esa-sceva/satcom-chunk-collection huggingface dataset:
# Adipolin, Chemerin, Neprilysin and Metabolic Disorders Associated with Obesity
## 4 Discussion
### Adipokines and Glucose Metabolism
demonstrated a positive relationship between blood adipokine levels and fasting glycemia and insulinemia, HOMA-IR, glycated hemoglobin, and duration of diabetes among patients with type 2 diabetes [37]. [PERSON] et al. compared neprilysin concentrations in men with metabolic syndrome and healthy men. They observed that its concentration was higher in the former group and increased with the number of components of the metabolic syndrome [24]. In our analyses, a negative correlation between neprilysin levels and fasting glycemia among all obese patients and with fasting insulinemia and HOMA-IR index values in the control group was found. Relating these observations to the findings of [PERSON] et al. [38], who believe not only that serum neprilysin levels are associated with the occurrence of diabetes but also predict future risk of developing diabetes, it can be concluded that our study included individuals who, despite high neprilysin levels, did not have impaired glucose metabolism but may be at risk of developing it in the future. Chemerin is also an adipokine involved in glucose metabolism. It reduces insulin-stimulated glucose uptake in the 3T3 LI cell line adipocytes and induces insulin resistance in skeletal muscle cells. This mechanism involves its effects at the level of IRS-I (Insulin Receptor Substrate 1), protein kinase 8, GSK3 (Glycogen Synthase Kinase 3), and glucose transporters [39]. Most researchers believe that chemerin levels increase with the severity of tissue insulin resistance. For example, [PERSON] et al., comparing the concentration of chemerin in diabetic and normoglycemic subjects, observed significantly higher levels of this adipokine in diabetic subjects [40]. Similar results were obtained by other researchers [41; 42; 43]. These observations were partially confirmed in our study. In a group of obese women, a positive correlation was observed between chemerin concentration and glycemia in the 120\({}^{\prime}\) OGTT. However, there are several studies in which no correlation between chemerin concentrations and parameters of glucose metabolism has been demonstrated [30; 44; 45]. In our study, we observed a negative correlation between serum chemerin concentration and fasting glycemia values in obese subjects with normal glucose tolerance. This result contradicts the general view and applies to obese people without abnormal glucose tolerance. This observation demonstrates the need for further research to look for reasons for the lack of negative consequences of obesity observed in some individuals referred to as \"metabolically healthy obese\", in which the role of blood chemerin concentration is worth considering.
# Signal Separation Based on Knowledge Representation
## 2 Theory
### Application of Knowledge Representations
#### 2.3.3 Application of Data-Driven Knowledge Representation  
In the case of a large number of samples, we aim for the predicted target signals by the neural network to be similar to the features and shapes represented in the knowledge representation. This implies that the cosine similarity \(L_{\text{cos}}(\mathbf{K}_{i},\mathbf{\tilde{S}}_{i})\) can be applied to the output of the network predictions while still ensuring differences exist between the predictedsignals. Therefore, the mathematical expression for applying knowledge representation is as follows:  
\[\begin{split}& M(\mathbf{\tilde{S}}_{1},\mathbf{K}_{1},\mathbf{ \tilde{S}}_{2},\mathbf{K}_{2},\cdots,\mathbf{\tilde{S}}_{N},\mathbf{K}_{N})= \sum_{i=1}^{N}a_{i}L_{\text{cos}}(\mathbf{K}_{i},\mathbf{\tilde{S}}_{i})\\ &+\sum_{i=1}^{N}\sum_{j=1}^{N}b_{i}\big{(}1-L_{\text{cos}}( \mathbf{\tilde{S}}_{i},\mathbf{\tilde{S}}_{j})\big{)}+\sum_{i=1}^{n}c_{i}| \mathbf{K}_{i}-\mathbf{\tilde{S}}_{i}|\end{split} \tag{16}\]  
Here, \(a_{i}\) represents the weight of similarity between the knowledge representation and the predicted separated signals, \(b_{i}\) represents the weight of feature differences among the predicted different target source signals, and \(c_{i}\) represents the weight of shape constraints between the knowledge representation and the predicted separated signals.
