esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 4 Discussion
### Trade-Off between Precision and Accuracy of Satellite Estimates
Meanwhile, WorldView 2 has a spectrum restricted to the visible and near infrared (0.4-1.04 \(\upmu\)m) [115]. Different terrain materials mimicking the spectrum of others are common in dense forests, and the use of more channels (i.e., bands) is a general recommendation for extracting more suitable endmembers [27]. Thus, the lower spectral resolution of WorldView 2 may affect the process of selecting endmembers, and thus the overall quality of the Spectral Mixture Analysis. The characteristics of the satellites we evaluated make them appropriate for specific conditions. While Landsat 8 and Sentinel 2 are freely available and cover the entire planet, WorldView 2 has high costs and covers only specific or by-demand regions. WorldView 2 has a 1.1-day revisiting time [115]; Sentinel 2 has a 5-day time [124], and Landsat 8 has a 16-day time [122]. Shorter revisiting time increases the chances of acquiring scenes shortly before and after the occurrence of windthrows. This aspect is essential to mitigate the contamination of NPV images due to fast natural regeneration. The high cloud cover in the Amazon [125] also limits the availability of images, and shorter revisit intervals can help overcome this limitation. Landsat has a longer-term collection of images than Sentinel 2 or WorldView 2 [126], which makes it suitable for landscape-scale studies aiming at mapping windthrows and monitoring forest recovery over time [2; 4]. However, Landsat may be inaccurate for quantifying and describing disturbances created by clusters of less than 6-8 fallen trees [23]. While restricted for the last 8 years, Sentinel 2 is a promising alternative for stand and landscape assessments of the extent and severity of forest disturbances at the individual-tree level. As Sentinel 2 produces estimates with overall low uncertainties, it may also perform better for quantifying diffuse tree mortality. Due to its continuity over the years [124], Sentinel 2 may soon allow for concealing longer-term analyses with high precision. WorldView 2 estimates had lower associated uncertainties and were more accurate in areas with <20% tree mortality. However, the relatively small collection of images reduces the potential of using WorldView 2 for monitoring disturbance dynamics and forest recovery. The recently launched Planet NICFI initiative [109; 127] conceals high resolution (3-5 m pixel) multispectral imagery with a revisiting interval of less than four days for most of the Amazon basin [128]. This offers a new possibility for quantifying and upscaling major mechanisms of tree damage and mortality regulating forest dynamics and functioning.
## 3 Mathematical Model and Thermodynamics Analysis
### Organic Rankine Cycle (ORC)  
The organic working fluid (n-Octane), which has a low boiling temperature and an appropriate operating pressure within the parameter range of the solar energy heat source in the ORC system, is evaporated using solar energy. The governing equations (Equations (1)-(11)) of the evaporator, condenser, turbine, pump, and ORC thermal efficiency are presented in the following manner based on the energy and mass balance [20]:  
