esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Platform to Fight Disinformation Using Crowd Wisdom and Artificial Intelligence
## 2 Background
### Blockchain and Smart Contracts  
The blockchain technology was made popular by Bitcoin [8] and since then it was extensively adopted by the developer community into a broad range of different implementations such as Ethereum, Litecoin, Cardano, Tezos, and Solana, to name only a few. Instead of executing transactions through a centralized entity, transactions are executed by a net work of participants, mediated by smart contract programs. These smart contracts usually run using open-source software built and maintained by a community of developers.  
The blockchain and smart contracts [9] technology enabled a wide range of applications in areas such as money transfer, cryptocurrencies [10], decentralized finance (DeFi) [11; 12], Internet of Things (IoT) [13], Self-Sovereign Identity (SSI) [14], healthcare [15], logistics [16], Non-Fungible Tokens (NFIs) [17], government, media and so on.  
The Blockchain and smart contracts technology enables the platform to inherently expose the following features:  
* _Zero downtime_--Since the smart contracts are deployed on the blockchain, the network will serve the clients. This also protects the platform from DDoS attacks.
* _Privacy_--The access to the platform is guaranteed to be anonymous with no links to real world profiles.
* _Resistance to censorship_--No single entity on the network can block users from interacting with the platform.
* _Complete data integrity_--Data stored on the blockchain is immutable and indispensable; this is guaranteed by cryptographic primitives. Malicious actors cannot forge transactions or other data that has already been made public.
* _Trustless computation and verifiable behavior_--Smart contracts can be analyzed and are guaranteed to be executed in predictable ways, without the need to trust a central authority.  
Smart contracts are designed to be _trustless_; this implies that the users don't have to trust third parties (developers, companies, or any other entities) to interact with a contract. By design, smart contracts are also _immutable_ and cannot be altered; a contract will only execute the business logic defined in the code at the time of deployment.  
Once the core of the platform is set on the blockchain, additional building blocks can come in place to provide a reliable and trusted anti-disinformation platform. These building blocks take the form specialized web crawlers and scrapers (distributed pieces of software fetching data from the Internet), Crowd Wisdom (human actors performing data analysis), and Artificial Intelligence Modules assembled into a federation.
Researchers have highlighted the importance of the position arrangement for mobile devices [14; 15] and tabletops [16]. However, do proxemics issues translate to the virtual world where avatar representations are used? To our knowledge, there is limited work that has explored whether this translates to users in the form of avatars in VR environments and how the proxemics in these environments influences collaborative behaviors. Therefore, this research aims to fill this gap and explores whether shared vs. non-shared interaction (or control) and view is beneficial to collaborative sense-making, learning, and task performance. Moreover, it investigates the effect of position arrangement in VR collaborative environments because it is helpful to know whether the position arrangement in these virtual environments influences the collaborative behaviors of users who need to perform visual analytics tasks. To explore the above issues, we have designed and developed a VR application that allows users to investigate and learn the structural and transformational properties of 3D geometric shapes. We used the tool in an experiment with two collaborative modes: (1) shared and (2) non-shared control/view. To explore the position arrangement, participants can freely switch between three positions in the virtual space: (1) side-by-side (S-S); (2) corner-to-corner (C-C); and (3) back-to-back (B-B). This paper presents the following main contributions: (1) an exploration of the task performance, engagement level, and collaboration behaviors and patterns of users in a VR environment that deals with visualizations; (2) an investigation into the effects of private/personal and public virtual workspace and virtual proxemics relationships and arrangements; (3) an understanding of the positive affordances and limitations of sharedand non-shared modes of collaborations; and (4) a set of recommendations for the design of VR systems aimed at supporting collaborative interaction with visualizations.
## 1 Introduction  
Time-domain analysis represents a prerogative for the comprehension and design of mechatronic devices. Often, the complexity of the numerical modeling of such systems is narrowed and simplified through the isolation of the main macro-phenomena, and thus is solved through dedicated languages, tools, and algorithms [1, 2]. However, in applications where the strict interdependence between the different physical domains and scales cannot be decoupled, such as mechatronic devices, strongly coupled electro-magnetic applications (WPT, electric vehicle re-charging, etc.), and active analog electronics controllers, the introduction of a complete multidomain co-simulation architecture is required [3, 4, 5, 6, 7].  
