esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamental Concepts
Negative Definiteness of the Fractional Derivative:  
\[{}^{C}D^{v}V(x)<0\quad\text{for }x\
eq 0. \tag{8}\]  
In FOS, the fractional derivative \({}^{C}D^{v}V(x)\) incorporates memory effects inherent in FC, making stability analysis dependent on the entire history of system states, not just the current state. Lemma 1 ([35]): _For the system (3), if there exists a convex and continuously differentiable Lyapunov function \(V:\mathbb{R}^{n}\to\mathbb{R}\) and the following inequalities hold, then the nonlinear FOS is asymptotically stable:_  
\[\alpha_{1}(\|x\|)|\leq V(x)\leq\alpha_{2}(\|x\|), \tag{9}\] \[{}^{C}D^{v}V(x)\leq-\alpha_{3}(\|x\|),\]  
_where \(\alpha_{i},i=1,2,3\) are class \(K\) functions, \(v\in(0,1)\) denotes the FO operator, and \(\|.\|\) is arbitrary norm._  
Lemma 2 ([36]): _Let \(x=0\) be an equilibrium point of the nonlinear system (3). If there exists a convex and continuously differentiable Lyapunov function \(V:\mathbb{R}^{n}\to\mathbb{R}\) such that \(V(0)=0\), then the following inequality holds for all \(t>0\):_  
\[{}^{C}D^{v}V(x)\leq\
abla_{x}V(x)\cdot{}^{C}D^{v}x. \tag{10}\]  
Lemma 3 ([37]): _Let \(x(t)\in R^{n}\) be a differentiable vector function. Then, for any time instant \(t\), the following inequality holds:_  
\[{}^{C}D^{v}_{t}(x^{T}(t)x(t))\leq 2x^{T}(t){}^{C}D^{v}_{t}x(t),\quad\forall v \in(0,1) \tag{11}\]  
Definition 1 ([38; 39]): A real \(n\times n\) matrix \(W=[w_{ij}]\) is an M-matrix if the element \(w_{ij}\leq 0\) for \(i\
eq j\), and all the principal minors of \(W\) are positive. Lemma 4 ([38]): _Let \(W\) be an M-matrix, which is a special class of matrices commonly used in stability analysis and control theory. Then, there exists a diagonal matrix \(Q=\text{diag}(q_{1},q_{2},\ldots,q_{N})\), where \(q_{i}>0\) for all \(i\), such that the matrix \(W^{T}Q+QW\) is positive-definite._  
This lemma plays a critical role in establishing stability criteria for FOS, as it ensures the existence of a Lyapunov function or matrix that can be used to verify the positive definiteness of the system's dynamics. The diagonal nature of \(Q\) simplifies the computational complexity, making it a practical choice for controller design and stability analysis.
## 4 Results Analysis
### Discussion  
The performance of the proposed IoT-Fog FCMN-based health monitoring system was compared with the standard market available devices to measure body temperature. The results of the evaluation are presented in Table 3 and Figure 8. The results show that the measurements of both devices were satisfactory with minimum errors. The proposed device was also found to be satisfactory in identifying GPS locations at different locations and also accurately captured facial images of the subjects under consideration. The device was found to be proficient in transmitting data to internet connected server immediately after fetching the temperature, location and facial images. The user experience while using the proposed framework was also captured during the evaluation process and the observations are presented in Tables 4 and 5. The results based on different user satisfaction parameters were found to be satisfactory. Around 92 % of participants gave an above average grading, which means more than 5 on a scale of 0 to 10, and the overall average was 6.47 on the scale of 0 to 10. Further, the statistical parameters presented in Table 4 were are positioned on the positive side of the proposed system with overall standard deviation, SD = 0.81 and Mean = 6.47 for the observed mean values with respect to individual participants. Symbols and notations are mentioned in Table A1.
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 4 Graph-Level Label-Only Membership Inference Attack
### Attack Methodology
#### 4.2.4 Perturbation Magnitude Estimation and Threshold Selection  
As described in Algorithm 1, a scaler \(s\) is used to adjust the perturbation range, which controls the perturbation magnitude of graphs. However, perturbations that are either too large or too small fail to identify the membership of the target graph, which is validated in Section 5.3. Therefore, appropriate perturbations need to be found to distinguish between members and non-members, which can be roughly estimated using the shadow dataset and model.  
