esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion
### Appling the Inversion Method to Validate the LBL Model  
Wesley [22] provides an experimental result for the thermal insulation performance of a 60-layer MLI with a double-aluminized mylar (DAM) and Dracon net. The cold boundary temperature is 77 K. The layer density is 9.5 N/cm. The heat flux of the MLI is measured as 0.262 W/m\({}^{2}\). Based on the modified LBL model and ELM algorithm, the interlayer pressure  
Figure 9: Heat fluxes in the MLI: (**a**) ideal vacuum condition; (**b**) testing case 4.  
is reconstructed as shown in Figure 10. It can be seen that the highest pressure in the MLI is 0.18 Pa near the cold boundary, which is 233-times higher than the vacuum chamber pressure of 7.7 \(\times\) 10\({}^{-4}\) Pa. The interlayer temperature determined by the modified LBL model is highly consistent with the measured temperature as shown in Figure 11.  
Besides the classical LBL model and the modified LBL model in this study, three kinds of Lockheed equations were put forward in Reference [22] to calculate the thermal insulation performance of the MLI. The deviations between the theoretical calculation and the experimental results are given in Table 5. It is worth noting that the interlayer pressure distribution is ignored in the Lockheed and classical LBL models, and the ideal vacuum condition (7.7 \(\times\) 10\({}^{-4}\) Pa) is used to calculate the residual gas conduction. It can be found that the calculation results by the Lockheed and classical LBL models deviate from the experimental results remarkably. However, the interlayer pressure distribution is reconstructed and considered in the modified LBL model, and the deviation between  
Figure 11: The interlayer temperature comparison simulation and experiment.  
Figure 10: The reconstruction of the interlayer pressure distribution.  
the calculation and the experiment is just 2.77%. It is very promising to use the proposed method to predict the thermal insulation performance of a practical large cryogen container with MLI.
# Noise Source Diagnosis Method Based on Transfer Path Analysis and Neural Network
###### Abstract  
Clarifying the noise source and the contribution of each path is essential for the system's noise control. The auxiliary converter cabinet, which is a crucial component of rail transportation, has numerous intricate noise sources. The contribution of each path point must be inverted-solved using known transfer functions and target point test values when identifying noise sources. This article suggests a method for diagnosing noise using transfer path analysis and neural networks (TPA-NN). Firstly, the principle and scheme for analyzing the transmission path of the converter cabinet are proposed. The transfer function of each path is obtained by selecting suitable path points, reference points, and target points for air and structure acoustic vibration experiments. The external target point data are then combined with the neural network's linear fitting function, and the contribution of each path is used as an output for network training while some path point contributions are rebuilt. The results indicate that the method's outcomes are most accurate when the converter cabinet's path point is 13 and the target point is 6. This approach offers an innovative technique for locating noise sources in intricate systems.  
noise source identification; transfer path analysis; neural network; path contribution +
Footnote †: journal: _aciences_
# A Practical and Efficient Node Blind SignCryption Scheme for the IoT Device Network
## 1 Introduction
One is that the sender device \(\mathcal{S}\) is already authenticated with the receiver \(\mathcal{R}\); they both inherently trust each other within the same IoT network environment. The authentication mechanism is beyond the scope of this research. Another limitation is that IoT device management is also beyond our research. We can adopt other proposed authentication mechanisms [5; 6; 7; 8; 9; 10] for devices to authenticate with each other in an IoT device network and also construct an IoT devices group with other devices. Our scheme focuses on the efficient signature and encryption scheme for these power limitation IoT devices such as the Zigbee chips or IoT sensor devices embedding less memory. To provide a mechanism to generate a signature and a cipher-text for IoT devices simultaneously, we propose an efficient and practical, fair sign-cryption scheme based on quadratic residue (QR for short) for the IoT device network. Not only does it offer an efficient and practical solution to IoT devices, but it also reduces the signature and cipher-text generation cost in our methodology. We also offer the formal security proof on our proposed scheme in the Appendix A and evaluate the efficiency of our mechanism in this research.
# Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect
## 3 Influence Maximization under DCM4 VM  
### Traditional Influence Maximization Problem  
Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence [4; 18].  
