esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated SDN-NFV 5G Network Performance and Management-Complexity Evaluation
## 2 Related Work
### Integration SDN and NFV in 5G Technology  
In a written paper [26], [PERSON] et. al. tried to solve the complex challenge of integrating SDN and NFV by proposing software-defined network virtualization (SDNV) architectural framework. The SDNV framework combines the SDN concept of splitting data and control planes with the NFV principle of decoupling service functions from infrastructures, providing a holistic view of SDN and NFV integration. To illustrate the relationship between SDN and NFV principles, [PERSON] et al. first discuss how SDN and NFV might benefit the model. Then, they proposed the SDNV framework architecture, which provides a high-level picture of SDN and NFV integration. Combining SDN and NFV would have many benefits, but integrating them is a challenge. There are two possible architectures for such integration: either the controller or the switch interacts with virtualized network functions (VNFs). [PERSON] et al. refer to the former as NFV under the controller (NFV-C) and the latter as NFV alongside the controller (NFV-AC). To the best of our knowledge, no analytical model exists for mathematically examining the performance of such architectures [27].  
In other articles, there is some research to compare the performance of software switches which relate to the integration of SDN-NFV. Still, adding a high-speed framework like DPDK or SR-IOV will accelerate the forwarding speed of the switch. Switches are important because most of the network components used in SDN and NFV platforms are routing capable switches. [PERSON] et al. investigate their respective design spaces and evaluate their performance in four representative test scenarios. Each scenario corresponds to a unique instance of NFV traffic routing between NICs and/or VNFs. Their experimental findings demonstrate that no single software switch is optimal in all circumstances. There  
Figure 3: Network function virtualization.  
fore, it is essential to select the one that is most suited to a given use case. The presented results and analysis improve comprehension of design tradeoffs and identify potential performance bottlenecks in software switches [28].  
From related work articles on integration that have been described, the most significant difference is in the article that the authors made describing the results of the integration seen from the time of operational convenience. In addition, the comparison made is a comparison between SDN only and SDN-NFV. Meanwhile, other articles on SDN and NFV integration are more focused on the integration process and performance.  
In Table 1, the comparison summary of SDN-NFV integration articles which are related to this experiment is presented to give a better understanding about this topic.
# KAN-Transformer Model for UltraShort-Term Wind Power Prediction Based on EWMA Data Processing
## 3 Experimental Analysis
### Comparion and Analysis of the Proposed Model with Other Models
#### 3.4.3 Comparison Between the Proposed Model and LSTM and GRU Series Models  
The proposed model is compared with the TCN-BiLSTM, TCN-BiGRU, LSTM, and GRU models. The comparison of the model prediction curves is shown in Figure 17, and the evaluation metrics for the test set are shown in Table 7.  
From Figure 17, it can be observed that the model proposed in this article has the highest prediction accuracy. In Table 7, the EWMA-KAN-Transformer model shows the following improvements compared to other models. Compared to the TCN-BiLSTM model, the MAE and RMSE are reduced by 4.16 MW and 5.78 MW, respectively, and R\({}^{2}\) increases by 2.76%. Compared to the TCN-BiGRU model, the MAE and RMSE are reduced by 2.28 MW and 1.59 MW, respectively, and R\({}^{2}\) increases by 0.60%. Compared to the LSTM model, the MAE and RMSE are reduced by 1.19 MW and 1.19 MW, respectively, and R\({}^{2}\) increases by 0.44%. Compared to the GRU model, the MAE and RMSE are reduced by 1.86 MW and 0.99 MW, respectively, and R\({}^{2}\) increases by 0.36%. This demonstrates that the proposed model achieves higher prediction accuracy compared to the GRU and LSTM series models.
