esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Assessment of the ARounD Communication Scheme
### Results and Discussion
In turn, Figure 32b shows the ARounD paths, where the communication paths are performed by repeater nodes, avoiding the cluster-tree path. Concerning the communication behaviour, Figure 33 shows the average end-to-end communication delays for many-to-one source-to-destination message streams, as a function of the background traffic load imposed by sensor nodes. As it can be observed, the ARounD approach provides smaller and constant end-to-end delays for all message streams, even for different background traffic loads. As previously mentioned, ARounD schedules message streams during the inactive periods of the sensor nodes, which allows communication without conten  
Figure 21:2 Scenarios considering multiples source-to-destination traffics: (**a**) Many-to-one message streams; (**b**) Many-to-many message streams. Figure 21:3 One of physical topologies for many-to-one scenario: (**a**) Cluster-tree paths of message streams; (**b**) ARounD paths of message streams. and avoids interference with the cluster-tree network. On the other hand, the standard approach provides higher end-to-end communication delays for multiples message streams, that tend to increase as the background traffic increases. Figure 34 illustrates a similar behaviour for the average end-to-end communication delays for many-to-many source-to-destination message streams. Finally, Figures 35 and 36 illustrate the packet loss rates for both many-to-one and many-to-many scenarios, respectively. The results are also presented as a function of the background traffic load imposed by sensor nodes. Note that, the Standard approach presents higher packet loss rates due to collisions and discarded messages, while ARounD scheme successfully delivered all source-to-destination messages. As previously mentioned, ARounD approach avoids collisions and discarded messages by using alternative paths from the source node to destination node to forward messages without contending for the channel access. Note also that, as the background traffic load increases, the number of lost messages by using the Standard approach also increases. Figure 21:4 Average end-to-end delay for multiple many-to-many message streams, considering the ARounD Approach vs. Standard Approach under different traffic loads imposed by sensor nodes. Figure 21:5 Average end-to-end delay for multiple many-to-one message streams, considering the ARounD Approach vs. Standard Approach under different traffic loads imposed by sensor nodes.
## 5 Analysis of the Numerical Results
### Longitudinal Settlement of the Ground Surface
#### 5.1.1 When C/H = 0.764
That is, the difference in surface settlement was small at the overlapping zone of the existing and new tunnels, which increased significantly after the new tunnel was separated from the existing tunnel. Meanwhile, backfilling of the existing tunnel aggravated the ground surface deformation. The main reason was found to be the excessively small D/W ratio of the new tunnel, at which point the thickness of slope rock pillar became the primary controlling factor of tunnel stability. After the tunnel excavation, the slope rock pillars were displaced toward the tunnel interior, while the lateral deformation of the surrounding rocks was coordinated and restricted by the settlement deformation of rocks in the tunnel upper part. Hence, when the existing tunnel was backfilled, the horizontal span of the new tunnel was reduced. At this time, a considerably higher C/H ratio than the D/W ratio made the surface settlement more severely affected by the lateral deformation such that the settlement became larger as well. On the contrary, when the existing tunnel was not backfilled, the span of the new tunnel could be considered to increase such that the enlarged C/H ratio also caused an enhanced ability to suppress the lateral deformation. Therefore, the settlement value decreased instead at this time. After repeated trial computations, D/W = 0.16 was determined as the critical position where the surface settlement exhibited the opposite law at C/H = 0.764.
###### Abstract  
This study evaluated the efficacy of ice-temperature storage (\(-1\,^{\circ}\)C) in preserving volatile compounds (VOCs) in blue honeysuckle (_Lonicera caerulea_ L.) as compared to conventional low-temperature (\(4\,^{\circ}\)C) and freezing (\(-3\,^{\circ}\)C) storage for 84 d with a 14 d interval. As a flavor-rich berry highly susceptible to postharvest VOC loss, VOC contents and ultrastructural variations were systematically analyzed by coupling gas chromatography-ion mobility spectrometry (GC-IMS), gas chromatography-mass spectrometry (GC-MS), and transmission electron microscopy (TEM). GC-IMS and GC-MS detected 25 and 62 VOCs, respectively, with ice-temperature storage demonstrating well maintaining VOC varieties and relative concentrations. Moreover, TEM analysis further revealed that ice-temperature storage maintained normal cellular ultrastructure integrity, particularly in cell wall organization and organellar morphology. These results conclusively establish ice-temperature storage as the optimal method for preserving both biochemical composition and cytological architecture in blue honeysuckle, thereby providing a scientific foundation for optimizing postharvest protocols and advancing cold-chain technologies for perishable berry fruits.  
blue honeysuckle; ice temperature storage; GC-IMS; GC-MS; volatile compounds; TEM; small berry
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 2 Materials and Methods
### Adsorption Isotherm  
In the adsorption process, study of the equilibrium isotherm is necessary and crucial to predict the adsorption behavior of the pollutants towards the adsorbent surface. In this study, two common adsorption models, the Langmuir and Freundlich isotherm models, were used to evaluate adsorption data.  
