esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 5 Experiment and Analysis of Ship's Wake Dual-Mode Fusion Detection
### Laser APD Detection Experiment  
The gain in the APD receiving circuit is set to 22 dB, and the current of molybdenum wire plate is set to 0.35 A. The data of the whole process from generation to disappearance of bubbles are collected and analyzed.  
In Figure 15, the x-axis represents the number of sampling points, one point corresponds to 0.4 ns, and the y-axis represents the amplitude of the received signal. It shows the single-cycle image of the original signal and the single-cycle image after integral processing. It can be observed from Figure 15a that the amplitude of the echo signal is stable at about 0.3 V and the pulse width is about 16 ns when the bubble generating device is not turned on. When the bubble generating device is turned on, the amplitude of the echo signal continuously jumps between 0.5 and 0.8 V, the pulse width continuously jumps between 18 and 22 ns, and the detection SBR can reach up to 2.67. It is observed from Figure 15b that after the signal is processed by the integral circuit, the amplitude of the echo signal is about 7.5 V-s when the bubble generating device is not turned on. When the bubble generating device is turned on, the amplitude of the echo signal jumps violently between 13 and 23 V-s, and the maximum detection SBR can reach 3.26. After integral processing, the SBR is increased by about 1.22 times.  
After transmitting all the collected signals to the computer, the envelope detection and mean filtering processing are performed to obtain the echo signal image, as shown in Figure 16.  
Figure 16: Multi-period echo signal image.  
Figure 15: Comparison of background signals between bubbles and water bodies: (**a**) original waveform; (**b**) waveform after integral output.  
It can be observed from Figure 16 that when the bubble generating device is not opened, the echo signal is very stable as a whole and remains floating around 0.29 V; when the bubble generating device is turned on, the amplitude of the echo signal will rise rapidly to 0.5-0.8 V, up to about 0.88 V, and the echo signal has obvious violent jitter. The received energy oscillation is mainly related to the number of bubbles generated by molybdenum wire electrolysis. When the bubble generating device is closed, the signal will quickly return to a stable state, and the detection SBR can reach up to 3.26. After signal processing, the signal change trend in the detection system tracking the ship's wake can be seen more intuitively, but the existence of false wake cannot be effectively judged.
## 3 Results
### Results of Individual Studies
They constructed and used a dataset of 817 patients with severe TBI diagnoses. The patients of the dataset had a median age of 30; 85% were male, and 15% were female. The patients constituting the dataset had a median GCS eye score and a GCS motor score of 7 and 5, respectively. Patient ICP, MAP, _end-tidal_ CO2 (ETco2), _oxygen saturation and brain tissue oxygenation_ (PbtO2) levels were recorded every 36 s. The study's dataset was collected over a period of 24 years, between 1989 and 2000 and between 2006 and 2013. The dataset was divided into three cohorts: (i) a _\"study cohort\"_ (collected between 1989 and 1996), used to develop candidatepredictive models; (ii) a _\"selection cohort\"_ (collected between 1996 and 2000), used to evaluate the performance of the candidate predictive models; (iii) a _\"validation cohort\"_ (collected between 2006 and 2013), used to test the performance of the best-performing models. A 30 min window of physiological data was used along with the time since the last crisis to predict impending crisis events, with prediction horizons ranging from 15 to 360 min. [PERSON] et al. suggested that a shorter prediction horizon of 30 min was clinically more appropriate. The predictive model that returned the maximal AUC-ROC reported was a five-feature logistic regression using ICP measurements and the time since the last crisis, which was able to predict an ICP crisis with a 30 min prediction horizon, with an AUC-ROC of 0.83 on the _\"validation cohort\"_. The authors reported that the two physiological signals most associated with precursors to ICP crises were ICP and change in ICP. There is significant methodological heterogeneity between the three studies, which renders a meta-analysis inappropriate. The studies presented three different solutions for predicting ICP crises. There was significant disparity in the sizes of the datasets used to train and test the models described in the studies. Although the main diagnosis in the [PERSON] et al. study was TBI (33.9%), the remaining diagnoses were not [_intracerebral haemorrhage_, _21.0%_; _stroke_, _16.1%_; _MISC_, _13.5%_; _subarachnoid haemorrhage_, _11.1%_; _tumour_, _4.4%_], unlike the two other studies.
