esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Traditional Chinese medicine (TCM), traditional Korean medicine (TKM), and kampo medicine (KM) have long been used in clinical practice for the treatment or prevention of various diseases in countries such as Korea, China, and Japan. The treatments are generally regarded as safe and have become standardized over centuries of refinement. However, with the development of modern science, research on these herbal prescriptions has become more frequent. Moreover, there is an increasing demand for regulatory documentation to certify the safety and standardization of TCM, TKM, and KM, and validated methods are therefore required.  
Sojadodamgangki-tang (SDGT) is a TKM consisting of 12 herbal medicines that has been used to treat asthma [1]. The effects of SDGT on SO\({}_{2}\)-induced respiratory injury, type I and type IV allergic reactions, and allergic asthma have been reported [2; 3; 4; 5].  
The main components in the medicinal herbs that make up SDGT are known to be as follows: phenylpropanoids (e.g., rosmarinic acid) and sesquiterpenoids (e.g., \(\beta\)-caryophyllene) from Perillae Fructus [6], phenols (e.g., homogentisic acid and 3,4-dihydroxybenzaldehyde) from Pinelliae Tuber [7], coumarins (e.g., decursin and decursinol angelate) from Angelicae Gigantis Radix [8], flavonoids (e.g., apigenin and schaftside) from Arisaemia Rhizoma [9; 10], flavonoids (e.g., hesperidin, nobiletin, and nariruttin) from Citri Unshius Pericarpium [11; 12], coumarins (e.g., praeruptorin A and B) from Peucedani Radix [13], lignans (e.g., honokiol and magnolol) from Magnoliae Cortex [14], flavonoids (e.g., poncirin and neopconcirin) from Poncini Fructus Immaturus [15], triterpenoids (e.g., pachymic acid, dehydrobrouricoic acid, and polyporenic acid C) from Poria Sclerotium [16; 17], flavonoids (e.g., liquiritgenin), flavonoid glycoside (e.g., liquiritn apioside and liquirittin), and triterpenoglycosides (e.g., glycerrhizin) from Glycyrrhizae Radix et Rhiza [18], phenols (e.g., 6- and 10-gingerol)from Zingiberis Rhiza Recens [19], and flavonoid glycosides (e.g., spinosin and 6''-feruloylspinosin) from Zizyphi Fructus [20].  
For the quality control of SDGT, we describe herein the development of the first simultaneous analysis of 14 marker components by high-performance liquid chromatography-photodiode array detection (HPLC-PDA) as well as liquid chromatography-mass spectrometry with tandem mass spectrometry (LC-MS/MS). The 14 marker components determined were: liquiritin apioside (LIQA), liquiritin (LIQ), and glycyrrhizin (GLY) (from Glycyrrhizae Radix et Rhiza), nodakenin (NOD), decursin (DEC), and decursinol angelate (DECA) (from Angelicae Gigantis), narirutin (NAR) and hesperidin (HES) (from Citri Unshius Pericarpium), naringin (NARG), neoponcinir (NPON), and poncinir (PON) (from Poncini Fructus Immaturus), apigenin (API) (from Arisaematis Rhiza), honokiol (HON) (from Magnoliae Cortex), and praeruptorin A (PRAA) (from Peucedani Radix).
## 5 Experimental Study  
### Medical Data Analyses  
For the database with rheumatoid arthritis patients, the following data were taken into account:  
* Input variables: the date of the patient evaluation, the age and gender of patient, the biological agent used at the time of the evaluation, the date of the biological therapy initiation, the starting date of biological agent used at the time of the evaluation, remissive therapy (conventional DMARDs), corticosteroid therapy, a pathological history of tuberculosis.
* Output variable: QuantiFERON-TB Gold test result. For the database with ankylosing spondylitis patients, the following were taken into account:
* Input variables: the date of the patient evaluation, the age and gender of patient, the biological agent used at the time of the evaluation, the date of the biological therapy initiation, the starting date of biological agent used at the time of the evaluation, NSAID treatment.
* Output variable: QuantiFERON-TB Gold test result.  
All patients with an initially negative QuantiFERON test result and who tested positive during the biological treatment were subsequently diagnosed with tuberculosis in the Pneumology Clinic and they received specific treatment. The reason why we considered the positive QuantiFERON test result as evidence of a reactivation of latent tuberculosis was that it was later confirmed by a specialist, and it was the first test to indicate this diagnosis, as it was performed as a screening method in patients undergoing biological treatment. Because we wanted to analyze whether the duration of the biological treatment influences the reactivation of TB, we decided to take into account the date of the positive QuantiFERON result and not the later date when the diagnosis was confirmed by the pneumologist.
