esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Consistency of C-V Response  
Three signal readings were taken to investigate the consistency of the C-V signal reading for all the five strips tested in this study. These signals were consecutive, and they were recorded at the same mAs value as seen in Figure 7. Consistent signals were obtained at 50, 85, 100, 125, and 250 mAs for the cold white strip; 100, 125, 250 mAs for the warm white strip; 20, 25, and 125 mAs for the red strip; 85, 100, 150 and 200 mAs for the green strip; and 150 and 250 mAs for the blue strip. Therefore, the cold white strip had the highest number of consistent signals at different mAs values: 50, 85, 100, 125, and 250, a total of five. On the other hand, the blue LED's signals were consistent at only two mAs values: 150 and 250. In some readings, there was no signal detected hence one or two bars are observed.  
The slight signal fluctuations observed in Figure 7 could be attributed to radiation-induced trapping effects [27]. Signal 1 could be low due to electrons being immobile in deep traps, and signals 2 and 3 could be high due to excitation of electrons out of the deep traps. Deep traps are located between valence and conduction energy bands [1; 27]. PN junction localized electrons may be propelled into these deep traps after absorbing a high amount of energy from the x-ray photons. As a consequence of charge trapping, charges are immobile hence lower radiation-induced excitations. However, absorption of x-ray photon energy by the trapped charge could free them from being interlocked in deep traps. Thus, higher signals would be observed in subsequent C-V response measurements. This phenomenon could, therefore, be a possible explanation for the slight signal fluctuations observed in Figure 7. Connecting a higher operating current to the LEDs for luminescence could be an annealing technique that would improve this signal fluctuation effect [34].  
Figure 7: _Cont._
# An Overview of Emerging Nuclear Sensor Technologies: Challenges, Advancements and Applications
## 4 Challenges and Future Directions
### Sensor Sensitivity  
The sensitivity of nuclear sensors is paramount for detecting trace levels of radiation; however, achieving a balance between high sensitivity and low false alarm ratesremains a significant challenge. Sensors must reliably detect low-intensity ionizing radiation (e.g., detection limits below 0.1 uGy/h in environmental monitoring [94]) while avoiding spurious responses to background noise or non-hazardous stimuli. This balance is often quantified by the sensor's signal-to-noise ratio (SNR) and detection threshold. For instance, a sensor designed for nuclear power plant monitoring must achieve a high SNR to discern minute changes in radiation levels yet incorporate efficient signal discrimination algorithms--such as adaptive filtering [95] machine learning [96; 97] techniques--to prevent false-positive detections that could lead to unnecessary operational disruptions or safety risks.
# Implementing a Circadian Adaptation Schedule after Eastward Flight in Young Male Athletes
## 1 Introduction  
Jet lag is a by-product of transmeridian travel and is characterised by difficulty maintaining nighttime sleep and feelings of daytime sleepiness and fatigue [1]. The primary mechanism responsible for jet lag is the misalignment between the body's endogenous circadian system and the local destination time. The effects of jet lag gradually subside as the circadian system aligns with environmental time cues (e.g., sunlight) [2].  
Appropriately scheduled light exposure can be an effective strategy to enhance circadian adaptation (via phase delay or phase advance) to destination time zones following travel [3]. The human circadian rhythms of melatonin and core body temperature are strongly linked with the sleep/wake cycle [4, 5]. Endogenous melatonin secretion typically begins 2 h prior to habitual bedtime [6], and the daily minimum of core body temperature (CBTmin) coincides with the low point of the circadian cycle [4]. The resetting of the endogenous circadian clock is most sensitive to retinal light exposure in the hours before and after CBTmin [7, 8]. Light exposure before CBTmin results in a phase delay (i.e., shift later in time) and light exposure after CBTmin results in a phase advance (i.e., shift earlier in time) [8]. The implementations of light exposure protocols to prevent or reduce jet lag are well established under laboratory conditions [2, 6, 9], but the effectiveness of these protocols in the field--particularly in elite athletes--have not been thoroughly examined.  
