esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the transition to electric vehicles has accelerated significantly. However, this shift does not imply the complete elimination of diesel engine vehicles, particularly in commercial and cargo transport, where diesel engines remain essential due to their high thermal efficiency and torque. Despite their advantages, diesel engines produce particulate matter (PM) in their exhaust, which poses environmental and health risks. To mitigate PM emissions, diesel particulate filters (DPFs) are integrated into exhaust systems. However, as PM accumulates in the DPF, pressure drops occur, increasing the load on the engine. Therefore, periodic removal of PM through oxidation, known as regeneration, is required. Optimizing the PM combustion temperature improves fuel efficiency, but since diesel engine exhaust temperatures typically range from 100 to 500 \({}^{\circ}\)C, catalysts that facilitate PM oxidation at lower temperatures are necessary. This study focuses on PM oxidation catalysts designed for low-temperature diesel exhaust conditions. One of the key challenges in this area is the difficulty in directly observing PM trapping and oxidation behavior within a catalyzed DPF. Additionally, changing the catalyst during experiments is not straightforward. To address these challenges, we have developed a numerical model that simulates the entire process--from PM deposition to oxidation--inside a DPF. This model allows for easy modification of catalyst properties, providing a flexible framework for analyzing PM oxidation behavior under various conditions. In this study, numerical simulations were conducted to analyze the PM deposition and oxidation processes within the DPF. The results were derived from a simplified model developed specifically for this research. The proposed calculation method allows for the qualitative assessment of DPF performance when catalysts are altered, contributing to the optimization of DPF design.  
PM oxidation; catalysts; DPF; emission Article
# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 2 System Model
Then, the outage probability \(P_{\text{out}}=\Pr\left[\text{SIR}_{0}\leq\beta\right]\) for each transmission attempt is as presented below (we assume that the distance \(r_{o}\) is fixed (but arbitrary) due to the fixed topology we assumed for the unlicensed network. However, this assumption could be easily relaxed following [PERSON] et al. work [50] by taking the expected value of the performance metric under investigation in relation to the distribution of \(r_{o}\).) where \(k=\pi r_{0}^{2}\Gamma\big{(}1-\frac{2}{\alpha}\big{)}\Gamma\big{(}1+\frac{2}{ \alpha}\big{)}\)[20]. \[P_{\text{out}}=1-e^{-k\lambda\beta^{2/\alpha}}. \tag{2}\]  
We are now able to evaluate the link throughput \(T\) in the reference link as:  
\[T=\frac{\log(1+\beta)}{1+\bar{m}}\left(1-P_{\text{out}}^{1+m}\right), \tag{3}\]  
It should be noted that in this throughput equation, \(m\) shows the retransmissions attempts whereas \(1+\bar{m}\) is the average number of transmissions for a successful transmission. Further details shall be seen in Section 3. As stated above, \(\left(1-P_{\text{out}}^{1+m}\right)\) is the probability of having a successful transmission.
## 3 PFR - A Probabilistic Forwarding Protocol
### The Energy Efficiency of PFR  
[9] considers the fictitious lattice \(G\) of the network area and let the event \(F\) hold. There is (at least) one particle inside each square. Now join all nearby particles of each particle to it, thus by forming a new graph \(G^{\prime}\) which is \"lattice-shaped\" but its elementary \"boxes\" may not be orthogonal and may have varied length. When squares of \(G\) become smaller and smaller, then \(G^{\prime}\) will look like \(G\). Thus, for reasons of analytic tractability, in [9] the authors assume that particles form a lattice (see Figure 7). They also assume length \(l=1\) in each square, for normalization purposes. Notice however that when \(l\to 0\) then \"\(G^{\prime}\to G\)\" and thus all results in this Section hold for any random deployment \"in the limit\".  
The analysis of the energy efficiency considers particles that are active but are as far as possible from \(ES\). Thus the approximation is suitable for remote particles.  
Figure 6: A Lattice Dissection \(G\).  
Figure 7: A Lattice Sensor Network.  
