esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Technology Development Strategy of TFIFT Systems
### Investigation into Emerging and Vacant Technology Fields
#### 4.3.1 IPC Network Analysis  
The patent-IPC matrix was applied to the ARM method to discover the relationships between IPC codes. The association rules were applied to find the relationship between the IPC codes representing previous technology and the IPC codes representing next technology in the vacant technology field. The rules with a high lift value represent a strong relationship between technology fields. Tables 6 and 7 summarize the top 5 rules by lift criteria as a result of applying ARM to the 9 th and 10 th patent vacuums.  
The ARM-based network is schematized, as shown in Figure 7. A network with too many rules applied is not intuitive; therefore, it is necessary to create and analyze a network based on the upper rules. The ARM-based network in this study was designed based on  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Previous** & **Consequent** & **Support (\%)** & **Confidence (\%)** & **Lift** \\ \hline
1 & B65D,E04B & B60P & 0.104 & 100 & 10.667 \\
2 & B65D,E04H & B60P & 0.104 & 100 & 10.667 \\
3 & B65D,E04H,E04B & B60P & 0.104 & 100 & 10.667 \\
4 & B61F,B61C & B61D & 0.208 & 100 & 6.038 \\
5 & B60F,B61G & B61D & 0.208 & 100 & 6.038 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The association rules for the 9 th patent vacuum.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Previous** & **Consequent** & **Support (\%)** & **Confidence (\%)** & **Lift** \\ \hline
1 & E01B,B61L & B65G & 0.104 & 100 & 9.600 \\
2 & B61B,B61L & B65G & 0.104 & 100 & 9.600 \\
3 & B63B,B61L & B65G & 0.104 & 100 & 9.600 \\
4 & B66C,B61L & B65G & 0.104 & 100 & 9.600 \\
5 & B61B,E01B & B65G & 0.104 & 100 & 8.400 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The association rules for the 10 th patent vacuum.  
the top 10 rules of the lift. ARM-based networks are represented by different circle sizes and chromas: the larger the Support, the larger the size of the circle and the larger the lift, the darker the color. The network analysis of the 9 th patent vacuum and the 10 th patent vacuum is as follows.  
The IPCs that belong to the 9 th patent vacuum are B60P and B61D. The technical fields affecting B60P were analyzed as E04H, B65D, and E04B, while the technical fields affecting B61D were analyzed as B62D, B60F, B61F, B61L, and G06Q. Hence, to develop the 9 th patent vacuum, these 8 IPCs need to be carefully analyzed. The IPCs that belong to the 10 th patent vacuum are B65G and B61D, and the technical fields that have the greatest impact on B65G and B61D are B61B, E01B, and B61L. From a broader perspective, B63B and B66C also need to be analyzed for technological development, making a total of 5 IPCs that need to be analyzed to develop the 10 th patent vacuum.
## 2 Results
### MB Tissue Treatment Identifies Histological Regions for RNA Microdissection
(**B**) Each box graph shows _18 SrRNA_ Ct values (\(\gamma\)-axis) from RNA isolated from six different FFPE MIBC tissues with (\(+\)) or without (\(-\)) MB prior staining using four different isolation methods (A–D) (Table 1); \(n=6\) technical replicates; *_p_ = 0.0481 Method A (graph in green). All other results either with (\(+\)) or without (\(-\)) MB were not statistically significant. (**C**) Each box graph shows _18 SrRNA_ Ct values (\(\gamma\)-axis) from RNA isolated from the six different FF patient matched MIBC tissues to FFPE tissues (see **B**) above with (\(+\)) or without (\(-\)) MB prior staining using five different isolation methods A–E (Table 1); A –MB to A +MB (green graph), **_p_ = 0.0043, B –MB to B +MB (green graph), *_p_ = 0.039, C –MB to C +MB (green graph), **_p_ = 0.0022, D –MB to D +MB (red graph), *_p_ = 0.026; all comparisons to A +MB with B +MB, C +MB, D +MB and E +MB were **_p_ = 0.0022; all were performed with \(n=6\) technical replicates. (**D**) Box graphs for isolated RNA from MIBC FFPE tissues after normalization of _18 SrRNA_ with _5.8 SrRNA_ (\(\Delta\)Ct). (**E**) Box graphs for isolated RNA from MIBC FF tissues after normalization of _18 SrRNA_ with _5.8 SrRNA_ (\(\Delta\)Ct). **_p_ = 0.0065 (A +MB) and **_p_ = 0.0022 (C +MB). In summary, our results show that, in the