esa-sceva/satcom-chunk-collection huggingface dataset:
In order to protect bacteriophages from the mentioned factors, microencapsulation methods (i.e., entrapment of bacteriophages in an encapsulation system) using freeze drying, spray drying, ultrasonic vacuum spray drying, electrospray, extrusion, emulsion, and supercritical techniques have been studied and reviewed intensively [21; 32; 33; 34; 35; 22]. As encapsulation materials, aginate solutions together with milk proteins such as alginate-casienate [36; 37] and alginate-whey protein [33; 38] formulations were employed successfully. In food processing, microencapsulation techniques are commonly utilized for sensitive food ingredients (e.g., proteins, oils, aroma, and probiotics) [34; 39; 40]. However, to the best of our knowledge, the application of encapsulated bacteriophages in a food system has not yet been studied. Though bacteriophage therapeutic products are available in some Eastern European countries (e.g., the Republic of Georgia), the use of bacteriophages in food products as functional ingredients still requires the approval of the European Union [28; 41; 42]. More research is needed in this field, since the functional application of bacteriophages via food products could demonstrate promising opportunities and lower the hurdles for their possible approval [29; 42]. In this study, bacteriophages were encapsulated using in situ complexation of alginate, calcium ions, and milk proteins during spray drying. The resulting capsules were incorporated into a food product (e.g., milk or cereal product) for comfortable reception. The capsules should have a particle diameter below 100 um in order not to influence the sensory properties. Furthermore, the capsules should be able to be stored for a period of time without loss of functionality, have adequate protection against the acidic conditions in the stomach, and should release the bacteriophages in intestinal conditions (in vitro) completely. Therefore, it was hypothesized that encapsulated bacteriophages can be transferred into the human gut via food to eliminate undesired bacteria and, thus, create a niche at their target. The newly generated niche could be directly repopulated, for example, by probiotic organisms. Hence, it would be advisable to consume bacteriophages in combination with probiotic organisms, exemplary via yoghurt (with probiotics) and a cereal cookie (with bacteriophages). In analogy to probiotics, we would like to introduce the neologism \"bacteriophage-biotics\" for therapeutically active bacteriophages. The present work exclusively focuses on in vitro studies using a harmless model system with a probiotic host (_Escherichia coli_ Nissle 1917) and bacteriophage K5 and serves as proof-of-concept in general. After successful demonstration of the functional principle, the model system could be used to perform in vivo human feasibility studies. In future work, the model bacteriophages could then be replaced by other bacteriophages that have a health-related or medically relevant purpose for humans.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 5 Formal Analysis of DTLS-SRTP Combined Protocol
### Sequential Combination of DTLS and SRTP  
We now proceed to prove the security properties of the combined protocol by combining the mutual authentication of the DTLS protocol and the secrecy of the SRTP protocol. In this process, we follow the previously mentioned method of proving sequential composition. According to the above proof process, \(\Gamma_{1}\) satisfies the mutual authentication property of the DTLS protocol, where \(\Gamma_{1}\) contains the set of formulas  
\[\Gamma_{1}\equiv\left\{\begin{aligned} & signer(s_{1})=A\Rightarrow( loc(s_{1})=A)\wedge key(e^{{}^{\prime}})=privateKey(A)\Rightarrow\\ &(loc(e^{{}^{\prime}}))=A\wedge key(e^{{}^{\prime\prime}})= privateKey(A)\Rightarrow(loc(e^{{}^{\prime\prime}})=A)\\ & signer(s_{2})=B\Rightarrow(loc(s_{2})=B)\wedge key(e^{{}^{ \prime}}_{1})=privateKey(B)\Rightarrow\\ &(loc(e^{{}^{\prime}}_{1}))=B\wedge key(e^{{}^{\prime\prime}}_{2 })=privateKey(B)\Rightarrow(loc(e^{{}^{\prime\prime}}_{2})=B)\end{aligned}\right\} \tag{16}\]  
where the invariant set \(\Gamma_{2}\) for the SRTP protocol is empty, as the honesty rules were not used in proving the secrecy property of the protocol. Therefore, the invariant set becomes \(\Gamma_{1}\cup\Gamma_{2}=\Gamma_{1}\). We can conclude that the security property of the DTLS protocol satisfies the combined invariant set.  
