esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Mixture Model-Based Analysis of Yarn Quality Parameters
## 3 Results and Discussion
### Distribution Fitting of Yarn Mechanical Properties
\[f(x)_{elongation}=0.33\frac{0.04^{320}x^{319}e^{-0.04x}}{\int_{0}^{\infty}t^{ 319}e^{-dt}}+0.67\frac{0.146^{110}x^{109}e^{-0.146x}}{\int_{0}^{\infty}t^{109} e^{-t}dt} \tag{72}\]  
\[f(x)_{strength}=0.52\frac{0.17^{120}x^{119}e^{-0.17x}}{\int_{0}^{\infty}t^{1 19}e^{-t}dt}+0.48\frac{0.09^{280}x^{279}e^{-0.09x}}{\int_{0}^{\infty}t^{279}e^ {-t}dt} \tag{73}\]  
In spinning mills, the variability in elongation and tensile strength directly affects the performance characteristics of the produced yarns. Establishing the probability density function for these properties is critical for effectively modeling the statistical behavior of elongation and tensile strength, providing insights that can be leveraged for quality control and predictive analysis in yarn production processes. In summary, employing a suitable FMM for both yarn irregularity and mechanical properties offers several key advantages, as follows:  
* Prediction and control: The obtained probability density functions allow for the prediction of the likelihood of specific parameter values occurring in future yarn production. This predictive capability is crucial for process control, enabling adjustments to maintain desired quality standards. When integrated into real-time quality monitoring systems, this predictive capability enables timely interventions by triggering alerts when the probability of conformance falls below critical thresholds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Quality Parameters**} & \multicolumn{2}{c}{**Observed Data**} & \multicolumn{2}{c}{**GMM Data**} \\ \cline{2-5}  & **Mean** & **Standard Dev.** & **Mean** & **Standard Dev.** \\ \hline Elongation & 14.97 & 2.03 & 14.98 & 2.01 \\ Tensile & 22.89 & 3.25 & 22.86 & 3.35 \\ strength & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean and standard deviation of elongation and tensile strength: observed data vs. gamma mixture model data. Figure 10: Elongation observed data and fitted gamma mixture data. * Modeling and simulation: Probability functions can be integrated into more complex models and simulations of textile manufacturing processes. This facilitates virtual experimentation and optimization, reducing reliance on costly and time-consuming physical trials. * Risk assessment: The obtained probability density functions allow for a more precise evaluation of the risk associated with producing fabrics that do not meet predefined performance specifications. By quantifying the probability of conformance, manufacturers can assess the likelihood of deviation from quality standards in advance. This type of risk assessment is particularly crucial in high-performance applications--such as medical textiles, aerospace composites, or protective fabrics--where yarn failure could lead to severe functional or safety consequences.
## 3 Model Application in a Simulated River Network  
### River Network Description  
A complex unsteady-flow river network with four levels of river channels from the literature [21] was used for case analysis as shown in Figure 3. In order to test the spatial detection ability of the developed model for sudden changes in roughness, various roughness values were set in different river channels. The roughness of River Channel 8 was set to 0.026, the roughness of River Channel 24 was 0.02, and the roughness values of the rest were 0.023. These values are regarded as the true values of roughness. River Channel 24 is far away from River Channel 8 (Figure 3), and there were a total of 41 river sections for roughness inversion.  
Sensitivity analyses were then conducted to test the robustness of the model, and identify the most important factors affecting the results and their optimized data range. By performing this, the uncertainty and potential errors in the roughness inversion results can be minimized, and the accuracy and reliability of the model can be improved. Since the amount of the observed data in practice is normally insufficient when so many bed roughness parameters are required at a number of river sections (i.e., 41 in this simulated river network) for inversion, the model simulation is an underdetermined problem for solving parameters. Regulation techniques that constrain the model parameters (i.e., the regularization term in Equation (1)) are used to render roughness estimation more reasonable and stable.
