esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preparation of Graphene Nanocomposite  
The nanocomposite was prepared prior to the experimental work using the two-step method. The essential requirements for nanocomposites are: a stable suspension, adequate durability, negligible agglomeration of particulates, no chemical change in the particulates or fluid, etc. The required quantity of graphene nanoplatelets was purchased from XG Sciences (Lansing, MI, USA). The scanning electron microscopy (SEM) (FEI 3D Versa Dual Beam, Hillsboro, Oregon, USA) and transmission electron microscopy (TEM) (JEOL JEM-2000 EX, Akishima, Tokyo, Japan) visualization of the GnP used is shown in Figure 1. The GnP-H\({}_{2}\)O was prepared using the covalent functionalization method. The GnPs were chemically functionalized with concentrated nitric acid (68 wt. %) to improve the dispersion of the particles and to avoid the use of surfactants. Five grams of graphene nanoplatelets were dispersed in 250 mL of concentrated nitric acid taken in a conical flask and then refluxed at a temperature of 100 \({}^{\circ}\)C for 2 h. For uniform dispersion, the fluid was stirred using a magnetic stirrer. To maintain a constant temperature during the process, the conical flask was placed in a constant temperature bath, which was maintained at 100 \({}^{\circ}\)C. After 2 h, the conical flask was taken out from the oil bath and allowed to cool down slowly to room temperature. The nanoplatelets were filtered then washed with DI water and dried in a furnace at 160 \({}^{\circ}\)C [PERSON] et al. (2008). Nitric acid treatment was used to chemically modify the surface of graphene platelets in order to increase the surface-active sites for electrochemical reactions because of the hydrophobic nature of GnP. The nitric acid treatment introduced more oxygen/nitrogen-containing functional groups onto the graphene surface, and clearly enhanced the hydrophilicity of the graphene. This was to promote the wettability of graphene when it was dispersed in DI water, which was the base fluid. For the experimental work, the volume percentages of GnP were 0.1%, 0.2%, 0.3%, 0.4%, and 0.5%. Depending on the volume percentage, the required quantity of chemically functionalized GnP was added to the DI water and was stirred for 30 min using a magnetic stirrer. After stirring, the fluid was ultra-sonicated using a digital sonicator (Qsonica, Newtown, CT, USA) for 2 h to enhance the stability. The graphene nanocomposites, thus prepared, were kept for observation and no particle sedimentation was observed at the bottom of the bottle even after two weeks.
## 6 Bounding the Perturbations of the Singular Values  
Equation (22) can also be used to determine linear and nonlinear estimates of the perturbations of the singular values. Considering the diagonal elements of this equation (high-lighted in green boxes) and taking into account that \(\text{diag}(\delta N_{U1})=0_{n},\ \ \text{diag}(\delta N_{V})=0_{n}\), we obtain  
\[\delta\Sigma_{n}=\text{diag}(\delta F_{1})-\text{diag}(\delta D_{U1}\Sigma_{n} -\Sigma_{n}\delta D_{V}+\Delta_{1})\]  
or  
\[\delta\sigma_{i}=\delta f_{ii}-\left(\delta D_{U1}\Sigma_{n}-\Sigma_{n}\delta D _{V}+\Delta_{1}\right)_{ii},\ \ i=1,2,\ldots,n, \tag{40}\]  
where \((.)_{ii}\) denotes the \(i\)th diagonal element of \((.)\). Neglecting the higher-order terms, we determine the componentwise asymptotic estimate  
\[|\delta\sigma_{i}|\leq\delta\sigma_{i}^{lin},\ \ \delta\sigma_{i}^{lin}:=| \delta f_{ii}|,\ \ i=1,2,\ldots,n. \tag{41}\]  
Bounding each diagonal element \(|\delta f_{ii}|\) by \(\|\delta A\|_{2}\), we find the normwise estimate of \(\delta\sigma_{i}\),  
\[|\delta\sigma_{i}|\leq\delta\sigma_{i},\ \ \delta\sigma_{i}:=\|\delta A\|_{2},\]  
which is in accordance with [PERSON]'s theorem (see (3)).  
