esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results
### Double-Balanced Down-Conversion Mixer Noise Calculation and Simulation
#### 4.2.2 Noise Due to LO Switching Pair
The noise voltage \(v_{n}<<\) LO signal, so it can be treated as a switched voltage of slowly varying amplitude. The switching edges will be a time constant, enabling a current through the capacitance \(C_{p}\) associated with transconductance stage Q1:  
\[I_{o,n}=\frac{2}{T_{LO}}.2I_{RF}.\frac{V_{n}}{S} \tag{14}\]1  
\[I_{o,n}=\frac{2}{T_{LO}}.2I_{RF}.\frac{V_{n}}{S} \tag{14}\]2  
This leads to the noise contribution:  
\[I_{o,n}=\frac{2}{T_{LO}}.2I_{RF}.\frac{V_{n}}{S} \tag{14}\]3  
In conclusion, indirect switching noise is much smaller than direct switching noise.
## 2 Topics and Results
### Change in Basic Blood Plasma Components
#### 2.2.1 Fibrinogen  
An elevated level of fibrinogen can be considered one of the main risk factors for the development of cardiovascular disease along with high blood pressure, obesity, smoking and diabetes [18]. Similarly there is a direct relation between whole blood viscosity and fibrinogen plasma concentration at different shear rates [19]. Therefore, the interest in the methods of lowering its levels in the blood plasma is also growing.  
Many authors find a direct relationship between physical exertion and fibrinogen level reduction. One of the first results in this direction was reported in Russia by [PERSON] et al. (1986) [20]. A group of 30 patients with cerebrovascular disease underwent regular exercise for 30 days. As a result, fibrinogen levels were significantly reduced [20].  
In the Scottish Cardiovascular Disease and Health Study [21], involving 8824 people aged 40 to 59 years, significantly increased plasma fibrinogen levels were found in physically inactive subjects compared to physically active subjects [21; 22].  
[PERSON] et al. (1991) [23] formed two target groups--One consisting of 10 young men aged 24-30 years and the other of 13 older men (60-82 years). All subjects were clinically healthy as demonstrated by routine physical and laboratory tests [23]. Both groups performed physical exercises for six months, 4 or 5 times a week, with increasing load. The experimental protocol included walking, running and cycling. A statistically significant decrease in fibrinogen levels was obtained in the elderly group. No changes were observed in young men.  
[PERSON] et al. (1992) [24] divided 55 patients into three groups after coronary artery surgery. The first group was made up of physically inactive patients, the second of people loaded with regular aerobic exercises for 6 months, and in the third there were patients subjected to strength loads for 6 months. The intensity of the loads is from 12 to 60 min, three times a week. A decrease in plasma fibrinogen levels was found in the second group of patients subjected to aerobic exercise [24].  
[PERSON] et al. (1991) [25] formed a group of 12 healthy subjects subjected to endurance exercise (28 h per week, for 9 weeks) and a group of 12 physically inactive subjects (control group). Again, the result is a decrease in fibrinogen levels under the influence of physical activity [22].  
The described examples of lowering the levels of fibrinogen in the blood plasma after different types, duration, frequency and degree of physical exercise show that physical activity is very important for maintaining the health status of the person. It has also been shown that the inclusion of regular physical activity, especially in elderly individuals, can serve as a natural endogenous regulation of fibrinogen levels and/or as a natural complementary regimen in support of ongoing drug therapy.