## 2 Background
### The motion model and sensor measurement model are all linear Gaussian.  
\[\begin{array}{l}f_{k|k-1}(x|\zeta)=\mathrm{N}(x:\mathrm{F}_{k-1}\zeta, \mathbf{Q}_{k-1})\\ g_{k}(z|x)=\mathrm{N}(z:\mathrm{H}_{k}x,\mathbf{R}_{k})\end{array} \tag{5}\]  
where \(\mathrm{F}_{k-1}\) is the state transition matrix, \(\mathrm{H}_{k}\) is the measurement matrix, and \(\mathbf{Q}_{k-1}\) and \(\mathbf{R}_{k}\) are the process noise covariance and measurement noise covariance respectively.  
1. The survival and detection probabilities for targets are state independent. \[\begin{array}{l}P_{S,k}(x)=P_{S,k}\\ P_{d,k}(x)=P_{d,k}\end{array}\] (6)
2. The target birth RFS is also a Gaussian mixture of the form. \[\gamma_{k}(x)=\sum_{i=1}^{I_{x,k}}\omega_{r,k}^{(i)}\mathrm{N}\Big{(}x: \mathbf{m}_{r,k}^{(i)},\mathbf{P}_{r,k}^{(i)}\Big{)}\] (7)  
Figure 2: Relationship between Probability Hypothesis Density (PHD) and Gaussian Mixture Probability Hypothesis Density (GM-PHD).  
where, \(J_{r,k}\), \(\omega_{r,k}^{(i)}\), \(\mathbf{m}_{r,k}^{(i)}\), \(\mathbf{P}_{r,k}^{(i)}\), \(i=1,\cdots,J_{r,k}\) are given parameters that determine the intensity of target birth; and \(\mathbf{m}_{r,k}^{(i)}\), \(\mathbf{P}_{r,k}^{(i)}\) are calculated using Kalman filter [24], which are used in many parts in the paper.  
Based on the assumption A.1-A.3, the posterior intensity at time \(k-1\) is a Gaussian mixture of the form in Equation (8). Here, the main prediction and update process of GM-PHD algorithm can be described as below.  
\[D_{k-1}(x)=\sum_{i=1}^{k-1}\omega_{k-1}^{(i)}\mathrm{N}\Big{(}x:\mathbf{m}_{k -1}^{(i)},\mathbf{P}_{k-1}^{(i)}\Big{)} \tag{8}\]  
Prediction:  
\[D_{k|k-1}(x)=D_{S,k|k-1}(x)+\gamma_{k}(x) \tag{9}\]  
where \(\gamma_{k}(x)\) is given in Equation (7), \(D_{S,k|k-1}(x)\) is shown in Equation (10).  
\[D_{S,k|k-1}(x)=P_{S,k}\sum_{j=1}^{k-1}\omega_{k-1}^{(j)}\mathrm{N}\Big{(}x: \mathbf{m}_{S,k|k-1}^{(j)},\mathbf{P}_{S,k|k-1}^{(j)}\Big{)} \tag{10}\]  
Update:  
\[D_{k}(x)=(1-P_{d,k})D_{k|k-1}(x)+\sum_{z\in\mathbf{Z}_{k}}\sum_{l=1}^{k-1} \omega_{k}^{(l)}\mathrm{N}\Big{(}x:\mathbf{m}_{k}^{(l)},\mathbf{P}_{k}^{(l)} \Big{)} \tag{11}\]  
where, \(\omega_{k}^{(l)}\), \(\mathbf{m}_{k}^{(l)}\), \(\mathbf{P}_{k}^{(l)}\) are shown in Equation (12).  
\[\omega_{k}^{(l)}=\frac{P_{d,k}\omega_{k|k-1}^{(l)}\mathrm{N}\Big{(}\mathbf{z} :\mathbf{z}_{k|k-1}^{(l)},\mathbf{S}_{k|k-1}^{(l)}\Big{)}}{\kappa_{k}+\sum \limits_{l=1}^{k_{k-1}}\omega_{k|k-1}^{(l)}\mathrm{N}\Big{(}\mathbf{z}: \mathbf{z}_{k|k-1}^{(l)},\mathbf{S}_{k|k-1}^{(l)}\Big{)}} \tag{12}\]  
For a more detailed mathematical derivation on GM-PHD filter, we can see it in literature [11]. The computational complexity of the PHD filtering algorithm is \(O(Nm_{k})\), where \(N\) is the target number of tracking and \(m_{k}\) is the cardinalized RFS measurement at each moment. Due to the interference of clutter, the GM-PHD filter algorithm has a large deviation and fluctuation in the estimation of the target number.
