esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Stray Node Detection Protocol and Method of Avoiding Message Collision
### Request-Response-Type Stray Node Detection Protocol  
#### 4.2.1 Processing Logic  
Each transmission node performs the following and a flowchart for this logic is shown in Figure 7.  
1. The area within which a message from the leader node can reach a node is adjusted by varying the transmission power of each node. Any node that is outside this area is regarded as a stray node.
2. The leader node sends a request message at intervals of T.
3. The receiving node checks IE1.  
1. If the node finds that the group ID in the message is different from its own group ID, it discards the message, go to Step 3.
2. If the message was sent by a node belonging to the same group. Go to Step 4.
4. The receiving node checks IE2 and IE4 to see if it has received the same message before.  
1. If it has, it discards the message, go to Step 3.
2. If it has not it checks IE5.  
1. Number of hops < hop limit, go to Step 5.
2. Number of hops = hop limit, it discards the message, go to Step 3.
3. The receiving node checks IE3.list  
1. If the message is a request message, go to Step 6.
2. If the message is a reply message, go to Step 7.
3. The receiving node sends a reply message and forwards the request message with the number of hops in IE5 increased by one, go to Step 3.
4. If the receiving node is the leader node, go to Step 8. If it is a normal node, forwards the received message with the number of hops in IE5 increased by one, go to Step 3.
5. The leader node examines IE5 in the message returned by each node.  
Figure 6: Message transmission sequence of the collision avoidance method using the autonomous response-type protocol.  
* If the number of hops is 1, it determines that the sending node is not a stray node, go to Step 3.
* If the number of hops is 2 or greater, it determines that the sending node is a stray node, and alerts the instructor, go to Step 3.
# Closed-Form Analytical Solutions for the Deflection of Elastic Beams in a Peridynamic Framework
## 3 Analytical Solutions to Peristatic Problems
### Simply Supported-Roller-Clamped Beam  
A simply supported-roller-supported beam subjected to transverse loading \(p(x)\) is illustrated in Figure 9. The PD boundary conditions in this case admit an anti-symmetric and symmetric relation between the fictitious and real domains  
\[w(-\xi)=-w(\xi),\quad w(L-\xi)=w(L+\xi). \tag{33}\]  
Based on relation (33), the displacement field can be extended over the entire real line such that it has a periodicity of \(4L\) and is anti-symmetric about the origin and symmetric about \(x=L\), as illustrated in Figure 10.  
Figure 8: Clamped–clamped beam: (**a**) original problem; (**b**) equivalent simply supported beam with applied moments.  
Therefore, the displacement can be expanded using the Fourier series as follows:  
\[\left\{\begin{array}{l}w(-x)=-w(x)\\ w(L-x)=w(L+x)\end{array}\right.\quad\Rightarrow\quad w(x)=\sum\limits_{n=1}^{ \infty}b_{n}\sin\frac{(2n-1)\pi x}{2L}. \tag{34}\]  
Following the similar approach given in Section 3.1, the PD solution to the simply supported-roller-supported beam can be expressed explicitly as follows:  
\[w(x)=\frac{2\delta^{2}}{LEL}\sum\limits_{n=1}^{\infty}\int\limits_{0}^{L}p(x) \sin\frac{(2n-1)\pi x}{2L}\mathrm{d}x\left[\int\limits_{-\delta}^{\delta}\frac {1}{\xi^{2}}\left(1-\cos\frac{(2n-1)\pi\xi}{2L}\right)\mathrm{d}\xi\right]^{-2 }\sin\frac{(2n-1)\pi x}{2L}. \tag{35}\]
###### Abstract  
Volatile organic metabolites (VOMs) present in different spices can provide distinct analytical biosignatures related to organoleptic properties and health benefits. This study aimed to establish the volatilomic fingerprint of six of the most consumed spices all over the world (saffron (_Crocus sativus_ L.), cinnamon (_Cinnamoun vermum_), cumin (_Cuminum cyninum_ L.), black pepper, (_Piper nigrum_ L.), sweet paprika (_Capsicum annuum_ L.), and curry (a mix of different herbs and spices)). Based on headspace solid phase microextraction (HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) analysis, this is a powerful strategy to explore and establish the spice's volatile pattern and unravel the potential health benefits related to the most important VOMs identified in each spice. This comprehensive knowledge will help in the definition of their authenticity, while simultaneously protecting against potential frauds and adulterations. A total of 162 VOMs were identified. Semi-quantitative assessments revealed that terpenoids and sesquiterpenoids amounted to the major volatile class in the investigated spices, except for cinnamon, where carbonyl compounds are the major group. Most of the studied spices comprised key characteristics of aroma and health bioactive compounds, e.g., dihydrouned in saffron, cinnamaldehyde in cinnamon, cuminaldehyde in cumin and curry, and carophyllene in black pepper. The principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) successfully discriminated the investigated spices, being \(\alpha\)-cubebene, 3-methyl butanal, \(\beta\)-patchoulene and \(\beta\)-selinene, the most important VOMs (highest VIP's) that contributed to its discrimination. Moreover, some VOMs have a high influence on the spice's bioactive potential, helping to prevent certain diseases including cancer, inflammatory-related diseases, diabetes, and cardiovascular diseases.  
