esa-sceva/satcom-chunk-collection huggingface dataset:
# Measurement of Diurnal Variation in Needle PRI and Shoot Photosynthesis in a Boreal Forest
## 4 Discussion
However, before this index can be applied routinely, several important complications need to be overcome. The effect of with-canopy shadowing must be accounted for [21], and correction for illumination geometry may remove apparent PRI variations. For the Hyytiala site and midsummer conditions, it is known that a variation of canopy PRI with the shadow fraction (the fraction of shaded foliage in the field of view of a remote sensor) does not necessarily indicate a variation of leaf (needle) PRI with PPFD [22]. Airborne imaging spectroscopy data acquired before noon, and after correction the spectral composition of light incident on sunlit and shaded foliage, contained no difference in sunlit and shaded needle PRI. Indeed, according to the measurements reported here, a PRI gradient with PPFD is only expected to develop at around noon in a boreal conifer forest, contrary to what has been reported for other species (e.g., [19]). To apply the correction for the spectral composition of incident light, at least the amount of sunlit foliage in the field of view of the sensor is required [42] and, preferably, also the dependence of PRI on this fraction [22; 43]. Unfortunately, this information was not available for the canopy PRI sensor. Further, in a relatively sparse boreal forest, it is also important to take into account the influence of the understory and lower canopy branches [8] as, in midsummer, the understory may contribute up to 40% of the reflected signal in the green spectral region [44]. With appropriate canopy reflectance models, this information can also be retrieved from spectral reflectance data. We have demonstrated that optical detection of the status of the xanthophyll cycle is, indeed, a promising tool for monitoring the LUE of conifer needles and shoots. It can be applied from a remote sensing platform to improve the mapping of carbon sequestration by vegetation on a global scale. The measurements described in this paper can be used to determine the typical daily curves for needle PRI, establish the dependence of needle LUE on environmental conditions and photosynthetic history, and improving the currently existing techniques for remote measurement of carbon sequestration. After quantifying and understanding the phenomena causing the daily needle PRI curve, we can establish physical models for interpreting the signals measured by future hyperspectral satellites.
## 5 Conclusions and Prospects  
In this paper, a new method to ensure the energy autonomy of a WSN has been proposed. It was a question of first setting up the PHPV to predict the amount of energy harvestable by each WS. The predictor's performance was compared with that of the previous predictor, the PHEV and the EWMA, which are widely used. The industrial process, of the drive diagram of a mill at the Larronde mine has been described (types and location of the various sensors). Vibration data was recorded for 31 days at 12 different locations on the process, with a sample taken every minute. To minimize prediction errors while saving data storage space, PHPV exploited the correlation between the predicted power and that measured at the preceding period during the day and at the same period of the earlier day. Compared to the PHEV predictor, PHPV improved the RMSE from 37.2 to 78.32%.  
Based on the possibility of estimating the energy level of each WS deployed in the process, a solution was implemented to better manage the entire wireless sensor network's energy. The objective of the method was to maximize the number of WSs that can communicate in a measurement cycle by promoting energy assistance betweenthe different WS. A minimum QoS was set, and a hierarchy was established between the different WSs. The HEBP was then set up to balance the energy level of the various WSs in the network to allow each of the WSs to communicate information during each measurement cycle. This new protocol allowed up to 100% of the WSs deployed on the system to communicate, compared to only 66% with previous protocols. Overall, the proposed method makes it possible to ensure the energy autonomy of a WSN and not of a single WS, as is the case in most studies. Note, however, that the performance achieved here may be affected during practical implementation by factors such as the dynamic nature of the channel between a needy WS and a self-sufficient WS as well as by losses in the energy-harvested shaping circuit.  