The Langmuir isotherm model assumes that the adsorption process occurs in a single layer of gas-solid two-phase adsorption and is given by Equation (2):  
\[\frac{C_{e}}{q_{e}}=\frac{C_{e}}{q_{\rm max}}+\frac{1}{K_{L}q_{\rm max}} \tag{2}\]  
where \(C_{e}\) is the concentration of Cs in a batch system at equilibrium (mg\(\cdot\)L\({}^{-1}\)), \(q_{e}\) is the amount of f Cs adsorption at equilibrium (mg\(\cdot\)g\({}^{-1}\)), \(q_{\rm max}\) is the maximum adsorption capacity of the adsorbent (mg\(\cdot\)g\({}^{-1}\)), and \(K_{L}\) is the equilibrium adsorption constant (L\(\cdot\)mg\({}^{-1}\)). A plot of \(C_{e}/q_{e}\) versus \(C_{e}\) gives a straight line with a slope of \(1/q_{\rm max}\) and an intercept of \(1/(K_{L}\)\(q_{\rm max})\). \(K_{L}\) can be applied to the adsorption free energy \(\Delta G_{\rm ads}\) (\(\cdot\)mol\({}^{-1}\)) by Equation (3):  
\[n\Delta G_{\rm ads}=-{\rm RT}\,\ln K_{L} \tag{3}\]  
where \(R\) is the gas constant (8.314\(\cdot\)K\({}^{-1}\)mol\({}^{-1}\)), T is the absolute temperature at equilibrium (K), and \(K_{L}\) is the equilibrium constant at temperature T.  
The equilibrium constant [32; 33]\(K_{L}\) can be calculated according to Equation (4):  
\[K_{L}=q_{e}/C_{e} \tag{4}\]  
where \(C_{e}\) and \(q_{e}\) are the same as in Equation (2).  
The Langmuir constant (\(K_{L}\)) can be used to determine the suitability of the adsorbent for the adsorbate by using the dimensionless parameter of the Hall separation coefficient (\(R_{L}\)), which is given by Equation (5):  
\[R_{L}=(\frac{1}{1+K_{L}C_{0}}) \tag{5}\]  
where \(C_{0}\) (mg\(\cdot\)L\({}^{-1}\)) is the initial concentration [34; 35].  
The Freundlich isotherm model is a multi-layer adsorption process that does not consider adsorption saturation, which occurs on a multi-layer heterogeneous surface. The isotherm of the linear Freundlich model is expressed by Equation (6):  
\[\ln q_{e}=\ln K_{F}+(1/n)\,\ln C_{e} \tag{6}\]  
where \(K_{F}\) is the adsorption capacity ((mg\(\cdot\)g\({}^{-1}\))\(\cdot\)(dm\({}^{-3}\)\(\cdot\)mg\({}^{-1}\))\({}^{1/n}\)), and \(1/n\) is the adsorption strength. The logarithmic scale graph of \(q_{e}\) versus \(C_{e}\) can be drawn to determine the \(1/n\) and \(K_{F}\) values, and the value of \(1/n\) can be used to judge the difficulty of the adsorption process [36].
## 2 Research Background
### AR technology Overview  
Fundamentally, AR is a technology that projects digital artifacts onto a display surface and aligns the coordinates of the artifact with objects in the real world. With increased interest in the medium as a technology to bridge between virtual and real domains, various hardware and software developments are continuously upgraded.  
AR hardware devices enable immersion and a sense of presence by providing visual information in the real world. Hardware is namely divided into wearable devices, such as head-mounted displays (HMDs), and AR-enabled handheld devices, such as smartphones or tablet computers. Although HMDs have their advantages, wearing them for significant amounts of time outdoors has not been readily adopted in the construction industry. HMDs have limited battery capacity and low brightness, and need to be separately acquired [29]. Comparatively, handheld devices are owned by personnel on-site and regularly used in the field, and thus their usage is less prone to resistance. Therefore, AROS has been developed and optimized for tablet devices.  
