esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
## 4 Specific Treatment of Burning Surfaces
### Blowing Effect  
In fire scenarios (e.g., related to pool fires and flame spread) which involve simultaneously both convective heat and mass transfer, consideration of mass transpiration effects (i.e., 'blowing' effect) has to be made. This correction (i.e., blocking) factor, applied to the calculation of the convective heat fluxes, represents a reduction in the heat transfer process as mass transfer increases (e.g., release of flammable fuel vapours in liquid pool fires or combustible solid surfaces) and can be calculated as [9]:  
\[f_{blowing}=\frac{(c_{p}\cdot\dot{m}^{\prime\prime})/h}{exp[(c_{p}\cdot\dot{m}^ {\prime\prime})/h]-1}, \tag{39}\]  
where \(c_{p}\) is the specific heat of the fluid, \(h\) is the convection coefficient and \(\dot{m}^{\prime\prime}\) is the fuel mass loss rate per unit area. The parameter \(f_{blowing}\), which varies between 0 and 1 (i.e., approaches 1 as \(\dot{m}^{\prime\prime}\to 0\)), acts to enlarge the boundary layer, due to blowing caused by the vaporized fuel, making it difficult to transfer heat across it [9]. For a detailed derivation of Equation (39) the reader is referred to [9].  
Figure 2 presents the magnitude of the \(f_{blowing}\) factor for several fuels (i.e., both sooty and non-sooty) and pool diameters (i.e., 7 cm-100 cm). The experimental data are taken from [59; 60] while a value of \(h/c_{p}=0.0065\) kg/(m\({}^{2}\cdot\) s) for turbulent lip fires [15] has been considered. The results, as expected, suggest that for sootier fuels (e.g., toluene) and for increasing burner diameters, the blowing factor values decrease (i.e., significant blockage of convective heat towards the fuel surface).  
Figure 2: Magnitude of the \(f_{blowing}\) (Equation (39)) factor for several fuels and pool diameters. Experimental data for the mass loss rate per unit area taken from [59; 60].  
The effect of mass transpiration (i.e., blockage of convective heat transfer with increasing mass transfer rates) has been reported experimentally in the past for upward spread scenarios over vertical PMM slabs [20]. Additionally, the inclusion of mass transpiration effects has also been considered when modelling flame spread using the approaches based on the law of the wall (e.g., [50]), based on the stagnant film theory (e.g., [12]) as well as on the convective heat transfer model of FireFOAM 2.2.x (e.g., [61; 62; 63]).
## 2 Theoretical Background  
### Harmony Search Algorithm  
Harmony Search (HS) is a relatively new meta-heuristic algorithm based on the improvisation process followed by musicians to find a pleasing harmony [1]. It possesses several advantages over the traditional optimization techniques: (1) it is a simple meta-heuristic algorithm that does not require an initial setting for decision variables; (2) it uses stochastic random searches, so derivative information is not necessary; and (3) it has a few parameters for fine tuning [13]. For these reasons, HS has been demonstrated in several studies to be a useful optimization algorithm for various engineering applications owing to its convenient implementation, rapid convergence, and reasonable computational cost. This can be conveniently found in the literature as applications to continuous and discrete optimization problems [14; 15; 16; 17; 18; 19].  
The concept of HS is based on the idea that during the improvisation process, musicians try different combinations of familiar (memorized) pitches, which is analogous to the optimization process applied to most engineering problems. Therefore, a feasible solution is called a harmony, and each decision variable corresponds to a note, which generates a value for finding the global optimum.  
The HS algorithm consists of the following steps [1]:  
1. Initialize the problem and algorithm parameters: The optimization problem is defined as \[\text{Minimize }(\text{or maximize})f(x)\] (1) \[\text{Such that }LB_{i}\leq x_{i}\leq UB_{i}\] (2) where \(f(x)\) is the objective function and \(x\) is a candidate solution consisting of \(N\) decision variables \(x_{i}\). \(LB_{i}\) and \(UB_{i}\) are the lower and upper bounds for each decision variable,respectively. In addition, the parameters of HS are the Harmony Memory Size (HMS), Harmony Memory Considering Rate (HMCR), pitch adjustment rate (PAR), bandwidth (bw), and the number of improvisations (NI) (the stopping criterion).  