Idea, A.M.; Conceptualization, A.M.; methodology, A.M. and H.M.; Data Acquisition, H.M.; Mechanical-electric conversion A.M. and H.M.; Predictor of the Harvestable Energy from vibrations, A.M. and H.M.; Hierarchical Energy-Balancing Protocol, A.M.; writing--original draft preparation, A.M.; writing--review and editing, H.M.; supervision, H.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Mitacs Canada, funder number: IT26434.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Performance Analysis  
In this section, we evaluate our proposed FGPQ scheme in terms of the communication overhead, computational costs and latency, respectively. Moreover, we give a comparison with the EPLQ scheme [18] and EPQ scheme [19].
## 1 Introduction
The proposed approach is systematically evaluated through numerical simulations under various measurement noise levels, initial state covariances, and across multiple climbing Starlink satellites. Its effectiveness is further assessed using a higher-fidelity dynamics model in which the atmospheric drag is explicitly accounted for.
# Dynamic Prediction Model for Progressive Surface Subsidence Based on MMF Time Function
## 2 Models and Methods  
### Dynamic Process Analysis of Mining Subsidence  
According to movement of a surface point P shown in Figure 1, the surface point P goes through a complicated process. The evolution of its subsidence velocity can be divided into three phases [16; 30; 31], as shown in Figure 2.  
In Figure 2, the initial phase is from the start of surface subsidence until the surface subsidence velocity reaches 1.67 mm/d. The active phase occurs when the velocity exceeds 1.67 mm/d. The weakening phase ends when the cumulative surface subsidence is less than 30 mm for six consecutive months.  
In general, the dynamic process of surface subsidence is a limited growth process [32]. The ideal time function used to describe the dynamic process must have the following characteristics:  
1. When \(t=0\), the subsidence (\(w\)), subsidence velocity (\(v\)) and subsidence acceleration (\(a\)) of a point on the surface are all zero (\(w(t)=0\), \(v(t)=0\), \(a(t)=0\));
2. During the initial phase, the subsidence, subsidence velocity and subsidence acceleration are all small, positive and gradually increasing;3. During the active phase, the subsidence velocity and acceleration are larger, the subsidence acceleration changes gradually to 0 after increasing to the peak, then becomes negative, and gradually decreases after the negative value reaches the peak (\(0\to v_{\max}\to v\), \(0\to+a_{\max}\to 0\to-a_{\max}\to-a\));
4. During the weakening phase, the subsidence velocity gradually becomes smaller and gradually tends to 0, the subsidence acceleration gradually increases from negative and gradually tends to 0 (\(v\to 0\), \(-a\to 0\)).  
As shown in Figure 2, a point \(x_{p}\) on the surface is considered. Its subsidence and subsidence velocity at the time t are \(W(t)\) and \(v(t)\), respectively. It is obvious that the subsidence velocity \(v(t)\) is correlated with the average subsidence velocity \((W(t)/t)\). Moreover, after the subsidence velocity of the surface point \(x_{p}\) reaches its maximum, the subsidence velocity \(v(t)\) is correlated with the potential subsidence \((W_{0}(x_{p})-W(t))\), where \(W_{0}(x_{p})\) is the final subsidence of the point \(x_{p}\). According to Figure 2, when the subsidence velocity of the surface point \(x_{p}\) reaches its maximum, the potential subsidence \((W_{0}(x_{p})\ -\ W(t))\) decreases, and correspondingly, the surface subsidence velocity also decreases. Therefore, the subsidence velocity at the time t is correlated with the average subsidence velocity and the potential subsidence ratio is \(((W_{0}(x_{p})\ -\ W(t))/W_{0}(x_{p}))\). This summary is the foundation of the surface dynamic subsidence modeling.
# Isolated Forest-Based Prediction of Container Resource Load Extremes
## 3 Load Prediction Model  
### Description of the Problem  
Container load prediction is a time-series data analysis task with the goal of estimating the CPU resource usage of the inner container in a given period. The data contain most of the normal values that contribute significantly to the overall predictability, as well as a few extreme values that must be accurately predicted. In addition, considering that container load is affected by a variety of factors, such as memory usage, disk occupancy, etc., we include these factors in our definition of container load data.  
