esa-sceva/satcom-chunk-collection huggingface dataset:
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 3 Reliability Model
### Traffic Flow Foundation  
There are two models used for traffic stream evaluation [29]. The first approach (known as the macroscopic approach) is based on a physical stream of traffic flow to describe traffic dynamics with the help of partial differential equations. The following correlations illustrate these aspects [30]:  
\[d_{m}=\frac{1000-(l_{m}\times p_{vch})}{p_{vch}} \tag{1}\]  
\[T_{m}=\frac{d_{m}}{v_{m}} \tag{2}\]  
\[q_{m}=\frac{1}{T_{m}} \tag{3}\]  
where:  
\(p_{vch}\): Traffic density;  
\(d_{m}\): average distance between vehicles;  
\(v_{m}\): average velocity;  
\(T_{m}\): average time gap;  
\(q_{m}\): average traffic stream;  
\(l_{m}\): average vehicle length.  
However, the other approach (microscopic approach) defines the vehicle behavior such as the change of lane, speeding up or down on the road. The connectivity of the network is assessed in the next step by analyzing the diversity of velocity instead of the vehicular flow of traffic. Vehicle speed is the most important factor in determining network topology dynamics. It also plays a significant role in determining the maximum communication time between two vehicles.
# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 4 Materials and Methods
### Data Model  
We introduce the notation used throughout the section below.  
* Participating students (\(\mathcal{S}\)): set of students who participated in a SQL laboratory session (i.e., in our experiments, 215 students);
* Lab duration (\(D\)): The time span corresponding to lab development (i.e., a 90-min time window, in our experiments);
* Time window (\(TW\)): A time span at a finer granularity than \(D\) (e.g., a 5-min time span);
* Events (\(\mathcal{E}\)): The set of events of interest that occurred in the SQL laboratory. An event, \(e\in\mathcal{E}\), that occurred at an arbitrary time point, \(t_{e}\in D\), and involved a specific student, \(s\in\mathcal{S}\).  
The analysis focuses on the most relevant temporal correlations between the events that occurred in the labs and are relative to the same student. Each event describes either a specific action made by the student (e.g., access to a new exercise), an achievement (e.g., exercise solved), a request for assistance, or an assistance intervention. As discussed later on, the selected events are deemed as relevant to quantify the key engagement indicators under analysis. For our convenience, hereafter each event will be represented by a symbol consisting of the number of the exercise surrounded by a colored shape that describes the type of the event. Specifically,  
* the symbol 1 represents an access to exercise 1;
* the symbol 1 represents the submission of a correct solution for exercise 1;
* the symbol 1 represents the failure of exercise 1;
* the symbol 1 represents an assistance request for exercise 1;
* the symbol 1 represents assistance for exercise 1.  
Since the main goal of the study is to quantify the engagement key indicators of the students attending an SQL laboratory using the most representative temporal sequences of events, we rely on an event data model consisting of a sequence database [31], as described in Section 4.1. Specifically, each symbol describing an event is an item and each subsequence is an ordered list of single events (or event sets) associated with a given student.  
For example, the subsequence 1 1 1 represents a student that accesses exercise 1, fails it, and then subbits the correct solution.
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 7 Conclusions  
In this comparative survey, the performance requirements of the MAC layer of a mmWave wireless cellular communication system were established in terms of the capacity, spectral efficiency, and QoS. This survey focused on the adjustments necessary at the MAC layer functionalities for synchronization, random access, handover, channelization, interference management, scheduling, and association. Analysis of different protocols showed that optimizing the MAC layer can help the mmWave 5G communication system achieve near-optimal quality of experience fairness and have an advantage over the traditional PHY layer designs. This survey also provides a significant platform for academics to be motivated and to improve the outcomes of various types of challenges in next-generation MAC protocol designs for mmWave networks.  
**Author Contributions:** Conceptualization, W.C.; methodology, P.T. and W.C.; software, P.T.; validation, P.T. and W.C.; formal analysis, P.T. and W.C.; investigation, P.T.; resources, W.C.; data curation, P.T. and W.C.; writing--original draft preparation, P.T.; writing--review and editing, W.C.; visualization, P.T.; supervision, W.C.; project administration, W.C.; funding acquisition, W.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2021R11A3050535.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 7 Results and Performance Evaluation
### Evaluation of Base Station-Centric (Centralized) Versus Node-Centric (Decentralized) Approaches
#### 7.6.4 Accuracy of Detection  
The centralized approach is decidedly more accurate than the decentralized approach because the former is based on local as well as global conditions while the latter is based only on the local conditions. This has amply been established by the high TDR and low FDR of our method, as discussed in preceding sections.