* Evaporator energy balance \[Q_{Evap}^{\bullet}=m_{ORC}^{\bullet}\Big{(}h_{f3}-h_{f2}\Big{)}\] (1) \[m_{ORC}^{\bullet}\Big{(}h_{f3}-h_{f2}\Big{)}=m_{oil}^{\bullet}\left(h_{t3}- h_{t4}\right)\] (2) \[\eta_{solar}=\frac{Q_{Evap}^{\bullet}}{I_{T,avg,daily}A_{solar}}\] (3)  
The current work takes the total daily average solar intensity [8]. Whereas the average annual value of the thermal efficiency of the evacuated tube solar collector is 63.2%, [27]  
* Condenser energy balance \[Q_{Cond}^{\bullet}=m_{ORC}^{\bullet}\Big{(}h_{f4}-h_{f1}\Big{)}\] (4) \[\varepsilon_{cond}=\left(\frac{m_{W}^{\bullet}(h_{u3}-h_{u2})+m_{R,a}^{ \bullet}(h_{a8}-h_{a7})}{m_{ORC}^{\bullet}\Big{(}h_{f4}-h_{f1}\Big{)}}\right)\] (5) \[MR=\frac{m_{ORC}^{\bullet}}{m_{R,a}^{\bullet}+m_{w}^{\bullet}}\] (6)
* Turbine power  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Dehumidifier efficiency, \(\eta_{\text{DH}}\)[8]. & 0.95 \\ Efficiency of the desiccant wheel, \(\eta_{\text{F1}}\)[25, 26] & 0.05 \\ Efficiency of the desiccant wheel, \(\eta_{\text{F2}}\)[25, 26] & 0.95 \\ Efficiency of evaporative cooler-1, \(\eta_{\text{DPC-1}}\)[25, 26] & 0.9 \\ Efficiency of evaporative cooler-2, \(\eta_{\text{DPC-2}}\)[25, 26] & 0.9 \\ Efficiency of heat exchangers, \(\eta_{\text{FHE-1}}\), \(\eta_{\text{HE-2}}\), \(\eta_{\text{HE-3}}\), \(\eta_{\text{HE-4}}\)[25, 26] & 0.8 \\ Efficiency of heat exchanger, \(\eta_{\text{HE-2}}\)[25, 26] & 1.0 \\ Efficiency of the evacuated tube solar collector, \(\eta_{\text{solar}}\)[27] & 0.632 \\ Efficiency of electrical generator of ORC, \(\eta_{\text{g}}\)[28] & 0.95 \\ ORC pump efficiency, \(\eta_{\text{pump}}\)[28] & 0.85 \\ ORC turbine efficiency, \(\eta_{\text{turbine}}\)[28] & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Efficiencies of the systems components.  
\[W_{t}^{\bullet}=m_{ORC}^{\bullet}\Big{(}h_{f3}-h_{f4}\Big{)}\eta_{t}\eta_{g} \tag{7}\]
* Pump power \[W_{p}^{\bullet}=m_{ORC}^{\bullet}c_{f1}\Big{(}P_{f2}-P_{f1}\Big{)}/\eta_{p}\] (8) \[W_{p}^{\bullet}=m_{ORC}^{\bullet}\Big{(}h_{f2,a}-h_{f1}\Big{)}\] (9)
* ORC net power and thermal efficacy \[W_{net}^{\bullet}=W_{t}^{\bullet}-W_{p}^{\bullet}\] (10) \[\eta_{ORC}=\frac{W_{net}^{\bullet}}{Q_{Evap}^{\bullet}}\] (11)
## 3 Implementation of Reconstruction Method
### Reconstruction Algorithm  
Based on the above-mentioned points, a schematic of the reconstruction algorithm is presented, which is shown in Figure 8. The algorithm mainly consists of four procedures: denoising and smoothing of the original image, analytic calculation of the surface gradient, correction of the mutational surface gradient, and calculation of the topographic relative height.  
(1) We read the data of the original image. The gray scale \(E(x,\,y)\) can be obtained without quantization errors by using the wavelet denoising algorithm.  
(2) Based on the gray scale \(E(x,y)\), the gray gradient \((E_{x},E_{y})\) can be obtained by the following equation:  
\[\left\{\begin{array}{l}E(x,y)_{x}=(E(x+1,y)-E(x-1,y))/2\\ E(x,y)_{y}=(E(x,y+1)-E(x,y-1))/2\end{array}\right. \tag{12}\]  
Moreover, the surface gradient corresponding to the gray scale \(E(x,y)\) of each point in the image is calculated by Equation (10).  
(3) The mutational points are found, and their corresponding calculated surface gradients are corrected by adopting the above-mentioned correction method.  