During estimation, \(s\) is gradually increased in small steps. At each step, the following processes are performed:  
(1) Calculate the robustness scores of the shadow data using the current \(s\).  
(2) Label the robustness scores of the shadow training data as \"member\", while labeling those of the shadow testing data as \"non-member\" to construct the attack dataset.  
(3) Maximize the classification accuracy of distinguishing between shadow members and non-members under current \(s\) by iteratively selecting a threshold using the ROC curve. Record the current AUC and accuracy, which jointly reflect the classification performance of the estimation.  
(4) If both the AUC and accuracy begin to decline, terminate the process, and record the last \(s\) and threshold before the decline for use in the attack on the target model. Otherwise, increment \(s\) by a small step and repeat steps (1) to (3).
# Cost Analysis and Optimization of Modern Power System Operations
## 6 Comparison of Case 1 and Case 2 at Same Load  
We have reformulated Case 2 to use the same 24 h dynamic load profile as Case 1. The only difference now is the addition of the spinning reserve constraint, set at 40% of hourly load demand, as originally described. This modification allows us to isolate and quantify the cost impact of spinning reserve provision. The results now show that with the spinning reserve constraint applied, the total operating cost increases by approximately 6.9%, rising from USD 485,240 (Case 1) to USD 518,765 (Case 2) for the same demand profile. This cost includes both fuel and reserve-related startup commitments.  
Figure 15 clearly shows how the addition of a 40% spinning reserve increases the generation commitment beyond the baseline load demand.
## 3 System Model and Problem Formation
### System Model  
Multi-hop WMNs (MWMNs) consist of a number of stationary WMRs, forming a wireless backbone. Some WMRs serve as access points (APs) for wireless multimedia users while remaining are serving as wireless mesh backbone relay routers only; as shown in Figure 1. Each WMR \(v_{l}\) is equipped with certain number of hybrid IEEE 802 radios denoted as \(\rm{R_{v_{i}}}\). One radio can work on one channel during the assignment process. The set of orthogonal channels available in the network is denoted as \(C\), where \(C=\{1,2,...,k_{max}\}\). The network topology can be modeled as an undirected graph \(G=(V,E)\), where \(V\) is the set of mesh nodes and \(E\) is the set of links. There is link \(e_{(i,j)}\) between WMR \(v_{i}\) and \(v_{j}\) if their Euclidean distance is smaller than the transmission range, assuming that all the WMRs have the same transmission ranges. Thus, multi-channel and multi-radio wireless backbone mesh topology provides multiple paths for each source and destination multimedia users.  
\begin{table}
\begin{tabular}{l l} \hline \(f_{l,k}^{j}\) & \(j^{th}\)flow from node \(v_{i}\) to \(v_{k}\) \\ \(p_{f_{l,k}^{j}}\) & The set of all available paths from node \(v_{i}\) to node \(v_{k}\)for flow \(f_{l,k}^{j}\) \\ \(UP_{f_{l,k}^{j}}\) & The used path by flow \(f_{i,j}^{k}\) starts from node \(v_{i}\) and ends at \(v_{k}\) \\ \(UNUP_{f_{l,k}^{j}}\) & Unused portion of the path for flow \(f_{i,k}^{j}\) \\ \(NUP_{f_{l,k}^{j}}\) & New usable path for flow \(f_{i,k}^{j}\) \\ \(PLR_{f_{l,k}^{j}}\) & The maximum affordable PLR for flow \(f_{i,k}^{j}\) \\ \(d_{f_{l,k}^{j}}\) & The maximum affordable delay limit for flow \(f_{i,k}^{j}\) \\ \(Q\)\(oSP_{f_{l,k}^{j}}\) & Vector contains QoS parameters of \(f_{i,k}^{j}\) \\ \(PP_{l,l-1}\) & The portion of usable path before the occurance of an underutilized link. \\ \([\bf{v_{1},v_{2},v_{3},v_{4}}]\) & A path formed by the sequence of nodes \(\bf{v_{1},v_{2},v_{3},v_{4}}\). \\ \hline \end{tabular}
\end{table}
Table 2: Notations for LNR-RCTD algorithm.  
