esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating Underwater Light Regime under Spatially Heterogeneous Sea Ice in the Arctic
## 2 Material and Methods
### In Situ Underwater Light Measurements  
During the campaign, a total of 83 vertical light profiles were acquired using a factory-calibrated ICE-Pro (an ice floe version of the C-OPS, or Compact-Optical Profiling System, from Biospherical Instruments Inc., San Diego, CA, USA) equipped with both downward plane irradiance \(E_{d}(z)\) (W m\({}^{-2}\)) and upward radiance \(L_{u}(z)\) (W m\({}^{-2}\) sr\({}^{-1}\)) radiometers. The ICE-Pro system is a negatively buoyant instrument with a cylindrical shape 10 inches in diameter and is not designed for free-fall casts (as opposed to its open-water version). To perform the profiles, the frame was manually lowered into an auger hole that had been cleaned of ice chunks. Once it was underneath the ice layer, fresh clean snow was shovelled back in the hole to prevent the creation of a bright spot right on top of the sensors. Great care was taken not to pollute the hole surroundings (footsteps, water and slush spillage from the auger drilling, etc.). The operator then stepped back 50 m, while keeping the sensors right under the ice, to avoid any human shadow on top of the profile. The frame was then lowered manually at a constant descent rate of approximately 0.3 m s\({}^{-1}\). The above-surface atmospheric reference sensor was fixed on a steady tripod standing on the floe approximately 2 m above the surface and above all neighbouring ice camp features. Data processing and validation were performed using a protocol inspired by the one proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (1988). Measurements were made at 19 wavelengths: 380, 395, 412, 443, 465, 490, 510, 532, 555, 560, 589, 625, 665, 683, 694, 710, 765, 780 and 875 nm. For this study, \(E_{d}\) and \(L_{u}\) spectra were interpolated linearly between 400 and 700 nm every 10 nm. In situ diffuse attenuation coefficients (\(K\)) for both \(E_{d}\) (\(K_{d}\)) and \(L_{u}\) (\(K_{Lu}\)) were calculated on a 5 m sliding window (10-15 m, 15-20 m, \(\ldots\), 70-75 m, 75-80 m) starting at 10 m depth to reduce the effects of surface heterogeneity. A total of 72,044 non-linear models were calculated to estimate both \(K\) coefficients from Equation (1) (83 profiles \(\times\) 14 depths \(\times\) 31 wavelengths \(\times\) 2 radiometric quantities (\(E_{d}\), \(L_{u}\))). A conservative \(R^{2}\) of 0.99 was used essentially to filter out noisy profiles. 42,407 models were kept for subsequent analysis.
# Energy Efficiency Improvement in Reconfigurable Photovoltaic Systems: An Evaluation of Team Systems
## 2 Sizing Ratio in GCPV Systems
### Simulation Procedure for the Optimal Sizing Ratio Estimation  
A simulation procedure for the sizing ratio optimal value (\(R_{S\;OPT}\)) computation, in terms of yearly energy production, is presented in [27]. Figure 2 shows the simplified block diagram of this procedure, which has been implemented using MATLAB.  
The available power at the inverter input (\(P_{DC}(t)\)) and the energy delivered by the PVG (\(E_{DC}\)) are computed from data of irradiance \(G(t)\) and ambient temperature \(T_{A}(t)\) at the considered location. Considering the inverter efficiency model, the inverter output power (\(P_{AC}(t)\)) and the value of the energy injected into the grid (\(E_{AC}\)) can also be estimated.  
Finally, the energy efficiency of the GCPV system (\(\eta_{E}\)), defined as the ratio between \(E_{AC}\) and \(E_{DC}\), is computed.  
Formally speaking, this simulation procedure only estimates the energy efficiency of the GCPV system (\(\eta_{E}\)) for a given value of \(R_{S}\), since the values of \(P_{INV}\) and \(P_{PVG(GTC)}\) remain constant throughout the simulation.  
Therefore, the determination of \(R_{S\,OPT}\) requires multiple and iterative executions of this procedure, introducing a slight modification on \(P_{INV}\) (and then on \(R_{S}\) modification) between simulations. Once all simulations have been completed, the value of \(R_{S\,OPT}\) will be the one corresponding to the highest energy efficiency (\(\eta_{E}\)) computed.  