# Surface Motion for P-Wave Scattering by an Arbitrary-Shaped Canyon in Saturated Half-Space
## 4 Verification  
When solving Equation (45), it is necessary to truncate the infinite series to obtain the convergent surface motion result. The series expansion term \(n\) and the number of weight functions \(m\) need to be tested in detail when truncating, and the test method can be seen [21]. Define the truncation term of the number of series expansion terms \(n\) of the wave potential function as \(N\), and the truncation term of the number of weight functions \(m\) as \(M\). To ensure that the coefficient matrix obtained is square, \(M=N\) is set in this paper.  
To consider the influence of canyon width and the wavelength \(\Lambda\), a dimensionless frequency is defined [3]  
\[\eta=\frac{2a}{\Lambda}=\frac{\omega a}{\pi c_{t}} \tag{48}\]  
To convert the displacement of the soil skeleton from \(u_{r}\) and \(u_{\theta}\) in the polar coordinate system to \(u_{x}\) and \(u_{y}\) in the rectangular coordinate system, the conversion formula in [21] is applied as follows  
\[\left\{\begin{array}{l}u_{x}\\ u_{y}\end{array}\right\}=\begin{bmatrix}\cos\theta&-\sin\theta\\ \sin\theta&\cos\theta\end{bmatrix}\left\{\begin{array}{l}u_{r}\\ u_{\theta}\end{array}\right\} \tag{49}\]  
In addition, the displacement in the analysis will be normalized. For an incident P-wave with an amplitude of 1, the normalized displacement is defined as follows  
\[\left|U_{x}\right|=\frac{\left|u_{x}\right|}{k_{1}}\text{, }\left|U_{y}\right|= \frac{\left|u_{y}\right|}{k_{1}} \tag{50}\]  
The correctness of the calculation is verified by comparing the degradation model with published results. Take the parameters \(n_{cr}\) = 0.36, \(K_{cr}\) = 200 Mpa, \(K_{g}\) = 36,000 Mpa, \(K_{f}\) = 2000 Mpa, \(\rho_{g}\) = 2650 kg/m\({}^{3}\), \(\rho_{f}\) = 1000 kg/m\({}^{3}\), \(v\) = 0.25 [35]. To degrade this arbitrary-shaped canyon model in a saturated half-space to a semicircular canyon model in a single-phase medium [21], the porosity of saturated soil is taken as a small value, \(n_{s}\) = 0.01, in which the saturated soil can be approximately considered as a single-phase medium soil. Taking the dimensionless frequencies of 2 and 4, and considering both vertical and oblique incidences, the comparative results are illustrated in Figure 2. The degenerated solution of this paper has a good coincidence with the model [21], which verifies the correctness of the calculation in this paper.  
Figure 2: _Cont._
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
### Limitations  
Section 1.1 reveals that simple hiding and masking techniques are extensively used in the literature to mitigate the DPA attack on the cryptographic algorithm(s) [10; 11; 12; 13]. Therefore, these techniques are not power efficient and increase the hardware resources (area). In other words, hardware-based countermeasures increased the implementation cost, which is not preferred in most cases (for area limited applications, i.e., radio-frequency identification, wireless sensor networks, and many more). Countermeasures based on hiding can easily be exposed to higher-order DPA because of the dependency of higher-order on first order equations [15]. In many papers, the AddRoundKey block of AES is altered into a logic-based design [25; 27; 29]. It breaks the dependency of the power trace during the execution of the encryption process (which is adequate). On the other hand, it alters the characteristics of the AES algorithm, making it vulnerable to cryptanalysis attacks. Subsequently, there is a need for area-efficient designs in light of countermeasures to mitigate the DPA attacks over cryptographic algorithm(s).