presence of MB, RNA isolation from FFPE tissues using Method A and from FF tissues using Methods A-C, statistically significantly improved RNA integrity using qPCR for _18 SrRNA_ Ct, and especially for Methods A and C from FF tissues even after normalization. In order to rule out any artifactual effects due to MB, which has an emission at 610 and 664 nm [33], we performed several control experiments. Due to the fact that MB interacts with nucleic acids, we asked whether MB could interfere with optical density readings affecting qPCR. Analyzing optical density measurements of isolated RNA from MIBC tissues from the different isolation methods, in the presence or absence of MB, showed an RNA absorbance at 260 nm, but not at 610 nm and 664 nm. Secondly, if MB was bound to RNA but below the level of detection, we tested the possibility, if MB could interfere with the qPCR results using SYBR-Green, which binds DNA with an absorbance of \(\sim\)497 nm and emission of \(\sim\)520 nm. We added MB in dilutions from 1:10 to 1:2000 to purified RNA and then performed cDNA-synthesis. In addition, MB was added to synthesized cDNA and then employed qPCR for _18 SrRNA_. Results showed for all experiments, except for the highest concentration of a 1:10 MB dilution with cDNA, no detectable differences in amplification signals in the presence or absence of MB (Figure S1). These results suggest that MB based upon its emission properties does not interfere with qPCR causing artifacts.
# Non-Magnetic Circulator Based on a Time-Varying Phase Modulator
## 5 Conclusions  
In the paper, the concept of a time-varying phase modulator (TVPM) is proposed. By taking advantage of the characteristic that the time-varying system can break the symmetry of time inversion, the general topological structure of the gyrator formed by the time-varying phase modulation method is proposed. The gyrator is composed of common differential phase shifters and different electrical control signals. Simulation results show that the proposed gyrator has a relatively high bandwidth and relatively compromised insertion loss. The circulator is implemented by using the gyrator and the appropriate transmission line. The simulation results show that the average insertion loss of the circulator is \(-2\) dB, and the average isolation is \(-18\) dB. Most of the insertion loss comes from the differential phase shifter, in which the initial part of the carrier is changed into another frequency during the phase conversion. The aim of the paper is to provide a compromised solution to the N-path filter and the conductivity modulation method of the circulator.  
It can be concluded that the circulator in this paper has good performance. The TVPM circuit is a common \(180^{\circ}\) differential phase shifter structure. In the future, it could have good development prospects in the field of high-speed communication.  
Methodology, X.J.; Formal analysis, X.J.; Writing--original draft, X.J.; Writing--review & editing, Y.J.; Supervision, Y.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China, under grant No. 61774078.  
The study didn't require ethical approval.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal
### Conceptual Basis of the U-CLX Model
#### 4.1.4. People, Roles, Learning Processes, Context, and Learning Experiences  
* People are the principal component and central element of the U-CLX Model. The idea of the model is to evaluate levels of ubiquitous learning according to people's learning experiences, as well as the context. There are three classes of participants in the model: Academic Manager, Learning Engineer, and Student.
* Learning processes are the cognitive mechanisms of reception, assimilation, and data analysis that allow the generation of information and production of knowledge, for the purpose of forming intelligence and obtaining wisdom to apply in different situations and contexts. Learning processes are individual and depend on students' characteristics, forms, skills, attitudes, and learning abilities.
* Learning contexts are the scenarios in which the ubiquitous learning processes and experiences occur. Each context is personal and unique, since each person creates their learning with different pedagogical and technological elements, which form the learning context.
