esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Works
### Concept Drift Adaptation  
Once drift is detected, it is crucial to effectively manage the observed changes so that the learning model can seamlessly adapt to the new data patterns. There are two broad categories for drift adaptation: incremental learning and online ensemble methods [39]. In the incremental learning strategy, data samples are processed sequentially in order to update the learning model as new data arrive. The Hoeffding Tree (HT) [40] is a variant of the decision tree that offers theoretical guarantees to make incremental drift adjustments for data streams. Unlike traditional decision trees that wait to evaluate the best possible split, HTs calculate the required sample size for choosing a splitting node, allowing for timely updates with incoming data. However, HTs have limitations due to their static internal nodes, which, once set, cannot be altered. Thus, their adaptability to concept drift is confined to the creation of new nodes, limiting their flexibility and responsiveness. To address these shortcomings, the Extremely Fast Decision Tree (EFDT) [41] was developed as an advanced version of the HT. The EFDT improves adaptability by allowing for dynamic adjustments to its decision nodes post creation. This means that an EFDT not only adds new nodes but can also revise existing splits if subsequent data suggest a more effective alternative. This approach enables the EFDT to respond more effectively to concept drifts than its predecessor, though it may still lag in rapid adaptation. Some prominent models for ensemble-based drift adaptation include Adaptive Random Forest (ARF) [42], Streaming Random Patches (SRP) [43] and Leverage Bagging (LB) [44]. ARF and SRP, both utilizing Hoeffding Trees as base learners and incorporating ADWIN for drift detection, aim to dynamically adjust the model ensemble by replacing underperforming trees. While ARF focuses on local subspace randomization, SRP employs a global strategy to enhance learner diversity, often at the cost of increased computational time [37]. On the other hand, the online ensemble bagging method, LB, primarily utilizes a resampling technique, often using a Poisson distribution to assign random weights to instances in the training data. This approach enhances the diversity of the training samples that each model in the ensemble sees, potentially increasing the robustness and accuracy of the ensemble. However, it falls short in scenarios where data evolve rapidly and unpredictably, as simple resampling may not be sufficient to capture and adapt to new patterns effectively.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## Appendix A Control Plane Path Allocation Procedure in Enhanced Mode Scenario
### Control Plane Path Allocation Procedure in Enhanced Mode with Unchanged gNB Behavior  
Figure 11 shows the control plane procedure for the path allocation. The MEO determines the appropriate SR policy to be enforced at SR Endpoint1. Based on that, it selects the corresponding BSID. For clarity, we assign label BSID1 to the selected BSID, which is an IPv6 address. This BSID serves as the designated destination address that gNB1 embeds within the IPv6 outer header to enforce the specified SR policy at Endpoint1. Firstly, the MEO asks the SMF to activate the SDN controller for the configuration of SR Endpoint3, as in the previous case. Then, the MEO instructs the SMF to trigger the SDN controller in configuring the End.M.GTP6.D rule on SR Endpoint1, if not installed yet, for the determined SR policy (BSID1). Finally, the MEO prompts the AMF to configure the GTP-U encapsulation rule on gNB1.
