esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Finally, they proposed FedSHE, and evaluation results indicated that FedSHE outperformed existing FL research based on HE in terms of model accuracy, computational efficiency, communication costs, and security levels. Moreover, although FL solutions have made progress in distributed scenarios, they still rely heavily on multi-party data consistency and trust mechanisms. In response, [PERSON] et al. [15] provided a detailed overview of privacy-preserving federated learning (PPFL) based on HE, discussing the background of FL, privacy protection technologies (such as DP and MPC), and threat models. To address these issues, as shown in Table 1, a data security training framework based on symmetric projection spaces and adversarial training is proposed, aiming to provide a general and efficient solution for balancing privacy protection and data utility. Specifically, the proposed method utilizes symmetric projection techniques, obfuscating the data while retaining the key information required for training and inference. Additionally, an autoencoder-based generative module is introduced, which generates abstract data representations through encoding and decoding processes. Unlike traditional noise-adding methods, the autoencoder module can accurately separate sensitive and nonsensitive information through the optimization of network weights, further improving the precision of data privacy protection and the utility of model training. In adversarial training, a generator-discriminator mechanism is designed, ensuring that the generated data not only maintain high privacy but also remain consistent with the original data distribution. Low-dimensional data abstraction based on symmetric projection: This method uses symmetry design and nonlinear transformations to project high-dimensional sensitive data into a lower-dimensional abstract space, achieving obfuscated data representation while effectively retaining key features required for training and inference. Privacy-preserving module combining adversarial training and autoencoders: Within the adversarial training framework, the generator is responsible for generating obfuscated data, while the discriminator optimizes its discriminative ability, forming a dynamic adversarial mechanism. Meanwhile, the introduction of the autoencoder enables the efficient separation and reconstruction of sensitive and nonsensitive information, ensuring both data privacy and enhanced model utility. Wide adaptability and performance validation for multi-task data: The proposed framework has been validated on multiple datasets, including time-series and image data, demonstrating its generality and performance advantages. The method shows outstanding results in terms of privacy protection and computational efficiency. In conclusion, by innovatively combining symmetric projection, autoencoders, and adversarial training technologies, this work provides a universal solution that balances security and utility in the field of privacy protection.
## 6 Conclusions  
In this paper, we proposed a hybrid-price auction-based secure routing (HPA-SR) protocol and an advanced speed and cosine similarity-based clustering (ASCS) protocol as an underlying structure to establish a secure route against sinkhole attacks and improve connectivity between nodes. The proposed HPA-SR protocol used the first- and second-price auction to avoid sinkhole attacks. Each node was used in the [PERSON] decision process to conditionally select which kind of auction method establishes the secure route against the sinkhole attack. Besides, to improve connectivity between nodes, the proposed ASCS clustering protocol that works as underlying structure used the node's speed and direction and then calculated the cosine similarity and distance between nodes. The numerical results showed that the use of hybrid-price auction and advanced speed cosine similarity improves the performance of routing in the network. The proposed HPA-SR with ASCS outperforms either the AODV+ASCS, HPA-SR, or AODV protocol in terms of the security in the network and packet delivery ratio. Additionally, the proposed HPA-SR with ASCS protocol are able to reduce the routing delay, packet loss ratio, number of packet loss, and control overhead.  