From a holistic and practical point of view, one of the main difficulties arises from the integration of the different platforms on which the partial models are developed. Among the others, Matlab/Simulink(r) represents one of the most advanced graphical programming environments for the simulation of dynamic systems, being developed for the analysis of multipurpose and general nature problems [8]. In this study, we propose a novel Matlab/Simulink-based architecture for multi-physics analysis, devoting particular emphasis to the interaction between the mechanical, magnetic, and analog electronic domains, taking efforts from two novel implemented bi-directional co-simulation routines based on Spice(r) and ESRF Radia(r) engines. In detail, this paper describes the logics and the sub-processes executed by the novel co-simulation algorithms for bi-directional communication of the effort and flow variables incoming and outgoing from the Matlab/Simulink leading routine and the analog electronic, and the magneto-static/dynamic ambient solved through equivalent circuit analysis (ECA) and 3D finite volume modeling (3 DFVM), respectively.  
To validate the proposed methodology, an innovative MEMS-based electromagnetic actuator was taken under analysis, and the numerical results obtained by the multi-physics model were compared with the experimental relatives reported by [PERSON] et al. in [PERSON] et al. (2018). In the following, Section 2 introduces the whole co-simulation architecture, highlighting the main interexchange processes and the related timing characteristics of the different domains. Importance is given to the two novel bi-directional co-simulation algorithms for the integration of the analog electronic and magneto-static domain through PySpice-Simulink and ESRF Radia-Simulink routines, faced in Sections 3 and 4, respectively. Section 5 highlights the potentiality of the proposed multi-domain architecture through a validation process based on the experimental results obtained from an electromagnetic actuated MEMS proposed in the literature. Finally, appropriate conclusions are drawn in Section 6.
## 3 Results
### Deep Learning Techniques
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** & **Year** & **Modality** & **Network Type** & **Biomarkers** & **Type of Study** & **Age Range** \\ \hline
[31] & 2024 & OCTA & CNN and GNN & FAZ area and neighboring vessels & Human & 66.42 (8.58) \\
[22] & 2024 & Fundus photography, OCT & VGG-19, ResNet-50... (ensemble model) & Optic nerve, macular regions & Human & 63.88 (9.63) \\
[29] & 2023 & Fundus photography & DenseNet-121 & Superior, inferior quadrants & Human & — \\
[30] & 2023 & OCT, Fundus photography & 5-layer CNN with an attention module & Vascular bifurcations, retinal layers & Human & — \\
[21] & 2022 & Fundus photography & EfficientNet-b2 with fusion module & — & Human & Multiple studies \\
[23] & 2022 & OCT & Modified Inception-v3 & — & Mice & 1–12 months \\
[32] & 2022 & Fundus photography & Modified MobileNetV3 & — & Human & — \\
[33] & 2022 & OCT, OCTA, UWF SLO, FAF & 5-layer CNN & — & Human & 71.08 (8.83) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of deep learning-based methods for AD detection. Inception-v3 network was used for the classification task. A total of 1144 OCT volume scans were acquired from 57 wild-type mice and 57 AD mice at different ages. OCT volumes were converted to mean-value fundus images, and therefore the dataset was composed of 1144 images. The model demonstrated 80.4% sensitivity, 86.5% specificity, and 83.3% F1 on the test set. Furthermore, in a different study, the DenseNet-121 network was used to classify 41 AD patients, 33 MCI patients, and 39 healthy subjects from fundus images, achieving a reported 97% F1 score, 99% sensitivity, and 90% specificity [29]. Similarly, a modified MobileNetV3 network was trained to differentiate 111 AD patients from 111 healthy individuals using fundus images, achieving 83.7% sensitivity, 89.1% specificity, and an AUC of 92.9% [32].
## 3 Experiment Dataset and Evaluation
### Model Comparisons  
FCN8s: FCN is based on VGG and completes the conversion from a classification model to a semantic segmentation model by replacing the fully concatenated layer with an inverse convolution layer [23], pioneering the application of fully convolutional networks to image segmentation. The FCN upsamples the feature maps obtained from the convolution layer and classifies them for each pixel while recovering the feature map size.  
PSPNet: [PERSON] et al. [46] proposed a pyramid pooling module (PSPNet) that aggregates contextual information based on different regions and has the ability to mine global contextual information. Based on the pixel-by-pixel prediction, PSPNet also mines the global information of the remote sensing imagery to improve the accuracy of predicting features through multi-scale information aggregation.  
