esa-sceva/satcom-chunk-collection huggingface dataset:
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## 1 Introduction
### HR or HRV?  
HR is considered as the simple count of heart beats per minute, whereas HRV refers to the variation in time intervals between consecutive R-peaks in the ECG signal. The body of evidence from physiological studies supports the validity of HR as a diagnostic and prognostic tool in cardiology. However, HR's clinical utility may be influenced by factors such as age and comorbidities, time of day, measurement duration, physical activity and medications; thus, standardized protocols are essential in order to improve reproducibility and comparability.  
HR can be used in different subject conditions; for example, at rest, an elevated resting heart rate is associated with increased cardiac morbidity and mortality, independent of other risk factors. It is considered a marker of sympathetic dominance, low cardiorespiratory fitness, or systemic inflammation [21]. During physical exercise, an abnormal HR response (blunted rise or delayed recovery) is associated with autonomic dysfunction, poor prognosis in heart failure and an increased risk of sudden cardiac death [22]. Moreover, circadian HR patterns can be studied and their changes can serve as early indicators of autonomic imbalance, athletes' overtraining, or the onset of sepsis or systemic inflammation in critical care patients [23]. Nevertheless, HR has some limitations, as its context-dependence is influenced by a wide range of non-pathological factors (e.g., temperature, emotional state, caffeine consumption, hydration) (low-specificity). A high HR may be pathological in one setting (e.g., resting tachycardia) and physiological in another (e.g., during exercise). In conclusion, HR provides a gross marker of cardiac workload and autonomic tone, but it is less sensitive to subtle regulatory changes than HRV, which captures beat-to-beat dynamics and offers a more detailed view of autonomic control mechanisms [24]. For these reasons, we chose HRV as crucial indicator of cardiac health and fatigue, since it refers to the variation in time intervals between consecutive heartbeats (RR time series) [25].  
HRV parameters can be easily recorded using a huge variety of wearable devices [8; 9; 20]. Therefore, the main research focus was to collect, present and discuss the existing literature on the effectiveness of AI-based methods for processing RR time series as a predictive biomarker for cardiac fatigue compared to commonly used questionnaires for this outcome in an adult population.
# Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices
## 5 Conclusions  
In this paper, it is assumed that each sensor in a WSN is equipped with a BSS. The received SNR at the desired AP is investigated to bolster the validity of the motivation in this paper and presented the optimization problem of (4). An algorithm is also proposed to solve this problem in an efficient manner. The simulation and experimental results showed that with the proper combination of the given parameters, our algorithm can increase the received SNR substantially with low computing resources and energy consumption. In future work, a reinforcement learning-based beam-switching algorithm will be designed to autonomously adjust to the complex and unpredictable propagation channel.  
[PERSON] designed the entire core architecture and performed the numerical analysis; [PERSON] was the corresponding author. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the BK21 FOUR project funded by the Ministry of Education, Korea (4199990113966). This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF2019R1A2C2005099), and Ministry of Education (NRF2018R1A6A1A03025109).  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; and in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} WSN & Wireless Sensor Network \\ AP & Access Point \\ CB & Collaborative Beamforming \\ BSS & Beam-Switching Structures \\ SNR & Signal-to-Noise Ratio \\ GS & Greedy Search \\ RF-MEMS & Radio-Frequency Micro-Electromechanical Systems \\ GPS & Global Positioning System \\ MCU & Micro Controller Unit \\ UART & Universal Asynchronous Receiver Transmitter \\ \end{tabular}  
Figure 12: (a) The supplied current versus \(\mathfrak{M}\) when the proposed algorithm is executed (execution mode) or not (normal mode). (b) The dissipated current (=supplied current\(\times\)execution time) versus \(\mathfrak{M}\).