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 6 Bias Estimation with Opportunity Traffic
### Recursive Bias Estimation  
After initialization, the values of the biases are estimated recursively with an Extended Kalman Filter. Biases are considered constant, so the prediction matrix (\(\Phi\)) is the identity matrix. Sub-index \(k\) indicates the temporal index of the state and the diacritic circumflex indicates that it is a predicted state. The predicted state and its covariance matrix at the time of the current pseudomeasurement are:  
\[\begin{split}\widehat{b_{k}}=\Phi b_{k-1}\\ \widehat{P_{k}}=P_{k-1}\Phi P_{k-1}^{\prime}+Q_{k-1}\end{split} \tag{70}\]  
In simulated data where the biases are constant, the noise plant covariance matrix can be set to zero, but in real scenarios the biases will be quasi-constant and the \(Q\) matrix must be set with small values of covariance. After that the residual and its covariance matrix must be estimated:  
\[\begin{split} X_{b,k}=X_{m,1}-X_{m,2}\\ r_{k}=X_{b,k}-H_{b,k}\widehat{b_{k}}\\ S_{k}=H_{b}\widehat{P_{k}}H_{b}^{\prime}+R_{k}\end{split} \tag{71}\]  
With these previous values, the filter gain is calculated and son the updated state so the updated covariance of the filter are calculated:  
\[\begin{split} K_{k}=\widehat{P_{k}}H_{b,k}^{\prime}S_{k}^{-1}\\ b_{k}=\widehat{b_{k}}+K_{k}r_{k}\\ P_{k}=\left(I-K_{k}H_{b,k}\right)\widehat{P_{k}}\end{split} \tag{72}\]
## 2 Materials and Methods
### Carotenoid Analysis by Reverse-Phase High-Performance Liquid Chromatography (RP-HPLC)  
For production of carotenoids in _E. coli_, 300 \(\upmu\)L of a 5 mL overnight culture was used to inoculate 15 mL auto-induction Terrific Broth (TB) base without trace elements (Formedium) with relevant antibiotics in 50 mL opaque falcon tubes. These cultures were incubated in darkness at 37 \({}^{\circ}\)C for 24 h in a rack set at a 45\({}^{\circ}\) angle on a shaking platform rotating at 220 rpm. All subsequent steps were performed in the dark. Cells were harvested by centrifugation at 4600\(\times\)_g_ for 10 min at 4 \({}^{\circ}\)C, washed with ice-cold 30 mM HEPES pH 7.4, and transferred to 2 mL screw-capped tubes. Pigments were extracted from cell pellets by adding 700 \(\upmu\)L of 7:2 acetone:methanol (\(\
u\)/\(\
u\)), vortex-mixing for 30 s, and incubating on ice for 20 min in the dark. The extracts were clarified by centrifugation (15,000\(\times\)_g_ for 5 min at 4 \({}^{\circ}\)C), supernatants were transferred to fresh tubes to which 30 \(\upmu\)L 5 M NaCl and 700 \(\upmu\)L hexane were added, mixed, and allowed to partition. The upper hexane phase was transferred to a 2 mL glass vial and evaporated to dryness in a vacuum centrifuge at ambient temperature. Dried pigments were dissolved in 150 \(\upmu\)L acetonitrile, and immediately analyzed on an Agilent 1200 HPLC system. \(\zeta\)-carotene species were separated on a YMC30 C30 RP column (3 \(\upmu\)m particle size; 250 mm \(\times\) 4.6 mm). An isocratic buffer composed of 40:30:30 acetonitrile:methanol:tetrahydrofuran (\(\
u\)/\(\
u\)/\(\
u\)) was used to separate carotenoids at 1 mL min\({}^{-1}\) at 40 \({}^{\circ}\)C, and elution of pigments was monitored by absorbance at 425 nm. Lycopene species were separated on a Discovery HS C18 column (5 \(\upmu\)m particle size; 250 mm \(\times\) 4.6 mm) according to the slightly modified method described in our previous work [28].  
_Synechocystis_ carotenoids were extracted from cell pellets washed with ice-cold 30 mM HEPES pH 7.4 by vortex mixing in 100% methanol for 30 s, the extracts were clarified by centrifugation (as above), and the supernatants were analyzed following the same protocol used for analysis of lycopene species in _E. coli_.