The problem can be described as follows:  
For container load prediction, we set up a k-dimensional time series \(\left(X_{t}=\left[x_{t}^{1},x_{t}^{2},\ldots,x_{t}^{k}\right]\right)\), where the components represent different system metrics, such as CPU utilization \(\left(x_{t}^{1}\right)\), memory usage \(\left(x_{t}^{2}\right)\), and disk usage \(\left(x_{t}^{k}\right)\). The prediction model \(f\) utilizes historical observations \(\left(X_{t-n+1},\ldots,X_{t}\right)\) to estimate the CPU utilization \(\left(\widehat{x_{t+1}^{1}}\right)\) at the next point in time \(\left(t+1\right)\).  
\[\widehat{x_{t+1}^{1}}=f(X_{t-n+1},\ldots,X_{t}) \tag{1}\]  
where \(f\) maps the historical data to predict future CPU utilization and represents the core component in multivariate time series forecasting.  
The choice to forecast data for only one point in time in the future, rather than multiple points in time, was primarily made to avoid the problem of error accumulation in long-term forecasts. Long-term forecasting can lead to an increase in error accumulation over time, affecting forecast accuracy. Therefore, focusing on the forecast at the next point in time minimizes errors and ensures the reliability of the forecast results.
# An Image Denoising Method for a Visible Light Camera in a Complex Sky-Based Background
###### Abstract  
For space target images captured by a sky-based visible light camera, various conditions are influenced by the imaging system itself and the observation environment; these conditions include uneven image background intensity, complex noise, stray light composition, and diverse target forms. A mean wavelet transform algorithm is proposed. This algorithm initially performs mean filtering and wavelet transform filtering on the noise-containing space target images and then performs a wavelet inverse transform on the filtered results to generate the final image. The experimental results show that our algorithm has good denoising performance and can effectively maintain the image details.  
sky-based visible light camera; space target images; denoising; wavelet transform; mean filtering +
Footnote †: journal: Applied sciences
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 5 Methodology and Measurements
The energy consumed in each step, from building to transmitting a packet, is dependent on the efficiency of the implementation and on the hardware used. Usually, the power usage of a microcontroller (\(P_{mcu}\)) is lower than the power required by the radio when transmitting (\(P_{tx}\)). Note that compression reduces the time of transmitting a packet and thus reduces the \(E_{tx}\). However, a 6 LoWPAN-GHC compression attempt may take quite some processing time without yielding enough gain in reducing the packet size. Compression might be impossible or hard and slow. Performing the 6 LoWPAN-GHC compression is only beneficial from an energy perspective when  
\[E_{c,ghc}-E_{c,nhc}<E_{tx,nhc}-E_{tx,ghc} \tag{3}\]  
The last parameter that we consider is the delay. In telecommunication systems, multiple causes of delay can be identified, such as processing, queuing, access, transmission and propagation. Processing and transmission delays are impacted by the compression mechanism. Together, they influence queuing delays. The transmission delay is defined as the packet size divided by the data rate. Compression attempts to decrease the packet size and transmission delay but increases the processing delay. To decrease the total delay, the following equation should be satisfied:  
\[d_{c,ghc}-d_{c,nhc}<d_{tx,nhc}-d_{tx,ghc} \tag{4}\]  
The variable \(d_{tx,ghc}\) represents the transmission delay with 6 LoWPAN-GHC compression, whereas \(d_{tx,nhc}\) represents the transmission delay without 6 LoWPAN-GHC compression. The 6 LoWPAN-GHC processing delay is denoted by \(d_{c,ghc}\), whereas \(d_{c,nhc}\) represents the processing delay for 6 LoWPAN-NHC.