## 3 Proposed Sensing Waveform
### Waveform Solution Based on the Simulated Annealing Algorithm
When subcarrier separation is performed, \(\tau\) is required to be smaller than \(T_{cp}\). \(f_{d_{l}}\) is the Doppler frequency shift, \(c_{l}(t)\) denotes the clutter of the \(l\)th sensing symbol, \(n_{l}(t)\) is the thermal noise of the \(l\)th sensing symbol, and \(\sigma\) is the attenuation factor caused by propagation loss, scattering, and the radar cross-section of the target. After performing the operations of carrier frequency removal, sampling, and subcarrier separation [18] on \(y_{l}(t)\), the resulting signal of \(l\)th sensing symbol is expressed as  
\[\hat{y}_{l}(m)= N_{c}\sigma e^{2\pi f_{d_{l}}n_{l}T_{s}}e^{-2\pi f_{0}\tau}e^{-2 \pi m\Delta f\tau}+\ell_{l}(m)+\hat{n}_{l}(m), \tag{13}\]  
where \(m=0,1,\ldots,N_{c}-1\). Therefore, the received sensing signal can be expressed in terms of a matrix by  
\[\mathbf{Y}=a\mathbf{\omega}_{f_{d}}\mathbf{\omega}_{\tau}+\mathbf{C}+\mathbf{N} \tag{14}\]  
where \(\mathbf{y}_{m}=\left[\hat{y}_{0}(m),\hat{y}_{1}(m),\ \ldots,\ \hat{y}_{L-1}(m)\right]^{T}\), \(\mathbf{Y}=\left[\mathbf{y}_{0},\ \mathbf{y}_{1},\ \ldots,\ \mathbf{y}_{N_{c}-1}\right]\), \(\mathbf{\omega}_{f_{d}}=\left[e^{2\pi f_{d}n_{0}T_{s}}\), \(e^{2\pi f_{d}n_{1}T_{s}}\), \(\ldots,\ e^{2\pi f_{d}n_{L-1}T_{s}}\right]^{T}\), \(a=N_{c}\sigma e^{-2\pi f_{0}\tau}\), \(\mathbf{\omega}_{\tau}=\left[1,e^{-2\pi 1\Delta f\tau},\ldots,\ e^{-2\pi(N_{c}-1)\Delta f\tau}\right]\), \(\mathbf{c}_{l}=\left[\mathcal{c}_{l}(1),\ \mathcal{c}_{l}(1),\ \mathcal{c}_{l}(1),\ \mathcal{c}_{l}(1 -1)\right]^{T}\), \(\mathbf{C}=\left[\mathcal{c}_{0},\mathcal{c}_{1},\ldots,\ \mathcal{c}_{L-1}\right]^{T}\), \(\mathbf{n}_{l}=\left[\mathcal{h}_{l}(0),\hat{n}_{l}(1),\ldots,\ \mathcal{h}_{l}(0),\ \mathcal{h}_{l}(1),\ \ldots,\ \mathcal{h}_{l}(0),\ \mathcal{h}_{l}(1),\ \ldots,\ \mathcal{h}_{l}(1),\ \mathcal{h}_{l}(0),\ \mathcal{h}_{l}(1),\ \ldots,\ \mathcal{h}_{l}(1),\ \mathcal{h}_{l}(1),\ \mathcal{h}_{l}(2),\ \mathcal{h}_{l}(2),\ \mathcal{h}_{l}(3),\ \mathcal{h}_{l}(\(\hat{n}_{l}(N_{c}-1)]^{T}\), \(\mathbf{N}=[\mathbf{n}_{0},\ \mathbf{n}_{1},\ldots,\ \mathbf{n}_{L-1}]^{T}\). In the next section, we will perform the detection and estimation of the received sensing signal based on \(\mathbf{Y}\) in (14).
## 3 Results and Discussion
### The Constitutive Model of Bond-Slip between RBFC and Rebar
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Group**} & \(l\) & \(d\) & \(s_{g}\) & \(s_{u}\) & \(s_{c}\) & \(\tau_{\theta}\) & \(\tau_{g}\) & \(\tau_{u}\) & \(\tau_{c}\) & \\  & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** \\ \hline
60-12-RBFC & 60 & 12 & 1.08 & 2.09 & 5.92 & 3.15 & 16.87 & 21.67 & 7.31 & 0.63 \\
60-14-RBFC & 60 & 14 & 1.02 & 2.05 & 5.86 & 3.10 & 16.81 & 20.88 & 7.28 & 0.72 \\
60-16-RBFC & 60 & 16 & 1.01 & 1.99 & 5.68 & 3.02 & 16.77 & 19.99 & 7.20 & 0.82 \\
80-12-RBFC & 80 & 12 & 1.23 & 2.07 & 5.33 & 2.67 & 15.60 & 18.25 & 5.79 & 0.28 \\
80-14-RBFC & 80 & 14 & 1.20 & 2.06 & 5.30 & 2.64 & 15.40 & 17.33 & 5.70 & 0.32 \\
80-16-RBFC & 80 & 16 & 1.15 & 2.04 & 5.26 & 2.60 & 15.29 & 16.17 & 5.56 & 0.46 \\
100-12-RBFC & 100 & 12 & 1.12 & 2.01 & 5.17 & 2.62 & 12.56 & 15.54 & 5.03 & 0.12 \\
100-14-RBFC & 100 & 14 & 1.04 & 1.95 & 5.13 & 2.57 & 11.06 & 14.48 & 4.86 & 0.21 \\
100-16-RBFC & 100 & 16 & 1.03 & 1.88 & 4.92 & 2.53 & 10.87 & 14.06 & 4.67 & 0.25 \\ \hline \end{tabular}
\end{table} 
Table 9: Statistics related to RBFC.
