esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Approaching Excavation on Adjacent Segments for Twin Tunnels
## 3 Description of Numerical Simulations
### Characteristics of Three-Dimensional Entity  
As shown in Figure 4, the component instances in this model are simulated by solid elements (C3D8R in ABAQUS), with a total of 264,672. The classical Mohr-coulomb constitutive model was utilized to model the soil. Descriptions of model parameters are listed in Table 1. In addition, the dilatancy angle \(\psi\) is assumed to depend on the friction angle \(\varphi\), namely \(\psi=\varphi-30^{\circ}\) if \(\varphi>30^{\circ}\), \(\psi=0\) if \(\varphi<30^{\circ}\)[28].  
Figure 3: Finite element model. (**a**) Perspective view; (**b**) Plan view; (**c**) Lateral view of Cross-section B.  
The TBM shield is assumed as a cylindrical shape with conicity ignored [29]. Due to the presence of the cutter-head as well as chamber, the elastic modulus and thickness of the shield in the models are 10 and 2 times greater than those in Table 2, respectively [30]. Nevertheless, the elastic modulus of the segments needs to be reduced due to the simulations of longitudinal or circumferential joints are ignored. Consequently, the modulus of the concrete segments is assumed as 25.9 GPa in the case of the reduction factor of 0.75 [30].  
According to the time-dependent elastic modulus of the grout material [31], the filling grout (Figure 4) between the excavation interface and the segmental outer wall is defined by the annular concrete elements with two-phase elastic behaviors. Combined with actual grouting timing and boring rate, the elastic moduli of fresh grout and hardened grout are assumed as 3 MPa and 500 MPa, respectively.  
Unlike the straight-line tunnel, overcutting at the inner side of the curved tunnel needs to be considered, which is simulated via equivalent, semicircular zone elements with low modulus and thin thickness (Figure 4). The mechanism of over-ex excavation for articulated TBM shield is illustrated in Figure 5, where \(L_{\text{MI}}\) and \(L_{\text{M2}}\) are the lengths of front shield body and rear shield body, respectively [32].  
Figure 4: Illustrated simulation of the TBM progressive advancement.  
Figure 5: Over-excavation of a curved tunnel.  
Considering promptly support after excavation, it is assumed that the overcutting \(\omega\) is equal to one-third of the theoretical value (\(G_{\text{a}}\)) [33; 34]:  
\[\omega\ =\ \frac{\sqrt{\left(Q+R\right)^{2}+L_{\text{M2}}^{2}}-\left(Q+R\right)}{3} \tag{2}\]  
where \(Q\) is the curvature radius of the curved tunnel, \(R\) is the external diameter of the TBM shield, \(L_{\text{M2}}=2b\), and \(b\) is equal to one ring width.  
The value of overcutting \(\omega\) considered in the numerical analyses is 1.14 cm, which is ten times the calculated value obtained by substituting the following parameters into Equation (2): \(Q\) = 840 m, \(R\) = 3.14 m, \(b\) = 1.2 m. Meanwhile, the modulus of the elements of overcutting is 13 MPa, which is one-tenth of the original value [35].
# Enhanced Learning Behaviors and Ability Knowledge Tracing
## 5 Experiments
### Datasets  
Our model is evaluated on three publicly available datasets: (1) ASSIST2012, (2) ASSISTChall, and (3) Algebra0607. These datasets are real-world datasets, taking the form of interaction records between students and intelligent tutoring systems in a temporal manner. They incorporate rich information regarding students and exercises as well as their interactions over time, such as'student_id','skill_id','startTime', 'endTime', and 'hint'--key features essential for our model's analysis. The basic statistics of these datasets are presented in Table 2. A simple description is as follows:  
* ASSIST2012 ([[https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-with-afect](https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-with-afect)]([https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-with-afect](https://sites.google.com/site/assistmentsdata/home/2012-13-school-data-with-afect)), accessed on 21 October 2024): This dataset was collected from September 2012 to October 2013 using the ASSISments online education website. This dataset consists of records that originate from practice problem sets, where students are required to complete similar exercises to enhance their skills. It includes interactive learning information such as student answer performance, knowledge concepts, answering time, and hint times. To improve the performance of the KT models, we have filtered the records without knowledge concepts when applied to this dataset. After processing, the dataset consists of 29,018 students, 265 concepts, and 53,091 exercises.