[9] estimates an upper bound on the number of particles in an \(n\times n\) (i.e. \(N=n\times n\)) lattice. If \(k\) is this number then \(r=\frac{k}{n^{2}}\) (\(0<r\leq 1\)) is the \"energy efficiency ratio\" of \(\mathsf{PFR}\).  
More specifically, in [9] the authors prove the (very satisfactory) result below. They consider the area around the \(ES\) line, whose particles participate in the propagation process. The number of active particles is thus, roughly speaking, captured by the size of this area, which in turn is equal to \(|ES|\) times the maximum distance from \(|ES|\) (where maximum is over all active particles).  
This maximum distance is clearly a random variable. To calculate the expectation and variance of this variable, the authors in [9] basically \"upper bound\" the stochastic process of the distance from \(ES\) by a random walk on the line, and subsequently \"upper bound\" this random walk by a well-known stochastic process (i.e. the \"discouraged arrivals\" birth and death Markovian process, see e.g. [31]). Thus they prove the following:  
**Theorem 1** ([9]): The energy efficiency of the \(\mathsf{PFR}\) protocol is \(\Theta\bigg{(}\Big{(}\frac{n_{0}}{n}\Big{)}^{2}\bigg{)}\) where \(n_{0}=|ES|\) and \(n=\sqrt{N}\), where \(N\) is the number of particles in the network. For \(n_{0}=|ES|=o(n)\), this is \(o(1)\).
## 3 Addressing the Dynamic Traveling Salesman Problem  
### Ant Colony Optimization Metaheuristic  
ACO has been designed for graph problems and, thus, it can be applied directly to the TSP, which is modeled as a weighted graph [24; 25; 26]. All arcs are associated with a pheromone trail value \(\tau_{ij}\) and some heuristic information value \(\eta_{ij}\). Initially, the pheromone trails are assigned with equal values, i.e., \(\tau_{ij}\leftarrow\tau_{0}\), \(\forall\in A\), where \(\tau_{0}\) is the initial pheromone value. The heuristic information at environment \(T\) is defined as \(\eta_{ij}=1/d_{ij}(T)\). A colony of \(\omega\) artificial ants is initially positioned in different randomly selected cities. Each ant \(k\) will select the next city to visit based on the existing pheromone trails \(\tau_{ij}\) and the defined heuristic information \(\eta_{ij}\) of arc \((i,j)\). Each ant will visit all cities once, in order to represent a feasible DTSP solution. Then, the pheromone trails associated with the \(\omega\) solutions constructed by the ants will be reinforced according to their quality value.
# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## 1 Introduction
With this aim, the main beneficiaries of this paper are researchers on educational technologies and designers of learning analytics systems who can benefit from the results to implement visual analytics systems, adaptation and prediction. Nevertheless, teachers and/or students would be indirect beneficiaries of this work as learning analytics systems would be designed from them. The objectives of this work are the following:  
* Propose a model to measure students' local persistence based on their interactions in a digital platform with online exercises. * Analyze the prevalence of the local persistence. * Analyze the evolution of local persistence over the time. * Analyze how local persistence is related to global persistence, and other variables about students' performance and engagement with videos. * Analyze the predictive power of local persistence in predictive models to predict global persistence and students' performance. This paper is an extension of the paper \"Analyzing Students' Persistence using an Event-Based Model\" [18], which was published in the Proceedings of the Learning Analytics Summer Institute Spain in Vigo, on June 27-28 2019. In that paper, we determined a model to define local persistence and analyzed the prevalence of local persistence and how local persistence was related to dropout, students' performance and engagement with videos. In this paper, we have incorporated a redefinition of the concept of persistence (distinguishing between global and local persistence). With these definitions, we have included an analysis of local persistence over time, and we have developed predictive models in different stages of the course to forecast global persistence and students' performance