From (40), we also have that  
\[\sqrt{\frac{n}{i=1}\delta\sigma_{i}^{2}}\leq\|\delta F_{1}\|_{F}+\|\delta D_{ U1}\Sigma_{n}-\Sigma_{n}\delta D_{V}\|_{F}+\|\Delta_{1}\|_{F}.\]  
In Table 3, we show the exact perturbations of the singular values of the matrix \(A\) of Example 1 along with the normwise bound \(\delta\sigma_{i}=\|\delta A\|_{2}\) and the asymptotic estimate \(\delta\sigma_{i}^{lin}\) obtained from (41) under the assumption that \(\delta A\) is known. The exact perturbations and their linear bounds are very close.
## 3 Analysis of Test Results
### Uniaxial Compression Test Results  
#### 3.3.1 Basic Mechanical Indicators  
Figure 7 shows the variational patterns of various physical and mechanical properties at different soaking times. All data points are average values of a group of samples. From Figure 7a,b, the data indicate that the pressure resistance of sandstone samples declines with extended soaking time. After 7, 14, 30, 60, and 90 days of soaking, the compressive strength percentages are 70.7%, 55.8%, 46.4%, 24.2%, and 17.7% of that of the dry sandstone, respectively. The most significant reductions in compressive strength occurred between 7 days and 14 days and 30 days and 60 days, with a decrease of 14.9% and 22.2%, respectively. This is consistent with the XRD and SEM results, indicating that water gradually infiltrated clay minerals over 7-14 days, and chlorite swelling fully occurred due to water absorption between 30 days and 60 days. During 60-90 days, the compressive strength decreased by 6.5%, which is consistent with the XRD results, indicating limited changes in chlorite and SEM observations of penetrative cracks.  
Figure 7c and Table 1 show that the elastic modulus of dry sandstone is 931.43 MPa, while the elastic modulus decreases to 813.76, 591.41, 440.28, 316.34, 210.03, and 108.2 MPa after 1, 7, 14, 30, 60, and 90 soaking days, respectively, exhibiting an exponential decline similar to the trend observed in compressive strength. As shown in Figure 7d, with increasing soaking time, the Poisson's ratio of the samples gradually increases, indicating softening of the samples and transitioning of the rock from brittle to plastic behavior during fracture. The variations in elastic modulus and Poisson's ratio are related to microstructural changes in the samples attributed to soaking, with detailed explanations provided in the Discussion Section.  
Figure 6: SEM images of three stages at different soaking times: (**a**) dry state; (**b**) immersion for 7 days; (**c**) immersion for 30 days; (**d**) immersion for 60 days.
## 4 Experimental Case Analysis and Discussion
### Imbalance Dataset
#### 4.4.2 Enhancement of Other Dataset  
The image processing technology is another standard data enhancement method. However, the time-frequency diagram is composed of vibration acceleration signals in a sequence of numbers; it is not suitable for generating images in a rotating manner. Therefore, this research case chooses vertical (horizontal) translation, scaling, and color enhancement to expand the short dataset. In Table 10, we compare it with the results of DCGAN. It can be found that if all generated data are used as the training set, the test accuracy of DCGAN under the wavelet transformation can be close to 80%, while the image processing technology is about 70%. In Table 11, for data after the over-sampling-balanced (Pro-Balanced) processing, both the two data enhancement technologies can increase the accuracy of the unbalanced training dataset to more than 90%. It is close to the accuracy of the actual balanced dataset, and only a slight difference in accuracy between the two. After processing the over-sampling-expanding (Pro-Balanced) method, there was no noticeable difference in the test accuracy of the two data enhancement techniques. However, the training dataset expanded by DCGAN had higher accuracy than image processing technology under wavelet transformation. If the deep learning method (DCGAN) can be effectively adjusted, its accuracy can be higher than image processing technology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Signal Processing**} & \multicolumn{1}{c}{**DCGAN Generated Data (All-Fake)**} & \multicolumn{1}{c}{**Image Processing Generated Data (All-Fake)**} \\ \hline WT & 76.22\% & 70.72\% \\ STFT & 65.71\% & 67.77\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of DCGAN and image processing techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Signal Processing**} & \multicolumn{1}{c}{**Real Data Balanced**} & \multicolumn{1}{c}{**DCGAN Generated Data (All-Fake)**} \\ \hline WT & 99.88\% & 76.22\% \\ STFT & 99.32\% & 65.71\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of different signal processing.
