esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 4 Results and Discussions
### Objective Analysis
From Table 3, the H value achieved by the proposed method is the best; for the indicators of MSE, PSNR, MAE, and MSSSIM, the corresponding values achieved by the ESIHE method are the best, but values obtained by the proposed technique are still second-best in these metrics; for the indicator GMSD, the ESIHE method is the best, and the values achieved by the LSCN and proposed methods are second and third, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HE** & **ESIHE** & **FLM** & **LSCN** & **Proposed** \\ \hline H & 5.8139 & 7.3840 & 5.4117 & 6.9739 & 7.7279 \\ MSE & 109.5441 & 52.9798 & 157.0788 & 133.5418 & 61.4857 \\ PSNR & 27.7349 & 30.8897 & 26.1696 & 26.8746 & 30.2431 \\ GMSD & 0.0916 & 0.0220 & 0.1275 & 0.0653 & 0.0831 \\ MAE & 9.0398 & 4.4681 & 22.2944 & 13.1778 & 7.5462 \\ MSSSIM & 0.9433 & 0.9604 & 0.8789 & 0.9546 & 0.9560 \\ \hline \hline \end{tabular} H, entropy; MSE, mean square error; PSNR, peak signal to noise ratio; GMSD, gradient magnitude similarity deviation; MAE, mean absolute error; MSSSIM, multi-scale structural similarity.  
\end{table} 
Table 1: The evaluation metrics of the five methods on Image 1.
[34] proposed mutual information-based incremental cooperative communication (MI-ICC) protocol, where several on-body relay nodes and one coordinator were attached to the patient's clothes. MI-ICC took into account the critical data, while the normal data does not get transmitted to the destination. It achieved better performance in comparison to the scheme using two relays, with the residual energy and network lifetime taken into account and improved. In what follows, the drawbacks and limitations of [27; 28; 29; 30; 31; 32; 33; 34] are shown in Table 1 and can be summarised as follows: MAC protocol was not considered (such as IEEE 802.15.6), e2e delay was not analysed, the best relay node selection was not considered, the duty cycle was not analysed, and the average power transmission was not studied, and the nature of the gathered data was not taken into account. To address the aforementioned issues and facilitate cooperative communication in WBAN, we propose a novel Critical-Data Incremental Cooperative Communication protocol based on the IEEE 802.15.6 CSMA policy. The contributions of this work are summarised as follows:  
1. A MAC protocol for the CD-ICC is proposed to coordinate the sensor to act as relay to carry out the retransmission process. 2. A new back-off time is proposed to achieve the selection of the best relay, where only the sensor that is nearest to the source can participate in cooperation. In addition, the back-off time accelerates the access of the selected best relay to the shared medium. 3. The gathered data natures have been considered. Where the critical data is transmitted over ICC, while the normal data is transmitted over DTM. It is meant that CD-ICC protocol supports multiple traffics. 4. The e2e delay, duty cycle and average power transmission of CD-ICC are mathematically modelled and analysed. 5. We show that the proposed protocol can reduce the e2e delay and the duty cycle and can enhance power saving of the WBAN compared to the existing work and DTM under IEEE 802.15.6 CSMA policy. The rest of the paper is organized as follows: system model and architecture is presented in Section 2. Section 3 describes and investigate the wireless link and successful transmission probability under lognormal shadowing model. Then, modeling and formulating of CD-ICC in details has been described in Section 4. In Section 5, e2e delay, duty cycle and average power transmission of CD-ICC are investigated, formulated and analyzed. Simulation and numerical results are addressed in Section 6. Finally, Section 7 draws the conclusion and future work.
# Mg,Sr-Cosubstituted Hydroxapatite with Improved Structural Properties  
[PERSON]  
1 ISTEC-CNR, Institute of Science and Technology for Ceramics-National Research Council, Via Granarolo 64, 48018 Faezna, Italy; [EMAIL_ADDRESS] (E.L.); [EMAIL_ADDRESS] (E.P.)  
