esa-sceva/satcom-chunk-collection huggingface dataset:
# Upgrading _In Vitro_ Digestion Protocols with Absorption Models
## 2 Technological Approaches of Intestinal Epithelium Models
### Non-Cell-Based Transport Models  
Non-cell-based transport models are useful when the goal is to screen time-efficiently as many samples as possible. These models may apply solubility (centrifugation or filtration) or dialyzability measurements [1; 33; 34]. Numerous widely used _in vitro_ digestion models, for example, the TIM systems, use dialysis to determine the intestinal permeability [33]. If a proper pore size (8000 Da MWCO) of the dialysis membrane is chosen, coupled with simulation of the motility of the small intestine, the human intestinal effective permeability of D-glucose can be predicted with the respective method [33]. Still, the dialysis membrane cannot reproduce active transport [35].  
Another approach may be the application of biomimetic membranes. These are filters or membranes that contain phospholipids or liposomes [22; 35].  
There are several types of biomimetic membranes available:  
* parallel artificial membrane permeability (PAMPA): a filter is infused with an organic solvent that contains phospholipids, in a manner that it imitates the lipid composition of the intestinal membrane [22].  
Figure 3: Approaches for the investigation of the absorption of food components. In Transwell® systems, primary cells, cell lines, or fragmented organoids derived from stem cells can be cultured.  
* vesicle-based permeation assay (PVPA): liposomes are deposited in the pores and on the surface of the filter [22].
* PermeaPad(r): two cellulose-hydrate membranes act as support layers in a sandwich-like structure, where the middle layer is made of dry phospholipids. This structure hinders the erosion of the middle layer and prevents the leakage of lipids into the solutions used during the experiment [22].  
They are only appropriate to evaluate the passive transcellular absorption of hydrophobic components, they do not reproduce active transport or paracellular permeability [12,35].
## 2 Methodology
### Mathematical Model  
The isobaric heating process in Figure 1 can be divided into 2 stages: Preheating (2-3) and evaporating. The temperature difference between state point 3 and the waste heat line vertically is called the pinch point temperature difference. Generally, the lower the pinch temperature, the higher the evaporation temperature of the working medium under the same heat source, the higher the inlet pressure of the expander, and the higher the output work and thermal efficiency. However, as the pinch point temperature becomes smaller, the heat exchange area between the preheater and the evaporator becomes larger, and so does the investment cost. Therefore, in order to balance thermal efficiency and economy, the pinch temperature is set to be 5 \({}^{\circ}\)C. The heat absorbed by the organic working fluid in the evaporator is  
\[Q_{2-4}=m_{h}(h_{7}-h_{8})=m_{f}(h_{4}-h_{2}) \tag{1}\]  
Figure 2: Simulation of basic ORC system.  
where \(m_{h}\) and \(m_{f}\) are the mass flow rates of the waste heat resource and the organic working fluid, respectively.  
The state of the expanded working fluid is marked as point 5. Due to the irreversibility of the expansion process, process 4-5 is a process of increasing entropy. The specific enthalpy of state point 5 can be calculated by the formula  
\[h_{5}=h_{4}-\eta_{t}(h_{4}-h_{5^{\prime}}) \tag{2}\]  
where \(\eta_{t}\) is the internal efficiency of the expander.  
The power output of the expander is  
\[W_{t}=m_{f}(h_{4}-h_{5}) \tag{3}\]  
The isobaric heat releasing of organic working fluid in the condenser can also be divided into 2 stages: Precooling (5-6) and condensing (6-1). The pinch point temperature difference is determined by state points 3 and the cooling water line. The heat released from organic working fluid in the condenser is  
\[Q_{5-1}=m_{c}(h_{10}-h_{9})=m_{f}(h_{5}-h_{1}) \tag{4}\]  
where \(m_{c}\) is the mass flow rate of cooling water.  