Problem 1. Given a social network modeled as an indirect graph \(G=(V,\,E,\,PE)\) (vertical \(V\) representing the individuals in the network, edges E denoting the relationship between individuals, and edge probabilities \(PE=\{Pu,v|u,\,v\in V\}\) illustrating the possibility that a node can influence its neighbor on the other side of the edge, and \(Pu,v\) illustrating the probability of both \(u\) and \(v\)), a specific propagation model called \"\(M\)\" and a small number called \"\(k\)\", the influence maximization problem is to find a initial set called \"\(S\)\" with k nodes in the graph such that under the propagation model \(M\), the expected number of nodes influenced by \(S\) is as large as possible. Assume \(\sigma\left(S\right)\) represents all influenced nodes, this problem can be formally defined as:\[S*=\max_{S\in V}\alpha(S) \tag{1}\]
## 3 The Main Sources of Bacterial Stress in the Intestinal Microbiota  
### Acid Stress  
One of the first sources of stress faced by bacterial organisms after ingestion is gastric acidity. Under fasting conditions, hydrochloric acid in gastric juice has a highly aggressive pH of 2, whereas _Enterobacteriaceae_ have an optimal pH in the neutral range, between 6.5 and 7.5 (pH 7 for _E. coli_). Intraluminal acidity is not exclusive to the stomach; the distal duodenum has an acid environment (pH 6), maintained by lactic acid bacteria, which increases until reaching an optimal pH of 7.4 in the terminal ileum, probably where most microbiota growth takes place. Again, acidity increases (probably because of the bacterial production of acids) to a pH of 5.7 in the cream, ultimately reaching a pH of 6.7 in the rectum. Some pathological conditions might increase intestinal acidity, such as ileoccal resection, chronic pancreatitis, cystic fibrosis, ulcerative colitis, or [PERSON]'s disease [17]. This increase could be a direct consequence of the altered microbiota, which also occurs with the use of probiotics; in both cases, organic acids are involved. Organic short-chain carboxylic acids, such as fumaric, propionic, acetic, lactic, and butyric acids, are frequently produced by intestinal microorganisms, lowering the pH, which results in stress for a number of bacterial populations [18].  
Adaptation to acid stress is an important factor for the transmission of intestinal microbes. In _E. coli_, resistance to acid stress is guaranteed by the GadE-regulated expression of glutamate and arginine decarboxylases associated with amino acid and Cl\({}^{-}\)/H\({}^{+}\) antiporters [19; 20]. Acid stress also forces _E. coli_ to alter the envelope structure and porins in the outer membrane and the cytoplasmic chaperones [21]. Changes in the envelope architecture and in the molecular folding have epistatic consequences altering cellular functions [22]. In fact, there are several overlapping acid survival systems with variable expression and efficacy depending on the growth phase [23; 24]. Resistance in _E. coli_ to short-chain organic carboxylic acids also involves changes in the _rpoA_ (influencing folding efficiency and/or chaperone-like activity), _rpoC_ (subunit of RNA polymerase), and _rpoS_ (alternative sigma factor inducing stationary phase) involved in stress response cascades, and probably _rho_ (transcription regulation) and _nagA_ (\(N\)-acetyl-d-glucosamine metabolism) [25; 26]. As we discuss in the following paragraphs, in addition to the relatively low duodenal pH, this upper part of the small intestine has other sources of bacterial stress [27].
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 2 Preliminaries
### Average Method for Calibration of MIESM
Then, they perform some comparison operations and simple substitution or subtraction based on the calculated CQI value. The computational complexity of the following operations is negligible compared to the first CQI calculation. Therefore, it can be said that the proposed CQI feedback schemes have almost the same computational complexity as the conventional CQI feedback scheme.
Sensors_**2015**, _15_, 24230-24256; doi:10.3390/s150924230  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
Cell Selection Game for Densely-Deployed Sensor and Mobile Devices In 5G Networks Integrating Heterogeneous Cells and the Internet of Things  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
School of Computer and Information, Hefei University of Technology, Anhui 230009, China;  
E-Mails: [EMAIL_ADDRESS] (Y.M.); [EMAIL_ADDRESS] (Z.D.);  
[EMAIL_ADDRESS] (X.W.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-551-6290-1552.  