[PERSON]  
2 DMec  
age [19], similar to Mg, and oral administration alone is poorly effective in restoring higher Sr levels [11].  
Numerous studies on Mg.Sr-cosubstituted calcium phosphates have been hitherto published [20; 21; 22; 23; 24]. Most of them mainly focused on the chemophysical and structural characterization of synthetic powders produced by wet synthesis [25; 26; 27; 28; 29]. However, though several in vitro and in vivo studies on single Mg- or Sr-substituted synthetic apatites are available [30; 31; 32; 33], the literature on cosubstituted Mg.Sr apatites as bone substitutes is still scarce. Obtaining sintered Mg,Sr-cosubstituted HA is challenging because the thermal treatments used for sintering improve the chemophysical superficial stability and the mechanical properties of apatite but decrease Mg stability in the HA lattice. Moreover, to the authors' knowledge, Mg,Sr-cosubstituted apatite still lacks the morphological 3D organization and structural properties typical of sintered porous apatite scaffolds.  
This research group has previously reported on the development of macro-granules of Mg.Sr cosubstituted HAs (MgSr-HAs) containing levels of Mg and Sr close to those observed in bones from young animals [28]. Due to its improved stability and release properties, this material displayed a better in vitro performance than similar materials, paving the way to biomimetic scaffolds capable of providing a prolonged direct supply of Mg and Sr ions to the surrounding bone. These granules, however, could not withstand the high temperatures required to attain effective sintering and were thus unsuitable to create scaffolds.  
The aim of this work was to design and prepare bioactive porous bone substitutes that exploit the synergy of Mg and Sr while preserving the desirable structural properties of a porous 3D architecture, as obtained by high-temperature consolidation. To achieve that, we enriched sintered Sr-HA scaffolds with magnesium via an ion-exchange process.
# A Study on the Heat Transfer of Surrounding Rock-Supporting Structures in High-Geothermal Tunnels
## 2 Establishment of Finite Difference Model  
### Fundamental Assumptions and Governing Equations  
The purpose of this study was to investigate the temperature distinguishing feature in a high geothermal tunnel during the construction process. Therefore, for simplicity, the one-dimensional unsteady heat transfer analysis of the cross-section of the high-temperature tunnel in this paper is based on the following assumptions:  
1. The tunnel cross-section is simplified to a circular shape [2; 24].
2. The tunnel lining and the surrounding rock are isotropic, and the thermal properties are stable and unaffected by temperature.  
3. The heat transfer conditions in the circumferential direction of the inner wall of the tunnel are the same, and the air temperature in the tunnel is uniform and constant.
4. There is no contact thermal resistance between the layers of the tunnel.
5. The heat transferring along the axis is much smaller than that along the radial direction, assuming that heat is only transferred in the radial direction, and there is no heat flow in the axial and circumferential directions.
6. There is no internal heat source in the medium of each layer of the tunnel. The heat transferring mode between the tunnel lining and surrounding rock is heat conduction and that between the lining and the air in the tunnel is heat convection. Heat radiation is neglected because of its relatively small magnitude [12].  
One-dimensional unsteady heat conduction [25] differential equations of the temperature field between lining-surrounding and rocks in polar coordinates can be written as [14]:  
\[\frac{\partial t_{k}}{\partial\tau}=a_{k}(\frac{1}{r}\frac{\partial t_{k}}{ \partial r}+\frac{\partial^{2}t_{k}}{\partial r^{2}}) \tag{1}\]  
where \(a_{k}\) and \(t_{k}\), respectively, represent the temperature coefficient and temperature of the k-th layer medium. \(\tau\) is the time. \(r\) is the distance from the tunnel axis.  
\[a_{k}=\frac{\lambda_{k}}{c_{k}\rho_{k}} \tag{2}\]  
where \(\lambda_{k}\), \(c_{k}\) and \(\rho_{k}\), respectively, represent thermal conductivity, specific heat capacity and intensity.  