SegNet: SegNet [25] is an FCN-based encoder-decoder structured semantic segmentation network with a max pooling operation. Such a structural design allows the network to upsample the underlying information input feature maps. Therefore, SegNet has better performance and efficiency in semantic segmentation.  
U-Net: U-Net [24] is a classical architecture with a symmetric encoder-decoder structure. The compressed paths in U-Net are used to better capture feature information and features, and their symmetric extended paths can restore feature map size, while fusion of feature maps is performed by jumping connections to retain the maximum amount of important feature information. Due to its good robustness, U-Net has been widely used as a base framework for many segmentation models in recent years.
## 2 Processing of HS-SPME-GC-MS Data on Wine Volatiles by Multivariate Statistics
### Hierarchical Cluster Analysis  
Cluster Analysis represents a set of unsupervised methods that aim at grouping different samples based on the similarity assessed from a set of multivariate data provided for them. One of the most common methods for Cluster Analysis is _Hierarchical Cluster Analysis_ (HCA), which allows a grouping of data without a prior knowledge of the number of clusters to be formed. In particular, the agglomerative approach is usually adopted for HCA, i.e., single samples are progressively grouped in clusters of increasing dimensions based on their distance in the multivariate space, with Euclidean distance being the most adopted. Different agglomerative algorithms, starting from these distances, can be used, like those referred to as \"_single_\" (shortest distance), \"_complete_\" (farthest distance) and \"_average_\" (unweighted average distance) linkage and [PERSON]'s method, based on the minimization of variance inside groups.  
Following the metrics and linkage criteria indicated by the user, HCA allows the building of a complete clustering dendrogram, through which a qualitative visualization of grouping among samples in a two-dimensional space is possible. This aspect has made HCA the preferred technique when Cluster Analysis is performed for exploratory purposes in the oenological field. Among reported applications, [PERSON] et al. [35] used HCA based on Euclidean distances and [PERSON]'s method of agglomeration to evaluate similarities between wine samples produced from the _Nebbiolo_ grape in the Langhe and Roero areas (Piedmont, Northern Italy) but differing in vintage (respectively, 3 years, 2 years, 1 year, 8 months and a few months) and production zone, starting from data on volatile compounds. [PERSON] et al. [60] demonstrated the possibility of classifying high quality wines according to their brand based on their volatile fingerprint using PCA and HCA analysis. HCA was exploited, in synergy with PCA, to study grape and wine aroma [61] i.e., as a tool to find the key aromatic series of pulp juice, skin, and whole berries. This type of investigation is also important in the context of fraud prevention in the oenological field. In research about the authenticity of red wines from Poland, [PERSON] et al. [62] used HCA analysis to assess that the Polish wines were separated thoroughly from wines produced in other European countries, notably France, Italy, and Spain, starting from data on ethyl phenylacetate, (hexan-1-ol, ethyl 2-hydroxy-4-methylpentanoate, (E)-3-hexen-1-ol, 2-phenylethyland 3-(methylsulfonyl))propan-1-ol. Recently, [PERSON] et al. [63] identified the chemical profile (including the volatile profile) that characterizes Chilean _Carmerere_ wines by using HCA and PCA, starting to establish a database for further analysis of the authenticity of South American wines. [PERSON] et al. [64] used HCA analysis coupled with other statistical techniques to evaluate, for the first time, the impact of manoproteins on the aroma quality of sparkling wines produced with the _Champenoise_ method.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 5 Bit Error Rate Analysis
### Uplink--OMAC  
Since the proposed wireless network operates with PLNC protocol, pair of nodes can transmit the data in a single time slot. Thus, the near node pair and far node pair send the data to the relay in the first and second time slots, respectively. Then, the relay node performs the bitwise PLNC operation \(b_{r_{1}}=b_{1}\oplus b_{2}\) where \(b_{r_{1}}\) is the PLNC bit generated by XORing \(b_{1}\) (first near node bit) and \(b_{2}\) (second near node bit) at the relay. In other hand, it can be produced by multiplying the near node transmitted symbols as \(x_{r_{1}}=x_{1}\times x_{2}\). Using ML detection rule, the symbol can be detected at the relay as given as [30].  