Academic Editor: [PERSON]  
_Received: 29 July 2015 / Accepted: 12 September 2015 / Published: 18 September 2015_  
**Abstract:** With the rapid development of wireless networking technologies, the Internet of Things and heterogeneous cellular networks (HCNs) tend to be integrated to form a promising wireless network paradigm for 5G. Hyper-dense sensor and mobile devices will be deployed under the coverage of heterogeneous cells, so that each of them could freely select any available cell covering it and compete for resource with others selecting the same cell, forming a cell selection (CS) game between these devices. Since different types of cells usually share the same portion of the spectrum, devices selecting overlapped cells can experience severe inter-cell interference (ICI). In this article, we study the CS game among a large amount of densely-deployed sensor and mobile devices for their uplink transmissions in a two-tier HCN. ICI is embedded with the traditional congestion game (TCG), forming a congestion game with ICI (CGI) and a congestion game with capacity (CGC). For the three games above, we theoretically find the circular boundaries between the devices selecting the macrocell and those selecting the picocells, indicated by the pure strategy Nash equilibria (PSNE). Meanwhile, through a number of simulations with different picocell radii and different path loss exponents, the collapse of the PSNE impacted by severe ICI (_i.e._, a large number of picocell devices change their CS preferences to the macrocell) is profoundly revealed, and the collapse points are identified.  
keywords:Internet of Things; dense sensor networks; heterogeneous cells; cell selection game; inter-cell interference +
Footnote †: journal: Journal of the American Statistical Association
## 1 Introduction
An alternating iterative weighted least squares (AIWLS) DOA estimation method [21] constructs the objective function for sparse signals and employs an iterative process to estimate source DOAs, achieving high accuracy in non-uniform noise environments. In recent years, deep neural networks (DNNs) have been employed in the field of direction of arrival (DOA) estimation, yielding numerous research advancements [22; 23; 24; 25]. Reference [22] addressed the challenge of underwater acoustic DOA estimation by framing it as an image classification task, leveraging the significant variations in covariance matrix characteristics across different directions. Reference [23] innovatively framed the problem as a multi-label classification task, leveraging convolutional neural networks (CNNs) to predict signal DOAs. Reference [24] presented the Deep-MUSIC approach, which employed multiple CNNs to model the intricate nonlinear relationship between received data and the MUSIC spatial spectrum, thereby facilitating multi-source DOA estimation. Reference [25] addressed the challenge of non-uniform noise by implementing a deep learning-based filter, effectively restoring the array covariance information. Despite these advancements, several limitations persist within the current methodologies. Firstly, existing research has overlooked effective DOA estimation methods in the presence of colored noise. Secondly, most existing methods only focused on the array covariance and its further improvement without considering the raw signal. Thirdly, traditional filtering algorithms cannot dynamically adjust weights, thus lacking the ability to process complex noise. In this paper, we propose an acoustic vector DOA estimation method based on the spectrum attention mechanism (SAM) [26]. Specifically, SAM is used to neglect the insignificant frequency components and highlight the important ones, which is similar to an adaptive filter for colored noise. Then, the filtered signal is sent to a double-branch neural network consisting of a convolutional neural network (CNN) and long short-term memory (LSTM). Thus, the spatial and temporal features of the array signal are extracted to achieve high-accuracy DOA estimation. The rest of the paper is organized as follows: Colored noise and DOA estimation tasks are described in Section 2. Section 3 presents the proposed method. Section 4 describes the experimental setup and discusses the results. Finally, conclusions are drawn in Section 5.