# A Study on PF-IFF-Based Diagnosis Model of Plant Equipment Failure
## 3 Approach
### Learning Process  
The learning process consisted of four steps. In the first step of the learning process, the data collected from the sensors attached to the equipment were processed by the appropriate technique for learning. In the second step, the feature vector was constructed by dividing the data interval and extracting the summation values for each interval. In the third step, abnormal and normal patterns according to the state of the equipment were extracted using the K-means clustering method and the Pattern Frequency-Inverse Failure mode Frequency (PF-IFF) technique-. A more detailed description of the PF-IFF is given in Section 3.1.3. In the final step, the classification model with the feature vector as input and the state of the equipment as output was built up. With the classification model, the state of the equipment was then able to be diagnosed in the future prediction process.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 4 Steam Leak Detection
### TEG Design
#### 4.1.2 Electronic Design  
The power electronic circuit is responsible for converting the energy harvested by each thermoelectric generator module (TGM) into a usable DC output voltage of 3.3 V, which powers the IIoT device. Figure 8 illustrates the power electronics system, which optimizes the operation of the TGMs, ensuring they operate at their maximum efficiency to maximize the harvested thermal energy. Energy is stored in a 5 F supercapacitor able to deliver enough energy to up to five complete acquisition/communication processes.  
Figure 9 shows the main circuit board. In particular, the left image shows the board location inside the PVC case to isolate the electronics from the heat source. In the right image, all subcircuits of the final circuit board are identified.  
1. Power management module.
2. LoRaWAN radio interface.
3. Temperature and ultrasound microphone connector.
4. Temperature sensor interface to control the hot surface of the module.
5. Main processor.
6. Programmer interface.
7. Log interface for debugging.  
Figure 8: DC/DC up-converter model to stabilize the output voltage at 3.3 V.  
Figure 7: Heat propagation considering 150 \({}^{\circ}\)C of heat in the pipeline.
# Numerical Study of Photoacoustic Pressure for Cancer Therapy
## 1 Introduction  
In the last decades, the theory and the applications of photoacoustics have been developed, in particular for the photothermal measurement techniques [1; 2]. The applications of photoacoustics include spectroscopy, monitoring deexcitation processes, probing physical properties of materials, tomography and applications in photothermal therapy [3; 4; 5]. Photothermal therapy and photoacoustic techniques are procedures using laser illumination of photoabsorbers. Such therapies are non-invasive when working in the near-infrared spectrum. Metallic nanoparticles embedded in the tumor site absorb the laser irradiation and transfer the thermal energy to the surrounding medium (i.e., cancer cells) [6; 7]. Photoacoustics refers to the generation of acoustic waves by modulating an optical radiation. Photoacoustic imaging is applied to measure temperature by tracking the temperature-induced changes in the photoacoustic signal amplitude [8].  
In this context, we propose a numerical model for the accurate computation of photoacoustic pressure around gold nanoparticle embedded in a cell and the analysis of the spatio-temporal evolution of the pressure. The numerical method, including a 3D version of the adaptive remeshing scheme with error estimator [9; 10], takes into account the local and global variations of the pressure ensuring the convergence of the solution to the physical solution. The organization of the paper is as follows: Section 2 describes the equations of the problem and the numerical method. In Section 3, the results of the simulations are presented and a discussion on the ability of the pressure to destroy a cell is acheived before concluding the paper.
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 4 Damage Constitutive Model of Rock
### Rock Damage Theory Based on the Drucker-Prager Criterion
#### 4.1.3 Total Damage of Rock under Thermo-Confining Pressure Coupling
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Rock Type** & \(\mathbf{T}^{c}\)**C** & \(\mathbf{\sigma_{3}}\)**/MPa** & \(\mathbf{\sigma_{p}}\)**/MPa** & \(\mathbf{\sigma^{p}}\)**/MPa** & **Specific Value** & \(\mathbf{\varepsilon_{p}}\) & \(\mathbf{\varepsilon_{p^{\prime}}}\) & **Specific Value** \\ \hline \multirow{2}{*}{G} & 20 & 0 & 119.73 & 121.31 & 1.01 & 0.32 & 0.32 & 1.00 \\  & 120 & 25 & 344.60 & 371.13 & 1.08 & 0.42 & 0.46 & 1.10 \\ \hline \multirow{2}{*}{RS} & 20 & 0 & 22.07 & 22.27 & 1.01 & 0.30 & 0.31 & 1.03 \\  & 120 & 25 & 17.04 & 18.20 & 1.09 & 0.13 & 0.17 & 1.31 \\ \hline \multirow{2}{*}{GS} & 20 & 0 & 150.22 & 154.48 & 1.03 & 0.45 & 0.46 & 1.02 \\  & 120 & 25 & 293.08 & 297.46 & 1.01 & 0.46 & 0.47 & 1.02 \\ \hline \multirow{2}{*}{SH} & 20 & 0 & 59.09 & 58.82 & 0.99 & 0.48 & 0.48 & 1.00 \\  & 120 & 25 & 102.43 & 105.59 & 1.03 & 0.63 & 0.77 & 1.00 \\ \hline \end{tabular}
\end{table} 
Table 6: Theoretical peak strain values for different rock types. Figure 17: Comparison of theoretical and experimental peak stress and peak strain: (**a**) comparison of theoretical and experimental peak stress values; (**b**) comparison of theoretical and experimental peak strain values. In conclusion, the constitutive model for the temperature-confining pressure coupled damage evolution equation presented in this paper successfully represents the stress-strain behavior of rocks. The mechanical parameters derived show strong consistency. A comparison of the theoretical peak points predicted by the damage constitutive model with the experimental peak points demonstrates a high level of correlation. This confirms that the model accurately captures the stress-strain peak characteristics of rocks during triaxial compression, highlighting its important engineering significance.