By replacing the post-event of the DTLS protocol with the pre-event of the SRTP protocol and applying the sequential composition rule, we obtain:  
\[\Gamma_{1}\vDash\mathrm{Send}(e)=\mathrm{Rcv}(e^{\prime})\vee\mathrm{Sign}(e) =\mathrm{Verify}(e^{\prime})\vee\mathrm{Decrypt}(e)=\mathrm{Encrypt}(e^{ \prime})\]  
From the proof processes of the DTLS and SRTP protocols, we can infer that the thread \(thr_{1}\) containing the last send event in the DTLS protocol and the thread \(thr_{2}\) containing the first receive event in the SRTP protocol are the same. Based on Lemma 1, when the conditions hold for the principals \(A\) and \(B\) in both sub-protocols, we can conclude that the invariant set \(\Gamma_{1}\cup\Gamma_{2}\) holds for the combined protocol \(Q\). Thus, the security property of the DTLS-SRTP composite protocol is established.
## 3 Results
### Antiwetting Properties of Models in Motion
#### 3.2.2 Liquid Repellency of the Linearly Moving Aquatic UAV  
A stepper motor was used to drive the hydrophilic model in a uniform linear motion (\(v_{\mathrm{s}}\) = 0.3 m/s), passing through the virtual-precipitation area, and the arrow indicates the vector direction (Figure 6a). At \(t\) = 583.98 ms, the device moved to the middle of the precipitation area, and obvious water adhesion was observed at the front of the model and at the leading edge of the wing. Due to the short residence time of the device in the precipitation area, only a few discontinuous liquid films were observed on the upper surface of the wing, where, as previously observed, continuous liquid films were prone to occur (the yellow dotted rectangular area in Figure 6a, \(t\) = 583.98 ms). Compared with the static state (Figure 3), the thickness of the liquid film on the surface was significantly reduced, and the attached water was mainly located at the junction of the curved surfaces and the upper surface of the wing. When the model was about to exit the field of view of the camera (\(t\) = 698.31 ms), only a small area of wetting was observed at its tail. This shows that the motion of the model slightly impairs the wetting, even on hydrophilic surfaces, which reduces the chance of it being continuously wetted (see details in \"Supporting Movie S5\" in the Supplementary Material).  
Figure 6: Comparison of antiwetting properties on the moving hydrophilic and superhydrophobic models. (**a**) Adhesion of water on moving hydrophilic surfaces. Moving reduces wetting of the surface compared with when the model is stationary; only the trailing edge of the wing is wetted (see the area selected in the yellow square). (**b**) Excellent antiwetting based on motion and superhydrophobic coupling. The movement of the surface and the superhydrophobicity together promote dynamic water repellency, with smaller-sized droplets occurring in the upstream direction (see elliptical area). No areas were wetted. (See details in “Supporting Movie S5” and “Supporting Movie S6” in the Supplementary Material).  
The positive antiwetting behavior of the moving model with a superhydrophobic surface is shown in Figure 6b. At \(t=143.66\) ms, the impact point of the droplet was on the side of the front of the model (part of the easily wetted area), and the droplet spread and sputtered. After 258.99 ms (\(t=402.65\) ms), the model was in a nonwetted state, and the easily wetted area was still in a dry state. The difference from the hydrophilic model was that more derived droplets appeared behind the model (the dashed area of the yellow ellipse in Figure 6b). When \(t=492.65\) ms, the derived droplet had a significant backward-movement trend. In the final stage (\(t=638.98\) ms), the derived droplets moved backward to the tail of the model. The overall antiwetting of the model is excellent, and its superhydrophobic surface can always maintain a dry state (see details in \"Supporting Movie S6\" in the Supplementary Material).