The state of working fluid after compressed is labeled as point 2. Processes 1-2 is entropy increasing, resulting from the irreversible loss in the compression process. The specific enthalpy of state point 2 can be determined by  
\[h_{2}=(h_{2^{\prime}}-h_{1})/\eta_{p} \tag{5}\]  
where \(\eta_{p}\) is the adiabatic efficiency of the pump. The power consumed in the pump is  
\[W_{p}=m_{f}(h_{2}-h_{1}). \tag{6}\]  
The net power output of the ORC system is  
\[W_{\mathbf{net}}=W_{t}-W_{p}. \tag{7}\]  
The exergy efficiency of the ORC system is defined as the ratio of the net power output to the exergy of waste heat before entering evaporation and is written as  
\[\eta_{\alpha\alpha}=\frac{W_{nt}}{m_{h}[(h_{7}-h_{0})-T_{0}(s_{7}-s_{0})]} \tag{8}\]  
where \(T_{0}\) is the environment temperature and are the specific enthalpy and specific entropy of waste heat resource at the environment temperature, respectively.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 2 Historical Overview of Post-Earthquake Fire (PEF) Events
### 1906 San Francisco PEF  
On 18 April 1906, a magnitude 8.3 earthquake struck San Francisco, originating from the San Andreas Fault, a fault system extending over 960 km [4]. The earthquake caused severe structural damage, but the most devastating impact came from over 50 fire ignitions that erupted shortly after the shock, leading to a four-day conflagration. Fire accounted for approximately 80% of the total destruction, with an economic loss exceeding USD 350 million (in 1906 dollars).  
The fires destroyed over 28,000 buildings and resulted in more than 3000 deaths. Key fire sources included ruptured gas pipes, broken chimneys, and electrical short circuits. The failure of the water supply system left firefighters unable to contain the flames [5], while misuse of dynamite by inexperienced responders further spread the fire. Some property owners also intentionally set fires to claim insurance, as policies did not cover earthquake damage.  
Compounding the crisis, disruptions to fire alarm systems and communication networks delayed firefighting efforts. Additionally, low humidity and shifting winds accelerated the fire's spread. This disaster underscored the vulnerability of urban areas to post-earthquake fires and emphasized the need for improved emergency response and fire mitigation strategies.
# Reversible Data Hiding for DICOM Image Using Lifting and Companding
###### Abstract  
In this work, a reversible watermarking technique is proposed for DICOM (Digital Imaging and Communications in Medicine) image that offers high embedding capacity (payload), security and fidelity of the watermarked image. The goal is achieved by embedding watermark based on companding in lifting based discrete wavelet transform (DWT) domain. In the embedding process, the companding technique is used to increase the data hiding capacity. On the other hand, a simple linear function is used in companding to make the scheme easy to implement, and content dependant watermark is used to make the scheme robust to collusion operation. Moreover, unlike previously proposed reversible watermarking techniques, this novel approach does not embed the location map in the host image that ultimately helps to achieve high fidelity of the watermarked image. The advantage of the proposed scheme is demonstrated by simulation results and also compared with selected other related schemes.  
DICOM image; reversible watermarking; lifting; companding; collusion +
Footnote †: journal: Accepted: 14 August 2019; Published: 19 August 2019
# Formal Analysis of Ratchet Protocols Based on Logic of Events
## 3 Formal Analysis of the Signal Protocol
### Formal Verification of Strong Authentication Properties  
In this formal analysis, we adopt the widely accepted Dolev-Yao adversary model, which is particularly well-suited for symbolic verification frameworks such as LoET. Under this model, the adversary is assumed to have full control over the communication channel, including the ability to intercept, modify, replay, delay, and inject arbitrary messages. However, the adversary is considered computationally bounded, and thus incapable of breaking cryptographic primitives--such as encryption schemes, digital signatures, or key derivation functions--unless the corresponding keys have already been compromised.  
