esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Classification of Routing Protocols
### Cross-Layered Routing Protocols
#### 4.5.2 Cascading Information Retrieval by Controlling Access with Dynamic Slot Assignment  
(CICADA)  
CICADA proposed in [72], is a cross-layered low energy protocol, designed for Time Division Multiple Access (TDMA) scheduling based on multi-hop mobile body area networks. This protocol builds a spanning tree same as WASP [71], as shown in Figure 14, redrawn from [71], and slots of the time are assigned in distributed manner and the known length of each cycle makes the slot synchronization possible. The parent nodes are responsible for telling their children nodes when they can communicate. CICADA is the improved version of WASP discussed in [71].  
This scheme divides each cycle into two sub-cycles: the control sub-cycle and the data sub-cycle. Each sub-cycle allocates the slots according to its own scheme: the control scheme and the data scheme, and both are forwarded during the control sub-cycle from the parent nodes to their children nodes. Each control scheme shows the sequence, in which the children nodes can send their control scheme to their parent nodes, the length of control sub-cycle and the depth of the tree. At the end of the control sub-cycle, the data sub-cycle starts. The data scheme consists of length of data period and length of waiting period. During the data period the children nodes send the data packets to their parent nodes while during the waiting period the nodes can go to sleep mode to save the energy. Each parent node constructs a table of its children nodes containing the number of slots they need to transmit the data to their parent node and the number of slots they required to receive the data from their children nodes. Each data sub-cycle has a slot for new nodes to join the tree. Each new child node is allowed to send JOIN-REQUEST message in that slot after hearing to the data scheme of the desired parent node.
## 3 Synthesis of Optical Coherence Function
### Distributed Lateral Force Sensing along a Polarization-Maintaining Optical Fiber with SOCF  
When a polarization-maintaining fiber has a lateral force, the two eigen-polarization modes are coupled with each other. With this phenomenon, a distributed lateral force sensing system can be realized, as shown in Figure 15[32]. In this system, only one linear polarization mode is launched at a fiber end. By applying lateral force or stress, the other orthogonal polarization mode takes place at the force-applied point. The two modes have a difference in their propagation speeds; then, the orthogonal polarized lightwave has a delay time corresponding to the coupling position. Through making an interference between the original polarization mode and the orthogonal one, as shown in Figure 15, and by additionally synthesizing a delta function-like coherence peak, the distributed measurement of the lateral force can be done. In Figure 15, a multiple-steps FM is used to synthesize the coherence peak, and then, phase modulation with the same waveform as that for the FM is used to sweep the coherence peak position along the fiber. The waveform is folded within \(+\pi\) and \(-\pi\). Figure 16 shows the measurement results, for which the SSG-DBR-LD was used to enlarge the FM amplitude [33]. Then, the spatial resolution was improved to be 20 cm [33]. By synthesizing the other shapes of the coherence functions, varieties of distributed lateral force sensing were performed [34; 35].  
Figure 14: Experimental demonstration of the quasi-distributed sensing in the system that is shown in Figure 13[31]. Bragg wavelength distribution along long-length FBGs are successfully obtained by the SOCF technology.
## 4 Creating a Dual Architecture from sPLCs
### Integrity of Safety against Accidental Failures  
The failure rate of channels A and B, shown in Figure 3, can be expressed by the relation  
\[\lambda_{A,B}=\sum_{i=1}^{n}\lambda_{i} \tag{5}\]  
where \(\lambda_{i}\) is the failure rate of the \(i\)-th element involved in the implementation of the safety function, and \(n\) is the number of these elements.  
The dangerous failure rate of the dual architecture (necessary for the determination of SIL) can be determined according to Equation (4).  
Tables 1 and 2 show the minimum failure rate of channel A and channel B (for simplicity, we assume identical channels) necessary to achieve the respective SIL, assuming a fault detection and negation time of 1 h (Table 1) and 24 h (Table 2). The considered detection and negation time is relatively large. With the proposed method of comparison (Section 4.2.1), these times are at the level of tens, at most hundreds of milliseconds. The tables also show the _Mean Time to Failure_ (MTTF) values, as manufacturers generally state these values.  
\[MTTF_{i}=\frac{1}{\lambda_{i}}. \tag{6}\]  
By comparing the MTTF values of commonly available sPLCs (obtained from ref. [20]) with the minimum values calculated in Tables 1 and 2, it can be stated that achieving SIL 1 to SIL 4 is possible using a dual sPLC-based architecture.  
