esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 State of the Art
Finally, in the asynchronous approach, instead of exchanging synchronization information, multiple techniques are implemented to avoid the packet overhead and collisions such as preamble sampling where all motes go to sleep asynchronously and often wakes up for checking channel activity [25]; the receiver-initiated approach where the sender waits for a beacon from the receiver [26]; or the random duty-cycling method, where motes are in sleep mode and wakeup randomly [27]. These duty-cycling mechanisms are integrated in operating systems such as _TinyOs_[28] and _Contiki_[29] yet these operating systems do not work in platforms where there is an external microcontroller like in our LOKA approach. Other techniques can be grouped in Low Power Listening (LPL) and Low Power Probe (LPP). In LPL if the sender energy level is above a predefined threshold, the receiver stays awake to receive the potential packet then, the receiver sends an ACK. In LPP, each receiver periodically wakes up to send a probe, the sender meets the receiver by successfully decoding the probe and send it the information. LPL and LPP suffer performance degradation in noisy environments with signal interference. In this way, [PERSON] et al. [30] implement _ZiSense_, a technique that minimizes the false positives due to interferences measuring the Received Signal Strength Indication (RSSI). This approach can be included into LOKA yet it is not the aim of our paper since LOKA focuses on delay buffer transmissions. Furthermore, duty-cycling approaches can be divided depending on the mote type that implements it. Some papers focus on the coordinator such as the work of [PERSON] [31] that incorporates a fuzzy logic control in the coordinator device (receiver) to predict when an issuer sends a message. This approach allows maximizing the coordinator sleep time when it is idle. A priori, this technique will not fit in our approach since it needs a lot of information or many receiver packets to allow the use of fuzzy logic and LOKA works in motes that receive few packets since these motes are sensor motes. Therefore, the goal of this research is to contribute to minimizing the power consumption by means of a duty-cycling algorithm, LOKA, in a sensor mote composed by a Cortex M0+ microcontroller and a ZigBee module included in a tiny embedded prototype. For that purpose the algorithm takes into account the transmission time interval and the power modes of the devices attached to the mote.
# Development of Cost and Schedule Data Integration Algorithm Based on Big Data Technology
## 2 Literature Review
### Big Data Technology in Construction Project  
The definition of big data refers to as \"a collection of data that cannot be captured, managed, and manipulated with conventional software tools for a period of time, requiring new processing models to have a greater decision-making power, a process optimization massive capacity, high growth rates, and diversified information assets\" [15]. It is regarded as a promising futuristic technology that generates user-customized and fit-for-purpose information, together with a more accurate prediction. According to statistics [16], the global big data market is projected to grow to 103 billion dollars by 2027, which indicates a 10.48% annual increase from 2018. It has been expected that this technology brings a new paradigm for competition, productivity growth, innovation, and consumer surplus [17].  
The application of big data technology has raised significant attention in construction project management. While data-driven analytics has been widely introduced in the building sector, the adoption of big data technology has been low [2]. [PERSON] and [PERSON] [18] used big data to develop a construction schedule evaluation model in terms of project risk. The proposed model is based on EVMS [12] and addressed cost and schedule data only for three types of activities at a single level. [PERSON] et al. [19] proposed a big data architecture to analyze construction project profitability. It is designed to provide insight into project margin based on project value and profit made. [PERSON] and [PERSON] [20] suggested a statistical model to identify sensitive materials for price change, using data for ten years. They defined essential elements for any cost item, such as materials, equipment, labor, and administrative expenses. [PERSON] et al. [21] employed big data technologies to build data from building information models for building projects in order to predict the quality of several construction activities. [PERSON] et al. [22] utilized big data to detect workers' unsafe behaviors in construction projects linked to WBS.  
The existing studies have centered on using big data to provide data-driven insight or to understand trends or patterns for specific purposes in construction management. There is a dearth of research exploring how big data technology can facilitate cost and schedule management. Few studies have dealt with cost and schedule data in their big data architecture; however, both data were mostly addressed respectively and simply connected to each other without an in-depth consideration of their relationships. Moreover, these studies represented all cost and schedule items at a single level. More research on the application of big data technology to extract, clean, and integrate cost-schedule data, which consider their intertwined and multi-hierarchical characteristics, is required to facilitate integrated management of cost and schedule.
