esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Architecture Design
### Pairing Process of SBD2D Communication  
In the proposed scenario of the SBD2D discovery phase, as indicated in Figure 2 above, for two devices to directly communicate with one another, they must first discover that they are near each other. Peer discovery is a randomized procedure, in which a device sends signals (UE1 to UE2 or UE3 to UE 4) without any knowledge about the location of the intended peer. Figure 3 depicts the transmission flow of a relay node, which involves the discovery phase.  
The knowledge of the nearby device location of the network is used to identify which device could benefit from D2D communication. When an SBD2D pair is found, the network coordinates the time and frequency allocation for the sending/scanning of devices. Due to the exchange of signals that is taking place, the signal-to-noise and interference ratio (SINR) of every paired device is computed. We intend to analyze the achievable network throughput of each SBD2D link and the consumed energy (energy efficiency) in this pairing scenario of SBD2D communication. As the beekeepers' equipment is connected to the BS and the relay, the network controller also selects which device will act as a relay node. The one that is connected to the serving BS directly is considered a relay to the other device. Consider the model (geometry) of the system illustrated in Figure 2, where it is assumed that it contains U cells. At the D2D receiver, the SINR is measured as \(\beta_{D_{R}}\), and can be computed as follows:  
\[\beta_{D_{R}}=\frac{\left|k_{D2D}\right|M_{D}l_{D}^{-\alpha}}{\sum_{l=1}^{U} \left|k_{nR}\right|^{2}M_{C_{n}}l_{C_{n}}^{-\alpha}+T_{0}} \tag{2}\]  
where \(k_{D2D}\) and \(k_{nR}\) are the fading coefficients of the D2D link and the link between the UE (D2D User Equipment) receiver and the Uth UE transmitter, respectively. The complex normal distribution, denoted as \(C_{n}\)(0,1), is followed by both the transmitter and receiver. \(\alpha\) symbolizes the path-loss exponent, while \(T_{0}\) is the additive white Gaussian noise (AWGN) variance. The transmit power of the D2D transmitter and the Uth CUE (Cellular User Equipment) are given by \(M_{D}\) and \(M_{C_{n}}\) respectively [28].
## 3 Results  
The results from the physiological, spatiotemporal, and neuromuscular variables of the RE tests are presented in Table 2. There were significant differences (\(p<\) 0.05) between time points (pre-post) in RE (expressed as oxygen cost [OC] and energy cost [EC]). However, there were no significant differences in spatiotemporal and neuromuscular variables during the RE test. OC and EC were significantly higher in the post-test (43.55 \(\pm\) 3.23 vs. 45.36 \(\pm\) 3.18 mL.kg\({}^{-1}\cdot\)min\({}^{-1}\) and 4.28 \(\pm\) 0.25 vs. 4.43 \(\pm\) 0.21 kJ\(\cdot\)kg\({}^{-1}\cdot\)km\({}^{-1}\), respectively).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & **Monday** & **Tuesday** & **Wednesday** & **Thursday** & **Friday** & **Saturday** & **Sunday** \\ \hline \multirow{6}{*}{Week 3} & Type of & Continuous & High- & & & & High- \\  & session & running & intensity & & & & & \\  & & 60 min & 55 min & & & & & \\  & Volume & 12–14 km & 10–12 km & 14–16 km & 12–14 km & 15–17 km & 15–17 km & 15–17 km \\  & Intensity & 60\% MAS & 105\% MAS & 65\% MAS & 55\% RM & 90\% MAS & 60\% MAS & 65\% MAS \\ \cline{2-8}  & Examples of high-intensity & & & & & & & \\  & interval sessions & & & & & & & \\  & & & & & & & & \\ \hline \multirow{6}{*}{Week 18} & Type of & Continuous & High- & & Continuous & High- & & \\  & session & running & intensity & & & & & \\  & & interval & & technique & & & & \\  & & 60 min & 70 min & 50 min & 80 min & 50 min & 90 min \\  & Volume & 10–12 km & 12–15 km & 8–10 km & 14–16 km & 18–20 km & \\  & Intensity & 60\% MAS & 105\% MAS & 60\% MAS & 90\% MAS & 65\% RM & 65\% MAS \\ \cline{2-8}  & Examples of high-intensity & & & & & & \\  & interval sessions & & & & & & \\  & & & & & & & \\ \hline \multirow{6}{*}{Strength session} & Strength & 4 bouts of 6 repetitions at & & & & & \\  & exercises & 60\% of 1 RM with 1 min & & & & & \\  & recovery & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & Core & 4 bouts of 12 repetitions & & Dead bug & Mountain climbers & & \\ \cline{1-1}  & exercises & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Example of two types microccles and a strength session of the participants.  