## 5 Testing and Evaluation
### Lab Testing and Objective Measurements  
#### 5.2.1 Drone Responsiveness  
When testing alternative drone control methods, an important consideration is the response time of the drone. In the presented solution, this is the latency between the user performing a gesture and the drone responding to this command. In the presented architecture, commands are transmitted from the HoloLens to the UAV interface app via a Kafka broker on the Internet or a local network. The location of the broker, as well as network conditions, can impact drone response latency.  
To measure this latency, a high-speed camera was used to capture both the user and the drone at 240 frames per second, also described in previous work [35]. Over a series of experiments, a counting of the number of elapsed frames between the user's gesture and the drone reaction yielded average latency timings. Different locations of the Kafka broker were considered and measured separately: the broker being on the same local network as the HoloLens and the UAV interface; the broker being in a different city in the same country; and the broker being in a different country. Figure 11 shows the minimum and the average timings measured in these experiments. With a camera frame rate of 240 frames per second, measurement error is less than 5 ms, which has has no significant impact on the conclusions of this experiment.  
Since a DJI drone consumes commands from its remote controller every 40-200 ms, that degree of latency is unavoidable. In a local area network, the observed latency was minimal, at less than 200 ms, therefore, no worse than that of conventional, handheld remote controls. At more remote broker locales, response latency increases. However, even at around 300 ms, it is still within acceptable limits for drone piloting.
## 2 Statistical Analysis of Tunnel Floor Heave
### Statistical Analysis of Tunnel Floor Heave during Construction  
Through an in-depth investigation of eight tunnels in five projects, the following conclusions are obtained:  
1. Among the eight tunnels investigated, a total of 15 tunnel floor heaves occurred, totaling 2.9 km, accounting for 3.0% of the total length of the eight tunnels. The influencing factors are shown in Figure 2.
2. It primarily happens due to the gently inclined surrounding rock and the water pressure at the tunnel's base.
3. There are six high-ground-stress and large-deformation sections (75%), and two gently inclined surrounding rock sections (25%).
4. The upper arch section mainly caused by high ground stress and large deformation is not abnormal at present after taking measurements; the upper arch section mainly caused by gently inclined surrounding rock was not abnormal after the measurements were taken.  
Figure 1: Proportion of influencing factors of tunnel floor heave during operation period.
# Digitized Reconfigurable Metal Reflectarray Surfaces for Millimeter-Wave Beam-Engineering
## 5 Conclusions  
In summary, we have designed digitized beam-steering metal reflectarray antennas composed of rectangular grooves on a metal plate for the millimeter-wave region. All depths of 1147 metal grooves in an aluminum plate are elaborately manipulated and quantized for designed phase control of high-gain reconfigurable reflectors. By the results of full-wave analysis and near-field experiments, there is no significant change of radiation patterns over two-quantized-bits. The radiation of designed digitized-steered-beam metal reflectarrays are well directed at \(\theta=20^{\circ}\) in a two-dimensional plane for a 2-bit quantized antenna. Remarkably, the radiation characteristics are almost the same as those of the non-quantized cases. The measured peak gain is higher than 31.7 dB for the full \(40^{\circ}\) steered region and gain loss of well-steered beams are 0.2 dB. These beam-aimed antenna in a millimeter wave band can be made by a simple machining, and the phase value of each element is digitized to save the time and cost required for production. The research results can be directly utilized as practical examples such as functional reflective surfaces for a changing channel path and increasing channel distance in millimeter wave and 6G band communication and a variety of wireless communication systems.  