## 4 Objective Function Setting
### Payback Period  
The payback period refers to the time required for the added benefits of a project to compensate for the investment made. It is a key indicator for assessing the feasibility of rural house retrofit projects. This study focuses on reducing energy consumption through passive envelope improvements and integrated photovoltaic panels, without considering construction costs. The initial investment includes the cost of insulation materials, installation fees, and the cost of photovoltaic components. This paper also considers the additional costs of using grid electricity to supplement electric heating when photovoltaic generation is insufficient to meet all heating needs.  
According to the \"Notice on the Adjustment of Electricity Prices for the Xinjiang Power Grid by the Development and Reform Commission of the Autonomous Region\", the unified electricity price for dispersed electric heating to households is 0.22 CNY/kWh.  
The \"Scheme for Perfecting the New Energy Pricing Mechanism in Our Region\" issued by the Development and Reform Commission of the Xinjiang Uygur Autonomous Region specifies the on-grid electricity price as 0.262 CNY/kWh. By comparing these costs with the long-term energy-saving benefits, the calculation formula is as follows:  
\[t=\frac{c}{c_{1}-c_{2}-c_{3}} \tag{20}\]  
In the formula, \(t\) is the payback period (years); \(c\) is the total initial investment cost; \(c_{1}\) is the annual energy saving cost (savings); \(c_{2}\) is the annual operation and maintenance cost; and \(c_{3}\) is the cost of supplementing electric heating with grid electricity.
## 2 The Proposed Method
### Mapping
As shown in Figure 4, \(P_{\text{m}}\)(x,y) is the target point to be estimated, and there are sixteen grid points \(P_{ij}\)(\(i\) = 0, 1, \(\dots\), 3, \(j\) = 0, 1, \(\dots\), 3) around \(P_{m}\). The main idea is to calculate the probability of target point \(P_{\text{m}}\) through the probability of the 16 grid points with a weighted sum strategy. The weight is expressed with \(W(z)\) and defined in Equation (4); it is a bicubic function used here, and \(z\) is the distance from the current point to the target \(P_{m}\). \[W(z)=\left\{ \begin{array}{ll}(a+2)|z|_{3}-(a+3)|z|_{2}+1&\text{ for }|z|\leq 1\\ a|z|_{3}-5a|z|_{2}+8a|z|-4a&\text{ for }1<|z|<2\\ 0&\text{ otherwise}\end{array} \right. \tag{4}\]  
The grid at line \(i\) and column \(j\) is expressed as:  
\[\mathbf{k}_{ij}=W(x-x_{i})W(y-y_{j}) \tag{5}\]  
The value of \(P_{\text{m}}\) is defined as:  
\[M(\mathbf{p}_{\text{m}})=\sum_{j=0}^{3}\sum_{i=0}^{3}M(\mathbf{p}_{ij})* \mathbf{k}_{ij} \tag{6}\]  
Then we can calculate the derivative of \(M(\mathbf{p}_{\text{m}})\) relative to x or y.  
Figure 4: The bicubic interpolation of the grid map. Figure 3: Simulation comparisons of map updating between the proposed strategy and the existing one.
## 4 Characterization of Real-Life Signals
### Experiments
#### 4.2.2 Experiment with Small-Scale RF Devices  
The previous experiment gives promising results for signal recognition based on the RSSI distribution. However, the results are obtained by a spectrum analyzer, which is capable of much more power analysis, and the added value of RSSI-based signal recognition for such a high-end device is rather trivial. Additionally, doing measurements with a spectrum analyzer is not always practical and cost efficient. Therefore, the preferred implementation platform is small-scale and low-cost RF devices. However, this raises another doubt: will the RSSI measurements obtained by low-end devices have sufficient diversities for signal recognition? In fact, the 10-MHz sample rate used in the last experiment is above the limit of many inexpensive devices (e.g., ZigBee sensors or Bluetooth dongles).  
To answer this question, we repeat the previous experiment with a low-end SDR device (USRP B200 mini) at the sample rate of 1 MHz. The amount of samples used to compute a single RSSI (denoted by \(N\) in Equation (1)) is scaled down to 20. In total, 50 k RSSI measurements are collected during the time frame of 1 s. Since a spectrogram of 1 MHz wide contains limited information, only the histograms of the RSSI measurements are displayed in Figure 7.  