(4) We assumed that \(z_{0}\) is the height of the reference point (\(x_{0},y_{0}\)) in the image, and subsequently set the height of the reference point. Thus, the relative height of any point on the object surface can be calculated as follows:  
\[z(x,y)=z_{0}+\int_{(x_{0},y_{0})}^{(x,y)}p_{c}dx+q_{c}dy \tag{13}\]  
Figure 8: Analytic reconstruction algorithm.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
###### Abstract  
The increase of data-rates that are provided by free-space optical (FSO) communications is essential in our data-driven society. When used in satellite and interplanetary networks, these optical links can ensure fast connections, yet they are susceptible to atmospheric disruptions and long orbital delays. The Delay and Disruption Tolerant Networking (DTN) architecture ensures a reliable connection between two end nodes, without the need for a direct connection. This can be an asset when used with FSO links, providing protocols that can handle the intermittent nature of the connection. This paper provides a review on the theoretical and state-of-the-art studies on FSO and DTN. The aim of this review is to provide motivation for the research of an optical wireless satellite network, with focus on the use of the Licklider Transmission Protocol. The assessment presented establishes the viability of these networks, providing many examples to rely on, and summarizing the most recent stage of the development of the technologies addressed.  
Delay Tolerant Networking; free-space optical communications; satellite and interplanetary networks +
Footnote †: journal: Electronics
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Subgraph  
So far, we have only studied unary graph operations in this work. However, in this subsection, we deviate from this path and study the unary graph transformation of _taking a subgraph_. This act of modifying a graph is not deterministic, since there are various subgraphs one can take from any given graph, and it is not explicitly defined which one should be taken. Consequently, taking a subgraph of a graph multiple times can result in different subgraphs, making the studied modification a graph transformation but not a graph operation.  
Note that taking a subgraph of a graph can be interpreted as a sequence of vertex-deletion and edge-deletion operations. Hence, every subgraph of a graph can be obtained by deleting selected vertices and edges from the original graph, such that the corollary below follows immediately from Theorems 3.1 and 3.2.  
Corollary 2: _For a graph \(G\) and any subgraph \(H\) of \(G\), it holds that \(tw(H)\leq tw(G)\) and \(pw(H)\leq pw(G)\)._
## 2 Theoretical Method and Physical Model  
### Solution of Oil Film Reynold Equation  
#### 2.1.1 Reynolds Equation  
The Reynolds equation of incompressible fluid in a cylindrical coordinate system is defined by the classical lubrication theory [20].  
\[\frac{1}{R^{2}}\frac{\partial}{\partial\theta}(h^{3}\frac{\partial p}{\partial \theta})+\frac{\partial}{\partial z}(h^{3}\frac{\partial p}{\partial z})=6\mu \dot{\psi}\frac{\partial h}{\partial\theta}+\frac{\partial h}{\partial t} \tag{1}\]  
where \(R\) is radius, \(P\) the oil film pressure, \(h\) the oil film clearance, \(\mu\) the fluid viscosity, \(z\) and \(\theta\) are the axial and circumferential coordinates. \(e\) and \(\psi\) are the journal eccentricity and deflection angle.  
Figure 1 illustrates the positional relationship of the various parts of the floating ring bearing when the journal whirl is eccentric. The solid line is the initial position, the dashed line is the changed position, \(C_{1}\) and \(C_{2}\) are the initial clearances of the inner and outer oil films, \(r(\theta)\) is the radial deformation of the floating ring, and \(h_{1}\) and \(h_{2}\) are the oil film clearances of the inner and outer layers, respectively. \(e\) is the eccentricity of the journal with respect to the bearing.  
The clearance of the inner oil film can be estimated as  
\[\left\{\begin{array}{c}h_{1}(\theta)=C_{1}+e\cos\theta+r(\theta)\\ \frac{\partial h_{1}}{\partial\theta}=\frac{\partial r}{\partial\theta}-e\sin \theta\\ \frac{\partial h_{1}}{\partial t}=\frac{\partial r}{\partial t}+\dot{e}\cos \theta\end{array}\right. \tag{2}\]  
The clearance of the outer oil film can be expressed as  
\[\left\{\begin{array}{c}h_{2}(\theta)=C_{2}-r(\theta)\\ \frac{\partial h_{2}}{\partial\theta}=-\frac{\partial r}{\partial\theta}\\ \frac{\partial h_{2}}{\partial t}=0\end{array}\right. \tag{3}\]  
Figure 1: The swirling form of the floating ring squeeze film damper.  