2. Initialize the Harmony Memory  
The Harmony Memory (HM) is a memory location where the solution vectors are stored, and is similar to the genetic pool in a GA [1,20]. The initial HS is generated from a uniform distribution of ranges, filling a matrix with a quantity of randomly generated solution vectors equal to the _HMS_.  
\[x_{i}^{l}=LB_{i}+Randx(UB_{i}-LB_{i}),\qquad l=1,2,\ldots HMS \tag{3}\]  
3. Improvise a new harmony  
The generation of a new harmony is called improvisation. This new harmony vector, \(x^{\prime}=(x_{1}^{\prime},x_{2}^{\prime},\ldots,x_{N}^{\prime})\), is generated based on three rules: memory consideration, pitch adjustment, and random selection.  
4. Update the Harmony Memory  
If the new vector is better than the worst harmony vector in the HM in terms of the objective function value, and no identical harmony vector is already stored in the HM, the new harmony is included in the HM, and the existing worst harmony is excluded.  
5. Check the Stopping Criterion  
The algorithm terminates when the maximum number of improvisations is reached. Otherwise, Steps 3 and 4 are repeated.
## 4 Existing Jamming Attack Detection Methods and Their Analysis
### Method Suggested by [PERSON] et al. [5]  
[PERSON] _et al._[5] have suggested a very effective method of detecting a reactive jammer ( which otherwise is so difficult to be detected) through Received Signal Strength (RSS) and Bit Error Rate (BER) samplings and inferring the presence of the reactive jammer in the event of high BER despite the RSS being normal or better than the normal. The method involves three steps: (1) error sample acquisition, (2) interference detection, and (3) sequential jamming test to infer presence or absence of reactive jamming.  
Error sample acquisition is done in two sub-steps: (a) packet reception and RSS recording, _i.e._, forming the tuple (m, s), where, m is the sampled message packet and s is the corresponding RSS, and (b) identifying the BER.  
They have suggested three methods for ascertaining whether a bit is correct or erroneous: (i) by XOR-ing the instant bit, m(i) with the predetermined value of the bit, m'(i) and concluding that the instant bit is faulty if the XOR result is true, (ii) using Error Correcting Codes (ECC), and (iii) through wired node chain (n-tuples) system, in which they generate error sample, e(i) as the result of theXOR of the bit received on the wireless link,wl(i) and the bit received by the same node from the same transmitter, transmitted simultaneously on a wired link, w(i), _i.e._, e(i) = wl(i) XOR w(i), and take the minimum of the RSSs received on the wireless and the wired links as the corresponding RSS, _i.e._, s(i) = min [swl(i), sw(i)] and form tuple [e(i), s(i)].  
In the second step, interference detection, they confirm the presence of interference if e (i) = 1 (true) and s (i) \(>\) S, where S is a predetermined threshold value of the RSS, and confirm absence of interference otherwise.  
In the third step, sequential jamming test, they take decision regarding presence or absence of jamming based on the values of three decision parameters: likelihood ratio \(\eta\)_(k)_, targeted probability of false alarm being true \(T_{FP}\), and targeted probability of false alarm being not true \(T_{FN}\).  
The method has a sound mathematical foundation and is capable of detecting all types of jamming attacks, including reactive jamming, but it cannot discriminate different types of jamming attacks. It also involves of sampling/fixing thresholds and values like those for \(p_{c}\), \(T_{FN}\), \(TFP\), and \(S\), the RSSthreshold, which have peculiar problems, as discussed before. Besides, it is taxing on a WSN node in terms of computational and memory-space resources.
## 2 Simulation Set-Up  
To analyze device operation characteristics according to ON pitch scaling, we implemented a simulation structure, as shown in Figure 1. Physical parameters were referenced from previous 3D NAND research results [11]. The blocking oxide, charge trap nitride, and tunneling oxide layers are 7 nm, 5.5 nm, and 4.5 nm, respectively. The gate length (Lg) of the reference structure shown in Figure 1a is 25 nm, the spacer length (Ls) is 20 nm, and the channel thickness is set to 7 nm. To simulate the scaling effects of Lg and Ls, four structures with different ON pitches were implemented through TCAD simulation. All other parameters were kept fixed while either Ls was reduced to 10 nm, as shown in Figure 1b, or Lg was reduced to 15 nm, as shown in Figure 1c. Additionally, to confirm the interaction when both Lg and Ls are reduced, a structure in which both parameters are reduced, as shown in Figure 1d, was also compared.  