## 3 Random Vibration Analysis  
Random vibrations simultaneously engage all frequencies within a structure, unlike sinusoidal vibrations that occur at specific frequencies [12]. They exhibit unpredictable fluctuations in both amplitude and phase over time. These vibrations are common inapplications such as vehicle dynamics, aerospace turbulence, seismic activity, machinery operation and wind turbines [13]. Engineers typically analyze them using statistical measures like power spectral density (PSD) and root mean square (RMS) acceleration. Understanding random vibrations is essential for designing robust and reliable structures, predicting fatigue life, and ensuring safe operation in dynamic environments [14].  
The PSD function is commonly used in engineering to describe vibration test loads by analyzing how power is distributed across different frequencies. When a product is tested against a specific PSD profile, acceleration time signals can be measured at various points on the structure. These signals are often random, but, for stationary processes, their statistical properties remain constant, allowing meaningful frequency-based analysis [15]. The PSD is typically derived using the fast Fourier transform (FFT), which converts time-domain signals into frequency components. By squaring these frequency amplitudes, the PSD function is obtained, providing valuable information for evaluating how structures respond to dynamic loads. This approach is essential for applications such as vibration testing, structural health monitoring, and optimizing system designs to ensure resilience against vibration-induced failures [16].  
As mentioned earlier, vibrations in real-world environments are seldom purely sinusoidal. In random vibration fatigue analysis, the [PERSON] method is widely applied to estimate component lifespan by integrating S-N curves and stress levels [17]. [PERSON]'s three-band method evaluates the Von Mises stress output from random vibration simulations. Assuming that the stress follows a Gaussian distribution, the method categorizes stresses into three distinct bands based on the standard deviation (\(\sigma\)) values, as depicted in Figure 1.  
* \(1\sigma\) Band: Covers approximately 68.27% of the vibration cycles (red region);
* \(2\sigma\) Band: Encompasses around 27.18% of the cycles (orange region);
* \(3\sigma\) Band: Represents about 4.28% of the cycles (pink region).  
Each band corresponds to a range of stress levels, enabling engineers to track completed cycles statistically and determine their contribution to fatigue damage. This approach offers a statistical framework to analyze fatigue life by accounting for the varying stress amplitudes experienced during vibration cycles. By integrating the number of completed  
Figure 1: [PERSON]’s three-band model in PSD analysis.  
cycles within each band, engineers can better predict potential failure points and optimize structural designs.
## 5 An Engineering Mechanics Experimental Method Based on the Wireless Sensor Network  
### The Application of the Wireless Sensor Algorithm Model to an Engineering Mechanics Experiment  
In 1996, U.S. scholar [PERSON] proposed the idea of replacing the wired system of an engineering mechanics experiment with wireless sensor technology, which introduced the application of wireless sensor network technology to the field of engineering mechanics experimentation and developed a real-time damage identification engineering structure health monitoring system. Based on the above work, [PERSON] and others developed a wireless sensor model using standard integrated circuits. The entire sensor node included an eight-bit microprocessor, and the detection unit was composed of a microacceleration chip. The integrated wireless sensor was verified in the laboratory. This laid a foundation for the application of engineering mechanics tests. [PERSON] et al. designed the Wireless Global Bridge Evaluation and Monitoring System (WGBEMS) for monitoring the environment and performance of engineering mechanics in 1997. The sensor nodes in this system were composed of battery cells, microprocessors, small transponders, and sensors [27]. One area controller included in the system was placed on the bridge shore, and the other sensor nodes were placed on the bridge. The sensor node conducted data acquisition, filtering, identification, quantification, etc.  
