esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A All Constraints in an MILP Formulation for Rank-2 Chemical Graphs
### Selecting A Subgraph  
We prepare a binary variable \(u(s,i)\) (resp., \(v(t,i)\)) for each vertex \(u_{s,i}\) in tree \(S_{s}\) (resp., \(v_{t,i}\) in tree \(T_{t}\)). We include constraints so that the path \((v_{1,1},v_{1,2},\ldots,v_{t^{*},1})\) is partitioned into subpaths \(P_{c}\), \(c\in[1,c^{*}]\), where possibly some \(P_{c}\) is empty, and the resulting subgraph \(H\) becomes a connected rank-2 graph, with \(n(H)=n^{*}\), \(\mathrm{cs}(H)=\mathrm{cs}^{*}\), \(\mathrm{ch}(H)=\mathrm{ch}^{*}\) and \(\theta(H)=\theta^{*}\).  
**constants:**  
Integers \(d_{\max}\in\{3,4\}\), \(\mathrm{ch}^{*}\geq 0\);  
Prepare the set \(\mathrm{Cld}(i)\) of the indices of children of a vertex \(v_{i}\)  
the index \(\mathrm{prt}(i)\) of the parent of a non-root vertex \(v_{i}\), and  
the set \(\mathrm{Dst}(h)\) of indices \(i\) such that the height of a vertex \(v_{i}\) is \(h\)  
in the rooted tree \(T(2,d_{\max}-1,\mathrm{ch}^{*})\);  
**variables:**  
\(u(s,i)\in\{0,1\}\), \(s\in[1,s^{*}]\), \(i\in[1,n_{\text{tree}}]\): \(u(s,i)\) represents vertex \(u_{s,i}\)  
(\(u(s,i)=1\Leftrightarrow\) vertex \(u_{s,i}\) is used in \(H\) and edge \(e^{\prime}_{s,i}\)\((i\geq 2)\) is used in \(H\));  
\(v(t,i)\in\{0,1\}\), \(t\in[1,t^{*}]\), \(i\in[1,n_{\text{tree}}]\): \(v(t,i)\) represents vertex \(v_{t,i}\)  
(\(v(t,i)=1\Leftrightarrow\) vertex \(v_{t,i}\) is used in \(H\) and edge \(e_{t,i}\)\((i\geq 2)\) is used in \(H\));  
\(e(t)\in\{0,1\}\), \(t\in[1,t^{*}+1]\): \(e(t)\) represents edge \(e_{t}=v_{t-1,1}v_{t,i}\),  
where \(e_{1,1}\) and \(e_{t^{*}+1,1}\) are fictitious edges (\(e(t)=1\Leftrightarrow\) edge \(e_{t}\) is used in \(H\));constraints:**  
\[u(s,1)=1, s\in[1,s^{*}], \tag{111}\] \[d_{\max}\cdot u(t,i)\geq\sum_{j\in\operatorname{Cd}(i)}u(t,j), t\in[1,\mathbf{cs}^{*}],i\in[2,n_{\min}],\] (112) \[v(t,1)=1, t\in[1,t^{*}],\] (113) \[d_{\max}\cdot v(t,i)\geq\sum_{j\in\operatorname{Cd}(i)}v(t,j), t\in[1,\mathbf{cs}^{*}],i\in[2,n_{\min}],\] (114) \[\sum_{s\in[1,s^{*}]\
eq[1,n_{\max}]}u(s,i)+\sum_{t\in[1,t^{*}],i \
eq[1,n_{\max}]}v(t,i)=n^{*},\] (115) \[\sum_{s\in[1,s^{*}]\
eq[\operatorname{DR}(\operatorname{d}^{*}) }u(s,i)+\sum_{t\in[1,t^{*}],i\in\operatorname{DR}(\operatorname{d}^{*})}v(t,i) \geq 1,\] (116) \[e(1)=e(t^{*}+1) =0,\] (117) \[e(t+1)+\sum_{s\in[1,s^{*}]}e(t,s) =1, t\in[1,t^{*}],\] (118) \[e(t)+\sum_{s\in[1,s^{*}]}e(s,t) =1, t\in[1,t^{*}],\] (119) \[c^{*}\geq\chi(1)\geq\chi(2)\geq\cdots\geq\chi(t^{*})\geq 1,\] (120) \[e(t+1)\geq 1+\chi(t+1)-\chi(t), t\in[1,t^{*}-1],\] (121) \[c^{*}\cdot(1-e(t+1))\geq\chi(t)-\chi(t+1), t\in[1,t^{*}-1]. \tag{122}\]
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 5 Proposed VDTN Solution  
According to our opinion, it is necessary to adjust the selection of next-SCF vehicle dynamically to the unpredictable drastic density changes. VDTNs are characterized by high mobility speed, hence the links between vehicles can be reformulated and split continuously and the disconnection time changes suffer this evolution as a results.  