Data in Tables 1 and 2 apply a diagnostic coverage (DC) of 100%. If the DC is \(<\)100%, the minimum fault intensities will be higher. However, given the considered margin of failure detection and negation, it can be reasonably assumed that the required SIL can be achieved through a dual sPLC-based architecture. A more detailed model needs to be developed to accurately determine the rate of dangerous faults in dual architectures with DC \(<\) 100%. Such a model can be created using Markov diagrams and deals with the influence of diagnostic coverage on the safety of dual architectures in more detail, e.g., in refs. [21; 22; 23]. This paper discusses the integrity of safety against systematic failures in more detail.  
Figure 3: Block diagram of the dual architecture based on sPLC.
## 4 Discussion
### Applicability and Limitations of the Model  
As demonstrated above, the proposed model, despite not requiring numerical computation, generally reproduces the temperature profiles of the trigger cell and adjacent cells in the simulation. While there is a tendency to predict a later onset of thermal runaway across all conditions, the prediction accuracy of the maximum cell temperature is high. This allows for a straightforward and numerically stable prediction of the approximate temperature rise and heat propagation behavior during cell heating. Furthermore, the linearization of the thermal decomposition reaction offers the advantage of easily evaluating the sensitivity of parameters such as latent heat and reaction rate. For further improvement of the model accuracy, the following approaches can be considered:  
* Examination of representative \(\alpha\) value selection: Clarifying the relationship between thermal decomposition reaction parameters (\(k_{0}\), \(C_{1}\), \(m\), \(n\), \(E\)) and the representative \(\alpha\);
* Representation using two linear functions, including the rising region in addition to the maximum slope;
- Representation using a quadratic function. The energy equation will become Riccati-type equations.
* Creation of an effective parameter database through parametric studies;
* Consideration of the physical meaning and improvement of the estimation accuracy of parameters \(R,f\), and \(\Delta t\) in multiple-cell systems.  
Moreover, this study treated multiple thermal decomposition reactions originating from the positive and negative electrodes as a single overall reaction. This assumption could be a factor causing discrepancies with the actual temperature rise behavior. These aspects will also be addressed in future work. Another crucial point is that approaches using analytical solutions for temperature prediction in multiple cells have limitations, and numerical simulation remains an effective means for accurate prediction that mimics a real-world battery module/pack.
## 3 Metrics for Jamming Attack Detection
### Bit Error Rate (BER)  
[PERSON] _et al._[5] have recommended the use of BER in combination with the received signal strength (RSS), as it is not only a very effective metric for detecting jamming attack, but is also capable of identifying the reactive jamming attack, which otherwise is very difficult to identify. The BER is calculated as the ratio of the number of corrupted bits to the number of total bits received by a node during a transmission session. We concur with the authors as far as the effectiveness of this metric is concerned, but find the calculation of the BER heavily taxing for a WSN node, especially in a networking environment where the node will have to keep track of the BER of all radio links with its one-hop neighbors. Calculation and updating of BER, even at the base station level, is not feasible because it involves collection of voluminous data regarding every bit of a valid and invalid packet from the nodes leading to over-taxing of the WSN.
# Research on Seismic Wave Quality Factor of Marble Jointed Rock Mass under SHPB Impact
## 4 Rock Mass Seismic Wave Quality Factor
### Acquisition of Seismic Wave Quality Factor of Jointed Rock Mass  
The software MATLAB was used to program Equations (12)-(14). Meanwhile, the time-history data of three-wave strain in Figure 8 were imported into the MATLAB workspace to establish the data matrix, and the data matrix was analyzed and processed based on the calculation function obtained by the program. Finally, the maximum storage energy \(W_{s}max\), dissipation energy \(W_{l}\) and seismic wave quality factor \(Q_{s}\) of the jointed rock mass under the loading condition of six groups of three specimens were obtained, as shown in Table 1.  