# Performance Analysis of a PEMFC-Based Grid-Connected Distributed Generation System
## 5 Conclusions  
The results and analysis were realized under transient and steady-state conditions. According to the results of the performance analysis, the important points to highlight in order to assist researchers working in this field are as follows. First, it can be extracted that the PV modules are more useful and efficient in urban applications than wind turbines. For example, we see barely 160 W produced by the wind turbine, with a nominal 400 W of power used in this system. The most important reason for this is that the wind turbines installed in places where there are many buildings cannot catch the necessary wind due to its dynamic nature. Another important consequence of the DG system is that the hydrogen required by the fuel cell is supplied by the electrolyzer, which is supplied from renewable energy instead of ready hydrogen tanks. However, the PEM-type fuel cell stack is not suitable for continuous operation if the electrolyzer has a low hydrogen-producing capacity. Although the nominal power of the fuel cell is 1200 W, it is operated with 250 W output power. The energy required for the operation of the electrolyzer is produced entirely from renewable energy. With the increase of the power and size of the electrolyzer, PEMFC's support for the DG system will increase even more.  
The key components of the system are power switching and power electronics components. The frequency of the power switching elements of the inverter used in the system during connection and disconnection from the grid affects the loads in the system. This particularly affects fluorescent lights, which operate with a gas discharge.  
A.N.A. and E.D. wrote the manuscript. E.D. performed the experimental design of the microgrid and data acquisition. A.N.A. operated the system. A.N.A., E.D. and Y.Y. gathered and evaluated the results. A.N.A., E.D. and Y.Y. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 16: The pie chart of power generation and grid support rates of the microgrid for a defined operation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(E_{Wind}\) & \(E_{PV}\) & \(E_{PEMTC}\) & \(E_{DG}\) = \(E_{Wind}\) + \(E_{PV}\) + \(E_{PEMFC}\) & \(E_{grid}\) & \(E_{demand}\) \\ \hline
228.493 & 1335.050 & 198 & 1761.453 & 2029.497 & 3791.040 \\ \hline \end{tabular}
\end{table}
Table 5: The DG and energy demands (kWh).  
**Funding:** This study was supported by grant from Scientific Research Projects Unit (BAPKO) project number: FEN-E-120314-0069.  
**Institutional Review Board Statement** Not applicable.  
**Informed Consent Statement** Not applicable.  
**Data Availability Statement** Not applicable; however, the data may be requested by reaching out to the authors through email.  
**Conflicts of Interest** The authors declare no conflict of interest.
## 2 Related Works
### Embedded Binary HTTP (EBHTTP)  
Embedded binary HTTP (EBHTTP) [34] is a binary-formatted, space-efficient, stateless encoding of the standard HTTP/1.1 protocol. EBHTTP is primarily designed as the transportation of small scale data between resource-constrained nodes, which follows a similar approach to CoAP. This protocol is focused on reducing the overhead of HTTP while maintaining the same HTTP semantics and communication paradigm. EBHTTP uses the UDP protocol instead of TCP. The basic EBHTTP header consists of 2 bytes specifying the method and a control field; all other data are carried as Type-Length-Value (TLV) encoded sections of the method. These may include a request URI, HTTPheaders (either compressed or uncompressed), and body data. This encoding also allows multiple EBHTTP messages to be packed into a single UDP datagram or TCP segment.  
Some of the most important weaknesses relay on the lack of reliability and in the processing power required to encode the HTTP protocol. In fact, although the processing required to encode the protocol would be less than what is required by a direct use of HTTP, it would be still expensive due to the limited resources of a sensor. Furthermore, although they are bound on UDP, they do not provide any reliability mechanism. Lastly, it is a protocol that has been conceived for the transmission of information at the application layer, so its applicability for lower ones would be ineffective or plainly impossible to perform.