## 1 Introduction
The situation becomes more significant in the presence of implant sensors with different QoS demandsand data rates. This highly dynamic nature prolongs the network convergence time that could cause stale network information to the source nodes while selecting the route. Besides these drawbacks, TMQoS only considers the available shortest path routes for traffic dissemination. However, the shortest-path might not always be the best end-to-end path considering the desired QoS metrics and temperature rise. In this paper, we present TLQoS, a Thermal-aware Localized QoS routing protocol for _in-vivo_ sensor nodes in WBAN, which exploits a localized approach in route selection with the aim of satisfying the required QoS demands of diverse _in-vivo_ nodes as well as preventing the formation of highly heated nodes, known as hotspot(s) along the network. TLQoS employs potential-based greedy routing approach that reflects its localized behavior requiring only local neighborhood information. TLQoS defines a number of routing potentials based on the QoS metrics and node temperature. To avoid the formation of routing loops which might be common in typical greedy approaches, and to route the packet towards the sink reducing unnecessary hop traversal, hybrid potentials are introduced, and a routing loop avoidance mechanism is presented in TLQoS. TLQoS exploits a completely modular approach to address the traffic according to their respective QoS demands. TLQoS effectively measures the parameters for route selection exploiting a cross-layer framework. Furthermore, the performance of TLQoS is evaluated compared to the state-of-the-art protocols using simulations. The rest of the paper is organized as follows: Section 2 summarizes the related works. Section 3 presents the system model and states the preliminaries behind the protocol. Section 4 describes the proposed protocol. Section 5 demonstrates the protocol performance using simulation. Finally, Section 6 presents the concluding remarks.
# A Generative Adversarial Network Structure for Learning with Small Numerical Data Sets
## 3 Methodology
### Virtual Sample Generation and Selection
#### 3.1.3 MTD in Range Estimation  
Using the MTD method, for one of the attributes of sample \(X_{j}\), the range of the upper (\(UB\)) and lower bound (\(LB\)) is estimated by Formulas (2)-(5). Note that \(min_{j}=\min\{x_{1j},\ x_{2j},\ldots,x_{nj}\}\), is the minimum value of the sample data, \(n\) is the number of data points in the dataset, \(max_{j}=\max\{x_{1j},\ x_{2j},\ldots,x_{nj}\}\) is the maximum value of the sample data, \(CL_{j}\). (Center Location) is the center point of the range of attribute value, and \(N_{j}^{L}\) and \(N_{j}^{U}\), respectively, denote the number of samples that are less than and greater than \(CL_{j}\). \(N_{j}^{L}/(N_{j}^{L}+N_{j}^{U})\) and \(N_{j}^{U}/\left(N_{j}^{L}+N_{j}^{U}\right)\) represent the expansion of the upper and lower boundaries, respectively; \(\sqrt{-2\left(\hat{s}_{j}^{2}/N_{j}^{L}\right)ln(10^{-20})}\) and \(\sqrt{-2\left(\hat{s}_{j}^{2}/N_{j}^{U}\right)ln(10^{-20})}\) represent the proportion of the left and right sides, respectively. This means that the expansion of information is influenced by the number of samples on the left and right sides of \(CL_{j}\), instead of symmetrical diffusion. As shown in Figure 3, when the observation \(min_{j}\) is less than the estimated \(LB_{j}\), the lower bound shifts to point \(min_{j}\), whereas when the observation \(max_{j}\) is greater than the estimated \(UB_{j}\), the upper bound shifts to point \(max_{j}\). Due to the scarcity of samples, the case of \(0\)-variance may also occur. [PERSON] et al. [15] indicates that \(LB_{j}\) and \(UB_{j}\) can be set to \(min_{j}/5\) and \(5 max_{j}\) in this case, based on their limited past experiences.  
Figure 3: The possible population range of MTD.  