Based on the above analysis, it can be seen that under different loading conditions of the same group of specimens, the measured maximum stored energy, dissipated energy and seismic wave quality factor are relatively close, and the maximum deviation of the quality factor \(Q_{s}\) is 3.2%, indicating that the loading conditions of this experiment are set reasonably and the conventional joint cracks are not opened by the first impact. In addition, although the experimental results show some discreteness, the overall deviation is not large and the average \(Q_{s}\) is 43.07.
## 3 Method
### Particle Filter Assimilation  
A particle filter (PF) is a fully non-linear filter with Bayesian conditional probability estimation based on Monte Carlo simulations [22; 80]. The basic idea is that the PF generates a set of probability-weighted posterior random samples (called 'particles') through the direct evaluation of the minimum variance of finite particles to approximate the posterior probability density function (PDF). When increasing the number of particles, the PDF of the particles gradually approaches the PDF of the state variables, so that the state reaches optimal Bayesian estimations.  
The PF is a method of sequential assimilation, in which every observation at one time is used to solve only one \"analysis\" in a recursive approximation process. In this study, the algorithm of residual resampling PF was used to assimilate the observed LAI into the CERES-Wheat model to estimate the winter wheat yield. The whole calculation process mainly includes the stages of forecast, update, resampling and state estimation. The detailed process was reported previously [22; 78], and the flowchart is shown in Figure 2.  
For the dynamic state-space model of CERES-Wheat, the LAI is not a state variable because its value is calculated independently at each time step as a function of the plant leaf area. The plant leaf area is grown dependent on a temperature control factor and has a potential value that is set by the number of plant leaves [68]. Thus, the plant leaf area is considered as a state variable, while the observational LAI was used as an observation vector in the data assimilation process in this study.  
With respect to the traditional PF method, particle degradation phenomenon is widespread, which could lead to an extensive computational burden for updating the PDF of particles with no contribution. Residual resampling is a critical operation to solve the degeneracy problem with particle filters. However, residual resampling can also bring the problems of sample impoverishment or lack of particle diversity when the particles with lager weight are copied many times, and the particles with smaller weight are ruled out after many iterations [45; 48]. To solve these above problems, we applied an approach of Gaussian repetitious perturbation to the resampled model states' particles. Based on this perturbation method, the imposing time variant model errors are more representative of model uncertainty, and the analysis deviation will be reduced.  
Figure 2: The flowchart of CERES-Wheat model data assimilation with PF.
# Service Reliability Based on Fault Prediction and Container Migration in Edge Computing
## 2 Related Work
### Service Migration in Edge Computing
By improving the ant-colony algorithm and establishing a local pheromone volatilization model and a global pheromone update model, the optimal migration of containers can be realized, the load balance can be improved, and the migration cost can be reduced. [PERSON] et al. (2020) proposed a container migration mechanism based on load balancing [27]. First, the timing of the container migration was determined based on the classical static resource utilization threshold model. Subsequently, a container migration model of load-balancing combined migration cost was established to minimize the impact of container migration while balancing the load of the edge network. The container migration mechanism proposed in this paper not only improves the load balancing of the edge network but also reduces the cost of container migration. However, these studies were based on the container migration caused by node overload. Most studies have only considered the load balancing of the network after migration, without considering the migration costs generated in the migration process. Additionally, no studies have considered a model in which migration timing is first determined by the fault prediction of devices in the edge environment and then the overall load balance of the edge network after migration and the migration cost caused by the migration of containers from the failed device to the healthy device are comprehensively considered. Therefore, this study proposes a migration strategy based on a GA, which considers fault prediction as the judgment condition of migration timing and minimizes the migration cost and load balancing of edge networks after migration.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 4 Chitosan
### Parameters Influencing the Antibacterial Properties of Chitosan
#### 4.1.4 Degree of Deacetylation  
Through the influences on the amount of free amino groups in the polysaccharides, the deacetylation degree of CS influences the solubility as well as the charge formation of the functional groups and is dependent on the positive charge density. The antibacterial effect of CS with a higher positive charge density is more effective [91].