The results from the physiological, spatiotemporal, and neuromuscular variables during the GXT are shown in Table 3. There were significant differences (\(p\) < 0.05) between time points (pre-post) in the speed of VT\({}_{1}\), VT\({}_{2}\), and MAS, in the oxygen cost of VT\({}_{1}\) and VT\({}_{2}\), in the relative VO\({}_{\rm 2 max}\), and in the percentage of VT\({}_{1}\). There was a significantly higher speed of VT\({}_{1}\) (13.44 \(\pm\) 1.65 vs. 14.67 \(\pm\) 1.61 km\(\cdot\)h\({}^{-1}\)), VT\({}_{2}\) (17.
## 2 Materials and Methods
### Experimental Data Collection and Processing
#### 2.1.2 Key Component Design  
To accommodate variations in crop height and complex terrain conditions, a compact and efficient LiDAR angle adjustment device was designed and integrated in this study. The structural layout of the device is shown in Figure 2. Specifically tailored for LiDAR installation, the device features a base measuring 80 mm \(\times\) 80 mm \(\times\) 20 mm, offering excellent adaptability for mounting and high structural stability. This ensures smooth operation of the LiDAR and maintains data accuracy during field deployment. The overall dimensions of the device are 80 mm \(\times\) 80 mm \(\times\) 126 mm, allowing for seamless integration with the mobile platform in space-constrained environments.  
To enhance the mechanical stability and data acquisition reliability of the device under varying operational conditions, multiple sets of positioning holes were incorporated into the design. These features ensure secure mounting on the mobile platform, thereby improving the overall durability and operational safety of the system. In addition, the angle adjustment mechanism adopts a manually operated hinged design, enabling flexible pitch adjustments of the LiDAR sensor within a range of 0\({}^{\circ}\) to 180\({}^{\circ}\). This provides robust mechanical support for multi-angle 3D perception in complex field environments.
# A Systematic Survey of Multi-Factor Authentication for Cloud Infrastructure
###### Abstract  
The unauthorized usage of various services and resources in cloud computing is something that must be protected against. Authentication and access control are the most significant concerns in cloud computing. Several researchers in this field suggest numerous approaches to enhance cloud authentication towards robustness. User names and associated passwords have been a common practice for long as Single Factor Authentication. However, advancements in the speed of computing and the usage of simple methods, starting from the Brute Force technique to the implementation of advanced and efficient cryptographic algorithms, have posed several threats and vulnerabilities for authentication systems, leading to the degradation of their effectiveness. Multi-factor authentication has emerged as a robust means of securing the cloud using simultaneous and multiple means of authentication factors. This employs multiple levels of cascaded authentication checks. This paper covers an extensive and systematic survey of various factors towards their adoption and suitability for authentication for multi-factor authentication mechanisms. The inference drawn from the survey is in terms of arriving at a unique authentication factor that does not require any additional, specialized hardware or software for multi-factor authentication. Such authentication also uses the distinct biometric characteristics of the concerned user in the process. This arrangement augments the secured and robust user authentication process. The mechanism is also assessed as an effective means against impersonation attacks.  