Thus, we introduce a combined swarm-based approach to cover this problem by adjusting the next-SCF vehicle selection with the assistance of the Firefly Algorithm (FA) and the Glowworm Swarm Optimization (GSO). Both approaches seek to find stochastically the optimum available SCF vehicle for each bundle towards its destination, according to a predefined set of SCF selection parameters.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 6 Conclusions  
Mobile communications security, having great features, is attractive among users as well as service providers. With the increase in its usage, security problems of confidentiality, integrity, and authentication are also on the rise. The mechanism to solve these problems has changed from symmetric key cryptography to public key cryptography. The available public key cryptographic approaches are good from a security point-of-view but they are computationally extensive and have more signaling overhead. Furthermore, public key cryptography is computationally extensive, and since many sources use it, the current security of mobile communications is not good enough. Therefore, it slows down the data rate. This paper has presented and analyzed some previous works with analysis: some of them are old and in use, while others are under development and standardization. Moreover, we presented some new ideas for the future of mobile communications security using a compatible PKI cryptosystem, especially for privacy preserving, user's anonymity, and AKA security. Hence the suggested new solutions can provide a secure cryptosystem while giving insights into physical security (PHY security) for the mobile communications systems.
## 5 Conclusions  
The AdaBoost algorithm is used to improve the SNR of different modulation techniques. It shows an improvement in the SNR of BPSK and QPSK. In comparison to the theoretical BER vs. SNR curve, the AdaBoost curve coincides with that of each modulation technique. A BER similar to that of the theoretical coherent one is achieved for each modulation technique without adding further complexity to the system. The AdaBoost algorithm is next used to improve the BER of the noncoherent detection of the used modulation techniques. AdaBoost helps in improving the noncoherent BER by detecting and calculating the value of the phase of the received bits. The achieved BER shows improvement over that of the theoretical noncoherent one. It yields a BER closer to that of the coherent detection. This result shows that by using the AdaBoost detection characteristics or capabilities, the value of the phase of the received signal could be calculated, which was unknown or highly noisy in the case of noncoherent detection. This helps to improve the noncoherent detection of the receiver without adding further complexity, which was originally the advantage (i.e. lower complexity) of the noncoherent over the coherent detection. The BER of the noncoherent detection is improved, which was the tradeoff versus that of the coherent detection. These improvements are achieved for the BPSK and QPSK noncoherent systems in both AWGN and Rayleigh fading channels.  
Conceptualization, M.E.K. and M.R.M.R.; Methodology, H.G.; Software, H.G.; Validation, H.G., M.E.K. and M.R.M.R.; Formal Analysis, H.G.; Investigation, H.G.; Resources, M.E.K.; Data Curation, H.G.; Writing-Original Draft Preparation, H.G.; Writing-Review & Editing, M.E.K., M.R.M.R., M.H.A. and N.E.L.; Visualization, H.G. and M.E.K.; Supervision, M.E.K., M.H.A., N.E.I. and M.R.M.R.; Project Administration, M.H.A., N.E.I. and M.R.M.R.  
This research received no external funding.  
The authors declare no conflict of interest.