* ASSISTChall ([[https://sites.google.com/view/assistmentsdatamining](https://sites.google.com/view/assistmentsdatamining)]([https://sites.google.com/view/assistmentsdatamining](https://sites.google.com/view/assistmentsdatamining)), accessed on 21 October 2024): Released in 2017 as part of a data mining competition, the full ASSISTment Challenge dataset contains the most descriptive information among the ASSISTment datasets. In this dataset, we also excluded the records without related knowledge concepts. It contains 3162 distinct questions answered by 1709 studentsover 102 knowledge concepts resulting in 942,816 interactions. On average, students have a longer learning sequence in this dataset than ASSIST2012.
* Algebra0607 ([[https://pslcdatashop.web.cmu.edu/KDDCup](https://pslcdatashop.web.cmu.edu/KDDCup)]([https://pslcdatashop.web.cmu.edu/KDDCup](https://pslcdatashop.web.cmu.edu/KDDCup)), accessed on 21 October 2024): This dataset is a subset of KDDCup dataset, which was presented at the KDDCup 2010 Educational Data Mining challenge and contains 13-14 years old students responses to Algebra questions from 2005 to 2007 extracted from the intelligent tutoring system called \"The Cognitive Tutors\" developed by Carnegie Learning Inc.in the US. Algebra0607 was collected from 2006 to 2007. This dataset records the steps of the students in answering the exercises, so we treat each step of each exercise as a separate exercise.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### Soundfield Representation  
For a soundfield within a source free region, the sound pressure at any point can be expressed in the 3D spherical coordinate as [86]  
\[P(x,w)\approx\sum_{n=0}^{N}\sum_{m=-N}^{N}\alpha_{n}^{m}(w)j_{n}(kr)Y_{n}^{m}( \hat{x}) \tag{1}\]  
where \(\alpha_{n}^{m}(\mathrm{w})\) are soundfield coefficients corresponding to the mode index \((n,m)\), \(j_{n}(kr)\) are Spherical Bessel functions of the first kind representing the mode amplitude at radius \(r\), \(Y_{n}^{m}(\hat{x})\) are Spherical Harmonics that are functions of the angular variables. Spherical Harmonics and Spherical Bessel functions together represent the propagation modes. Especially, due to the low-pass characteristics of the Spherical Bessel function, given the radius of a region of interest (ROI) \(r_{0}\) and the wavenumber \(k\), the truncation number \(N\approx\lceil kr_{0}\rceil\)[87]. This means that the soundfield within the ROI can be represented by a finite number of, i.e., \(D=\left(N+1\right)^{2}\), coefficients.  
In soundfield recording and analysis, normally, the soundfield coefficients \(\alpha_{n}^{m}(w)\) and the Spherical Bessel functions \(j_{n}(kr)\) are integrated as one component. Equation (1) becomes an expansion with respect to Spherical Harmonics solely. The expansion order \(N\) is also the order of the system. For example, when \(N=1\), the system is called the first-order system (or the widely known Ambisonics); and when \(N\geq 2\), the system is called the higher-order system (or higher-order Ambisonics).
## 3 Platform for Real-Life Testing of Communication Protocols
### Monitor Messaging  
* When creating GNU radio applications, the monitoring messages typically come from a GNU radio block and are built within the block's work thread. This means it is important to find efficient methods for constructing these messages. Additionally, since the broker combines messages from multiple sources, improving deserialization is crucial. Refer to Figure 10 for the main components involved in transferring monitoring messages. The message generators are in charge of constructing messages and operate in the same thread as the GNU radio blocks.
* The monitor probes collect messages from multiple generators within the GNU radio application and send them to the broker.
* The broker is responsible for receiving messages from various probes and subsequently processing them.  
A message being sent from its origin to a collector (such as a broker) goes through two distinct channels:  
* Message passing uses the GNU radio messaging system between individual blocks. This system uses PMTs (polymorphic types) [49] to carry different objects as messages between blocks.
* The transport channel carries the message from the GNU radio application to the message collector (i.e., the broker).  
This paper suggests using the protocol buffers library [50] to reduce the size of monitoring messages and the time spent building and parsing them. This library is a language-neutral, platform-neutral extensible mechanism for serializing structured data, similar to JSON [51]. Since both the message generators and probes need to add information to the monitoring messages, the library must be available to both application-side components. While most of the monitoring data come from the GNU radio block that does the work, the probe has information that should be tracked, such as messages sent over the transport channel and the message passing queue size.
