esa-sceva/satcom-chunk-collection huggingface dataset:
# A Wireless EEG System for Neurofeedback Training
## 2 Related Work
### Electrode Sensors and EEG Cap  
Both EEG sensors (dry and wet) could be appropriate to EEG data acquisition for systems with 24 bit analog-to-digital convertors (ADCs) because of their high input impedance (1 M\(\Omega\)) and CMRR (-97/110 dB), with achievable sampling rates up to 1 KHz [1; 24]. The advantages of dry sensors were greater than those of wet electrodes in recent studies [24; 25]. The application of traditional wet-sensor systems is largely limited by their difficult portability and long preparation time to outdoor settings.
## 5 Conclusions  
A 3-D model for CP-\(\pi\) RFA has been established and tested with experimental results. The simulated results show the flow, heat transfer characteristics, and oxygen distributions in the CP-\(\pi\) RFA. Simulated results allow the following conclusions:  
In the initial stage Ad step, there is a distinct pressure peak near the outer hole. In addition, the raw material gas enters the adsorber and N\({}_{2}\) is adsorbed by adsorbent bed, which causes the pressure change obviously, and the pressure in the adsorber increases slightly at the end of the Ad step. In the Pr step, the gas flow velocity decreases to 2.2 m\(\cdot\)s\({}^{-1}\) near the outer hole. In the Ad step, the gas flow velocity near the outer hole and the central hole are 5 m\(\cdot\)s\({}^{-1}\) and 2.5 m\(\cdot\)s\({}^{-1}\), respectively. The maximum speed near the central hole is about 62 m\(\cdot\)s\({}^{-1}\) and 6 m\(\cdot\)s\({}^{-1}\) in the Cb and Pu step. In a stable cycle, the temperature of adsorbent bed in the CP-\(\pi\) adsorber changes significantly. The radial temperature difference in the adsorbent bed reaches 15 K after the Pr step, and the temperature increases to 287 K after the Ad step. The temperature of the adsorbent decreases to about 277 K in the Pu step. The oxygen purity is achieved to be 98.6% after the Ad step, and the oxygen purity in outlet of bed can reach up to 84.8% with purging by highly concentrated product in the end of Pu step. This model can be used in multicomponent PSA process. The pressure changes during the Pr step and Cb step are calculated by the other process variables, not semi-empirical formulas. This work could provide a reference for the application of CP-\(\pi\) RFA and improve the design of CP-\(\pi\) RFA. In the future, detailed research will be mainly focused on the optimization of structural parameters and industrial applications. Figure 13: Changes of cycle times and product flow rate for the CP-\(\pi\) RFA: (**a**) changes in oxygen purity with cycles times, (**b**) changes in oxygen purity with product flow rate. **Author Contributions:** Conceptualization, H.W. and X.Y. ; methodology, H.W., C.Z., X.Y. and Z.L. ; writing, H.W., and X.Y. ; project administration, Y.L., C.L., Y.H., X.M. and H.W. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by Beijing Natural Science Foundation (No. 8182019), National Natural Science Foundation of China (No.51578065), Beijing Education Commission General Project (No.KM202011417007), Premium Funding Project for Academic Human Resources Development in Beijing Union University. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
# Full-Field Strain Determination for Additively Manufactured Parts Using Radial Basis Functions
###### Abstract  
Additively manufactured components, especially those produced in deposition welding processes, have a rough curvilinear surface. Strain and surface deformation analysis of such components is increasingly performed using digital image correlation (DIC) methods, which raises questions regarding interpretability of the results. Furthermore, in triangulation or local tangential plane based DIC strain analysis, the principal strain directions are difficult to be calculated at any point, which is due to the non-continuity of the approach. Thus, both questions will be addressed in this article. Apart from classical local strain analysis based on triangulation or local linearization concepts, the application of globally formulated radial basis functions (RBF) is investigated for the first time, with the advantage that it is possible to evaluate all interesting quantities at arbitrary points. This is performed for both interpolation and regression. Both approaches are studied at three-dimensional, curvilinear verification examples and real additively manufactured cylindrical specimens. It is found out that, if real applications are investigated, the RBF-approach based on interpolation and regression has to be considered carefully due to so-called boundary effects. This can be circumvented by only considering the region that has a certain distance to the edges of the evaluation domain. Independent of the evaluation scheme, the error of the maximum principal strains increases with increasing surface roughness, which has to be kept in mind for such applications when interpreting or evaluating the results of manufactured parts. However, the entire scheme offers interesting properties for the treatment of DIC-data.  