At first sight, the RSSI measurements from the USRP are stronger than the ones from the spectrum analyzer. Taking the most static signal DVB-T as an example, the average RSSI measured by USRP is around \(-\)54 dB, while only \(-\)72 dB is obtained by the spectrum analyzer. Since the RSSI measurements are not calibrated, many factors (e.g., hardware, gain) can contribute to the difference, which are out of the scope of this paper. The important message here is that the histograms obtained by USRP at a lower sample rate are highly consistent with their counterparts obtained by the spectrum analyzer. Therefore, RSSI measurements obtained at a much lower sampling rate, by small-scale devices, also have potential to be applied for signal recognition.
# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 2 Materials and Methods
### Quality and Risk of Bias Assessment  
Risk of bias assessment of the included studies was carried out independently in pairs using the validated Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool [31]. The Grading Quality of Evidence and Strength of Recommendations methodology was applied to rate the quality of evidence [32]. Reasons used to downgrade the quality of evidence were study limitations, inconsistency, and imprecision. Indirectness was not used as a downgrading reason because studies that were not relevant were excluded before the quality assessment. Publication bias was not formally assessed so the possibility of this bias was not excluded but not considered sufficient to require downgrading the quality of evidence.
# An Energy-Efficient Coverage Algorithm for Macrocell--Small Cell Network Systems
## 4 Proposed Algorithm
Figure 9: Conceptual illustrations of (**a**) SSD and (**b**) SSO. Case 3. The end point of user \(i\) will fall within the coverage hole, and the starting point is in the MBS service state. 1. The relay process has SBSs providing support overlay. 1. There is at least one available SBS next to the coverage hole center. In this case, the cell of the SBS closest to the coverage hole center, which is denoted as SBStag, is enlarged. 1. The starting point can be covered by the SBS in the relay process closest to the starting point after adjusting its power level to \(P_{\text{s}}^{\text{(high)}}\). In this case (NYD), service is provided by transferring the starting point connection to this cell-enlarged SBS, with the corresponding SBSs being used in the relay process, and SBStag being used at the end point. An example is shown in Figure 12a. 2. However, for the above power adjustment, the starting point cannot be covered. In this case (NXD), the processing method is the same as the corresponding processing method in Case 2.B.a.ii. An example is shown in Figure 12b. 3. There is no available SBS next to the coverage hole center. In this case, the power level of the SBS deployed in the coverage hole, which is denoted as SBShtag, is set to \(P_{\text{s}}^{\text{(low)}}\). 1. The starting point can be covered by the SBS in the relay process closest to the starting point after adjusting its power level to \(P_{\text{s}}^{\text{(high)}}\). In this case (NYO), the processing method is almost identical to that used in Case 3.A.a.i; the only difference is that we use SBShtag for covering the end point. An example is shown in Figure 13a. 2. However, for the above power adjustment, the starting point cannot be covered. In this case (NXO), the processing method is the same as the corresponding processing method in Case 2.B.b.ii. An example is shown in Figure 13b. 4. There is no available SBS next to the coverage hole center. In this case (NNO), the processing method is identical to that used in Case 3.A.b.ii. An example is shown in Figure 14b. Figure 3:0 Conceptual illustrations of (**a**) YNO and (**b**) XNO. Finally, after the execution of the algorithm, the system will close SBSs that have not been used (that is, \(P_{\mathrm{s}}^{\mathrm{(high)}}\) or \(P_{\mathrm{s}}^{\mathrm{(low)}}\) will be adjusted to 0). Last, but not least, the worst-case performance of the proposed algorithm, with regard to the time complexity as a function of the number of users \(M\) and the number of SBSs \(K\), is provided as follows. We first sort the \(M\) users in order of priority, \(O(M\log M)\). Next, we process the \(M\) users, which require \(M\) rounds, with \(c\) comparisons in each round, where \(c\) is a constant, \(O(M)\). Finally, we deactivate SBSs that have not been used, \(O(K)\). Accordingly, the worst-case time complexity of the proposed algorithm can be expressed as \(O(M\log M+M+K)\), and it indicates that the complexity level is low.