using local persistence. For these models, which represent a new contribution with respect to our previous work [18], several common variables in the literature and local persistence have also been used to analyze the effect of local persistence in predictive models. Therefore, we also contribute with the analysis of prediction in different stages of the course using local persistence as predictor. The structure of the paper is as follows: Section 2 presents an overview of what has been researched on persistence (global and local); Section 3 describes the methodology of the paper, including the context and data collection techniques, the variables and measures, and analytical methods; Section 4 details the model to measure local persistence; the analysis and discussion of the results are provided in Section 5; finally, the main conclusions are detailed in Section 6.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
###### Abstract  
Localization is a basic issue for underwater acoustic networks (UANs). Currently, most localization algorithms only perform well in one-hop networks or need more anchors which are not suitable for the underwater environment. In this paper, we proposed a double rate localization algorithm with one anchor for multi-hop underwater acoustic networks (DRL). The algorithm firstly presents a double rate scheme which separates the localization procedure into two modes to increase the ranging accuracy in multi-hop UANs while maintaining the transmission rate. Then an optimal selection scheme of reference nodes was proposed to reduce the influence of references' topology on localization performance. The proposed DRL algorithm can be used in the multi-hop UANs to increase the localization accuracy and reduce the usage of anchor nodes. The simulation and experimental results demonstrated that the proposed DRL algorithm has a better localization performance than the previous algorithms in many aspects such as accuracy and communication cost, and is more suitable to the underwater environment.  
localization; underwater acoustic networks (UANs); double rate; reference nodes +
Footnote †: journal: Sensors
## 2 Dynamic Model of Drilling System
### Scaling  
The stick-slip vibration is essentially for the development of the torsional vibrations. So the torsional natural frequency of the drill string is used to simplify the time scale and the torsional rigidity is used to simplify the axial cutting depth. In order to describe the influence of control parameters \(\Omega_{rot}\), \(V_{0}\) on the drilling system, a control parameter \(\rho\) was defined. For deep hole drilling, these control parameters are from Table 2.  
\[\tau=\omega_{T}t,\psi=\frac{sa^{2}N}{2k_{T}},\rho=\frac{V_{0}}{\Omega_{rot}} \tag{18}\]  
\[k_{r}=\frac{2\zeta k_{T}}{ak_{A}},\ v=\frac{\omega_{T}}{\omega_{A}},\ Q_{A}= \frac{m\omega_{A}}{\beta_{A}},\ Q_{T}=\frac{I\omega_{T}}{\beta_{T}},\ \Omega=\frac{\Omega_{rot}}{\omega_{T}} \tag{19}\]  
Figure 4: The profile located between two successive blades of a PDC bit.  
Using Equation (18) to simplify the SDD, CD model:  
\[v^{2}\ddot{z}(\tau)+Q_{A}^{-1}v\dot{z}(\tau)+z(\tau)=-\psi k_{r}[z(\tau)-z(\tau- \tau_{n})-\rho(\phi(\tau)-\phi(\tau-\tau_{n}))] \tag{20}\]  
\[\ddot{\phi}(\tau)+Q_{T}^{-1}\dot{\phi}(\tau)+\phi(\tau)=-\psi[z(\tau)-z(\tau- \tau_{n})-\rho(\phi(\tau)-\phi(\tau-\tau_{n}))] \tag{21}\]  
\[v^{2}\ddot{z}(\tau)+Q_{A}^{-1}v\dot{z}(\tau)+z(\tau)=-\psi k_{r}[z(\tau)-z( \tau-\tau_{n})] \tag{22}\]  
\[\ddot{\phi}(\tau)+Q_{T}^{-1}\dot{\phi}(\tau)+\phi(\tau)=-\psi[z(\tau)-z(\tau- \tau_{n})], \tag{23}\]  
where  
\[\tau_{n}(\tau)=\tau_{n0}\bigg{[}1-\frac{N}{2\pi}(\phi(\tau)-\phi(\tau-\tau_{n} (\tau)))\bigg{]},\ \tau_{n0}=\frac{2\pi}{N\Omega}. \tag{24}\]  
Parameter \(\tau_{n}\) is the dimensionless delay time, \(\omega_{A}\) is the natural frequency of the axial vibrations, \(Q_{A}\) and \(Q_{T}\) are the axial and torsional amplification factors, respectively. The parameter \(k_{r}\) characterizes the torsional stiffness and bit structure. The parameters used in the numerical studies are \(\
u=0.2\), \(N=4\), \(k_{r}=10\), \(Q_{A}=Q_{T}=5\). The dimensionless rotational speed \(\Omega\) is usually less than 5 (\(\Omega<5\)) and the parameter \(\psi\) is less than 1 (\(\psi<1\)).