Some Chinese scientific research institutions and universities, such as Harbin University of Technology, Tsinghua University, the Ministry of Aerospace, and the Chinese Academy of Sciences, have initially researched sensors and wireless sensor networks [28]. As for the research and development of wireless sensors used for the mechanical monitoring of engineering structures, China is also in the initial stage. In general, the mechanical test monitoring of engineering structures belongs to a new research direction in the engineering field, and the application of wireless sensor networks to the mechanical monitoring of engineering structures is a hot spot of development. However, as wireless sensor network technology also belongs to a new discipline, it needs the cooperation of more disciplines to develop quickly, which may also slow the development of wireless sensor networks in the field of engineering mechanics experiments. Based on the application in the field of engineering structure monitoring, we tested the developed wireless sensor and its network system on representative structures such as reinforced concrete beams. This research foused on the testing and analysis of the wireless sensor algorithm model on the engineering mechanics test and further improved the wireless sensor hardware performance, software collaborative design, and other topics based on the analysis.
# A Generic Automated Surface Defect Detection Based on a Bilinear Model
## 2 Methodology
### Defect Classification
#### 2.1.2 Bilinear Model  
The bilinear model is composed of two-factors, and is mathematically separable, i.e., when one factor remains constant, its output is linear in any factor. A bilinear model \(B\) for defect classification consists of a quaternion function, as shown in Equation (1).  
\[B=\left(f_{A},f_{B},P,F\right) \tag{1}\]  
where \(f_{A}\) and \(f_{B}\) are feature functions, D-VGG16 is used in this paper, \(P\) represents the pooling function, and \(F\) represents the classification function, which here refers to the soft-max classifier.  
The output of the feature function, \(f_{A}\) and \(f_{B}\), are combined at each position of the image \(I\) using the matrix inner product, as shown in Equation (2).  
\[bilinear(i,f_{A},f_{B})=f_{A}(i)^{T}f_{B}(i) \tag{2}\]  
where \(i\in I\). The feature dimensions of \(f_{A}\) and \(f_{B}\) must be equal, and the value should be greater than 1 to represent various descriptors that can be written as bilinear models.  
To obtain the descriptor of the image, the pooling function \(P\) aggregates the bilinear features across all of the locations in the image. The pooling function can use the weighted sum of all bilinear features of the image, i.e., the sum of all bilinear features, which was calculated as follows.  
\[\Phi(I)=\sum\
olimits_{i\in I}bilinear(i,f_{A},f_{B}) \tag{3}\]  
If the feature sizes of the \(f_{A}\) and \(f_{B}\) output are \(C\times M\) and \(C\times N\) respectively, then the size of the bilinear vector \(\Phi(I)\) is \(M\times N\), and its corresponding class probability can be obtained by inputting the \(\Phi(I)\) reshaped size \(MN\times 1\) into the classification function \(F\). The data stream of this bilinear model is shown in the Figure 3.  
From an intuitive point of view, the structure of the bilinear can make the output features of the feature extraction function, \(f_{A}\) and \(f_{B}\), to be fine tuned on each other by considering all of their pairwise interactions similar to quadratic kernel expansion. Because the entire network is a directed acyclic graph, and parameters of the network can be trained by the gradient of back-propagating loss.
## 5 Physical Layer: Five Decades of Evolution (from 45 Mb/s to 100+ Tb/s)
### Two Key Factors of Evolution
As it is evident, the fiber spectrum is segregated into five nominal amplification bands: original (O)-band, extended (E)-band, small (S)-band, conventional (C)-band and long (L)-band. According to Figure 28, the impact of fiber attenuation decreases as the transmitted wavelength increases, attaining a minimum around C-band. This is the main reason that modern optical systems work mainly in C-band, since the number of optical amplification elements can be minimized. Next, we can observe that the dispersion parameter is proportional to the transmitted wavelength, showing a value of zero around 1300 nm. This was an attractive feature for early fiber-optic systems that emitted light in the O-band, overcoming, in this way, the impact of dispersion. In currently deployed systems, the O-band is still exploited for optical access networks, where dispersion compensation modules can be saved in order to reduce the overall network costs.
## 3 Results
### Main Results  
No significant differences were found in the main outcomes between groups, with the only notable difference being the total duration of activity (\(p<0.01\)). These findings are summarized in Table 2.  
Heart rate was presented as the minimum, mean, and maximum throughout the exercise time. No significant differences in minimum HR (\(p=0.158\)), mean HR (\(p=0.391\)) or maximum HR (\(p=0.1\)) were observed.  