Figure 1: Wireless mesh backbone network architecture.
## 1 Introduction
Traditionally, the Lagrange girds are generated on the surface of solid body to identify the velocity of the solid points ([PERSON], 2016; [PERSON], 2016). In contrast, the level set (LS) function \(\phi\) is defined as a signed distance to the fluid-solid interface, of which the value is positive and negative in the fluids and solid bodies, respectively. The fluid-solid interface is defined as the iso-contour \(\phi\) = 0, determined by the geometry of the solid body. Most importantly, we use LS function to identify and track the normal direction of solid points on the surface of solid body. This is totally different from the traditional approach ([PERSON], 2016), in which the solid/fluid nodes around the interface are used. In contrast, [PERSON] et al. (2017) and [PERSON] et al. (2017) used the LS function to track the solid-fluid interface, but they used the cut-cell methods to solve the transient fluid flow, with the drawbacks of small time step and gird size. In this study, a modified LS-based Cartesian-IB method is developed to simulate the interactions between fluid flows and the deformed fish body. For the self-propelled model, the midline motions of carangiform fish are described, and the LS function needs to be regenerated at every time step. In contrast to the numerical method in reference ([PERSON], 2016), the reinitialization procedure is used to fix the LS function of the fish body, rather than solving the LS function. In this study, the travelling index of the midline motion, as well as the amplitude and tail-beat frequency, is changed to be up to 50% larger or smaller than the biological value. Their influences on swimming performance are evaluated by the forward speed and swimming efficiency. Overall, the major contributions of the present work are: (1) development of a modified LS-IBM method to simulate the swimming fish; and (2) use of the travelling index to investigate the influences of the fish-like motions on their swimming performance. This paper is organized as follows: the problem definition of carangiform swimmers and numerical method are given in Sections 2 and 3, respectively, Section 4 presents the simulation results, and Sections 5 and 6 show the discussion and conclusions, respectively.
## 4 Discussion
### Limitations and Future Research Directions  
The major findings in this study can provide guidance for selecting training samples in patch-based classification; however, further investigation and confirmation are necessitated. In this study, crop type information from ground truth data was used to analyze the effects of class purity in a training patch and quantify the spatial homogeneity of the study area. From a practical viewpoint, however, such ground truth information is not always available, which renders it impossible to calculate the class purity and other quantitative indices. This limitation of the sampling strategy using class purity and quantitative indices presented herein may be relieved using a past land-cover map in the study area of interest. Unless significant changes have occurred in the study area, one can quantify the class homogeneity of the study area using land-cover types in a past land-cover map. This class homogeneity information can be used as a prior basis to determine the appropriate class purity and patch size values in the study area. However, more extensive experiments should be conducted to determine the appropriate reference or threshold value of class purity and the optimal patch size. In this study, the strategy for selecting training patches based on class purity is based fully on class-type information, but may not account for the spectral information of class type. The same class type may exhibit different spectral characteristics, and mixed pixel effects are also typical in classifications using coarse spatial resolution remote sensing images [13; 14]. Hence, the effects of spectral purity should be analyzed via spectral unmixing [48; 49] in conjunction with those of class purity of training patches in future work.
