esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 6 Conclusions  
This paper first adopts a time slice division strategy based on the number of satellites per orbit according to the characteristics of the LEO mega constellation and takes 66 time slices for simulation. Then, based on the divided population density, a call model based on  
Figure 8: Comparison of the total delay in the cycle for the six topologies.  
global service capacity distribution is determined, and two optimized dynamic topology strategies, DTLS1 and DTLS2, are designed using the dynamic link selection algorithm to compare with the static topology STLS and the three benchmark topologies of Time, Distance, and MHD. The Dijkstra algorithm is used to obtain random paths between any 10,000 pairs of urban ground stations. The average call distance, packet-forwarding hops, and total delay of transmitted packets within 66 time slices are obtained and compared to verify the effectiveness of the dynamic topology algorithm adopted in this paper and the degree of improvement of the satellite network performance.  
The simulation results indicate that incorporating dynamic links in the inter-satellite topology reduces the average call distance and effectively decreases the total time delay and the number of hops during data transmission. Among various topologies, the DTLS1 topology, which comprises two intra-OP ISLs and one AOPL, exhibits optimal comprehensive performance while ensuring a maximum connection degree of four for each satellite. Additionally, it can rapidly transmit traffic in any direction through the reverse orbit.  
Conceptualization, Q.Z., X.W. and Y.C.; methodology, Q.Z. and H.Y.; software, Q.Z.; validation, H.Y.; formal analysis, Q.Z.; investigation, Q.Z.; resources, X.W., Q.Z. and Y.C.; writing--original draft preparation, Q.Z.; writing--review and editing, Q.Z. and Y.C.; visualization, Q.Z.; supervision, Y.C. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
We would like to thank everyone who helped with this paper, especially our supervisor and colleagues who made this paper possible.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
LEO Low earth orbit  
ISLs Inter-satellite links  
STLS Static topology of satellites  
DTLS Dynamic topology of satellites  
LPCR Low-complexity probability routing algorithm  
MHD Manhattan network  
NOPL Non-adjacent orbital plane link  
OP Orbital plane  
AOPL Adjacent OP ISL
# Privacy-Preserving Federated Learning Using Homomorphic Encryption
## 6 Performance Analysis  
### Computational Overhead  
#### 6.1.1 Computational Overhead on the Client's Side  
In the proposed PPFL algorithm, additional encryption operations are performed to protect the trained local model parameters, and extra decryption operations are performedto reflect the global model parameters to the learning model on the client's side. In the PCK scheme used in the proposed algorithm, the exponentiation operation has a dominant effect on encryption and decryption. The exponentiation operation \(g^{r}\) requires \(1.5\times N_{r}\) multiplications, where \(g\) is a generator of order \((p-1)(q-1)/2\), \(r\in\mathbb{Z}_{K}\) is a random number, and \(N_{r}\) is the length of \(r\) in the DHC scheme in [21]. Thus, the computational complexity of the encryption operation in the proposed PPFL algorithm is given as \(\mathcal{O}(N_{r}\cdot N_{w})\), where \(N_{w}\) is the number of elements of the local model vector. Similarly, the computational complexity of the decryption in the proposed PPFL algorithm is also represented as \(\mathcal{O}(N_{r}\cdot N_{w})\).