authenticity; bioactive properties; chemometrics; fingerprint; HS-SPME/GC-MS; spices; volatile organic metabolites +
Footnote †: journal: molecules
## 3 Methodology
### Optical Flow Estimation Module
#### 3.4.1 Design of the Optical Flow Estimation Module
The hidden state output from the GRU module is subsequently passed through two convolutional layers to produce the optical flow update \(\Delta f\). To reduce computational cost, the resolution of the predicted flow is set to 1/8 of the input image size. During both training and evaluation, the predicted optical flow is upsampled to match the resolution of the ground-truth flow, enabling accurate supervision and performance assessment. The overall structure of the optical flow estimation module is illustrated in Figure 7.
## 3 AIGC's Approach to Global Stress Reconstruction
### The Process of Establishing an ANN for Global Stress  
The process of establishing an ANN involves several key aspects, including data sample planning, a feedforward neural network architecture, backpropagation, convergence assessment, and a generalization performance evaluation. These aspects will be explored in detail through specific case studies. Figure 1 offers a concise overview of the process involved in constructing an ANN for the reconstruction of global stress. The following workflow is proposed.  
1. Acquire Sufficient Training Data [22]: Obtain ample training data since direct measurements of spatial structural global stress are impractical. Finite element simulation data are used as the training sample. These data should be divided into three sets: training, validation, and testing sets.
2. Select Feature Elements: Identify and select the characteristic or representative elements that contribute significantly to the overall stress field. These feature elements will serve as the input to the ANN, and details will be given in Section 4.3.  
3. Design ANN Architecture [23]: Construct a feedforward neural network architecture specifically tailored to the prediction of global stress. The training set is employed to train the network in capturing global stress. The network is optimized using the backpropagation algorithm with gradient descent, which allows for parameter adjustments to minimize the loss function between predicted and target data. 4. Address Convergence and Overfitting [22]: Evaluate the convergence of the ANN during the training process. The validation set assesses the network's convergence and serves as a basis in determining when to conclude the training process. If the network demonstrates underfitting (insufficient learning) or overfitting (overly adapted to training data), appropriate adjustments and optimizations should be implemented to achieve a balance between accuracy and generalization. 5. Evaluate Generalization Performance [24]: Assess the ANN's ability to generalize by quantifying its prediction accuracy. The test set evaluates the network's predictive accuracy. Additionally, the dispersion or variability of the network's predictions of global stress should be quantitatively measured to assess its performance.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 4 Results and Discussion  
### The Energy Consumption of the Oxygen Separation Process  
_Reference Measurements_  
For the reference point, analyses on the oxygen generator with the factory settings were performed. The factory settings were as follows: the adsorption pressure 6 bar(g), transfer time 15 s, the oxygen reception stream 0.6 normal m\({}^{3}\)/h. To determine the energy consumption with factory settings, 12 measurements were made. On average, one measurement takes approximately 5 h.  
The obtained data show a distribution of values of a random nature. The random uncertainty of the measurement was calculated as the standard deviation. Taking into account the uncertainty of the calibration of the measuring instruments, the measured energy consumption of the oxygen separation process at the factory settings is \(2\,490\pm 140\,\mathrm{kWh/t}\). The relative measurement uncertainty is equal to 5.6%.  