This model is chosen for its ability to provide a rigorous yet tractable abstraction of real-world adversaries. It enables us to concentrate on the logical structure and causal relationships within the protocol, without relying on the underlying algebraic properties of cryptographic algorithms. The Dolev-Yao model represents a widely accepted assumption in  
Figure 2: Flowchart for verifying the strong authentication of the Signal protocol using LoET.  
symbolic protocol analysis and provides a solid theoretical foundation for verifying strong authentication guarantees in the Signal protocol under ideal cryptographic conditions.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 3 Results and Discussions
### Solar Thermal Cooling System
As the initial cost for installing the solar thermal cooling system on one building was $350k, the cost will rise to $3.150M if it is installed on 9 buildings throughout the campus, while the initial cost for the central air conditioning and PV system is $3M. When discounted by 6% per year, the present worth of future cash payments is approximately $9,745,000, whereas the solar thermal cooling system will not reach the breakeven point with a value of negative $1,730,000, as illustrated in Figure 6. The solar thermal cooling system's LCOE value was $2.35/kWh, whereas the value for the coupled PV and air conditioning systems was under $0.05/kWh. However, assuming the solar cooling system will run at it is maximum COP of 0.79, this will increase the feasibility of the project and the payback time of the project to around 10 years, which is still way below the coupled PV and air-conditioning systems. The internal rate of return (IRR) to estimate the profitability of different options is summarized in Table 1.  
In addition to the extensive maintenance needs, the reduced COP, along with the practicality and feasibility of the solar thermal cooling systems, mean these kinds of technologies are under significant pressure to remain competitive when faced with the development of new air conditioning and PV technologies.
# Application of Soil Moisture Active Passive (SMAP) Satellite Data in Seismic Response Assessment
## 3 Results
### Moisture Differences
Furthermore, although the SMAP data time interval is 72 h for most of the target earthquakes (see Table 3), an \(MDI>0\) was recorded in all earthquakes, except for seismic zones in Elazig, Turkey and Puebla, Mexico. Numerous factors such as the geological setting of the region, topographic conditions, groundwater level, earthquake characteristics, soil density, and pre-event soil moisture conditions are critical regarding how and to what extent surface soil moisture increases as a result of earthquakes. Future research is needed to study the effects of each of these factors on seismic-induced soil moisture change. For instance, the large number of grids without an earthquake-induced soil moisture increase (i.e., grids with an \(MDI<0\)) in Elazig, Turkey, in comparison with other seismic zones that have the same SMAP data time interval, may be attributed to: (1) the low pre-event soil moisture of this region; (2) a deeper groundwater level than the other study regions; and/or (3) a smaller earthquake magnitude than the other study regions.
# Robust Fingerprint Minutiae Extraction and Matching Based on Improved SIFT Features
###### Abstract
_Global features:_ These features form a special pattern of ridge and valleys, called singularities or Singular Point (SP), and they can further be divided into three types: loop, delta, and whorl. The significant points are the core and the delta. The core is defined as the most points on the innermost ridges, and the delta is defined as the central point where three different trend flows converge (see Figure 1a). It can be argued that these features provide the most useful and crucial information for fingerprint classification, fingerprint matching, and fingerprint alignment [7,8]. 2. _Local features:_ At the local level, ridge characteristics, collectively called minutia, represent the most widely used features to match fingerprints. There are several types of minutiae, but for practical purposes, just two types of minutiae [9,10] are considered as the two most prominent ridge characteristics: ridge ending (point where ridge ends abruptly) and ridge bifurcation (point where a ridge forks or diverges into branch ridges), as shown in Figure 1b,c. The primary objective of this work is the development of a fully automated minutiae extraction and matching framework for fingerprint identification and verification purposes, in which an adaptive SIFT detector is applied to high-contrast fingerprints preprocessed by means of denoising, binarization, thinning, dilation and enhancement to improve the quality of latent fingerprints. As a result, an optimized set of highly-reliable salient points discriminating fingerprint minutiae is identified. The SIFT descriptors of the local keypoints in a given fingerprint are then matched with those of the stored templates using a brute force algorithm. Finally, a dual-threshold filter is adaptively applied, which can potentially eliminate almost all the false matches, while losing less than 4% of correct matches. The remainder of the paper is organized as follows: in Section 2, we briefly review closely related prior work on feature extraction and matching algorithms developed for fingerprint recognition. The details of the proposed methodology for fingerprint minutiae extraction and matching and its components and stages are elaborately described in Section 3. Section 4 provides the experimental results and discusses their implications. Finally, concluding remarks and avenues for future research are drawn in Section 5.