access control; authentication management; authorization management; biometrics; cloud computing; cloud IAM; multi-factor authentication; user identity +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 5 Multi-Identity Based Full Homomorphic Encryption Scheme
### Construction
**Eval algorithm except for the dimension expansion, the matrix \(\hat{\mathbf{M}}\) and the randomization function \(\hat{\mathbf{M}}^{-1}(\cdot)\). The following is the homomorphism analysis of the mIBFHE scheme:  
**Definition 9**. : Let \(\hat{\mathbf{C}}_{1},\hat{\mathbf{C}}_{2}\) be an extended ciphertext matrix corresponding to plaintext \(\mu_{1},\mu_{2}\), respectively, and the secret key is \(\hat{\mathbf{s}}\in\mathbb{Z}_{q}^{D^{\prime}}\), satisfying \(\hat{\mathbf{s}}^{\text{T}}\hat{\mathbf{C}}_{1}=\mu_{1}\hat{\mathbf{s}}^{\text{T}}\hat{\mathbf{ M}}+\mathbf{\hat{e}}_{1}\), \(\hat{\mathbf{s}}^{\text{T}}\hat{\mathbf{C}}_{2}=\mu_{2}\hat{\mathbf{s}}^{\text{T}}\hat{\mathbf{M}}+ \mathbf{\hat{e}}_{2}\), where \(\mu_{1},\mu_{2}\in\{0,1\}\), \(\|\mathbf{\hat{e}}_{1}\|_{\infty}\leq\hat{\beta}_{1}\), \(\|\mathbf{\hat{e}}_{2}\|_{\infty}\leq\hat{\beta}_{2}\). \[\begin{array}{ll}\text{(1)}&\text{\bf{Homomorphic addition:}}\hat{\mathbf{C}}_{Add}=\hat{\mathbf{C}}_{1}+\hat{\mathbf{C}}_{2}, \text{ satisfy }\hat{\mathbf{s}}^{\text{T}}\hat{\mathbf{C}}_{Add}=\hat{\mathbf{s}}^{\text{T}}\big{(}\hat{ \mathbf{C}}_{1}+\hat{\mathbf{C}}_{2}\big{)}=(\mathbf{u}_{1}+\mu_{2})\\ \hat{\mathbf{s}}^{\text{T}}\hat{\mathbf{M}}+\mathbf{\hat{e}}^{+},\text{where }\hat{\mathbf{e}}^{+}=\hat{\mathbf{e}}_{1}+\hat{\mathbf{e}}_{2}\). Obviously \(\hat{\mathbf{C}}_{Add}\) is \(\hat{\beta}  
* **Homomorphic multiplication:**\(\hat{\mathcal{C}}_{Mult}=\hat{\mathcal{C}}_{1}\hat{\mathcal{M}}^{-1}(\hat{ \mathcal{C}}_{2}),\text{satisfy}\,\hat{\mathfrak{s}}^{T}\hat{\mathcal{C}}_{Mult}= \hat{\mathfrak{s}}^{T}\hat{\mathcal{C}}_{1}M^{-1}(\hat{\mathcal{C}}_{2})=(\mu_ {1}\mu_{2})\hat{\mathfrak{s}}^{T}\hat{\mathcal{M}}+\hat{\mathfrak{e}}^{\times},\) where \(\hat{\mathfrak{e}}^{\times}=\hat{\varrho}_{1}\hat{\mathcal{M}}^{-1}(\hat{ \mathcal{C}}_{2})+\mu_{1}\hat{\varrho}_{2}.\) Obviously \(\parallel\hat{e}^{\prime\prime}\parallel_{\infty}\leq\left(\sqrt{DN}\hat{ \beta}_{1}+\hat{\beta}_{2}\right)\), \(\hat{\mathcal{C}}_{Mult}\) is \(\left(\sqrt{DN}\hat{\beta}_{1}+\hat{\beta}_{2}\right)\) noise ciphertext. The same calculation is also applicable to NAND gates. **Multi-identity ciphertext security**. If the IBE scheme constructed in this paper is IND-sID-CPA secure, then the mIBFHE scheme proposed in this paper is also IND-sID-CPA secure. By using constructive proof, the masking scheme constructed by **LinkMask** algorithm is IND-sID-CPA security. From theorem 2, it can be seen that the IBFHE scheme is IND-sID-CPA security. In summary, the mIBFHE scheme is also IND-sID-CPA security.