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 3 Results
### Inducing Effect of Carbon Source Inducers on Holocellulae Production  
The effect of different LC wastes and carbon components on inducing cellulase and xylanase production by the _Streptomyces_ strain was examined and is summarized in Table 1. Cellulase production was induced by three of the carbon sources; CMC, arabin, and mannose with activities of 0.070 \(\pm\) 0.01, 0.061 \(\pm\) 0.01, and 1.33 \(\pm\) 0.01 U/mL respectively. In addition, wheat straw slightly induced cellulase production, with an activity of 0.01 \(\pm\) 0. On the other hand, xylanase production was induced by the entire carbon sources, and wheat straw had the highest inducing effect, followed by beechwood xylan, galactose, and xylose with activities of 14.86 \(\pm\) 0.56, 14.15 \(\pm\) 0.07, 10.35 \(\pm\) 0.65, and 8.02 \(\pm\) 0.65 U/mL, respectively. Whereas wheat straw induced the highest xylanase production by the _Streptomyces_ strain with slight cellulase activity, there was no significant difference between xylanase activity detected in the wheat straw and xylan cultures, although no cellulase activity was detected in culture of the latter. Moreover, CMC, arabin, mannose, and wheat straw induced production of both cellulase and xylanase as indicated by cellulase and xylanase activities in the culture supernatant with statistical significance (\(p<0.05\)) between xylanase and cellulase production (Table 1). In addition, low xylanase activity of 0.17 \(\pm\) 0.01 U/mL was detected in the control basal medium which had no mono- or polysaccharide carbon source; however, no cellulase activity was detected.
# Emergency Planning for UAV-Controlled Crowd Evacuations
## 4 Results and Discussion
### Mortality  
The second performance measure was percentage mortality, which examined the efficiency of the suggested evacuation approach. In Figure 4, the percentage mortality is plotted for different UAV locations as the number of pedestrians increases from 450 to 7200 for opposite and adjacent exit doors. When the evacuation process was run without utilizing UAVs, the percentage mortality was worst, especially with adjacent exit doors: approximately 30% when CD = 0.5, and rising to above 60% when CD = 2. For exit doors placed opposite each other, the mortality percentage was better with low crowd density, and increasing considerably, to more than 60%, when CD = 2.  
Figure 3: Average evacuation time for partial guidance model.  
Using UAVs in all three different positions (middle, near exit, and random) to guide pedestrians to the nearest exit show low mortality percentage when compared with a None approach. With opposite exit doors, the mortality percentage was around 0%, even when the hall is full (CD = 1), rising to around 20% with adjacent exit doors regardless of the position of UAV. Having a crowded hall with CD = 2, the mortality percentage was around 35%, but it increased to more than 40% when the exit doors were adjacent.  
Overall, these results clearly show that adopting UAVs in the evacuation process leads to a more efficient evacuation process compared with using none, as it reduces AET and mortality regardless of the location of UAVs and exit doors. The two placements of exit doors gave similar results in terms of AET, but the mortality rate decreased considerably when the exit doors were opposite each other for all three UAV positions.  
Figure 4: Mortality for partial guidance model.
# A New Orbiting Deployable System for Small Satellite Observations for Ecology and Earth Observation
## 1 Introduction
In Section 3, environmental studies, focused on the potential ecological hazards to the habitat, are described: for each application and observation strategy, the most favorable characteristics of the satellite are defined. In Section 4, we introduce the data fusion techniques as a powerful tool to combine multisensory data to better analyze a given topic. In Section 5, we illustrate the radiometric analysis carried out to evaluate the performances of the deployable telescope. Finally, in Section 6, conclusions and future perspectives are outlined.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 4 Performance Analysis
### Protocol Specification
#### 4.2.2 Distributed Beaconless Dissemination  
Distributed beaconless dissemination (DBD) protocol performs receiver-based dissemination and is specifically built to transfer video data in highway scenarios [11]. The scheme does not have a forwarding zone criterion, since the transmission range of vehicles is usually greater than the width of the highway roads and, hence, they are able to notify their data packet transmission to other vehicles. However, the vehicles that are traveling in opposite direction with respect to the sender discard the received data packet and do not take part in the contention. For the waiting time criterion, a node after receiving data packet sets the waiting time solely based on its geographic progress towards the destination, which is as follows:  
\[\gamma=\gamma_{geo}\times\Gamma, \tag{25}\]  
where \(\gamma_{geo}\) is calculated according to Equation (2) and \(\Gamma\) is set to 50 ms which is the same value as used in ILBFC and is indicated in Table 3. For the winner relay management, the relay node stays in the \"winner\" state until its distance from the sender (\(d\)) is such that \(0.75\times r\leq d\leq r\). The rest of the features (redundant packet elimination and loop protection) are similar to ILBFC. Performance of DBD is analyzed to test the compatibility of a receiver-based routing scheme developed for highways in an urban scenario.