## 4 Analysis of Results  
### Confirmatory Factor Analysis  
#### 4.1.1 Perceived Ease of Use  
After the confirmatory factor analysis of the perceived ease of use, the GFI value was 0.976, the CFI value was 0.988, and the RMSEA value was 0.105, demonstrating that the fit has reached the critical value of ideal fit. In addition, the standardized factor loadings except for PEOU6 less than 0.5, it is shaved in the sample, while the other samples were all above 0.5 (0.916, 0.856, 0.932, 0.786, and 0.855 respectively) indicating that the observed variables of this research model reached the level of measuring potential dimensions. Table 1 shows a well-fitted model of the perceived ease of use dimension through CFA.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Construct** & **Indicator** & \multicolumn{2}{c}{**Standardized Factor**} & \multicolumn{2}{c}{**Unstandardized Factor Loading**} & \multicolumn{1}{c}{**\(p\) Value**} & \multicolumn{1}{c}{**GFI**} & \multicolumn{1}{c}{**CFI**} & \multicolumn{1}{c}{**RMSEA**} \\ \hline  & PEOU1 & 0.916 & 1 & *** & & & & \\  & PEOU2 & 0.856 & 1.004 & *** & & & & \\ PEOU & PEOU3 & 0.932 & 1.070 & *** & 0.976 & 0.988 & 0.105 \\  & PEOU4 & 0.786 & 0.889 & *** & & & & \\  & PEOU5 & 0.855 & 0.947 & *** & & & & \\ \hline \hline \end{tabular} \({}^{***}p<0.001\). Source: calculated by this research.  
\end{table}
Table 1: TAM of perceived ease of use.
## 2 Materials and Methods
### Life Cycle Impact Categories  
The categories of energy consumption and environmental impact employed in this work were developed by the Centre for Environmental Science at Leiden University (CML 2001 Method) [34; 35] and updated in April 2015: use of primary energy (PE) and global warming potential (GWP). PE is defined as the consumption of direct and indirect energy; that is, the energy used directly in the system and the energy required in the raw material production and outputs management. PE is determined according to Equation (1):  
\[PE=e_{d}+e_{i} \tag{1}\]  
where \(e_{d}\) is the direct energy consumption and \(e_{i}\) represents the indirect energy consumption. \(e_{d}\) and \(e_{i}\) can be calculated following Equations (2) and (3):  
\[e_{d}=\sum_{k=1}^{k}q_{k}.h_{k} \tag{2}\]  
\[e_{i}=\sum_{n=1}^{n}q_{n}.h_{n} \tag{3}\]  
where \(k\) represents electricity or other energy sources, \(n\) presents the rest of inputs used in the system, \(q\) is the energy equivalent measured in M] per unit of input flow, and \(h\) is the activity whose units depend on the input flow.  
Regarding GWP, this indicator is defined following Equation (4), where \(i\) represents the substances that contribute to the GWP, \(m\) is the weight of substance \(i\) emitted (kg/FU), and EF is the emissions factor of substance \(i\) (kg CO\({}_{2}\) eq./kg \(i\)).  
\[\text{GWP}=\sum_{i=1}^{i}m_{i}.E\text{F}_{I} \tag{4}\]  
The largest source of greenhouse gas emissions from human activities is from burning fossil fuels for electricity, heat, and transportation. Public concern about climate change is a hot topic nowadays, and this environmental impact is directly measured through GWP. The largest source of greenhouse gas emissions from human activities is from burning fossil fuels for electricity, heat, and transportation. On the one hand, the PE category, expressed in M], accounted for the total consumption of both renewable and non-renewable energy sources. On the other hand, the GWP category, expressed in kg CO\({}_{2}\) eq., represented the harmful chemicals emitted into the atmosphere that are responsible for the greenhouse effect: carbon dioxide (CO\({}_{2}\)), methane (CH\({}_{4}\)), nitrous oxide (N\({}_{2}\)O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF\({}_{6}\)).
# Multi-Objective Decision-Making Meets Dynamic Shortest Path: Challenges and Prospects +
## 6 Applications
### Unmanned Aerial Vehicles  
Unmanned Aerial Vehicles (UAVs)/drones are increasingly used in many activities, reducing life-threatening conditions or in environments with restrictive access. For example, UAVs can deliver packages, film live sports, and search for people for diverse purposes [83; 84; 85]. For these applications, UAVs should consider other obstacles along the path, such as other drones or even trees and traffic signs; moreover, they should also reduce energy consumption to cover larger areas [86]. To this end, the UAVs fit the definition of the MODSP to accomplish the dynamic changes in the environment and multiple objectives' cost.