## 4 Collision Free Path Optimization
### The Secondary Optimization of ACO (SO-ACO)  
The path solved based on the ACO is not the shortest path because of its discontinuous step length and direction. Hence, a secondary optimization (SO) is proposed to obtain better optimization results. Here, \(rout\) represents the optimized path based on ACO, and \(routi\) represents improved path based on SO-ACO. The optimization process is presented as follows:  
Step 1. Initialize \(routi\) as void. Step 2. Add the first point of \(rout\) to \(routi\). Step 3. Suppose the last point of \(routi\) is \(p\), and the last point in \(rout\) is \(q\). Step 4. If \(q\) is the next point to \(p\), the location of \(p\) in \(routi\) and \(q\) in \(rout\) are shown in Figure 8, jump to Step 6. Otherwise, jump to Step 5. Step 5. Connect \(p\) and \(q\) to form a line. If the line encounters the obstacles, set the point in front of \(q\) in \(rout\) as \(q\), and return to Step 4. Otherwise, jump to Step 6. Step 6. Add \(q\) to \(routi\). Step 7. If \(q\) is the last point in \(rout\), jump to Step 8; otherwise, return to Step 3. Step 8. Compare the length of \(rout\) and \(routi\). If the length is the same, jump to Step 10. Otherwise, replace the sequence in rout with that in routi and jump to Step 9. Step 9. If the length of each segment in rout is greater than 5 mm, the segment is split into points, and the new points are added to rout. Then, reverse the order of the rout and return to Step 1. Step 10. Set \(routi\) as the optimized result. Figure 8: The location of \(p\) and \(q\). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{\alpha}\) & \(\mathbf{\beta}\) & \(\mathbf{\rho}\) & \(\mathbf{Q}\) & \(\mathbf{N}\) & \(\mathbf{M}\) & **Initial Pheromone** \\ \hline
1 & 11 & 0.9 & 5 & 50 & 50 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ant colony algorithm (ACO) Parameters. Figure 7: The iterative process of the ant colony algorithm. According to the above steps, when the Weld Joints 3 and 7 are selected as the starting joint and the terminal joint, respectively, the parameters of the ACO are the same as Section 4.2. The path planning result based on the SO-ACO is shown in Figure 9 (a and b are a different view of the same result). The path length optimized based on ACO is 330 mm, while the path length optimized based on the SO-ACO is 203.290 mm. Different iterations \(N\) and population sizes \(M\) are selected to prove the effectiveness of the SO-ACO, which are shown in Table 3. Other parameters refer to Section 4.2. The algorithm is conducted 20 times for every condition, and the minimum, maximum, average, variance of path length, and time for every conditions are shown in Table 3.  