* Learning experiences are characterized by the capture of all data generated in the ubiquitous learning process, followed by data analysis to develop information, knowledge, intelligence, and wisdom, thus allowing ubiquitous learning to occur.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 3 Problem Statement and Modeling
### Edge Environment Model
\begin{table}
\begin{tabular}{c l} \hline \hline
**Symbol** & **Description** \\ \hline  & _Entities_ \\ \hline \(H\) & Set of edge nodes (hosts) \\ \(L\) & Set of links between edge nodes \\ \(U\) & Set of Users \\ \(V\) & Set of VNF types \\ \(I_{v}\) & Set of VNF instance of type \(v\) \\ \(S\) & Set of SFC types \\ \(I_{s}\) & Set of SFC instances of type \(s\) \\ \hline  & _Edge Node (Host) Attributes_ \\ \hline \(\mathcal{A}\) & Set of resource types \\ \(\alpha\) & A specific type of resource in \(\mathcal{A}\) \\ \(R^{\alpha}(h)\) & The amount of resource type \(\alpha\) of host h \\ \(TR_{h}\) & Total resources of host \(h\) \\ \hline  & _User Attributes_ \\ \hline \(b(l_{uh})\) & Bandwidth between user \(u\) and host \(h\) \\ \(d(l_{uh})\) & Delay between user \(u\) and host \(h\) \\ \(w(l_{uh})\) & Loss rate between user \(u\) and host \(h\) \\ \(S_{u}\) & Set of SSCs requested by user \(u\) \\ \hline  & _VNF Type Attributes_ \\ \hline \(v_{inst}\) & VNF instance of type \(v\) \\ \(IM_{v}\) & Set of hosts with the image of VNF type \(v\) \\ \(r^{\alpha}(v)\) & The demanded resource \(\alpha\) by VNF type \(v\) \\ \(TR_{v}\) & Total required resources of VNF \(v\) \\ \hline  & _SFC Attributes_ \\ \hline \(d_{tol}\) & End-to-end delay tolerance of an SFC type \\ \(b(s)\) & Min bandwidth of SFC \(s\) \\ \(jit(s)\) & Jitter of SFC \(s\) \\ \(SLA(s)\) & SLA of SFC \(s\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Entities and attributes notation.
## 4 Discussion
### Isokinetic Temperature Again  
In order to clarify the methodology of the experimental determination of isokinetic temperature, the parameters of the Arrhenius equation were used.  
For the assumptions: \(f(\alpha_{0})=1\), \(g(\alpha_{0})=\alpha_{0}\), Equation (22) determines the activation energy:  
\[E=R\frac{T_{0}}{\alpha_{0}} \tag{66}\]  
and using both Equation (19) and Equation (66) we obtain:  
\[\ln A=\frac{1}{\alpha_{0}}+\ln\left(\frac{q}{T_{0}}\right) \tag{67}\]  
The data illustrated in Figure 11\((N=7)\) and according to Equations (66) and (67) \((N=7)\) were combined with an additional coordinate considered in this work as a reference in [35] (e.g., [PERSON]) \((N=15)\) and are presented in Figure 7.  
The determined activation energies presented in Figure 7 form two resultant groups, in fact, they are combined in the form of one simple _KCE_. Results of the scoring method (much higher values-red) can be classified as quasi-isoconvorsional (\(\alpha_{0}\cong\text{const}\)). On the other hand, values that are lower (blue) or close to the reference data (\(E=191\) kJ/mol, \(\ln A=15.4\)/ A in s\({}^{-1}\)) [35], e.g., [PERSON]) are determined for individual heating rate-dependent curves by the Coats\(-\)Redfern equation for a range of changes \(\alpha\in[0.1-0.9]\).  
For the data presented in Figure 7 for Equation (46) and \(T_{iso}=914.29\) K, \(r^{2}=0.9980\) and temperatures: \(T_{low}=976\) K, \(T_{high}=1069.27\) K (for \(\alpha\in[0.1-0.9]\), see [5]) and setting up \(T^{*}\), \(T_{high}\), \(T_{hm}=1020.51\) K for \(N=15\) and probability at least 99% (sl = 0.01) (Equation (48)):  
\[CQF=0.7017,\quad CQF_{99}=0.29+\frac{1.41}{15^{0.57}}0.591\]  
and from Equation (49):  
\[K=2.50\]  
This result shows the reliability of the determined isokinetic temperature, which is ahead of the range of tested temperatures (\(T_{iso}<T_{low}\)). It therefore has the character of an extrapolated quantity.
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 4 Model Simulations and Discussion
### Different Vegetation Parameters at Various Polarizations on the Scattering Characteristics
However, as the above two variables set constants for different vegetation parameters (Figures 5-7), the bistatic scattering properties almost keep the same for some cases, and have apparent changes for other cases, which are related to the observation geometries and polarizations. Therefore, if we well utilize the geometry and polarization, then the vegetation parameters have the potential to be detected. In other words, if we ignore or take less consideration of the information of observation geometry and polarization, it is hard to detect. Given the importance of observation geometry and polarizations for the final vegetation parameters' detection, we need the corresponding measurement data to validate this information. However, due to the limits of the present in-situ campaigns, we cannot find abundant data to back up this assumption. We have to ask for the theoretical model and the corresponding simulations for help.