Dependence of the Purity of the Separated Oxygen on the Pressure Difference between the Adsorption Bed and the Oxygen Tank  
The influence of the pressure difference \(\Delta P_{Oxy}\) on the obtained oxygen purity was checked for the adsorption pressure equal to 3, 4, 5, and 6 bar. The flow from the oxygen reservoir was then carefully controlled to obtain the desired \(\Delta P_{Oxy}\) value. After the condition stabilized, the purity of the separated oxygen was read. In total, about 60 measurements were made for various conditions of adsorption pressure and pressure in the oxygen tank. The number of measurements for individual adsorption pressures is different due to technical requirements that prohibit the stable operation of the device if the pressure in the oxygen tank drops below 2 bar. The results of the purity measurements as a function of the tested pressure difference are shown in Figure 5.  
In Figure 5 it can be seen that high oxygen purity (95%) is maintained if the difference between the adsorption pressure and the pressure in the oxygen reservoir is less than 0.5 bar. Above this value, there is a decrease in the purity of the separated oxygen. When the pressure difference exceeds 3.5 bar, the oxygen purity drops sharply. It is, therefore, possible to control the required oxygen purity with the control of the \(\Delta P_{Oxy}\) value.  
Laboratory tests confirm that the high \(\Delta P_{Oxy}\) values, the rapid gas flow through the adsorption bed shortens the air residence time in the adsorber and may cause local pressure reduction in the porous bed. At extremely high values of \(\Delta P_{Oxy}\), adsorbed nitrogen molecules can be purged into the oxygen reservoir. That could explain a sudden drop in purity for \(\Delta P_{Oxy}\) above 3.5 bar visible in Figure 5.  
According to the obtained results, to maintain high purity of oxygen, the pressure in the oxygen tank should not be lower than 0.5 bar than the adsorption pressure.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Disturbs
#### 3.6.1 Read Disturb  
The bias condition of a NAND block during read has been discussed in Section 2.2 and is shown in Figure 29: a suitable read voltage is applied to the selected WLs to sense whether the cells in the page are in the on or off condition, while all other cells in the string are biased to a pass voltage higher than their maximum \(V_{T}\). The pass bias is hence analogous to a program operation, albeit performed at a lower gate bias, and as such it can induce a \(V_{T}\) shift in the cells. This _read disturb_ (RD) cumulates with the number of read operations and could eventually lead to a failure. Note that this is a recoverable error, that is nulled after the block is erased and reprogrammed.  
The oxide thickness and bias conditions are designed to assure that no significant \(V_{T}\) shift is expected after RD. However, this might no longer be the case after heavy P/E cycling, when the gate oxide quality is degraded and SILC and charge detrapping arise, enhancing the conduction at low fields (see the \(I\)-\(V\) characteristics in Figure 18, left). RD is hence exacerbated in cycled cells [251; 252; 253] and in read-intensive applications such as database access, explaining why a lower endurance is sometimes specified in such cases.  
Experimental data for the memory lifetime report a power-law dependence on \(N_{C}\), with exponent ranging from \(-0.3\) to \(-0.4\)[254; 251]. Data for the \(V_{T}\) shift as a function of the number of reads feature different dependences [255; 256], while raw bit-error rate (RBER) data vs. the number of reads reveal a linear dependence whose slope increases with cycles [255; 60; 257], with a higher BER observed on SLCs than on MLCs. As with SILC, several process optimization [258; 259], voltage tuning [255; 260] or system-level correction [261] proposals have been put forward to alleviate the issue.  
Typical RD characteristics are reported in Figure 30[255]: the \(V_{T}\) shift is larger for the erased state and decreases for higher programmed \(V_{T}\) levels, in agreement with the reduction on the tunnel oxide field. As a consequence, more errors are expected in the logical pages corresponding to those states. It has also been found [262] that repeated RD on partially-programmed blocks leads to an increased BER when the remaining pages are programmed, suggesting that extensive RD can lead to  
Figure 29: NAND array bias condition during read. Cells subjected to RD are circled.  
a degradation of the program characteristic of the cell. In any case, it has been claimed [61] that RD errors are not the prevalent ones in SSDs used in servers.