Y.P.: Conceptualization, Validation, Writing--original draft. R.H.Y.P.: Conceptualization, Validation, Writing--review and editing. T.-N.T.: Conceptualization, Validation, Writing--review and editing. K.S.: Conceptualization, Validation, Writing--review and editing. B.A.: Conceptualization, Funding acquisition, Project administration, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1A2B5B01001190). [PERSON] is the corresponding author.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Recently, two-dimensional (2D) nanomaterial-based SERS substrates have garnered considerable attention for their excellent SERS activities. 2D nanomaterials can serve as a stable \"nano-island\" to support the metal nanoparticles in liquid environment. Additionally, an amount of SERS \"hot spots\" form in the interstices among the fixed nanoparticles that provide fine electromagnetic enhancement effect [18; 19]. Moreover, the 2D materials can offer extra chemical enhancement originated from the charge transfer between the nanosheet and molecule [20]. As a novel 2D nanomaterial, black phosphorus (BP) has exhibited promising potentials for theranostic applications [21; 22; 23; 24; 25; 26]. BP can also serve as a neuroprotective nanomaterial to protect neuronal cells from copper ion-induced neurotoxicity [27]. Recently, we have proposed a facile strategy for the synthesis of BP-Au nanocomposites; the nanohybrids showed outstanding SERS activity, which allowed for real-time in vivo SERS monitoring of the changes in the biochemical components of tumor tissues during the photothermal therapy, indicating the potential for SERS bioanalysis [28]. For neuroscience research, it is valuable to describe the chemical composition of brain tissue in a large region which is difficult to achieve using normal Raman spectroscopy. Herein, label-free, full-scale and rapid SERS imaging of brain tissue section of mouse using the BP-based 2D SERS substrate was carried out for the first time. The Raman spectral lines were also extracted from the Raman images to elaborate the molecular information of biochemical components in various encephalic regions.
## 4 Experiments and Results
### Results
Overall, it can be concluded that the transformer models have significantly higher scores when compared to the previous state-of-the-art machine learning and deep learning models for regret detection. This suggests that the transformer models are more effective at classifying Reddit posts in terms of the expression of different types of regret or no regret. The transformer models' ability to capture long-range dependencies and handle large amounts of data may allow them to better analyze the texts and titles of the Reddit posts and accurately classify them. A graphical representation of this comparison between the best-performing models for the regret detection task is presented in Figure 2.  
Similarly, in Table 6, the performances of the various transformer models is evaluated for multi-class hope speech detection from tweets. The bert-based, uncased model has the highest scores, with an averaged macro F1-score of 0.72. The roberta-based and distilbert-based, uncased models have a slightly lower precision and recall, but still perform well, with an averaged macro F1-score of 0.70. The alert v2-based, xhnet-based, cased, and google/electro-based models have similar performances, with slight differences in precision and recall, and averaged macro F1-scores ranging from 0.68 to 0.69. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Learning Approach** & **Averaged-Weighted F1** & **Averaged-Macro F1** \\ \hline \multicolumn{4}{c}{only text} \\ \hline LR & Machine learning & 0.64 & 0.56 \\ CNN with GloVe & Deep learning & 0.71 & 0.61 \\ \hline \multicolumn{4}{c}{text + title} \\ \hline LR & Machine learning & 0.63 & 0.55 \\ BiLSTM with GloVe & Deep learning & 0.76 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Previous state-of-the-art results on regret detection. Figure 2: Comparison of best performing models for regret detection. Comparing the results of the ReDDIT and PolyHope datasets represented in Figure 3 reveals that the transformers perform well for both tasks. However, the specific performance of each model may vary depending on the task and dataset. The roberta-based model achieved the highest performance for regret detection, with an averaged macro F1-score of 0.83. On the other hand, for the task of hope speech detection, the bert-based, uncased model outperformed the rest of the models, with an averaged macro F1-score of 0.72. It is worth noting that the regret detection from Reddit posts may provide more context, due to the larger amount of data and the inclusion of post titles, which could explain the higher scores achieved by the transformer models in the ReDDIT dataset when compared to the PolyHope dataset.
# Deep Neural Network Equalization for Optical Short Reach Communication
## 3 Experimental Verification and Discussion
### Measurement Setup  
A coherent single carrier transmission system over a single mode fiber (SSMF) is employed to experimentally evaluate the performance of the proposed schemes. The dual polarization 600 Gb/s experimental setup including the offline DSP stack is shown in Figure 4. The measurements were performed BtB at 1550 nm with amplified spontaneous emission (ASE) noise loading, in order to compare bit error rates before applying any forward error correction (pre-FEC BER) at varying optical signal to noise ratios (OSNR). For a net bitrate of 600 Gb/s, 704 Gb/s of pseudo random data including overheads of FEC and training have been transmitted at 88 GBd for 16 QAM, at 70 GBd for 32 QAM and at 68 GBd for 64 QAM.  