# Automated Enemy Avoidance of Unmanned Aerial Vehicles Based on Reinforcement Learning
## 6 Implementation and Results
### Policy Comparison Results  
To test the effectiveness of the policy learned with the Sarsa based framework, another set of experiments are carried on the same HITL simulation platform. For comparison, the fixed-rule policy and the random policy are also tested with the same experimental settings. Each policy is run for 1200 episodes, and the results are summarized as follows.  
Firstly, the numbers of steps taken in each episode for using different policies are compared in Figure 10. The figure is drawn with data filtered with a window of 100 episodes. It can be seen that the number of steps are stable in the testing process, no matter which policy is used. However, it takes different numbers of steps to end an episode when different policies are used. Overall, the random policy takes the least number of steps, while the policy learned with the Sarsa based framework takes the most. Though more number of steps means more cost, it is also a reflection of higher possibility to reach the goal point and better policy to avoid the enemy UAV. Such understanding is from the following two aspects. First, the episode ending for reaching the goal point takes more steps than the episode ending before reaching the goal point for other reasons, because of the longer distance. On the other hand, it needs more steps to fly away to avoid the collision with the enemy UAV than to fly straightly towards the goal. To more accurately show the performance of different policies, more results are presented below.
## 3 Underlying Terminology and Concepts
### Traceability and Uncertainty
At the moment, an attempt of providing per-pixel uncertainty at Level 1, with a qualitative assessment of pixel correlation structures in the spatial, temporal and spectral dimensions, is the S2 Radiometric Uncertainty Tool (RUT) [43; 44]. The S2 RUT is currently provided as part of the Sentinels Application Platform (SNAP) toolbox [45] to be run on the user side. The integration of S2 RUT at an operational level will be investigated over the coming years. A similar exercise is currently on-going in the frame of S3 OLCI and SLSTR MPC [46].
## 1 Introduction
The vertical reference frame, the North American Vertical Datum of 1988 (NAVD 88), was realized based on a nationwide passive network determined using differential geodetic leveling. With recent advances, such as precise hybrid geoids (for example, GEOID12A), the costly, time-consuming leveling process can be replaced by GNSS-derived heights, though the extent depends on the specific application. Given the recent advancement in real-time technology, new specifications and guidelines are needed for reliable determination of GNSS-derived heights, including consideration of data collection methods and other factors. While most of the studies on the performance of RTK/RTN deal with the accuracy and/or precision of the final coordinates in a controlled environment [2; 3; 7], we tested the applicability of RTN for height determination using Continuously Operating Reference Station (CORS) networks under realistic field conditions. Since the performance of the RTN is related to many factors, we also focused on the effect of different types of antennas, baseline lengths, solution types, and data collection method of RTN.
## 2 Materials and Methods
### Optimization and Test Strategy
#### 2.6.1 Materials  
This study uses a proprietary clinical ECG database (Schiller Medical, Wissembourg, France) provided for research purposes and for the retrospective investigation of cardiac arrest rhythms. The database was collected by Schiller AEDs (FRED EASY in 2011 and DEFIGARD TOUCH 7 in 2017) used during OHCA interventions by the Paris Fire Brigade (BSPP, Brigade des Sapeurs-Pompiers de Paris). The reanimation protocol applied CPR with a 30:2 compression/ventilation ratio and chest compression rate of 100-120 min\({}^{-1}\), paused every 2 min for a standard AED rhythm analysis, following the European Research Council Adult Basic Life Support guidelines [82; 83]. The database was anonymized before the study to ensure the medical confidentiality, without information about the patient identity, epidemiological data, diagnosis, drug therapy, or outcome.  
Single-lead ECG signals were acquired via defibrillation pads placed in antero-apical position on the patient thorax. These signals were filtered by the AED hardware within a 1-30 Hz bandwidth to suppress baseline drift and high-frequency noise, then sampled at 500 Hz. To reduce computational load, the ECG signals were downsampled to 125 Hz to match the DNN input format shown in Figure 2. For each OHCA intervention, 10 s ECG episodes collected during standard AED rhythm analysis were extracted and annotated by consensus among three experts, following the American Heart Association (AHA) rhythm annotation guidelines [84]:  
Figure 5: Optimization and test workflow of the DNN models in the target platforms. DynQ: dynamic range quantization; IntQ: integer quantization; Full-IntQ: full-integer quantization; RDS: representative dataset; MAE(pVF): mean absolute error of the probability for detection of ventricular fibrillation.  
