esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenge-Response Pair Mechanisms and Multi-Factor Authentication Schemes to Protect Private Keys
###### Abstract  
Crypto wallets store and protect the private keys needed to sign transactions for crypto currencies; they are secured by multi-factor authentication schemes. However, the loss of a wallet, or a dysfunctional factor of authentication, can be catastrophic, as the keys are then lost as well as the crypto currencies. Such difficult tradeoffs between the protection of the private keys and factors of authentication that are easy to use are also present in public key infrastructures, banking cards, smartphones and smartcards. In this paper, we present protocols based on novel challenge-response pair mechanisms that protect private keys, while using factors of authentication that can be lost or misplaced without negative consequences. Examples of factors that are analyzed include passwords, tokens, wearable devices, biometry, and blockchain-based non-fungible tokens. In normal operations, the terminal device uses all factors of authentication to retrieve an ephemeral key, decrypt the private key, and finally sign a transaction. With our solution, users can download the software stack into multiple terminal devices, turning all of them into backups. We present a zero-knowledge multi-factor authentication scheme allowing the secure recovery of private keys when one of the factors is lost, such as the token. The challenge-response pair mechanisms also enable a novel key pair generation protocol in which private keys can be kept secret by the user, while a Keystore can securely authenticate the user and transmit the public key to a distributed network. The standardized LWE post-quantum cryptographic CRYSTALS Dilithium protocol was selected in the experimental section.  
crypto wallet; multi-factor authentication; challenge-response pair Article
## 1 Introduction
The Austrian standard ONORM M 7135:2000 [11] stipulates that the briquettes must have a minimum value of 850 kg/m\({}^{3}\), regardless of their nature, i.e., if they are made of wood or vegetable remains, and other authors [2] have specified a minimum value of 650 kg/m\({}^{3}\). The density of the briquettes differs depending on the type and power of the production machine, with values of about 800 kg/m\({}^{3}\) for hydraulic installations and 1100 kg/m\({}^{3}\) for presses with helical screws or connecting rod-crank mechanisms [12]. The briquettes can be obtained separately from straw or mixed with wood or inferior coal [13; 14; 15; 16], with the lignocellulosic particles being native or torrefied [17; 18; 19]. Regarding the properties of briquettes, such as any lignocellulosic material, briquettes have physical, mechanical, chemical, and calorific properties. The physical-mechanical properties of briquettes are based on the dimensions of the briquettes, usually their diameter and length. The specifications of the standards in the field of briquettes and pellets [20] provide that the length should be about five times greater than the diameter. The moisture content of briquettes is limited to 10% by the British BioGen/UK standard [20] and 18% by ONORM M7135/Austria [11]. Unit density is limited above 525 kg/m3 by the standard CTI-R04/5/Italy [21] and is a minimum of 1000 kg/m\({}^{3}\) in DIN 51731/Germany [22] and ONORM M7135/Austria [20]. The calorific value is limited to less than 16.7 MJ/kg by British BioGen/UK and to 18 MJ/kg by ONORM M7135/Austria. Ash content is limited above 1.5% by SS 18 71 20/Sweden, CTI-R 04/5, and DIN 51731, and at 6% by ONORM M7135/Austria [20]. Many authors [10; 23; 24; 25; 26; 27; 28; 29] have analyzed the physical-mechanical and caloric properties of lignocellulosic briquettes. The main objective of this work was to compare briquettes with hollows and those without hollows, obtained from wheat straw. In this sense, the physical-mechanical and calorific properties of the briquettes, obtained with a connecting rod-crank mechanism with high densities, were analyzed. We sought to determine properties such as density, compression resistance, splitting resistance, moisture content, calorific value, energy density, and ash content. Additionally, a comparison was made of briquettes made of straw with the properties of briquettes obtained from the biomass of beech sawdust.