AR content is created using AR software development kits (SDK) that provide various resources and libraries for 2D/3D positioning tracking and graphics processing. AR SDK provides a development environment for Android and iOS, and the functions can be linked to game engines such as Unity's Unity3D or Epic Games' Unreal engine. Representative SDKs include the Vuforia SDK by PTC, ARKit by Apple, and ARCore by Google.  
Projecting a virtual model onto a real-world scene can be classified into marker-based and markerless methods. Marker-based methods use 2D images with easy-to-extract visual features (e.g., QR codes) and overlay the virtual objects using them as location-specific markers. Markerless methods do not rely on markers, but create virtual objects by tracking extracted feature points of objects within the real world or using location-tracking technologies, such as Global Positioning Systems (GPS) and Radio Frequency Identification (RFID). Table 1 shows exemplary target methods for each of the projection methods. The 'Image Target' is a well-known marker-based method, whereas 'Object Target,' '3D Model Target,' and 'Spatial Target' are common markerless methods.
###### Abstract  
The aim of this study was to check the impact of the addition of selected flavoring herbs on the organoleptic and antioxidant properties of homemade tofu, as well as the possibility of its storage extension by freezing. The optimization of homemade tofu production based on organic soy and the enrichment (2% \(w/w\)) of obtained tofu with dried plant additives (wild garlic, basil, Herbes de Provence, black cumin seeds, and dried cranberries) was performed. The manufactured tofu was compared to commercial tofu (control) in terms of antioxidant activity, total polyphenol content, and polyphenol profile using thin-layer chromatography (HPTLC). Moreover, the texture profile and organoleptic assessment of the compared products were performed. The antioxidant and texture parameters were also controlled in tofu fixed by different methods: cold herbal infusion and prolonged freezing of vacuum-packed products. It was shown that homemade tofu was characterized by better sensory and texture properties than commercial product. Moreover, the introduction of plant additives tofu increased the antioxidant activity and the content of polyphenols in the final product; the greater the activity of the herbs used, the higher the degree of tofu enrichment was achieved. In terms of all analyzed parameters, cranberries and Herbes de Provence additives tofu were found to be the first choice. It was found that the addition of herbs to tofu allowed us to create new sensory features, enhanced the pro-healthy value, and extended the storage time. Due to no significant changes in texture and antioxidant properties of frozen tofu, as well as its acceptable microbial quality, freezing vacuum-packed tofu can be recommended as a beneficial way to extend the storage time at home condition. The addition of herbs allows for the design of new versions of tofu in accordance with consumer preferences, and the proposed packaging method can be applied on an industrial scale.  
tofu; soy; herbs; antioxidant activity; phenolic compounds; HPTLC; storage Article
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 4 Security Analysis
### Informal Security Analysis
#### 4.1.8 Resisting Known Session Key Attacks  
During the computation of session key \(\mathcal{SK}_{k-f-csp}=h((r_{3}\oplus r_{5}\oplus r_{7})\|N_{1}\|ID_{csp})\), session specific random numbers \(r_{3}\), \(r_{5}\) and \(r_{7}\) are used. So, the computed session key of each session is not dependent on any other session. Therefore, even if the session key of one session is leaked, there will be no impact on the privacy of other session keys.
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
###### Abstract  
When locating wall-climbing robots with vision-based methods, locating and controlling the wall-climbing robot in the pixel coordinate of the wall map is an effective alternative that eliminates the need to calibrate the internal and external parameters of the camera. The estimation accuracy of the homography matrix between the camera image and the wall map directly impacts the pixel positioning accuracy of the wall-climbing robot in the wall map. In this study, we focused on the homography estimation between the camera image and wall map. We proposed HomographyFpnNet and obtained a smaller homography estimation error for a center-aligned image pair compared with the state of the art. The proposed hierarchical HomographyFpnNet for a non-center-aligned image pair significantly outperforms the method based on artificially designed features + Random Sample Consensus. The experiments conducted with a trained three-stage hierarchical HomographyFpnNet model on wall images of climbing robots also achieved small mean corner pixel error and proved its potential for estimating the homography between the wall map and camera images. The three-stage hierarchical HomographyFpnNet model has an average processing time of 10.8 ms on a GPU. The real-time processing speed satisfies the requirements of wall-climbing robots.  