The numerical models used for the characterization of the PVG and the central inverter are described in the next subsection.
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 3 Results
### Factors Influencing the Success of Afforestation on Sandy Dunes  
The success of afforestation works from sand dunes depends on several factors, from which the most important ones are typography, soil moisture, and microsite conditions.  
A great example of the importance of topography comes from a _Tamarix aphylla_ plantation from Israel. Here, the trees from steep dunes were larger, older, and better supported by the litter layer and water-repellent soil horizon. On the other hand, trees from flat dunes were more rare, stunted, and lacked both the litter and water-repellent soil. Trees had better growth rates on steep slopes as these facilitated a better subsurface water accumulation, while the tree litter allowed for a better moisture retention. Flat dunes lacked this dynamic, having scarcer water infiltration and poorer tree performance [91].  
Similar cases were recorded all around the Mediterranean. _Pinus pinea_ and _P. halepensis_ plantations from Tunisia's coastal dunes exhibited stress and reduced growth [100]. Scots pines from the Baltic Sea were sensitive to drought and temperature, regardless of the local site conditions, with positive results during winter-spring and summer, and declining ones during warm and dry periods [100].  
Other examples of the impact of micro-typography come from Southern Thailand, where the tree species from Bang Boet dunes showcased more diversity and abundance on the sheltered side, where wind and salt exposure were more reduced [72]. _Casuarina equistifolia_ trees from Senegal also showed better results in interdune depressions and dune flanks.  
Finally, the Culbin Forest from Scotland is a great example of the long-term impact of afforestation on dune characteristics. Even though sand texture remained constant, older trees improved moisture retention as organic matter accumulated over time [101].  
In summary, we can identify the main factors that determine the success of afforestation works on sand dunes as: Topography (with its impact on water flow and exposure conditions), Soil moisture (especially retention capacity and water-repellent soil layers), and Microsite variations (like slope orientation, wind exposure, or salt spray).  
Secondary factors are the specific species response to seasonal moisture and the long-term soil changes brought about by organic matter.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 2 Background
### Tamper-Evident Logging Using Merkle Trees  
#### 2.2.1 Merkle Tree Structure  
A Merkle tree is a binary hash tree in which each leaf is labeled with the cryptographic hash of a data block and every interior (non-leaf) element is labeled with the hash of the concatenated labels of its child elements. It enables quick verification of the integrity of individual data elements without requiring the entire dataset. The topmost hash, known as the root hash or Merkle root, functions as a digest of all the data underneath it. Any changes in data result in a corresponding change in the root hash.
## 1 Introduction  
Strokes and spinal cord injuries (SCIs) are primary causes of long-term difficulties in walking, which can significantly impact a person's mental, emotional, and social well-being [1]. After experiencing a stroke, approximately 66% of survivors encounter challenges with walking right away. Even six months later, more than 30% continue to require assistance for walking [2]. These walking disabilities impose limitations that increase the risk of developing other chronic health conditions, including osteoporosis, cancer, cardiovascular diseases, and diabetes. Furthermore, individuals with walking limitations often face restricted activities at home and within their communities, leading to social isolation and confinement [3]. Consequently, gait rehabilitation programs are typically recommended for stroke victims and SCI patients to prevent additional health issues and facilitate the recovery of their walking abilities. The most common types of gait rehabilitation used by therapists include treadmill exercise and traditional overground gait training. Overground gait training, which is a commonly used approach, involves a physical therapist observing and adjusting an individual's walking on a regular floor surface [4; 5]. It encompasses practice on various terrains and includes specific exercises for gait improvement. This approach is used across diverse healthcare settings, highlighting its functional role in physical therapist education and practice. An emerging approach to gait rehabilitation, utilizing a treadmill with body weight support (BWS), holds significant promise in the field. This method offers an avenue for severely affected patients to align with contemporary principles of motor learning, emphasizing a task-specific approach. This approach involves the assistance of two therapists who facilitate limb placement and oversee trunk movements [6]. When compared to conventional overground walking, patients exhibit increased symmetry, reduced spasticity, and enhanced efficiency during treadmill-based walking with BWS [4; 7]. Advancements in technology have led to the development of orthotic devices prioritizing biomechanical alignment in lower extremities. Robot-assisted gait training, coupled with physical therapy, increases the likelihood of stroke survivors achieving independent gait [8]. Various organizations are designing exoskeletal devices, with the Lokomat [9] and GEAR [10] being prominent rehabilitation tools using a body-weight-support treadmill system to enhance movement in weakened limbs. While proven effective, these exoskeletal devices often still incorporate body-weight-support harnesses, causing challenges and discomfort for therapists and patients [11; 12]. Refer to Table 1 for examples of harness-free exoskeletal devices.