The initial conditions are,  
\[t(\tau,r)=t_{0},\;\tau=0. \tag{3}\]  
The boundary conditions are [26],  
\[\left\{\begin{array}{c}-\lambda\frac{\partial t}{\partial r}=-h\Big{(}t-t_{ f}\Big{)},r=r_{0},\tau>0(\text{{Convection heat exchange}})\\ t(\tau,\infty)=t_{0},\tau>0(\text{{outer boundary}})\end{array}\right.. \tag{4}\]  
The interface continuity conditions are,  
\[\left\{\begin{array}{c}t_{k}(\tau,r_{k})=t_{k+1}(\tau,r_{k}),\tau>0\\ \lambda_{k}\frac{\partial t_{k}}{\partial r}=\lambda_{k+1}\frac{\partial t_{k +1}}{\partial r},\tau>0\end{array}\right.. \tag{5}\]  
where \(t_{0}\), \(t_{f}\), \(r_{0}\), respectively, represent the initial temperature of rock, humidity of air and radius of tunnel. \(h\) represents convective heat transfer coefficient between lining and air.
# A Cross-Medium Uplink Communication Model Merging Acoustic and Millimeter Waves
## 2 Model of Cross-Medium Communication Channel
### Model Adjustments and Analysis
#### 2.2.2 Background Noise Target Component Correction  
Despite the placement of the speaker at the bottom of the tank and the use of silencing cotton, the vibration remained unavoidable. This vibration at the water-air interface introduced a noise component at the target frequency, which affected the accurate calculation of the SNR. To address this, a correction coefficient \(\epsilon\) was introduced to the signal energy. The formal representation of this correction is as follows:  
\[E=E_{S}+\epsilon \tag{8}\]
# Influence of Macrophyte and Gut Microbiota on Mercury Contamination in Fish: A Microcosms Study
## 3 Results
### Distribution and Transformation of Isotopic Tracer \({}^{199}\)Hg in Contaminated Units
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Condition** & **Matrix** & **[199]**Hg** & **[199]**MMHg** & **\% Methylation** & **n** \\ \hline \multirow{4}{*}{Ti} & Plant-Hg & Water & 109.2–41.8 & 24.1–9.2 & 18.1–18.1 & 2 \\  & Plant & Plant & 2061.1–2572.8 & 311.4–22.9 & 13.1–0.9 & 2 \\ \cline{2-6}  & Plant+sediment-Hg & Water & \(41\pm 15.4\) & \(0.8\pm 0.7\) & \(3.7\pm 3.4\) & 3 \\  & Plant+sediment-Hg & Sediment & \(2.9\pm 1.5\) & \(0.6\pm 0.3\) & \(13.9\pm 6.1\) & 3 \\  & Plant & \(1362\pm 272.2\) & \(92.8\pm 36\) & \(6.2\pm 2.4\) & 3 \\ \cline{2-6}  & Sediment-Hg & Water & \(244.2\pm 27.7\) & \(0.2\pm 0\) & \(0.1\pm 0\) & 3 \\  & Sediment & \(1.8\pm 0.1\) & \(0\pm 0\) & \(2.3\pm 1.6\) & 3 \\ \hline \multirow{4}{*}{T} & Plant-Hg & Water & \(10.9\)–9.5 & 8–0.6 & 42.4 - 6.4 & 2 \\  & Plant+sediment-Hg & Plant & 587.2–397.1 & 55.8 & 12.3 & 2-1 \\  & Fish & \(63.7\pm 25.1\) & \(180.7\pm 69.1\) & \(77.3\pm 4.9\) & 6 \\ \cline{1-1} \cline{2-6}  & Plant+sediment-Hg & Water & \(0.4\pm 0.1\) & \(0\pm 0\) & \(13\pm 6\) & 3 \\  & Plant+sediment-Hg & Sediment & \(3.4\pm 0.3\) & \(0.3\pm 0.1\) & \(8.2\pm 3.2\) & 3 \\  & Plant+sediment-Hg & Plant & \(44.4\pm 4\) & \(16.8\pm 2.3\) & \(27.4\pm 0.9\) & 3 \\  & Fish & \(15\pm 3.1\) & \(153.9\pm 39.7\) & \(90\pm 1.4\) & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Concentrations of \({}^{199}\)IHg and \({}^{199}\)MeHg (ng:g\({}^{-1}\)_dw_), Hg methylation potentials (%) in the water, sediment, plants, and _Pseudancistrus_ sp. fish muscle for different conditions before fish introduction in units (Tf) and after 15 days of incubation (Tf). For each unit, a mass budget of \({}^{199}\)Hg species was calculated (each Hg species was compared to the total quantity of Hg species in a unit) and the results are expressed in