esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Impact of Accuracy and CV on the Practical Application of Tools and Parameters  
The need for reporting accuracy can be better understood following an example reported by the United Nations Industrial Development Organization [24]. Thus, in practical terms, if the accuracy of a tool is \(\pm\)3, this means that if 100 would be the reading displayed on a tool during a measurement, then the actual value could be anywhere between 97 and 103, including 97 and 103. In this regard, it is important to note that some tools did not present the required accuracy for the experimental design performed in each study. For example, the accuracy of an infrared camera is \(\pm\)2 \({}^{\circ}\)C, and the applied protocol in the study provided for changes in body temperature ranged between 0.3 and 1.5 \({}^{\circ}\)C [98]. Moreover, in EPTS with a sampling rate of 10 Hz, the accuracy for high accelerations (>4 m/s\({}^{2}\)) is compromised, which may have an impact on the interpretation of results [79] because of the poor estimation of instantaneous velocity when performing these very high accelerations (>4 m/s\({}^{2}\)) during player tracking in team sports [135]. Therefore, tools with inadequate accuracy for the required interventions need to improve their accuracy or else they willgenerate an input noise, thus compromising the outputs of a Big Data approach.  
A common concern of sports scientists as well as strength and conditioning coaches of high-level soccer clubs when using monitoring tools refers to reliability, which is one of the main factors related to the discrepancy between the expected and actual effectiveness of monitoring, thus representing a potential barrier for successful interventions [136]. For this purpose, the classic classification of the CV is considered for the decision-making process: >30% = large, <30% and >10% = medium, and <10% = small [40]. In the present systematic review, extremely large CVs, such as 193.5% for sprinting at speeds \(\geq\)25.2 km/h, are measured with EPTS [129]. Moreover, 137% for the time between peak power and peak force during a CMJ [137] and 679% for the variance from mean bedtime assessed by Actigraphy [138] have been found in the scientific literature. Large CVs make it extremely difficult to detect the real differences between moments after an intervention unless these differences were also very large [139]. Therefore, caution should be taken when using parameters with a CV greater than 30%.
## 2 System Model
### NB-IoT via LEO Satellite-Based Networks  
Figure 1 shows a system architecture of LEO satellite-based IoT networks in remote areas, which consists of multiple NB-IoT terminals, LEO satellites, and a gateway station. The LEO satellite transponder is equipped with a regenerative transponder. In this way, the onboard demodulation of data packets is executed by the LEO satellite itself. When the gateway station is not visible to the current service satellite, data packets will then be transferred to other LEO satellites in the gateway station's line of sight through Inter-Satellite Links (ISLs). Subsequently, the data packets are further transmitted to the gateway station through the feeder link within the line of sight of the gateway station. We consider that each NB-IoT terminal is equipped with a GNSS receiver to pre-compensate Doppler shift and time advance (TA), which are caused by the high dynamics and long delay of the LEO satellite channel. This is possible thanks to the knowledge of satellite ephemeris and available terminal locations. Depending on the prediction accuracy, additional delay and frequency offsets are assumed to be handled by the pre-compensation. In addition, NB-IoT terminals are divided into different CE groups according to their downlink Reference Signal Receiving Power (RSRP) measurement. For the different RACH success probability requirement applications, such as data collection, asset tracking, and alarm traffic, the NB-IoT terminal needs suitable RACH parameter settings to optimize the power consumption. In this paper, we focus on the relationship between RACH success probability and the power consumption of the NB-IoT terminal for different IoT applications.
###### Abstract  
Currently, there are three types of optical communication networks based on the communication channel between the transmitter and receiver: the optical fiber channel, visible light channel, and optical wireless channel networks. The last type has several advantages for underwater communication, wireless sensors, and military communication networks. However, this type of optical network suffers from weather conditions in free-space communications and attenuation owing to the scattering and absorption mechanisms for underwater communication. In this study, we present a new transceiver architecture of a coherent optical code-division multiple-access (OCDMA) system based on a hybrid M-ary differential pulse position modulation scheme and a spreading code sequence called weighted modified prime code for underwater communication to minimize channel dispersion, increase the transmission rate per second, enhance the network bit error rate (BER) performance, and improve network security. Using an OCDMA system, we can simultaneously expand the network coverage area and increase the number of users sharing the network over the same channel bandwidth. The simulation results in this study proved that the proposed system can accommodate 1310 active users and a network throughput of 180 Gbps*user over a transmission distance of 930 m without any repeater at a \(10^{-9}\) BER performance, compared to the 45 Gbps*user network throughput and 100 m transmission distance reported in the literature.  