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Adaptive-DecayRank Algorithm with Bayesian Updating
#### 4.2.2 Bayesian Updating in Adaptive-DecayRank  
In the context of Adaptive-DecayRank, Bayesian inference is employed to update the decay factor, \(\delta_{u}\), for each node, \(u\). The Gamma distribution is used as a conjugate prior, ensuring computational efficiency and tractability during updates on the observed changes in node scores. The probability density function of the **prior Gamma distribution** for \(\delta_{u}\) is defined as  
\[f(\delta_{u}\mid\alpha_{u},\beta_{u})=\frac{\beta_{u}^{\delta_{u}}}{\Gamma( \alpha_{u})}\delta_{u}^{\alpha_{u}-1}e^{-\beta_{u}\delta_{u}}, \tag{6}\]  
where \(\delta_{u}\) is a continuous variable taking non-negative values, \((\delta_{u}\geq 0)\), \(\alpha_{u}\) is the _shape parameter_ that controls the shape of the distribution, \(\beta_{u}\) is the _rate parameter_ that controls the scale of the distribution, and \(\Gamma(\alpha_{u})\) is the Gamma function, which generalizes the factorial function.
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 6 Experimental Results
### Impact of Mobility Models
This primarily is due to the low packet loss rates (refer to Equation (4)). In sum, as a key characteristic, the mobility of nodes has significant, negative impacts on the network performance of MANETs.
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 2 A Cellular Automaton Model for Urban Roads Considering Vehicle Heterogeneity
### Road Network Model  
As shown in Figure 1, to probe the traffic flow characteristics of a moving bottleneck, we created a virtual road network, which consists of an arterial road and several access roads, with intersections formed where the arterial road and access roads meet. Both the arterial road and access roads are bidirectional four-lane roads, and the intersections are controlled using a fixed signal cycle. We also assumed that there are only two types of vehicles in the road network: small cars and heavy-duty trucks. Furthermore, apart from intersections, there are no other fixed traffic bottlenecks, such as entrance and exit ramps in the road network.  
In this cellular automaton model, the road is divided into 2 \(\times\) N cells for each direction, where N represents the number of cells in a single lane. \(x_{n}\) (in meters, m) and \(v_{n}\) (in meters per second, m/s) represent the position and speed of the nth vehicle on a certain road segment, respectively. The speed can take any integer value between 0 and \(v_{max}\) (in meters per second, m/s), where \(v_{max}\) is the maximum speed, \(a_{n}\) (in meters per second squared, m/s\({}^{2}\)) is the acceleration, \(E_{n}\) (in meters per second squared, m/s\({}^{2}\)) is the emergency braking acceleration, and \(P_{n}\) is the deceleration probability. \(L_{n}\) (in meters, m) represents the distance between the nth vehicle and the vehicle ahead of it, \(S_{n}\) (in meters, m) represents the distance between the nth vehicle and the intersection ahead, \(L_{n,font}\) (in meters, m) represents the distance between the nth vehicle and the vehicle ahead of it after changing to the adjacent lane, and \(L_{n,back}\) (in meters, m) represents the distance between the nth vehicle and the vehicle behind it after changing to the adjacent lane. The research hypothesized that the arrival of vehicles meets a uniform distribution, with two types of vehicles entering from  
Figure 1: A scheme of the road network.  
the initiation of the entrance lane. Upon reaching the intersection, they can proceed straight, turn left, or turn right, and then exit after reaching the end of the exit lane.
## 4 Experimental Test Results
### Compressive Strength  
Figure 6a presents the average compressive strengths of concrete samples at 7 days and 28 days. In addition, the relative strength ratio of the ERA series, ERB series, and ERR mix are shown in Figure 6b-d, compared to the compressive strength of the control specimens (CNC).  
In Figure 6b,c, the relative strengths of the ERA concrete series and ERB concrete series are compared to the CNC concrete, respectively. Test results showed that the compressive strengths decreased by 24% in the ERA2 mix (S = 2), compared to the control CNC mix and ERA1 mix (S = 1). It should be noted that the water-cement ratio, slump, and air content were kept to be almost the same in these mixes in Table 3 and Figure 4a,b, thus not affecting compressive strengths. However, the FA/a ratio (see Table 3) of ERA1 and ERA2 is 32.4% and 38.7%, respectively, whereas that of ERB1 and ERB2 is 34.3% and 39.5%. Because of variation of the FA/a ratio, the compressive strength was ERA1 > ERA2 and ERB1 > ERB2. Obviously, poor water absorption of the RA (3.82%) and RB (6.61%) aggregates resulted in a decrease in the compressive strength, whereas the NA aggregate showed good water absorption (0.37%).  