# A Case Study Comparing Methods for Coal Thickness Identification in Complex Geological Conditions
A comparative analysis of two methods for coal thickness identification under complex geological conditions revealed that the BP neural network is not only effective in standard geological environments but also highly adaptable to more complex settings. This adaptability enhances the reliability of inversion results, allowing for more accurate quantitative interpretations of coal thickness. These improvements boost both the precision and reliability of geological interpretations and resource estimations. The integration of seismic multi-attribute methods with well log-constrained impedance inversion technology proves highly effective for predicting coal thickness. Moreover, by leveraging simulation technology for improved situational awareness and conducting hypothetical scenario analysis for proactive risk assessment [22], the efficiency and safety of mining operations are enhanced. These advancements provide a robust technical foundation for the intelligent development of coalmines, marking a significant step forward in mining innovation.
# Replication-Based Dynamic Energy-Aware Resource Provisioning for Scientific Workflows
## 2 Related Work
Recovery is the final phase in the CFT paradigm. Fitness checking is used to determine whether the expected failure is caused by the task or by the virtual machine. When a job fails, the post-prediction check-pointing (PPC) mechanism is utilized to recover. The post- or pre-replication overlapped migration approach can be used to recover VM failure. Experiments show that the suggested CFT model improves process execution reliability in a cloud context. The authors in [35] presented a replication-based partial critical path (R-PCP) workflow scheduling strategy for upcoming data transmission and completing process activities while staying within deadlines and financial restrictions. The first data placement step in the proposed method clusters and distributes datasets depending on their relationships. R-PCP schedules activities based on data proximity and dynamically maintains a dependency matrix for locating created datasets. To reduce runtime-dataset migration, the authors use inter-datacenter task replication and dataset replication to assure dataset availability. R-PCP beats previous approaches by successfully reducing data transmission and performing all chosen operations within the user-specified budget and timescale, as evidenced by simulation results with four workflow applications [35]. Various energy saving protocols, such as a budget-constrained energy consumption optimization approach and particle swarm optimization, have been highlighted in [36; 37; 38]. The proposed R-DEAR strategy saves energy through optimization of resource utilization as compared with the existing energy-saving protocols. An overview of the related work has been given in Table 1.
###### Abstract  
The objective of this paper was to evaluate the use of in situ normalized difference vegetation index (\(\mathrm{NDVI}_{\mathrm{is}}\)) and Moderate Resolution Imaging Spectroradiometer NDVI (\(\mathrm{NDVI}_{\mathrm{MD}}\)) time series data as proxies for ecosystem gross primary productivity (GPP) to improve GPP upscaling. We used GPP flux data from 21 global FLUXNET sites across main global biomes (forest, grassland, and cropland) and derived MODIS NDVI at contrasting spatial resolutions (between \(0.5\times 0.5\) km and \(3.5\times 3.5\) km) centered at flux tower location. The goodness of the relationship between \(\mathrm{NDVI}_{\mathrm{is}}\) and \(\mathrm{NDVI}_{\mathrm{MD}}\) varied across biomes, sites, and MODIS spatial resolutions. We found a strong relationship with a low variability across sites and within year variability in deciduous broadleaf forests and a poor correlation in evergreen forests. Best performances were obtained for the highest spatial resolution at \(0.5\times 0.5\) km). Both \(\mathrm{NDVI}_{\mathrm{is}}\) and \(\mathrm{NDVI}_{\mathrm{MD}}\) elicited roughly three weeks later the starting of the growing season compared to GPP data. Our results confirm that to improve the accuracy of upscaling in situ data of site GPP seasonal responses, in situ radiation measurement biomes should use larger field of view to sense an area, or more sensors should be placed in the flux footprint area to allow optimal match with satellite sensor pixel size.  