The photos show a VEGA 100 GSa/s 4-channel DAC by Micram with 40 GHz bandwidth and 4.5 ENOB and two 2-channel 160 GSa/s Keysight Infiniium oscilloscopes with 63 GHz bandwidth. Their nonlinear effects are mixed with nonlinear distortions from SHF 804 A drivers, a Fujitsu 64 GBd DP-IQM and a NeoPhotonics 64 GBd class-40 HB-\(\mu\)ICR (40 GHz). Unlike in this BtB setup, longer  
Figure 4: Back-to-back offline measurement setup including Tx and Rx digital signal processing (DSP) with different methods to compute the L-values.  
fiber spans as well as a WDM-setup would introduce further nonlinearities, such as single, cross and multichannel nonlinear interferences.  
The transmitter DSP inserts a CAZAC (constant amplitude zero autocorrelation) based training sequence. These training symbols are used for framing, carrier frequency offset estimation, 2 \(\times\) 2 MIMO equalization and residual chromatic dispersion compensation. These sequences of 4096 training symbols are sent prior to more than 10\({}^{5}\) payload data symbols repetitively. This makes it a minimal frame structure, of which the oscilloscope's memory stores up to four per capture. The DSP uses a linear preemphasis filter on the TX side against linear O/E component distortions.  
The receiver DSP stack includes classical signal recovery blocks [17; 18]. The different NLEs are added after the timing and phase recovery and run at one sample per symbol. For fair performance comparisons, the different NLE types operate on identical power normalized data. Regardless of the type of NLE in use, training on the particular nonlinearities is essential before deployment. CAZAC sequences do not capture nonlinearities very well. Hence, NLE training is done instead upon the payload of the first captured frame for one of the OSNR captures. Once trained, the NLE can equalize all other captures without further training.
###### Abstract  
To support a massive number of connected cars, a novel channel access scheme for next-generation vehicle-to-anything (V2X) systems is proposed in this paper. In the design of the proposed scheme, two essential aspects are carefully considered: backward compatibility and massive V2X support. Since IEEE 802.11p-based V2X networks are already being deployed and used for intelligent transport systems, next-generation V2X shall be designed considering IEEE 802.11p-based V2X networks to provide backward compatibility. Since all future cars are expected to be equipped with a V2X communication device, a dense V2X communication scenario will be common and massive V2X communication support will be required. In the proposed scheme, IEEE 802.11-based extension is employed to provide backward compatibility and the emerging IEEE 802.11 ax standard-based orthogonal frequency-division multiple access is adopted and extended to provide massive V2X support. The proposed scheme is further extended with a dedicated V2X channel and a scheduled V2X channel access to ensure high capacity and low latency to meet the requirements of the future V2X communication systems. To demonstrate the performance of the proposed scheme thoroughly and rigorously, the proposed scheme is mathematically analyzed using a Markov model and extensive simulations are performed. In the dense V2X communication networks of the future, the proposed V2X communication scheme will provide high performance and reliability.  
V2X; OFDMA; random access; UORA; wireless LAN; resource unit; trigger frame; channel access +
Footnote †: journal: Applied Soft. 2018
# Parametric Analyses of the Response of Masonry Walls with Reinforced Plaster
## 3 Numerical Simulations
### Retrofitted Masometry--Number of Connectors  
Of the parameters that influence the structural performance of a wall retrofitted with reinforced plaster, the number of connectors is investigated in this section. Four models were implemented with five, seven, ten (Figure 20), and no anchors, respectively. The anchors' positions were defined according to the Italian technical code [14], which suggests using a quincunx pattern with an equilateral triangle. The nail diameter was 10 mm, while its length was 200 mm. The nail was placed horizontally (i.e., 0 deg). Nonlinear analyses were performed on specimens with 30-mm plaster thickness. The mechanical and interaction properties, loading conditions, and mesh size were those defined in Sections 2 and 3.  
