  
Figure 2: Characteristics of the BOCcos(5,5) and BPSK(5) composite signals. (**a**) \(\alpha:\beta=0.3:0.7\); (**b**) The power spectrum of different \(\alpha\) and \(\beta\) ratios.
# CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
## 3 Results and Discussion
### Analysis of the Separation Efficiency  
The main purpose of utilizing a cyclone is to separate the particles from gas, and in this regard, the separation capability of a cyclone is defined in two ways. First, the cut-off particle size (designated as \(x_{50}\))--defined as the particle size that corresponds to 50% separation efficiency on the grade efficiency curve (GEC). Second, the overall separation efficiency is calculated as the ratio of the number of particles separated by the cyclone separator to the number of particles entering the cyclone.  
Figure 8 represents the GEC of all variants at \(U_{in}=10\) m/s. The change in dimensions and shape of individual sections of the cyclone separator did not significantly change the profile of GEC curves. Similar observations were made by [PERSON] et al. [39] and [PERSON] et al. [37]. For all cylinder lengths in each variant, the variations in the GECs are from mild to moderate--CC variants are more sensitive to the cylinder height than the ST variant, whereas in the CV variant the variations are very mild. Complete information on the cut-off size for all \(U_{in}\) is presented in Figure 9. Two important observations here are: with an increase in cylinder length, \(x_{50}\) increases mildly at all \(U_{in}\), whereas with an increase in \(U_{in}\), the cut-off size reduces significantly in all variants. For short cylinder height and lower \(U_{in}\), the CC variant outperforms the ST as well as CV variants, whereas, at higher \(U_{in}\), the \(x_{50}\) values in the ST variant are greater than other variants by a very small margin.  
Figure 10 illustrates the overall separation efficiency in all variants at different \(U_{in}\). With an increase in \(U_{in}\), the overall separation efficiency in each variant increases significantly, whereas the increase in the cylinder height mildly reduces the collection efficiency at a given \(U_{in}\). The efficiency of different models in the CC variant is slightly better than the standard ST variant, and in the case of CV, the efficiency is the lowest.  
Figure 8: Grade efficiency curves for all cyclone models in ST variant, CV variant and CC variant at inlet velocities \(U_{in}=10\) m/s with particle sizes ranging from 0.4 to 8.0 \(\upmu\)m.  
Figure 10: Overall separation efficiency in all the cyclone models of ST variant, CV variant and CC variant at inlet velocities \(U_{in}\) = 10, 15 and 20 m/s.  
Figure 9: Cut-off sizes in all cyclone models of ST variant, CV variant, and CC variant at inlet velocities \(U_{in}\) = 10, 15 and 20 m/s.  
Table 3 provides complete information on (percent change in) overall separation efficiency and cut-off size with respect to the base variant ST2, at different \(U_{in}\), in all variants.
# SOLIOT--Decentralized Data Control and Interactions for IoT
## 5 SOLID Characteristics
### Functional Characteristics  
The core SOLID design principle is the decentralized management of personal data. Therefore, the _distributed identity management_ is the foundation for any interaction process. The usage of WebIDs (F5) allows the creation of identity proofs without a CA chain. Clients obtain a dereferenceable URI as their WebID, by which the obtained certificate is linked to the public key in the user's profile. The relevant data is managed as a _encapsulated data entity_ in the form of a personal online data store (Pod), which can be seen as a Digital Twin of the human user. This Digital Twin frames a manageable entity where interactions and manipulations can be applied.  
Interoperability (F1) and transportability of pods are gained through _Uniform interfaces_ based on the Linked Data Platform specification and SPARQL interfaces. Obviously, the focus is on the Cloud (F1.3) and Web Integration (F1.4) stages. These two patterns also provide the interaction methods with the resources and state transitions (F2). _Complex data retrieval_ is required whenever single resources cannot directly fulfill an information need, which to some degree can be accomplished using SPARQL queries. However, SOLID does not specify any methods to call complex operations (F4) on the hosted resources. The design decision to rely on _state-less_, _RESTful interactions_ enables the usage of current available web browsers and Web-based clients for interactions but restricts the direct applicability of richer operations.  
_Access control_ (F7) on pods and their subcontainers is implemented by Access Control Lists (ACL) formulating restrictions with terms and concepts of the Web Access Control (WAC) ontology [37], using the WebID or a user certificate (F6). The language states which groups or individual users, identified by their WebIDs, have access, write or control rights on each container. Each container comes with its own _._acl file, containing the respective permissions encoded in RDF itself.  