# Flash Flood Early Warning System in Colima, Mexico
## 2 Materials and Methods
### Alert System
Figure 10: Rendered frame of the hydraulic model used to determine the overflow alert levels and zones. Figure 9: Zoom in of the state of Colima, in México. The following section provides a discussion of the principle aspects of the software that maintain network functioning. Simplified alerting system methodology. 1. Simulation of the hydraulic model to obtain the overflow zones and levels around the area of interest. 2. Topographic survey of each section of the river where a node will be placed. 3. Determine the corresponding formula according to the characteristics of the terrain. 4. Deploy RiverCore at the point of interest. 5. Input characteristic of each section to the server. 6. Process each data package sent by the installed RiverCores to determine the alert level. 7. The information gets displayed on the web interface and an alert is triggered if it corresponds; in this case, the local authorities will be automatically notified.
# Thermo-Viscoelastic Characterization of 3D Printing Polymers
## 2 Thermo-Viscoelasticity  
Viscoelasticity theoretically models material behavior whose mechanical properties have rate-dependency and memory effects. For thermorheologically simple materials, rheological stress-strain relationships for linear viscoelastic materials subjected to mechanical loads can be described as [8; 12]:  
\[\sigma(\mathbf{x},t)=\int_{-\infty}^{t}E(T_{r},\ \zeta-\zeta^{\prime})\frac{ \partial\epsilon(\mathbf{x},t^{\prime})}{\partial t^{\prime}}\ dt^{\prime} \tag{1}\]  
where \(t\) is time, \(T\) is temperature, \(\sigma\) is stress, \(\epsilon\) is strain, \(E\) is the relaxation function, the subscript \(r\) denotes the reference condition, and \(\zeta\) is reduced time, which is related to the shift function, \(a_{T}\), in the following manner:  
\[\zeta(t)=\int_{0}^{t}a_{T}(T,\ s)\ ds \tag{2}\]  
and:  
\[\zeta^{\prime}(t^{\prime})=\int_{0}^{t^{\prime}}a_{T}(T,\ s)\ ds \tag{3}\]  
When the strain history is a harmonic function of time as:  
\[\epsilon(\mathbf{x},t)=\epsilon_{o}\exp(i\omega t) \tag{4}\]  
where \(\epsilon_{o}\) is the amplitude and \(\omega\) is the frequency of oscillation, by substituting Equation (4) into (1), with the steady-state conditions, the stress becomes [13]:  
\[\sigma(\mathbf{x},t) = \bar{E}(\mathbf{x},i\omega)\epsilon_{o}\exp(i\omega t) \tag{5}\]  
where \(\bar{E}(\mathbf{x},i\omega)=\bar{E}_{R}(\mathbf{x},\omega)+i\bar{E}_{I}( \mathbf{x},\omega)\) is decomposed into real and imaginary parts:  
\[\bar{E}_{R}(\mathbf{x},\omega)=\omega\int_{0}^{\infty}E(\mathbf{x},t^{\prime })\sin(\omega t^{\prime})dt^{\prime} \tag{6}\]  
and:  
\[\bar{E}_{I}(\mathbf{x},\omega)=\omega\int_{0}^{\infty}E(\mathbf{x},t^{\prime })\cos(\omega t^{\prime})dt^{\prime} \tag{7}\]  
where \(\bar{E}_{R}(\mathbf{x},\omega)\) and \(\bar{E}_{I}(\mathbf{x},\omega)\) are referred to as the storage and loss moduli, respectively.  
Equations (6) and (7) are Fourier sin and cosine transforms. Therefore, the complex moduli can be obtained by taking Fourier transform of the relaxation moduli defined in the time domain.  
Dynamical mechanical analysis is a well-established technique to characterize viscoelastic properties such as \(\bar{E}_{R}(\mathbf{x},\omega)\) and \(\bar{E}_{I}(\mathbf{x},\omega)\) of polymeric materials as a function of temperature and frequency. In this study, viscoelastic behaviors of various 3D printing polymeric materials have been characterized by using a dynamical mechanical analyzer in the time and frequency domains and obtaining master curves and shift functions.
# Global IoT Mobility: A Path Based Forwarding Approach
## 5 Simulation and Cost Evaluation
### Total Cost Evaluation for Global vs. Micro Mobility Support  
Figure 11 shows the total PDC and SC incurred from the simulation scenarios above. We observe from the figure that IP-over-ICN incurs a total PDC of approximately \(7.2\times 10^{9}\) Bytes to support global mobility compared to \(5.8\times 10^{9}\) Bytes to support micro mobility. In terms of SC, IP-over-ICN incurs a total cost of approximately \(10.7\times 10^{6}\) Bytes to support global mobility, compared to \(7.9\times 10^{6}\) for micro mobility. Note that, in this figure, the inset graph is magnified from the main graph to show the scale of SC. It can be seen from the figure that the total SC is only a very small fraction of the total PDC, while it offers flexible and efficient global mobility support within and across network domains, which is a service essentially required by many of the emerging IoT use cases.  