# Analysis of Available Components and Performance Estimation of Optical Multi-Band Systems
## 4 Discussion
The proposed physical layer analysis revealed that by using the currently available components and in particular rare earth amplifiers, we can exceed a capacity of 130 Tb/s for shorter distances, e.g., 150 km, and 40 Tb/s for longer ones, e.g., \(>\)2000 km. Moreover, we highlighted a trade-off between the modulation format and the attainable reach, as the higher cardinality modulation formats require a higher OSNIR in order to attain the same BER. This resulted in a decrease in the bit rate times reach product, when a higher modulation format is employed. Finally, the adopted physical layer formalism balanced the impact of ASE, FWM, and SRS, leading to small differences of OSNIR between all bands; however, in order to attain a completely flat OSNIR within a band, a large number of channels has to be incorporated in the optimization procedure, which is the scope of an ongoing work.
## 5 Conclusions  
In this paper, we proposed an efficient connected dominating set clustering based routing protocol with dynamic channel selection in multi-channel cognitive mobile ad hoc networks. First, we proposed underlying techniques that can support CRD efficiency, such as CDS selection algorithm, CDS size reduction algorithm and CDS based clustering algorithm. Particularly, the CDS selection algorithm  
Figure 14: Energy Consumption (in Joules) versus node mobility.  
Figure 13: Packet delivery ratio versus node mobility.  
helped to establish a set of intermediate nodes (CDS), which was later used as a route searching space for routing. The CDS size reduction algorithm used the weighted function and some rules to reduce the size of CDS, making cluster head election more efficient. Because the weighted function was a combination of stability function, remaining energy ratio of node and speed of node in the connected network, the proposed CDS based clustering used the CDS and weighted function to elect cluster heads to provide high stable clusters. Second, the proposed sending channel-based focus region selection supported intermediate nodes to select dynamically a channel for finding a route which avoided the affected region of a primary user. The sending channel-based focus region selection algorithm also supported the CRD protocol in order to reduce the delay and increase the ability to find the route from a source to a destination. From the evaluation results, the system performances of proposed CRD protocol outperformed the CR-AODV in terms of control overheads, PDR, delay and energy consumption. Moreover, the CRD protocol can apply for VANET, Internet of Things (IoT) and 5G. In this paper, we only consider one primary user and a random waypoint mobility model. We did not consider issues of limiting bandwidth and physical security in MANETs. In future works, we will extend the CRD protocol in multicast multi-channel cognitive MANETs, which is a complex and challenging issue. This motivates us to apply machine learning techniques to achieve its solution.  
The main contributions of T.N.T. are idea creation, algorithm design, protocol implementation in OMNET++ and writing-original draft. The main contributions of T.-V.N. are idea creation, revision and editing. The main contributions of B.A. are idea creation, revision, editing, project administration and funding acquisition.  
This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF), Ministry of Education, under Grant 2016R1 DIA1B03934898 and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1A2C1083996).  
The authors declare no conflicts of interest.
## Appendix B Sample implementation guideline for security controls.
### Key Management  
Cryptographic key management and establishment can be performed using manual procedures or automated mechanisms. NIST 800-53 propose to use NIST FIPS-compliant or NSA-approved key management technology to produce, control and distribute symmetric cryptographic keys. In this study ISO/IEC 1170 and NIST 800-56A key management guidelines are used for key generation, control and distribution.  
Source:  
NIST 800-53 \(\tau\)5: AU-2, AU-3, AU-5, AU-6, AU-7, AU-8, AU-9, AU-5  
ISO IEC 27002/ISO 27799: 12.4.1, 12.4.2  
Guidelines:  
A policy on the use, protection and lifetime of cryptographic keys should be developed and implemented through their whole lifecycle (NIST 800-53 SC-12, ISO 27002 10.1.2).  
Create keys with appropriate key size and block size. Do not use a laptop or random application to generate the key. Only generate the key using any application or service provider which supports hardware security modules (HSMs) (ISO/IEC 11770).  
Do not use any random cryptographic algorithms. Select only which are recognized by different standards. For example, AES is currently recognized by the Federal Government standard body for symmetric techniques (NIST 800-175B).  
Consider the proper key size during cryptographic algorithms. For AES 128, 168 or 256-bits key size can be used (NIST 800-175B).  
Generated keys need to be distributed securely by keeping confidentiality and integrity (ISO/IEC 11770).  
Use key wrapping techniques to exchange the key between mobile applications and devices. Diffie-Hellman provides the capability for two parties to agree upon a shared secret for exchanging keys over a public channel (NIST 800-56A).  