## 1 Introduction  
In recent years, with the rapid development of drone technology and the reduction in manufacturing costs, drones have been widely applied across various industries. However, the high mobility of UAVs leads to a significant variation in the size of objects within images and potential occlusion between objects, resulting in a large number of small objects--those with fewer pixels. The existing object detection algorithms often focus less on small objects during cross-scale detection, leading to feature loss during the convolution process. Therefore, maintaining attention on small objects while accurately detecting large ones is a critical challenge. At the same time, in order to embed the model into the drone platform, it is necessary to control the model parameters within a certain range and maintain a relative balance with the detection accuracy.  
Most current research methods encounter accuracy issues when dealing with UAV aerial imagery, particularly in scenarios with a high proportion of small objects. To improve the focus on small objects while maintaining high accuracy for large object detection, this study proposes a Recursive Pyramid Structure-based YOLO (RPS-YOLO) algorithm, which incorporates a recursive feature pyramid structure. The specific improvements are reflected in the following three aspects:  
* The YOLOv8 network is enhanced with a recursive feature pyramid (RFP) structure to perform two stages of \"processing\" on the image--i.e., two rounds of feature extraction. During the first stage, a single downsampling operation is reduced, enabling the model to place greater emphasis on small object detection in multi-scale scenarios.
* In order to prevent feature loss during the convolution process, we designed a new attention mechanism that can supplement salient information with detailed information from both spatial and channel perspectives, thus enabling feature recovery during the convolution process.
* A new bounding box loss function is proposed by incorporating angle loss, accelerating the regression of predicted bounding boxes.  
The experimental outcomes on both the UAVDT and VisDrone-DET2021 datasets indicate that, when compared with other leading lightweight networks, the RPS-YOLO model demonstrates enhanced detection accuracy while maintaining an effective balance between computational efficiency and the number of parameters.  
The structure of the remaining sections of this paper is outlined as follows: Section 2 provides an overview of related work in drone detection and introduces YOLOv8 and the recursive feature pyramid structure. Section 3 introduces our proposed model and the structure and application of each module. Section 4 presents the results of the experiments, Section 5 discusses them, and Section 6 concludes this paper.
# Single-Cell Patterning Based on Immunocapture and a Surface Modified Substrate
## 2 Preparation of the Substrate for Single Cell Patterning  
To realize cell separation and patterning specifically, we studied a biocompatible template using biotin-BSA (Bovine Serum Albumin) and streptavidin. Based on the immune reactions between the biotinylated antibodies and the antigens on the surface of the cell's membrane, HL-60 cells were successfully patterned on the glass substrate.
# Partitioning Evapotranspirationiration in a Cotton Field under
## 2 Materials and Methods
### EC System
#### 2.4.3 Calculate GPP  
NEE was partitioned into \(GPP\) and Ecosystem Respiration (\(R_{eco}\)).  
\[GPP=R_{eco}-NEE \tag{1}\]  
Based on the empirical index model proposed by [PERSON], [PERSON] and [PERSON] established the Lloyd and Taylor Respiratory Model [40], which provided a more accurate unbiased estimate of \(R_{eco}\):  
\[R_{eco}=a\times e^{(b\times T_{air})} \tag{2}\]  
where \(a\) and \(b\) are the parameters for exponential fitting of \(NEE\) and \(T_{air}\), and \(T_{air}\) is the temperature, \({}^{\circ}\)C.  