## 4 Recent Progress in Beamlines
### Time-Resolved X-ray Absorption Spectroscopy with Tender X-ray FEL
The measured X-ray fluorescence signal is normalized by the I\({}_{0}\) signal, which is collected by the quadrant beam position monitor  
Figure 7: Designed chamber for XAS of liquid circulated system at NCI. The sample injector mounted on the 3 axis stages (yellow color) guides the liquid jet. The X-ray pulse and optical laser pulse are focused on the sample position, and they are almost parallel to each other. The X-ray fluorescence signal from the sample is detected by an avalanche photodiode (APD, brown color). (QBPM). After data normalization at each X-ray pulse, the transient spectrum is obtained by the subtraction from a pumped to unpumped X-ray absorption signal. Ru L\({}_{3}\)-edge TR-XAS results are shown in Figure 8. The sample is an aqueous solution of 50 mM [Ru(bpy)\({}_{3}\)]\({}^{2+}\) (Tris(bipyridine) ruthenium(II) chloride, Sigma-Aldrich) that is well known as a photosensitizer with intra-electron transfer. The top panel of Figure 8 shows the ground static spectrum, and the bottom panel shows the transient X-ray absorption spectrum at 10 ps time delay. These data were collected over 15 s (450 shots for laser-on XFEL, 450 shots for laser-off XFEL) at each energy point. The center panel of Figure 8 presents the kinetic spectrum at 2.844 KeV, which is a mean of 150 shots for laser on and off. The instrument response function (IRF) was fitted to 136 \(\pm\) 44 fs. Currently, TR-XAS experiments can run smoothly at the PAL-XFEL for the study of various chemical and biological fields. In photoactivated reactions, the 136 fs IRF from the PAL-XFEL is not sufficiently short. One way to reduce the IRF is to reduce the group velocity mismatch by using a thinner liquid jet. Currently, the liquid injector at the PAL-XFEL provides a 100 \(\mu\)m or 150 \(\mu\)m round nozzle. We plan to reduce the liquid jet thickness and increase the jet stability so that the IRF can be reduced to 100 fs. Recently, the PAL-XFEL succeeded in the generation of a bright self-seeded FEL, which had a peak intensity more than fivefold stronger than that of a normal XFEL from SASE. We plan to use self-seeded FEL with an energy scanning function. We expect the TR-XAS with a self-seeding FEL to be able to measure signals even from a sample at very low concentration with an increased signal-to-noise ratio.
## 4 Discussion
For example, telling a patient that they have a higher chance of developing glaucoma and recommending biannual checkups or close monitoring would be more beneficial for the individual than a false negative report where the patient would miss an early warning of glaucoma onset that would develop into glaucoma in the near future. Overall, the research suggests that the higher accuracy rates obtained from the RF, XGB, and MLP models using the balanced dataset can be considered for predicting glaucoma in a real-world setting. With the present dataset, the prediction accuracy for glaucoma onset was about 81%, which is reasonably significant for glaucoma detection using only EHR data and without observing/analyzing traditional fundus images or optical coherence tomography (OCT) results. Though early diagnosis of glaucoma using ocular coherence tomography (OCT) has been considered significant, it also comes with an inherent error rate of ~20%. [PERSON] et al. have reported that, in general, only up to 82% of glaucoma diagnoses can be determined using the information provided by medical history and OCT image data [43]. Studies have reported many risk factors for the development and progression of glaucoma. Though age has been reported as a well-known common risk factor for glaucoma [36], understanding exactly how other preventable risk factors increase the risk of glaucoma is necessary to tackle this growing problem. Because several risk factors are implicit with aging, it becomes challenging to draw a line between the aging process and disease progression [37]. Since the present study used age-matched glaucoma and non-glaucoma groups, we did not incur any inferences based on age. Consistent with the existing literature, the present study identified significant risk factors or comorbidities for the development of glaucoma, including cataracts. This analysis also revealed additional risk factors, such as lacrimal disorders, with an OR of about 1.14. While cataracts have long been associated with glaucoma formation, other factors such as primary and chronic angle closure, lacrimal disorders, and obesity are unknown risk factors associated with the development of glaucoma. These findings suggest that this information can be used to inform risk stratification to mitigate glaucoma onset as needed. Further, an independent study is needed to understand the role of lacrimal disorders and obesity in the development of glaucoma onset.