# Effect of Aggregate Crystalline Surface Anisotropy on
## 1 Introduction
[4] selected the (0 0 1) and (1 0 1) planes for \(\alpha\)-SiO\({}_{2}\) crystals due to their stability, and, for CaCO\({}_{3}\) crystals, they chose the (1 0 4) plane for its stability, large layer spacing, and low surface energy, noting that (0 1 8) and (2 1 4) facets are also common. [PERSON] et al. [15] cut SiO\({}_{2}\) cells to obtain the stable (0 0 1) facet in their modeling and found that the (0 1 8) direction provided the strongest adhesion when CaCO\({}_{3}\) cells were cleaved. [PERSON] et al. [16] modeled SiO\({}_{2}\) crystals by cutting cells along the (1 0 1) direction, explaining that low-Miller-index surfaces (0 0 1), (0 1 0), and (1 0 0) are common for \(\beta\)-SiO\({}_{2}\) crystals. [PERSON] et al. [17] selected four minerals for the modeling of aggregates, using the (1 0 0) surface for the smallest lattice mismatch. [PERSON] et al. [3] indicated that (1 0 0) and (1 0 1) facets are also commonly exposed surfaces for SiO\({}_{2}\) crystals, in addition to the stable (0 0 1) surface, and they identified (1 0 4) as the most common solvation surface for calcite (CaCO\({}_{3}\)), also noting the (2 1 4) and (0 1 8) surfaces as common. The exposed surfaces of SiO\({}_{2}\) and CaCO\({}_{3}\) aggregate crystals are shown in Table 1.  
Research on the characteristics and mechanisms of asphalt-aggregate interfacial interaction has garnered attention both domestically and internationally. However, limited studies have focused on the influence of aggregate grain surface anisotropy on the interfacial interaction. The objective of this research was to utilize the MD simulation method to establish an aggregate-asphal interface model with various crystalline surfaces. The \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multicolumn{2}{c}{**Surface**} \\ \cline{2-3}  & **SiO\({}_{2}\)** & **CaCO\({}_{3}\)** \\ \hline [PERSON] et al. [4] & (0 0 1), (1 0 1) & (1 0 4), (0 1 8), (2 1 4) \\ [PERSON] et al. [15] & (0 0 1) & (0 1 8) \\ [PERSON] et al. [16] & (0 0 1), (1 0 1), (0 1 0 0) & - \\ [PERSON] et al. [17] & (1 0 0) & - \\ [PERSON] et al. [3] & (0 0 1), (1 0 1), (1 0 0) & (1 0 4), (0 1 8), (2 1 4) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Exposed surfaces of SiO\({}_{2}\) and CaCO\({}_{3}\) aggregate crystals. interactions between asphalt and aggregates under different crystalline surfaces, including the adhesion performance, diffusion behavior, and radial distribution function (_RDF_), were analyzed to comprehensively understand the impact of aggregate crystalline surface anisotropy on the adhesion performance. The findings provide a theoretical basis for the enhancement of asphalt mixture performance by proposing targeted measures for different aggregates.