# Study on Denoising Method of Weld Defect Signal Based on SSA-VMD-WPD
## 1 Introduction
In addition, in terms of the decomposition effect, VMD had more stable numerical stability and denoising effect than EMD, which could better maintain the local characteristics of the signal and had better adaptability to non-smooth signals and signals containing noise. EMD may have the problem of overfitting or underfitting when dealing with non-smooth signals, and it may lead to inaccurate decomposition results for signals with more high-frequency noise, which further illustrates the advantages of the SSA-VMD-WPD method in this paper. [PERSON], [PERSON] et al. [22] concluded that EWT performs better than other noise reduction algorithms in locating different parts of abnormal biomedical signal data, but since EMG signals are non-smooth, the time-frequency application Capon was used for optimization. [PERSON] [23] introduced a particle swarm optimization algorithm to optimize the decomposition layer K and penalty factor \(\alpha\) of variational mode decomposition, removing noise components from the signal. [PERSON] et al. [24] optimized the variational mode decomposition (VMD) algorithm based on the mean instantaneous frequency, solving the problem of selecting the decomposition mode number K. In addition, there have been some research achievements in signal denoising have been made using combined machine learning methods. [PERSON] et al. [25] combined basic statistical analysis with machine learning to develop a denoising autoencoder, demonstrating stable and excellent performance and denoising capability through experiments. Comparatively, SSA was used for algorithm optimization in the method proposed in this paper, and the algorithm showed good qualities in terms of stability and convergence ability. Building upon this foundation, this study customized carbon steel butt weld test blocks and conducted defect detection based on phased array ultrasonics according to standard requirements, collecting defect backscatter signals. Taking the defect A-scan signal obtained from the detection experiment as the research object, based on the sparrow optimization variational mode decomposition-wavelet packet analysis, applicable denoising methods were studied, and the acquired defect signals were denoized to extract effective defect information. This method improves the accuracy of ultrasonic phased array inspection signal interpretation and provides accurate defect information for subsequent studies.
## 5 Data Imbalance
### Severity of the Data Imbalance Problem  
The severity of the data imbalance problem cannot be ignored, as it directly limits the precision and robustness of Intrusion Detection Systems (IDS). Therefore, addressing this issue is essential for enhancing the reliability and accuracy of IDS. Existing techniques to mitigate this problem include oversampling and undersampling methods, which adjust the data distribution to balance the sample sizes of different classes. Oversampling techniques, such as the Synthetic Minority Over-sampling Technique (SMOTE), generate synthetic data points to increase the number of minority class samples, while undersampling reduces the majority class samples randomly to achieve balance. However, although these methods can improve the balance of the data distribution to some extent, they also introduce new challenges. For example, oversampling can lead to overfitting, and undersampling can result in the loss of important attack features. Therefore, before implementing these techniques, it is critical to recognize the complexity and severity of the data imbalance problem and carefully select appropriate strategies to enhance the overall detection capability and robustness of the IDS.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Pairing Security  
Device pairing builds a secure communication channel between two previously unassociated devices communicating over some insecure channel. Researchers prefer to design secure and efficient pairing protocols that are suitable for various scenarios.  
Perception [114] is a novel context-based pairing mechanism for IoT devices with different sensor types. Devices that are always deployed in the same place can perceive more of the same events over time. This mechanism uses sensory perception of events to calculate event fingerprint similarity as a threshold to detect attacker devices. The perception protocol is evaluated in different settings, and the results show that the fingerprint similarity between legitimate devices is 94.9% on average, which is higher than that of the invalid device (68.9%).  
HoloPair [115] is a secure and usable pairing protocol for augmented reality (AR) headsets. First, the devices exchange their public keys over the insecure high bandwidth and then submit and reach an agreement on a specific instance of a weak hash. Finally, relying on an AR display's unobservability, the participants confirm the authenticity of the exchanged keys in the low-bandwidth visual channel. The authors accomplished a prototype of the system with two HoloLens devices and implemented a comprehensive user study with 22 participants. As the experiments show, most participants detected man-in-the-middle attacks and confirmed successful pairing in 8 s.  
[PERSON] and [PERSON] [96] developed a noisy vibration scheme to cloak vibration sounds during pairing against eavesdropping attacks. In this scheme, the speaker emits a band-limited white noise-based masking signal and low-frequency tones through an external speaker during key transmission. The \"colocated\" adversary can compromise the onboard motion sensors (e.g., accelerometer) and learn the transferred secret via vibrations. Therefore, we need to inject fake accelerometer readings to cover the actual vibration effect.
## 3 Results
### Assessment of the Models' Level of Detail and Feature Recognizability
#### 3.4.3 Recognition of Architectural Details  
In order to assess the real usability of IMMS data in an urban/architectural environment, some comparisons were made with respect to the possibility of not only recognizing the main built volumes, but also some typical architectural details such as doors, windows, lamps, and so on.  
First of all, in the test field, we considered the corner defined by two facades shown in Figure 19. Here, the recognizability of details from the static reference cloud (**b**) can be easily appreciated.  