* Coarse ventricular fibrillation (VF) with peak-to-peak ECG amplitude > 200 \(\upmu\)V;
* Normal sinus rhythm (NSR) with visible P-QRS-T waves and heart rate 40-100 bpm;
* Other non-shockable rhythm (ONR), including atrial fibrillation/flutter, sinus bradycardia, supra-ventricular tachycardia, premature ventricular contractions, heart blocks, etc.;
* Asystole (ASYS) with low-amplitude ECG, having peak-to-peak signal deflection \(\leq\) 100 \(\upmu\)V for more than 4 s.  
Following the AHA statement [84], the annotations were categorized into shockable (VF) and non-shockable (NSR, ONR, ASYS) rhythms. The data were divided patient-wise into independent datasets for calibration, validation, and testing, with the number of 10 s ECG episodes defined in Table 2. Rhyth distribution within and between datasets was not controlled but reflected the content of the OHCA interventions recorded during non-overlapping periods and predefined before the study--calibration dataset (733 patients in 2011), validation dataset (1604 patients in 2017), and test dataset (1312 patients in 2017). This approach assures generalizing out-of-distribution [85], which is critical and challenging in real applications, especially in the case of critical medicine.
## 3 Methodology and Materials
### Methodology
#### 3.1.2 OD Connection  
To evaluate the strength of linkages between Jurong and its neighboring cities, we developed three metrics based on origin-destination (OD) flow analysis: total flow volume, relative intensity, and flow balance.  
(1) Total flow volume  
The total flow volume measures bidirectional human mobility between cities (Figure 3). For two cities \(A_{i}\) and \(B_{i}\), the following equation is used:  
\[V_{A_{i}B_{i}}=V_{B_{i}A_{i}}=T_{A_{i}B_{i}}+T_{B_{i}A_{i}} \tag{1}\]  
where  
\(T_{A_{i}B_{i}}\) denotes the daily flow from origin \(A_{i}\) (primary residence) to destination \(B_{i}\);  
\(T_{B_{i}A_{i}}\) denotes the daily flow from origin \(B_{i}\) (primary residence) to destination \(A_{i}\).  
(2) Relative Intensity  
This metric normalizes the flow volume relative to the population scale to assess the linkage strength:  
\[Q_{A_{i}B_{i}}=\frac{V_{A_{i}B_{i}}/P_{A_{i}}}{V_{B_{i}}/P_{total}} \tag{2}\]  
where  
\(V_{A_{i}B_{i}}\) denotes the total bidirectional flow between \(A_{i}\) and \(B_{i}\);  
\(P_{A_{i}}\) denotes the permanent population of \(A_{i}\);  
\(V_{B_{i}}\) denotes the total bidirectional flows between \(B_{i}\) and all A connected cities;  
\(P_{total}\) denotes the combined population of all A cities.  
(3) Flow Balance  
This index quantifies the flow symmetry between cities:  
\[X_{a}=\frac{T_{B_{i}A_{i}}-T_{A_{i}B_{i}}}{T_{B_{i}A_{i}}+T_{A_{i}B_{i}}} \tag{3}\]
## 2 Materials and Methods
### Boundary Conditions  
The three primary BCs in the TMCE models are reflecting, escape, and extended BCs. A reflecting BC describes a perfectly reflecting situation wherein all the photon energy is recycled to the computational domain via reflection (as shown in Figure 4a). This approach considerably accelerates computation and yields acceptable results for wide-beam irradiation, such that its diameter is significantly larger than the computational domain.  
Figure 4: Three boundary conditions (BCs) for laser transportation. The computational domain is marked by a black rectangle. (**a**) Reflecting BC, (**b**) Escape BC and (**c**) Extended BC. The red dashed line represents the terminated photon.  
Figure 3: Diagram of photon–tetrahedron interaction. Planes ABC and BCD satisfy the judgment. _t_\({}_{BCD}\) represents the photon propagation distance to plane BCD, and _N_\({}_{BCD}\) denotes the inward unit normal vector of plane BCD.  