## 5 Experimental Results and Discussion
### Benchmark Simulation Data: Tennessee Eastman Process
As a result, determining fault conditions becomes challenging, leading to an increased type II error. As shown in the results of Figure 8b,c, kNN and LOF show type I errors of 23.75 and 22.88, respectively, indicating they do not demonstrate sufficient reliability on the training data, making it difficult to consider them reliable fault detection models. This performance limitation arises because kNN and LOF select neighbors strictly based on a predefined \(k\) value, without considering the relative position of fault data. As a result, they utilize the distances of all selected neighbors, leading to reduced accuracy in fault detection. AAKR calculates type I and II errors of 3.63 and 32.25, respectively, similar to AASKR-NP (type I = 3.00, type II = 32.55). As shown in Figure 8f, these results indicate that AASKR does not provide significant improvement without applying a penalty to the distance value. The proposed method calculates a type I error that is 1.5%p higher than AASKR-NP but a type II error of 29.48%p, which is 2.07%p lower than AAKR and 3.07%p lower than AASKR-NP. This demonstrates that the proposed method improves the fault detection performance of AAKR. Table 1 compares the type I and II errors of the proposed method with conventional methods across 21 fault cases in TEP. For a comparison of fault detection performance, the fault thresholds for each model were set at a 99% confidence limit value  
Figure 7: Comparison of fault detection performance between conventional and proposed methods for the TEP Fault 6 case: (**a**) PCA; (**b**) kNN; (**c**) LOF; (**d**) AAKR; (**e**) AASKR–NP; (**f**) AASKR. (\(\alpha\) = 0.01). In TEP, fault cases 3, 9, and 15 show high type II errors for all detection models, as shown in Table 1, due to the difficulty in identifying fault-related signs from the data. When comparing the type I and II errors for each model across 18 fault cases, excluding cases 3, 9, and 15, the proposed method outperforms all others, except LOF and kNN, which have high type I and II errors. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**PCA**} & \multicolumn{2}{c}{**kNN**} & \multicolumn{2}{c}{**LOF**} & \multicolumn{2}{c}{**AAKR**} & \multicolumn{2}{c}{**AASKR-NP**} & \multicolumn{2}{c}{**AASKR**} \\ \cline{2-13}  & **Type I** & **Type II** & **Type I** & **Type II** & **Type I** & **Type II** & **Type I** & **Type II** & **Type I** & **Type II** & **Type I** & **Type II** \\ \hline
1 & 3.75 & 0
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 4 Results
### Relationship between Coal Seam Thickness and Slowness  
However, the slowness distribution result can only be used to qualitatively estimate the variation of coal thickness in the detection area. In order to analyze the variation of coal thickness quantitatively, the slowness result has to be translated into coal thickness. The coal thickness disclosed along with the head entry (150 to 550 m) and the belt entry (250 to 550m) was used to estimate the correlation of slowness with the disclosed thickness values. Figure 5 shows that a second order polynomial can better reflect the relationship between slowness and coal thickness where the determination coefficient \(R^{2}\)= 0.9654 and the root mean square error \(RMSE\) = 0.087.
## 2 Data and Methods
### Ancillary Data
#### 2.2.2 Surface Temperature (T\({}_{S}\))  
At microwave frequencies below 117 GHz, [PERSON]'s law of electromagnetic radiation can be simplified using the Rayleigh-Jeans approximation, resulting in T\({}_{B}\) being the product of the physical temperature (T\({}_{Ph}\)) and the ice emissivity with an error of <1 % [44]. Hereby, the emissivity contains the actual information about the sea ice composition--including SIT--of the radiating layer. Figure 2 shows the relationship between T\({}_{B}\) and SIT as a function of T\({}_{Ph}\) and sea ice types that are based on a radiative transfer model considering two nadir-pointing observations at frequencies corresponding to L-band (1.4 GHz) and P-band (500 MHz), respectively. The model assumes a horizontally-layered column of sea ice above water (without snow on top) using empirically determined values for sea ice properties, such as salinity and surface roughness [45]. While the L-band signal already saturates around 0.6 m, the model reveals that observations at P-band contain information on SIT beyond 1.5 m and, in principle, they can be used to extent the sensitivity range of current retrieval algorithms. T\({}_{Ph}\) can strongly vary among the pole areas and it has a gradient along the ice profile, which influences the penetration depth of the emitted signal at L-band. Additional physical properties, such as density and ice type, can further depend  
Figure 1: Maps of the input features for 11 November 2020, comprising (**a**) FMPL-2 T\({}_{B}\), (**b**) OSI-SAF OSI-401-b SIC, (**c**) ECMWF Skin temperature, and (**d**) CryoSat-2 sea ice Fb (weekly composite).  
on the distribution of temperature, which makes a direct correction of T\({}_{Ph}\) based on T\({}_{B}\) complicated. The skin temperature (T\({}_{S}\)) product provided by the ECMWF represents the temperature value of the uppermost surface layer that satisfies the surface energy balance equation [46]. Daily T\({}_{S}\) maps were considered to be relevant input features for model training. They were linearly interpolated to a 12.5 km EASE-Grid 2.0 for Northern and Southern hemispheres.