## 5 Results and Analysis
### Outdoor Performance of LoRaWAN  
Uplink and downlink success rates are reported in Table 4 with RSSI, SNR, and channel performance reported in Table 5. The channel performance was reported since performance  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & \multicolumn{2}{c}{**Top-Floor Gateway**} & \multicolumn{2}{c}{**Ground-Floor Gateway**} \\ \hline
**Location \({}^{*}\)** & **RSSI (dBm)** & **Std (dB)** & **RSSI (dBm)** & **Std (dB)** \\ \hline G-N & \(-57.1\) & \(\pm 4.3\) & \(-54.7\) & \(\pm 4.6\) \\ \hline G-V § & \(-69.4\) & \(\pm 6.5\) & \(-37.9\) & \(\pm 5.2\) \\ \hline G-F & \(-76.1\) & \(\pm 6.0\) & \(-68.5\) & \(\pm 7.3\) \\ \hline M-N & \(-51.7\) & \(\pm 7.3\) & \(-66.0\) & \(\pm 6.9\) \\ \hline M-F & \(-73.3\) & \(\pm 6.2\) & \(-70.3\) & \(\pm 6.1\) \\ \hline T-N & \(-48.5\) & \(\pm 5.7\) & \(-79.3\) & \(\pm 5.2\) \\ \hline T-F & \(-68.1\) & \(\pm 10.0\) & \(-72.2\) & \(\pm 7.3\) \\ \hline G-S & \(-92.8\) & \(\pm 5.7\) & \(-84.5\) & \(\pm 6.3\) \\ \hline \hline \end{tabular} \({}^{*}\)Location Code. The first character is the floor, G = ground, M = middle, T = top. Second character: N = end near to gateway, F = end far from gateway, V = various, S = showers. § mode was left sitting close to the ground-floor gateway for a period hence the high receive level.  
\end{table}
Table 2: Note to gateway rssi mean and standard deviation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mode** & \multicolumn{2}{c}{**Top-Floor Gateway**} & \multicolumn{2}{c}{**Ground-Floor Gateway**} \\ \hline
**Location \({}^{*}\)** & **SNR (dB)** & **Std (dB)** & **SNR (dB)** & **Std (dB)** \\ \hline G-N & \(11.1\) & \(\pm 2.0\) & \(11.4\) & \(\pm 1.9\) \\ \hline G-V § & \(8.8\) & \(\pm 1.4\) & \(9\) & \(\pm 1.2\) \\ \hline G-F & \(11.5\) & \(\pm 1.9\) & \(11.5\) & \(\pm 1.9\) \\ \hline M-N & \(11.5\) & \(\pm 1.8\) & \(11.1\) & \(\pm 1.8\) \\ \hline M-F & \(10.7\) & \(\pm 2.0\) & \(11.5\) & \(\pm 1.8\) \\ \hline T-N & \(11.2\) & \(\pm 1.9\) & \(11.4\) & \(\pm 1.9\) \\ \hline T-F & \(across the channels varied. Deduplication and other gateway and network performance measures were reported in Table 6.
# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 3 Materials, Methods, and Tools Used by the CloudMonitor Framework
### Dynamic Information Flow Tracking (DIFT)  
DIFT is used to tag, track, and verify the authenticity of information flows (which may represent nefarious activity such as exfiltration, destruction/disruption, or other malicious activity), such as logging into a computer system from, assumed to be, untrusted input channels. The basic DIFT mechanisms correlate well with tagging and tracking interesting data as they propagate during program execution. The process of \"DIFTing\" is characterized by three primary flow tracking aspects described here and detailed in the following three subsections (Sections 3.2-3.4).  
1. Data sources are usually represented by either a program or a memory location, and they come into play after a function or system call has been made.
2. Data tracking is a process of labeled data tracking during program execution as they are copied/moved and/or altered by program instructions.
3. Data traps are either program or memory locations where the existence of tagged data can be verified for data flow inspection and/or policy enforcement.  
DIFT can serve as a valuable tool to support malware prevention, functioning as a detection mechanism for zero-day vulnerabilities, cross-site scripting, and buffer overflow attacks. Moreover, DIFT can be instrumental in identifying and preventing information leakage by implementing tags.  