## 2 Materials and Methods
### Description of the GHP System  
The heat pump unit (DHGW 5N-C4-02, Daesung Heat Enersys, Seoul, Korea) was installed in the pig house (Figures 2 and 3). The system consisted of a 150 m-deep double U-tube borehole exchanger (BHE), a fan coil unit (FCU), water-circulating pumps, and a water tank. The cooling ability of the heat pump was 20.59 kW, with a 4.93 kW rating of electric power consumption. The freezing capacity of the system was 2.1 RT, and R-410A was used as a refrigerant. The water storage capacity of the tank was approximately 260 L. The FCU can generate different wind speeds, with a temperature range of 5-45 \({}^{\circ}\)C. Three circulating pumps (Wilo Pump, Ansan, Korea) were attached to the system: (1) a PH-200M with a flow rate of 136 L/min, which was used for the transfer of ground water to the heat pump; (2) a PH-080M with a flow rate of 75 L/min, which was used for the transfer of water from the heat pump to the tank; and (3) a PB-600 MA with a flow rate of 80 L/min, which was used for the transfer of water from the tank to the pig house.  
The heat pump unit had a rolling compressor piston, a thermostatic expansion valve, and a copper tube with tube heat exchangers. The exchangers increased (in heating mode) and decreased (in cooling mode) the temperature of air, and distributed it through the pig house via plastic ducts. The air inside the pig house was replaced with incoming air. The volumetric flow of air through the heat pump was 700 m\({}^{3}\)/min.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Weaning Pigs** & **Growing Pigs** & **Finishing Pigs** \\  & **0–5 Weeks** & **5–9 Weeks** & **10–19 Weeks** \\ \hline \multicolumn{4}{c}{Ingredients (\% as fed basis)} \\ Yellow corn & 47.85 & 51.34 & 54.00 \\ Rice bran & 14.00 & 7.00 & 9.00 \\ Rapeseed oil & 0.00 & 1.72 & 3.00 \\ DDGS & 0.00 & 6.00 & 6.00 \\ Soybean meal & 22.10 & 21.80 & 18.00 \\ Limestone & 0.70 & 0.86 & 1.00 \\ Calcium Phosphate & 0.70 & 0.10 & 0.20 \\ Salt & 0.15 & 0.30 & 0.30 \\ Vit-min premix 1 & 0.50 & 0.45 & 0.20 \\ Animal fat & 7.00 & 6.78 & 4.86 \\ Molasses & 2.00 & 2.50 & 2.50 \\ Amino Acid additive & 5.00 & 1.15 & 0.94 \\ \multicolumn{4}{c}{Analyzed composition (g/kg dry matter)} \\ Dry matter & 879 & 876 & 881 \\ Crude protein & 190 & 180 & 170 \\ Crude fat & 43 & 44 & 44 \\ Crude fiber & 39 & 38 & 38 \\ Ca & 7.0 & 8.0 & 8.0 \\ Available P & 4.4 & 3.4 & 3.4 \\ Lysine & 10.35 & 10.20 & 10.10 \\ Methionine & 5.3 & 3.7 & 3.1 \\ \hline \hline \end{tabular} \({}^{1}\) Vit-min premix contained following nutrients/kg: vitamin D\({}_{5}\), 800 IU; vitamin A, 600 IU; vitamin K\({}_{3}\), 2 mg; vitamin E, 20 IU; thiamin, 2 mg; riboflavin, 4 mg; vitamin B\({}_{12}\), 1 mg; niacin, 10 mg; pantothelic acid, 11 mg; biotin, 0.02 mg; vitamin B\({}_{6}\), 20 IU; manganese, 90 mg; copper, 21 mg; iron (ferrous), 100 mg; zinc, 60 mg; iodine, 1.0 mg; cobalt, 0.3 mg; selenium, 0.3 mg.  