## 3 Results
### User Behavior
#### 3.1.2 Self-Adjustment Situations  
To investigate the potentially increased need for personalized adjustments in certain everyday situations, the prevailing acoustic environments at the time of self-adjustment were analyzed and are presented in Figure 6. It was found that 38% of all self-adjustments occurred in quiet situations with speech and approximately 29% in situations with speech with background noise. These two conditions accounted for nearly two-thirds of all observed everyday situations where self-adjustments were performed. However, the pattern in this distribution mirrored the general distribution of acoustic situations in daily life, as shown in Figure 4. To account for the inherent bias introduced by this natural distribution, an adjustment was made by factoring out the general distribution of acoustic scenarios. Specifically, inverse factors based on the data in Figure 4 were applied to the self-adjustment data in Figure 6. The adjusted data, shown in Figure 7, revealed a more homogeneous distribution. A detailed analysis using a two-sided binomial test showed that self-adjustments were made significantly more frequently in the \"speech in noise\" situation (\(p<0.01\)) and significantly less frequently in the \"noise\" situation (\(p<0.05\)).  
Figure 4: General overview of the everyday acoustic environment of the participants.  
Figure 5: Overview for each individual participant of the acoustic environment situations in which they most frequently found themselves.  
Figure 6: The environment during self-adjustment of all participants.
# Virtual Replication of IoT Hubs in the Cloud:
## 3 Architecture
### Operational Scenarios
#### 3.2.1 Polling Resources  
If the smart object is a CoAP server, it can receive requests to access its hosted resources. In this case, shown in Figure 3, the message flow is as follows: (1) the external client sends a HTTP or CoAP request to the Cloud platform front-end, which (2) forwards the request to one selected replica of the IoT Hub.  
Figure 3: Message flow for the Polling scenario: a HTTP/Constrained Application Protocol (CoAP) client requests a resource to the Cloud platform, which internally selects a suitable IoT Hub replica and forwards the request. The request reaches the smart object only if neither the replica nor the IoT Hub have stored a fresh cached representation of the resource. Other replicas (which are not in the path of the request) are kept in sync using the synchronization protocol.  
Figure 2: Protocol stack and functional modules implemented by IoT Hub.  
The CoAP protocol [4] defines the Max-Age option to indicate the maximum time a response may be cached before it is considered not fresh. Since Smart Objects, the IoT Hub, and Virtual Replicas all implement CoAP they are able invalidate their caches autonomously based on this information.  
If the replica has a matching cached \"fresh\" resource, it can return it immediately; otherwise, (3) the replica forwards the request to the actual IoT Hub (which is securely connected through a VPN tunnel): if the IoT Hub has a matching cached fresh resource, it can return it immediately; otherwise, (4) it acts as a reverse proxy and forwards a (and, if needed, translated) CoAP request to the CoAP server, which (5) returns the resource. At this point, the returned resource is cached by the IoT Hub, and (6) returned to the replica, which, in turn, (7, 8) sends it back to the client. The resource cached at the IoT Hub is then (6\({}^{\prime}\) and 6\({}^{\prime\prime}\)) synchronized with its replicas, in order to speed up and efficiently manage subsequent requests targeting the same resource.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 2 Basic Principles
### Support Vector Machines  
The basic principle of an SVM is to achieve the correct classification of samples by finding the optimal hyperplane and using support vectors to determine the location of this hyperplane [20]. When the training samples are not linearly separable in the original space, the introduction of a kernel function can make an otherwise complex, nonlinearly separable problem linearly separable [21; 22].  
For the binary classification problem, let the input feature vector be \(x_{i}\) (\(i\) = 1, 2,..., \(n\)) and the classification label, \(y\in\{-1,\) +1\(\}\). The SVM can determine an optimal classification hyperplane from the input feature vector. The expression is given by:  
\[f(x)=\omega^{T}+b=0 \tag{10}\]  
where: \(\omega\) is the hyperplane normal vector, which determines the direction of the hyperplane, and \(b\) is the intercept, which determines the distance between the hyperplane and the origin.