A tag is metadata that indicates the sensitivity of data flow. DIFT tags from untrusted sources as potentially malicious [57]. The status tag provides information that propagates through the system relying on predefined rules, either dataflow or data and control-flow-based. DIFT inspects tagged flows at places known as \"data sinks\" (or \"traps\"), to determine if a tagged information flow indicates any malicious activity. Logic related to DIFT can be injected into the original program code in two different ways described here.
## 4 Results of Probabilistic Stability and Performance Assessment
### The Impact of PLL Bandwidth on System Stability and Performance
#### 4.3.3 Time-Domain Simulations  
In the first test, the system response for a 20-degree phase jump at grid voltage under different PLL bandwidths was investigated. As can be concluded from Figure 11, very low PLL bandwidths provided a lower amount of oscillations in the inverter current at the cost of a slower dynamic response; it took a long time to reach a steady-state value. On the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SCR** & **10** & **5** & **2.5** & **2** & **1.67** \\ \hline \hline \multirow{2}{*}{Damping Factor} & \multicolumn{4}{c}{\(f_{PLL}=1\) Hz} \\ \hline Damping Factor & PLL (0.99) & PLL (0.99) & PLL (0.97) & PLL (0.94) & PLL (0.92) \\ \hline \multirow{2}{*}{Damping Ratio} & HPF (0.31), Delay (0.27), & HPF (33), Delay (0.22), \(\mathrm{i_{dq}}\) & HPF (0.34), Delay (0.21), \(\mathrm{i_{dq}}\) (0.24), & HPF (0.24), \(\mathrm{i_{dq}}\) (0.25), \(\mathrm{v_{cdq}}\) (0.14) & LC (0.2), \(\mathrm{i_{dq}}\) (0.25), \(\mathrm{v_{cdq}}\) (0.14) & LC (0.2), AVC \\  & \(\mathrm{i_{dq}}\) (0.16), \(\mathrm{v_{cdq}}\) (0.14) & \(\mathrm{v_{cdq}}\) (0.14) & \(\mathrm{v_{cdq}}\) (0.14) & \(\mathrm{v_{cdq}}\) (0.13) & (0.06), APB (0.09) \\ \hline \hline \multirow{2}{*}{Damping Factor} & \multicolumn{4}{c}{\(f_{PLL}=4\) Hz} \\ \hline \hline \multirow{2}{*}{Damping Factor} & \multirow{2}{*}{AVC (0.99)} & \multirow{2}{*}{AVC (0.96)} & PLL (0.5), DVC (0.14), AVC (0.14), & PLL (0.42), DVC (0.16), AVC (0.18), & PLL (0.38), DVC (0.14), AVC (0.21), \(\mathrm{ABP}\) (0.16) & PLL (0.38), DVC (0.14), AVC (0.21), \(\mathrm{ABP}\) (0.17) \\ \hline \multirow{2}{*}{Damping Ratio} & HPF (0.3), Delay (0.27), & HPF (33), Delay (0.23), \(\mathrm{i_{dq}}\) (0.24), & HPF (34), Delay (0.2), \(\mathrm{i_{dq}}\) (0.24), & HPF (34), Delay (0.2), \(\mathrm{i_{dq}}\) (0.25), \(\mathrm{v_{cdq}}\) (0.14) & LC (0.2), \(\mathrm{i_{dq}}\) (0.25), \(\mathrm{v_{cdq}}\) (0.14) & LC (0.05), AVC \\  & \(\mathrm{i_{dq}}\) (0.16), \(\mathrm{v_{cdq}}\) (0.  
other hand, higher PLL bandwidths provided a fast transient response at the price of higher output oscillations. Therefore, the PLL bandwidth selection is a compromise between quick dynamic response, output fluctuations, and system stability.  
In the second test, the system response for a 10% voltage sag at grid voltage under different grid inductances was studied and the results are shown in Figure 12. Two distinct values for PLL bandwidth, 1 Hz and 12 Hz, were considered to carry out the mentioned scenario.  
As expected due to the previous statistical analysis, under lower PLL bandwidths, the system response was not sensitive to grid inductance variations, but the dynamic response was prolonged. In contrast, under higher PLL bandwidths, the dynamic response was relatively faster but it was highly affected by grid inductance variations.