## Design and Performance Evaluation of a Dual Antenna Joint Carrier Tracking Loop  
**[PERSON] 1, [PERSON] 2, [PERSON] 1,*, [PERSON] 1 and [PERSON] 1**  
1 GNSS Research Center, Wuhan University, Wuhan 430079, China;  
E-Mails: [EMAIL_ADDRESS] (W.G.); [EMAIL_ADDRESS] (C.S.); [EMAIL_ADDRESS] (H.Z.)  
2 Department of Geomatrics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-186-0270-8312; Fax: +86-27-6877-8890.  
Academic Editor: [PERSON]  
_Received: 11 March 2015 / Accepted: 28 September 2015 / Published: 1 October 2015_  
**Abstract:** In order to track the carrier phases of Global Navigation Satellite Systems (GNSS) signals in signal degraded environments, a dual antenna joint carrier tracking loop is proposed and evaluated. This proposed tracking loop processes inputs from two antennas, namely the master antenna and the slave antenna. The master antenna captures signals in open-sky environments, while the slave antenna capture signals in degraded environments. In this architecture, a Phase Lock Loop (PLL) is adopted as a master loop to track the carrier phase of the open-sky signals. The Doppler frequency estimated by this master loop is utilized to assist weak carrier tracking in the slave loop. As both antennas experience similar signal dynamics due to satellite motion and clock frequency variations, a much narrower loop bandwidth and possibly a longer coherent integration can be adopted to track the weak signals in slave channels, by utilizing the Doppler aid from master channels. PLL tracking performance is affected by the satellite/user dynamics, clock instability, and thermal noise. In this paper, their impacts on the proposed phase tracking loop are analyzed and verified by both simulation and field data. Theoretical analysis and experimental results show that the proposed loop structure can track degraded signals (_i.e._, 18 dB-Hz) with a very narrow loop bandwidth (_i.e._, 0.5 Hz) and a TCXO clock.  
**Keywords:** GNSS receiver; carrier phase tracking; differential carrier phase; weak signal; loop bandwidth +
Footnote †: journal: Journal of Scientific Computing
# Design, Implementation, and Analysis of a Block Cipher Based on a Secure Chaotic Generator
## 3 Experiments Results and Security Analysis
### Performance Analysis of the Proposed PRNG-CS
#### 3.2.2 NIST  
The NIST Test Suite [48] is a statistical package consisting of 15 tests that were developed to test the randomness of binary sequences produced by PRNGs. Some tests are decomposable into a variety of subtests. To determine the result of each test, a \(p\)-value is computed. A \(p\)-value \(\geq 0.01\) means that the sequence is considered to be random with a confidence of 99%. A \(p\)-value \(\leq 0.01\) means that the sequence is nonrandom with a confidence of 99%.  
To carry out the NIST test, we produced 100 different sequences of 3,125,100 32-bit samples each, using 100 random secret keys. Only 3,125,000 samples per sequence (i.e., 10\({}^{8}\) bits) were used (the first 100 samples for each sequence, corresponding to the transient regime, were not useful for the various robustness tests).  
We give in Table 5 the \(p\)-values obtained for the 15 tests by the proposed PRNG-SC. All \(p\)-values were distinctly larger than the critical value 0.01, which proved the high degree of randomness of the generated sequences. Therefore, from the statistical results obtained previously, we could confirm that the proposed PRNG-CS was robust against statistical attacks.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Chi-Square Test** & **PRNG-CS** \\ \hline \(\chi^{2}_{\chi^{2}}\) & 967.700 \\ \(\chi^{2}_{th}\) (1000, 0.05) & 1073.642 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chi-square results on the tested histograms.  
Figure 6: An example of a histogram of a generated sequence \(X(n)\).