F 2019 11, 1656; doi:10.3390/rs11141656
## 4 Simulation Results
### Current Response in the d-q Axis  
Figure 13 presents the \(d\)-\(q\) current simulation responses. The proposed method is effective for the sensorless control strategy. In the sensorless mode, the current reference \(i_{q}\)* was discovered to increase or decrease in accordance with the speed setting. For example, if the speed setting was increased from 0.26 p.u-speed to 0.32 p.u-speed, the motor required more current, causing an increase in the current \(i_{q}\)* from 0.0308 to 0.0310 p.u-current, and vice versa. In general, the current \(i_{q}\) smoothly followed the reference current \(i_{q}\)*. The current \(i_{d}\) was close to zero, following the setting of the current reference \(i_{d}\)*. A small spike in \(i_{d}\) was observed when the speed setting changed. A similar spike was observed for \(i_{q}\)* and \(i_{q}\) in response to a stepped speed change. Larger speed changes resulted in larger spikes.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Speed (PU)**} & \multirow{2}{*}{**Speed (rpm)**} & \multirow{2}{*}{**Electrical Rotation Time (s)**} & \multirow{2}{*}{**Mechanical Rotation Time (s)**} & \multirow{2}{*}{**Speed Response (rpm)**} & \multirow{2}{*}{**Error rpm (\%)**} \\  & & & & & \\ \hline
0.18 & 738 & 0.0203 & 0.0812 & 738.92 & 0.12 \\
0.32 & 1312 & 0.0114 & 0.0456 & 1315.79 & 0.29 \\
0.48 & 1968 & 0.0076 & 0.0304 & 1973.68 & 0.29 \\
0.65 & 2665 & 0.0056 & 0.0224 & 2678.57 & 0.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Speed calculations from rotor position response.  
Figure 13: Simulation results for current responses in the \(d\)-\(q\) axis.
## 1 Introduction  
Interconnecting space nodes are the current developmental trend and will soon be an inevitable requirement for future intelligent constellation communication in part due to satellite networks. One of the most important design objectives of satellite network communication is to allocate system resources to balance the needs of communication and perception [1; 2; 3; 4]. Intelligent satellites carry out cooperative transmission and resource management which have become the key to real-time observation and low delay information transmission. Although the spread spectrum system can realize the common transmission of new services, the inter-symbol interference (ISI) is caused by the fact that the spread spectrum codewords cannot be completely orthogonal [5; 6]. In this paper, the cooperative relay transmission of interference management is realized by optimizing the signal space structure to improve the transmission efficiency and quality of satellite networks.
# Argininemia: Pathophysiology and Novel Methods for Evaluation of the Disease
## 3 Pathophysiology
### Arginase 1/Arginase 2  
Arginase, initially identified in mammalian liver tissue in 1904, serves as the fifth and final enzyme of the urea cycle [36]. It catalyzes the hydrolysis of L-arginine to ornithine and urea. Ornithine is subsequently recycled for future urea production and serves as a precursor to polyamines, proline, and other products. Meanwhile, urea is transported through blood to the kidneys to be excreted in urine [23].  
Two major isoforms of arginase exist, namely ARG1 and arginase 2 (ARG2), which are encoded by different genes in mammals. While the two isoforms share enzymatic properties, they differ in approximately 40% of their amino acid sequence, as well as in their cellular location, tissue distribution, and metabolic functions [19; 36; 37].  
ARG1 is mainly expressed in the liver cytosol, but can also be detected in erythrocytes, vasculature, and immune cells, such as M2-like macrophages [19; 38; 39]. It is a 195 kDa metalloprotein composed of three similar subunits, which requires manganese (Mn\({}^{2+}\)) metal ions for maximal catalytic activity and structural stabilization. Each subunit contains a highly conserved binuclear Mn\({}^{2+}\) cluster with metal-coordinating histidine and aspartic acid residues at the active site [23; 40].  
Mutations in the ARG1 gene lead to deficient or absent ARG1 activity, resulting in the accumulation of arginine and other nitrogenous metabolites. Arginemia is characterized by an almost 50-fold elevation of intracellular hepatic arginine [41].  