## 3 Results and Analysis
### Transcriptome-Based Analysis of the Key Regulatory Factors of Pentaleneene Synthesis Efficiency
Several key genes involved in this pathway exhibited significant down-regulation (Table 1), including _NDH_ (complex I: NADH dehydrogenase), _petB_, _petC_ (complex III: cytochrome bc1 complex), _cyoC_, _cyoB_ (complex IV: cytochrome oxidase), and _atpA_, _atpC_ (ATP synthase) genes, indicating possible activation of the oxidative stress response [31]. Since oxidative phosphorylation is the primary route for ATP production, the down-regulation of ATP synthase genes may lead to ATP deficiency [32], limiting the activity of MVA pathway enzymes and restricting terpenoid precursor synthesis and transport. In the glutathione metabolism pathway, most DEGs were down-regulated, but the key gene _GSR_ (glutathione reductase) in the pathway was up-regulated by 1.6-times. This response confirms the occurrence of oxidative stress, which was likely induced by the introduction of the heterologous pathway. The resulting oxidative stress triggered increased glutathione synthesis as an antioxidant defense mechanism [33], consequently leading to increased NADPH consumption [34]. In the pentose phosphate pathway, a large number of genes were down-regulated (up/down ratio = 3:11), including _G6 PDH_ (glucose-6-phosphate dehydrogenase), which showed a 2.3-fold reduction in expression. As G6 PDH catalyzes the generation of NADPH, its repression leads to reduced NADPH availability [35]. NADPH is required for both the antioxidant defense functions of the glutathione metabolism pathway and the catalytic activity of HMGR in the MVA pathway. Limited NADPH availability may lead to competition between these two processes, resulting in cofactor imbalance and reduced efficiency in the synthesis of the terpenoid precursor IPP. In the thiamine synthesis pathway, key genes, such as _DXS_, _thiM_, _tenA_, _thiI_, _thiG_, and _thiK_, were significantly down-regulated (Table 1), indicating impaired thiamine production. As an essential coenzyme in cellular metabolism, thiamine is required for the activity of the pyruvate dehydrogenase complex. Thiamine deficiency, thus, limits the conversion of pyruvate to acetyl-CoA [36; 37]. Given that acetyl-CoA serves as a critical precursor in the MVA pathway, its insufficient supply directly constrains pathway flux [38] and ultimately reduces pentalene biosynthetic efficiency. In summary, the introduction of the heterologous MVA pathway disrupted the metabolic balance in _V. natrigens_, resulting in limited cofactor availability and precursor supply. These imbalances restricted MVA pathway flux and inhibited pentalene synthesis. Figure 5: The top 20 KEGG pathways enriched after plasmid-induced expression with the number of differential genes.
## 4 Evaluation
### Simulation Setup of NB-IoT Signal
Regarding the throughput, the following closed form formula has been used to estimate its value, i.e.,  
\[\text{THR}(\delta,R_{c})=\frac{N_{b}\,R_{c}\,(1-\text{BLER})}{(1+\rho)\,(1- \delta)}, \tag{34}\]  
where the code rate \(R_{c}\) for the two first cases under study corresponds to the code rate of the NB-IoT waveform. For the third case, the value of \(R_{c}\) represents the joint code rate of the NB-IoT and DVB-S2(X) waveforms, which is given in (33). Without loss of generality, the roll-off factor is kept fixed in all simulations (i.e., \(\rho=0.15\)). Thus, according to the overlapping factors and code rates that have been previously presented, the maximum achievable throughput in each situation are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(R_{c}\) & \(\delta\) = 0\% & \(\delta\) = 15\% & \  
This maximum achievable throughput may not be reached due to the presence of a residual time-packing interference, unexpected variations in the optical channel gain, and noise in the optical feeder link may increase the BLER. So, simulation results give information about the transmission structure that provides the best in terms of both BLER and throughput. Note that lowest code rates offer a very low BLER but, in contrast, worst results in terms of throughput. On the contrary, high code rates may provide the highest achievable throughput but, at the same time, the worst BLER. For this reason, a target BLER \(\leq 10^{-2}\) has been considered as requirement for a normal service according to what is stated in the 5G specifications. This condition helps to decide the best transmission architecture, such that the case that satisfies the target BLER and provides the best throughput will be categorized as the most convenient transmission architecture. However, in order to compare the three cases under study, additional figures of merit needs to be defined in the following section.