However, the GAN can only be used in the context of a large number of pictures, and cannot be applied to numerical small datasets. Therefore, a means must be developed to enable GAN to be applied to numerical small datasets. In previous research, mega-trend-diffusion [15] provided a way to enable numeric small samples to generate virtual samples that are similar to real samples. By using MTD to find the upper and lower bounds, and estimating the population within a reasonable range, as shown in Figure 3, we can randomly generate the virtual sample with a membership function (Figure 4) using small datasets. The upper bound, lower bound, and membership function formulas are listed as:  
\[LB_{j}=CL_{j}-\frac{N_{j}^{L}}{N_{j}^{L}+N_{j}^{ll}}\times\sqrt{-2\times\frac {\hat{S_{j}}^{2}}{N_{j}^{L}}\times ln(10^{-20})} \tag{2}\]  
\[LB_{j}=\left\{\begin{array}{l}LB_{j},LB_{j}<\min_{j}\\ \min_{j},LB_{j}>\min_{j}\\ \frac{\min_{j}}{5},\hat{S_{j}}^{2}=0\end{array}\right. \tag{3}\]  
\[UB_{j}=CL_{j}-\frac{N_{j}^{ll}}{N_{j}^{L}+N_{j}^{ll}}\times\sqrt{-2\times \frac{\hat{S_{j}}^{2}}{N_{j}^{ll}}\times ln(10^{-20})} \tag{4}\]  
\[UB_{j}=\left\{\begin{array}{l}UB_{j},UB_{j}<max_{j}\\ max_{j},UB_{j}>max_{j}\\ max_{j}\times 5,\hat{S_{j}}^{2}=0\end{array}\right. \tag{5}\]
## 2 Related Works
Existing research exploring the combined effect of using GGBFS and RCA on the primary properties of concrete is limited. To begin with, one study used machine learning to accurately predict concrete's compressive strength utilizing a dataset of 125 different materials, including waste by-products for eco-friendly concrete. The most efficient ensemble models were LRF and CNN-LSTM [21]. Building on this, another study aimed to develop a Multivariate Polynomial Regression (MPR) model for predicting the compressive strength of eco-friendly concrete containing RCA and GGBFS. The MPR outperformed linear regression and SVM models in accuracy [22]. Continuing this trend of advancement, a subsequent study created a Multivariate Adaptive Regression Splines (MARS) machine learning model to predict the compressive strength of eco-friendly concrete, which outperformed SVM and RF models according to R\({}^{2}\) and RMSE metrics [23]. Further enhancing the field, a GBRT model integrated with grid search cross-validation was utilized to precisely predict the compressive strength of eco-friendly concrete. Remarkably, this Grid Search Cross-Validation GBRT (GSC-GBRT) model surpassed the default GBRT model, providing improved accuracy metrics [24]. More recently, a study developed an ANN model to predict the mechanical and durability properties of concrete incorporating waste materials. Experimental results showed that while increased recycled aggregate deteriorates concrete properties, adding GGBFS can counteract these negative effects, improving both mechanical characteristics and long-term durability [25].
## 3 Application Instance
### Data Preprocessing  
Taking the collected rock mass acoustic emission signals as an example, the upper and lower envelope curves of the rock mass acoustic emission signals are obtained using the sliding average method, the cubic spline interpolation method, and the optimal empirical envelope method. For the sake of presentation, only the data of the first 400 sampling points in the results are taken. The results are shown in Figures 6-8. The envelope curves constructed by the optimal empirical envelope method exhibit a smooth trend, closely matching the original curve, and almost no over-enveloping or under-enveloping phenomena are observed. On the other hand, both the cubic spline interpolation method and the sliding average method exhibit more pronounced over-enveloping phenomena, with the cubic spline interpolation method also displaying under-enveloping issues. Hence, it can be observed that the optimal empirical envelope method can replace the sliding average method for envelope curve determination.  
Taking the collected rock mass acoustic emission signals as an example, both the CELMD and improved CELMD methods were employed for decomposition. The decomposition results are shown in Figures 9 and 10. From the figures, it can be observed that CELMD decomposes a larger number of PF components, but the later components contain more false components, resulting in poor decomposition accuracy. Moreover, noticeable endpoint effects are evident at the left endpoint, and components PF3 to PF5 exhibit significant mode mixing phenomena. In contrast, the decomposition results of the improved CELMD method show almost no false components, and the components of different frequency signals are well decomposed. The influence of white noise is effectively eliminated, and no abnormal amplitude or frequency is observed at the endpoints. There are fewer occurrences of endpoint effects and mode mixing compared to the CELMD method, indicating the significant advantages of the improved CELMD method.  
Figure 8: Signal envelope curve constructed by the empirical optimal envelope method.  
Figure 6: Signal envelope curve constructed by the sliding average method.  
Figure 7: Signal envelope curve constructed by the cubic spline interpolation method.  
Figure 9: CELMD decomposition results of the RS signal.
## 3 Results and Discussion
### Equivalent Viscous Damping  
As shown in Figure 12, the equivalent damping ratio can be computed by Equation (3):  
\[\xi_{\text{eq}}=\frac{1}{2\pi}\times\frac{\text{S}_{\text{(ABC+CDA)}}}{\text{S}_{ \text{(OBE+ODF)}}} \tag{3}\]  
where \(\text{S}_{\text{(ABC+CDA)}}\) denotes the area enclosed by the hysteretic loop and \(\text{S}_{\text{(OBE+ODF)}}\) denotes the area of triangles that represents the rigid, perfectly plastic curve.  