## 3 Results and Discussions
### The Dataset Overview
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Pathogen**} & \multirow{2}{*}{**Season**} & \multicolumn{4}{c}{**Site**} \\ \cline{3-6}  & & **MA04** & **MA05** & **MA10** & **MA11** \\ \hline \multirow{3}{*}{_L. monocytogenes_} & \multirow{3}{*}{Non-growing} & 0.76 \(\pm\) 0.15 \({}^{1}\) & 5.92 \(\pm\) 0.32 & 0.03 \(\pm\) 0.00 & 0.03 \(\pm\) 0.00 \\  & & (58.8\%) \({}^{2}\) & (93.8\%) & (11.1\%) & (12.5\%) \\  & & (10/17) \({}^{3}\) & (15/16) & (2/18) & (2/16) \\ \hline \multirow{3}{*}{_L. monocytogenes_} & \multirow{3}{*}{Growing} & 0.30 \(\pm\) 0.05 & 3.63 \(\pm\) 0.27 & 0.62 \(\pm\) 0.14 & 0.02 \(\pm\) 0.00 \\  & & (35.3\%) & (100\%) & (5.6\%) & (11.1\%) \\  & & (6/17) & (16/16) & (1/18) & (2/18) \\ \hline \multirow{3}{*}{_S. enterica_} & \multirow{3}{*}{Non-growing} & 1.05 \(\pm\) 0.08 & 0.86 \(\pm\) 0.16 & 0.04 \(\pm\) 0.00 & 0.06 \(\pm\) 0.00 \\  & & (94.1\%) & (62.5\%) & (11.8\%) & (25.0\%) \\ \cline{1-1}  & & (16/17) & (10/16) & (2/17) & (4/16) \\ \hline \multirow{3}{*}{_S. enterica_} & \multirow{3}{*}{Growing} & 1.83 \(\pm\) 0.20 & 1.31 \(\pm\) 0.16 & 0.05 \(\pm\) 0.00 & 0.33 \(\pm\) 0.05 \\  & & (70.6\%) & (93.3\%) & (27.8\%) & (33.3\%) \\ \cline{1-1}  & & (12/17) & (14/15) & (5/18) & (6/18) \\ \hline \hline \end{tabular}  
\({}^{1}\) mean \(\pm\) standard error. \({}^{2}\) Prevalence, %. \({}^{3}\) Number of positive pathogens samples/number of total samples.  
\end{table} 
Table 1: Mean levels (MPN/L) of _L. monocytogenes_ and _S. enterica_, followed by prevalence (percentage and number of samples which were positive for the pathogen) in non-growing (October–April) and produce-growing (May–September) seasons. much better than in ponds. One of the reasons for such a relationship can be the strong effect of bottom sediment disturbance at high flows on the _Salmonella_ spp prevalence. Bottom sediment was shown to be a possible substantial source of _Salmonella_ spp. in agricultural and forested watersheds [40]). The higher correlation of the _Salmonella_ prevalence with Rain1 compared with Rain7 supports the assumption of the role of sediment disturbance in increased _Salmonella_ prevalence in streams after rainfall events.
# Quality Characteristics and Flavor Analysis of Five Mulberry Varieties
## 2 Materials and Methods
### Nutritional Index Measurements
#### 2.3.2 Determination of Total Flavonoid Content  
The total flavonoid content was measured using the aluminum nitrate method as described previously [24]. First, mulberries were ground, diluted by 5 times with 80% ethanol water, subjected to ultrasonic treatment for 30 min, and filtered to form a diluent. One mL of the diluted sample was added to a 25 mL colorimetric tube, along with 5 mL of 30% ethanol and 1 mL of 5% sodium nitrite solution. The mixture was vigorously shaken, reacted for 6 min, and supplemented with 1 mL of 10% aluminum nitrate solution. The mixture was shaken again and incubated for 6 min. The precisely measured quantities (10 mL) of 1 mol/L sodium hydroxide solution were then added to this mixture. Finally, the colorimetric tube was filled up to the desired level with additional amounts of the aforementioned 30% ethanol and left undisturbed for 15 min. A blank control without samples was used as a reference for the absorbance analysis of the reaction mixture at 510 nm by using a visible spectrophotometer (Shanghai Jinghua Science and Technology Instrument Co., Ltd., Shanghai, China). The rutin standard solution (purity \(\geq\) 98%, Fuzhou Feijing Biological Technology Co., Ltd., Fuzhou, China) was used for calibration. The result was calculated according to the standard curve equation. Each treatment was repeated 3 times.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### Figure of Merit (FOM)  
Evaluating an SRAM cell requires balancing multiple performance metrics that often exhibit trade-off relationships. To enable a comprehensive comparison that accounts for these trade-offs, a Figure of Merit (FOM) is employed. This FOM integrates key parameters related to stability (HSNM, RSNM, WSNM), effective critical charge (Qc), speed (RAT, WAT), and power consumption (hold power). The specific formula used to compute the FOM in this study, as presented in Equation (3), was adopted with reference to previous works [9; 10; 11; 12; 13; 14; 15; 16; 17]:  
\[FOM=\frac{HSNM\times RSNM\times WSNM\times Q_{C}}{RAT\times WAT\times HoldPower} \tag{3}\]  
As shown in Figure 13, the proposed RMLP-18T cell achieves the highest FOM value among all evaluated designs, with a score of 22.44, significantly surpassing the others. This high FOM value underscores the cell's well-balanced performance, particularly its excellent read stability and low hold power consumption, despite its relatively slower access times. Table 1 presents a performance comparison between the proposed RMLP-18T cell and other existing radiation-hardened SRAM cell architectures.  