OCDMA; UOWC; M-ary DPPM; WMPC; BER performance
# Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery
## 4 Application and Validation
### Experimental Analysis of Faulty Gear
#### 4.1.5 The Proposed Method  
In this method, the collected signal is decomposed with EEMD first, and then the number of fault signals can be estimated with SVD. The ratios of neighboring singular values are shown in Table 1.  
As shown in Table 1, the sequence number of the maximum NSVR is 1. Therefore, the number of fault signals is 1. According to the correlation coefficient of each IMF, the observation signal is reconstructed with high-correlation IMFs. Finally, the fault signals can be extracted from the new observation signal with KICA. The Hilbert envelope spectra of the extracted fault signal is shown in Figure 8.  
The result in Figure 8 shows that the motor rotation frequency \(f_{r}=14\) Hz and its double frequency and triple frequency have been extracted clearly, and \(f_{z}=283.3\) Hz is also extracted clearly. The amplitude of \(f_{r}\) is much larger than \(f_{z}\), so it can be clearly judged to be the broken tooth fault.
## 5 Experimental Study and Result Analysis  
The proposed RDH scheme, data extraction, and image restoration algorithms are implemented using Matlab 2020. We have considered natural images from the USC-SIPI image dataset [42] and medical images from [43].  
This section mainly discusses the experimental results that we observed during the study. The following efficiency parameters are considered during the analysis:  
* Embedding rate;
* Security analysis;
* Analysis of image recovery;
* Time complexity analysis.
# Modeling the Energy Performance of LoRaWAN
## 5 Evaluation  
In this section, we use the models derived in Section 4 in order to evaluate LoRaWAN end-device current consumption and lifetime, as well as the energy cost of data delivery. As an additional validation of the evaluation results, we have performed average current consumption measurements of up to 4-min duration (i.e., close to the maximum duration allowed by the power analyzer used), comprising several message transmissions from the end-device, for different configurations in terms of DR, notification period, and acknowledged or unacknowledged transmission. We have found an almost exact match between the average measured current consumption and the one computed by using the analytical models. Note that this is an expected result, since the analytical models have been derived on the basis of measurement results.  
The section is divided into three subsections, which focus on each one of the performance parameters mentioned, respectively. Unless explicitly mentioned otherwise, we assume the maximum-sized frame payload for each DR. As it will be shown, this is the most energy-efficient approach per delivered bit, under the assumption that the network comprises a single end-device,and therefore the probability of collision is zero. Nevertheless, the approach of a single-byte payload, which is the least efficient under the same conditions, is also evaluated in the section.
###### Abstract  
The Shaximiao Formation in the Southwestern Sichuan Basin is rich in natural gas resources, but the genetic types of channel sandbodies remain unclear. This study investigated the sedimentary characteristics and sandbody genetic types of the Jurassic Shaximiao Formation using well core, logging, and reservoir property data. The results revealed that: (1) The Shaximiao Formation is predominated by shallow-water delta front facies, with delta plain facies present only in the Sha-1 Member. Lithologically, it exhibits a \"mudstone-overlying-sandstone\" pattern with channel sedimentary sequences portraying normal grading and various microfacies. (2) The sandstones of the Shaximiao Formation developed seven types of lithofacies. Based on the characteristics of lithofacies associations, six genetic types of sandbodies, i.e., deeply incised abrupt-transition, deeply incised gradual-transition, growth gradual-transition, progradational superimposed, progradational mouth bar, and suspension accretion sandbodies are recognized. The deeply incised abrupt-transition and deeply incised gradual-transition sandbodies have the best reservoir physical properties, while the suspension accretion sandbody has the worst. (3) These genetic types of sandbodies are controlled by the relative position of the channels and their relation to mouth bars, with reservoirs in deeply incised abrupt-transition and deeply incised gradual-transition sandwic developed in the delta plain. (4) High-quality reservoirs are found in deeply incised abrupt-transition and gradual-transition sandbodies, where sedimentary processes strongly influence reservoir physical properties. This study provides new insights into the relationship between tight sandstone reservoirs and channel sandbody genetic types, offering guidance for exploration and development in similar regions.  
tight sandstone reservoir; sedimentary facies; lithofacies assemblage; sandbody genetic types; Shaximiao Formation; Sichuan Basin.