From Table 3, it can be seen that the SO-ACO is obviously superior to the basic ACO; satisfactory results were obtained when the iterations \(N\) and population sizes \(M\) were 10. For ACO, with the increase in the iteration \(N\), population size \(M\), and searching time, the path length of the ACO decreases, but it is still larger than the optimization results based on SO-ACO. Hence, it can be concluded that
## 2 Theoretical Model
### Adaptive Compensation
When the Laguerre-Gaussian beam propagates in atmospheric turbulence, in order to obtain the weight of a new vortex mode component, the disturbed electric field is decomposed into a series of spiral harmonics with a corresponding coefficient as follows:  
\[U(r,\theta,z)=\frac{1}{\sqrt{2\pi}}\sum_{-\infty}^{+\infty}a_{l}(r,z)\exp(il \theta), \tag{15}\]  
where the coefficient \(a_{l}(r,z)\) is given by the integral  
\[a_{l}(r,z)=\frac{1}{\sqrt{2\pi}}\int_{0}^{2\pi}U(r,\theta,z)\exp(-il\theta)d\theta. \tag{16}\]  
Radial integration of the above formula can obtain the relative energy using single OAM mode, as for a finite-aperture receiver with radius \(R\), which has the form of  
\[C_{l}=\int_{0}^{R}|a_{l}(r,z)|^{2}rdr. \tag{17}\]  
In the conditions of atmospheric turbulence, there is a transfer of energy between the emitted OAM modes, which leads to channel crosstalk. If a single OAM mode with an angle index of \(l\) is transmitted, the received optical field after the transmission through turbulent atmospheric channel can now be regarded as a superposition of all OAM modes. Therefore, the signal intensity in the receiving terminal is expressed as  
\[C=\sum_{l}g_{l}C_{l}, \tag{18}\]  
where \(g_{l}\) is the weight coefficient of the OAM mode of angle index \(l\). Because the OAM pattern is orthogonal, the crosstalk between OAM modes can be described by the following conditional probability. \[P(l|l_{0})=\left|\frac{\langle C|C_{l}\rangle}{C}\right|^{2}=|C_{l}|^{2} \tag{19}\]  
We use the formal model in calculating the channel capacity. The channel capacity can be calculated by  
\[B=\text{max}[H(Y)-H(Y|X)], \tag{20}\]  
where \(X\) and \(Y\) is the input and output OAM mode, respectively, and the symbol \(H\) denotes entropy.
## 4 Discussion  
### Datasets and Parameters Selection  
The source images used in this study were selected from two widely recognized datasets: TNO [44] and M3 FD [18], which provide diverse multi-spectral scenarios. To achieve an optimal balance between computational efficiency and performance, we set the SWGF filter radius \(r\) to 7 and the regularization coefficient \(\varepsilon\) to 0.01. For the contrast enhancement in Equation (2), the parameters were empirically determined as \(C\) = 6, \(\alpha\) = 10, and \(\beta\) = 0.1. The detail enhancement factor G in Equation (7) was set to 0.45 through comprehensive visual assessment. To prevent over-enhancement, we configured the regularization parameters in Equation (7) as \(\lambda_{1}\) = 0.95 and \(\lambda_{2}\) = 0.45. In the saliency fusion layer (Equation (17)), the parameter \(\lambda\) controls infrared feature prominence--increasing values enhance thermal visibility while maintaining background integrity. Our experiments demonstrate that \(\lambda\) = 20 achieves the optimal trade-off between infrared detail preservation and natural background representation.
# Context-Aware Beam Selection for IRS-Assisted mmWave V2I Communications
###### Abstract  
Millimeter wave (mmWave) technology, with its ultra-high bandwidth and low latency, holds significant promise for vehicle-to-everything (V2X) communications. However, it faces challenges such as high propagation losses and limited coverage in dense urban vehicular environments. Intelligent Reflecting Surfaces (IRSs) help address these issues by enhancing mmWave signal paths around obstacles, thereby maintaining reliable communication. This paper introduces a novel Contextual Multi-Armed Bandit (C-MAB) algorithm designed to dynamically adapt beam and IRS selections based on real-time environmental context. Simulation results demonstrate that the proposed C-MAB approach significantly improves link stability, doubling average beam sojourn times compared to traditional SNR-based strategies and standard MAB methods, and achieving gains of up to four times the performance in scenarios with IRS assistance. This approach enables optimized resource allocation and significantly improves coverage, data rate, and resource utilization compared to conventional methods.  