Athletes that compete and train internationally are required to cross multiple time zones, which makes them susceptible to stressors such as jet lag [1,10,11]. In addition, athletes may be required to compete and/or train very soon after arrival--potentially before they are fully adapted to the new time zone. In such situations, strategies that facilitate adaptation would be useful. Therefore, the aim of this study was to examine the effectiveness of an electronic reminder system to enforce a light exposure schedule to enhance adaptation to destination time zones in travelling athletes.
# LEO Precise Orbit Determination with Inter-satellite Links
## 3 Simulation and Method  
### LEO Constellation Design  
Since most of the LEO constellations are still in the preparatory stage, three kinds of LEO constellations are designed for the simulation of ISLs. As for satellite numbers, 60, 120, and 192 are selected for comprehensive analysis. It is noted that the 60-satellite design is also adopted by the HongYan constellation program. All of these LEO satellites are evenly distributed in 10 or 12 equally spaced orbital planes with an altitude of 1000 km. In the design of orbit type, polar orbit is chosen. For simplicity, the pseudo random noise (PRN) codes of these constellations are arranged from 1 to 60, 1 to 120, 1 to 192, respectively. Detailed parameters of the designed LEO constellations are shown in Table 1 and the configurations are displayed in Figure 2.  
Figure 2: Configuration of 60-LEO (**left**), 120-LEO (**middle**), and 192-LEO (**right**) constellation.
## 3 Experimental Research and Analysis
### Experiments and Analysis
#### 3.3.3 Heatmap Evaluation  
Gradient-weighted Class Activation Mapping (Grad-CAM) is used to visualize heatmaps as a method to interpret the decisions of convolutional neural networks. Heatmaps can be enlarged and overlaid on the original images to highlight the regions the model focuses on most during classification. An advantage of Grad-CAM is that it can be applied to any convolutional neural network without requiring structural modifications or retraining. It provides a simple yet intuitive way to understand the model's decision for a given input. Experimental results show that the deeper the red hue and the stronger the color intensity in a region, the better the object detection performance.  
Comparison of Grad-CAM heatmaps between YOLO11 and HSS-YOLO:  
As introduced above, the deeper the red hue and the stronger the color intensity in a region, the better the object detection performance. By comparing the heatmaps in Figures 13-15, it is clear that HSS-YOLO showed deeper colors than YOLO11 for key target detection under the same experimental scenarios, indicating that HSS-YOLO achieved better accuracy in detecting key targets.  
Figure 14: Comparison of the heat maps of the test set that comes with the dataset (2).  
Figure 12: Comparison of built-in test sets of datasets (5).  
Figure 13: Comparison of the heat maps of the test set that comes with the dataset (1).
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 4 Results
### Optimization of Conventional State-Based Maintenance Operations
Therefore, more frequent maintenance contributes significantly to enhancing both the minimum and average reliability of the system, but it can lead to significantly increasing costs and idle times. Figure 5: The maintenance period–system reliability function in the case of a maintenance period length of 3 h.  