RPE was measured at mid-session and the end of the session, and no significant differences were found at half-time (\(p=0.626\)) or at the end of the exercise time (\(p=0.74\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Group 1** & **Group 2** & \\  & **Mean (SD) (95\% CI)** & **Mean (SD) (95\% CI)** & **\(p\) Value** \\ \hline Min HR (ppm) & 72.13 (13.4) & 67.94 (9.45) & \(p=0.158\)* \\ Mean HR (bpm) & 85.83 (15.9) & 86.75 (11.59) & \(p=0.391\)* \\ Max HR (ppm) & 98.13 (17.67) & 105.75 (15.2) & \(p=0.1\)* \\ Half-time RPE (6–20) & 9.53 (2.12) & 9.31 (2.182) & \(p=0.626\)** \\ End-time RPE (6–20) & 11.2 (2.65) & 11.5 (2.83) & \(p=0.74\)** \\ Total time mean (SD) & 0:13:08 (0:00:48) & 0:27:55 (0:00:57) & \(p<0.01\)** \\ Enjoyment PACES & 35.13 (5.24) & 36.94 (4.39) & \(p=0.287\)** \\ Non-enjoyment PACES & 11.62 (4.91) & 10.31 (3.03) & \(p=0.616\)** \\ Adverse effects & 1.44 (0.96) & 1.81 (0.83) & \(p=0.287\)** \\ Fatigue 24 h mean & 1.69 (1.45) & 1.31 (0.87) & \(p=0.564\)** \\ Fatigue 48 h mean & 1.31 (0.873) & 1.19 (0.54) & \(p=0.956\)** \\ DOMS 24 h mean (SD) & 1.69 (1.01) & 1.94 (1.12) & \(p=0.539\)** \\ DOMS 48 h mean (SD) & 1.25 (0.58) & 1.19 (0.4) & \(p=0.956\)** \\ \hline \hline \end{tabular}  
* \(t\)-test was used, ** Mann–Whitney U test was used. CI: Confidence Interval, Bpm: beats per minute.  
\end{table}
Table 2: Comparison between groups.  
Figure 2: Flow diagram.  
The Spanish PACES scale was divided into two parts, enjoyment and non-enjoyment items, and no differences were found in enjoyment (\(p\) = 0.287) or non-enjoyment (\(p\) = 0.616).  
There were no differences between groups in terms of total adverse effects (\(p\) = 0.287), nor in fatigue at 24 h (\(p\) = 0.564) and 48 h (\(p\) = 0.956) post-exercise, or DOMS during the same period (24 h (\(p\) = 0.539) and 48 h (\(p\) = 0.956)).
## 7 Conclusions  
Congestion has a severe influence on network performance, which results in a large number of missing packets, unfair status of network and significant wasted energy due to the repeated sending packets. Focusing on the problems, we propose a cross-layer active predictive congestion control protocol (CL-APCC) for WSNs. The basic concept of the CL-APCC protocol is described as below.  
Each node adopts a single service window of mixed queuing model M/M/1/m to predict and resolve node-level congestion. CL-APCC predicts the probability of congestion caused by the grid in period \(t\), and the probability is used to control the inputting and outputting rate of the grid, which effectively solves the system-level congestion problems of the network. Therefore, the congestion is solved through the combination of node-level control method and system-level control method. At the same  
Figure 18: (a) CL-APCC in comparison with the ESRT;.(b) CL-APCC in comparison with the ESRT and (RT)\({}^{2}\).  
time, the IEEE 802.11 protocol is revised in CL-APCC to meet the requirement of fairness of networks, which is depending on the service time at the node and original priority of data packets. We make a complete analysis and simulate on the proposed scheme, which shows that it exceeds other methods relying on not only RPR and lifetime of network but fairness and timeliness of data packets.  
However, the kind of congestion control protocols under the circumstances where nodes can be moved have not been considered. Still, we have not yet found an optimal method to set communication radius for the node. Nevertheless, such issues are to be studied in the future so that CL-APCC protocol has much wider applications.