## 5 III-V Lasers Grown on InP-SOI
### III-V on SOI FP Laser with Hybrid Facets  
The width of the Si waveguide can be adjusted to optimize the optical mode confinement in the MQW. The confinement factor in the Si waveguide as a function of its width is plotted in Figure 12a. The width of the Si waveguide was chosen so that the mode was 80% in the Si waveguide in order to minimize losses induced by the p-doped layers. The optical mode simulation is presented in Figure 12b. A 2.7 mm-long bar was cleaved and soldered on a thermally conductive submount. A cross-section of the hybrid FP laser was observed by SEM in order to control the alignment between the two waveguides (Figure 13a). Then, the laser was characterized by coupling light through a single facet using a broad detector and tested up to 50 \({}^{\circ}\)C under continuous-wave regime. The light-current characteristics are shown in Figure 13b. The threshold current measured at 20 \({}^{\circ}\)C is of 89 mA with a maximum output power of 2.8 mW. The threshold current is increased up to 160 mA at 40 \({}^{\circ}\)C. This result demonstrates the successful mode hybridization between III-V and Si. Laser demonstration is achieved up to 50 \({}^{\circ}\)C, which is lower than for the lasers grown on InPSi. This lower thermal behavior can be attributed to poorer heat dissipation induced by the thickness of the buried oxide (BOX) of 2 \(\upmu\)m for InP-SOI as compared to a value of 200 nm for InP-Si. Thermal stability of our components could be further improved by burying the III-V waveguide through InP regrowth steps [26].  
Figure 11: LIV Characteristics measured under CW regime up to 70 \({}^{\circ}\)C for the lasers emitting at 1515, 1580 and 1610 nm.
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 6 Results and Discussion
### Control Policy
#### 6.2.2 Impact on Policy Entropy  
We hypothesize that this difference in performance between the distillation losses is mainly due to the maintained accuracy of the policy stochasticity. This has particular importance to reach a high degree of fidelity with teachers such as SAC, which are optimized to maximize an entropy-regularized return [11]. To verify this, we measure the entropy of the action distribution predicted by the students during testing, as can be seen in Figure 10. This clearly shows that the relative order of the experiments is the same as for the average return, but in reverse. The student trained using our KL-divergence-based distillation loss indeed matches the entropy of the teacher the closest, and the more similar the entropy is to the teacher, the higher the average return that is obtained. However, it seems that the entropy is overestimated when using the other losses, resulting in more actions being taken that deviate too much from the teacher policy.  
Figure 8: The average return during training for student and teacher-driven distillation on the HalfCheetah-v3 environment and an SAC teacher.  
Figure 9: The exponential running mean of the average return during training for student and teacher-driven distillation on the Ant-v3 environment and an SAC teacher.  
So, the KL-divergence loss strikes a good balance between learning a stochastic policy, which our results confirm is optimal for this teacher, while staying closer to the teacher policy by not overestimating the entropy either. In the student-driven experiments, the entropy is initially higher, before gradually converging to the teacher entropy. This is beneficial for training, as the data collected during the first epochs will contain more exploratory behavior and thus results in faster learning and reducing once the control policy is stabilizing. These values are also a lot more stable and have much less inter-run variance compared to the teacher-driven experiments, which only seem to become worse over time.
## 2 Materials and Methods
### Near Infrared Spectroscopy  
The setup used in this study consisted of two spectrometers (AvaSpec-ULS2048L and AvaSpec-NIR256-2.5-HSC, Avantes BV, Apeldoorn, The Netherlands), a light source (AvaLight-HAL-(S)-Mini, Avantes BV), and a customized arthroscopic optical fiber probe (Avantes BV, Apeldoorn, The Netherlands). A similar set-up was used to study articular cartilage and subchondral bone by [PERSON] et al. (2001). The optical fiber probe is not initially designed for liquid measurements. For the AvaSpec-ULS2048L, the integration time was 1.5 ms for pure L-fucose, aqueous L-fucose, and L-proline solutions and saliva sample 2. For pure L-proline and saliva sample 1, the reference spectrum was saturated with the integration time of 1.5 ms; thus, the time was adjusted to 1.05 ms. The acquired spectral region was 319-1093 nm, the resolution of the spectrometer was 0.6 nm, and the averaging number was 100 scans. For the AvaSpec-NIR256-2.5-HSC, the integration time was 30 ms, the averaging