## 2 System Model
### Multi-USV System  
We considered a two-dimensional environment in which \(K\) USVs were represented by \(U\), each USV's position at time \(t\) is denoted by \(\left(x_{i}^{U}(t),y_{i}^{U}(t)\right)\), and the matching target is represented by \(T_{i}\), among which the obstacle is denoted by \(O_{i}\), and the positions of targets and obstacles are represented by \(\left(x_{i}^{T},y_{i}^{T}\right)\) and \(\left(x_{i}^{O},y_{i}^{O}\right)\), respectively. For an arbitrary USV, the path taken can be denoted by \(P_{i}=\left\{(x_{i}^{U}(0),y_{i}^{U}(0)),\left(x_{i}^{U}(1),y_{i}^{U}(1) \right),\ldots,\left(x_{i}^{U}(n),y_{i}^{U}(n)\right)\right\}\) and the path length by \(\sum\limits_{i}^{K_{U}}d_{i}\). Thus, the discrete dynamics model of each USV can be expressed as:  
\[\begin{cases}v_{i}^{t+1}=v_{i}^{t}+\frac{F_{i}^{t}}{m}\Delta t\\ p_{i}^{t+1}=p_{i}^{t}+v_{i}^{t}\Delta t\end{cases} \tag{1}\]  
where \(v_{i}^{t}\) and \(p_{i}^{t}\), respectively, represent the speed and position of the \(i_{th}\) USV at time \(t\), and \(\Delta t\) represents the sampling period where \(F\) is a force vector. The distance between the USV and the obstacle is represented by \(d_{i}^{O}\), and the distance between USVs is represented by \(d_{i}^{U}\), while the distance from the USV to the matching target is represented by \(d_{i}^{T}\).
# Polyphenol-Rich Black Elderberry Extract Stimulates Transintestinal Cholesterol Excretion
## 3 Results
### Effects of BEE on the Genes Involved in Cholesterol Synthesis and Absorption  
To investigate the effects of BEE on intestinal cholesterol metabolism, we first measured the mRNA expressions of genes involved in the synthesis and absorption of cholesterol in BEE-treated Caco-2 cells. BEE significantly and dose-dependently decreased the expression of _HMGR_ and _SREBP2_, i.e., the rate-controlling enzyme in cholesterol biosynthesis and its transcription factor, respectively (Figure 2). The expression of genes for cholesterol absorption, such as _NPC1L1_ and _SR-B1_, was dose-dependently and significantly decreased by BEE. Furthermore, the mRNA abundance of _ABCA1_, the basolateral transporter of cholesterol, was significantly decreased in BEE-treated cells. For the genes involved in chloromicron assembly, the mRNA abundance of microsomal triglyceride transfer protein (_MTTP_) and acetyl-CoA Acetyltransferase 2 (_ACAT2_) was significantly decreased in BEE-treated Caco-2 cells (Figure 2). Consistent with the mRNA results, the protein levels of HMGR and SREBP2 were decreased by BEE treatment. Furthermore, the protein levels of transporters for cholesterol absorption, i.e., NPC1L1, SR-B1, and ABCA1, were noticeably lowered in BEE-treated Caco-2 cells (Figure 3).  
Figure 1: Cytotoxicity of polyphenol-rich black elderberry extract (BEE)-treated Caco-2 cells. Cells were treated with increasing concentrations (0–200 \(\upmu\)g/mL) of BEE for 24 h. The viability (%) is presented relative to controls without BEE treatment. Data with different letters are significantly different (\(p<0.05\)). Values are means \(\pm\) SEM; \(n=6\).  
Figure 2: The effects of BEE on the genes involved in the synthesis and absorption of cholesterol in Caco-2 cells. Cells were treated with 50 or 100 \(\upmu\)g/mL BEE for 24 h. The messenger RNA (mRNA) abundance was measured by qRT-PCR analysis. Data are expressed as relative expressions to control. Bars with different letters are significantly different (\(p<0.05\)). Values are means \(\pm\) SEM; \(n=6\).