Figure 19: Corner of the chosen building for assessing the recognizability of architectural details; (**a**) photo; (**b**) reference dataset statically acquired by terrestrial laser scanning (TLS) and down-sampled with 1-cm average resolution.  
Figure 18: Three datasets with a 14.5 cm-diameter sphere, pinpointed by the arrow, attached to a wall: (**a**) Kaarta Stencil; (**b**) Leica Pegasus Backpack; (**c**) GeoSlam Zeb-Revo.  
On observing Figure 20, the same linear scanning direction in Figures 17 and 18 is clearly visible, here denoting a denser resolution: in the Stencil and Pegasus datasets, the different TLS tilt during acquisition is evident. The Stencil's linear laser rotates around a pseudo-vertical axis (therefore the profile tracks are almost horizontal), while the Pegasus's laser rotates around a pseudo-horizontal axis (so the profile tracks are almost vertical). The Zeb-Revo resolution is instead more homogeneous thanks to the rotating acquisition system. On the other hand, the Zeb-Revo dataset resolution is lower, and the same considered features are barely recognizable.  
In sum, in this part of the test field, only the Stencil cloud has an LoD enabling the reconstruction of \"small objects\", namely architectural details.  
All of the datasets in Figure 20 are visualized in the CloudCompare environment, without any shading or visualization improvements. As is well known, rendering techniques can significantly modify the effectiveness of data visualization and exploration, so for each dataset we tried to set up the best way to render it: obviously, this is an arbitrary and subjective choice, but it aims to qualitatively use the acquired clouds in the best way possible.  
For this purpose, we considered the two walls of the aforementioned octagonal room, shown in Figure 21. To be compared with the other datasets acquired without texture, the Pegasus dataset is also visualized with intensity values in Figure 22.  
Figure 20: _Cont._  
On comparing the representations in Figure 22, despite missing the upper part, the intensity readability of the Stencil cloud is good, with a similar colour scale to the scales of grey Pegasus data; the grey scale intensity of the Zeb-Revo instead has a very poor interval and is not very readable.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 5 Implementation and Results
### Deployment  
One of the key points of FIWARE is FIWARE Lab ([[https://account.lab.fiware.org](https://account.lab.fiware.org)]([https://account.lab.fiware.org](https://account.lab.fiware.org)) [Last accessed on 3 October 2017]), a public working instance of FIWARE GEri's available for experimentation. The entry point of this laboratory is an instance of our KeyRock IdP implementation, where developers can create an account and start registering applications, IoT devices, etc., for free. FIWARE Lab also includes an AuthZFForce (the PDP and PAP component explained above) PDP and PAP instance, connected to the IdP to manage permissions and AC policies. Furthermore, the implementations of the GEs are available to be deployed in the FIWARE Cloud ecosystem, which is based on Openstack. This includes the Wilma PEP Proxy, which can be instantiated and deployed on top of a service with just a click, by running the available software image.  
FIWARE Lab offers cloud infrastructure in 13 regions across Europe ([[http://infographic.lab.fiware.org](http://infographic.lab.fiware.org)]([http://infographic.lab.fiware.org](http://infographic.lab.fiware.org)) [Last accessed on 3 October 2017]), with more than 8500 registered users as of the date this paper was written. Security components are deployed in the Spanish node, owned by one of the partners of the project consortium.  
Figure 4 shows in detail the deployment in which the experiments exposed in this paper have been performed. Table 1 describes the hardware used to deploy each component. As explained before, the IdP is based on Openstack and therefore split into two components, Horizon and Keystone, for the front-end and the back-end, respectively. In order to provide a high availability solution, we deployed two instances of Keystone, which is the module that handles a higher number of requests. Both of them are connected to the same MySQL database, hosted in the Keystone 2 instance. An external disk is mounted in this instance, in order to store the data that are periodically backed-up from the database. The High Availability (HA) proxy just redirects the incoming requests to one of the Keystone instances. Finally, the _Policies DB_ is hosted in the same instance where the AuthZFForce component is running. The figure also shows the public ports exposed by each component.