\end{table}
Table 1: Diet formulae and chemical composition of the experimental diets.
## 2 Methods
### Synthesis of Two EIAs into a Single DV-EIA  
To generate the DV zones corresponding to each of the left and right views, two EIAs captured from each of the two different 3-D objects were multiplexed into a single EIA, which is called DV-EIA, based on the SSIM method, as mentioned above. Figure 4 shows a conceptual diagram of the SSIM method, which is composed of a four-step process.  
At the 1 st step, two EIA were captured from each of the two different 3-D objects, which were assigned to the L-EIA and R-EIA, respectively. At the second step, L-EIA and R-EIA were transformed  
Figure 3: Two captured EIAs from each of the two different 3-D objects of ‘Dice’ and ‘Car’ on the on-axis integral imaging system (**a**) captured EIA from the ‘Dice’ (**b**) captured EIA from the ‘Car’.  
Figure 2: Two types of the EIA pickup systems: (**a**) Off-axis pickup, (**b**) on-axis pickup.  
into the corresponding sub-image arrays (SIAs), which are called L-SIA and R-SIA, respectively, based on the EIA-to-SIA transformation (EST) method [26]. At the 3 rd step, odd and even number of components of the EIs were selectively chosen from each of the L-SIA and R-EIA and mapped into their corresponding left-half and right-half parts of the dual-view sub-image array (DV-SIA), respectively.  
Here, \(N_{L}\) and \(N_{R}\), respectively, represent the total number of SIs selected from the L-SIA and R-SIA, and allocated to their corresponding left and right-half parts of the DV-EIA. \(N_{DV}\) (=\(N_{L}+N_{R}\)) also denotes the total number of SIs mapped into the DV-SIA along the horizontal direction, where the number of pixels in each EI was equal to the number of SIs. This DV-SIA was then transformed into its corresponding DV-EIA using the inverse-EST (IEST) method. At the 4 th step, the DV-EIAs for the two L-EIA and R-EIA were finally generated and used for separate displaying of two different 3-D objects into their viewing zones. Meanwhile, in the conventional system, the left and right viewing zones are set to be equal and physically fixed by the employed optical elements, such as the lens array and lenticular lens array, whereas in the proposed system, a parameter of \(\alpha\) can be employed for controlling the relative sizes of the left and right viewing zones, where \(\alpha\) is defined as a relative portion of the \(N_{L}\) in \(N_{DV}\) and expressed by Equation (1).  
\[\alpha=\frac{N_{L}}{N_{DV}} \tag{1}\]  
According to the relationship between the EIA and SIA, \(N_{DV}\) and \(N_{L}\) become equal to the total number of pixels of the DV-EI and number of pixels of the L-EI allocated to the left-hand side of the DV-EI, respectively. Thus, \(N_{L}\) turn out to be \(\alpha\times N_{DV}\) and the other portion of the \(N_{\rm DV}\), \(N_{\rm R}\) becomes \((1-\alpha)\times N_{DV}\), which represents the number of pixels of the R-EI allocated to the right-hand side of the DV-EI. This scalability of the left and right viewing zones is another property of the proposed system in the practical application.