percentage (Figure 2). For all conditions, % of \({}^{199}\)IHg and \({}^{199}\)MeHg in water were very low (0% in the \"plant+sediment\" condition to 4% in the \"plant\" condition). \({}^{199}\)IHg was mainly distributed in sediment (99% in the \"sediment\" condition; 83% in the \"plant+sediment\" condition) and plant (87% in the \"plant\" condition and 16% in the \"plant+sediment\" condition), and significantly lower in fish muscle for all conditions (minimum value: 0.7 and 0.8% in the \"sediment\" condition and the \"plant+sediment\" condition, respectively). \({}^{199}\)MeHg was significantly higher in fish muscle for the \"plant\" condition (75%) than in fish for the other conditions (Kruskall-Wallis test, \(p<0.05\)), but also for the other compartments. The rest of the \({}^{199}\)MeHg was distributed between sediment and plants (maximum value: sediment = 61% in the \"sediment\" condition). In the \"plant\" condition, \({}^{199}\)MeHg in plants presented a high heterogeneity between replicates (\(21.4\pm 20.1\)%).
## 6 Simulations and Numerical Analysis  
### Simulation Scenario  
The simulation scenario is a coal mine with three layers (Layers A, B and C), as shown in Figure 3. The vertical tunnel connects the three layers. In this section, the simulation will be conducted at the vertical tunnel and one horizontal layer (Layer A). The figure of Layer A is shown in Figure 4. At each intersection of the vertical and horizontal tunnels, there is one sub-base station, which has the duty of collecting the data of its own layer and forwarding them to the base station.  
Several sensor nodes are installed along the mine tracks of the vertical tunnel and horizontal tunnels. There are also some of them installed at pit faces to collect the environmental information. At the entrance of each branch of horizontal tunnel, a relay node is placed in case of wireless signal loss. The nodes' placement is shown in Figure 4. The sensor nodes are labeled with dot symbols. The relay nodes at branch entrances and the sub-base station are denoted as hollow circles. The sojourn spot of the recharging locomotive is located at the sub-base station. After recharging each node, the recharging locomotive will stay at the sojourn spot receiving maintenance.  
Figure 4: A sketch map for the sensor nodes deployment in Layer A.  
Figure 3: A sketch map for a coal mine with three layers.
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 2 Materials and Methods  
### Plant Material  
Blackcurrants (_Ribes rubrum_) of the variety 'Tofino' were cultivated at the Western Agricultural Research Center of Montana State University since 2015. Ripe fruits with \(14.5\pm 0.3\,^{\circ}\)Brix of SSC and 2.9 \(\pm\) 0.1 pH were hand harvested on 1 August 2023 from three plants with minimum soluble solid contents and pH variations and selected with an even diameter of 10 mm to 15 mm. Blackcurrants with visible damage or deformation were removed.
# Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
## 2 Identification Problem Statement  
Let us consider the following segment of the truncated Volterra series (1) for \(n=2\)  
\[y(t)=\int\limits_{0}^{t}K_{1}(s)x(t-s)\,ds+\int\limits_{0}^{t}\int\limits_{0}^ {t}K_{2}(s_{1},s_{2})x(t-s_{1})x(t-s_{2})\,ds_{1}ds_{2},\ t\in[0,T]. \tag{3}\]  
Our current problem in this section is to determine the kernels \(K_{1}(s)\) and \(K_{2}(s_{1},s_{2})\) by a known input and output pair \(\big{(}x(t),y(t)\big{)}\).  