Figure 21 shows the load vs. displacement curves for the different anchor configurations. The higher stiffness in the first branch of the curve was detected in all the reinforced specimens compared to the unreinforced one, and this stiffness was the same regardless of the considered number of anchors. No significant variation in the peak load was detected by increasing the number of anchors from zero to five and from seven to ten, whereas from five to seven, there was an increase of about 10%.  
Figure 22 shows the out-of-plane displacement along the compressed diagonal for the different anchor configurations at the peak load (a) and at failure (b). The peaks in the different curves represent the crack openings. It is evident that, at the maximum load, the anchors were not activated, and the out-of-plane displacements were similar in the different  
Figure 21: Load–displacement curves for different number of anchors.  
Figure 20: Anchors patterns: (**a**) N5; (**b**) N7; (**c**) N10.  
configurations (less than 0.5 mm). After the peak, the displacements started to increase significantly. At collapse, the anchors showed their retaining effect, with higher out-of-plane displacement in the configuration without anchors and lower in the configuration with 10 anchors.  
Even if the increase in the number of connectors does not significantly affect the collapse load, their role is fundamental to inhibit instability by the number of limiting out-of-plane displacements. Moreover, the numerical results show that the anchors can guarantee a certain resistance (about 40% of the peak load) even close to the failure point due to the activation of a different resistant mechanism, which involves the connectors.
## 4 HMBAC Design and Implementation
### Janus Implementation
#### 4.3.3 Inter-Blockchain API
The Congestion Level \(CL\) is calculated using the formula:  
\[CL=\frac{\textit{max}\#\textit{ConcurrentRequests}}{\#\textit{QueuedRequests}}\]  
This means that, for example, if the PBC receives 1500 requests and accepts 400 concurrent requests, then 1100 of them will be pending in queue. The congestion level is then: \(CL=400/1100=0.36\), which is _High_ according to Table 3. The system willlower _monitor interval_ from 5 s to 2 s, and throttle requests sent from IBC-API to PBC will be recalculated with multiplier 0.1. If the default value is 400, then 40 concurrent requests will be sent until the PBC is discongested. (b) Voting Management  
To reach management decisions that require agreement between stakeholders, an asynchronous voting mechanism is implemented through smart contracts (described in Section 4.3.4 below). The Inter-Blockchain API enables the execution of this asynchronous model through the following components. As each stakeholder runs an instance of the Inter-Blockchain API, it deploys an instance of an _Event Manager_ and a _Voting Manager_. The Event Manager continuously checks for new events related to active voting processes and expired elections. The Voting Manager fetches all the active elections related with the relevant stakeholder running the Inter-Blockchain instance, and it constantly awaits to receive relevant data (e.g., new votes). It also keeps a log of the submitted votes for each Election ID, and communicates with the PSC chaincode when majority is reached or time expiration occurs for a particular election.
# Time-Sensitive Networking in IEEE 802.11 be: On the Way to Low-Latency WiFi 7
## 5 Use Cases
### Industrial  
During the next years, wireless networks will have increasing weight in the industry, leading a trend towards more flexible production sites. The Industry 4.0 concept, based on the cyber-physical transformation of processes, systems, and methods of manufacturing in  
Figure 6: (**a**) Google Stadia’s Tom Raider traffic load according to video resolution and (**b**) Measured RTT when the network has a bandwidth limit of 30 Mbps (dashed line corresponds to video resolution).  
the industrial sector, will enable autonomous and decentralized operation while ensuring proper coordination with commercial and logistics systems.  