# RRM Prediction of Erythrocyte Band3 Protein as Alternative Receptor for SARS-CoV-2 Virus
## 3 Results
To confirm that the S1 spike from SARS-CoV-2 can interact with human RBC Band3 surface proteins, we calculated phases for those two proteins at an RRM characteristic frequency of 0.3145 to find out if they are opposite, as required by RRM for successful interaction. The phase for S1 spike was found to be at -1.74 rad, while the phase for human RBC Band3 surface protein was found to be at +1.91 rad, indicating that their phase difference is 2.43 rad, which is empirically shown to be opposite enough to enable successful interaction. The results above show that the RBC Band3 surface protein has the same RRM characteristics as the ACE2 protein regarding interaction with the S1 spike protein from coronavirus. Thus, it can be considered as another pathway for SARS-CoV-2 virus to affect human cells. In addition, it has been shown that the human RBC Band3 surface protein has not only the same RRM frequency of 0.3145 as the human ACE2 receptor, but also that it has a very similar phase at this characteristic frequency, approximately opposite to the phase for the S1 spike from SARS-CoV-2, as presented in Figure 3 (within the phase circle). Figure 1: Resonant Recognition Model (RRM) cross-spectrum for interaction between S1 spike proteins and angiotensin-converting enzyme 2 (ACE2) receptors with common RRM characteristic frequency of 0.3145. These findings indicate, according to RRM principles, that the RBC Band3 surface protein is most probably another pathway for SARS-CoV-2 to enter into human cells.
# Steganalysis of Quantization Index Modulation Steganography in G.723.1 Codec
## 5 Experiment and Results Analysis
### Experimental Environment  
The experiment environment is shown in Figure 4. Sample data is encoded by the G.723.1 speech encoder and steganographed by CNV-QIM. After the packet is sealed, it is transmitted to the receiving end via VoIP network. On the receiving end, the corresponding feature vector is constructed.  
Figure 3: Change rate \(\gamma 1\) and \(\gamma 2\).  
The classification model is obtained by learning and training a large number of common carrier samples and those containing dense carrier samples. The classification results are obtained [20].  
The computer system used in experiment is Window7 Professional Service Pack 1, and the processor parameter is Intel(R) Core(TM) i3-6100 [EMAIL_ADDRESS] GHz. Software mainly used are Visual Studio 2012 and Matlab 2016 software. Embedding secret information uses the G.723.1 speech coder with the method of [21]. The experimental sample includes five categories, namely Chinese male voice (CM), Chinese women voice (CW), English male voice (EM), English women voice (EW), and hybrid voice (Hybrid). Samples of each category are prepared for a total of 100 speech segments of 9 seconds and a total of 500. After steganography, the total number of samples reaches.
## 6 Broadcast Communication
### Key Management for Broadcast Communication  
The publishing of DR projects and information on energy prices are both messages that may be delivered using the broadcasting technique in order to ensure the message's secrecy and authenticity; the session keys must be updated before each broadcast session. The three stages of detailed key management for broadcast communication are depicted in Figure 7.  
The variables Count\({}_{0}\) and the root key \({}_{0}\) are used in Step 1.1 and Step 1.2 to produce the session key \({}_{0}\). The information for transfer is encrypted and signed using this session key before being sent in steps 1.3 and 1.4. In step 2, the data that have been encrypted together with the signature value are then sent to all the recipients of SXs. Step 3.1 and Step 3.2 generate the session key at the recipients then Step 3.3 calculates the receiver signature. In Step 3.4, the recipients compare the transmitter signature and recipient signature, if there are equal, and the information is decrypted.
## 3 Results  
A total of 37 eyes of 37 patients with NPDR and DME, of whom 12 were women and 25 were men, were enrolled according to the selection criteria. Thirty patients were treatment-naive to intravitreal dexamethasone injections, while seven were switched from anti-VEGF therapy. Eight patients (five naive patients and three switched from anti-VEGF) exhibited a poor response to intravitreal dexamethasone treatment and were classified as poor responders according to the criteria of [PERSON] et al. (2014) (Figure 1).  
Data analysis did not reveal statistically significant differences in the parameters of CVI, LA, SA, and TA between baseline and the 2- and 4-month post-intravitreal dexamethasone follow-up (Table 1).  