In the escape BC, the probability of photon reflection or refraction is determined by the Fresnel coefficients when hitting a boundary. The photon is terminated when it exits the boundary via refraction, as shown in Figure 4b.  
In the extended BC, photons are launched in accordance with the profile of the laser beam intensity and propagate in laterally infinite tissue layers until they deposit all their energy or escape into the air (Figure 4c). In this approach, photons exiting the finely discretized computational domain are propagated further in semi-infinite tissue layers until they return to the computational domain, escape into the air, or lose all their energy.  
In this study, a small-diameter laser beam is adopted to simulate light transport in skin with complex vessel structures. Therefore, reflecting BC is unsuitable. The cuboid domain has six boundaries (Figure 4). Escape BC is applied to the top surface, which is a real physical boundary connected to the air. The principle of extended BC is applied to the bottom and the four side boundaries.
# The Carbon Sequestration Potential of Degraded Agricultural Land in the Amhara Region of Ethiopia
###### Abstract  
Forests are a key player within the global carbon cycle and reforestation is an important climate change mitigation mechanism. In this study, we identify potentially suitable areas for reforestation to assess the carbon sequestration potential in the highly deforested and degraded Amhara region of Ethiopia. We apply biogeochemical mechanistic ecosystem modelling to predict the amount of carbon that can be potentially sequestered within different time horizons. Since human intervention plays a key role within the Amhara region, three different forest management scenarios and five different rotation periods following reforestation are tested: (i) unthinned; (ii) removal of 5% of the stem carbon every 20 years (thinning 1); and (iii) removal of 10% stem carbon every 20 years (thinning 2), as well as a rotation period of 10, 30, 50, 100, and 150 years. Sustainable management of reforested land is addressed by implementing the so called 'Normal-forest' system (equal representation of every age class). This ensures the long term sequestration effect of reforested areas. The study shows that 3.4 Mha (Mha = Million hectare) of land, including bare land (0.7 Mha), grass land (1.2 Mha), and shrub land (1.5 Mha) can be considered as ecologically potentially suitable for reforestation. Assuming a 100 year rotation period in a 'Normal-forest' system, this shows that a total net carbon sequestration potential of 177 Tg C (10.8 Tg C in the soil and 165.9 Tg C aboveground; Teragram = 10\({}^{12}\) g) is possible, if all 3.4 Mha are replanted. The highest total net carbon sequestration (soil and aboveground) was evident for the Highland-wet agro-ecological zone, whereas the lowest values are typically in the Midland-dry zone. The highest net aboveground carbon sequestration was predicted for reforestations on current grass land and shrub land versus bare land, whereas the highest net soil carbon sequestration was predicted on current bare land, followed by grass land and shrub land.  
reforestation; carbon sequestration; forest management; Normal-forest; Ethiopia +
Footnote †: journal: Physics Letters B
## 4 Proposed Scheme  
The proposed system provides data integrity, authenticity, and confidentiality by embedding a watermark at random locations within the data and using symmetric encryption. The proposed system consists of three phases as shown in Figure 3: (1) _Setup and Key Management_; (2) _Sensing and Reporting_; and (3) _Verifying and Aggregation_.  
In the _Setup and Key Management_ phase, the clusters are formed, and all authentication information is assigned to the sensors. After that, in the case of an event or based on a periodic interval, sensor nodes will start to sense information, encrypt it, embed the watermark at a random place, and then send the final report \(R_{i}\) to the cluster head to start the verification step. In the _Verifying and Aggregation_ phase, the cluster head checks whether the received report is valid or not to filter bogus packets. The verification is done by regenerating the watermark from the extracted data. For each valid \(R_{i}\) of the received reports, \(CH\) aggregates the sensed data, embeds a new watermark in the aggregated data, and forwards it to the access point. The AP individually decrypts and verifies the reports of each cluster, as it is assumed that the AP has all of the necessary keys and parameters.  
Following this, the phases of RWFS are described in detail. All of the notation that is used later is listed in Table 1.
## 8 Experiments and Result Analysis  
To verify the effectiveness of the method in this study, experiments were carried out on three open-source projects using three non-machine learning design pattern detection methods and five design pattern detection methods based on traditional machine learning algorithms as well as the method in this paper. In addition, the experimental results were analyzed and discussed from precision and recall.