## 4 Experimental Results
### Driving Performance Evaluation
#### 4.2.3 Case 3: Overtaking  
In Figure 11, both mandatory and discretionary lane changes (overtaking) occur. The gray areas denote the predetermined guidance lane, indicating mandatory lane changes for the ego vehicle. To assess each agent's performance in following sub-goals, Figure 12 shows the maneuvers of each agent with sub-goals at each time step. Both agents must opt to change lanes to the right at 1000 m as a mandatory lane change. While our method successfully follows the target lane, the TD3 agent deviates from the target lane after 1000 m. A closer examination of the overtaking action is provided in the section from 1100 to 1470 m in Figure 13, where the maneuver is depicted in both global Figure 13a,b coordinates. In relative coordinates, the origin is set as the obstacle vehicle. Here, the agent in our method executes an overtaking maneuver while maintaining a safe distance from the obstacle vehicle.  
Figure 11: Scenario 3, comparison of drive paths for TD3 + CER agent and TD3 agent.  
Figure 10: Case study of lane-changing performance: Section from 1100 to 1300 m in Scenario 2. The lower label of the vehicle (rectangle box) indicates the time step, while the upper label indicates the velocity of the vehicle at each time step. The maneuver of the suggested method in (**a**) follows CoL while maintaining the desired velocity range. In contrast, the maneuver without CER, depicted in (**b**), fails to follow CoL and exceeds the desired velocity range.  
Figure 9: Scenario 2, comparison of trajectories between TD3 + CER agent and TD3 agent.
## 4 Non-Thermal Plasma
### Active Plasma Agents  
The main active agents present in the plasma are radicals, charged particles, reactive oxygen species (ROS-O, O\({}_{2}\)*, O\({}_{3}\), OH), reactive nitrogen species (RNS-NO, NO\({}_{2}\)), UV radiation, and electrical field. These active agents in combination are believed to be responsible for the antimicrobial efficacy. The reactive species (ROS and RNS) possess strong oxidative effects on the outer cellular structure [74]. ROS in cellular level leads to lipid peroxidation, DNA damage, protein modulation and programmed cell death in microorganisms [97; 98]. Some of the molecular marker that is involved in plasma treatment are listed below in Table 3. UV radiation has fewer effects on bacteria as NTP at atmospheric pressure is a poor source of UV [97]. Similarly, charged particles play a vital role in rupturing the bacterial outer cell membrane. The electrostatic force created by the charge accumulation on the outer cell membrane overcomes its tensile strength and ruptures it [74].  
Figure 4: Schematic diagram and photograph of the regular DBD plasma setup. (**a**) demonstrates the schematic diagram of the regular DBD plasma and (**b**) shows the actual experimental setup of DBD plasma [70].  
Figure 5: Schematic diagram and photograph of the jet plasma setup. (**a**) demonstrates the schematic diagram of the jet plasma and (**b**) shows the actual experimental setup of jet plasma [69; 96].
# Novel Calibration of MIESM and Reduction of CQI Feedback for Improved Fast Link Adaptation
## 5 Simulation Results
### Verification Algorithm for FLA  
The simulation was performed at the intermediate SNR of the interval where the SNR\({}_{\text{eff}}\) distribution ratio of each CQI was 5% or more. The information bits were encoded according to the modulation order and coding rate corresponding to 15 CQIs, and were passed through the generated channel and noise. We decoded the received data and calculated BLER\({}_{\text{realS}}\) for all CQIs from the link-level simulation for a given channel and noise. Then, we determined a final CQI (CQI\({}_{\text{real}}\)) by selecting the largest CQI index such that it satisfied the condition of the target BLER among the BLER\({}_{\text{real}}\) corresponding to each CQI. At the same time, SNR\({}_{\text{eff}}\) corresponding to the current channel was calculated, and the effective CQI (CQI\({}_{\text{eff}}\)) was determined by calculation. If the CQI\({}_{\text{real}}\) and CQI\({}_{\text{eff}}\) were the same, the link quality was considered correct; then, verification proceeded to the next channel. Otherwise, the number of errors was increased by one before verification proceeded to the next channel.