# Multi-Spacecraft Tracking and Data Association Based on Uncertainty Propagation
## 4 Numerical Simulation  
In this section, numerical simulations are implemented to validate the performance of the proposed multi-spacecraft tracking method. A case with four targets is considered, and the accuracy of the proposed method is investigated.
## 3 Numerical Analysis
### Governing Equations  
The study employed the \(\kappa\)-\(\epsilon\) turbulence model to address turbulence in a simplified manner. The \(\kappa\)-\(\epsilon\) model is extensively used in the literature for modelling fluid flow in various channels. The variable '\(\kappa\)' signifies the variance of velocity fluctuations, which is commonly known as turbulence kinetic energy, whilst the term '\(\epsilon\)' denotes the rate of velocity fluctuation dissipation, which is known as turbulence eddy dissipation. Technical term abbreviations are clarified upon their initial usage. The equations in the \(\kappa\)-\(\epsilon\) model include two additional variables [17]:  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(}\rho U_ {j}\big{)}=0 \tag{4}\]  
Momentum equation:  
\[\frac{\partial\rho U_{i}}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho U_{i}U_{j}\big{)}=-\frac{\partial p^{\prime}}{\partial x_{i}}+\frac{ \partial}{\partial x_{j}}\Bigg{[}\mu_{eff}\Bigg{(}\frac{\partial U_{i}}{ \partial x_{j}}+\frac{\partial U_{j}}{\partial x_{i}}\Bigg{)}\Bigg{]}+S_{M} \tag{5}\]  
where \(p^{\prime}\) is the modified pressure, \(S_{M}\) is the sum of body forces, and \(\mu_{eff}\) is the effective viscosity that accounts for turbulence [17]. Similar to the zero-equation model, the \(\kappa\)-\(\epsilon\) model is predicated on the idea of eddy viscosity, meaning that:  
\[\mu_{eff}=\mu+\mu_{t} \tag{6}\]where \(\mu_{t}\) denotes the turbulence viscosity, and the \(\kappa\)-\(\varepsilon\) model postulates that it is associated with the turbulence kinetic energy and dissipation through the relation.  
\[\mu_{t}=C_{\mu}\rho+\frac{\kappa^{2}}{\varepsilon} \tag{7}\]  
The value of the \(\kappa\)-\(\varepsilon\) turbulence model constant \(C_{\mu}\) is 0.09. The differential transport equations for turbulence kinetic energy and turbulence dissipation rate yield the values of \(\kappa\) and \(\varepsilon\):  
\[\frac{\partial(\rho\kappa)}{\partial t}+\frac{\partial}{\partial x_{j}}\big{(} \rho U_{j}\kappa\big{)}=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\kappa}}\bigg{)}\frac{\partial\kappa}{\partial x_{j}} \Bigg{]}+P_{\kappa}-\rho\varepsilon+P_{\kappa b} \tag{8}\]  
\[\frac{\partial(\rho\varepsilon)}{\partial t}+\frac{\partial}{\partial x_{j}} \big{(}\rho U_{j}\varepsilon\big{)}=\frac{\partial}{\partial x_{j}}\Bigg{[} \bigg{(}\mu+\frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial \varepsilon}{\partial x_{j}}\Bigg{]}+\frac{\varepsilon}{\kappa}(C_{ \varepsilon 1}P_{\kappa}-C_{\varepsilon 2}\rho\varepsilon+C_{\varepsilon 3}P_{ \varepsilon b}) \tag{9}\]  
For incompressible flow, \(\partial U_{k}/\partial x_{k}\) is small, and the second term on the right-hand side of the equation is negligible in terms of its contribution to the production. For compressible flow, \(\partial U_{k}/\partial x_{k}\) is only large in regions with high velocity divergence, such as at shocks.