The 2D NAND structure set in this way was implemented as a 3D NAND structure through the cylindrical function. The design allows for the use of either a Drain Select Line (DSL) or Source Select Line (SSL), and the 3D NAND structure is composed of a total of five cells, including the Select Line connected to the Bit Line (BL) and Source Line (SL). The simulation was conducted with these configurations. Tungsten was used as the gate material, and the gate edge was analyzed based on a rounded shape of 2 nm each, considering the actual process implementation of the device. The source and drain were doped with phosphorus at a concentration of \(1\times 10^{19}\) cm\({}^{-3}\), and the polysilicon channel was doped with boron at a concentration of \(1\times 10^{15}\) cm\({}^{-3}\).  
The operating conditions for the program, erase, and read operations are listed in Table 1. During the program operation, 17 V was applied to the selected cell, and 5 V was applied as the pass voltage. The erase voltage was achieved by applying 0 V to all gates and 20 V to BL and SL to generate a Gate-Induced Drain Leakage (GIDL) current. During the read operation, 3.3 V was applied to SSL and DSL, 0.5 V to BL, and 5.5 V to all unselected cells. A voltage ranging from \(-\)5 V to 5 V was applied to the selected WL to check the Id-Vg curve, and the gate voltage at which 50 nA flows  
Figure 1: Simulated structures. (**a**) Reference structure, (**b**) Ls scaling, (**c**) Lg scaling, and (**d**) both Lg and Ls scaling. (**e**) Corresponding simulated parameters of reference structure.  
Z-interference was defined as the shift in the victim cell's Vth after programming the attack cell. This measurement was performed by keeping the victim cell Vth constant after erase, considering that the initial Vth of each ON pitch device varies greatly, and then changing the programmed Vth of the attack cell equally.
# Reliability Assessment of Space Station Based on Multi-Layer and Multi-Type Risks
## 2 Framework of Space Station Configuration Reliability Assessment  
According to the above analysis, the framework of the space station configuration reliability assessment is determined and shown in Figure 1. The framework can be divided into five steps: risk definition and identification, risk features analysis, reliability modeling, risk data collection, and reliability assessment. The details are shown as follows.  
(1) Risk definition and identification  
The space station configuration risks consist of multi-layer and multi-type risks in the flight missions, and risk definition and identification are the beginning of the other steps. This step determines the domain of the space station configuration reliability modeling and assessment work.  
(2) Risk features analysis  
Traditional risk feature analysis mainly focuses on occurrence and consequence features, but the propagation feature is the most important issue of the space station configuration risk. This paper adopts qualitative and quantitative methods to analyze the risk features. This step identifies the key risks in all of the space station configuration risks.  
Figure 1: Framework of the space station configuration reliability assessment.  
(3) Reliability modeling  
In the reliability modeling step, the traditional PRA method including the Event Tree (ET) [29; 30] and the Fault Tree (FT) [31; 32] methods are improved by incorporating complex network theory [33; 34; 35; 36] to describe the risk propagation features. Based on the risk definition, identification and features analysis, the configuration reliability model can be built using the PRPA method.  
(4) Risk data collection  
After the risk features analysis, the risk data collection has been determined, and the static and dynamic features data are necessary. The static features data comes from the existing operational database, and the dynamic features data comes from the propagation feature analysis results.  
(5) Reliability assessment  
All the model building and data collection works are the basis of reliability assessment. Then, the reliability assessment results, which contain the space station configuration mission final state probabilities, can be evaluated in this step. The risk control plan can also be made based on the assessment results.
# Energy Efficient Constellation for Wireless Connectivity of IoT Devices
###### Abstract  
Reducing energy consumption is one of the most important task of the approaching Internet of Things (IoT) paradigm. Existing communication standards, such as 3G/4G, use complex protocols (active mode, sleep modes) in order to address the waste of energy. These protocols are forced to transmit when one frame is only partially filled with information symbols. The hard task to adapt the power-saving mode with low latency to the discontinuity of the source is mainly due to the fact that the receiver cannot know a priori when the source has something to transmit. In this paper, we propose a modified signalling/constellation which can save energy by mapping a zero-energy symbol in the information source. This paper addresses the fundamentals of this new technique: the maximum a posteriori probability (MAP) criterion, the probability of error, the (energy) entropy, the (energy) capacity as well as the energy cost of the proposed technique are derived for the binary signalling case.  