## 3 Results and Analysis  
### Gas and Water Production  
Figure 5a depicts the trends in \(Q_{g}\) and \(V_{g}\) both for the cases without and with whole wellbore heating (WWH) and continuous heating (CH) within 1080 days of production. The \(Q_{g}\) in the heating case (heating power set to 100 W/m) is higher than in the case without heating, and in the case assisted with heating, the \(V_{g}\) increases from 3.420 \(\times\) 10\({}^{6}\) to 4.055 \(\times\) 10\({}^{6}\) ST m\({}^{3}\), an increase of 118.56%. The simulation results show that heating technology has a good effect on increasing the production of hydrate depressurization mining. It is worth noting that after approximately 420 days of production, the \(Q_{g}\) of the case without heating begins to exceed that of the heating case, and then the \(Q_{g}\) of both is consistent around 900 days. This may be caused by the following reasons: firstly, around 420 days, the bottom warm water of the case without heating began to enter the wellbore, and the high-saturation secondary hydrates formed at the TPL began to accelerate dissociation. Secondly, in the heating case, because the high saturation secondary hydrates were not formed around the wellbore, and the gas around the wellbore was quickly extracted, while the gas further away from the wellbore slowly into the wellbore. Thirdly, the non-fluid heating method can only heat the reservoir at a certain distance around the wellbore, and the heating effect gradually weakened in the later stage. The water production rate \(Q_{w}\) and gas-to-water ratio \(R_{gw}\) evolutions within 1080 days of production are depicted in Figure 5b. The \(Q_{w}\) curve trends in every case are in line with the \(Q_{g}\) and show that heating has a positive effect on gas production as well as making it easier to produce water, which raises the \(Q_{w}\). The heating results in a slightly higher \(R_{gw}\) than the case without heating in the first 720 days.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 4 Validation Using CST Microwave Studio for 5G Antenna Arrays
### Antenna Design
The pattern might exhibit a relatively narrow beamwidth, which suggests that the antenna is highly directional at this frequency, focusing its energy towards a specific region in space. Side lobes and back lobes might also appear, indicating minor radiation in directions other than the main beam. The low sidelobe levels typically indicate minimal unwanted radiation, contributing to higher efficiency in the desired direction. (b) At 37.88 GHz: At this higher frequency, the antenna's radiation pattern could differ significantly, reflecting a change in its directional properties. The main lobe might shift in angle or broaden, depending on the antenna design and its impedance matching at this frequency. Higher frequencies typically result in a narrower beamwidth, but the antenna might also demonstrate a more complex radiation pattern due to factors like frequency-dependent impedance mismatch or interference effects. The side lobes may also appear differently, and the overall pattern might show reduced efficiency in certain directions due to different propagation characteristics at this higher frequency. In both cases, the patterns highlight the dual-band performance of the antenna, which is crucial for applications requiring high efficiency in multiple frequency bands. The optimal radiation patterns at 28.14 GHz and 37.88 GHz ensure that the antenna can be effectively used in these bands, minimizing reflection losses and maximizing signal transmission in their respective regions.
# Protecting Hybrid ITS Networks: A Comprehensive Security Approach
## 2 Background and Related Work
### Architectures for Securing ITS Networks
#### 2.4.1 DLAPP  
[PERSON] et al. [13] proposed a decentralised, lightweight authentication and privacy protocol (DLAPP) that offers authentication and privacy protection. The protocol utilises a biometric device (BD) for driver identification and authentication and a tamper-proof device (TPD) for secure storage and processing. Its objective is to decentralise the CA's tasks by allowing each vehicle to locally generate its own pseudo-identity and private keys rather than relying on frequent communication with a central CA, thus preserving privacy and authentication while reducing the communication workload on the CA.  
For message exchanges, it is necessary to perform the pseudo-identity and the hash chain generation. Then, to transmit a message (\(m\)), the transmitter calculates a message authentication code (MAC) using a randomly selected chain key (\(ki\)) identified by index \(k_{index}\), as shown in Equation (1). For this, it is also necessary to choose a pseudo-identity (\(PID_{i}\)) from the generated set and to extract the current timestamp (\(T_{s}\)).  
\[Sig_{ki}=mac_{ki}(PID_{i}\mid m\mid T_{s}) \tag{1}\]  
The protocol's proposed message format attaches to the message, the pseudo-identity, the MAC value, the index of the selected key and the current timestamp (Figure 4).  