## 1 Introduction
The final product retains a high moisture content (approximately 75%), low levels of salt (around 1%) and maintains a pH 4.3-4.5. Storage is conducted at temperatures of 4-5 \({}^{\circ}\)C for up to 4 days [10; 11]. The microbiota of dairy products, such as cheese, consist of starter or adjunct cultures, as well as non-starter lactic acid bacteria [12]. The incorporation of functional cultures in dairy products has a profound impact on their microbiome, particularly the composition, functionality, and sensory characteristics. More specifically, lactic acid bacteria ferment lactose and produce lactic acid, creating an acidic environment that inhibits the growth of undesirable microorganisms, while promoting their growth since they are well-adapted to thrive in this environment [12]. Notably, when proper safety measures are neglected during the production process (improper pasteurization, post-pasteurization contamination, etc. ), cheese constitutes an optimal environment for the proliferation of pathogenic bacteria, including _Salmonella_ spp., _Listeria monocytogenes_, and _Escherichia coli_[13]. Moreover, soft and semi-soft cheese products are susceptible to the growth of molds and bacteria that produce harmful toxins [14]. Microbial hazards have a great economic and legal impact on the food industry, intensifying the need for preservatives. Considering consumer demand for natural alternatives, several studies have suggested the use of lactic acid bacteria as biopreservatives [15]. Additionally, they contribute to the breakdown of proteins and lipids, releasing flavor-enhancing compounds and enzymes that influence the sensory attributes of the cheese. Mechanistically, lactic acid bacteria adhere to the surface of cheese cards, forming biofilms that protect against contamination and provide an environment for microbial interactions. They also produce exopolysaccharide, contributing to the cheese's texture and rheological properties [16]. Hence, the incorporation of probiotictics in cheese production not only enhances nutritional value and promotes health benefits, but also shapes microbial dynamics, ultimately influencing the quality, flavor, and texture of the cheese. Several lactic acid bacteria, such as _Lactococcus_, _Lactobacillus_, and _Streptococcus_ species, are commonly employed as starter cultures in cheese production [12]. However, for the selection of a suitable strain for the production of functional food, several criteria should be considered regarding its potential beneficial properties. In this vein, _P. acidilactici_ ORE5, a wild-type strain isolated from keff, was initially evaluated for potential probiotic characteristics, and subsequently, it was used for the production of a novel functional Katiki Domokou-type cheese enriched with immobilized _P. acidilactici_ ORE5 on pistachio nuts, assessing cell survival, along with the physicochemical and microbiome changes during storage. Moreover, the potential growth suppression of _L. monocytogenes_ was investigated after deliberate contamination, a food-borne pathogen usually associated with cheese safety.
## 2 Literature Review
(2017) analyzed game theory extensively within the context of disaster response stages but proposed its extension into other phases such as disaster reduction, preparedness, and recovery [30]. [PERSON] et al. (2021) investigated an emergency management framework specific to a region in New Zealand and discovered that small-scale recurrent disasters were overlooked when formulating policies related to emergency management. Therefore, they advocated incorporating these impacts into policy systems to eliminate disparities in risk management at lower administrative levels [31]. In terms of the emergency response in rural areas, [PERSON] (2023) discovered that post-earthquake outbreaks of lice and scabies infections often arise due to overcrowding and inadequate sanitation in rural regions. Tetanus and its associated complications also frequently occur as a result of insufficient or lacking vaccination [32]. Additionally, [PERSON] (2019) identified distinct healthcare needs for rural areas following natural disasters compared to urban areas [33]. [PERSON] (2013) and [PERSON] (2013) argued that numerous rural communities in certain Asian countries have a significant proportion of elderly individuals and children, necessitating increased attention towards specific health risks during emergency management. These risks include respiratory problems caused by indoor waste pollution resulting from energy poverty [34; 35]. [PERSON] (2024) demonstrated that the provision of financial resourcesduring natural disasters can enhance the mental health and well-being of rural victims [36]. Regarding legislation and regulations, [PERSON] (2023) examined the necessity for regulation within the digital context [37], [PERSON] (2022) discussed administrative justice reform's role in Poland during the COVID-19 pandemic [38], while [PERSON] (2023) proposed the theoretical possibility of establishing an interdisciplinary legal mechanism through pooling key capabilities [39]. Through the aforementioned literature analysis, it is evident that current research predominantly focuses on the integration of new-generation information technology in government planning and the strategic analysis of emergency management, primarily within the context of smart cities. However, there is a noticeable lack of emphasis on rural informatization. Furthermore, model construction primarily revolves around the singular perspective of emergency material provision, with limited attention given to the application of digital technologies such as artificial intelligence, big data, and cloud computing throughout the entire process of rural emergency management. Therefore, this paper presents an innovative comprehensive emergency management model encompassing early warning, command scheduling, material allocation, vehicle coordination, and rescue team deployment.