energy efficient communication; MAP detector; zero energy symbol; signal constellation 2020, 20, 3991; doi:10.3390/s20143991
## 2 Multibody Dynamics Model
### Vehicle Model
#### 2.1.2 Target Roll Angle  
Target tilting (roll) angle is given as shown in Equation (1), to balance against virtual lateral acceleration, which is simply calculated from tire steered angles, wheel base and vehicle speed. Torsion torque of the active stabilizer to get the target roll angle is given by general proportional-integral-derivative (PID) tracking control. Initial values of control gain are shown in Equation (2).  
\[TRA\ =\ A\tan^{-1}\left(\frac{\sin(\delta)v^{2}}{lg}\right) \tag{1}\]  
\[\mathrm{P}\ =\ 4000,\ \mathrm{I}\ =\ 100,\ \mathrm{D}\ =\ 0 \tag{2}\]  
_TRA_: target roll angle  
_A_: user amplification factor  
\(\delta\): tire steered angle  
_v_: vehicle speed  
_l_: wheel base  
P: proportional gain  
I: integral gain  
D: derivative gain
# Experimental Study on the Influence of Low Temperature on the Gas Permeability of Granite
###### Abstract
As the distance from the storage decreases, the temperature gradually decreases from the original formation temperature to approximately \(-162\)\({}^{\circ}\)C. In accordance with earlier research [17; 29; 30], saturated coarse and fine-grained granite specimens with distinct particle sizes were subjected to freeze-thaw cycling at target temperatures of \(-20\)\({}^{\circ}\)C, \(-40\)\({}^{\circ}\)C, \(-60\)\({}^{\circ}\)C, \(-90\)\({}^{\circ}\)C, and \(-120\)\({}^{\circ}\)C. One-dimensional gas permeability tests were conducted following the freeze-thaw cycles to assess changes in permeability performance. To further investigate the influence of cryogenic damage on pore structure evolution, nuclear magnetic resonance (NMR) analysis was employed to characterize microstructural variations in both granite types under different freezing conditions. The permeability test results were then analyzed alongside the NMR data to elucidate the freeze-thaw damage mechanisms and their effects on permeability evolution across varying particle sizes. Additionally, a custom-designed 2D real-time low-temperature gas permeability testing apparatus was developed to evaluate the gas permeability of granite under actual cryogenic conditions. This enabled a direct comparison between the permeability characteristics observed during real-time low-temperature evaluations and those measured after freeze-thaw cycling. The findings of this work are expected to provide theoretical insights and technical guidance for ensuring structural integrity and sealing performance of underground LNG storage systems operating in ultra-low temperature environments.
# An Energy-Efficient Routing Algorithm
## 2 Background on Compressed Sensing  
Compressed sensing is a thriving theory which is capable of achieving sampling and compressing together, and can effectively reduce the amount of data transmitted in USNs when an appropriate routing protocol is integrated.  
Consider a discrete signal \(\mathbf{x}\in R^{N\times 1}\), which can be expanded in an orthogonal basis:  
\[\mathbf{x}=\sum_{i=1}^{N}\theta_{i}\psi_{i}=\Psi\theta \tag{1}\]  
where \(\Psi=[\psi_{1},\psi_{2},\cdots\psi_{N}]\), \(\psi_{i}\in R^{N\times 1}\). If the coefficient vector \(\theta\) have only \(K\) (\(K\ll N\)) non-zero elements, the original signal \(\mathbf{x}\) is compressible and called _K-sparse_. CS concentrates on acquiring \(\mathbf{x}\) by measuring \(M\) (\(M\ll N\)) projections. The useful information in the compressible signal \(\mathbf{x}\) can be captured by the observation process  
\[\mathbf{y}=\Phi\mathbf{x}=\Phi\Psi\theta=\widetilde{\Phi}\theta \tag{2}\]  
where \(\mathbf{y}\) is an \(M\times 1\) column vector, \(\widetilde{\Phi}=\Phi\Psi\) is the \(M\times N\) sensing matrix, which satisfies the rule of restricted isometry property (RIP) [4].  
The original signal \(\mathbf{x}\) can be reconstructed accurately when the number of random measurements \(M\) is larger than \(CK\log N\) (\(C\) is a constant irrelevant to \(N\) and \(K\)). There are plenty of reconstruction algorithms, such as the basis pursuit (BP) algorithm. The greedy search approach is designed to further reduce the computational complexity of BP; an example is the orthogonal matching pursuit (OMP) algorithm [7].