Figure 6: The number of unnecessary maintenance operations as a function of the maintenance interval. It is worth analyzing how the length of the maintenance period influences the maintenance-related costs, the productivity resulting from system availability, and the income that can be determined as a result of these factors within the selected time window of the analysis. If the length of the maintenance period decreases, the cost of maintenance should also decrease. The appropriate formula can be written as follows:  
\[c(t)=c_{0}.t^{-\theta} \tag{10}\]  
where \(c(t)\) is the cost of maintenance as a function of the length of the maintenance period, \(c_{0}\) is the initial base maintenance cost (when \(t=1\)), and \(\theta\) is a positive parameter that determines how much the cost decreases as maintenance becomes more frequent (the more often maintenance is performed, the cheaper each maintenance operation becomes). In this formula, as the cycle time decreases, the maintenance cost also decreases. The parameter \(\theta\) controls how rapidly the cost decreases with shorter cycle times. For example, if \(\theta=1\), the cost is inversely proportional to the cycle time, meaning that the more frequently maintenance is performed, the lower the cost per maintenance. If \(\theta\) is greater, the cost decreases more rapidly. We can use the same logic to define the relationship between productivity and reliability of the system. A possible equation is the following:  
\[p(r)=p_{0}.r^{\alpha} \tag{11}\]  
where \(p(r)\) is the productivity of the production system as a function of its reliability, \(p_{0}\) is the maximum available productivity (when the production system operates with 100% reliability), \(r\) is the system's reliability, ranging from 0 to 1, and \(\alpha\) is a parameter that determines how strongly reliability affects productivity. Based on these two equations, it is possible to calculate the maintenance cost for the analyzed time window, the income related to the productivity of the analyzed production system, and the profit depending on the maintenance cost and the income based on productivity. The relationship between the maintenance intervals and the profit, depending on the maintenance cost and the income based on productivity, can be modeled using a third-degree polynomial with a coefficient of determination value of \(R^{2}>0.99\). Figure 8  
Figure 7: The impact of the maintenance period on the minimum and average value of system reliability. shows the relationship between the maintenance periods and the specific profit depending on the maintenance cost and the income based on productivity.
## 2 UAM Vehicle and Airspace Environment Analysis
### UAM Airspace Environment Analysis through Concept of Operations (ConOps)  
For this study, the characteristics of the low-altitude environment for operations of UAM vehicles have been analyzed through the UAM concept of operations. Conventional aircraft fly at high altitudes to avoid obstacles after takeoff. Therefore, they are not restricted by obstacles when cruising or performing various maneuvers such as holding, climbing and descending. Aircraft operating at high altitudes only need to maintain their separation from other nearby aircraft (traffic). The separation from other aircraft is under the charge of the air traffic controller (ATC). Additionally, the flight crew monitors the tracks of other aircraft visually and using radar.  
Unlike the operations of conventional aircraft, UAM vehicles are exposed to various obstacles during operations. In particular, since UAM vehicles fly very close to urban buildings, structures, and natural obstacles, it is necessary to apply precision navigation systems and communication systems in UAM vehicles to enable more precise flight operations than for conventional aircraft. The U.S. Federal Aviation Administration (FAA) is establishing the concept that low-altitude unmanned aerial vehicles (drones) controlled by UTM (UAS traffic management) systems will operate at or below 400 ft above ground level (AGL). Additionally, UAM vehicles will operate within a UAM corridor when cruising above 400 ft AGL. Conventional aircraft operate over the entire airspace [5]. Figure 2 shows the FAA concept UAM, UTM, and air traffic management (ATM).  
Konkuk University and Korean Air are also establishing a similar concept of airspace by altitude. According to the concept they have established, UAM vehicles operate along the UAM corridor at altitudes between the airways of conventional aircraft and at or above 500 ft AGL. The provider services for UAM (PSU) manage the traffic flow through the urban air traffic management (UATM) systems so that UAM vehicles can operate safely. Below 500 ft AGL, low-altitude unmanned aerial vehicles oper  
Figure 2: FAA airspace concept for UAM, UTM and ATM [5].  
in aircraft operate in airways or approved airspace. Figure 3 shows the interaction concept of ATM, UATM, and UTM [4].  
In order for UAM vehicles, low-altitude unmanned aerial vehicles, and conventional aircraft to operate safely without collisions, flight track information should be exchanged through each traffic flow management system. For strategic collision avoidance, all vehicles and aircraft should submit operational flight plans, and for tactical collision avoidance, precise detect and avoid (DAA) systems should be applied in each vehicle and aircraft.