For the convenience of the finite difference method, introducing non-dimensional parameters \(\lambda=2z/l\), \(H_{\rm i}=h_{\rm i}/C_{1}\), \(P=p{C_{1}}^{2}/2\Omega\mu r^{2}\), \(l\) is the axial width of the floating ring. The influence of the deformation of the floating ring is ignored here. Equation (1) can be rewritten as follows:  
\[\frac{\partial}{\partial\theta}(H^{3}\frac{\partial P}{\partial\theta})+(\frac {2R}{l})^{2}\frac{\partial}{\partial\lambda}(H^{3}\frac{\partial P}{\partial \lambda})=6\frac{\partial H}{\partial\lambda}+\frac{6}{Rc\dot{\psi}}\frac{ \partial H}{\partial t} \tag{4}\]  
Equations (2) and (3) are estimated as  
\[\left\{\begin{aligned} H_{1}(\theta)=1+w(\theta)+\varepsilon\cos \theta\\ H_{2}(\theta)=c_{3}-w(\theta)\end{aligned}\right. \tag{5}\]  
where \(\varepsilon=e/C_{1}\), \(c_{3}=C_{2}/C_{1}\) is the eccentricity ratio and \(w(\theta)\) is the deformation function of the floating ring.
# Vegetation Health in China Is Severely Compromised by Drought, Wet and Heat Stress Events
## 4 Discussion
### Characteristics of Stress Events and Their Impact on Vegetation Health
Forest ecosystems, such as MCBF and BF, may undergo shifts in species composition and reduced carbon sink capacity under prolonged heat stress. Elevated temperatures accelerate respiration and suppress photosynthesis, directly reducing carbon fixation [58]. GL and MD may experience severe species loss and altered community functions under heat stress, further compromising their ecosystem services [59; 60]. Although AV is more resilient to heat stress due to long-term adaptation to low-temperature environments, extreme heat can still damage its photosystem. Moreover, global climate change has intensified the frequency of stress events, resulting in increased negative anomalies in vegetation health. This is particularly evident under warm and humid climatic conditions, where vegetation exhibits heightened sensitivity to moisture and temperature changes [61]. The differences in vegetation responses to stress across regions are primarily determined by their respective geographic and climatic characteristics [62; 63]. For example, vegetation in the northern arid and semi-arid regions, having adapted to low precipitation over the years, exhibits lower positive sensitivity to CPA stress. In contrast, vegetation in more humid areas, such as South China, may display higher positive sensitivity to the same stress. These differences manifest as significant spatial heterogeneity, with certain areas like the Northeast Plain, Huang-Huai-Hai Plain, and Yunnan-Guizhou Plateau showing more pronounced sensitivity to specific stresses. Additionally, residual analysis based on the linear relationship between climate variables and vegetation, due to its simplicity and ease of use, is widely employed to separate the contributions of climate change and human activities to vegetation. As the impact of global climate change intensifies, contributions from stress events and other factors to vegetation should also be considered. Our results indicate that stress events play a dominant role in influencing vegetation health, whereas the effects of other factors, such as human activities, are gradually diminishing. The abrupt nature of these stress events impairs the ability of vegetation to adjust its physiological activities. SM CD causes difficulties in water uptake by vegetation roots, and CH reduces the normal physiological functioning of vegetation leaves exposed to the air. The concurrent occurrence of stress events exerts cumulative negative impacts on vegetation growth [64], ultimately leading to a decline in vegetation health.