Figure 2: Spectral domain optical coherence tomography (SD-OCT) raster scan of a diabetic patient with diabetic macular edema. The binarized image of the 3 mm area centered on the fovea superimposed on the SD-OCT image, with the region of interest highlighted in yellow, corresponding to the choroidal area.  
Data were successively evaluated by repeating the analysis following the exclusion of the poor responders, and a statistically significant reduction in CVI was observed over the follow-up period (\(p\) = 0.049) (Table 2). Post hoc analysis revealed a statistically significant reduction in CVI at 4 months post-intravitreal dexamethasone compared to baseline, but not compared to 2 months post-intravitreal dexamethasone (Table 3). No statistically significant differences emerged in analyzing the dexamethasone responders regarding LA, SA, and TA parameters. However, in absolute values, we observed a decrease in LA and an increase in SA values throughout the follow-up period; TA showed a decrease at 2 months post-intravitreal dexamethasone with an increase at 4 months post-intravitreal dexamethasone.  
However, the multiple linear regression, which included CMT as an additional variable, confirmed the statistical significance of CMT in influencing the CVI at 2 months post-intravitreal injection.  
Table 4 summarizes the descriptive characteristics of the retinal morphological parameters analyzed at baseline, 2 months, and 4 months of follow-up after the intravitreal dexamethasone injection.
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
###### Abstract  
Water pasteurization has the potential to overcome some of the drawbacks of more conventional disinfection techniques such as chlorination, ozonation and ultraviolet radiation treatment. However, the high throughput of community water systems requires energy-intensive processes, and renewable energy sources have the potential to improve the sustainability of water pasteurization plants. In case of water pasteurization by solar thermal treatment, the continuity of operation is limited by the intermittent availability of the solar irradiance. Here we show that this problem can be addressed by a proper design of the plant layout, which includes a thermal energy storage system and an auxiliary gas boiler. Based on a target pasteurization protocol validated by experiments, a complete lumped-component model of the plant is developed and used to determine the operating parameters and size of the components for a given delivery flow rate. Finally, we report an economic analysis of the proposed plant layout, which allows its optimization for different scenarios based on two design variables, namely the solar multiple and the duration of the thermal energy storage. Based on the analyzed cases, it is found that the proposed plant layouts may yield a unit cost of water treatment ranging from \(\approx\)32 EUR-cents m\({}^{-3}\) to \(\approx\)25 EUR-cents m\({}^{-3}\).  
water treatment; pasteurization; solar thermal energy; thermal energy storage +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
The results of the study show the potential of using MAP and edible films with antimicrobial agents to extend the shelf life and improve the commercial value of broiler chicken cuts. The use of edible coatings enriched with glucono-\(\delta\)-lactone led to a 2-day extended shelf life of chicken samples stored at 5 \({}^{\circ}\)C. MAP and active coatings with citrus extract showed a similar effect on the shelf life of chicken samples. The results of the microbiological analysis showed that the shelf life for the control, glucono-\(\delta\)-lactone coating, MAP, and citrus extract-coated samples were 6, 8, 9, and 9 days, respectively, at 5 \({}^{\circ}\)C, revealing an important antimicrobial effect of the edible coatings used in the present study. The use of active packaging applied to the chicken cuts led to the extension of the shelf life and the maintenance of the quality of the chicken meat; therefore, it could be used as an alternative to the conventional packaging commonly used for poultry meat. The proposed active packaging systems may enable a reduction in the use of the currently applied conventional plastic materials in the meat and poultry industry, without limiting product shelf life, thus reducing both food and packaging waste. Further studies are required to validate the developed shelf life predictive models under realistic and dynamic temperature conditions of the real cold chain of perishable food products, such as meat and poultry.  
Conceptualisation, T.T. and P.T.; methodology, A.N., A.K. and T.T.; validation, A.N. and T.T.; formal analysis, A.N. and A.K.; investigation, A.N. and A.K.; writing--original draft preparation, A.N. and T.T.; writing--review and editing, A.N., A.K., T.T. and P.T.; supervision P.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data that support the findings of this study are available from the corresponding authors upon reasonable request.  
The authors declare no conflict of interest.