## 3 Empirical Analysis
### Topological Properties of the CATN
#### 3.2.2 Temporal Property of Node Degree  
Node degree changes over time partially reflect the temporal characteristics of airport operation. Based on the CTAN constructed on 1 October 2018, the spatiotemporal characteristics of the air transportation network were illustrated at 6:00, 12:00, 18:00, and 24:00 by integrating the spatial information of airports' locations. The results revealed that, from 6:00 to 7:00 in the morning, routes were sparse with a small number of travelers, and most departures were from economically developed cities (Figure 6). After 8:00, a significant number of small and medium-sized airports joined the network and aggregated around the nearest hub airports with a central radiation structure. This gave rise to a rapid increase in the degree of hub nodes, and the entire civil aviation network entered its peak operation period. At noon, routes became denser, and there was a wider node distribution with the highest node degree value compared to other time periods. Although the air transportation network at 18:00 still contained some high-degree nodes, the number of such nodes gradually decreased due to the absence of takeoffs or landings at some small airports, leading to the disconnection of those airports from hub airports. At 24:00, the node distribution of the network was sparse with a small number of travelers, and most departures were from economically developed areas.  
The six airports with the highest average node degree value in the air transportation network were Xi'an Xianyang, Beijing Capital, Chengdu Shuangliu, Chongqing Jiangbei, Shanghai Pudong, and Guangzhou Baiyun Airport, with values of 166, 165, 158, 154, 150, and 149, respectively. The high node degree of these airports indicated that they were the most closely connected airports in the air transportation network.
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
In this case, data from the Train Control and Monitoring System (TCMS) was used to detect defects, and they evaluated the normalities of vehicle equipment using a neural network with Long Short Term Memory (LSTM) [14]. [PERSON] et al. proposed a method to detect wheel flat defects by measuring the vertical acceleration on the axle box, and this method was verified by both simulation and test. At this time, the algorithm that the wheel flat index proposed was able to detect small flats and estimate their severity [15]. [PERSON] et al. proposed a method to detect wheel flats using measurement by ultrasound. At this time, this paper differs from other approaches, using ultrasonic pulses (Rayleigh waves) being sent over a measuring rail; this method provides the loss of material and the length of the flat originally formed by abrasion [16]. In this paper, firstly, two signal processing methods and CNN (Convolutional Neural Networks) among the deep learning algorithms were used to study the anomaly detection method for wheel flat parts in CBM. At this time, anomaly detection was applied by classifying the normality and anomaly according to the safety standards of urban railway vehicles. Finally, through the anomaly detection method, the data with signal processing and the data without signal processing were compared using deep learning results. The deep learning results were confirmed through accuracy and ROC curve, together with the AUC value being confirmed through ROC curve.
###### Abstract  
It is a critical issue to reduce the enormous amount of data in the processing, storage and transmission of a hologram in digital format. In photograph compression, the JPEG standard is commonly supported by almost every system and device. It will be favorable if JPEG standard is applicable to hologram compression, with advantages of universal compatibility. However, the reconstructed image from a JPEG compressed hologram suffers from severe quality degradation since some high frequency features in the hologram will be lost during the compression process. In this work, we employ a deep convolutional neural network to reduce the artifacts in a JPEG compressed hologram. Simulation and experimental results reveal that our proposed \"JPEG + deep learning\" hologram compression scheme can achieve satisfactory reconstruction results for a computer-generated phase-only hologram after compression.  
Keywords: hologram; holography; phase-only; compression; deep learning; JPEG; convolutional neural network +
Footnote †: journal: Medical Imaging
## 3 Case Study: SmartLab Concept and 3D Printing Environment
### Main Functions of SmartLab Concept  
The goal of the SmartLab concept was to create an environment where an idea or need could be manufactured into a product. Figure 3 presents the SmartLab concept in more detail, with its main functions.  