mmWave; V2X; RIS; machine learning; multi-armed bandit +
Footnote †: journal: Journal of Robotics and Automation
# Reservoir Performance Prediction in Steam Huff and Puff Injection Using Proxy Modelling
## 2 Methodology
### Generation of Simulation Experimental Data  
Simulation experiment cases were generated by varying the values of 28 input parameters within a provided range. They were designed using the Latin Hypercube Method, where this method has an advantage over classic experimental design in orthogonality and space-filling, resulting in a unique input parameter value for each simulation case [47]. The range of each parameter is determined from the screening criteria of steam huff and puff applications obtained from the literature study. A study from [PERSON] et al. [48] provided the screening criteria in which a data cleaning process had been conducted to remove outliers and duplicates from the database. Furthermore, the distribution of each screening parameter was determined and used in this study to generate the experimental cases. The range of input parameter values of [PERSON]'s equations was selected to satisfy practical fluid flow conditions and accommodate the different pore size distributions of sandstone rock. Combining these with the former screening criteria by [PERSON] [49] and [PERSON] et al. [50], the list of input parameters with their range for this study was determined and is presented in Table 1.  
A sampling control was conducted before running the simulation to avoid unrealistic experiment cases, which result in reservoir models that physically do not exist. Combinations of input parameters needed to be controlled, such as oil density, reservoir pressure and temperature, porosity-permeability, rock-fluid properties, and drainage area. After sampling control, 6304 simulation experiment cases were ready to be simulated using a reservoir simulator. The reservoir performance of steam huff and puff injection was represented in cumulative oil production, maximum oil rate per cycle, and the oil rate at the end of the cycle. Multiple cycle injection performance can be estimated by re-inputting  
Figure 3: Validation of the synthetic 3D reservoir model to the field data [46].  
parameters into the proxy model, as well as the input parameter that may have changed after conducting the previous huff and puff cycle. The input parameters that may have changed and were modeled in this study are reservoir pressure, reservoir temperature, and water saturation in the reservoir. All of the outputs from each simulation result are captured as a response variable to build the predictive model.
Computers_**2015**, \(4\), 215-233; doi:10.3390/computers4030215  
_Article_  
**Security Property Validation of the Sensor Network Encryption Protocol (SNEP)**  
**[PERSON]**  
Department of Computer Science and Engineering, United International University,  
Dhaka 1209, Bangladesh; E-Mail: [EMAIL_ADDRESS]; Tel.: +88-2-9125912;  
Fax: +88-2-9118170.  
Academic Editor: [PERSON]  
_Received: 30 May 2015 / Accepted: 17 July 2015 / Published: 24 July 2015_  
**Abstract:** Since wireless sensor networks (WSNs) have been designed to be deployed in an unsecured, public environment, secured communication is really vital for their wide-spread use. Among all of the communication protocols developed for WSN, the Security Protocols for Sensor Networks (SPINS) is exceptional, as it has been designed with security as a goal. SPINS is composed of two building blocks: Secure Network Encryption Protocol (SNEP) and the \"micro\" version of the Timed Efficient Streaming Loss-tolerant Authentication (TESLA), named \(\mu\)TESLA. From the inception of SPINS, a number of efforts have been made to validate its security properties. In this paper, we have validated the security properties of SNEP by using an automated security protocol validation tool, named AVISPA. Using the protocol specification language, HLPSL, we model two combined scenarios--node to node key agreement and counter exchange protocols--followed by data transmission. Next, we validate the security properties of these combined protocols, using different AVISPA back-ends. AVISPA reports the models we have developed free from attacks. However, by analyzing the key distribution sub-protocol, we find one threat of a potential DoS attack that we have demonstrated by modeling in AVISPA. Finally, we propose a modification, and AVISPA reports this modified version free from the potential DoS attack.  