If any user and/or device is identified as compromised, the respective key of the user or device needs to be removed from the application and key management server. After
# A CNN-LSTM-Attention Model for Near-Crash Event Identification on Mountainous Roads
## 3 Data Collection and Processing  
### Data Collection  
This study utilizes naturalistic driving data collected from 20 drivers, accumulating over 8000 km and more than 150 h of driving on mountainous roads in Yunnan Province, China. The chosen experimental routes include the G78 National Expressway and the G324 National Highway, among others. The naturalistic driving data collection system, as shown in Figure 2, includes the following components:  
1. Four high-definition cameras. Operating at a 25 Hz frame rate, these cameras are strategically positioned to capture both frontal and rear views of the vehicle, along with the upper body and foot movements of the driver. The image data serves primarily for manual verification and calibration of near-crash events.
2. A nine-axis inertial sensor. This sensor is deployed to collect data on x-axis, y-axis, and z-axis acceleration, along with angular velocity and angular data at a frequency of 20 Hz. The orientation of these axes is detailed in Figure 2.
3. A GNSS positioning system. It provides detailed information on longitudinal velocity, latitude, longitude, and altitude with a sampling rate of 1 Hz.
4. Two industrial computers. These units are used for processing and storing the voluminous data collected by the aforementioned devices.  
Figure 2: Demonstration of the Naturalistic Driving Data Collection System.  
Previous studies [13; 24] have found that in emergency situations, over 90% of drivers would make emergency evasive reactions, which were prominently reflected in the changes observed in vehicular dynamics parameters. Consequently, this study primarily utilizes vehicle dynamics data collected via the inertial sensor to identify near-crash events. Additionally, the video footage from the onboard cameras are also used in labeling near-crash events.
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
## 2 Normalized Energy Intensity (NEI)  
Research by [PERSON] and [PERSON] first presented the NEI in 2015 [11,12], using the concentrations of gases and the enthalpy values of the gases CH\({}_{4}\), C\({}_{2}\)H\({}_{6}\), C\({}_{2}\)H\({}_{4}\), and C\({}_{2}\)H\({}_{2}\) from n-octane (C\({}_{8}\)H\({}_{18}\)) to calculate the NEI that directly reflects the energy from faults occurring in transformers. The equation for NEI from hydrocarbon gases (NEI\({}_{\rm HC}\)) is as follows:  
\[NEI_{HC}=\frac{77.7 CH_{4}+93.5C_{2}H_{6}+104.1C_{2}H_{4}+278.3C_{2}H_{2}}{22400} \tag{1}\]  
The IEEE C57.104-2019 standard has incorporated the Normalized Energy Intensity (NEI) from the concentrations of carbon monoxide and carbon dioxide gases (NEI\({}_{\rm CO}\)) produced by faults affecting solid insulation, particularly the paper wrapping around windings. This value can effectively monitor the degradation of solid insulation, since both types of carbon oxide gases usually occur gradually. The equation for NEI\({}_{\rm CO}\) is  
\[NEI_{\rm CO}=\frac{101.4 CO+30.2 CO_{2}}{22400} \tag{2}\]  
The IEEE standard also provides a detailed correlation between the NEI\({}_{\rm CO}\) value and the CO\({}_{2}\)/CO ratio. It specifically states that a fault affecting paper insulation has occurred if the NEI\({}_{\rm CO}\) value exhibits a rapid increase while the CO\({}_{2}\)/CO ratio decreases. This information is of paramount importance for monitoring the severity of faults from online DGA measuring devices.
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 5 Conclusions  
In this article, we gave an overview of compressed sensing with three key aspects: sensing model, reconstruction algorithm, and its applications. We first introduced several sensing models, including sparse-dictionary, block-compressed, chaotic-compressed, deep-learning compressed, and semitensor-product compressed-sensing methods. We also presented a detailed introduction of reconstruction algorithms, such as the convex-optimization, greedy, Bayesian, and noniterative-reconstruction algorithms. Lastly, we provided a brief introduction for CS applications that widely cover many fields, such as data compression, image encryption, cryptography, channel estimation, analog-to-information conversion, channel coding, radar reconstruction, and radar remote sensing. The approaches discussed in this paper provide the theoretical basis for the improvement and new applications of of CS.  