homography estimation; convolutional neural network; wall-climbing robot
## 5 THG Efficiency in Dependence of SOD Coefficients and Phase Mismatching between Interacting Waves
### Anomalous Dispersion of a Medium at FW Wavelength  
Let us briefly discuss THG in a medium with the anomalous dispersion occurring at the frequency of FW. The SOD coefficient \(D_{1}\) is positive. We consider a propagation of the incident pulse with duration about 100 fs and with wavelength about 1064 nm in KDP crystal (see, for instance, [64]). If \(\Delta_{21}k>0\) that corresponds to decompression of the FW pulse and compression of the pulse at tripled frequency, then the efficiency of the FW energy conversion to the TH achieves about 60% in the section \(z=15\) (Figure 7). It is accompanied with good TH pulse shape and its broadened spectrum in comparison with the incident pulse spectrum at the FF. The FW pulse shape has three intensity maxima and possesses smaller intensity at the pulse center in comparison with its value occurring at the pulse propagation in a medium with the normal dispersion at the FF. Nevertheless, we can see in Figure 7 that the FW pulse spectrum is more broadened in comparison with the incident pulse spectrum.  
If the phase mismatching between FW and SH is negative (\(\Delta_{21}k<0\)), then the pulse compression at FF occurs, and decompression of the SH and TH pulses is observed. This leads to highly effective THG (Figure 8): more than 80% of the FW energy converts to the TH wave. However, the TH intensity is less than its value at a positive phase mismatching \(\Delta_{21}k\), and the TH spectrum is smooth and broadened.  
Figure 6: Pulse shapes (**a**), FW and TH spectra (**b**) in the section \(z=30\). Pulse intensities evolution at their centers (**c**), and the frequency conversion efficiency evolution (**d**) along \(z\)-coordinate computed for the parameters \(\gamma=1\), \(\Delta_{21}k=-20\), \(D_{1}=-0.0032\), \(D_{2}=-0.0083\), \(D_{3}=-0.0199\).
# Supporting Heterogenous Traffic on Top of Point-to-Multipoint Light-Trees +
###### Abstract
OCS digitally slices the optical constellation of a single carrier to transport a dynamically defined combination of P2P and P2 MP traffic flows. The slicing is performed by dedicating a different subset of constellation points (CP) to different receivers. This paper studies the feasibility of OCS for a dual-polarization (DP), 64 GBd system. We compare the throughput and relative capital (CAPEX) and operational (OPEX) expenditures of OCS and DSCM technologies for different optical P2 MP scenarios. Many previous works have compared different solutions in terms of CAPEX and OPEX. For instance, the authors in [17] compared next-generation central offices for metro networks, the authors in [2] envision an optical transport extending from the core to the network edge, and the authors in [18] studied the cost of solutions for a multilayer packet over optical networks. Additionally, this paper addresses data privacy P2 MP connections. To prevent data breach, an independent lookup table (LUT) for each traffic flow is used to implement a substitution cipher [19]. As in our previous paper [20] for securing lightpaths, the LUT is used at the source to encode the transmitted data which also makes sure that only the destination(s) with the corresponding LUT can decode the data that have been assigned to it. Note that although substitution ciphers do not provide perfect secrecy, they provide enough degree of privacy for the type of applications that we target. The rest of the paper is as follows. Section 2 investigates P2P and P2 MP connectivity considering the limits of the existing deployed P2P network and shows how P2 MP at the optical layer might support both P2P and P2 MP packet traffic and make the transport architecture more efficient. Two optical technologies, DSCM and OCS, are selected as candidates to implement P2 MP connectivity using coherent optical communication systems. Section 3 discusses OCS in detail and highlights the construction, implementation, and throughputs of OCS compared to that of DSCM. Section 4 compares the optical performance of OCS and DSCM by simulation and presents numerical results to provide a quantitative comparison between them under dynamic traffic scenarios. Finally, Section 5 draws the main conclusions of the paper.