This access control mechanism therefore enables SOLID to connect the identity claims of user agents with the identity information behind the dereferenceable WebID. The approach combines identity claims, self-information of the user, and asymmetric keys into a decentralized system without a CA hierarchy. Usage Control (F8) in its wider sense is not regarded.
# MPH-M, AODV-M and DSR-M Performance Evaluation under Jamming Attacks
## 1 Introduction  
Wireless Sensor Networks (WSNs) [1] are a set of interconnected electronic devices with small processing and (wireless) communication capabilities. For many applications, it is desirable that wireless sensor devices remain fully operational for a long period. For example, a WSN can be designed to transmit prominent information about a forest, such as a temperature or humidity. Frontier research in WSNs hence involves the development of mechanisms for efficient energy management, enabling sensors to work for a longer time without replacing the battery.  
Recent advances in both computing and communications have caused a significant shift in research on sensor networks, i.e., the use of cheap and tiny sensors based on micro-electromechanical systems technology, connected via wireless communication, and making inexpensive low-power processors. This allows for the deployment of ad hoc wireless networks suitable for many applications, including environmental monitoring, security monitoring or object tracking.  
There are applications where sensitive information is transferred through the WSN, i.e., cryptographic key exchange. Typical threats may be communications eavesdropping, nodes impersonation, data corruption, etc. A first countermeasure could involve the use of cryptography mechanisms, i.e., the IEEE 802.15.4 standard describes a series of cryptography mechanisms that guarantee a set of properties implying secure communication [2].  
However, the implementation of cryptographic algorithms cannot reduce the risk due to other threats in wireless communications; for example, intentional interference, called jamming, where the receivers are saturated with spurious signals avoiding the reception of legitimate data. Usual effects of jamming are higher energy consumption when the nodes increase the re-transmissions rate leading to a lower performance of the network.  
We propose in this work a mitigation mechanism for reducing the effects of jamming attacks in WSNs, we have modified MPH (Multi-Parent Hierarchical) [3], AODV (Ad hoc On Demand Distance Vector) [4] and DSR (Dynamic Source Routing) [5], the new versions of the protocols that include the mitigation scheme are called MPH-M, AODV-M and DSR-M.  
Detection of jamming attacks is performed with an anomaly model, which considers the following variables: packet re-transmissions rate, number of CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) retries waiting for an idle channel, and energy consumption. After a positive detection is done, a reactive phase starts, where the self-configuring capabilities of the routing protocols are triggered for avoiding the jamming area and reducing the impact on the whole network.
## 9 Conclusions  
Jamming devices introduce unwanted jamming signals in the network to disrupt the primary communication of the devices. The efficiency of the jamming signals is characterized by the number and distribution of the jammers. This article characterized UAV-assisted C-V2X communications and considered the impact of clustered jammers as well as the fluctuating antenna beam on the equipment's performance. The results described the networks' performance in terms of coverage and SE with various network settings such as V-Ns, MBSs, and UAVs. The results showed that the V2X network's performance reduces with jammers and that this performance declines with the increasing variations of the antenna's beam. Thus, network designers must focus on designing advanced counter-jamming techniques whenever jamming signals are expected in vehicular networks.  
The future directions to extend this research include designing advanced anti-jamming techniques to reduce the impact of clustered jamming on the vehicular communications. This research can also be extended to investigate a full-duplex environment for UAV-assisted C-V2X networks.  
Conceptualization, M.A.; methodology, M.A., W.K., A.I. and S.W.K.; formal analysis, M.A.; generation of simulation results and comparison, M.A.; validation, M.A., W.K., A.I. and S.W.K.; writing--review and editing, M.A., W.K., A.I. and S.W.K. All authors have read and agreed to the final version of the manuscript.  
This research was supported in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (RS-2023-00228316), in part by the Basic Science Research Program through the National Research Foundation  
Figure 11: Jamming cluster’s influence on equipment’s spectrum.  
of Korea(NRF) funded by the Ministry of Education (NRF-2021R1A6A1A03039493), and in part by the NRF grant funded by the Korean government (MSIT) (NRF-2022R1A2C1004401).  
**Data Availability Statement:** No data were used for the research described in the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.