# Integrating Merkle Trees with Transformer Networks for Secure Financial Computation
## 3 Materials and Method
### Merkle-Transformer
#### 3.3.5 Merkle Loss Function  
In this paper, a novel loss function, the Merkle loss function, is introduced. This function is a significant supplement and expansion of traditional loss functions, designed to integrate the data verification mechanism of Merkle trees with the optimization process of deep learning models. The Merkle loss function is conceptualized to consider not only prediction errors at every step of model training but also the integrity and security of data. Traditional loss functions, such as mean squared error (MSE) or cross-entropy, primarily focus on the model's predictive performance, i.e., the discrepancy between predicted and actual values. The Merkle loss function, however, introduces an additional term that measures the consistency between the model output and the root hash value of the Merkle tree. This new loss function is represented as follows:  
\[L_{\text{Merkle}}=L_{\text{pred}}+\lambda\times L_{\text{merkle}} \tag{12}\]  
where \(L_{\text{pred}}\) is the traditional prediction loss term, such as MSE or cross-entropy, \(L_{\text{merkle}}\) is the loss term related to the root hash value of the Merkle tree, and \(\lambda\) is a hyperparameter balancing the importance of the two. The above approach is designed to conform to the general expression of the loss function. In practice, by setting \(\lambda\) to a very large number (e.g., 10,000), it allows the model to immediately stop training upon detecting data integrity issues, similar to a penalty term in optimization algorithms.
# Optimized Scheduling of IoT Devices in Healthcare Facilities: Balancing Cost and Quality of Care
## Appendix B System Architecture and Workflow  
The system employs a distributed architectural paradigm, as depicted in Figure 11, to efficiently manage and schedule IoT devices for patient monitoring. This architecture consists of several key components, each serving a specific role in optimizing data flow and processing. Client nodes (user terminals) act as the primary interface for healthcare professionals, including nurses and administrative staff, allowing them to input data, retrieve optimized schedules, and monitor device assignments. IoT devices, which are medical-grade and deployed to patients, continuously capture real-time physiological data and transmit them to the backend for further processing. Backend processing is responsible for executing an optimization algorithm, such as NSGA-II, to determine the most efficient allocation of IoT devices while adhering to predefined constraints and objectives. To ensure seamless data management, a distributed database stores patient records, device availability, scheduling results, and historical data, guaranteeing data integrity and easy retrieval of past assignments.  
The sequence diagram in Figure 12 visually represents the operational workflow for IoT-based patient monitoring. It illustrates how different system components interact throughout the workflow, from data input to real-time re-optimization.  
The operational workflow begins with data acquisition, where healthcare personnel input relevant information, including available IoT device inventories and patient monitoring requirements. The optimization procedure follows, in which the backend processes the input data and applies the optimization algorithm to generate an efficient device-to-patient assignment, taking into account factors such as patient priority, device availability, and clinical conditions. The final decision is made by the decision-maker, who considers all the generated alternatives. The result storage and distribution phase ensures that the optimized schedule is persistently stored in the distributed database and made accessible to authorized healthcare personnel. User interaction allows nurses and healthcare staff to retrieve assigned schedules via client terminals, ensuring adherence to the proposed plan for each patient. Lastly, the system supports dynamic updating, which allows real-time re-optimization in response to new patient admissions or evolving clinical conditions.  
This architecture establishes a scalable and automated approach for managing patient-device assignments, reducing manual errors, and enhancing overall healthcare operational efficiency.
## 2 Monitoring Protocols and Methods
### Soil-Gas Concentrations  
Soil-gas concentrations were measured at 1m-depth after drilling a small hole in the soil with a battery-powered drill (1m-depth, 1 cm diameter) and inserting a sampling probe (copper tube, 1 m long and 1 cm diameter). Due to land-use constraints, it was not possible to keep the sampling probe in place between each monitoring campaign, which would have been the best option. The holes were surface-capped with locally present soil. For each new measurement, probes were re-inserted after a new hole was drilled. Soil-gas concentrations were directly monitored using with an LFG20 Infra-Red Gas Analyser (IRGA--ADC Gas Analysis Ltd., UK) using low-rate pumping (200 mL\(\cdot\)min\({}^{-1}\)) to avoid disturbing the soil atmosphere. After purging of the tubes and pipes, stable readings were reached within tens of seconds. The stability of the readings, during measurement and gas-sample collection, was regularly checked and, if necessary, the site was resampled. The analytical precision for CO\({}_{2}\) and CH\({}_{4}\) was \(\pm\)0.5% (of the reading) at low concentrations (0.01-10% vol.) and \(\pm\)3% (of the reading) at higher ranges (10-50% vol.). Oxygen precision for was \(\pm\)0.4% (of the reading) in the range 0-25%. About 27% of the field measurements were cross-checked by gas-chromatography analyses at laboratory allowed determining that the overall error (sampling error induced by re-drilling + instrumental error + error on replicates) is less than 10% for CO\({}_{2}\) concentrations greater than 8% and less than 5% for CO\({}_{2}\) concentrations lesser than 6%.