digital image correlation; strain analysis; radial basis function; additive manufacturing; WAAM; tension; torsion; verification
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 4 Architecture and Procedures for MEC-Based Mobile Services
### SRv6-Based Path Allocation  
Based on the architecture presented so far, we now discuss the configuration of a 5 GC network in the case of MEC-based services. The procedures are based on [13], which considers two operation modes: _Enhanced Mode_ and _Enhanced Mode with Unchanged gNB Behavior_. The first mode belongs to a full SRv6-aware 5G core network, where the SRv6 domain is extended to the gNBs. The gNBs tunnel the packet across the 5 GC network through an IPv6-SRv6 encapsulation. The second concerns the case of a 5G network with SRv6-unaware gNBs. This second mode guarantees the retro compatibility, by instructing the first SRv6 node of the 5 GC network, called the Ingress Router, to remove the GTP-U encapsulation and add the SRv6 one. As anticipated in Section 4, GTP-U is a protocol that allows the establishment of a tunnel between the RAN and the 5 GC to carry user information. A GTP-U tunnel is uniquely identified by a Tunnel Endpoint Identifier (TEID). Using Figure 4 as a reference, we describe the SRv6-based path allocation procedure in both modes.  
The path allocation procedure is triggered by the first request for an edge service by a client. Initially, the MEC system instantiates a container to host the requested service, assigning it an IPv6 address from the appropriate pool. This IPv6 address is retained throughout the container's lifecycle. Hence, any subsequent migration does not alter its assigned address. Subsequently, the MEO triggers the AMF and the SMF to configure routing rules. Due to the characteristics of SRv6, rule configuration is confined to devices positioned at the network edge and any other potential endpoints. Core routers within the 5 GC network are preconfigured with necessary rules to allow packet forwarding to SR endpoints. The SIDs format permits aggregation, thus enhancing scalability. It is worth noting that core routers may lack SRv6 capabilities and route the packets toward the endpoint using the IPv6 routing mechanism.  
In both modes, the H.Encaps.Red behavior is employed. By encapsulating IPv6 packets within an outer IPv6 Header, a uniform SRv6 tunnel can be utilized for all packets necessitating the same path and functions within the 5 GC network, increasing scalability. This approach is possible thanks to the exclusion of the container address from the segment list, with the container's IPv6 address placed as the destination address within the inner IPv6 packet. Instead, adopting the inline version of the SRH precludes this capability, as it includes the container's IPv6 address as part of the segments list. Moreover, the use of H.Encaps.Red instead of H.Insert offers higher security and efficiency, as it avoids the need for modifying the original IPv6 packet. However, our architecture also supports the H.Insert method, although in the Enhanced Mode with Unchanged gNB mode, an extension of the End.M.GTP6.D behavior is required.
## 1 Introduction  
Medical nutrition therapy is the keystone of managing type 2 diabetes (T2 DM) and preventing its onset [1, 2, 3]. Nutritional recommendations have been delivered by several scientific societies to support clinicians and dietitians in their choice of the most suitable medical nutrition therapy for individuals with T2 DM [4, 5, 6]; however, adherence to these recommendations in clinical practice is generally poor [7, 8]. In many cases, these nutritional recommendations are based on macro- and micronutrients, which often prevent patients' understanding and adherence in real life. In the last decade, human nutrition science has shifted to focus not only on \"which nutrients to take\" or \"how much to eat\", but also on to \"how to eat\", considering aspects such as food order [9, 10, 11], eating speed [12], mealtime [13], and intermittent fasting [14].  