The equivalent damping ratio versus lateral drift is plotted in Figure 13. At the beginning of the test, the equivalent damping ratio was high, ranging from 0.26 to 0.45. Then, the equivalent damping ratio dropped rapidly to approximately 0.10 at the lateral displacement of 3 mm. As the lateral displacement increased, the equivalent damping ratio increased to a steady value of 0.26 and 0.13 for prefabricated walls and cast-in-place walls, respectively. However, the equivalent viscous damping of reinforced concrete masonry structures was assumed, ranging from 0.07 to 0.10. This indicated that the structures with RMSWs can provide a high level of damping and, hence, reduce the seismic demand. Regarding to the construction method, the equivalent damping ratios of cast-in-place walls were higher than those of prefabricated walls, which was also found in previous literature [21].  
Figure 11: Energy dissipation: (**a**) single cycle and (**b**) cumulative.  
Figure 12: Schematic of the load–deformation hysteresis curve.
## 3 Test Results and Discussions
### Crack Propagation and Failure Mode
Figure 7: Typical failure characteristics of longitudinal reinforcement at the interface between the beam and column in F-series specimens. The S-series specimens exhibited similar crack propagation to the common observation in the specimen F-H-C with respect to the initial flexural crack development at the maximum moment zone and the diagonal cracks in the column. However, these specimens were governed by shear with the occurrence of the diagonal cracks in the web of the beam. After the first cycle of \(d_{r}=0.75\%\), a couple of diagonal shear cracks suddenly developed in the web of each beam and then rapidly propagated toward loading points and the corner of the column interface as the beam displacement increased. The web of each beam was eventually mostly covered by intersecting diagonal cracks, as shown in Figure 6b. Overall, almost identical crack distributions in the beam were observed in both specimens S-H-C and S-H-G. Meanwhile, the GFRP bars underwent tensile rupture near the column interface of the specimen S-H-G with the increase in the width of the diagonal crack after the peak load. This may be attributed to the fact that the low elongation capacity of the GFRP bars is not capable of affording the extensive width of the diagonal crack. Overall, the testing of the specimen S-H-G was terminated at \(d_{r}=3.2\%\).
## 3 Results
### Proof-of-Concept Demonstration
#### 3.2.1 Downlink Communication Performance  
For the downlink communication, Figure 8a shows the bit-error-rate (BER) performance using the 2-PAM scheme. BERs with various data rates are measured at four different distances. The results show that BER gets worse by increasing the distance. This is because the downlink light intensity is decreased by the distance. The result at 85 cm is the worst, achieving only a Mb/s link with less than \(10^{-3}\) BER (the minimum measurable error floor in this test). The data rate increased to 5 Mb/s at distances of 0 cm, 25 cm and 50 cm, showing a similar tendency. It is mainly because the impact of inter-symbol-interference (ISI) due to the limited bandwidth of the white-LED light is the dominant factor degrading the BER performances.  
Figure 8b shows the BER performance with a decision feedback equalizer (DFE) to compensate for the limited bandwidth. Overall, the BER performances are improved, leading to less than \(10^{-3}\) BER at 5 Mb/s, 15 Mb/s, and 20 Mb/s for the case of less than 85 cm, 50 cm and 25 cm, respectively. Figure 9a,b show the eye diagrams before and after applying the DFE at 25 cm, with the data rate of 20 Mb/s. It can be seen that there is no clear eye-opening because of the severe ISI in the received signal. However, after applying the DFE, a clear eye-opening is observed, showing a BER less than \(10^{-3}\).  
Figure 6: (**a**) Experimental setup; (**b**) reflected light from the vegetable for uplink communications.  
Figure 7: Camera image at the distance of (**a**) 0 cm; (**b**) 25 cm; (**c**) 50 cm; (**d**) 85 cm from the vegetable.
# GraphSAGE-Based Multi-Path Reliable Routing Algorithm for Wireless Mesh Networks
## 2 System Model
### Notations  
_Head_: Head node, the first element of the queue.  
_Queue_: Queue used to store neighbor nodes.  
_Visited_: Visited status, if \(Visited=True\), the neighbor node has been visited and has been in the queue. Otherwise \(Visited=False\).  
_Searched_: Searched status, if \(Searched=True\), the neighbor node has already been searched, and can be skipped.  
\(HopCount(v)\): The shortest hop count from node \(v\) to the source. \(FrontCount(v)\): The number of predecessor nodes of node \(v\).  
\(FrontPoint(v)\): Set of predecessor nodes of node \(v\). There are \(FrontCount(v)\) elements in the set.  
_Array_: Neighbor node list used to store all neighbor nodes of the top element of the main stack.  
_Path_: Set of the shortest paths used to store the shortest paths output by multi-path routing algorithm based on the improved breadth first search.