esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
The TNF-\(\alpha\) also decreased in AIA + hazelnut oil (143.085 \(\pm\) 14.842 pg/mL) and the AIA + cashew nut oil (183.721 \(\pm\) 27.419 pg/mL). The maximum decrease in the levels of IL-6 was observed in the groups of rats that received AIA + walnut oil (138.60 \(\pm\) 35.748 pg/mL) followed by the AIA + peanut oil (164.16 \(\pm\) 48.261 pg/mL) versus AIA group (344.72 \(\pm\) 58.827 pg/mL). The IL-1\(\beta\) level decreased in all treatment groups, but the lowest level was observed in the AIA + peanut oil (121.58 \(\pm\) 37.45 pg/mL) and AIA + walnut oil (122.69 \(\pm\) 34.07 pg/mL) groups, although the difference was insignificant (\(p\) = 1.000) among them. A significant (\(p\)\(\leq\) 0.05) decrease in the AIA + cashew nut oil (210.36 \(\pm\) 31.69 pg/mL) and the AIA + hazelnut oil (235.69 \(\pm\) 37 \(\pm\) 59 pg/mL) groups versus the AIA group (263.41 \(\pm\) 52.29 pg/mL) has also been observed, but the difference was insignificant (\(p\) = 0.890). Effect of Different Nuts' Oil on Serum Levels of Prostaglandin E2 in Adjuvant-Induced Arthritis (AIA) Rat Model  
Figure 6 summarizes the effect of different oils on PGE2. The serum levels of PGE2 were significantly (\(p\) =0.00) increased in AIA model rats versus control rats. The levels of PGE2 were significantly (\(p\)\(\leq\) 0.05) decreased in the AIA-treated rats, which received the walnut, peanut, cashew, and hazelnut oils. The maximum decrease in the levels of PGE2 was shown in AIA + walnut oil (59.78 \(\pm\) 12.66 pg/mL) and AIA + peanut oil (72.88 \(\pm\) 13.58 pg/mL)-treated rats versus AIA-induced rats (168.57 \(\pm\) 31.49 pg/mL). However, no significant (\(p\) = 0.838) variation in the levels of PGE2 was noticed in the AIA + walnut and AIA + peanut-treated rats, and their levels were higher than in the control group. Similarly, no significant (\(p\) = 1.00) differences in the level of PGE2 were noticed between AIA + cashew oil (96.26 \(\pm\) 8.75 pg/mL) and AIA + hazelnut-treated rats (99.32 \(\pm\) 13.55 pg/mL). Figure 5: Effect of different nuts’ oil on serum levels of pro-inflammatory cytokines TNF-\(\alpha\) (**A**), IL-6 (**B**), and IL-1\(\beta\) (**C**) adjuvant-induced arthritis (AIA) rats. Model: One-way analysis of variance (ANOVA) with a statistical significance assumed at \(p\)\(\leq\) 0.05 followed by [PERSON]’s HSD post hoc test for statistically significant \(p\)-value. Data are presented as mean \(\pm\) SD for \(n\) = 5 rats/groups. **a**: Different from the control rats. **b**: Significantly different from the adjuvant-induced arthritis (AIA) model rats. **c**: Significantly different from AIA + walnut oil. **d**: Significantly different from AIA + peanut oil. **e**: significantly different from AIA + cashew nut oil.
## 3 Index Structure
### Grid Index  
In our model, the uncertainty of objects' locations is represented as an approximate region with rectangular shape. We use a grid index to efficiently manage the approximate regions. The data space is first divided into \(N\)*\(N\) grid cells, and then each grid cell stores information of the objects, and the sensor inside it. Consider the example in Figure 3. There are five objects \(o_{1}\) to \(o_{5}\) moving in the sensor network. The entire space of network is divided into 3*3 grid cells and monitored by nine sensors \(s_{1}\) to \(s_{9}\). That is,  
Figure 2: Uncertainty model.  
each sensor is located at the center of a grid cell and responsible for monitoring this cell. In the sequel, we describe the data structures used for storing information of the sensors and the objects.