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
## 4 Discussion  
By using data from different types of receivers and constructing models to evaluate signal power, code noise, and multipath effects, the satellite and signal performance of BDS and other systems such as GPS, Galileo were analyzed and compared.  
Figure 11: Comparison of average MPC STD values in different stations. The horizontal axis in subfigure (**a**) represents 15 stations with receiver type JAVAD, while that in subfigure (**b**) represents 24 stations with receiver type LEICA, that in subfigure (**c**) represents 29 stations with receiver type SEPT, and that in subfigure (**d**) represents 40 stations with receiver type TRIMBLE. In each subgraph, the 4 panels are for BDS-3 B1I/B3I/B2a, BDS-2 B1I/B2I/B3I, GPS L1/L2/L5, and Galileo E1/E5a/E6/E5b/E5, respectively.  
The receiver type has little influence on _C/N0_ values. For BDS, signal powers of BDS-3 satellites are approximate 2-3 dB-Hz higher than those of BDS-2 satellites at the same frequency, which may be related to its advance modulation, and B2a of BDS-3, E5 of Galileo, and L5 of GPS have stronger signal powers.  
The pseudorange noise levels of different signals have little relationship with the receiver type. It is worth noting that the Galileo E5 signal exhibits optimal performance due to its high signal power. The observation accuracy of E5 can reach 0.2 m, which is the highest among all the signals, regardless of receiver types, and next highest are BDS-3 B2a and GPS L5.  
Similar characteristics can be found, namely that the multipath values have little relationship with the receiver type. Not being affected by the systematic code errors of BDS-2, the observations of BDS-3 satellites contain smaller multipath errors than that of BDS-2 satellites. As for the multipath suppression performance, the BDS-3 signal B2a, GPS L5, and Galileo E5b perform better than the other signals, which may be related to their wide signal bandwidths. The Galileo E5 signal exhibits superior performance compared to all other signals, regardless of receiver type.  
In conclusion, the signals B2a, E5, and L5 exhibit advantages over the other signals, which may be attributed to the advanced signal modulation method.
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 2 Literature Overview
### Scenario Selection Methods
#### 2.4.4 Falsification-Based Methods  
Falsification-based methods focus on optimization techniques in order to obtain more challenging scenarios. Some papers use accident databases as a starting point [54]. Other papers define criticality metrics or complexity metrics to quantify scenario properties. For example, [PERSON] and [PERSON] [55] calculate the safe area next to the AV as the measure of criticality. Afterwards they use evolutionary algorithms for optimization by minimizing the safe area and thus maximizing the criticality. [PERSON] et al. [56] built phenomenological sensor models to identify complex test scenarios for the AV's perception capabilities. In addition, some papers do not perform the optimization in advance, but include the AV in the feedback loop. In each iteration, they take the assessment results of the actual AV from the current and previous scenarios to determine the subsequent scenario. Thus, they get more and more critical scenarios with each iteration. [PERSON] et al. [57] apply reinforcement learning techniques for optimization, [PERSON] et al. [58] use a model-based kriging approach with an iterative zooming-in algorithm and [PERSON] et al. [59] apply simulated annealing to falsify formal system requirements.
## 3 Results and Discussion  
Different models are employed to forecast the electrical generation for Kuwait up to 2030 using real data from the Kuwait Ministry of electrical and water. The simulations were carried out within Rstudio and Matlab software environments. The period between January 2015 to December 2018 was used for estimation and adjustment, while the period from January 2019-May 2020 was used for the validation, and the forecasting horizon is between June 2020 to December 2030.  
Single (PSR) and multiple (PMR) regressors were used for Prophet model, and three methods of seasonality Holt-Winters models: single (HWSS), double (HWDS), and triple (HWTS). Each model's performance is assessed from different angles by accuracy indicators. The future electrical generation forecasting of Kuwait until the year 2030 using the five models is plotted in Figure 2a,b. The models are assessed in the following section and then discussed, and different fuel cost scenarios are investigated.  
Figure 2: The generation of daily loads data in Kuwait: the trained, predicted, and forecasted data using the five models of (**a**) two types of the Prophet model and (**b**) three types of the Holt-Winters model.