## 2 Measurements
### Setup  
The arrangement of the measurement setups within the designated measurement area is illustrated in Figure 3. The platform has a width of 53 m and a length of 110 m, with the latter dimension aligned in the direction of train travel. Below it are three tracks spaced at intervals of 5 m from each other, each positioned at a distance of 3.3 m from the nearest structural column.  
As depicted in Figure 3b, the train operation area is situated beneath the platform, with an accelerometer, G1, positioned adjacent to the nearest structural column on the soil. Simultaneously, an accelerometer, C1, was affixed to the structural column at a height of 0.5 m above ground level. This arrangement aims to investigate vibration propagation laws and responses between soil and building columns. As illustrated in Figure 3c, an accelerometer, C2, was strategically placed on the first floor of the over-track building, approximately 8.3 m away from track #2.  
Figure 3: Measurement setup. (**a**) Elevation view; (**b**) plan view of train operation area; (**c**) plan view within the building.  
According to the functional issues pertaining to structural members and similar site selection problems discussed in reference [7], as well as the actual site and building layout constraints, this study selects C1, C2, and G1 for analysis. The chosen locations enable the examination of the dynamic interaction and inertial interaction between the soil and structure [28].
## 2 Materials and Methods
### Methods
#### 2.3.2 Trend Variation and Correlation Analysis  
We utilized Theil-Sen linear regression to assess the temporal trends of summer GPP, RSM, and PRE from 2000 to 2022. Theil-Sen regression is a robust method for estimating linear trends, as it calculates the median slope derived from all possible pairwise linear fits among two-dimensional data points [45]. To discern the independent contributions of PRE and RSM to variations in GPP, we implemented a pixel-level partial correlation analysis. This approach enabled us to evaluate the relationship between RSM and GPP while statistically controlling for the effect of PRE. Conversely, we assessed the association between PRE and GPP by accounting for the influence of RSM [18]. The partial correlation was computed using the following formula:  
\[R_{(\alpha,\beta|\gamma)}=\frac{R_{\alpha\beta}-R_{\alpha\gamma}\times R_{ \beta\gamma}}{\sqrt{1-R^{2}{}_{\alpha\gamma}}\times\sqrt{1-R^{2}{}_{\beta \gamma}}} \tag{2}\]  
where \(R_{(\alpha\beta|\gamma)}\) denotes the partial correlation coefficient between variables \(\alpha\) and \(\beta\) after \(\gamma\) controlling for variable \(\alpha\), while \(R_{\alpha\beta}\), \(R_{\alpha\gamma}\), and \(R_{\beta\gamma}\) represent the partial correlation coefficient between variables \(\alpha\) and \(\beta\), \(\alpha\) and \(\gamma\), and \(\beta\) and \(\gamma\), respectively.
## 3 Theoretical Background and Methodology
### Queueing Theory  
The study of queueing systems allows one to evaluate various metrics to assess the performance and the quality of a service such as the mean number of items in the system (queue length), the mean waiting time, the service occupancy, etc. [34]. A queuing system consists of items (i.e., customers or jobs in a queue) and servers.  
Kendall Notation (A/B/C/D/E/F) is used to synthetize the hypotheses of a queuing model [35] where A is the arrival process, B is the service process, C is the service mechanism, D is the system capacity, E is the population and F is the queue discipline.  
The simplest queueing model (Figure 1) is when, for each item, both interarrival and service time are independent among each other (i.i.d), and it is possible to model the corresponding probability distributions with the negative exponential distributions, assuming, respectively, a constant rate \(\lambda\)= \(\
u\)MTA (i.e., the Mean Time of Arrive) and a constant service rate \(\mu\)= \(1\)MTS (i.e., the Mean Time of Service).  
This modelling obeys the following hypotheses:  
* **Hp1:** Interarrival of items can be modelled with a random variable characterized by a mean interarrival time.
* **Hp2:** The service time can be modelled with a random variable characterized by a mean service time.  
These hypotheses bring about a special type of Continuous Time Markov Chain (CTMC), the well-known birth and death process [36] in which jumps exist only among the neighbouring states and each state of the Markov Chain represents the number of items in the queue (Figure 2).  
Figure 2: Birth and Death process.  