# Surface Acoustic Wave Sensor for C-Reactive Protein Detection
## 3 Results and Discussion
After the immobilization processes, we changed the washing buffer from PBS solution to PBS/DI water, because we found some residues on the sensing area after only PBS washing. To ensure the reliability of the measurement of the CRP/anti-CRP interaction, we added the DI water washing in the cleaning processes, which might affect the mass changes or the pH values of the sensing area. The insertion loss of the SAW sensors with a CRP concentration of 0.1 \(\mu\)g/mL was increased to -31 dB. Further, the insertion loss was continuously attenuated from \(-\)31 dB to \(-\)41 dB by increasing CRP concentrations for the specific recognition. The sensitivity of proposed SAW sensor was estimated by partially linear fitting to amplitude attenuation at low concentrations (i.e., 0.1, 1, and 10 \(\mu\)g/mL) [43]. The corresponding slope is taken as a measure for the sensitivity of the commercial standard CRP solution. The sensitivity of proposed SAW sensor for CRP detection is about 0.2711 dB/\(\mu\)g. It is noted that this is just an estimated value. It may have a large error owing to a large variation of amplitude response. Recently, [PERSON] et al. [44] reported that bovine serum albumin (BSA, 1%) was immobilized as an effective enhancer to obtain ultrasensitive detection with high selectivity in comparison with the reported ethanolamine (EA, 1%). To enhance the sensitivity, BSA may be a good choice for blocking the proposed SAW sensor. It is well known that blocking methods have the advantage of washing away unbound and non-specific binding of antibody active sites from the surface. In reference [45], it is mentioned that the use of long chain blocking agents helps to orient the antibody to extend further into the solvent phase. This will make it more favorable for specific hybridization. In addition, a higher hydrophilicity will help the CRP in an aqueous solution to move closer to the electrode surface. It means that the CRP is closer to the immobilized antibody and thus can result in a more efficient hybridization. Therefore, different blocking agents have been used to minimize the non-specific binding onto the sensor surface and to improve the selectivity and  
Figure 7: The amplitude response of SAW sensor for measurements taken three times for each CRP concentration. sensitivity of the biosensor. To achieve higher sensitivity and lower detection limit, [PERSON] et al. also reported that the optimization of the concentration of antibody and the incubation time of detecting antigens was an important factor and should be considered cautiously. In the future, except the use of BSA for blocking, we can also optimize the concentration of anti-CRP and the incubation time of detecting CRP solution for the proposed SAW sensor.
## 4 Protocol Design
### Initial Authentication Stage  
When the user has not been authenticated or the authentication has expired and initial authentication is required, the user at this stage sends the initial authentication request to the nearest available satellite and pays the amount of cryptocurrency to obtain the satellite service. Moreover, the user can return all the cryptocurrencies to the authentication center when the user needs to cancel the unexpired service, as shown in Figure 5, which is divided into three steps.  
Figure 4: Registration stage.  
1. The user submits a transaction to the satellite, and the transaction information includes the user's public key, registered user address, authentication center address, requested service type, service duration, cryptocurrency quantity, timestamp, and signature. The calculation method is shown in (3). \[\begin{array}{c}m3=Pk,From,To,Server,Duration,Amount,Timestamp,\\ Sign(Pk,From,To,Server,Duration,Amount,Timestamp)\end{array}\] (3)
2. The satellite node receives the trade request; verifies the public key, address, service type, service duration, cryptocurrency quantity, timestamp, and signature; and checks whether the balance in the address is enough to pay for services. If authentication is successful, then the transaction is included in the buffer pool waiting for packaging; otherwise, the request is rejected. After the transaction is packaged, the session key, service type, service duration, and timestamp encrypted with the user's public key are returned to the user. The calculation method is shown in (4). \[\begin{array}{c}m4=Enc_{pk}(K,Server,Duration,Timestamp),\\ Sign(Server,Duration,Timestamp)\end{array}\] (4)
3. The user receives the returned information; decrypts it with the private key; obtains the session key; verifies the correctness of the service type, service duration and timestamp; and obtains the service from the satellite with the session key.
## 3 The 3D Seismic Fine Identification Technology
### Spectrum Decomposition  
Spectral decomposition consists in the continuous time-frequency analysis of a single seismic trace, with the objective of obtaining the corresponding time-frequency spectrum. In this way, a 1D seismic signal can be decomposed in a time-frequency plane, on which part of the seismic signal can be continuously analyzed and compared.  
For the spectral decomposition of a specific signal, the noise introduced for different time window types will vary.  