# Design, Modeling, and Analysis of Piezoelectric-Actuated Device for Blood Sampling
## 2 Physical Modeling of the Device
### Microneedle  
A Microneedle of stainless steel was attached at the bottom of the pump chamber, flush with its inner wall, as shown in Figure 1b. The purpose of the microneedle is to extract the blood from the human body, so as to provide sufficient blood sample for diagnosis at the biosensor location. A regular, pentagonal microneedle of 1.8-mm length having a hydraulic diameter of 60 \(\upmu\)m was considered [23; 26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials** & **Properties** & & **Value** \\ \hline \multirow{4}{*}{Piezoelectric} & Piezoelectric charge constant (C/N) & \(d_{31}=d_{32}\) & \(-320\times 10^{-12}\) \\  & & \(d_{33}\) & \(650\times 10^{-12}\) \\  & Relative dielectric constant & \(\epsilon\) & \(3600\) \\  & Density (kg/m\({}^{3}\)) & \(\rho\) & \(7800\) \\ \multirow{2}{*}{Epoxy Glue} & Module of elasticity (GPa) & 2.478 \\  & Density (kg/m\({}^{3}\)) & 1400 \\ \multirow{2}{*}{Stainless steel} & Module of elasticity (GPa) & 200 \\  & Density (kg/m\({}^{3}\)) & 7700 \\ \multirow{2}{*}{Silicon} & Module of elasticity (GPa) & 168.3 \\  & Density (kg/m\({}^{3}\)) & 2329 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of materials.  
Figure 1: Schematic diagram of blood sampling device, (**a**) design of wristwatch with micropumps, and (**b**) detail representation of a piezoelectric micropump with FQPB actuator.
###### Abstract  
Structural health monitoring (SHM) techniques, which are also considered as online nondestructive testing methods, are significant in modern structural engineering due to their ability to guarantee structure safety while reducing maintenance cost. It is often necessary to combine different SHM methods to achieve a more reliable damage detection result. However, the hardware of the SHM systems is usually expensive, bulky, and heavy when they are designed separately. Therefore, this paper proposes a three-layer architecture for designing an integrated multi-function SHM system to achieve a small, lightweight, and low power consumption SHM system. Based on the architecture, an integrated SHM system with impact monitoring and electromechanical impedance measurement is developed. In addition, a scheduling module is developed to manage the two functions of the system. Furthermore, an integrated interface is developed to transfer the data and the command. Then, an integrated printed circuit board is designed and manufactured to achieve the aforementioned functions. The designed system is applied for impact monitoring and damage detection for a supporting structure of a sailplane.  
structural health monitoring; piezoelectric transducer; system integration; impact monitoring; electromechanical impedance measurement +
Footnote †: journal: Applied Sciences
# A Multi-Scale Video Longformer Network for Action Recognition
## 4 Experiments
### Hyperparameter Tuning
#### 4.4.2 Number of 3D Longformer Attemblocks and Mlpblocks  
We investigated the impact of different numbers (\(n\)) of 3D Longformer AttenBlock and MlpBlock layers in each stage. To ensure a fair experiment, we set the total count of n to  
Figure 8: The accuracy and loss of MSVL with four different network configurations on the HMDB51 dataset, employing the same training strategy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Top-1/\%** & **Top-5/\%** & **FLOPs/G** & **Params/M** \\ \hline Ape & 68.62 & 86.95 & 157 & 27.3 \\ Rpe & 71.38 & 88.92 & 158 & 27.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablations on different position encoding of MSVL on the HMDB51 dataset.  
Figure 9: Visualization of absolute 3D position encoding and relative 3D position encoding.  
be equal. To be specific, we employed two network structures--(1, 1, 9, 1), resembling a transformer hierarchy, and (1, 2, 8, 1), resembling a convolutional network hierarchy--both using relative 3D position encoding. As shown in Table 4, the (1, 1, 9, 1) structure reduced FLOPs by 2G but increased Params by 1.2M compared to (1, 2, 8, 1). Furthermore, the (1, 1, 9, 1) structure lagged behind (1, 2, 8, 1) by 1.97% in top-1 accuracy and 0.97% in top-5 accuracy.  