## 5 Impact Assessments on Estimated Parameters at Collocated Stations
### Impact of Precise Products on ZTD and Gradient Estimates  
Table 7 summarizes the ZTD and horizontal gradient statistics indicating the impact of different orbit and clock products and, again, the backward smoothing, all using the same processing strategy and the software. The first value in each table column represents the result of the Kalman filter processing and the second value represents the result of the backward smoothing. Comparison of the GPS-only solutions to the COM and GBM MGEX products are included too, both indicating that the usage of IGS RTS products might still degrade the accuracy up to a factor of 2. A significant difference is observed also in statistics of IGS RT products (IGS01 and IGS03). The IGS03 solution performed better than IGS01 during September 2017, which seems to be in contradiction to the results of a long-term evaluation in Section 2.1. Indeed, the precision (SDEV) of the IGS RTS clocks was 3.5 cm and 3.0 cm during this month for IGS01 and IGS03, respectively, indicating a high variability in the actual performance of the real-time products.
# Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification
###### Abstract  
This paper presents an interval-based LDA (Linear Discriminant Analysis) algorithm for individual verification using ECG (Electrocardiogram). In this algorithm, at first, unwanted noise and power-line interference are removed from the ECG signal. Then, the autocorrelation profile (ACP) of the ECG signal, which is a mathematical representation of the degree of similarity between a given time series and a lagged version of itself over successive time intervals, is calculated. Finally, the interval-based LDA algorithm is applied to extract unique individual feature vectors that represent distance and angle characteristics on short ACP segments. These feature vectors are used during the processes of enrollment and verification of individual identification. To validate our algorithm, we conducted experiments using the MIT-BIH ECG and achieved EERs (Equal Error Rate) of 0.143%, showing that the proposed algorithm is practically effective and robust in verifying the individual's identity.  
Individual verification; Electrocardiogram (ECG); Interval based LDA; biometrics Article
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 4 Theoretical Basis for ANNs to Improve Lighting System Reliability  
### Brief of ANNs  
Artificial neural networks (ANNs), also referred to as neural networks (NNs) or connection models (CM). ANN is a modeling technique and algorithmic mathematical model that imitates the behavior characteristics of human neural networks and performs distributed and parallel information processing. ANNs are able to establish empirical relationships between independent and dependent variables and extract subtle information and complex knowledge from representative data sets [33]. Depending on the complexity of the system, the network achieves the purpose of processing information by adjusting the connection relationship among a large number of neurons. ANNs can be categorized from different classification criteria as shown in Table 13.  
\begin{table}
\begin{tabular}{c l} \hline \hline \(\bullet\) & **Major Functions**. \\ \hline
1 & Predefined intelligent lighting strategies reside in TSM. \\ \hline
2 & Real-time response to light data from LESC. \\ \hline
3 & Assembly lighting adjustment command. \\ \hline \(\bullet\) & **Input parameters: Influence factors of lighting parameters for tunnel zones.** \\ \hline
1 & Driving direction, bidirectional or unidirectional. \\ \hline
2 & Outside weather conditions, normal or abnormal. \\ \hline
3 & Design speed, 40/60/80/100/120 (km/h). \\ \hline
4 & Traffic volume (veh/(h-ln)). \\ \hline
5 & Outside color temperature (K). \\ \hline
6 & Outside luminance (cd/m\({}^{2}\)). \\ \hline \(\bullet\) & **Output parameters: Inside target lighting parameters for tunnel zones**. \\ \hline
1 & Target inside color temperature (K). \\ \hline
2 & Target inside luminance (cd/m\({}^{2}\)). \\ \hline \(\bullet\) & **Requirements or limitations for lighting adjustment.** \\ \hline
1 & Step-less mode for color temperature adjustment. \\ \hline
2 & Step-less mode for luminance adjustment. \\ \hline
3 & Each adjustment of color temperature or luminance finishes within 1 s. \\ \hline
4 & Cycle to adjust color temperature or luminance is configurable with default setting of 10 min. \\ \hline
5 & This strategy works for each tunnel lighting zone. \\ \hline
6 & This strategy can be easily switched to stable time sequence control mode. \\ \hline
7 & This strategy can be easily switched to emergency lighting mode. \\ \hline
8 & This strategy can be easily switched to manual control mode. \\ \hline
9 & This strategy differentiates daytime from night automatically. \\ \hline \hline \end{tabular}
\end{table}
Table 12: Major functions or customer requirements of IASM.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 3 Routing Protocols in MANET
### Hybrid Routing Protocols  
Hybrid routing protocols combine features of both proactive and reactive protocols to balance the trade-offs. Table 3 summarizes hybrid routing protocols that combine features of both proactive and reactive approaches to balance efficiency and adaptability. Hybrid protocols aim to leverage the low latency of proactive routing within local regions while employing reactive methods for distant nodes. ZRP divides the network into zones and uses proactive routing within zones and reactive routing between zones, balancing the benefits of both approaches while managing the complexity of zone maintenance. GRP uses geographic information to improve routing decisions, combining proactive and reactive strategies to efficiently use geographic data for routing, though it requires accurate location information. These protocols optimize performance by leveraging the strengths of both proactive and reactive strategies, making them ideal for diverse network conditions where both local stability and global adaptability are needed.