Because crops do not undergo photosynthesis at night, the \(R_{eco}\) of the ecosystem at night is equal to \(NEE\) in value. In this study, the exponential function relationship between \(NEE\) and \(T_{air}\) at night was used to estimate the \(R_{eco}\) of cotton at daytime [41]. The night-fitting results for two years are shown in Figure 2.  
According to [PERSON]'s radiation efficiency theory, the missing \(GPP\) can be simulated by the nonrectangular hyperbolic model [42], and the equation is as follows:  
\[GPP=\frac{1}{2\theta}\bigg{(}\alpha\times PAR+A_{max}-\sqrt{(\alpha\times PAR +A_{max})-4\alpha\theta A_{max}\times PAR}\bigg{)} \tag{3}\]  
where \(\theta\) is the curvature parameter of the light response curve (\(0\leq\theta\leq 1\)); \(\alpha\) is the apparent quantum efficiency; \(PAR\) is photosynthetic active radiation, \(\mu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\); \(A_{max}\) is the maximum net photosynthetic rate of the ecosystem, \(\mu\)mol\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\).
###### Abstract  
Background:The PATHINDER-CHD Registry is a prospective, multicenter, non-interventional registry across tertiary care centers in Germany. The aim is to analyze real-world data on adults with congenital heart defects (ACHD) or hereditary connective tissue disorders who have manifest heart failure (HF), a history of HF, or are at significant risk of developing HF. This analysis investigates the prevalence and clinical impact of overweight and obesity in this unique population. **Methods:** As of 1 st February, 2025, a total of 1490 ACHD had been enrolled. The mean age was 39.4 \(\pm\) 12.4 years, and 47.9% were female. Patients were categorized according to [PERSON]'s functional class and the Munich Heart Failure Classification for Congenital Heart Disease (MUC-HF-Class). **Results:** The most common congenital heart disease (CHD) in this cohort was Tetralogy of Fallot, transposition of the great arteries, and congenital aortic valve disease. Marfan syndrome was the most common hereditary connective tissue disease. Of the patients, 46.1% were classified as overweight (32.8%) or obese (13.3%), while 4.8% were underweight. The highest prevalence of overweight (47.1%) was observed among patients who had undergonepalliative surgery, whereas untreated patients showed the highest proportion of normal weight (57.2%). Cyanotic patients were predominantly of normal weight. Patients with univentricular circulation exhibited significantly lower rates of overweight and obesity (35%; \(p\) = 0.001). Overweight and obesity were statistically significantly associated with arterial hypertension, diabetes mellitus, and sleep apnea (all \(p\) < 0.001). High BMI was linked to increased use of HF-specific medications, including SGLT2 inhibitors (\(p\) = 0.040), diuretics (\(p\) = 0.014), and angiotensin receptor blockers (\(p\) = 0.005). **Conclusions:** The data highlight the clinical relevance of overweight and obesity in ACHD with HF, emphasizing the need for individualized prevention and treatment strategies. The registry serves as a critical foundation for the optimization of long-term care in this population.  
**Keywords:** congenital heart disease; heart failure; obesity; overweight; real-world data; registry
# Determinants of Memory Encoding of Altruistic Messages: M-Delphi and F-DEMATEL Approach
## 2 Literature Review
### Determinants of Successful Communication by Non-Profit Organizations  
Apart from performing the essential functions for which they exist, organizations are also concerned with communicating their values to their target audiences [18]. Prior research has focused on examining the drivers of organizations' successful communication of their functions. However, most of these studies have not examined the communication processes in the context of digital mass media, particularly television. The drivers identified by the prior studies can be placed into several broad categories. One category is network connectivity. Non-profit organizations that work with other stakeholders to promote essential issues are more successful in communication [19; 29]. The support provided by the network organization increases the receptiveness of the audience to the messages being communicated. Other scholars have studied the factors that shape effective communication among non-profit organizations working on refugee welfare from a network perspective [5].  