## 2 Related Work
This can be simpler, compatible with existing infrastructure, and efficient, but it can have limited granularity. Assigning labels to data packets is a straightforward process that does not require significant computational resources or expertise. This approach can be compatible with existing IoT systems since many IoT systems already use some form of data labeling, such as metadata or tags, to identify and manage data. It can also be more efficient than tracking the flow of data in systems with limited resources. Labeling data can have limited granularity, and its appropriateness depends on the context. For example, labeling an entire data packet with a single label can be an efficient approach if the packet contains multiple data items of similar sensitivity. [PERSON] et al. [16] focus on smart home environments, and their approach may be less applicable to other IoT domains. Our approach can be applied to any IoT domain beyond smart homes and proposes a solution for securely sharing data between different IoT domains. Our approach also provides a comprehensive analysis of the requirements for IoT data flow configuration and presents a detailed architecture for implementation using SDN. Papers dealing with SDN security usually tackle security factors such as the exchange and deployment of security policies within the network in the case of SDN domains, intrusion detection, security against external attacks, etc. [17, 18]. Several of the proposed security solutions use cryptographic algorithms that may be difficult to implement in sensors, and in any case, they can be used in conjunction with our method. As mentioned, our approach does not require cryptography, although cryptography can be used in combination with it. Several of the reviewed papers are short and present only ideas for solutions. Many do not concentrate on data security. Our work is the first to present a data flow control method for security based on SDN routing, and thus a direct comparison of results is not possible. Surely, some of the techniques proposed may be compatible with our approach and, in combination with it, may lead to efficiency and security improvements; this will be the subject of further research.
## 6 Discussion and Future Directions
Adaptive control algorithms are emerging as a critical area of focus, offering the ability to dynamically optimize beam-steering parameters in real time. Algorithms powered by machine learning can analyze live data to adjust beam direction, enhance signal quality, and respond to disruptions caused by orbital motion or interference. These algorithms ensure robust and reliable communication links, even in challenging operational environments.
## 2 Literature Review
### Empirical Methods for the Prediction of Ground Settlements  
Empirical methods adopt statistical methods to extract trends from historical in-site settlement-monitoring data, with the aim of developing empirical formulae to assist the prediction of ground settlements, based upon various parameters. Some researchers adopted multi-linear and non-linear regression analyses for developing regression models using parameters, such as deformation modulus, cohesion, internal friction angle, compressive strength, and depth-to-diameter ratio, to predict the maximum ground settlement [13; 14; 15; 16]. [PERSON] [17] proposed mathematical formulae, named as \"[PERSON]'s model\", for the prediction of the maximum ground settlement during tunneling and used a normal distribution function curve to describe the transverse settlement trough over a single tunnel, as shown in Equations (1)-(4):  
\[S_{v}(x)=S_{v,max}\exp\left(-\frac{x^{2}}{2i_{x}^{2}}\right) \tag{1}\]  
\[S_{v,max}=\frac{V_{S}}{\sqrt{2\pi i_{x}}} \tag{2}\]  
\[V_{S}=V_{I}\pi R^{2} \tag{3}\]  
\[i_{x}=\frac{Z}{\sqrt{2\pi}tg(45^{\circ}-\varphi/2)} \tag{4}\]  
where \(S_{v}(x)\) is ground settlement at a transverse distance \(x\) from the tunnel centerline; \(x\) is the horizontal distance between the location and the tunnel centerline in the transverse direction; \(S_{v,max}\) is the maximum transverse settlement at \(x=0\); \(V_{S}\) is the stratum loss resulted from tunneling excavation; \(V_{I}\) is stratum loss ratio; \(R\) is radius of the tunnel; \(\varphi\) is friction angle. [PERSON] [18] further modified [PERSON]'s model to predict ground movements associated with soft ground tunneling. Moreover, [PERSON] and [PERSON] [6] refined and improved [PERSON]'s model by adopting Gaussian curves to describe short-term ground settlement over a tunnel. [PERSON] et al. [19] used statistical software to perform multiple regression analysis, aiming at obtaining linear or nonlinear models to predict the maximum ground settlement. Although these formulae can generally present the maximum ground settlements under different soil types, the prediction accuracies of these formulae are uncertain due to uncertainties of ground conditions and the execution process since those aspectscannot be fully understood at the design stage [4; 9]. Additionally, empirical methods fail to fulfill the dynamic requirements of ground settlement prediction during construction [20].