In this process, a Birth is an arrival of a new item into the queueing system, with the rate of \(\lambda\), and a Death is a departure of a served item, with the rate of \(\mu\). Under this assumption, the amount of time \(\theta_{i}\) the system spends until another birth or death happens is exponentially distributed with rate \(\theta\) = \(\lambda+\mu\), and the sojourn probability in the state \(i\)th can be computed as shown in Equation (1):  
\[P\{\theta_{i}\leq t\}=1-e^{-\theta t},t\geq 0 \tag{1}\]  
For a long run, it is possible to evaluate the steady-state system probabilities:  
\[\rho=\frac{\lambda}{\mu} \tag{2}\]  
\[\pi_{n}=(1-\rho)\rho^{n} \tag{3}\]  
\[L=\frac{\lambda}{\mu-\lambda} \tag{4}\]  
\[W=L/\lambda \tag{5}\]  
where  
* \(\rho\) is the probability of being served;
* \(\pi_{n}\) is the probability of having n items in the queueing system;
* \(L\) is the mean number of items in the queue;
* \(W\) is the mean waiting time in the queue: \(W=L/\lambda\).  
As it can be understood, this simple queuing model is very unlikely in real case scenarios for which the complexity of the corresponding processes cannot really be captured with those assumptions. Therefore, the study of a generic G/G/k must be carried on with other computational models such as the hybrid simulation model proposed in Section 4.
## 2 Results and Discussion
### Phosphine Oxide Derivatives  
Blue TADF emitters are generally composed of a strong acceptor and a weak donor. [PERSON] et al. used the opposite strategy since they connected triphenylphosphine oxide (TPPO) that is a weak  
Figure 3: Triazine-based TADF emitters.  
electron-withdrawing group to phenoxazine (POZ), which is a strong electron-releasing group. A series of weak acceptor\(-\)strong donor (WA-SD)-type emitters was thus obtained and the photophysical properties were thus examined [85]. Three molecules were synthesized **18**, **19**, and **20** (See Figure 4). In solution, the three compounds exhibited similar absorption properties, with three transitions being detected at 370, 320, and 240 nm. One peak was attributed to an n\(\rightarrow\)\(\pi^{\star}\) transition (POZ \(\rightarrow\) TPPO). The two other transitions correspond to \(\pi\)\(\rightarrow\)\(\pi^{\star}\) transitions (POZ and phenyl moieties). Interestingly, the intensity of the peaks was proportional to the number of POZ. The three compounds gave almost identical PL spectra with relatively high PLQYs (45%, 57%, and 65%, for **18-20**, respectively), evidencing the almost complete isolation of the TPPO group from the rest of the molecule. This also highlights the pertinence of the WA-SD strategy to preserve the emission colour. \(\Delta E_{\text{ST}}\) values of 0.26, 0.19, and 0.11 eV were determined for **18-20**, respectively, which are in perfect accordance with the TD-DFT data. PLQY of **20**-based films (67%) was higher than that of **19**- and **18**-doped films. In accordance with these results, lifetimes of the prompt fluorescence increased from 8 to 13 to 20 ns for **18-20**, whereas the opposite situation was found for the delayed contribution where lifetimes of 95, 31, and 17 \(\upmu\)s were respectively measured for **18-20**. Parallel to this, the quantum yields also increase from 36% for **18** to 45% for **19**, and to 51% for the most emissive candidate **20**, respectively. Unfortunately, none of these emitters could provide really deep blue OLEDs. **18**-based OLEDs were the bluest devices of the series and an electroluminescence centred at 448 nm could be detected.  
**19**-based OLEDs emitted a blue emission light at 460 nm (with CIE coordinates of (0.16, 0.20)), while **20** gave OLEDs emitting a pure-blue emission peaking at 464 nm (with CIE coordinates of (0.17, 0.20)), an EQE of 15.3% and a low efficiency roll-off.