# Unstimulated Parotid Saliva Is a Better Method for Blood Glucose Prediction
## 4 Discussion
It should be noted that many unknown factors and unstable elements will affect the properties of mixed saliva. Saliva collected directly from a single gland is stable and not affected by oral conditions. Thus, it can accurately reflect blood glucose status. Saliva from the parotid gland is easily collected under unstimulated and stimulated conditions. [PERSON] et al. [29] reported that, when saliva is collected under unstimulated conditions, the concentration of glucose in saliva is higher than under stimulated conditions, which is consistent with the conclusions obtained in this study. Other studies have found that there is no significant difference in the concentration of glucose in saliva collected under unstimulated and stimulated conditions [30], because participants may not be willing to accept acid stimulation, and the water concentration in stimulated saliva is higher. Besides, unstimulated saliva may be more representative of a normal physiological state. [PERSON] et al. [31] measured the saliva chemical concentration of healthy subjects under different conditions and found that, compared with stimulated saliva, almost all metabolites in unstimulated saliva were higher. [PERSON] et al. [32] also found that, compared with stimulated saliva, the average saliva glucose level in unstimulated saliva of control and non-control DM patients was higher. Saliva collected directly from a single gland is stable and not affected by oral conditions. Therefore, it may accurately reflect blood glucose status. Moreover, as far as we know, this is the first study focused structurally on comparing the glucose expression of whole saliva and glandular saliva in a cohort of careful characterization and clinical examination. The results indicate that different collection methods provide significant differences in the snapshots of saliva glucose. The limitation of our study is the relatively small sample size. Further studies with a larger sample size are necessary to confirm the correlation between blood glucose and saliva glucose, so as to design a saliva-based diagnostic test method for DM. In addition, there are still many problems in this study that need to be resolved and further explored. For example, the submandibular glands and sublingual glands are closely located, so it is difficult to separate saliva from these glands with certainty, which is why saliva is collected from both glands. How to distinguish sublingual saliva from submandibular saliva is also a direction that needs further research. In summary, the results of this study indicate that different saliva collection methods provide significant differences in the snapshots of saliva glucose. Based on the comparison of unstimulated and stimulated saliva collection methods, it can be shown that, based on the simplicity and low variability of the collection method, UPS may be a preferred collection method. The results emphasize the importance of consistency when collecting saliva samples, which should be more important than the collection method itself.
# Prediction of RF-EMF Exposure by Outdoor Drive Test Measurements
## 2 Measurement Description  
### Measurement Equipment  
Drive test measurement campaigns were carried out in Paris using a portable spectrum analyzer, i.e., RSA306B from Tektronix [22]. A wide frequency band from 700 to 2700 MHz was selected in the drive test, covering all RF bands from 2G, 3G and 4G networks. Isotropic measurements of the E-field are performed by connecting the one-port Tektronix to a 3-axisdipole antenna via a switch. The 3-axis antenna was fixed on the top of the vehicle while conducting the drive measurement. The measurement equipment is shown in Figure 1.  
The signal acquisition was performed on the Tektronix RSA306B, while the data analysis, storage and replay were performed on the laptop (PC) side. Managing the PC separately from the acquisition hardware makes processing upgrades easy and minimizes IT management issues. We developed a python-based graphical user interface (GUI) to configure and control the RF-EMF measurements by transferring the measurement setup to Tektronix. The setup information includes resolution bandwidth (RBW), reference level, center frequency and span. For each measurement record, the isotropic E-field strength is obtained by scanning over the three ports of the antenna and computed as \(E=\sqrt{\sum{E_{j}}^{2}}\) where \(j\in\{1,2,3\}\). Then, the process is repeated continuously. The measurement steps can be found in Figure 2.  
Figure 1: Measurement system: 3-axis antenna, Tektronix and switch.  
Figure 2: Flow chart of the measurement procedure.
## 5 Conclusions  
We have developed a technique for generating spheroids that consists of 2D cell culture on ECM dot patterns deposited on a fluoropolymer scaffold, followed by cell colony detachment from the substrate and suspension culture. The ECM substrate was weakly bound onto the fluoropolymer surface, and thus, the adherent cell colonies were easily detached by gentle pipetting; we theorized that van der Waals forces were responsible for this weak adhesion. Spheroid formation was successful with all tested combinations of five ECM substrates (Matrigel, iMatrix, fibronectin, vitronectin, and collagen IV) and two cell lines (HepG2 and MCF7). When forming spheroids during the differentiation process of pluripotent stem cells, in contrast to conventional methods, our technique enabled spheroidization without chemical (via enzymatic treatment) or physical stimulation, and thus, a more stable spheroid production could be obtained. For spheroids such as cancer cells and cells established from tissues, however, the chemical and physical stimuli are the same as those for the conventional method. Moreover, the technique is relatively uncomplicated and is expected to be low in cost. In future studies, we will test the effect of the ECM pattern size on the spheroid size and assess differentiation induction using this scaffold to establish size controllability and a method for iPSC spheroid/organoid production. We anticipate that such spheroid/organoids can be used for drug discovery and regenerative medicine.  