Conceptualization, M.Y. and S.Y.; methodology, M.Y. and Y.B.; software, M.Y.; validation, M.Y. and Y.B.; formal analysis, M.Y. and S.Y.; investigation, M.Y.; resources, M.Y. and J.S.; data curation, M.Y. and Y.B.; writing--original draft preparation, M.Y.; writing--review and editing, Y.B., S.Y. and J.S.; visualization, M.Y. and Y.B.; supervision, J.S.; project administration, J.S.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: Measured (solid line) and simulated (dashed line) antenna gain and return loss (\(\theta=-20^{\circ}\)) versus millimeter-wave frequency of the reconfigurable reflectarray
## 4 Results and Discussion
### Binder Testing Results and Discussion
#### 4.1.5 SEM Test Analysis  
The micro morphologies of virgin binder, SBR-modified binder, EMAA/SBR compound-modified binder are presented in Figure 8. The virgin binder appears homogeneous under microscope, as shown in Figure 8a, but the images of SBR-modified binder shows that the SBR polymer disperse as floccules morphology in virgin binder, see Figure 8b. This phenomenon implies the poor stability of SBR-modified binder. The reason is probably because the low molecular oil fraction of binder was absorbed by SBR polymer, which caused the separation between asphaltene and resin. However, the micro morphology of EMAA/SBR compound-modified binder shows that both EMAA particles and SBR disperse uniformly in virgin binder, as seen in Figure 8c. Therefore, when EMAA was added to SBR-modified binder, the small concentration and aggregation of polymer was weakened. The storage stability of EMAA/SBR compound-modified binder improved. Moreover, the interfacial structure between EMAA, SBR polymer, and binder could prevent the movement of macromolecular chains of modified binder, and also lead to the reduction of initiation and propagation of microcracks in binder. Therefore, the anti-fracture property and the mechanical strength of EMAA/SBR compound modified binder improved.  
Figure 8: SEM photographs of different binders: (**a**) Virgin; (**b**) 3% SBR; (**c**) 4%EMAA/3%SBR.  
Figure 7: Fluorescence micrographs of different binders: (**a**) Virgin; (**b**) 4%EMAA; (**c**) 3%SBR; (**d**) 4%EMAA/3%SBR.
## 2 Literature Overview
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Definitions** & **Symbols** & **Definitions** \\ \hline P\({}_{\text{t}}\) & Transmitted optical power of LED & d\({}_{\text{overlap}}\) & Overlapping distance between two camera coverages \\ N & Total number of neighboring light & & Threshold below which BLE outage occurs \\ N & sources & & \\ N\({}_{0}\) & Noise spectral density & N\({}_{\text{b}}\) & Total number of sources interfering with the BLE spectrum \\ f\({}_{\text{r}}\) & Frame rate & I\({}_{\text{T}}\) & Power received from an interfering source \\ \(\Gamma\) & Total number of strips & d\({}_{\text{\alpha},\text{\beta}}^{\text{max}}\) & Maximum possible distance between LED and camera \\ f\({}_{\text{on}}\) & ON frequency of LED & d\({}_{\text{\alpha},\text{b}}^{\text{crit}}\) & Maximum communication range occurs \\ f\({}_{\text{off}}\) & OFF frequency of LED & \(\Phi_{\text{ins}}\) & Instantaneous data rate \\ t\({}_{\text{r}}\) & Read-out time of a pixel & \(\Phi_{\text{tar}}\) & Target data rate \\ \(\Gamma_{\text{min}}\) & Minimum number of generated strips & & Instantaneous \\  & needed for data decoding & \(\eta_{\text{ins}}\) & signal-to-interference-plus-noise ratio \\ P\({}_{\text{tb}}\) & Power received by BLE receiver & \(\eta_{\text{tar}}\) & Target SINR \\ P\({}_{0}\) & Received power from a reference & \(\kappa\) & Bit error rate \\  & distance by BLE receiver & & \\ d\({}_{\text{r}}\) & Communication distance between & & \\  & BLE transmitter and receiver & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._room or far from the patient. Most studies do not focus on intensive-care scenarios. This type of environment has life or death implications when the connection reliability is questioned. It can be seen from Table 2 that most of the monitoring systems particularly focus on devices with low-power consumption. However, new methods are yet to be proposed to increase reliability. A system focusing on low power, low cost, high security, and enhanced reliability needs to be developed for intensive-care environments.