ARG2 is found in the mitochondria of the kidneys and the prostate, as well as in lower amounts in other extrahepatic tissues, including the brain, gastrointestinal tract, lactating mammary glands, and macrophages [42; 43; 44]. Its role encompasses arginine homeostasis and the biosynthesis of polyamines, proline, creatine, citrulline, \(\gamma\)-aminobutyric acid (GABA), glutamate, and nitric oxide (NO) [43; 45].
## 2 Methodological Background
### PID Control
#### 2.2.1. Effects of \(K_{p}\), \(K_{i}\), and \(K_{d}\) on the PID Controller  
The coefficient of the proportional term \(K_{p}\) is directly multiplied by the error term \(E\) so the input error term \(E\) is proportional to the output \(K_{p}\)*\(E\). If only \(K_{p}\) is used in the controlled system and it is a non-first-order system (type one), there will be a steady-state error in the output [21]. Generally, steady-state errors will be eliminated by \(K_{i}\) to achieve system stability, which will be explained in the next paragraph.  
To eliminate the steady-state error, the integral control term \(K_{i}\) can be added to increase the bias term in the system (i.e., biasing). Integrating the error term over a period of time and multiplying it by the integral gain value \(K_{i}\) yields the output value of the integral control. Over time, even if the error is small, the value of the error integration will become increasingly larger, causing the steady-state error to gradually decrease until it equals zero.  
The differential control term \(K_{d}\) utilizes the first-order differentiation to consider future errors. It is multiplied with the first derivative of the error as the output value. For example, when the automatic controlled system adjusts the error, the system may oscillate or even be unstable. At this time, the  
Figure 2: Flowchart modeling the environmental control system.  
differential control can predict the trend of the error change. The larger the value of differentiation is, the more rapidly the controlled system can react.  
The three gain control parameters in PID control do not necessarily have to be used at the same time. Instead, they can be selected according to different application requirements to reduce the computational burden. For example, a proportional and derivative controller (PD controller) can improve the dynamic characteristics of the system during the adjustment process. However, although the rise time of the PD controller is shorter, it cannot effectively suppress the steady-state errors of the system. A proportional and integral controller (PI controller) has a slower response time. However, as it can eliminate the steady-state error, it is often applied in the industry. In this paper, we supposed to use \"PID controller\" just for generic name, but may including PI controller or PD controller.
## 6 Simulations and Discussion
### Simulations
#### 6.1.2 Simulation Results  
Simulation 1  
Table 3 shows the network traffic frames recorded from the simulation. The simulation result shows that there was no lost frame during the communication. Therefore, the DSP algorithm can provide fault-tolerant communication for WSNs.  
Simulation 2  
Figures 11-13 show comparisons of the network traffic under the DSP-based approach and the standard HSR protocol in cases 1, 2, and 3, respectively. The horizontal axis of these figures shows the number of unicast frames sent from the source to the destination, and the vertical axis shows the number of network traffic frames generated and delivered in the network when the source node sends the unicast frames to the destination node.  
Figure 11: A comparison of traffic performance in Case 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Sent Frames** & **Number of Received Frames** & **Number of Lost Frames** \\ \hline
10 & 10 & 0 \\
20 & 20 & 0 \\
30 & 30 & 0 \\
40 & 40 & 0 \\
50 & 50 & 0 \\
60 & 60 & 0 \\
70 & 70 & 0 \\
80 & 80 & 0 \\
90 & 90 & 0 \\
100 & 100 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of Simulation 1.  
The simulation results in Case 1 show that the proposed DSP-based approach reduces network traffic by up to 88%, 80%, and 76% compared with the standard HSR protocol, the QR technique, and the PL technique, respectively. The network traffic of the DSP-based approach is the same as that of the DVP approach.  
The simulation results in Case 2 show that the proposed DSP-based approach reduces network traffic by up to 84%, 73%, and 68% compared with the standard HSR protocol, the QR technique, and the PL technique, respectively. The network traffic of the DSP-based approach is the same as that of the DVP approach.  
Figure 12: A comparison of traffic performance in Case 2.  
Figure 13: A comparison of traffic performance in Case 3.  
The simulation results in Case 3 show that the proposed DSP-based approach reduces network traffic by up to 80%, 66%, and 59% compared with the standard HSR protocol, the QR technique, and the PL technique, respectively. The network traffic of the DSP-based approach is the same as that of the DVP approach.