\[\begin{gathered}\text{if}\quad\exp\left(\frac{-X(1,1)}{\sigma^{2 }}\right)+\exp\left(\frac{-X(-1,-1)}{\sigma^{2}}\right)>\\ \exp\left(\frac{-X(1,-1)}{\sigma^{2}}\right)+\exp\left(\frac{-X(- 1,1)}{\sigma^{2}}\right)\\ \hat{x}_{r_{1}}=1\quad\text{else}\quad\hat{x}_{r_{1}}=-1,\end{gathered} \tag{38}\]  
where \(X(x_{1},x_{2})=|z_{r,1}\ -\ \sqrt{P_{t}}x_{1}h_{1}\ +\ \sqrt{P_{t}}x_{2}h_{2}|^{2}\). By taking logarithm of (38) and simplifying, it can be written as  
\[\begin{gathered}\min(X(1,1),X(-1,-1))>\min(X(1,-1),X(-1,1))\\ \hat{x}_{r_{1}}=1\quad\text{else}\quad\hat{x}_{r_{1}}=-1,\end{gathered} \tag{39}\]  
Similarly, the relay decodes the second time slot received signal, \(z_{r,2}\). Using NOMA scheme, these two signals are combined with different power levels, then the resulting signal forwarded to all four nodes in the third time slot simultaneously. The upper bounded instantaneous symbol error probability (SEP) of the OMAC link in the first time slot can be written as [30]  
\[p_{OMAC_{1}}<Q(\sqrt{2\gamma_{1}^{u}})+Q(\sqrt{2\gamma_{2}^{u}}), \tag{40}\]  
where \(Q(.)\) is the Q-function. Please note that the SEP and BER are same for the BPSK modulation. Similarly, in the second time slot it can be written as  
\[p_{OMAC_{2}}<Q(\sqrt{2\gamma_{3}^{u}})+Q(\sqrt{2\gamma_{4}^{u}}), \tag{41}\]By combining (40) and (41), the average SEP of the OMAC link can be computed as  
\[E[p_{OMAC}]<E[p_{OMAC_{1}}]+E[p_{OMAC_{2}}] \tag{42}\]  
where \(E(.)\) is the expectation operator. By substituting the respective PDFs and then integrating, the simplified SEP can be written as  
\[E[p_{OMAC}]<\psi_{1}(\overline{\gamma}_{1}^{u})+\psi_{2}(\overline{\gamma}_{2} ^{u})+\psi_{3}(\overline{\gamma}_{3}^{u})+\psi_{4}(\overline{\gamma}_{4}^{u}) \tag{43}\]  
where \(\psi_{i}(\overline{\gamma}_{i}^{u})=\left(1-\sqrt{\overline{\gamma}_{1}^{u}/( \overline{\gamma}_{1}^{u}+1)}\right)/2\), \(i\in(1,2,3,4)\) is the average BER, \(\overline{\gamma}_{i}^{u}=P_{i}\bar{h}_{i}/\sigma^{2}\) is the average SNR and \(\bar{h}_{i}=E(|h_{i}|^{2})\) is the average channel gain.
## 4 Performance Evaluation in Edge Scenarios
### Performance in Network Programming
#### 4.4.1 Impact of Concurrency on Throughput  
Since Occlum requires the use of the spawn method for process creation and does not support the fork system call, and fork is used in Lighttpd, the Lighttpd code needs to be modified to replace fork calls with spawn system calls. The code modification is planned for future implementation, and in this paper, testing is only performed on native Linux and Gramine. In the Gramine tests, the maximum number of threads for Lighttpd was set to 25, and the file downloaded by the client was a randomly generated file of 10 KB.  
Figure 5 shows that as concurrency increases, both the native Linux and Gramine exhibit a trend where throughput initially increases and then stabilizes. When concurrency is low, Gramine's performance is significantly lower. For instance, when the concurrency is 1, Gramine achieves a throughput of only 7.6, while the native Linux exceeds 4000. The main reason for this is that with such a low load, the proportion of time spent handling download tasks decreases significantly, making the overhead of creating enclaves in Gramine particularly costly. However, as the load increases, both native Linux and Gramine show a rapid increase in throughput as concurrency rises from 1 to 50. When concurrency increases from 50 to 300, Gramine's throughput continues to rise, while the native Linux throughput stabilizes, and both eventually reach their maximum throughput. The test results indicate that Gramine's maximum throughput is about 75\(\sim\)80% of that of native Linux.  