In contrast to the linear case \(n=1\), when it is sufficient to specify a single pair \(\big{(}x(t),y(t)\big{)}\) to determine the kernel \(K_{1}(s)\), in the nonlinear case \(n=2\), for the unique identification of the two-dimensional kernel \(K_{2}(s_{1},s_{2})\), it is necessary to specify a two-dimensional continuum of equalities. This means that problem (4) has an infinite set of solutions.  
**Remark 1**.: _It should be noted that if we consider this problem as an integral equation with two unknown functions \(K_{1}(s)\) and \(K_{2}(s_{1},s_{2})\), then this problem is essentially ill-posed. There are an infinite number of solutions and this problem is insufficiently defined. In this regard, no classical numerical methods designed for integral equations are applicable in this case. And as a result, there are no any attempts to solve the problem in this form in the literature._  
**Remark 2**.: _A fundamentally different situation takes place in the problem of determining an unknown input signal \(x(t)\) with a known output signal \(y(t)\) after kernels identification. It is to be noted that in this case we have the problem of nonlinear Volterra integral equations' solution. Here, readers may refer to Section 9 in book [11], papers [12; 13; 14] and references therein regarding the [PERSON] principal solutions and the blow-up phenomenon._Within the framework of this paper, from a practical point of view, we will be satisfied with any pair of approximately found kernels \(\widetilde{K}_{1}(s)\) and \(\widetilde{K}_{2}(s_{1},s_{2})\) that provides a sufficiently small residual norm  
\[\varepsilon=\max_{t\in[0,T]}\left|y(t)-\int\limits_{0}^{t}\widetilde{K}_{1}(s) x(t-s)\,ds-\int\limits_{0}^{t}\int\limits_{0}^{t}\widetilde{K}_{2}(s_{1},s_{2})x(t-s _{1})x(t-s_{2})\,ds_{1}ds_{2}\right|. \tag{4}\]  
Denoted by \(B_{i}(t),\ i=0,1,2,\dots\), the basis functions form a complete orthogonal system of functions on the segment \([0,T]\).  
We look for an approximate solution of the problem (3) in the form of segments of series of expansions according to the selected system of basis functions  
\[\widetilde{K}_{1,m}(s)=\sum\limits_{i=0}^{m-1}A_{i}B_{i}(s),\quad\widetilde{K} _{2,m_{1},m_{2}}(s_{1},s_{2})=\sum\limits_{i=0}^{m_{1}-1}\sum\limits_{j=0}^{m_ {2}-1}C_{ij}B_{i}(s_{1})B_{j}(s_{2}). \tag{5}\]
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 5 Power Optimization Strategies
### Designing an Energy-Conscious Algorithm
#### 5.1.6 Fused-Logic Model Implementation  
Fusion of operations across adjacent layers or fusion of adjacent layers itself combines multiple computational steps into a single step to reduce memory access and improve computational efficiency, which can reduce resource and power consumption. Such fusion of operations can increase the overall throughput and reduce the latency of the system, which is important for real-time physiological monitoring, where timely data processing is critical. However, such operations are highly optimized for a particular scenario or application, which reduces the flexibility of the system to adapt to changing requirements. The authors of [99] integrated their feature selection process with the classifier to form a single architecture for their EEG-based seizure detection system. This strategy improved performance along with a reduction in computational complexity and power consumption, resulting in 2.018 uW of power.  
In another approach, the authors fused their batch normalization with convolution to reduce unnecessary computations in their PPG-based heart rate estimator [100]. The convolution operation mainly consists of multiplication and addition operations. Usually, the next layer is a batch normalization (BN) layer, where the main operations are subtraction and division, which are resource- and power-hungry. These computations can be fused with the previous layer's computations to remove repeating computations. This results in a reduction in computational complexity and can thus also reduce resource and energy consumption. The authors of [67] fused their qMLP with the bCNN model to enable an end-to-end architecture for their binary image-based ECG classifier for resource and power reduction.  