## 2 Theory of TRM-SBDC UWA Communication
### Principles of SBDC
For the first symbol, it is assumed that the time delay error caused by Doppler is not enough to make the decoding error, so the time delay shift of the first symbol can be obtained as:  
\[\tau_{d1}^{\prime}=T_{p1}^{\prime}-T_{1} \tag{12}\]  
According to Equation (9), the Doppler time delay error of the first symbol can be calculated as:  
\[\Delta_{D1}^{\prime}=\tau_{d1}-\tau_{d1}^{\prime}=\Delta_{D0}+\Delta_{D1}=k_{ 1}\cdot\Delta\tau-\tau_{d1}^{\prime} \tag{13}\]  
then \(\Delta_{D1}^{\prime}\) is used for Doppler compensation of the second symbol, we can obtain:  
\[T_{2}^{\prime}=T_{2}-\Delta_{D1}^{\prime}=T_{2}-\Delta_{D1}-\Delta_{D0} \tag{14}\]  
\[\tau_{d2}^{\prime}=T_{p2}^{\prime}-T_{2}^{\prime}=\tau_{d2}-\Delta_{D2} \tag{15}\]  
\[\Delta_{D2}^{\prime}=\Delta_{D2}=k_{2}\cdot\Delta\tau-\tau_{d2}^{\prime} \tag{16}\]  
and so on, after the SBDC processing, the start position of the \(i\)th symbol is:  
\[ \begin{array}{ll}T_{i}^{\prime}&=T_{i}-\sum\limits_{k=1}^{i-1}\Delta_{Di}^{ \prime}\\ &=T_{i}-\sum\limits_{k=1}^{i-1}\Delta_{Di}-\Delta_{D0}\end{array} ,\;i\geq 2 \tag{17}\]  
thus the corresponding time delay shift and time delay error resulting from Doppler are respectively expressed as:  
\[ \begin{array}{ll}\tau_{di}^{\prime}&=\tau_{di}-\Delta_{Di}\\ &=k_{i}\cdot\Delta\tau-\Delta_{Di}\end{array} ,\;i\geq 2 \tag{18}\]  
\[\Delta_{Di}^{\prime}=\Delta_{Di},\;i\geq 2 \tag{19}\]  
Comparing Equations (11) and (18), each symbol obviously only needs to withstand its respective Doppler time delay error after SBDC, rather than the error accumulation of the preceding symbols. Consequently, no decoding error will occur as long as the Doppler time delay error of each symbol is within the error tolerance:  
\[-\Delta\tau/2<\Delta_{Di}^{\prime}<\Delta\tau/2 \tag{20}\]  
To analyze the performance of SBDC, we assume that the Doppler coefficient is \(\alpha(t)\), and it is defined as:  
\[\alpha(t)=v(t)/c(t) \tag{21}\]where \(v(t)\) is the relative radial speed between the transmitter and receiver, and \(c(t)\) is the sound speed in the water. Then the time delay error can be expressed as:  
\[ \begin{cases}\Delta_{D0}=\alpha(t)\cdot(T_{s}+T_{ISI}),&i=0\\ \Delta_{Di}=\alpha(t)\cdot T_{0},&i\geq 1\end{cases}  \tag{22}\]  
For the PDS communication scheme, the key to correct decoding is to ensure that the time delay error of each symbol satisfies the Equation (20). The corresponding expression is:  
\[ \begin{cases}\left|\Delta_{D1}^{\prime}\right|=\left|\Delta_{D0}+\Delta_{D1} \right|<\Delta\tau/2,&i=1\\ \left|\Delta_{Di}^{\prime}\right|=\left|\Delta_{Di}\right|<\Delta\tau/2,&i\geq 2 \end{cases}  \tag{23}\]  
According to Equations (21)-(23), we can get:  
\[\left|\alpha(t)\right|<\frac{\Delta\tau}{2(T_{s}+T_{ISI}+T_{0})} \tag{24}\]  
\[\left|v(t)\right|<\frac{\Delta\tau}{2(T_{s}+T_{ISI}+T_{0})}\cdot c(t) \tag{25}\]  
that is to say, as long as the relative radial speed between the transmitter and receiver satisfies the Equation (25), the SBDC method will obtain a good decoding performance.