# Blockchain-Based Authentication in Internet of Vehicles: A Survey
## 3 Security Issues in IoV and VANETs
### Security Threats and Attacks in Vehicular Networks
* GPS Deception/Spoofing Attack: It refers to the interception and manipulation of the GPS signals while being sent between two legitimate IoV nodes, to mislead the receiving entity by providing wrong location information about the sending vehicles. This can directly harm the drivers and the passengers by affecting their path decisions. Additionally, when a GPS receiver of a vehicle is attacked, it could provide the vehicle with false information about its location, speed, and other GPS information, which can greatly affect some IoV applications that depend on this GPS information such as navigation devices. * Masquerading/Impersonation Attack: A node pretends to be another node by stealing its identity. Here, both the legitimate vehicle and the adversary vehicle can use the same identity simultaneously which can create a chaos in the network since other vehicles may receive contradictory information from the same identity. The malicious node can also enjoy the access privileges of the spoofed identity. * Worm Hole Attack: Also known as tunneling attack, refers to the attack where at least two malicious vehicles create a private tunnel known as a worm hole which is used to forward the intercepted data between the two of them [89]. In this attack, the true distance information is faked where other entities are forced to route through the created tunnel thus controlling all the traffic flowing in the network. * Replay Attack: In this attack, an adversary keeps iterating the old messages in the network to deceive the other nodes by dropping the messages with high priority from the queue. This can greatly affect the system's efficiency and increase the cost of the bandwidth. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attack Targets** & **Description** & \multicolumn{2}{c}{**Examples**} & **Attack Type** \\ \hline \multirow{4}{*}{Availability} & Attacks that affect the IoV system’s availability & - & DoS attack & Active \\ \cline{3-4}  & normally use techniques that can make the bandwidth & - & DDoS attack & Active \\ \cline{3-4}  & and transmission power of the IoV system unusable by & - & Channel interference attack & Active \\ \hline \multirow{4}{*}{Authentication} & \multirow{2}{*}{Attacks that fake the identity of the original sender and act on its behalf, which could be used to spread harmful information in the network.} & - & - \\ \cline{3-4}  & & - & Masquerading/Impersonation attack & Active \\ \cline{3-4}  & & - & Wormhole/tunnelling attack & Passive/Active \\ \cline{3-4}  & & - & GPS spoofing attack & Active \\ \cline{3-4}  & & - & Replay attack & Active \\ \hline \multirow{3}{*}{Data Integrity} & Attacks that tamper with the original message content to badly affect the decisions of the receiving entity which could threaten the overall system. & - & Message manipulation attack & Active \\ \cline{3-4}  & & - & Malware attack & Active \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Possible security attacks in IoV and VANETs categorized by security requirements.
# Mitigation of Electro Magnetic Interference by Using C-Shaped Composite Cylindrical Device
## 2 Experimental and Simulation Setup
### Materials  
A high-permeability material and a permalloy material were purchased from Starek Scientific Co., Ltd. and used as received without further treatment [9]. In Figure 1, the radius and the length of the high-permeability material were 1.25 cm and 50 cm, respectively. The thicknesses of the high-permeability material and the permalloy pipe were 0.20 cm, and the width of the C-shaped opening gap was \(\sim\)0.05 cm.
# Radio Wave Propagation and WSN Deployment in Complex Utility Tunnel Environments +
## 4 WSN Deployment
### Technology Analysis: ZigBee 868 MHz vs. LoRaWAN Range within the Tunnels  
3D-RL simulations were shown to be accurate for an optimized radio-planning analysis in this complex and particular underground environment. Based on these preliminary results, the coverage for both Zigbee and LoRaWAN wireless devices can be estimated (i.e., the number of wireless nodes required for the whole tunnel) considering their corresponding device sensitivities, which are around  
Figure 9: Coherence bandwidth within the considered utility tunnel for a 2-m height for 868-MHz frequency.  
\(-100\) dBm and \(-148\) dBm, respectively. In order to provide clear insight, range estimations within the tunnel for both wireless technologies were obtained. For that purpose, a transmitter was placed on the ceiling at the beginning of the tunnel with a length equal to the longest tunnel section of the scenario under analysis (327 m, as it is presented in Figure 3a). The transmitted powers were set to 13 dBm and 14 dBm for ZigBee and LoRaWAN, respectively. As can be concluded from the results depicted in Figure 10, both wireless technologies covered the entire length of the tunnel, although LoRaWAN presented better results due to its better sensitivity (\(-148\) dBm) compared to ZigBee (\(-100\) dBm). Thus, in terms of wireless link distance within a straight tunnel of these characteristics, both wireless technologies could be employed.  
Nevertheless, in the real environment, different phenomena can lead to channel impairments, such as the presence of people within the tunnels or worsening the radio propagation conditions. Therefore, a further study is required in order to provide safe, robust, reliable and efficient WSN deployment.