We opt for the S-transform (ST) to conduct spectral decomposition due to its capability to produce high-resolution spectral decomposition images, as demonstrated in previous studies [35]. This method proves advantageous in noise removal and analysis of thin interbeds. The S-transform of a function \(h(t)\) can be expressed as:  
\[S_{f}(\tau,f)=\int\limits_{-\infty}^{\infty}h(t)w(t-\tau,f)\exp(-i2\pi ft)dt \tag{6}\]  
\[w(t)=\frac{|f|}{\sqrt{2\pi}}\exp\frac{-f^{2}t^{2}}{2} \tag{7}\]  
Therefore, the following result can be obtained:  
\[S_{f}(\tau,f)=\int\limits_{-\infty}^{\infty}h(t)\frac{|f|}{\sqrt{2\pi}}\exp[ \frac{-f^{2}(\tau-t)^{2}}{2}]\exp(-i2\pi ft)dt \tag{8}\]  
where \(f\) is frequency, \(t\) is time, \(\tau\) is the time on control the gauss window position on the timeline.  
However, even for the same time window type, the noise introduced for different time window widths will also vary quite a lot (Figure 7). Notably, a Gaussian time window will be used. Figure 7 shows that, in the case of a narrow time window, the spectrum will be lower and flatter, and the frequency range of influence will be larger than in the case of a wide time window. In the first case, not only the low frequency band will be affected, but the middle and high frequency bands will be impacted even more. Notably, in the case of a wide time window the spectrum will be steeper, and the influence frequency range will be smaller (mainly concentrated in the low frequency band). With the enlargement of the time window, the corresponding spectrum will change its shape from straight to steep.  
Regarding spectral decomposition, its effect does not only depend on the selected time window, but also on the characteristics of the seismic signals. The influence of a certain time window on seismic signals with different dominant frequencies will vary (Figure 8). Moreover, for a certain window width, the higher the main frequency of the seismic signal, the closer will the spectrum be to its true value. In the case of seismic signals with different dominant frequencies, the selection of a time window for the spectral decomposition processing should be adjusted accordingly.
# Current Filaments in Asymmetric Surface Dielectric Barrier Discharge
## 5 Experimental Results
### Rogowski Coil  
Every oscilloscope channel acquisition has \(41\times 10^{6}\) points, sampled every 0.25 ns. That means that every acquisition length is 1.025 ms. To increase the statistics, we analyzed 30 realizations for each voltage, yielding a total time span of 30.75 ms. A typical one-cycle signal acquisition is plotted in Figure 7a, where the left axis refers to the applied voltage (black line) and the right axis refers to the current measured by the Rogowski coil (red line).  
When the applied voltage \(V>0\), the electrons go from the exposed electrode to the dielectric, and the current is defined as negative. The discharges that occur in this case are denoted as a forward stroke (FS). The discharges that occur in the opposite condition, \(V<0\), are denoted as a backward stroke (BS), and have an associated current defined as positive. For this reason, if a spike is positive (negative) in a forward (backward) stroke, it is classified as noise. We also consider as noise a peak that both develops within 25 ns after a burst and has a height lower than 75% of the previous burst. These spurious signals are due to the finite system bandwidth.  
We performed a fit of the HV signal with a sine function to determine its amplitude, frequency, and argument at each point. From the current signal, we can notice the presence of a constant baseline and a series of spikes that occur at specific HV phases. These spikes are the current signal caused by the plasma microdischarges. Detail of a microdischarge (called burst) is shown in Figure 7b. We characterize a burst by its charge (area of the peak), maximum current intensity (height), time duration (width), and HV phase position. Since we are interested only in the plasma current, carried by the bursts, we have to determine a background threshold above which a peak can be defined as a burst. We choose as background threshold, \(e_{\mathrm{B}}=2\sigma\), i.e., twice the standard deviation over the whole Rogowski coil data. We identify a peak as a burst if its height is greater than the value of \(4\sigma\). The peak duration is given by the peak width at \(e_{\mathrm{B}}\).
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 5 Evaluation
### Complexity Analysis
#### 5.5.2 Space Complexity  
The data in the network consist of nodes and edges. Hence, the space complexity of our method is \(O(|V|+|E|+|V_{\text{C}}|+|E_{\text{C}}|)\), where \(|E|\) is the number of edges, and \(|E_{\text{C}}|\) is the number of edges between communities.