Conceptualization, L.L. ([PERSON]), Y.F., and L.L. ([PERSON]); formal analysis, H.P. and Y.F.; investigation, L.L. ([PERSON]) and L.L. ([PERSON]); supervision, J.K., H.P., and Y.Y.; writing--original draft, Y.F. and L.L. ([PERSON]); writing--review and editing, [PERSON] ([PERSON]), J.K., and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 61972051, 61771071 and 61932005.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CS & compressed sensing \\ DFT & discrete Fourier transform \\ DWT & discrete wavelet transform \\ DCT & discrete cosine transform \\ RIP & restricted isometry property \\ DTI & diffusion tensor imaging \\ BCS & block-compressed sensing \\ PSNR & peak signal-to-noise ratio \\ \end{tabular}  
Figure 5: Identification of complex network model based on compressed sensing and QR decomposition [81].
## 3 Proposed Framework
### Asynchronous Aggregation with Edge and Cloud Interaction  
The aggregation process in our framework takes place across both the edge and cloud layers, incorporating dynamic interactions to ensure efficient and timely model updates. Unlike traditional hierarchical federated learning, where the cloud server merely waits foredge nodes to complete aggregation, our approach allows for active cloud-level control and feedback, enhancing the overall efficiency and model performance.  
To further enhance edge-level aggregation, we employ a sliding window control mechanism. The sliding window allows each edge node to dynamically adjust the aggregation interval based on the recent arrival rate of updates from IoT devices. Specifically, the window size is adjusted according to the device participation rate and training time, inspired by concepts from queue theory. Let \(w_{window}\) denote the window size, which is updated as follows:  
\[w_{window}^{(t+1)}=w_{window}^{(t)}+\alpha(p_{desired}-p_{actual}), \tag{4}\]  
where \(\alpha\) is a hyperparameter, \(p_{desired}\) is the desired participation rate, and \(p_{actual}\) is the actual participation rate observed in the previous window. This mechanism ensures that the aggregation window size is neither too short nor too long, therefore balancing responsiveness and stability.  
Sliding window control is a versatile technique commonly used in networking and data transmission to improve efficiency and reliability, especially in dynamic environments. Adjusting the window size in real time helps prevent data loss, adapt to changes in device participation, and maintain a balance between accuracy and responsiveness. In the context of FL, sliding window control enables real-time adaptability to heterogeneous IoT environments, aligning aggregation intervals with actual participation to maintain system robustness.
## 5 Antibiotic Resistance in Healthcare: Challenges in Treating Common Infections
### Cost Implications of Antimicrobial Resistance  
Antimicrobial resistance (AMR) has significant cost implications for healthcare systems worldwide, primarily due to the increased expenses associated with managing resistant infections. These costs stem from multiple factors, including the use of more expensive alternative medications, extended hospital stays, additional diagnostic tests, and the need for more intensive and specialized care. Patients infected with resistant bacteria, such as penicillin-resistant _pneumococci_, MRSA, and ESBL-producing _E. coli_ and _Klebsiella_ spp., often require complex treatment regimens. This reliance on second-line or last-resort antibiotics, which are generally more costly than standard therapies, significantly drives up the cost of care [81; 83].  
Moreover, the longer duration of hospitalization associated with resistant infections exacerbates these costs. For example, infections caused by MRSA or ESBL-producing organisms frequently necessitate prolonged monitoring, additional interventions such as repeat surgeries, and extended antibiotic therapy, all of which contribute to increased healthcare expenditure [84]. Studies have demonstrated that the financial burden of treating patients with resistant infections can be several times higher than that of managing infections caused by susceptible strains. Additionally, indirect costs, such as lost productivity due to extended illness or time away from work, further amplify the economic impact. The burden of AMR also extends to healthcare infrastructure, as hospitals and clinics must allocate additional resources to infection control measures, staff training, and the implementation of antimicrobial stewardship programs. The financial strain caused by AMR highlightsthe urgent need for comprehensive strategies to curb its spread. Investments in rapid diagnostic tools, novel antimicrobial development, and global collaboration to implement effective antibiotic stewardship are essential to mitigate the rising costs associated with resistant infections and ensure sustainable healthcare delivery [81].