As Figure 3 illustrates, the SmartLab concept consists of two parts, the design and the manufacturing phase; hence, the principle, from idea to product. The need or product idea is developed in the design phase, in which the Lapland UAS mechanical engineering students perform engineering design work to produce the necessary product data (e.g., CAD files and documents such as instructions, etc.). These assignments can either be learning projects or even assignments from the partners of outside customers. The output is the product data that is uploaded to the product data management system (PDM), enabling the control of the data (e.g., distribution and version control). The PDM system is part of a larger digital product lifecycle management system (PLM) called Teamcenter software [46]. Teamcenter connects the product lifecycle with the SmartLab processes by providing an interface for controlling product development, manufacturing, and quality, to mention only a few [46]. The manufacturing part consists of digital and traditional manufacturing systems and AM. Digital manufacturing consists of a Festo Cyber-Physical (CP) Lab environment [47] with automated modules such as turning, milling, robotics, camera inspection, pick-by-light assembly, the pressing station, and storage. The system is a compact learning system where the I4.0 principles can be practiced with different learning scenarios. The product or part is transferred between the modules on top of a pallet via conveyor belts. The system uses radio frequency identification (RFID) to recognize the pallet in the different stages of the process. The system enables the assembly of a product  
Figure 3: The SmartLab concept functions.  
via a manual pick-by-light assembly station. The system can be expanded with different application modules [48]. Conventional manufacturing consists of welding with multiple methods, an NC plasma cutter, an NC controlled sheet metal bending machine, and other tools and equipment for manual manufacturing and finishing. The AM department consists of polymer printing technologies, enabling the direct manufacturing of products and prototypes. In addition, AM provides parts for the products manufactured and assembled in the digital/traditional manufacturing departments. This enables hybrid products in which all these three manufacturing alternatives meet. Digital and traditional manufacturing are monitored through data (e.g., production data such as lead time, efficiency, and energy consumption). The whole is controlled and monitored through the Teamcenter interface, enabling enterprise resource planning (ERP) activities. From the education perspective, the whole provides novel scenarios for learning modern manufacturing.
# Water Quality Evaluation and Prediction Based on a Combined Model
## 4 Results and Discussion
### Water Quality Prediction Model
#### 4.2.4 Water Quality Evaluation in 2016  
The results in presented in Section 4.1.3 prove that the trained T-S fuzzy neural network described in this paper has excellent ability in the determination of water quality grade. On this basis, the water quality index data of two sections predicted for 2016 were substituted for water quality evaluation. The results are shown in Tables 10 and 11 and Figures 22 and 23.  
Figure 21: Combined model compared to other models.  
Figure 20: Comparison of model prediction of NH\({}_{3}\)-N.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Week** & \begin{tabular}{c} **Evaluation** \\ **Results** \\ \end{tabular} & \begin{tabular}{c} **True Water** \\ **Quality Rating** \\ \end{tabular} & **Week** & \begin{tabular}{c} **Evaluation** \\ **Results** \\ \end{tabular} & \begin{tabular}{c} **True Water** \\ **Quality Rating** \\ \end{tabular} & **Week** & \begin{tabular}{c} **Evaluation** \\ **Results** \\ \end{tabular} & \begin{tabular}{c} **True Water** \\ **Quality Rating** \\ \end{tabular} & **Week** & \
\begin{tabular}{c} **Evaluation** \\ **Results** \\ \end{tabular} &
\begin{tabular}{c} **True Water** \\ **Results** \\ \end{tabular} \\ \hline
1 & II & II & II & 14 & II & II & 27 & II & II & II & 40 & II & III \\
2 & II & II & 15 & II & II & 28 & II & II & 41 & II & II \\
3 & II & II & 16 & II & II & 29 & III & III & 4  
Water quality evaluation using the T-S fuzzy neural network trained as described in this paper revealed that among the 52 weeks of 2016, water quality was misjudged in the Lanzhou Xincheng Bridge section of the Yellow River Basin and the Longdong section of Panzhihua, Sichuan, the Yangtze River Basin for 7 and 10 weeks, respectively, with total correct judgment rates of 86.54% and 80.77%, respectively. Because the input data contain errors, this result is acceptable, verifying the reliability of the water quality evaluation model trained as described in this paper.