# Game Theory Meets Wireless Sensor Networks Security Requirements and Threats Mitigation: A Survey
## 4 Game Theory Defense Strategies Against WSN Attacks
### Clear and Passive Attacks
#### 4.3.1 Clear Attack  
The clear attack is capable of disrupting the traffic stream. It can transmit, respond, modify, and/or delete specific messages inside the stream due to the manipulation that the attacker applies. This attack can impersonate one end of a conversation or a third party. Consequently, it can prevent the infected sensor node from communicating with others.  
The following papers consider the clear attack types in WSNs. In [80], the authors propose a defense attack strategy which concentrates on the jamming attack. An intrusion detection system is introduced against the attacks based on the NE concept that represents a defense strategy against the most vulnerable sensor nodes [41; 81]. In [2], the attack targets the data trustworthiness of the sensor network which is mitigated by a game theoretic approach to protect the sensor nodes based on a trust score of the data item observed by those nodes. Consequently, the higher the data items trust scores are, the more trustful the nodes are. In [53], a deleterious attack scenario is considered, where a node turns malicious and then drops selected packets from a traffic stream.  
Interestingly, clear attacks can cause some manipulations to the whole sensor network. Therefore, a great deal of information is needed for the defense mechanisms to be able to mitigate this type of attacks. For example, a malicious sensor node has the ability to drop incoming packets or issue route error messages to misdirect the path (e.g., blackhole attack) as presented in [82]. Also, in [83], the authors propose a game-theoretic approach to confront the attack impact of dropping the passing messages within a cluster.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 2 Related Works
### SPIHT-Based Image Compression
SPIHT is highly vulnerable to bit corruption since a single bit error can introduce major image distortion, depending on its location. \[S_{n}(T)=\left\{ \begin{array}{ll}1&\max_{(i,j)\in T}\{\big{[}\big{|}c_{ij} \big{|}\big{]}\geq 2^{n}\\ 0&otherwise\end{array} \right. \tag{5}\]
## 2 Materials and Methods
### Analysis of Non-Volatile Metabolites by UPLC-MS/MS
#### 2.4.2 UPLC Conditions  
The YH and FHYH extracts were examined using UPLC-ESI-MS/MS (UPLC, SHIMADZU Nexera X2; MS, Applied Biosystems 4500 Q TRAP, Thermo Fisher Scientific, Shanghai City, China). For UPLC, an Agilent SB-C18 column (1.8 m, 2.1 mm \(\times\) 100 mm) was employed. Both solvents contained 0.1% formic acid: solvent A, which was pure water, and solvent B (mobile phase), which was acetonitrile. The initial gradient for the sample analyses was 95% A and 5% B. Within 9 min, a gradient that was linear of 5% A and 95% B was established and kept constant for 1 min. Within 1.1 min, the gradient was altered to 95% A and 5% B, where it remained for 2.9 min. The flow velocity was 0.35 mL per minute, the injection volume was 4 \(\upmu\)L, and the column oven was set to 40 \({}^{\circ}\)C. The effluent and an ESI-triple quadrupole-linear ion trap (QTRAP)-MS were connected differently.