number was 100 scans, the acquired spectral region was 943-2503 nm, and the resolution was 6.4 nm. The measured data is provided as supplementary materials (Table S1: Data sheet). Sample drops were placed on a reflective standard (Spectralon, SRS-99, Labsphere Inc., North Sutton, NH, USA) on top of a sample holder (Figure 1b), which was controlled by the motion controller (Figure 1a). The reflective standard was cleaned with tissue paper after each measurement before pipetting another drop on top of the reflective standard, making the sample deposition simple, fast, and inexpensive. Spectral measurements were performed at the exact same coordinates (x, y, z) with each drop. Preliminary tests revealed that optimal signal-to-noise ratio (SNR) was achieved with the sample volume of 20 \(\upmu\)L which was standardized for the actual measurements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metabolite** & **OSCC [\(\upmu\)M]** & **Controls [\(\upmu\)M]** \\ \hline Acetate & 2559.8\(-\)9344.8 & 1977.7\(-\)5239.5 \\ Alanine & 47.1\(-\)515.9 & 53.0\(-\)173.0 \\ Butanol & 17.2\(-\)190.5 & 16.8\(-\)84.3 \\ Butyrate & 33.9\(-\)266.4 & 25.9\(-\)128.4 \\ Choline & 12.2\(-\)43.7 & 14.2\(-\)24.7 \\ Formate & 191.4\(-\)426.2 & 77.0\(-\)433.3 \\ L-fucose & 302.0\(-\)1527.2 & 100.6\(-\)284.7 \\ Glycine & 103.2\(-\)719.1 & 241.1\(-\)923.6 \\ Lactate & 71.5\(-\)1132.9 & 140.4\(-\)324.6 \\ Methanol & 36.6\(-\)208.1 & 51.4\(-\)121.5 \\ Methylamine & 1.7\(-\)66.5 & 1.9\(-\)5.7 \\ Phenylalanine & 41.9\(-\)147.6 & 59.1\(-\)123.6 \\ L-proline & 104.1\(-\)799.9 & 318.5\(-\)1244.3 \\ Propionate & 319.9\(-\)2157.6 & 251.1\(-\)1028.4 \\ Pyruvic acid & 12.6\(-\)73.1 & 7.2\(-\)33.3 \\ Succinate & 24.5\(-\)214.3 & 47.1\(-\)71.9 \\ Taurine & 72.1\(-\)195.4 & 104.7\(-\)205.1 \\ Tyrosine & 42.3\(-\)173.5 & 55.3\(-\)165.5 \\
1,2-propanediol & 32.7\(-\)2465.4 & 21.7\(-\)54.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Concentrations of salivary metabolites of oral squamous cell cancer (OSCC) (n = 8) patients and their healthy controls (n = 30) identified in an NMR study by [PERSON] et al. (2015).
# Functional Yogurt: Types and Health Benefits
## 2 Functional Yogurt Types
### Probiotic
The samples encapsulated in the multilayer emulsion had the highest viscosity, followed by those encapsulated in the conventional emulsion and those in the free emulsion. Notably, the sample with _L. plantarum_ encapsulated in the multilayer emulsion had a higher viscosity than the sample with _L. rhamnus_. Additionally, the encapsulation methods improved the syneresis and water-holding capacity of the products. The viability of the probiotic bacteria was evaluated via various methods, and it was concluded that the samples encapsulated with multilayer emulsions had relatively high encapsulation efficiencies and greater viability. Sensory analysis results indicated that the form of encapsulated probiotic bacteria did not differ noticeably from the free form in terms of taste, smell, appearance, or texture, as perceived by consumers [66]. Similarly, in another study investigating the effects of different encapsulation methods on the viability, physicochemical, and sensory properties of _Bifidobacterium animalis_ subsp. _lactis_, it was found that encapsulated probiotics resulted in greater viability in yogurt than free probiotics did [58]. Additionally, compared with encapsulated probiotics, free probiotic bacteria undergo a faster fermentation process, and encapsulated probiotics, in turn, have a shorter fermentation process than traditional probiotics. When evaluating product quality, yogurt containing encapsulated probiotic bacteria was characterized as having the best values in terms of viscosity and texture. In conclusion, the encapsulation method used resulted in significant differences in the properties of the products. Drying methods are also techniques used to preserve the health benefits of probiotics at high viability [69; 129; 100]. In an article where free and spray-dried _L. plantarum_ bacteria were used during yogurt production, the addition of spray-dried _L. plantarum_ resulted in increased viability of the bacteria in the yogurt [69]. Similarly, another study demonstrated that the freeze-drying technique can help probiotics remain alive during yogurt production [100]. In summary, probiotic yogurt is superior to traditional yogurt both nutritionally and functionally. The use of various probiotics enhances the textural, physicochemical, and sensory properties of the product. At this point, the viability and functionality of probiotics are important, and various techniques can improve these characteristics. The microencapsulation method, in particular, offers a suitable approach to enhance the stability and viability of probiotic bacteria in the yogurt matrix. The type of probiotic used and the fermentation efficiency are highly important.