# Syntax-Informed Self-Attention Network for Span-Based Joint Entity and Relation Extraction
###### Abstract  
Current state-of-the-art joint entity and relation extraction framework is based on span-level entity classification and relation identification between pairs of entity mentions. However, while maintaining an efficient exhaustive search on spans, the importance of syntactic features is not taken into consideration. It will lead to a problem that the prediction of a relation between two entities is related based on corresponding entity types, but in fact they are not related in the sentence. In addition, although previous works have proven that extract local context is beneficial for the task, it still lacks in-depth learning of contextual features in local context. In this paper, we propose to incorporate syntax knowledge into multi-head self-attention by employing part of heads to focus on syntactic parents of each token from pruned dependency trees, and we use it to model the global context to fuse syntactic and semantic features. In addition, in order to get richer contextual features from the local context, we apply local focus mechanism on entity pairs and corresponding context. Based on applying the two strategies, we perform joint entity and relation extraction on span-level. Experimental results show that our model achieves significant improvements on both Conll04 and SciERC dataset compared to strong competitors.  
entity recognition; relation extraction; dependency tree; self-attention
## 4 Results and Empirical Approach
### Hierarchical Regression Testing  
Hierarchical regression testing is usually accessed by moderating effects test [52]. The next step aimed to test the moderating effects of ISA and ISC using a hierarchical regression model. As shown in Table 5, sex and age were the control variables for this study as these might have presented alternative demonstrations for the hypothesized relationships. The control variables were included only in Model 1 and Model 4. The main effects of ISM, RSC, ISI, ISA, and ISC were set into proper constructs, respectively (see Model 2 and Model 5). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Measure** & **AVE \({}^{\text{a}}\)** & **Construct Reliability \({}^{\text{b}}\)** & **SMC** & **RSC** & **ISI** & **ISA** & **ISC** \\ \hline ISM & 0.50 & 0.89 & 1 \({}^{\text{c}}\) & & & \\ RSC & 0.65 & 0.88 & \(-\)0.22 ** & 1 & & \\ ISI & 0.59 & 0.88 & 0.50 ** & \(-\)0.30 ** & 1 & \\ ISA & 0.63 & 0.87 & 0.07 ** & \(-\)0.13 ** & 0.00 ** & 1 & \\ ISC & 0.65 & 0.90 & 0.27 ** & 0.11 & 0.12 ** & 0.21 ** & 1 \\ \hline \hline \end{tabular}  
* (standardized loading)\({}^{2}\); \({}^{\text{b}}\) Construct reliability = (sum of standardized loadings)\({}^{2}\)/[(sum of standardized loadings)\({}^{2}\) + (sum of indicator measurement error)], and indicator measurement error is calculated as 1
- (standardized loading)\({}^{2}\); \({}^{\text{c}}\) The square root of the shared variance between the constructs and their measures are provided in the diagonal.  
\end{table} 
Table 4: Average variance extracted, composite reliability, and collections. The Durbin-Watson value was assessed to examine the multicollinearity of the regression equation. The results showed that it ranged from 0.840 to 1.696 (>0.60), proving that there were no problems concerning correlated residuals. In Model 1 and Model 4, sex and age had no significant impact on the dependent variables. ISM had a negative effect on RSC in Model 2 and a positive influence on ISI in Model 5, at a significance level of 0.01. This outcome met the SEM test. The moderating effects were investigated in Model 3, Model 6, Model 7, and Model 8. In Model 3 and Model 6, ISA had a robust positive significant impact on the relationship between ISM and RSC (\(\beta\) = 0.166, \(p\) < 0.05) and ISM and ISI (\(\beta\) = 0.157, \(p\) < 0.01). ISC was verified as an important moderating variable for ISI (\(\beta\) = 0.156, \(p\) < 0.05) in Model 8. Conversely, ISA was not found to have a significant impact on the relationship between the model and the model. Figure 2: Depiction of results. (Note: \({}^{**}\)\(p\) < 0.05).