## 8 Limitation and Future Work  
FPS games demand that players focus more on audio cues, which provide strategic information to gameplay rather than merely serving as ambiance or decoration. This study acknowledges its potential limitations beyond the FPS genre, such as in role-playing, racing, or fighting games, where the significance of audio cues might be less pronounced. In addition, our game is a 3D game, and the perception of audio in 3D and 2D games may be different. As a result, more detailed studies are required for other game genres and 2D games.  
Furthermore, this study focuses solely on controlling the volume of audio cues. However, detailed information such as decibels, sound pressure levels, and loudness is not provided here. We will test and present this information to enhance the audio design. Additionally, we will explore a more detailed player perception of audio loudness in the future, considering adaptation, order, and summation during auditory stimuli. This approach will provide designers with precise statistical data to inform their audio design in games. Our future research will focus on extending this study to various game genres and 2D games to emphasize the ongoing exploration of audio cue significance, revealing decibels, sound pressure levels, and loudness. This should contribute to the advancement of audio cue studies, whether aimed at enhancing gaming enjoyment, educational significance, or aiding visually impaired individuals.  
Conceptualization, X.L. and R.T.; methodology, X.L., Y.X. and R.T.; software, X.L.; validation, Y.X.; formal analysis, X.L.; investigation, X.L.; resources, R.T.; data curation, X.L., Y.X., M.C.G., X.Y. and S.C.; writing--original draft preparation, X.L.; writing--review and editing, Y.X., M.C.G., S.C. and R.T.; visualization, X.L.; supervision, R.T.; project administration, X.L.; funding acquisition, R.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ritsumeikan University does not require approval for this kind of nonmedical research. We consulted with the Research Ethics Review Committee Executive Office before conducting similar studies.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are openly available in GitHub at [[https://github.com/Lxx007/Anonymous](https://github.com/Lxx007/Anonymous)]([https://github.com/Lxx007/Anonymous](https://github.com/Lxx007/Anonymous)) (accessed on 5 November 2024).  
The authors declare no conflicts of interest.
# Turbofan Engine Health Prediction Model Based on ESO-BP Neural Network
## 3 Model Construction
### Introduction to Datasets  
This study uses the publicly available turbofan condition monitoring data from the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset provided by the National Aeronautics and Space Administration (NASA) to predict the remaining health index of turbofan engines [38]. The C-MAPSS dataset comprises four subsets, each containing various operating and faulty working conditions. Each subset is then divided into a training set and test set, as shown in Table 1.  
The training set records various state parameters of the turbofan engine. These variables include the number of operating cycles and 24 pieces of sensor information. Data are recorded as time series throughout its complete lifespan cycle, from the normal state to the fault state. In contrast, in the test set randomly stops collecting the cycle parameters, meaning that the complete life cycle is not recorded for each engine.  