# A Novel Covert Channel for IEEE 802.11 Networks Utilizing MAC Address Randomization
## 3 MAC Address Randomization
### User Information Disclosure through MAC Address
Specifically, for devices that remain in the vicinity for 2 to 3 min, the collected PRs provide sufficient data to estimate crowd density accurately. The study [25] introduces two passive analysis methods, namely service set identifier (SSID) analysis and inter-frame arrival time analysis (IFAT), for device identification using  
Figure 2: IEEE 802.11 probe request frame structure. PR frames. These methods leverage the MAC address as a key parameter in the PR frames and other associated values. The first method focuses on SSID fingerprinting, where the MAC address is linked to the SSID contained within the probe request. User behavior or location can be inferred by analyzing the SSID, which often provides information about commercial spots, public places, or private property addresses. The study demonstrates that the SSID fingerprinting method accurately achieves an efficiency rate of 73.19%. The second method, IFAT analysis, examines the inter-frame arrival time values within the probe requests. Unique device characteristics can be extracted by analyzing the timing patterns between successive PR frames. However, the study shows that the IFAT fingerprinting method has a lower efficiency rate of 33.73% compared to SSID fingerprinting. The study [26] explores utilizing Wi-Fi data for estimating passenger mobility on buses. To facilitate this, buses have Wi-Fi sensors that continuously collect real-time data. Each data capture includes the MAC address, signal strength, and timestamp, which are stored alongside GPS coordinates and timestamps in separate files. These files link the Wi-Fi and GPS data, enabling the analysis of passenger movements. A similar approach is demonstrated in the research [27], where Wi-Fi data are employed to estimate the number of passengers using public transportation. This method relies on identifying devices based on probe requests transmitted during discovery. In this case, the buses are equipped with multiple sensors, and the accuracy of determining whether a device is inside the vehicle depends on factors such as the frequency of MAC addresses and additional sensor information. To map user movements on a map based on MAC addresses, a combination of Wi-Fi and GPS data is employed as outlined in the research [28]. This integration allows for visualizing user trajectories and their corresponding MAC addresses.
# Prevention of Controller Area Network (CAN) Attacks on Electric Autonomous Vehicles
## 5 Data Integrity Algorithms
### HMAC SHA 256 Algorithm  
HMAC algorithms are message authentication code algorithms that are based on hash functions. MAC algorithms use symmetric cryptographic keys to generate a digest value for the software data that ensures both the software's integrity and authenticity. HMAC performs multiple hashing and xor operations on the software code, the secret key, and predefined constants to generate a unique digest value. This digest value would change with any modifications of the software or the secret key [41].  
HMAC algorithms inherit their strength from the underlying hash algorithm's strength plus the strength provided by the HMAC structure [42]. Therefore, only HMAC algorithmsthat use NIST-approved hash functions should be used in secure boot processes. Also, because HMAC functions use variable-length cryptographic keys, the used key derivation and key management mechanisms highly affect the algorithm strength. Therefore, it is recommended to derive a unique HMAC key for each ECU [29] and to only use HMAC keys with equal lengths to the output length [42] because keys with a smaller length than the output length decrease the algorithm strength, and keys with a larger length than the output length do not significantly increase the algorithm strength [43].  
The HMAC SHA 256 algorithm uses the SHA 256 hash algorithm to calculate a 256-bit output MAC to be used as a validation value in the secure boot process. In general, the calculation speed of the SHA 256 and the HMAC SHA 256 algorithms are comparable.  