The Mediterranean diet is considered to be one of the healthiest dietary patterns [15], and is effective in improving both glycemic control and cardiovascular risk in people with T2 DM. The characteristics of the Mediterranean diet represent a dietary pattern rich in vegetables, fruits, nuts, wholegrains, and seafood, in addition to highlighting food-related cultural and social activities. These activities help to establish a lifestyle that encourages enjoying cooking and taking a long time to eat with other people [16]. Particularly in the Mediterranean region, people traditionally enjoy full-course meals, consuming one dish at a time, with intervals between courses, over several hours. In contrast, Japanese people are known for eating quickly within a limited timeframe, often without commensality. However, they occasionally share meals with family and friends in a full-course meal manner over extended periods. Many studies have shown that eating in a short period of time increases the risk of diabetes [17], obesity [18, 19], cardiovascular diseases [20], and metabolic syndrome [21]. We have previously reported that eating in a short period of time demonstrated significantly higher glycemic and insulin responses [12]. Moreover, our previous study demonstrated no significant differences in glycemic and insulin responses between eating meals in 10 min and eating meals in 20 min when following a food order of consuming vegetables first, followed by the main dish, and carbohydrates last [22]. While various studies have investigated the effects of different eating speeds or times required for meal and food orders [9, 10, 11, 12, 17, 18, 19, 20, 21, 22], the impact of consuming a full-course meal, with extended intervals between dishes, on blood glucose and insulin responses remains unknown. This study aimed to evaluate the impact of consuming a meal in a full-course meal manner over one hour, with intervals between courses, on glycemic and insulin responses in young healthy women, using a randomized controlled crossover trial.
## 5 Summary and Discussion
### Challenges and Limitations
#### 5.2.1 Vibration Sensor Challenges  
* Environmental vulnerability: Marine environments present high humidity, salinity, and temperature fluctuations, all of which can impair sensor accuracy and durability. Sensor corrosion is a major concern, necessitating the use of protective housing and robust sealing strategies.
* Installation and calibration complexity: Appropriate sensor placement is crucial for accurate signal acquisition. However, installation on large, geometrically complex structures, such as STSCs or RTGCs, is logistically demanding. Calibration procedures are equally labor-intensive and require periodic readjustments to maintain the measurement integrity.
* Data management constraints: Vibration sensors produce voluminous time-series data, posing challenges for data transmission, especially in areas with limited wireless coverage, and require efficient preprocessing and filtering algorithms to extract actionable insights.
## 3 Modeling  
### Physico-Chemical Model  
A formidable challenge lies in the thermodynamic model used for formaldehyde-containing solutions. The system used in this paper (formaldehyde + methanol + water) performs complicated thermodynamic behaviors. Formaldehyde (FA, CH\({}_{2}\)O) reacts with water (H\({}_{2}\)O) to poly(oxymethylene) glycols (MG\({}_{\text{n}}\), HO-(CH\({}_{2}\)O)n-H) [48]. The reactions are described by Equations (1) and (2).  
\[\text{FA}+\text{H}_{2}\text{O}\rightleftharpoons\text{MG}_{1} \tag{1}\]  
\[\text{FA}+\text{MG}_{\text{n-1}}\rightleftharpoons\text{MG}_{\text{n}};\text {n}>2 \tag{2}\]  
Formaldehyde reacts with methanol (MeOH, CH\({}_{3}\)OH) to poly(oxymethylene) hemiformals (HF\({}_{\text{n}}\), HO-(CH\({}_{2}\)O)n-CH3) [48]. The reactions of formaldehyde with methanol are described by Equations (3) and (4).  
\[\text{FA}+\text{MeOH}\rightleftharpoons\text{HF}_{1} \tag{3}\]  
\[\text{FA}+\text{HF}_{\text{n-1}}\rightleftharpoons\text{HF}_{\text{n}};\text {n}>2 \tag{4}\]  
Equations (1)-(4) occur in the system everywhere without any catalyst, and their equilibria are such that the amount of monomeric formaldehyde is negligible and most of them form oligomers. In this work, we describe the ternary mixture with UNIFAC model and treat the unstable species  
Figure 3: Vapor–liquid equilibrium diagram. (\(\blacksquare\)) Experimental results. Lines: Model.  