The following are available online at [[https://www.mdpicom/article/10.3390/app112110495/s1](https://www.mdpicom/article/10.3390/app112110495/s1)]([https://www.mdpicom/article/10.3390/app112110495/s1](https://www.mdpicom/article/10.3390/app112110495/s1)), Figure S1: Detailed measurement at each stage.  
Conceptualization, H.T. (Hidetaka Togo), H.T. ([PERSON]) and M.K.; methodology, H.T. (Hidetaka Togo), H.T. ([PERSON]) and M.K.; validation, H.T. (Hidetaka Togo), K.Y.-T. and Y.H.; formal analysis, H.T. (Hidetaka Togo); investigation, H.T. (Hidetaka Togo), K.Y.-T. and Y.H.; resources, H.T. ([PERSON]) and M.K.; data curation, H.T. (Hidetaka Togo) and K.Y.-T.; writing--original draft preparation, H.T. (Hidetaka Togo); writing--review and editing, H.T. ([PERSON]) and M.K.; visualization, H.T. (Hidetaka Togo); supervision, H.N.; project administration, M.K.; funding acquisition, H.T. ([PERSON]) and M.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by JST A-STE, grant number JPM/TR20 UC (to M.K.) and JSPS KAKENHI grant numbers JP18K16768, 20K18188 (to H.T. ([PERSON])).  
Not applicable.  
The authors declare no conflict of interest.
# Acoustic Voice Analysis as a Tool for Assessing Nasal Obstruction: A Systematic Review
## 3 Results  
The initial search yielded 174 abstracts; after removing duplicates, 118 remained. Eighty-one studies were excluded upon the title and abstract check for clearly not meeting the inclusion criteria. The full texts of 37 articles were reviewed in detail. We excluded 27 studies for the following reasons: non-human studies (n = 1), wrong intervention (surgical interventions that lacked both preoperative and postoperative objective evaluations of nasal obstruction, n = 17), wrong study design (n = 1), pediatric population (n = 4), wrong patient population (n = 1), no full text availability (n = 3). Ten studies that specifically included a correlation of nasal obstruction (evaluated with any approach, including PROM, objective measurements, and physician assessment) with acoustic voice analysis were included. Figure 1 shows the PRISMA flowchart for this systematic review.  
Figure 1: The PRISMA flowchart used in the present review.  
The methodological quality of the included studies, assessed using the modified Downs and Black checklist [29] indicated that the overall methodological quality was generally fair, with a mean score of 65%. The minimum score was 55% [18; 26], while the maximum score was 75% [19].  
These results suggest that most studies had adequate reporting and internal validity; however, limitations were identified in aspects such as statistical power, external validity, and the management of confounding variables. The score range of 55% to 75% reflects variability in rigor among studies, which should be considered when interpreting the findings. [PERSON]'s classification indicates that none of the studies fell below the threshold for poor quality; however, the limited number of high-quality studies highlights the necessity for more methodologically robust studies. The evaluation of the quality of each study is included in the Supplementary Material (File S2).  
Many different approaches have been used to assess nasal obstruction (Section 3.1) and many different voice assessment tools have been evaluated for their correlation with nasal obstruction itself (Section 3.2); we listed them below.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Evaluation of the SPME Arrow Procedure
#### 3.1.4 Selection of Extraction Time  
The evaluation of extraction time is a critical step for the SPME Arrow method. Therefore, different extraction time spans were investigated (i.e., 5, 10, 20, 40, 60 and 80 min). Since SPME is an equilibrium technique, the equilibrium time has to be reached in order to extract the maximum amount of analyte at various extraction times. As it can be observed, for the SPME Arrow method, equilibrium is reached at 60 min, and the use of longer extraction times even appears to decrease the response again. As a result, an extraction time span of 60 min was chosen. Figure 3 shows the effect of extraction time in the SPME Arrow extraction procedure.