HMAC algorithms are used in systems that require integrity and authenticity checks, such as secure booting and secure flashing algorithms. Also, HMAC is widely used in vehicles' secure onboard communication (SecOC) by appending an HMAC value to the transmitted message to be checked by the receiver for integrity and authenticity. One of the main applications of HMAC is the HMAC-based key derivation algorithm (HKDF) [44], which is used at the start of a communication session to derive a variable-length key from a secret input, a random value, and an optional identifier for the key. This key is then used throughout the communication session.
# LBFT: An Asynchronous Committee-Based Blockchain Storage Strategy on Zero Trust Model
## 2 System and Problem Statement
### Design Goals  
LBFT is constructed as an asynchronous committee-based storage strategy for the blockchain. We consider its strengths and functionalities listed in detail in Section 1 from the following aspects:  
1. **Decentralization and Security.** To carry forward the conception of decentralization, we should eliminate the reliance on a certain third trust party during the whole operation process of LBFT as far as possible. For system security, all the network traffic should be inspected and logged, and the transmitted values should be verified before they are accessed and operated. Access to certain resources and applications should also be strictly enforced [36].
2. **Frugal Communication.** LBFT is constructed on a zero-trust model. Moreover, in the previous work BFT-Store, suffering from \(O(n^{2})\) communication complexity in the decoding process, we aim to construct a protocol that can remove the \(O(n^{2})\) term during each decoding round, on the premise that the system can recover the original block successfully for client requests.
3. **Robustness.** Unlike the synchronous schemes that can only be applied to the relatively private and well-connected networks, we hope that LBFT can suit more large-scale, manifold, and unstable networks and can make progress even if the timing assumption of message delivery does not hold.
4. **Efficiency.** To make LBFT an efficient strategy, we have twofold expectations: (1) minimize the loss of performance resulting from the extra encoding and decoding rounds (compared to the primordial blockchain storage scheme); (2) free the nodes from the constraint of the pre-set timing assumption, so that they can respond immediately after the messages are delivered.
5. **Scalability.** We expect that LBFT, which is designed to be deployed in promising blockchain systems, can improve the system scalability as well as maintain the original functionalities of the blockchain. In LBFT, the storage costs of the blocks should maintain a constant level in regard to the number of nodes \(n\) in the system, on the premise that the data stored on the chain is consistent and accessible at any time.
## 4 SRF-IoT Design
### Monitoring/Updating Trust  
Keeping the trust value up to date is significant to avoid interruption of network operation from malicious actors. Hence, SRF-IDS constantly monitors the network and sends updated metrics to the monitored nodes. SRF-IDS sends the updated metrics in two modes: interval-based and trickle-based. Interval-based transmission occurs every 3 min from SRF-IDS detectors to devices in monitored network so that malicious nodes are identified in a short time. Before packet transmission, SRF-IDS detectors verify that new metrics are actually available to send to monitored nodes; otherwise they skip the procedure.  
Trickle-based transmission is based on RPL trickle timer implementation for transmitting DIO packets [35]. Trickle timer is a dynamic mechanism embedded into RPL that tries to minimise the transmission of RPL control packets. SRF-IDS detectors send packets with updated metrics each time the trickle timer resets. Sending packets in two different modes ensures that SRF-IDS packets arrive successfully and at the proper time to monitored nodes to choose their best parent. Without any metrics, benign nodes use MRHOF. Once a benign monitored node receives a packet from SRF-IDS, it calculates candidate parent's confidence value based on the new measurements. Metrics for monitored nodes are stored in SRF-IDS detectors and they are reset every 15 min to avoid storage capacity problems.  
Trust values have different scales as shown in Table 2. We defined the interval for the first three trust levels to be 25 because the weight factor may affect the calculations and cause rapid changes to the trust value. As a result, a big interval was needed to avoid unnecessary changes when the best parent algorithm is executed. In the higher levels of \"High Trust\" and \"Full Trust\", the interval is defined to be 11 and 14, respectively. There is no specific reason for the interval. However, it is important to note that once a node reaches a trust value of 76 or more, it is considered trusted and the weight factor becomes zero. Therefore, the node will be in the \"Full Trust\" scale. If trust falls below the _min_threshold_ (less than 26), then a device is considered malicious and it is blacklisted. In addition, RPL local repair is triggered to allow nodes find new parents. In the opposite case, if a node is blacklisted and the trust value is above 50, the node is removed from the list of malicious nodes. Initially, trust value 63 is assigned by default to all nodes joining the network. This is to allow nodes to choose the best parent using other metrics, such as rank, until trust metrics become available. Lists with malicious nodes are stored locally in each benign node so that parent selection algorithm avoids blacklisted nodes. In the case where a node stops attacking, the SRF-IDS will recalculate it's trust value. However, weights are adjusted and the node will gradually become fully trusted.