Comparing the test results between the ERA series and ERB series in 'S = 1', the compressive strength was ERA1 > ERB1. Once again, the same water-cement ratio, slump, and air content were employed. A lower FA/a ratio of ERA1 might account for greater compressive strength gain. [PERSON] et al. [42] found an increase of compressive strength of RCA concrete at early ages of 3 days and 7 days with greater replacement of RCA up to 100%. They asserted that the mortar strength of this concrete was likely superior to that of the control mixes, due to the reduction of the effective W/C ratio in the RCA mixes. An increase of compressive strength of RCA concrete with up to 30% replacement of RCA was also reported by [PERSON] et al. [43], while equivalent compressive strength of RCA concrete was attained with 50% replacement and 100% replacement of RCA by [PERSON] [44].  
Figure 6: Compressive strength results of concrete samples (**a**) strength, (**b**) relative value of ERA series, (**c**) relative value of ERB series, and (**d**) relative value of ERR mix.  
Next, in 'S = 2', however, the compressive strength was ERA2 < ERB2. Very poor density of ERA2 in Figure 6, which might be ascribed to it having the lowest density (see Figure 5), yielded the converse test result.  
Test results in Figure 6d indicates that the compressive strength of the ERR mix is superior to that of the control CNC mix. It is due to the high quality of the RCA manufactured from the PC concrete sleepers. This was explained in the author's former study [28]: RCA produced from wastes of precast structural concrete is of high quality and clean.
## 3 Materials and Methods
### Rotating Classification Model  
We found that the current dataset contains many low-quality images with rotation angles exceeding 90 degrees. This problem may be caused by different shooting methods of users. When we input these images into the Pix2 Pix model for training, the generated high-quality images are significantly different from the original low-quality ones. Therefore, we collected 2352 images with such problems and trained a classification model. As ResNet has powerful feature classification capabilities, ResNet34 was chosen as the classification network. The classification results include three categories: not rotated, rotated clockwise by 90 degrees and rotated counterclockwise by 90 degrees. Before inputting low-quality images into the generation model, the rotation classification model will determine whether there is a rotation problem in the input image.
## 4 Conclusions  
1. Compared with the 6 xxx-1(1.6 Mg/Si) profile, the 6 xxx-2(1.33 Mg/Si) and 6 xxx-3 (1.14 Mg/Si) profiles have the most severe cracking. The crushing energy absorption of the three Al-Mg-Si alloy thin-walled profiles arranged in order from highest to lowest are: 6 xxx-1(1.6 Mg/Si) > 6 xxx-3(1.14 Mg/Si) > 6 xxx-2(1.33 Mg/Si), indicating that if the Mg + Si contents are too high or too low, the crush performance of the alloy thin-walled profiles will decrease.
2. As the total Mg + Si content increases from 1.26% to 1.89%, the yield strength and tensile strength of the profiles increase from 273 MPa and 288 MPa to 355 MPa and 367 MPa, respectively, and the elongation decreases from 11.2% to 8.2%. This is because the increase in the total Mg + Si content increases the number of intragranular \(\beta^{\prime\prime}\) phases in Al-Mg-Si alloy thin-walled profiles, and large numbers of \(\beta^{\prime\prime}\) phases hinder grain dislocation, increasing the profile strength. However, with the increase in the total Mg + Si content, the grain size is coarser and the grain boundary PFZs are wider, thereby reducing ductility.  
Figure 13: TEM images of grain boundary precipitation of 6 xxx-1(1.6 Mg/Si) profile after different aging process treatments: (**a**) 175 \({}^{\circ}\)C/10 h; (**b**) 175 \({}^{\circ}\)C/16 h; (**c**) 205 \({}^{\circ}\)C/6 h.  
3. Compared with the under-aged and near-peak-aged states, the three Al-Mg-Si alloy thin-walled profiles in the over-aged state have increased bending angles and effective crushing energy absorption, suggesting that profiles in the over-aged state have better toughness and crush performance.