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 4 Probiotics and Gut Microbiota  
Changes in the gut microbiota can promote colorectal carcinogenesis. [PERSON] et al. analyzed the microbial community in the human colorectal mucosae at different stages of colorectal tumor development. The results revealed significant mucosal microbiota alterations throughout the various stages of colorectal carcinogenesis. The microbial communities in the tumor and tumor-adjacent mucosae also exhibited inconsistency [104]. Subsequent investigation revealed that fecal samples from CRC patients can induce tumorigenesis in germ-free mice, indicating that microbial dysbiosis contributes to the initiation and progression of CRC [105]. Specific bacteria from gut microbiota with dysregulation play a dominant role in promoting carcinogenesis. _F. nucleatum_ abundantly accumulates in the tumor tissues of late-stage CRC patients [106]. The bacterial adhesin FadA, secreted by Fn, facilitates bacterial adherence and the invasion of colonic epithelial cells, resulting in the activation of cancer-related signaling pathways such as \(\beta\)-catenin and Wnt, ultimately inducing colonic tissue carcinogenesis [107]. _Enterotoxic Bacteroides fragilis_ (ETBF), an additional CRC-associated pathogenic bacterium, can colonize the intestinal mucosa and continuously release the _Bacteroides fragilis_ toxin (BFT) [108]. The accumulated BFT stimulates the development of colonic precancerous lesions and carcinogenesis, suggesting that it has the potential to serve as a biomarker for the early detection of CRC [109]. _E. coli_ harboring the pathogenic pks gene island (pks\({}^{+}\)_E. coli_) are frequently present in the guts of healthy individuals as commensal bacteria with conditional pathogenic potential. The abundance of pks\({}^{+}\)_E. coli_ in the colonic mucosa of CRC patients is significantly higher compared with that of healthy individuals [110, 111]. The pks gene island enables pks\({}^{+}\)_E. coli_ to produce and secrete the genotoxic coli, interacting with host cell DNA to cause DNA strand crosslinking, breakage, and the induction of specific T-base substitutions, deletions, and insertional mutations [112].
## 2 Background and Motivation
### Rolling Shutter  
In rolling shutter mode, the photodiode does not collect all the light simultaneously, but instead scans the pixels sequentially using the CMOS image sensor (IS). Images are generated by row by row (or column by column) scanning and exposing one row (or column) of pixels at a time, with each row (or column) pixel in the sensor array having a different time to start and end the exposure. Thus, the delay in the exposure time allows us to record the relative structure of the target based on time. In this mode, different pixels obtained from the rolling shutter image sensor have different light signal intensities, which means that there are multiple bits of information. Precisely, the receiver can capture multiple switching states of the illumination source in the image. When the LED luminaire is \"ON\", a bright stripe is generated in the image, representing the binary data \"1\", and when the LED luminaire is \"OFF\", a dark stripe is generated in the image, representing the binary data \"0\", as shown in Figure 1. The faster the LED blinks, the greater the number of stripes and the narrower the width, so multiple bits of information can be encoded with this pattern [24; 33].
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 1 Introduction
Therefore, the ISL SSV should also include these aspects. The received power is related to the length of the propagation path, and the pseudorange accuracy is determined by the clock error, ephemeris error, and received power [18]. The significant difference between the ISL and the navigation signal is the availability due to the time division spot beam. Thus, this paper focuses on the availability characteristics of the ISL SSV. The next section describes the relative movements of the satellites in a navigation constellation. Section 3 analyzes the availability of the ISL signal. The performance of a time division ISL is simulated in Section 4. The final section presents our conclusions.
## 4 Simulation Studies and Numerical Justifications  
### Simulation Setup and Evaluation Method  
To justify the performance improvement of the RASMR algorithm on data of massive sizes, we conduct extensive simulations on a main-effects GLM as follows:  
\[E(Y_{i})=\mu_{i}\text{ and }\eta_{i}=g(\mu_{i})=\mathbf{X}_{i}^{T}\boldsymbol{ \beta}=\beta_{0}+\beta_{1}x_{i1}+\cdots+\beta_{d}x_{id}\, \tag{8}\]  
where \(i=1,\ldots,N\). Following [43; 56], we choose \(d=7\), \(\beta_{0}=0\), and \(\beta_{1}=\cdots=\beta_{7}=0.5\). The feature variables \(\mathbf{x}_{i}=(x_{i1},x_{i2},\ldots,x_{id})^{T}\) are randomly generated from one of seven different distributions, namely mzNormal, nzNormal, ueNormal, mixNormal, \(T_{3}\), EXP, and BETA (see Section S5 in the Supplementary Materials for more details).  
For Bernoulli regression models (see Example 1 in Section 4.2), we consider four commonly used link functions, namely logit, cloglog, probit, and cauchit. We first simulate \(N=10^{6}\) data points from each of the seven distributions, respectively, under the logit link. We then obtain the data blocks \(I_{1},\ldots,I_{K}\) using \(K\)-means clustering with \(K=1000\). By assuming each of the four link functions, we obtain the corresponding parameter estimates using the MR, SMR, and RASMR algorithms, respectively. We then compare the estimate \(\hat{\beta}_{i}\) with the full data estimate \(\hat{\beta}_{i}\) under the corresponding link function. The performance of estimation is measured by the root mean square error (RMSE, that is, \([\sum_{i=1}^{7}(\hat{\beta}_{i}-\hat{\beta}_{i})^{2}/7]^{1/2}\)). A smaller RMSE indicates a better approach.  