Figure 10: Signalling cost (SC) for IP-over-ICN micro and global mobility support.
# Aerodynamic Analysis of Simple Grider Bridges under Construction Phase
## 1 Introduction
### Case Study  
The present research work is focused on the aerodynamic behavior of steel-concrete simple girder bridges. In particular, the studied section is formed by two I-beams supporting a concrete slab (Figure 1), but the analysis was made when only one or both beams were already built before constructing the concrete slab. These bridges are usually 30-50 m long, and they are widely used in Sweden.  
During the construction phase, both girders are placed in their final position one by one. Later, the bracing between both of them is done, and finally, the concrete slab is built. In this study, only the situations previous to the slab construction were analyzed. Therefore, the following three different case studies were considered here:  
1. Only one beam is installed;
2. Both beams are installed;
3. Both beams are installed, but the space between them is isolated from the external flow with wood or steel plates (Figure 2).  
The second case covers both before and after the construction of the bracing. Therefore, the actions on the first beam, the second beam, and both beams joined were analyzed. Regarding the third situation, it was included to see if any improvement is observed in the aerodynamic parameters of the section, which would then be a possible solution to reduce its vulnerability.  
During the construction of the plate, there is a period of time when the concrete is not hardened yet, and the structure does not have its final stiffness. At this stage, the added mass leads the structure to be less vulnerable to wind effects. Thus, this situation was not considered in the present study. Since the assessment was made for different construction stages, the expected winds endured by the structure are lower than in the  
Figure 1: Cross-section of the Banafjall Bridge, a railway structure of the Bothnia Line in Sweden. The bridge shows a typical simple girder bridge cross-section with two girders. Source: Royal Institute of Technology (single column, B&W in the printed version).  
Figure 2: Construction stages considered in the parametric study (single or double column, B&W in the printed version).  
final stage. The exposure time is in the order of days or weeks, so extreme winds are unlikely to occur during the studied situations. Vortex shedding effects are dangerous, with wind speeds between 5 and 15 m/s [25], while buffeting effects usually need much higher winds to cause large vibrations [26]. Therefore, the analysis concentrated on the vortex shedding phenomenon rather than on the bridge's response to turbulent winds. However, the aerodynamic coefficients extracted during the study can also be applied to analytical methods to calculate the structural response to turbulence. This response is proportional to the drag coefficient, so a decrease in it would also imply a reduction in dynamic wind effects induced by turbulence.
## 4 Discussion
### Comparison of Flow Time  
According to the proposed flow algorithm in the stairway, the passengers on board will receive the public address system alarm simultaneously in case of fire. Therefore, part of the passenger on the deck escaped earlier and did not participate in the escaping flow of the various decks. As a result, the flow time of passengers in the stairway is smaller than that computed by the conventional method. At the same time, the proposed local congestion method based on the social force model defined a lower flow in the state of extreme congestion, which decreases the flow speed of passengers when moving to congested doors. The calculations based on both methods are shown in Figures 6-8. As shown in Figures 6-8, the flow time of the No. 1, No. 4, and No. 5 main vertical zones are calculated by using the original model-based method and the model-based method proposed in this paper. The flow time of the method proposed in this paper is 25%, 20%, and 33% lower than that of the original method. In other words, the flow speed in the method proposed in this paper is higher than that of the original model. This is mainly because the dislocation accumulation model established in this paper reduces the flow of people in the convergence of the stairway, which increase the flow speed in the stairway.  
Figure 6: Comparison of flow time in MVZ 1/Original simplified evacuation analysis method: MSC/Cir.1533; Revised simplified evacuation analysis method: The proposed method in this paper.  
Figure 8: Comparison of flow time in MVZ 5/Original simplified evacuation analysis method: MSC/Cir.1533; Revised simplified evacuation analysis method: The proposed method in this paper.  
Figure 7: Comparison of flow time in MVZ 4/Original simplified evacuation analysis method: MSC/Cir.1533; Revised simplified evacuation analysis method: The proposed method in this paper.