## 3 Results and Reports
### Applying the Normal Distribution to the Generated Samples
#### 3.6.1 Integrating Knowledge Based on the Normal Distribution  
According to the results shown in Figure 14, we clearly distinguished the different metrics that represent the abnormal values. The green (\(\gamma\)) and the red crosses (dots) represent the detected values belonging to Class 1 and Class 2 respectively. Thus, given a new entry, the system would automatically issue alerts according to the classifications model made of the recorded values. Since this approach  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Probabilistic Analysis** & **Class\_1\_Sys** & **Class\_2\_Sys** & **Class\_1\_Dias** & **Class\_2\_Dias** \\ \hline Patient 1 (Pre-high BP) & 62 & 20 & 59 & 31 \\ Patient 2 (high BP) & 8 & 18 & 71 & 17 \\ Patient 3 (Low BP) & 73 & 26 & 88 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of anomalies calculations.  
Figure 13: (**a**) Anomalies area within the normal distribution of the Sys measures and (**b**) anomalies area within the normal distribution of the Dias measures.  
classified the anomalies, it was essential to reiterate that observations with probabilities that ranged in [\(\mu-\sigma\), \(\mu+\sigma\)] did not show any threat and were therefore subject to neither this classification process nor the previous process using SVM.  
As explained in this paper, values belonging to Class 1 did not represent an immediate danger. This involves detecting several anomalies by using SVM first, then classifying the returned abnormal values and issuing the corresponding alerts according to their dangerousness. The third plot of Figure 14 corresponding to panel (b), unlike all other plots in which we noticed the presence of Class 1 anomalies either to the right or to the left of the mean, represents an interesting case, since most of its parameters and statistical values belonged to the abnormal blood pressure that ranged in [-3, -1] and some measures belonging to both classes that ranged in [1, 3] for diastolic records. This is due to the fluctuation of data around the corresponding means. The approach supported by these representations therefore returned a very satisfactory result since we had chosen datasets from which patients suffered from BPH, PHBP and LBP respectively for Dts_1, Dts_2 and Dts_3. As explained in Table 2, HBP and PHBP measures generally represented anomalies that were strongly present to the right of the mean, unlike LBP where anomalies were displayed to the right of the mean. This enabled us to pinpoint the areas that were a threat to the patient's health.  
Figure 14: (**a**) Anomaly classes 1 and 2 of the normalized Sys measures and (**b**) anomaly classes 1 and 2 of the normalized Dias measures.
# Indoor Microclimate Analysis of the San Panfilo Church in Tornimparte, Italy
## 2 Materials and Methods
### Computation of Specific Humidity, Dev Point Temperature, and Dev Point Spread  
Specific humidity is defined as the ratio between the mass of water vapor and the mass of moist air. In microclimatic analysis this physical quantity is useful to recognize the presence of evaporation, condensation, the path of an air mass, leakage, window openings and the presence of people [1]. Specific humidity (\(SH\)) was computed using the following formula:  
\[SH=0.62197.\frac{e}{p-e}100\]  
where \(e\) is the vapor pressure and \(p\) is the pressure at the altitude of Tornimparte. The vapor pressure \(e\) was computed using the equation:  
\[e=\frac{RH\cdot e_{w}}{100}\]  
with \(RH\) representing relative humidity and \(e_{w}\) representing the saturation vapor from Bolton parametrization [20]:  
\[e_{w}=6.112\cdot\exp\left(\frac{17.67\cdot T}{T+243.5}\right)\]  
where \(T\) is the measured temperature (\({}^{\circ}\)C).  
The dew point temperature is defined as the temperature to which a parcel of moist air must be cooled at constant atmospheric pressure and constant water vapor content in order for saturation to occur. Dew point temperature (\(T_{d}\)) was computed using the relation:  
\[T_{d}=b\frac{a\cdot T+(b+T)\cdot log\left(\frac{RH}{100}\right)}{a\cdot b-(b+T )\cdot log\left(\frac{RH}{100}\right)}\]  
where \(a=7.5\) and \(b=237.3\)\({}^{\circ}\)C are the Magnus and Tetens coefficients for vapor in equilibrium with the liquid phase [1].  
Dew point spread is computed as the difference between the temperature and the dew point temperature. It shows how close or far the air temperature is from the dew point temperature. In conservation science, the dew point spread gives information about the possibility of condensation over the surfaces.