The fusion of operations and layers can significantly enhance efficiency, performance, and power optimization. However, it can limit the flexibility of the system.
# Assessment of Spatiotemporal Fusion Algorithms for Planet and Worldview Images
## 2 Spatiotemporal Fusion Approaches
### Performance Metrics  
Although there are many performance metrics in the literature, we selected the following ones: peak signal-to-noise ratio (PSNR) [1], structural similarity (SSIM) [8], Q2N [9], absolution difference (AD) [9], root mean squared error (RMSE) [18], spectral angle mapper (SAM) [18], cross correlation  
Figure 4: Proposed prediction approach based on local mapping.  
Figure 3: The Hybrid Color Mapping (HCM) approach for image fusion.  
(CC) [18], Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) [18], In addition to the above metrics, we also use SSIM maps to visualize the similarity between two images.  
* Absolute Difference (AD). The AD of two vectorized images \(\mathbf{S}\) (ground truth) and \(\mathbf{\hat{S}}\) (prediction) is defined as \[\mathrm{AD}(\mathbf{S},\mathbf{\hat{S}})=\frac{1}{Z}\sum_{j=1}^{Z}\abs{s_{j}- \hat{s}_{j}}\] (7) where \(Z\) is the number of pixels in each image. The ideal value of AD is 0 if the prediction is perfect.
* RMSE (Root Mean Squared Error). The RMSE of two vectorized images \(\mathbf{S}\) (ground truth) and \(\mathbf{\hat{S}}\) (prediction) is defined as \[\mathrm{RMSE}(\mathbf{S},\mathbf{\hat{S}})=\sqrt{\frac{1}{Z}\sum_{j=1}^{Z} \bigl{(}s_{j}-\hat{s}_{j}\bigr{)}^{2}}\] (8) where \(Z\) is the number of pixels in each image. The ideal value of RMSE is 0 if the prediction is perfect.
* PSNR (Peak Signal to Noise Ratio). PSNR is related to RMSE defined in (8). If the image pixels are expressed in doubles with values between 0 and 1, then \[\mathrm{PSNR}=20\log(1/\mathrm{RMSE}(\mathbf{S},\mathbf{\hat{S}}))\] (9)
* CC (Cross-Correlation). We used the codes from Open Remote Sensing website ([[https://openremotesensing.net/](https://openremotesensing.net/)]([https://openremotesensing.net/](https://openremotesensing.net/))). The ideal value of CC is 1 if the prediction is perfect.
* ERGAS (Erreur Relative. Globale Adimensionnelle de Synthese). The ERGAS is defined as \[\mathrm{EGARS}(\mathbf{S},\mathbf{\hat{S}})=100d\frac{\mathrm{RMSE}}{\mu}\] (10) for some constant \(d\) depending on the resolution and \(\mu\) is the mean of the ground truth images. The ideal value of ERGAS is 0 if a prediction algorithm is perfect.
* SSIM (Structural Similarity). This is a metric to reflect the similarity between two images. An equation for SSIM can be found in [8]. The ideal value of SSIM is 1 for perfect prediction. We also use the SSIM map to display the similarity values at each pixel location. Bright pixels have high similarity.
* SAM (Spectral Angle Mapper). The spectral angle mapper measures the angle between two vectors. The ideal value of SAM is 0 for perfect reconstruction. For single bands, the angle is zero between two scalar pixels. The exact definition of SAM can be found in [17].
* Q2N: A definition for Q2N can be found in [17]. The ideal value of Q2N is 1. The codes can be downloaded from Open Remote Sensing website ([[http://openremotesensing.net/](http://openremotesensing.net/)]([http://openremotesensing.net/](http://openremotesensing.net/))).