From the data presented above, it is evident that the (1, 2, 8, 1) structure, which resembles a convolutional network hierarchy, is better suited for MSVL. We believe this is related to the feature extraction process and network architecture of MSVL. MSVL employs high resolution feature maps for extracting local information in shallow layers and low resolution feature maps for capturing global information in deeper layers, all while progressively increasing the feature depth, which aligns more closely with the characteristics of convolutional neural networks.
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 2 Materials and Methods
### Simulation of the Photovoltaic Energy Supply System  
The solar energy supply system and the estimated fan performance of the two fans of the ISD were simulated using simulation und model-based design software (MATLAB Simulink, The Mathworks Inc., Natick, MA, USA) using weather data of Nairobi, Kenya. For this purpose, the weather data set KEN_Nairobi-Kenyatta.637400_SWERA was obtained (Meteonorm, Meteotest AG, Bern, Switzerland). The simulation model of the solar battery and the charge controller was set according to [PERSON] et al. [20]. Based on the climate data input, the voltage of the PV modules, state-of-charge (SOC) of the battery, and fan performance were simulated [21].  
The voltage-dependent characteristics per single fan were measured in preparatory tests in a range between 5.4 and 14.8 V, and linear functions were established:  
\[I=0.20\cdot U-0.43,\qquad R^{2}=0.997 \tag{1}\]  
\[n=138.94\cdot U+176.44,\qquad R^{2}=0.995 \tag{2}\]  
\[m=0.028\cdot U+0.015,\qquad R^{2}=0.987 \tag{3}\]  
with voltage \(U\) (V), current \(I\) (A), rotational speed \(n\) (rpm), and air mass flow \(m\) (kg/s) at a temperature of 25 \({}^{\circ}\)C.  
The operating mode was assumed to be as follows: initial SOC of the battery is 90%, the system shut downs when SOC falls below 40%, and the system restarts when SOC again exceeds 45%.
# The Development and Evaluation of a Training Monitoring System for Amateur Rugby Union
## 5 Conclusions
### What Are the Key Practical Applications for Coaches?  
* This study provides further support to the use of subjective measures as a method of monitoring training. Their ease of use, accessibility and low cost make them practically applicable in the amateur setting.
* For players to engage with a TMS and give consistent data, they need regular feedback and evidence that the data are informing their training. This should aid in achieving consistent data from players and in turn improve the effectiveness of the TMS.
* S&C coaches should be aware that the temporal robustness of sRPE may alleviate the difficulties around collecting post-match sRPE.  
* It is important that S&C coaches have the full support and assistance of all members of the coaching staff to ensure player compliance and to share the associated training monitoring and analysis demands.
# Removal of Metals and Dyes in Water Using Low-Cost Agro-Industrial Waste Materials
## 3 Results and Discussion
### Adsorption Parameters Effect
#### 3.2.2 Effect of the Adsorbent Dose  
The adsorbent dose effect study was performed for MB adsorption, which functioned as a reference to determine the best dose and use it in Cr (VI) adsorption tests. Analyzing this parameter, it is considered that the greater the amount of adsorbent, the greater the number of active sites, which favors the adsorption process. This explanation is also valid when the surface area of the adsorbent is increased, or the particle size of the solid is reduced [28]. The effect of the dosage allows us to evaluate the adsorption capacity of a dye such as MB when working with less amount of adsorbent; this allows us to recognize the capacity of a dye from an economic point of view [45].  
Figure 6: Effect of the pH onto MB adsorption at room conditions and 200 rpm.  
Figure 8 shows a proportional behavior between the adsorbent amount and the removal efficiency of MB. On the other hand, an increase in the adsorbent dose causes the diminution of the adsorption capacity, which implies that the active sites present in the carbon are occupied to a great extent by the pollutant molecules; however, the higher the dose, the greater the number of active sites, denoting the presence of free active sites upon reaching equilibrium and obtaining a lower adsorption capacity. Regarding the effect of the initial concentration, the higher the concentration, the greater the adsorption capacity obtained, as in the case of 10 mg, but at 30 mg, the higher the concentration, the lower the adsorption capacity since active sites remain free to occupy. The same behavior is observed for the Cr (VI) removal process in Figure 9.  
Figure 8: Effect of the adsorbent dose and initial concentration over the MB adsorption using MWBC.  
Figure 7: Effect of pH onto Cr (VI) adsorption at room conditions and 200 rpm.