They established that the network position of a non-profit organization ensures effective communication of refugee welfare issues. [PERSON] et al., 2015 [7], examining non-profit organizations' communication of climate policy issues, found that frequent meetings between organizations and their audiences lead to effective information dissemination. Clarity is another determinant of effective communication by non-profit organizations. It reduces confusion about the message and clarifies the point [30]. Extensive discussion of the primary subject of communication leads to effective communication. [PERSON] [6] established that explaining climate change's impacts, actions, and efficacy guarantees effective communication of climate issues. Thus, a holistic explanation of the specifics of the issue being communicated enhances the audience's understanding. Other studies showed that considering audience characteristics, such as culture, in the communication process enhances communication effectiveness. Non-profit organizations operate in multiple areas with diverse cultures [31]. Failure to consider the audience's culture results in psychological and social conflict between the organization and the audience. As such, the message is resisted, rendering the communication a failure [32].  
Because non-profit organizations exist to drive social change and to help disadvantaged groups, altruism lies at the core of their functions [33]. Altruism drives individuals and entities to help others. If it is communicated effectively, it draws an imprint on the memory of the audience [34]. It is, therefore, crucial to understand how altruism can be communicated effectively so that the communications are encoded into the audience's memory.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 4 Results and Discussion
### IMU Measurements as a Function of Sea Conditions  
Through wave buoy data, sampled every 30 min, we identify two representative samples of calm and rough sea conditions to illustrate different aspects of the buoy's motion. For observing the temporal evolution of the IMU data, we extract the last 30 s of the 30-min samples for visualization. The sea conditions are selected using the mean wave height \(\overline{H_{w}}\) and the mean period \(\overline{P_{w}}\) of all waves. As wave height and period are highly correlated (Figure 13b), calm conditions are represented by low values (\(\overline{H_{w}}=28\) cm, \(\overline{P_{w}}=3.7\) s) and rough conditions by high values (\(\overline{H_{w}}=142\) cm, \(\overline{P_{w}}=6.8\) s). The measurements of the six degrees of freedom of the inertial station on the picked 30-s samples are presented in the Figure 3.  
Figure 3: Measurements of the six degrees of freedom from the IMU on board of the WINDSEA_02 buoy. Rotational angles are presented for calm; and (**a**) rough; (**c**) sea conditions. Translational velocities are presented for calm; and (**b**) rough; (**d**) sea conditions.  
We first discuss the rotational angle measurements (Figure 3a,c). In calm seas (a), the buoy performs a flapping motion introducing tilt movements (roll and pitch) with an amplitude of a few degrees and a period of the order of 4 to 5 s. At the same time, the buoy rotates, introducing yaw measurements with a high amplitude of about 30 degrees and a period of 30 s. In rough seas (c), the buoy's flapping is intensified as the waves are more intense. The tilt motions (roll and pitch) exhibit an amplitude of 5 to 10 degrees with a period of about 4 to 5 s. The yaw behaves very differently in rough seas compared to calm seas, it is strongly affected by the waves and its evolution is similar to the tilt. Its amplitude is of the order of 10 degrees and its period of about 4 to 5 s.  
For the translational velocity measurements (Figure 3b,d), in calm seas (b), the surge, sway and heave have a roughly similar amplitude of about 0.1 m/s. The period of the surge and sway is of the same order as the tilt flap, about 4 to 5 s. The heave has a shorter period because it reacts to every small change in sea surface height. In rough seas (d), the three translational degrees of freedom have a similar amplitude of the order of 0.5 m/s, we note that the heave tends to have larger peak values than the surge and sway. The periods of the translational velocities are still about 4 to 5 s.  
We can now use these observations to estimate the theoretical error induced by a sinusoidal movement of the same magnitude. We note that these measurements are specific to the WINDSEA_02 buoy used in this study. Its dimensions, shape, anchorage and means of supply (turbine in the wave tank) have a strong impact on its dynamic behavior.