###### Abstract  
Thanks to the rapid advancements in Connected and Automated Vehicles (CAVs) and vehicular communication technologies, the concept of the Internet of Vehicles (IoVs) combined with Artificial Intelligence (AI) and big data promotes the vision of an Intelligent Transportation System (ITS). An ITS is critical in enhancing road safety, traffic efficiency, and the overall driving experience by enabling a comprehensive data exchange platform. However, the open and dynamic nature of IoV networks brings significant performance and security challenges to IoV data acquisition, storage, and usage. To comprehensively tackle these challenges, this paper proposes a **D**ecentralized **D**igital **W**atermarking framework for smart **V**hicular networks (D2 WaVe). D2 WaVe consists of two core components: FIAE-GAN, a novel feature-integrated and attention-enhanced robust image watermarking model based on a Generative Adversarial Network (GAN), and BloVA, a **B**lockchain-based **V**ideo frames **A**uthentication scheme. By leveraging an encoder-noise-decoder framework, trained FIAE-GAN watermarking models can achieve the invisibility and robustness of watermarks that can be embedded in video frames to verify the authenticity of video data. BloVA ensures the integrity and auditability of IoV data in the storing and sharing stages. Experimental results based on a proof-of-concept prototype implementation validate the feasibility and effectiveness of our D2 WaVe scheme for securing and auditing video data exchange in smart vehicular networks.  
Intelligent Transportation System (ITS); Internet of Vehicles (IoVs); digital watermarking; deep learning; blockchain; security +
Footnote †: journal: _Future Internet_  
participants and interactions of ITSs based on a crossroad scenario. The information interaction between vehicles and other entities refers to vehicle-to-everything (V2X) models, which include vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), vehicle-to-roadside unit (V2R), vehicle-to-grid (V2G), and vehicle-to-pedestrian (V2P) [1]. By enabling a comprehensive data exchange platform supporting the mobility of CAVs and heterogeneity of vehicular services, the ITS provides diverse intelligent and safe vehicular applications, like enhanced pedestrian and driving safety, efficient traffic planning, smart parking, and entertainment services [4]. Meanwhile, the ITS requires in-vehicle data transmission and traffic information sharing among participants. This inevitably incurs new performance and security challenges such as collecting, storing, and transacting large amounts of IoV data.
# Cooperative Localization Using Distance Measurements for Mobile Nodes
## 1 Introduction
Unlike existing algorithms that operate in an iterative manner the proposed algorithm presents a closed-form solution that is computationally efficient. The algorithm is first derived in the noise-free case and the theoretical result is given. Subsruquently, the noisy case is considered and the associated closed-form estimator is presented. The proposed algorithm is supported by a rigorous theoretical derivation which provides optimal parameters. The simulation results support the theoretical analysis and indicate that the algorithm outperforms alternative methods. This paper is organized, as follows: Section 2 introduces the background to the MDS algorithm and the associated ambiguity problem. The theoretical solution to the ambiguity problem is then presented in Section 3. Based on the theoretical analysis, Section 4 describes the impact of noise and introduces the proposed closed-form estimator for determining positions in noisy scenarios. Section 5 presents simulations for validating the proposed algorithm and, finally, Section 6 concludes the paper.
## 3 Data Protection Risks of ML Systems and Relations with GDPR Provisions
### Member Inference Attacks  
Another important type of privacy attack is the so-called member inference attack, which aims to infer, based on the corresponding output of the ML model, whether a sample belonged to the training dataset (a good survey on these attacks can be found in [46]). For example, an adversary can mount such an attack to find out if a particular person's personal data have been used to train a machine learning model that predicts whether a person has a particular disease. Such attacks identify and exploit the differences in the ML model's predictions regarding the samples that were used to train the model in conjunction with those samples that were not used. Such attacks are investigated in [47] through training ML models that received, as input, the correct label of a sample and the machine learning model's prediction that predicted whether the sample belonged to the training dataset or not. Interestingly enough, the researchers investigated this issue in the most difficult setting, where the adversary's access to the model was limited to \"black-box\" queries that returned the model's output on a given input.  
All the above attacks actually illustrate how difficult it is to ensure the data minimization principle in practice when all technically possible data protection risks are considered.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 5 Experiments and Results
### Research Questions and Findings  
With the above experimental setup and design, our study aimed to answer the following research questions:  
* What are the current limitations of existing approaches for processing static analysis alarms?
* How effective is our proposed DL-based approach in accurately identifying actionable alarms from static analysis warnings?
* What are the benefits of using a DL-based approach to handle with static analysis alarms?  
By tackling these research questions, our objective was to assess the effectiveness and feasibility of employing DL for processing static analysis alarms, providing insights into its potential advantages and limitations in this work.