These sensor data include information about the engine's operating parameters, pressure, and temperature for 24 lots of sensor information, as shown in Table 2, which can be used to train and test fault diagnosis and prediction models. This dataset is widely used in the fields of machine learning and data mining, which provides valuable data support for aero-engine health management.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**FD001**} & \multicolumn{2}{c}{**FD002**} & \multicolumn{2}{c}{**FD003**} & \multicolumn{2}{c}{**FD004**} \\ \cline{2-10}
**Sub-Dataset** & \begin{tabular}{c} **Training** \\ **Set** \\ \end{tabular} & \begin{tabular}{c} **Test Set** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Set** \\ \end{tabular} & \begin{tabular}{c} **Test Set** \\ \end{tabular} & \begin{tabular}{c} **Training** \\ **Set** \\ \end{tabular} & \begin{tabular}{c} **Test Set** \\ \end{tabular} & \
\begin{tabular}{c} **Training** \\ **Set** \\ \end{tabular} &
\begin{tabular}{c} **Test Set** \\ \end{tabular} \\ \hline Operating condition & \(1\) & \(1\) & \(6\) & \(6\) & \(1\) & \(1\) & \(6\) & \(6\) \\ Fault mode & \(1\) & \(1\) & \(1\) & \(1\) & \(2\) & \(2\) & \(2\) & \(2\) \\ Number of engines & 100 & 100 & 260 & 259 & 100 & 100 & 249 & 248 \\ Number of operating cycles & 20,631 & 13,096 & 53,759 & 33,991 & 24,720 & 16,596 & 61,249 & 41,214 \\ Mean of engine operating cycles & 206.31 & 130.96 & 206.77 & 131.24 & 247.20 & 165.96 & 245.98 & 166.19 \\ Standard error of engine operating cycles & 0.32 & 0.47 & 0.20 & 0.34 & 0.55 & 0.67 & 0.30 & 0.45 \\ Maximum of engine operating cycles & 362 & 303 & 378 & 367 & 525 & 475 & 543 & 486 \\ Minimum of engine operating cycles & 128 & 31 & 128 & 21 & 145 & 38 & 128 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 1: C-MAPSS dataset information.  
Figure 4: Comparison of RHI and RUL degradation functions.
# Smart Warehouses: Rationale, Challenges and Solution Directions
## 2 Background  
At the end of the 18 th century, the first iron-framed warehouse was constructed [8]. From the 18 th century onwards, the scale use of warehouses grew rapidly and became more specialized. The 20 th century altered the use of warehouses with the introduction of standardization and efficiency. The introduction of trucks and forklifts, and the replacement of basic skids, boxes, and crates used since the horrea with pallets and adjustable pallet racks, enabled efficient vertical storage.  
The last evolution for warehouses is the technological revolution from the 1960s onwards. The most significant impact is made by the introduction of the barcode, enabling the first form of real-time warehouse management system (WMS) and automation [8]. In addition to barcode and automation technologies, Industry 4.0 is now more powerful with several technologies such as NarrowBand IoT (NB-IoT), near-field communication (NFC), radio-frequency identification (RFID), global positioning system (GPS), wireless sensor network, Wi-Fi, and robotics [9; 10; 11]. Although conventional warehouses were the most useful and successful so far, there are several problems. First of all, storing and fetching inventories is not an easy task and is time-consuming in many cases. Another drawback is that there is a huge need for storekeepers in this traditional type of warehouse, and this dependency causes the waste of human resources. The last problem is related to the record of stocks on account books, which in turn is not an environmentally friendly approach. On the other hand, a smart warehouse is mostly unmanned, paperless, and automated during the pickup, delivery, and bookkeeping operations [9].  
With the help of the adoption of new technologies such as the Internet of things (IoT), blockchain, big data analytics, artificial intelligence, machine learning, deep learning, and robotics, it is highly possible that the operations of warehouses will be radically transformed.  
The main advantages of smart warehouses are described as follows [12]:  
* Smart warehouses provide real-time information, which is not possible in traditional warehouses
* In smart warehouses, manual tasks are minimized, and automation of tasks is maximized so that high-value tasks are performed by the employees
* Operational scalability is improved with smart warehouses because updating the infrastructure is easier than the update of the human capital within the organization In smart warehouses, automated decisions are made using different prediction models
* When customer demands or processes change, smart warehouses are easily adapted to the new cases compared to the traditional warehouses
* Smart warehouses use smart sensors to monitor expensive equipment and therefore, downtime is minimized The disadvantages are the following:
* The cost of building a smart warehouse is more expensive than the traditional warehouses
* The transition to a smart warehouse is a time-consuming process, which requires a lot of effort
* The transition to a smart warehouse requires the support of top management
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Antarctica Project
#### 3.5.3 Measurement Setup  
The Antarctic sounder is composed of a custom system that generates and processes the signals at transmitter and receiver, respectively [58; 59]. The use of commercial components was discarded since the transmission was designed to use a bandwidth exceeding 3 kHz. The research group implemented  
Figure 8: Geographical link characteristics. The transmitter is located in the Spanish Antarctic Station on Livingston Island, and the receiver is placed in Cambrils, Spain.  