One of the multiple existing Industry 4.0 applications is known as connected factory, involving monitoring, management, and direct control of machines, robots, and other industrial assets. The critical nature of some specific manufacturing processes, with typical latency requirements from 1 to 200 ms, makes crucial to guarantee reliability with determinism; that is, that each message must reach its destination within its scheduled period [59].  
Future industrial communications will probably rely on the coexistence among wired (e.g., Fieldbus-based and Industrial Ethernet), wireless (from RFID to LoRa, to cite two examples), and 5G/6G-based cellular technologies. WiFi 7 is also expected to get a foothold in this sector, not only because of its inherited features (namely, flexibility, ease of installation, scalability, and interoperability), but also thanks to its new enhancements, particularly in terms of improved resource management and support to deterministic communications.
## 8 Discussion
### Open Issues and Future Research Directions  
* The need for a systematic comparison method for an affinity-based model is still a major open issue. Research is required for the development of appropriate comparison criteria.
* An affinity analysis in social media is an emerging and very crucial application domain [49; 52; 53]. Most research studies have focused on the affinity within a single social media platform. This research can be extended to incorporate the affinity measure of a user (or group) between multiple social media platforms.
* Applying affinity analysis methodologies in developing fields of study (e.g., sentiment analysis, text classification, Internet of Things, and sensor networks) could open up new possibilities.
* Data privacy and security have always been prime concerns in data mining approaches. This is a crucial issue, especially in bibliomining and technology mining. Various types of data are associated with academic libraries, such as user credentials, digital article copies, billing information, and subscription data. The privacy and security of these data need to be guaranteed. Few approaches regarding privacy and security in bibliomining can be found in the literature. Securing data privacy can be considered a potentially fruitful research direction.
* The application of blockchains in various aspects of technology mining is still in the emerging phase. Blockchain is a cryptography-based decentralized database system, which can be very handy for securely storing associated data and managing authentication schemes.
# Transactive Energy to Thwart Load Altering Attacks on Power Distribution Systems
## 6 Case Studies
### Increasing the Safety Margin in Thwarting the Attacks  
As it was discussed in Section 4.1, maintaining the frequency of sinusoidal voltages in the interval of (7) secures the reliable operation of the power grid. However, when a load altering attack is detected in a power grid, restricting the frequency of sinusoidal voltages in a closer proximity to rated frequency allows a safety margin to preclude emergency load curtailment. Fortunately, in the proposed attack-thwarting framework the proximity of voltages' frequency to the rated frequency is adjustable. The adjustment is carried out by means of altering the rate of energy exchanges between the power system operator and the flexible loads. Namely, when the frequency of sinusoidal voltages is declining, the pace of frequency drop can be reduced by increasing the rate of energy purchase from the flexible loads. Similarly, when the frequency of sinusoidal voltages is surging, the pace of frequency surge can be reduced by increasing the rate of energy sale to the flexible loads.  
Figure 3: The impact of a Load Altering Attack on a power grid with no attack-thwarting system (**a**) Total power consumption of the compromised loads (**b**) The deviation in frequency of sinusoidal voltages in the power grid.  
We note that the energy exchanges in the transactive energy framework are conducted in successive transaction cycles that come one after the other [27]. The amount of energy exchanges in each transaction cycle is upper bounded by a certain fixed amount. Therefore, for altering the rate of energy exchanges in the transactive energy framework the rate of transaction cycles should be modified. Figure 5 shows the frequency of sinusoidal voltages under the attack scenario of Section 6.3 in five different cases. In these cases, the rates of energy transactions in the transactive energy framework are 100, 200, 300, 400, and 500 transaction cycles per second. From Figure 5, increasing the rate of transaction cycle increases the proximity of the voltages' frequency to the rated frequency of 60 Hz.  
Figure 4: The impact of a Load Altering Attack on a power grid armed with the proposed attack-thwarting system: (**a**) Total power consumption of the compromised loads (**b**) The deviation in frequency of sinusoidal voltages in the power grid.