2. The yield strength and tensile strength of the three Al-Mg-Si alloy thin-walled profiles increased significantly by near-peak aging treatment. This is because of the significant increase of \(\beta^{\prime\prime}\) phase precipitation in the near-peak-aged state of the three profiles.  
Conceptualization, H.G. and Z.L.; methodology, H.G.; software, H.G.; validation, H.G. and Z.L.; formal analysis, H.G.; investigation, H.G.; resources, H.G.; data curation, H.G.; writing--original draft preparation, H.G. and Z.L.; writing--review and editing, H.G. and J.T.; visualization, H.G. and Z.L.; supervision, H.G.; project administration, H.G. and J.T.; funding acquisition, J.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Basic Research Program of China (Grant No. 2017 YFB0306300).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Ningbo Xusheng Group Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 4 Mathematical Analysis  
In this section, we calculate the probability equations for BFD and TNFD communications, because most of communications will be either BFD communications or TNFD communications, if it is considered that all nodes have data to send. The probability equations are calculated using the Packet Generation Rate (PGR) by each node (\(\lambda\)). We assume that the total number of nodes are 'n' and the percentage of hidden nodes is '\(\alpha\)'. Here,  
Figure 6: Time sequence of the proposed MAC for source-based TNFD (the rest of the nodes except MS initiate the transmission).  
Figure 7: Time sequence of the proposed MAC for destination-based TNFD (the rest of the nodes except MS initiate the transmission).  
it is considered that packet arrival process is the 'Poisson process' and the service time distribution is 'exponential'.  
In general, packets wait in the queue before transmission. The average waiting time before transmission is \(T_{WAIT}\) and it is assumed as an M/M/1 queueing system. Therefore, the equation of the average waiting time is,  
\[T_{WAIT}=\frac{total\ packet\ generation\ rate}{(service\ rate-total\ packet\ generation\ rate)\times service\ rate}=\frac{\lambda_{tot}}{(\mu-\lambda_{tot})\times\mu} \tag{1}\]  
where, \(\lambda_{tot}\) is the total PGR and \(\mu\) is the average service rate of the MS.  
The symbols and description of different variables that we use to calculate probability equations are listed in Table 1.
## 1 Introduction  
The plain of Guigou, located in the center of Morocco between the town of Timahdite and the village of Almis Guigou, has seen an important development in agricultural activities in recent decades, especially in the production of potatoes and onions, which are very well known and in demand in the Moroccan market. Indeed, this crop, which is very demanding in terms of irrigation water, is at the origin of the overexploitation of the water table in the said region. This need is manifested by the decrease in pumped flows and, consequently, the phenomenal drop in the piezometric level [1].  
In this respect, detailed knowledge of the aquifer system is of great interest for the optimal management of groundwater resources in such a region [2; 3; 4; 5; 6]. The aquifer exploited in the region is formed by Plio-Quaternary basalts with Miocene and Cretaceous marl and limestone formations as a semi-impermeable bedrock. The fracturing affecting these basalts plays a key role in the circulation of groundwater and impacts the productivity of hydrogeological boreholes. In addition, there are few hydrogeological studies of the Plio-Quaternary basaltic aquifer in the region [1; 7], and there are none based on geophysical surveys, which means that this aquifer is still poorly known and needs to be explored in detail.  
The geoelectrical method, with its three most widely used techniques (tomography, profiling and vertical drilling), has long been considered the most widely used geophysical method for the characterization of aquifer systems in the world [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21].  
Due to its simplicity of implementation and cost-effectiveness compared to other methods, the vertical electrical sounding (VES) technique has proven to be very useful in mapping aquifer systems, the geological layers forming their impermeable bedrock and the detection of structural anomalies corresponding to faults and fractures [4; 22; 23; 24; 25].  
In this study, we used the vertical electrical sounding (VES) technique to map the aquifer system of the Guigou plain. The main objectives of this study included the following: (i) to identify the electrical horizons of the subsoil, (ii) to determine the geological layers forming the aquifer; (iii) to determine the geometry of the aquifer system; (iv) to estimate the depth of the impermeable bedrock; and (v) to locate the geoelectrical discontinuities and determine their role in groundwater drainage.