Figure 10: (a) Complete block diagram of the Antarctica project transmitter and (b) Complete block diagram of the Antarctica project receiver.  
Figure 9: (a) The single transmitter antenna in Antarctica and (b) the three antennas of the receiver in Cambrils.  
a custom system to fit the strictest requirements of the channel. The solution was based on a software defined radio platform, whose last update was on the 2009/2010 campaign.  
The main parts and features of this platform are the following:  
* An embedded PC is used to control the entire system and storage data.
* A Virtex-4 XtremeDSP development platform from Xilinx, with three FPGAs that conduct all the signal processing operations needed, including the up and down conversion of the signal to be transmitted and received. This card is provided with 2 xADC and 2 xDAC of 14-bits. This systems is plugged to the embedded PC through PCI bus.
* A GPS unit was installed with the PPS signal, to obtain a time synchronization with an accuracy of up to 1 \(\mu\)s, since the absolute propagation time of the wave is a possible measurement in the receiver.
* A 100 MHz Oven Controlled Crystal Oscillator (OCXO) was installed in both the transmitter and the receiver side. The frequency stability in temperature of this oscillator is \(\pm\)0.1 ppb.  
The transmitter has also a Bonn HF power amplifier configured at 200 W of output power that feeds the antenna subsystem. The antenna subsystem is composed of a monopole of 7.5 m, several - radials to improve the ground plane and an impedance tuner [26]. The signal to be transmitted is directly synthesized in the FPGA card by the DAC. The transmitter diagram block is depicted in Figure 10a.  
The receiver is composed of three different antennas [58], a monopole, an inverted V and a Yagi with a resonance frequency of 14 MHz, in order to increase the information of the channel in terms of sounding. However, the monopole and the inverted V are not wideband antennas and a workaround has been implemented for each one. The inverted V requires a balun to flat the frequency response and an antenna tuner adapts the input impedance of the monopole to the input impedance of the receiver. The FPGA card samples the filtered signal provided by the antennas in their ADCs. Two - software define radio platforms are required to process the data of the 3 antennas, since every system only contains 2 ADCs. The transmitter diagram block is depicted in Figure 10b.  
The system is described in detail in Reference [58].
## 4 Experimental Results
### Evaluation Metric  
We validate the detection performance by employing precision-recall curves, which are generally applied to the evaluation of human detection performance. To verify the detection results, the predictedbounding boxes generated by the detector are compared with the ground-truth bounding boxes and checked as either true positive (TP), false positive (FP), or false negative (FN), via measuring the overlap between the bounding boxes. TP indicates the quantity of properly detected pedestrians, FP indicates the number of mistakenly detected pedestrians by the detector, and FN indicates the number of pedestrians that are not detected by the detector. As the detection criteria, the detected bounding box is considered to be TP if the overlap ratio between the detected bounding box and the ground-truth bounding box exceeds 50%. TP/(TP + FP) calculates the precision and TP/(TP + FN) computes the recall. The average precision (AP) describes the tendency of the precision-recall curve and is calculated by averaging the precision at several evenly spaced recall levels via changing the threshold of the detection scores. In our experiments, the AP is obtained by averaging the precision values at 11 evenly spaced recall levels between 0 and 1.
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 4 Proposed Protocol Methodology for E-Commerce
### Implicated Symmetric and Asymmetric Cryptography
#### 4.1.3 Utilizing the CRT Technique  
The CRT technique can be used to support randomness issues for encryption algorithms. In addition, it can be used to support performance issues in encryption, decryption and keys generation. This technique is used with the ElGamal algorithm to generate random keys, especially private ones, to increase the randomness, and it is used with the ElGamal algorithm with a faster operating speed, a smaller key, and an increase in the working time capacity with the system. The keys are later used in the process of encryption and decryption for the encryption process, i.e., changing the original text into ciphertext using a public key and for the decryption procedure, which changes the ciphertext into original text/plaintext using a private key. This technique works to represent and process large operands in ciphertext polynomials and to reduce large numbers to small ones. Namely, we use CRT to reduce the private key length and to support its randomness. In ourprotocol, we relied on CRT to calculate Salt to support the random handling of encryption and decomposition in AES.