HF\({}_{\rm n}\) and MG\({}_{\rm n}\) as electrolytes in the feature of the Chemistry section of Aspen Plus. In the gas phase, it ignores Equations (2) and (4), and since the vapor pressure of MG\({}_{\rm n}\) and HF\({}_{\rm n}\) with n \(\geq\) 2 is deemed sufficiently low [48], the influence would be imperceptible even if there is an estimation deviation. The chemical equilibrium of Equations (1)-(4) in the aqueous and methanol solution is modeled by activity-based chemical equilibrium constants, which are taken from [PERSON] et al. [49] In this paper, the maximal chain length of MG\({}_{\rm n}\) and HF\({}_{\rm n}\) in this model is limited to n = 8. The species MG\({}_{\rm n}\) and HF\({}_{\rm n}\) for n \(\leq\) 3 are the main oligomers, and increasing the chain length does not significantly affect the calculation results [49]. In reactive systems containing formaldehyde, methanol, and water, two different ways of describing the composition are used. In this work, overall concentrations are depicted instead of the true composition of all components.  
In accordance with the original models [28], we considered an ideal vapor phase and describe non-ideality of the liquid phase through the UNIFAC model. The structural groups in the system (FA + water + methanol + methylal + PODE\({}_{\rm n}\) + TOX) are given in Table S2, which also contains the size parameters R and surface parameters Q of the groups. The UNIFAC group assignment is given in Table S3. The UNIFAC group interaction parameters are given in Table S4. Interaction parameters between groups 1-9 were adopted from [PERSON] et al. [45], and group 10, namely (CH\({}_{2}\)O)\({}_{\rm OME}\), was adopted from [PERSON] et al. [44].
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### Qualitative and Semi-Quantification Analysis of Volatile Compounds in the Fish Sauces
#### 3.3.1 Amine and Sulfide  
The changes in flavor compounds detected in the fish sauce samples are presented in Figure 3. Trimethylamine is a key compound responsible for the characteristic fishy odor of aquatic products, distinguishing them from other foods. It is produced through the degradation of trimethylamine oxide by facultative anaerobic bacteria during continuous fermentation [23]. In this study, the concentration of trimethylamine ranged from 365.35 to 1038.54 \(\mathrm{\SIUnitSymbolMicro g}\)/kg, with the highest concentrations in B30, followed by F10 and B60. These three groups also exhibited the highest concentrations of total volatile compounds (Figure 4). Methional is produced via the Strecker degradation of methionine, which serves as the precursor and is crucial in food flavor [24]. The methional content was significantly affected  
Figure 4: Stacked graph of volatile compounds in fish sauces following different cooking processes. U (unheated); B10, B30, and B60 (heated at 100 \(\mathrm{\SIUnitSymbolCelsius}\) for 10, 30, and 60 min, respectively); F10, F30, and F60 (heated at 220 \(\mathrm{\SIUnitSymbolCelsius}\) for 10, 30, and 60 s, respectively). Significant differences among different cooking fish sauces are indicated by different lowercase letters (\(p<0.05\)).  
Figure 3: Heatmap visualization of volatile compounds in fish sauces following different cooking processes. U (unheated); B10, B30, and B60 (heated at 100 \(\mathrm{\SIUnitSymbolCelsius}\) for 10, 30, and 60 min, respectively); F10, F30, and F60 (heated at 220 \(\mathrm{\SIUnitSymbolCelsius}\) for 10, 30, and 60 s, respectively).  
by cooking processes. Excluding F30, the methional content was higher in cooked samples than in unheated samples in this study.
# Development of Hydraulic Turbodrills for Deep Well Drilling
## 4 Comparative Analysis of the Developed Turbodrill  
After we received the technical characteristics of the developed turbine pair, we carried out a comparative analysis based on the equality of the power of the turbodrills.  
For comparison, let us take the TSSh-178T and Neyrfor 2 7/8 turbodrills (Table 3).  