## 2 The Proposed Method
### Coordinate Transformation  
To improve the success rate of closed-loop detection, a local map which is composed of the latest 10 continuous frames is defined. A coordinate \(X^{R}O^{R}Y^{R}\) is defined for the local map. The scanned points are represented in LiDAR coordinates and defined as:  
\[b^{i}=\left(b^{i}x,b^{i}y\right)_{i=0,1,\ldots,I} \tag{7}\]  
The pose of the LiDAR at the time \(n\) related to the local map coordinate of \(m\) is expressed as:  
\[\xi_{m}{}^{n}=\left(\xi_{x}^{n},\xi_{y}^{n},\xi_{\theta}^{n}\right) \tag{8}\]  
The pose of the local map \(m\) related to the world coordinate system is defined as:  
\[\psi^{m}=\left(\psi^{m}x,\psi^{m}y,\psi^{m}\right) \tag{9}\]  
Then the transformation from the LiDAR coordinate to the local map coordinate is expressed as:  
\[G^{i}(\xi^{m})=\left(\begin{array}{cc}\cos\xi_{\theta}^{n}&-\sin\xi_{\theta}^ {n}\\ \sin\xi_{\theta}^{n}&\cos\xi_{\theta}^{n}\end{array}\right)\left(\begin{array} []{c}b^{i}x\\ b^{i}y\end{array}\right)+\left(\begin{array}{c}\xi_{x}^{n}\\ \xi_{y}^{n}\end{array}\right)=\left(\begin{array}{c}b^{i}x\\ b^{i}y\end{array}\right)_{X^{R}O^{R}Y^{R}} \tag{10}\]  
The transformation between the pose of LiDAR from the local map coordinate to the world coordinate system is defined as:  
\[L^{n}(\psi^{m})=\left(\begin{array}{cc}\cos\psi^{m}{}_{\theta}&-\sin\psi^{m }{}_{\theta}&0\\ \sin\psi^{m}{}_{\theta}&\cos\psi^{m}{}_{\theta}&0\\ 0&0&1\end{array}\right)\left(\begin{array}{c}\xi_{x}^{n}\\ \xi_{y}^{n}\\ \xi_{\theta}^{n}\end{array}\right)+\left(\begin{array}{c}\psi^{m}{}_{x}\\ \psi^{m}{}_{y}\\ \psi^{m}{}_{\theta}\end{array}\right)=\left(\begin{array}{c}\xi_{x}^{n}\\ \xi_{y}^{n}\\ \xi_{\theta}^{n}\end{array}\right)_{X^{R}O^{R}Y^{R}} \tag{11}\]
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 4 Proposed Method
### On the Senders' Side
#### 4.1.2 Data Compression, Encryption, and Critical Information Concealment  
The step given in Equation (9) simultaneously completes the processes including data compression, encryption, and critical information concealment.  
\[y_{e}=(A+H)s \tag{9}\]  
where \(y_{e}\in\mathbb{R}^{M\times 1}\) is the compressed, encrypted, and concealed signal.  
Easily, it can be deduced that,  
\[y_{e}=(A+H)\Psi^{\mathrm{T}}x=\Phi^{*}x \tag{10}\]  
where \(\Phi^{*}=(A+H)\Psi^{\mathrm{T}}\) acts as the sensing matrix that will be used to reconstruct the signal \(x\) through the OMP algorithm by receivers with full authorization.  
We can also rewrite Equation (9) as below:  
\[y_{e}=A\Psi^{\mathrm{T}}x+H\Psi^{\mathrm{T}}x=\Phi x+\epsilon \tag{11}\]  
where \(\epsilon=H\Psi^{\mathrm{T}}x\) denotes noise. The matrix \(\Phi=A\Psi^{\mathrm{T}}\) acts as the sensing matrix that will be used to reconstruct the signal \(x\) through the OMP algorithm by receivers with restricted authorization.