The receiver validates \(T_{s}\). If valid, it uses the received \(ki\) to extract the corresponding key from the locally generated chain and verifies the received MAC. If the calculated MAC, \(Sig^{*}_{ki}\) (via Equation (2)) and the received one (\(Sig_{ki}\)) do not match, the message is discarded.  
\[Sig^{*}_{ki}=mac_{ki}(PID_{i}\mid m\mid T_{s}) \tag{2}\]  
According to the authors, simulations conducted using the NS-3 simulator demonstrate that DLAPP can sign 60,000 messages per second, up to 55 times higher than other protocols the paper compares itself to. The authors state that DLAPP achieves a communication overhead reduction of 20% to 85% compared to other protocols. They conclude that the DLAPP is well suited to time-critical applications such as large-scale V2X networks.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Document** & **Title** & **Version** \\ \hline
[32] & TR 102 893 & Threat, Vulnerability and Risk Analysis (TVRA) & v1.2.1 2017 \\
[25] & TS 102 940 & ITS Communications Security Architecture & v2.1.1 2021 \\
[33] & TS 102 941 & Trust and Privacy Management & v1.4.1 2021 \\
[34] & TS 102 943 & Confidentiality Services & v1.1.1 2012 \\
[35] & TS 103 097 & Security Header and Certificate Formats & v2.1.1 2021 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Security standards summary for C-ITS PKI.  
Figure 4: DLAPP proposed message format.
# Fall Recognition Based on Time-Level Decision Fusion Classification
## 3 Fall Detection System
### Time-Level Decision Fusion  
Assembling multiple models to enhance generalization performance is a common practice [36; 37; 38; 39]. Combining the outcomes of separately trained models to improve the overall performance is a typical ensemble approach. However, falls involve temporal changes in posture. Therefore, if the skeleton data at time intervals \([t,t+1,\ldots,t+n]\) indicate a fall, it is likely that the skeleton data at time intervals \([t-1,t,\ldots,t+n-1]\) and \([t+1,t+2,\ldots,t+n+1]\) also correspond to a fall. Therefore, in this study, the results from different time points of the classifier were assembled. Fluctuations in the skeleton sequence can occur due to incorrect bounding boxes in the person detection stage, leading to a degradation in pose estimation performance or performance issues within the pose estimation stage itself. Fusion can help alleviate the impact of skeleton sequence fluctua  
Figure 4: Structure of ST-GCN.  
tions on the classification performance of the ST-GCN. Additionally, situations where falls are challenging to detect, such as actions similar to falling, can be overcome by fusing the results from multiple perspectives of the ST-GCN. To aggregate the classifiers at the time level, we denote the keypoint values obtained at time \(t\) as \(Y_{t}\). The input features \(X_{t}\) for a classifier are defined as follows:  
\[X_{t}=[Y_{t-w+1}\ Y_{t-w+2}\ \dots\ Y_{t}] \tag{1}\]  
where \(w\) denotes the key points' list size. The output result \(\omega_{t}\) of the classifier is defined as  
\[\omega_{t}=f(X_{t}) \tag{2}\]  
where \(f(\cdot)\) represents the classifier function. \(\omega_{t}\) is represented as a vector indicating the confidence scores for each class and can be expressed as follows:  
\[\omega_{t}=\left[\omega_{t}^{1}\ \omega_{t}^{2}\ \dots\ \omega_{t}^{c}\right]^{T} \text{, where }0\leq\omega_{t}^{i}\leq 1 \tag{3}\]  
Here, \(c\) represents the number of predefined classes. The final result \(\omega^{out}\), obtained by fusing \(\omega_{t}\) from time \(t\) to \(t-N+1\), is defined as follows:  
\[\omega^{out}=g([\omega_{t-N+1}\ \omega_{t-N+2}\ \dots\ \omega_{t}]) \tag{4}\]  
where \(N\) denotes the number of feature windows, and \(g(\cdot)\) represents the function that fuses the results of \(N\) classifiers. The fusion module process is summarized in Figure 5.  
We used the following methods to obtain \(g(\cdot)\).