## 3 Results
### The Effect of DE on the Hydration Process of Cement Mortar
#### 3.5.1 The Influence of DE on the Chemically Bound Water Content of Cement Mortar  
The curves of chemically bound water are shown in Figure 8.  
From Figure 8, it can be observed that the chemically bound water content rapidly increases in the first 3 days. Compared to the baseline group (A1), the chemically bound water content in the groups doped with DE increased by 1.79%, 2.47%, and 2.81%, respectively. However, the growth becomes slower from 7 d to 28 d, with growth rates of 0.23%, 0.27%, and 0.6%, respectively. It can be seen that as the dosage of DE increases, the chemically bound water content of the cementitious material also increases. This can be attributed to two main reasons. Firstly, under the same water-binder ratio, the incorporation of DE releases water gradually when the internal relative humidity of the slurry decreases, which promotes the hydration of the slurry and thus increases the chemically bound water content of the hardened slurry. Secondly, in the middle and late stages of hydration (7-28 d), the pozzolanic activity of DE gradually takes effect. Under alkaline  
Figure 8: Chemically bound water content.  
conditions, some of the reactive SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) in DE react with the hydration product Ca(OH)\({}_{2}\) of cement to produce secondary hydration products such as hydrated calcium silicate and hydrated calcium silicoaluminate, which increases the amount of hydration products and subsequently increases the chemically bound water content.
###### Abstract  
To investigate the source of the bitter almond taste in whole corn silage (WPCS), headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), headspace gas chromatography-ion migration spectrometry (HS-GC-IMS), and electronic nose (E-nose) technology were employed. The study analyzed the differences in volatile compounds between two WPCS samples with distinct odors from the same cellar. GC-IMS and GC-MS identified 32 and 101 volatile organic compounds (VOCs), respectively, including aldehydes, alcohols, esters, ketones, and other compounds. Three characteristic volatile organic compounds associated with the bitter almond taste were detected: benzaldehyde, cyanide, and isocyanate. The electronic nose demonstrated varying sensitivities across its sensors, and principal component analysis (PCA) combined with variable importance projection (VIP) analysis revealed that WSS (nitrogen oxides) could differentiate between the two distinct silage odors. This finding was consistent with the GC-MS results, which identified 34 nitrogen-containing heterocyclic compounds in the abnormal silage sample, accounting for 77% of the total nitrogen-containing compounds. In summary, significant differences in aroma composition were observed between the bitter almond-flavored silage and the other silage in the same cellar. These differences were primarily attributed to changes in volatile organic compounds, which could serve as indicators for identifying bitter almond-flavored silage.  
WPCS; VOCs; HS-SPME-GC-MS; HS-GC-IMS; E-nose Article
# Enhancing Data Security Through VLSM Subnetting and TCP/IP Model in an ENT
## 2 Literature Review
### Routing Protocols
#### 2.3.5 Hierarchical Network Topology  
The hierarchical network topology is designed for scalability and optimisation on the performance through TCP/IP layers. This enhances efficiency and resilience in contemporary networks. Figure 3 represents a hierarchical network topology which relates to an ENT with various devices such as routers, switches, servers, laptops, smartphones, tablets, desktop computers, links to the devices, and different locations. The ENT shown in Figure 3 will be discussed in Section 3.  
It is best practice to model the network design with hierarchical network topology comprising an upper logical topology, which determines network performance and a lower physical topology depicting the actual network, including physical devices and links [15]. In wireless sensor networks (WSNs), a new hierarchical topology sustains energy efficiency and increases durability [16], whereas a distributed hierarchical algorithm applies to clustering tree topologies in WSN environments [26].