In comparison to the domestic turbodrill, the developed turbodrill shows a 13-fold reduction in length and a 3-fold reduction in torque, provided that the maximum power has increased by 1.5 times. When comparing the developed turbodrill to a foreign one,  
Figure 4: Distribution of the flow velocity along the length of the working pair.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Indicator**} & \multicolumn{4}{c}{**Model of Turbodrill**} \\ \cline{3-5}  & & **TSSh-178T** & **Neyfor 2 7/8** & **Developed Turbodrill** \\ \hline
1 & Outer diameter of the case, mm & 178 & 73 & 195 \\
2 & Turbodrill length, mm & 7100 & 4572 & 520 \\
3 & Drilling fluid flow rate, l/s & 28–38 & 6.3–7.5 & 45–55 \\
4 & Drilling mud density, g/cm\({}^{3}\) & 1.0 & Before 2.16 & 1.0 \\
5 & Torque, Nm & 1630–3000 & Before 203 & 679–1013 \\
6 & Operating speed, rpm & 945–1283 & 2000–2500 & 2250–2750 \\
7 & Turbine pressure drop, MPa & 5.4–9.2 & 7.6–11.4 & 3.6–5.3 \\
8 & Maximum power, kW & 80–201 & 24–43 & 160–292 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of technical characteristics.  
there is a decrease in length by 8.5 times, an increase in torque by 5 times, and an increase in maximum power by 6.5 times.  
In addition, it is important to note that the outer diameter of the turbodrill, relative to the existing high-speed models, is larger (the developed turbine pair has 195 mm, and the Russian counterparts have 178 mm), and, accordingly, the working area of the blades is larger (by 100%).
## 3 Sound Source Localization Fusion Algorithm of a Three-Plane Five-Element Microphone Array
### The Three-Plane Five-Element Microphone Array Localization Fusion Algorithm  
The sound source spherical coordinates \(S_{0}(r_{0},\theta_{0},\varphi_{0})\), \(S_{1}(r_{1},\theta_{1},\varphi_{1})\), and \(S_{2}(r_{2},\theta_{2},\varphi_{2})\) correspond to the same target sound source S and are actually under the same coordinate system. The coordinates are obtained from three different planes based on a five-element microphone array. Each coordinate should be identical; however, since errors are introduced in the calculations for the time-delay value, sound velocity, and array element spacing, especially in the estimation of time delay, the three coordinates will be different.  
To reduce the measuring error and improve the localization precision and stability, we used repeated simulations to obtain the best compound weighting coefficients (i.e., the sine value of the elevation angle within the X0Y plane and the cosine value of the elevation angle within the Y0Z plane) to construct the three-plane five-element microphone array localization fusion algorithm. The weighted coefficients are set as \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\), and the fusion algorithm is used to obtain the sound source coordinate \((r,\theta,\varphi)\) as follows:  
\[\left\{\begin{array}{l}r=k_{3}(k_{2}\tau_{0}+k_{1}r_{1})+k_{4}r_{2}\\ \theta=k_{3}(k_{2}\theta_{0}+k_{1}\theta_{1})+k_{4}\theta_{2}\\ \varphi=k_{3}(k_{2}\varphi_{0}+k_{1}\varphi_{1})+k_{4}\varphi_{2}\end{array}\right. \tag{8}\]  
where \(k_{2}=sin\theta_{0}\), \(k_{1}=1-k_{2}\), \(k_{4}=cos\theta_{2}\), and \(k_{3}=1-k_{4}\).  
The calculation steps of the three-plane five-element microphone array sound source localization fusion algorithm are as follows:  
Step 1: derive the spherical coordinate calculation formula of sound source S in the first X0Y plane;  
Step 2: derive the spherical coordinate calculation formula of sound source S in the second X0Z plane;  
Step 3: derive the spherical coordinate calculation formula of sound source S in the third Y0Z plane;  
Step 4: design a composite weighting coefficient based on the sine value of the elevation angle within the X0Y plane and the cosine value of the elevation angle within the Y0Z plane;  
Step 5: introduce the three-plane five-element microphone array sound source localization fusion algorithm to obtain the sound source coordinate.  
Performance Analysis of the Sound Source Localization Fusion Algorithm Based on a Three-Plane Five-Element Microphone Array  
The sound source localization performance is related to the error of the time-delay estimation, the sound velocity, the array element spacing, the distance from the sound source to the coordinate origin, the elevation angle, and the horizontal angle. The precision of the time-delay estimation plays a key role in localization performance when the sound velocity and array element spacing are fixed.  
Let the standard deviation of the time-delay estimation be \(\sigma_{\tau_{0}}\), \(i\)= 1, 2, 3, 4, 5, and 6, where all are equal to \(\sigma_{\tau}\).