## 2 Problem Description
Additionally, the cognitive control system can adaptively adjust transmission paths or priorities to minimize the impact of network interference on data quality. Through these measures, the cognitive control system ensures that BEMS can receive complete and reliable data support in complex network environments, enabling accurate load forecasting and optimized scheduling while maintaining robustness and efficiency in dynamic and uncertain conditions. However, challenges such as communication instability, data loss, and transmission interference during data collection and transmission pose significant threats to the BEMS's load forecasting and scheduling effectiveness. To address these issues, the BEMS integrates a cognitive control system to enhance data transmission integrity and reliability. The core task of the cognitive control system is to monitor data transmission status in real time, promptly identifying potential issues like packet loss, transmission delay, or interference. By doing so, the cognitive control system effectively reduces the negative impact of data loss on system forecasting and decision-making. It can also adaptively adjust transmission paths and priorities, further enhancing data transmission stability by minimizing the effects of network interference. The integration of the cognitive control system not only improves data transmission integrity and reliability but also strengthens the BEMS's adaptability and robustness in uncertain environments. By flexibly responding to data loss and transmission delays, the system ensures that the BEMS can still receive complete and reliable data support even in complex network environments, thus achieving more accurate load forecasting and optimized scheduling. With the cognitive control system, BEMS can maintain stable operation even in scenarios with significant fluctuations in EV demand, allowing the system to handle load changes more flexibly and efficiently.
## 3 Key Modules
### Channel Estimation and Compensation
#### 3.4.1 Orthogonal Matching Pursuit Channel Estimation  
The UWA channel is a time domain sparse channel whose length is \(L\) and sparsity is \(K\), so we can use the OMP algorithm of compressive sensing to do channel estimation.  
After adding the cyclic prefix, the linear convolution will be changed to circular convolution, and the circular convolution can be expressed as:  
\[r=\left[\begin{array}{c}r(0)\\ r(1)\\ r(2)\\ \vdots\\ r(N-1)\end{array}\right]=\left[\begin{array}{cccc}P(0)&P(N-1)&P(N-2)&\cdots &P(N-L)\\ P(1)&P(0)&P(N-1)&\cdots&P(N-L+1)\\ P(2)&P(2)&P(0)&\cdots&P(N-L+2)\\ \vdots&\vdots&\vdots&\cdots&\vdots\\ P(N-1)&P(N-2)&P(N-1)&\cdots&P(N-L-1)\end{array}\right]\left[\begin{array}{c}h (0)\\ h(1)\\ h(2)\\ \vdots\\ h(L-1)\end{array}\right]+\left[\begin{array}{c}w(0)\\ w(1)\\ w(2)\\ \vdots\\ w(N-1)\end{array}\right] \tag{12}\]  
Figure 5: Channel estimation and equalization process.  
We ignore the noise \(w\) and Equation (12) can be written as  
\[r=P\cdot h \tag{13}\]  
where, \(P\) means the pilot matrix, \(h\) represents the sparse acoustic channel, and \(w\) is Gaussian noise.  
Equation (13) conforms to the general form of the OMP algorithm. The \(h\) is a sparse vector and the observation matrix satisfies the RIP criteria [21]. The orthogonal matching pursuit algorithm is shown in Algorithm 1.  
```
1:Initial residual \(r_{k}=y\), Observation matrix \(\Phi=P\), Label set \(\Lambda_{0}=O\), Sparsity \(K\)
2:for each t=1:\(K\)do
3: Find the number of columns of the strongest correlation column with the residual \(r\) in the
4:observation matrix \(\phi_{j}\lambda_{t}=\arg\max_{j=1,2,-N}\left|\left\langle r_{t-1},\phi_{j} \right\rangle\right|\).
5: Update the index set \(\Lambda_{t}=\Lambda_{t-1}\cup\{\lambda_{t}\}\), reconstruct collection \(\Phi_{t}=\left[\Phi_{t},\phi_{\lambda_{t}}\right]\).
6: Compute \(\theta_{t}=\text{argmin}\|y-\Phi_{t}\theta\|_{2}\) by least squares method.
7: Update residuals \(r_{t}=y-\Phi_{t}\theta_{t}\).
8:endfor
9:return the channel estimation result in \(\hat{\theta}\)
```  
**Algorithm 1** Orthogonal matching pursuit
# Coverage Optimization of WSNs Based on Enhanced Multi-Objective Salp Swarm Algorithm
## 5 Results and Discussion  
The simulation experiments in this paper were conducted using Matlab 2021a, with identical parameters set for all simulations. To effectively demonstrate the performance of the EMSSA algorithm proposed in this paper, this section conducts convergence tests on this algorithm, the original MSSA [12] algorithm, and the multi-objective optimization genetic algorithm (NSGA-II) [33] on four different test functions. A comparative analysis is performed among the three different algorithms. Subsequently, coverage optimization is carried out using these three algorithms at various coverage thresholds to derive experimental conclusions.