## 2 Overview of DME and JTIDS
### First Principles and Baseband Signal of JTIDS  
JTIDS refers to the communications component of Link 16, which is the designation of a tactical data link that is being fully integrated into operations of the Joint Services, the forces of the North Atlantic Treaty Organization (NATO), and other Allies. JTIDS has the integrated capability of communication, navigation and identification. JTIDS uses the principle of Time Division Multiple Access (TDMA), all JTIDS units are preassigned sets of time slots in which to transmit their data and in which to receive data from other units. Each time slot is 1/128 s, or 7.8125 milliseconds (ms), in duration. The time distribution of a JTIDS slot is shown in Figure 2, there is a random jittering time \(t_{f}\) at the beginning of a slot time, then pulse signals time \(\tau_{f}\) follows, safeguard time \(t_{g}\) is in the last  
Figure 1: The basic pulse pair of DME signal.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Signal** & **Operation** & **Pulse Duration** & **Pulse Interval** & **Signal Duration** & **PRF(Hz)** \\
**Type** & **Mode** & \(w_{m}\) (\(\upmu\)s) & \(s_{m}\) (\(\upmu\)s) & \(\tau_{m}\) (\(\upmu\)s) & **Tracking** & **Searching** \\ \hline \multirow{2}{*}{Interrogation} & X & & 12 & 15.5 & \multirow{2}{*}{10–30} & \multirow{2}{*}{40–150} \\  & Y & & 36 & 39.5 & & \\ \multirow{2}{*}{Reply} & X & & 12 & 15.5 & \multirow{2}{*}{700–2700} \\  & Y & & 30 & 33.5 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: DME pulse pair characteristics.  
The single pulse symbol packet consists of one 6.4 us pulse of modulated carrier followed a 6.6 us dead time for a total pulse symbol packet duration of 13 us. The period of pulse signals is composed of 258 pulses when the data pulse format of the time slot is packed in standard double pulse or packed-2 single pulse, thus, the pulse signals lasted 3.354 ms. If the data pulse format of the time slot is packed in packed-2 double pulse or packed-4 single pulse, the period of pulse signals is composed of 444 pulses, jittering time is 0, safeguard time is 2.0405 ms, and then the pulse signal is 5.772 ms [14; 15].
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 2 Proposed RMLP-18T Cell and Its Operations
### Memory Operation of the Proposed RHLP-18T Cell  
#### 2.1.1 Hold Mode  
During the hold mode, the memory cell maintains its stored data without external access by deactivating access transistors (N5, N6, P11, P12). This is achieved by setting the word line (WL) to ground (GND) and the write word line (WWL) to the supply voltage (VDD). Consider the case where the cell stores a logic '1' (internal node Q = 1 and QB = 0). In this state, P4, P5, P6, P8, P9, N1, and N3 are on, while P1, P2, P3, P7, P10, N2, and N4 are off. This transistor configuration ensures the internal storage nodes (Q and QB) maintain their states.