# Underwater Target Recognition Based on Multi-Decision
## 2 System Model
### Signal Decomposition Algorithm Based on Resonance  
In traditional signal processing, Fourier transform is usually used to analyze in the frequency domain or time-frequency domain, but these methods are only valid for periodic stationary signals [25]. However, due to the generation mechanism of ship radiated noise and the complex channel conditions in the marine environment, the ship radiated noise collected by hydrophones is usually the mixture of oscillating signals and transient non-oscillating signals [20]. The harmonic component (or oscillation component) of the ship's radiated noise plays an important role in the identification of underwater targets. Therefore, a signal decomposition algorithm based on resonance that effectively responds to nonlinear signals is used to preprocess the signal. Based on the oscillation characteristics rather than the frequency or scale, the method can obtain a signal composed of multiple simultaneous and continuous oscillations (high-resonance component). To some extent, it weakens the transient non-oscillation signal of uncertain duration (low-resonance component) and gaussian white noise (residual component) which is conducive to feature extraction.  
The RSSD algorithm regards resonance as the basis for signal decomposition [26], and the \(Q\) factor quantifies the degree of signal resonance. Specifically, high-resonance signals exhibit a higher degree of frequency aggregation in the time domain, more simultaneous oscillating waveforms with a larger \(Q\) factor. Low-resonance signals appear non-oscillating and indefinite transient signal with a smaller \(Q\) factor. Therefore, the basic theory of the RSSD algorithm is that using two different wavelet basis functions (corresponding to \(Q\) factors of different sizes), we can find a sparse representation of a complex signal and reconstruct the signal.  
The algorithm mentioned in this section is divided into adjustable Q-Factor Wavelet Transform (TQWT) [27] and Morphological Component Analysis (MCA) [28]. Its algorithm framework is shown in Figure 2.  
Figure 1: Deep-learning underwater target-recognition framework based on multi-step decision LOFAR line spectrum enhancement.
## 2 Related Work
### Recognition of Music-Induced Emotion  
Existing MER research has been dominated by the music-centered paradigm, focusing on perceived emotion [8; 9], while the MER task on music-induced emotion started later yet is gaining increasing interest [17; 18; 19]. The Emotiy dataset constructed by [PERSON] et al. [17] is one of the few datasets with music-induced emotion annotated by human annotators. It has since been frequently used in subsequent studies focusing on modeling emotion induced by music listening through content-based analysis (e.g., [18; 19]). In these studies, different sets of acoustic features that capture music characteristics in loudness, rhythm, timbre, pitch, and harmony have been exploited [17; 18; 19]. As pointed out in [17], results in these studies indicate that the content-based modeling of induced emotion seems to be more feasible for some emotion categories (e.g., tenderness, joyful activation) than others. Although later studies (e.g., [19]) further improved the prediction performance on other emotion categories such as calmness and power, there is still room for improvement on emotions such as amazement and sadness. One possible direction of further research suggested by [17] was to incorporate contextual information for modeling emotion categories.  
Physiological signals, as a user- and context-related information source, have gained increasing attention for recognizing emotion induced by music in recent years, where both brain signals [20; 21; 22; 23] and peripheral physiological signals such as electrocardiography (ECG) [24] were exploited for this research topic. For example, refs. [21; 22; 23] conducted comprehensive laboratory experiments to collect participants' electroencephalography (EEG) under music stimuli and self-reported emotion states. Statistical regression models [21] and several machine learning algorithms [22] were modeled to predict induced emotion. In particular, apart from the features extracted from EEG signals, ref. [21] leveraged music acoustic features into the models and resulted in significantly better prediction on the induced valence and arousal. In addition, multimodal views of people's emotional response to music, which comprehensively considered acoustic characteristics of music and physiological responses of listeners, have also contributed to unravelling the underlying mechanism of individuals' affective perception and emotional experience of music [25; 26]. For instance, refs. [25; 26] showed that loudness-, rhythm-, and harmony-related acoustic features were informative for predicting people's emotional responses to music, and both studies revealed associations between acoustic features and physiological measures. These findings provide empirical evidence for understanding the interplay between the psychoacoustic characteristics of music and listeners' emotional and physiological responses to music.