## 4 Performance Evaluation
### Evaluation of MRSS-based Handoff Algorithm
However, they need to consume more power for network discovery. MACD, MRSS and DMRSS trigger a handoff based on the user motion state, which result in a lower number of handoffs. A fast motion detection service can quickly trigger a handoff when the MN is leaving and quickly terminate a handoff process when the MN is stationary. Thus, DMRSS and MRSS perform better than MACD. Figure 15(d) presents the accumulated number of failure handoff. The hysteresis combined methods have a higher number of failed handoff because an MN starts the network discovery process late. The motion detection based handoff approaches have a lower number of failed handoff.
# Effective Strategies for Enhancing Real-Time Weapons Detection in Industry
## 4 Results
### First Phase
#### 4.1.2 Background Images  
The aim of this experiment is to determine the optimal percentage of background images that enables the model to perform optimally. Adding too few background images can result in a high false positive rate, while an excess of background images may lead to a deterioration in the model's accuracy in detecting weapons. According to the YOLOv5's documentation ([[https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/)]([https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/](https://docs.ultralytics.com/yolov5/tutorials/tips_for_best_training_results/)), accessed on 24 January 2024), it is recommended that this percentage should fall between 0 and 10%. In this case, the experiment involved using \(0\%,2\%,5\%,7\%\), and 10% of background images for the _Disarm-Dataset_. Examples of the added background images can be seen in Figure 8, sourced from live streams of various streets on YouTube, selected images from _usdataset_, _usdataset_ (_Synthetic_), and _HOD-Dataset_ (refer to Section 3.1), along with some videos captured in a workspace.  
As depicted in Table 6, the addition of more background images results in the model detecting fewer false positives. However, this also leads to a slight decrease in some metrics. Only when 5% background images are added with respect to the dataset does it show an increase in accuracy, although its false positive count is not as low as the other percentages. Nevertheless, as evident in both Table 6 and Figure 9, the inclusion of this percentage outperforms the previously mentioned percentages and the base model,  
Figure 8: False positive sample images.  
Figure 7: Comparison between models using _Disarm-Dataset_ according to training time in hours.  
while also achieving a slightly better average precision than YOLOv8l but with fewer false positives.
# Binanual Movements and Chronic Stroke Rehabilitation: Looking Back and Looking Forward
## 2 Chronic Stroke Rehabilitation
### Bimanual Movement Interventions  
Compelling evidence suggests that assimilation occurs between the left and right arms during neural control of symmetrical bimanual motor actions [24; 38]. Promising findings on chronic stroke interventions have been identified when participants perform the same movement with both limbs. Further, producing the same forces on both arms with homologous muscles firing simultaneously post-stroke assists in making progress toward motor recovery. Early bimanual coordination or bimanual coordination studies consistently reported synchronization among effectors in concurrently performed movements [24; 39; 40; 51]. Importantly, [PERSON]'s classic argument that both arms are centrally linked as a coordinative structure holds, and upper extremities function in a homologous coupling of muscle groups on both sides of the body [52].  
A series of chronic stroke studies focused on bimanual movements executed concurrently and supplemented with neuromuscular-triggered electrical stimulation revealed consistent motor improvement findings. Manipulating treatment protocols centered on bimanual movements as well as EMG-triggered stimulation generates progress toward motor recovery in the upper extremities [21; 38; 53; 54; 55; 56; 57; 58]. Positive experience-dependent and active stimulation findings include increased motor capabilities in short-term and longitudinal post-testing. Moreover, adding a proportional load to the non-paretic arm while requiring bimanual movements produced less dysfunctional motor actions in the impaired arm/hand. In a systematic review and meta-analysis on bimanual movement coordination (i.e., interlimb coordination) protocols post-stroke indicated that the chronic stroke groups improved performance while executing both synchronous and asynchronous bimanual movements [54]. Further, [PERSON] and colleagues found asynchronous support when they strapped the paretic and non-paretic arms to cars attached to a trackway and required participants to perform rhythmic alternating (asynchronous) bimanual movements [59].