# Smart Deployable Scissor Lift Brace to Mitigate Earthquake Risks of Soft-Story Buildings
### Soft-Story Mechanism
In 2022, the Chihshang earthquake, Taiwan, in the southern Hualien and northern Taitung region which had a high seismic intensity, had one notable case which involved the collapse of a three-story residential and commercial mixed-use RC building with a soft-story feature in the downtown area of the Yuli Township, Hualien [24].The recent 2023 Turkey-Syria earthquakes damaged over 100,000 buildings, caused more than 10,000 buildings to collapse, and killed more than 50,000 people. Many soft-story buildings were damaged during this earthquake [25]. Over the decades, retrofitting soft-story buildings to mitigate earthquake effects has focused on the strengthening of the vulnerable first story to improve the overall seismic resilience. Common techniques include the introduction of steel bracing, shear walls, column strengthening, base isolation systems, and the addition of infill walls. [PERSON] and [PERSON] [26] proposed a displacement-based design method for the seismic retrofitting of RC buildings using hysteretic dissipative braces which addresses the main issue of the effect of soft-story irregularities and applied it to a real case study of a three-story RC school building in Vibo Valentia, Calabria, Italy, to assess its application potential in current practice. To create open spaces in buildings, such as parking areas where adding more columns is not feasible, the incorporation of shear walls can be highly beneficial. These walls can resist lateral and shear forces that typically cause significant damage during an earthquake, reducing the need for additional columns [27]. Column strengthening techniques such as FRP jacketing [28; 29], steel jacketing [30; 31; 32], and reinforcement caging have been found to be effective methods for the retrofitting of vulnerable columns and for effectively countering the soft-story mechanism commonly observed in buildings with RC frames. [PERSON] et al. [33] and [PERSON] et al. [34] introduced a novel engineering solution for the seismic isolation of columns that can be utilized to retrofit existing buildings with inadequate soft stories or to construct new structures with soft first stories for architectural or functional purposes. Adding infill walls has been the most practical and affordable way of countering the soft-story effect in buildings. Infill walls function as struts, reducing the lateral load effects on the columns and beams [35].
## 7 Conclusions  
This study investigates Beam Ant Colony System (BACS) for solving the highly constrained multi-objective (reliability and communication overhead) component deployment problem. Four variants of BACS are investigated where each variant is different with respect to the estimates used to guide the search towards feasible regions. We find that three of the four BACS variants are more effective than ACS with respect to feasibility. These are the variants that are based on stochastic sampling, respecting memory constraints and enforcing co-localisation constraints.  
The variant of BACS that enforces the communication constraints struggles with feasibility. Regarding solution quality, we find that ACS is effective on small problems when feasibility is found. With increasing problem size, the BACS variants are more effective, and in particular, BACS(Mem) (respecting memory constraints) is the best performing algorithm in large problem. On the other hand, BACS(Col) which prioritises co-localisation constraint is the overall best performing method, both in terms of feasibility and solutions quality hence we recommend it for the highly constrained multi-objective component deployment problem.  
In the future, we plan to solve more difficult instances with a larger number of software components and hardware units. In order to do this effectively, we will consider parallel implementations of our methods via multi-core shared memory and message passing interface (e.g., using approaches such as that of References [45; 46; 47]). Furthermore, approaches tailored for multi-objective problems which deal with hard constraints effectively, can be of great potential [48; 49; 50].  
[PERSON] developed the algorithms and ensuing implementations and led the writing of the manuscript. [PERSON] carried out the data analysis, prepared the visualisation, and wrote the experiment and results section. [PERSON] provided the motivation for the problem, developed a problem generator, ideas for the algorithms, and wrote parts of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Model/Data Comparison
The extracted Doppler shift fluctuates due to contamination by multi-path scattering at an early time and the time variability of the towed system and environment, while the overall trend is clearly revealed. We compare it with the Doppler shift extracted from the modeled reverberation using Equation (6) again. The red solid line shows the results without a correction of the bistatic geometry, which means that the range of the source and receiver is zero. The blue solid line represents the results with the true bistatic geometry. It can be seen that the use of the bistatic geometry is better for predicting the Doppler shift of the CW reverberation in the active towed array system than that of a depth-bistatic geometry. Figure 14: Comparison of the modeled Doppler shift and the extracted Doppler shift from data for six relative beam directions at ping 120. The black dotted line indicates the extracted result. The blue solid line and the red solid line indicate the modeled result of a bistatic geometry and of a depth-bistatic geometry. (**a**) 60\({}^{\circ}\), (**b**) 90\({}^{\circ}\), (**c**) 120\({}^{\circ}\), (**d**) 240\({}^{\circ}\), (**e**) 270\({}^{\circ}\), and (**f**) 300\({}^{\circ}\). Figure 13: Modeled spectrogram using the data information of Figure 10a. (**a**) Considering the platform motion and (**b**) not considering the platform motion.