Figure 12: Critical charge comparison between the proposed RMLP-18T cell and previous works.  
Figure 13: Comparison of Figures of Merit (FOM).
# Cargo Terminal Intelligent-Scheduling Strategies Based on Improved Bee Colony Algorithms
## 2 The Scheduling Model of Freight Station  
The airport freight station consists of a container storage area, a bulk cargo storage area, and an unhandled cargo area. As the core of the whole station, the container storage area is a three-dimensional warehouse used for handling the containerized cargo which is unloaded from aircraft on the air side or unbounded from the bulk cargo storage area on the land side. The shelves in the warehouse consist of two rows with 16 entrances and exits, and each row has eight layers and 60 columns.  
ETV is employed for handling cargoes between different entrances and exits. During the pickup and delivery operations, ETVs experience three stages, which are acceleration, constant speed, and deceleration, in the horizontal and vertical directions. Thus, the time needed to finish a whole delivery process is determined by the maximum value between the horizontal travelling time \(Tx\) and the vertical lifting time \(Ty\), which are defined as Equations (1) and (2).  
\[\left\{\begin{array}{l}T_{x}=2*\sqrt{\frac{w*u}{x_{1}}},\;\;w*u\leq D_{x};\\ T_{x}=T_{xi}+\frac{w*u-D_{x}}{V_{x}},\;\;\;w*u>D_{x}.\end{array}\right. \tag{1}\]\[\left\{\begin{array}{l}T_{y}=2*\sqrt{\frac{h*e}{dy}},\ h*e\leq D_{y};\\ T_{y}=T_{yj}+\frac{h*e-D_{y}}{V_{y}},\ h*e>D_{y}.\end{array}\right. \tag{2}\]  
Here, \(e\) and \(u\) are the differences of layers and columns between the initial position and the destination, \(h\) and \(w\) are the height and width of storage location, and \(a_{x}\), \(a_{y}\), \(V_{x}\), and \(V_{y}\) are the accelerations and velocities in horizontal and vertical directions, respectively.  
Equations (3) and (4) describe the time costs \(T_{xi}\) and \(T_{yj}\), and the travelled distances \(D_{x}\) and \(D_{y}\), when ETV accelerates to the maximum speed and immediately decelerates to 0 in different directions  
\[T_{xi}=2*V_{x}/a_{x},T_{yj}=2*V_{y}/a_{y} \tag{3}\]  
\[D_{x}=\frac{1}{4}*\ a_{x}*\ T_{xi}^{2},\ D_{y}=\frac{1}{4}*\ a_{y}*\ T_{yj}^{2} \tag{4}\]  
Based on the motion analysis for ETVs, the time cost \(T_{i}\) needed to execute the \(i\)th task, including picking up, releasing, as well as moving the assigned cargoes, is expressed as Equation (5),  
\[T_{i}=T_{i_{0}}+T_{i_{1}}+2\zeta \tag{5}\]  
Here, \(T_{i_{0}}\) is the time needed to move from its current position to the cargo's position, \(T_{i_{1}}\) is the time used to move to the destination after an ETV receives the cargo, and \(\zeta\) is the time used to load or unload the cargo.  
Thus, if there are \(n\) independent inbound and outbound tasks in the cargo terminal, in order to improve its operational efficiency, the sequence of inbound and outbound tasks considering the actions of the ETV should be scheduled, and the total time cost \(T\) defined as Equation (6) should be minimized.  
\[Minimize\ T=\sum_{i=1}^{n}T_{i}=\sum_{i=1}^{n}\bigl{(}T_{i_{0}}+T_{i_{1}} \bigr{)}+2n\zeta \tag{6}\]  
In order to solve the minimization problem mentioned above, an effective optimization algorithm should be introduced.