**Keywords:** wireless sensor networks (WSN); Secure Network Encryption Protocol (SNEP); key distribution, strong freshness, validating security protocols; AVISPA; DoS attack
## 3 Capacity Analysis of the IWMSNs
### The Optimal Joint Decoding Capacity
 \tag{27}\]  
To compute the optimal capacity approximation (27), \(\theta_{i}\) can be determined as \(\theta_{i}\approx\lambda_{i}^{2}(\mathbf{T})\), and \(\lambda_{i}(\mathbf{T})\) is approximately expressed as Equations (12) and (13) respectively for the hybrid 1D beam model and modified hybrid 1D beam model. Since \(\lambda_{(l-1)M+m}(\overline{\mathbf{T}}_{\text{2D}})\) of the hybrid 2D beam model are affected by \(L\) and \(M\) and computed repeatedly in (27), we use the integral of the inverse function [51] to get \(\theta_{i}\) for the hybrid 2D beam model,  
\[\text{F}_{\overline{\mathbf{T}}_{\text{2D}}}(x)=\frac{1}{\pi}\int_{0}^{1} \arccos(\frac{1}{2\cos 0.5\pi t}(\frac{x-\frac{1}{1+\beta}}{2\alpha}-\cos\pi t))dt \tag{28}\]  
which can be computed as \(\text{F}_{\overline{\mathbf{T}}_{\text{2D}}^{2}}^{-1}(x)\). The distribution of \(x\) was determined by \(\lambda_{(l-1)M+m}(\overline{\mathbf{T}}_{\text{2D}})\) and by using the standard probability theory [51]. For the Rician fading channel, \(f_{\xi}(x)\) is a noncentral chi-squared density [56],  
\[f_{\xi}(x)=(\kappa+1)\bigg{(}\frac{(\kappa+1)x}{K\kappa}\bigg{)}^{\frac{K-1}{ 2}}e^{-((\kappa+1)x+K\kappa)}I_{K-1}\left(2\sqrt{K\kappa(\kappa+1)x}\right) \tag{29}\]  
where \(I_{n}(x)\) is the first kind of modified Bessel function and the order is \(n\). Thus, \(\zeta_{j}\) can be computed as \(\zeta_{j}\approx\text{F}_{\xi^{2}}(x)\).
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 3 Results and Discussion
In general terms, using bifacial panels in BIPV systems enhances the efficiency of the panel. Since there is no significant price difference between bifacial panels and monofacial panels, it does not increase the installation costs either.
## 2 Materials and Methods
### Determination of phenolic compounds  
The UPLC analysis of the phenolic acids was performed using a Waters Acquity Ultra-Performance TM LC system (Waters), equipped with a quaternary pump system (Waters). The Acquity high-strength silica (HSS) T3 column (150 mm 2.1 mm id, 1.8 mm particle size) from Waters was used. The stationary phase was 100% silica particles. The phenolic acids were separated with a mobile phase that consisted of 0.1% Formic acid (eluent A) and acetonitrile (eluent B), The flow-rate was 0.4 mL/min and the gradient elution was initial, 5% B; 0-4.5 min, 5%-20% B; 4.5-6.45 min, isocratic 20% B; 6.45-13.5 min, 20%-45% B; 13.5-16.5 min 45%-100% B; 16.5-19.5 min isocratic 100% B; 19.5-19.52 min 5%-100% B; 19.52-22.5 min isocratic 5% B. The analyses were carried out on a TQD mass spectrometer (Waters) equipped with a Z-spray electrospray interface. The analysis was performed in negative mode and the data was acquired through multiple reactions monitoring (MRM). The ionization source parameters were capillary voltage, 2.75 kV; source temperature, 140 \({}^{\circ}\)C; cone gas flow rate, 80 L/h and desolvation gas flow rate, 900 L/h; desolvation temperature, 350 \({}^{\circ}\)C. Nitrogen (99% purity) and argon (99% purity) were used as nebulizing and collision gases, respectively. Data acquisition was carried out with the Mass Lynx 4.1 software.
# Biomedical Applications of Polymeric Cryogels
## 3 Cryogels in Medical Applications  
Cryogels are good alternatives to traditional protein-binding matrices with many different biomedical applications. The advantages of cryogels such as high blood-compatibility, high water content, resistance to degradation, non-toxicity, and pressure drop properties allow them to be used without any diffusion problems when working with biological macromolecules. The main applications of cryogel matrices are immobilization of biomolecules, capturing of target molecules, controlled drug delivery, cell separation, tissue engineering, bioreactor, cell separation, and scaffolds.