Figure 5: Impact of concurrency on throughput.
## 3 Results
### Basic Performance
Figure 5: The spatial distribution of precipitation prediction RMSE (mm) by PM and GFS models with different lead times: (**a**) PM with 24 h, (**b**) PM with 48 h, (**c**) PM with 72 h, (**d**) GFS with 24 h, (**e**) GFS with 48 h, (**f**) GFS with 72 h. The spatial distribution of the RMSE (mm) difference in precipitation prediction between GFS model and PM model with different lead times: (**g**) 24 h, (**h**) 48 h, and (**i**) 72 h.  
Figure 6 illustrates the TSs for different models to forecast precipitation with different thresholds at various lead times, while Figure 7 displays the mean TS variations at different lead times. The figures demonstrate that the VGG model produces the lowest TSs for all thresholds, followed by the GFS model. There is a significant decrease in TSs for all models as the lead time increases, particularly at higher precipitation thresholds (50 mm/day and 100 mm/day) (Figures 6c,d and 7c,d). Figure 6: Boxplots of TSs for precipitation prediction at different precipitation thresholds: (**a**) 10 mm/day, (**b**) 25 mm/day, (**c**) 50 mm/day, and (**d**) 100 mm/day. Figure 7a shows that when the precipitation threshold is 10 mm/day, the mean TSs for GFS models are 0.52, 0.55, and 0.55 at lead times of 24 h, 48 h, and 72 h, respectively. These values are lower than most of the mean TSs for UNet-based models at the same lead times. Similarly, when the precipitation thresholds are 25 mm/day, 50 mm/day, and 100 mm/day, UNet-based models produce higher TSs than GFS models at most lead times. On average, the TS scores of U-Net, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are raised by 12.8%, 21.3%, 19.3%, 20.7%, 22.5%, and 22.9%, respectively, when compared with the GFS model. The models with improved U-Net structures, namely UNet3+, SE-UNet, and SE-UNet3+, show a more effective improvement in TS scores than the standard U-Net model. Meanwhile, the integrated models, AVR and PM, exhibit higher TS scores than any separate precipitation prediction models. Overall, the neural network model used in this study performs well in reducing the RMSE and raising the TS of precipitation forecasts. In particular, the U-Net architecture represents an advancement in deep learning applications for precipitation forecasting [15]. This study further utilizes improved U-Net structures, such as UNet3+, SE-UNet, and SE-UNet3+, as well as an ensemble of outputs from all these models. Compared with the GFS model, the traditional U-Net model reduces the RMSE by 8.7% and improves the TS by 12.8% in precipitation forecasts. In contrast, our best ensemble model, the PM model, reduces the RMSE by 11.5% and improves the TS by 22.9% in precipitation forecasts. Figure 7: TSs in precipitation prediction by different models with different lead times for various precipitation thresholds: (**a**) 10 mm/day, (**b**) 25 mm/day, (**c**) 50 mm/day, and (**d**) 100 mm/day.
## 3 Results and Discussion
### Double-Notch Filter with Improved Rejection  
Although infinite rejection appears impossible in multinotch filters, rejection can be improved by adjusting \(\lambda\). Figure 8b compares theoretical and measured S-parameters for a two-notch filter with \(\kappa_{c}=0.3\) with \(\lambda\) chosen to equalise rejection at the two notches. A significant performance improvement in a rejection of over 15 dB is obtained, once again at the expense of port 3 isolation. As regards single-notch filters, lower values of \(\lambda\) are needed for higher values of \(\mu\). Low insertion loss is observed around the resonance frequency but increases elsewhere as matching between the guides and load deteriorates.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Key Generation
#### 4.1.3 QBER Calculation  
Both RTU and MTU extract a small portion from their sifted key and use it to calculate the error rate. The RTU obtains the quantum bit error rate (QBER) by computing the ratio of the number of errors and the total number of bits in MTU's extracted key. Both the units discard the extracted portion and acquire the sub-sifted key. In our simulation, we set the threshold of noise or QBER up to 25%. If QBER \(>\) 25%, both units discard the process, and when the QBER \(\leq\) 25%, both units proceed to the next sub-phase.