Similarly, we consider a Poisson regression model with a log link (see Example 2 in Section 4.2) and a Gamma regression model with a reciprocal link (see Example 3 in Section 4.2). For each example in Sections 4.2 and 4.3, the corresponding simulation is repeated 100 times. The summarized results, including the average RMSE and the sample standard deviation (std in parenthesis) of 100 RMSEs, are listed in the corresponding tables.  
Note that the \(K\)-means clustering algorithm performed for each simulation study is used for illustration purposes. It generates a partition with blocks whose data points are homogeneous. Other clustering methods, such as \(k\)-medoids (see, for example, [57] and references therein), may be used for the same purpose as well. Our goal here is to evaluate the performance of MR, SMR, and RASMR given the same partition, not to compare different clustering algorithms.  
For SMR, each simulation study recommends a number of iterations, \(T\), set to 3, as suggested by [43], as improvement beyond the third iteration is often negligible (see Figure S12 in the Supplementary Materials). In contrast, RASMR typically continues to improve its parameter estimates as \(T\) increases, provided the number of data blocks is sufficiently large (see Figure S12). Overall, we recommend setting \(T\) to 10 for RASMR algorithms to balance estimation accuracy with computational time. It should be noted that RASMR still outperforms SMR even with the same number of iterations (see Table 6).
# Approaching Breakthrough: Resource-Efficient Micropolitan Removal with MBR-GAC Configuration
## 3 Results
### Removal Efficiency for PFASs  
Concentrations of PFAS11 (provided in Figure A2 in the Appendix A) show variations between the various sampling occasions and different levels for different PFASs. In difference to pharmaceuticals, only a few PFASs are affected by the main treatment process also the quaternary treatment does not imply a significant reduction of most analyzed PFASs. For PFOS, however, concentrations are significant lower already after the main treatment process (MBR permeate), with variations of concentrations in the MBR permeate and after the quaternary treatment related to incoming variations.  
Figure 3 presents removal rates for PFOS and \(\underline{\Sigma}\)PFAS11 over the MBR process and the entire treatment process, including quaternary treatment. Unlike pharmaceutical removal, PFOS removal is primarily achieved in the MBR process, with the GAC filters contributing with only minor removal. Removal efficiency for PFOS over both pilot lines (MBR-GAC  
Figure 2: Removal efficiency in the MBR-GAC pilot for all analyzed pharmaceuticals and the sixteen most persistent pharmaceuticals for sample occasions (calendar week and treated EBVs).  
L1 and L2) generally exceeded 80%, except for a few occasions. For \(\Sigma\)PFAS11, the results differ, with a negative removal already observed in the MBR process. Except for the last sampling campaign, where incoming PFAS concentrations were lower than in previous sampling occasions, removal of \(\Sigma\)PFAS11 varies between low to moderate rates. However, as there are currently no defined removal targets for PFASs at WWTPs, an assessment of the removal efficiency towards such target was not possible.
## 3 Proposed Model
### The Effects of the New Parameters on the TYM
#### 3.1.3 The Impact of Window Size on Signal Strength  
Here, we shall explain some of the features of window size. Specifically, the effect of both the metal frame shape over the deviation style and the window size; both must be taken into consideration, in addition to the view of a computational accuracy spot.  
Another important factor that needs to be considered is the effect of window size on the form of the deviation style [9,10]. For the window size, the complete area of the entire window is calculated in this paper within the same selected floor. In fact, this process was performed by conducting calculations for the average size of each entire window. Then, we performed a multiplication for the available number of windows.  
In relation to the window size inside the building, we calculated the full surface area for the window in the same specific floor. This was accomplished by multiplying the average size for all the windows in the same floor by the number of the windows (Equation (5)).  
\[S_{win}=\frac{1}{N}\sum_{i=1}^{N}a_{i}=\frac{1}{N}=(win_{1}+win_{2}+\ldots+win _{n}) \tag{5}\]  
**Definition 5**.: \((S_{win})\) _is window size, \((N)\) is the number of windows, \((win_{n})\) is number of windows in each floor._