esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Their mapping of soil properties was dependent on convergence with satellite-derived estimates of LAI\({}_{\text{max}}\), which saturated at 7.0 m\({}^{2}\) m\({}^{-2}\). We performed sensitivity tests, whereby baseline values for the available water storage capacity (ASWC) of soils defined by [PERSON] _et al._[24] were varied by \(\pm\)50% to assess the effect on modeled predictions of the current distribution of 20 western tree species. In three cases, altering ASWC values shifted the predicted ranges of species by \(>\)20% from their known distributions on surveyed field plots [25]. In this paper, we accept the baseline values of soil properties delineated for forested regions by [PERSON] _et al._[24] and expand on the approach to assess the extent that soil properties might buffer or magnify the effects of climatic variation on LAI\({}_{\text{max}}\) and potential forest productivity in western North America. Specifically, we contrast conditions in a cool, wet period (1950-1975) with a notably generally warmer and drier one (2000-2009). If climatic variation were sufficient to affect GPP and growth potential between the two periods, seasonal changes in the frequency of frost, the intensity of drought and evaporative demand should be evident, along with changes in LAI\({}_{\text{max}}\). With the exception of the presence or absence of a heavy snowpack, which favor some species over others, climatic effects should be most important during the growing season when solar irradiance is high and canopy development peaks. We recognize, however, that in places where the temperature remains above freezing, considerable photosynthesis is also possible during early spring and late autumn [26]. To account for photosynthesis that might occur during the dormant season, we parameterize a forest growth model, 3-PG (Physiological Principles Predicting Growth) developed by [PERSON] and [PERSON] [27] for the most widely distributed evergreen conifer in the study area, Douglas-fir (_Pseudotstuga menziesii_) [28].
# Enhanced Pet Behavior Prediction via S2 GAN-Based Heterogeneous Data Synthesis
## 3 Pet Behavior Prediction via S2 GAN-based Data Synthesis
### S2 GAN-Based Data Synthesis  
S2 GAN-based data synthesis (Figure 4) was performed when pets moved out of the camera's capturing range or when skeleton extraction failed. The S2 GAN model has two key components: a generator and a discriminator. The generator receives sensor sequence data and action class labels as inputs to synthesize fake skeleton features to supplement the real data, filling in missing parts of the dataset.  
Figure 3: Key points used for skeleton extraction.  
Meanwhile, the discriminator distinguishes between the fake data produced by the generator and the real data. Throughout the training process, the discriminator learns to discern authenticity while the generator progressively improves and crafts sophisticated fake data to deceive the discriminator.  
Figure 5 shows the generator and discriminator structures. The generator takes in preprocessed sensor sequence data and one-hot encoded class labels to generate x and y coordinates for skeleton points within a 2D image. To match the shape of the incoming sequence data, class data are processed through a dense layer and concatenated based on their features. The structure is designed such that while it comprises the same layers, the x and y coordinates are processed separately and concatenated toward the end. This arrangement necessitates passing through two instances of time-distributed convolution 2D transpose layers and one convolution LSTM 2D layer to ensure spatial consistency and temporal continuity across all frames.  
The discriminator receives real and fake video sequences and class labels as inputs to determine the authenticity of the given video sequence. Similar to the generator, class data are processed through a dense layer to conform to the shape of the incoming sequence data and concatenated with the video sequences based on their features. The network subsequently passes through two time-distributed convolution 2D layers and one max-pooling 2D layer, thereby reducing dimensions and distilling features. Finally, the convolutional  
Figure 4: S2 GAN-based data synthesis process.  
Figure 5: S2 GAN-based data synthesis network structure. (**a**) Represents the generator, and (**b**) represents the discriminator.  
LSTM 2D layer considers features at each timestep through convolutional operations, extracting refined features in a reduced dimension. The process culminates in a dense layer with a sigmoid activation function, outputting the probability of the input video sequence being real or fake.
## 2 Remote Sensing Technologies for the Monitoring of Coastal and River Delta Regions
### Ground Deformation Analyses
Coastal ground subsidence has been investigated worldwide, in locations such as Houston (U.S.) [246], Mexico City, the Mississippi River delta (U.S.) [247], Norfolk and Miami Beach on the U.S. Atlantic coast [248], the Ionian coast of south-eastern Sicily (Italy) [249], the Volturno River Plain (Italy) [250], the Friuli Venezia Giulia coastal plain (Italy) [251], the Venice coastland (Italy) [252], the Gippsland Basin (Australia), and many other locations. Deltaic regions are significantly more susceptible to subsidence than other coastal areas. Previous studies have reported that many modern river delta regions are sinking due to ground subsidence and SLR [253], such as the Ganges-Brahmaputra-Meghna delta, the Mekong Delta [51], the Mississippi River Delta [254], the Yellow River Delta [255; 256], the Yangtze River Delta, and the Pearl River Delta [257]. The Ganges-Brahmaputra-Meghna delta is the second largest globally, with over 100 million residents. A wide range of subsidence rates has been reported in the literature; however, the causes of subsidence in the delta are still not well understood, and reliable subsidence data for the whole region are scarce [258]. Ground subsidence has also been reported in the Yellow River Delta, mainly due to oil extraction and sediment consolidation and compaction [255; 256]. Excessive groundwater withdrawal, rapid urbanization, sediment consolidation, and compaction are responsible for the ground subsidence of the Yangtze River Delta [182; 259]. Ground subsidence in densely populated deltas is also a significant source of uncertainty in RSLR calculations. To show the potential of InSAR methodologies, we portray in Figure 2 some examples of ground displacement maps. Specifically, Figure 2 shows the mean ground displacement velocity maps of the Saint Petersburg (a), Shenzhen (b1,b2), and Shanghai (c) regions, respectively. As detailed in [260; 261; 240], the ground deformation in these coastal regions is mainly due to terrain subsidence linked to land reclamation projects. Figure 2: The geocoded mean ground displacement velocity maps of the coastal regions of Saint Petersburg (**a**), Shenzhen (**b1**,**b2**), and Shanghai (**c**). (**a**) The geocoded mean line-of-sight deformation velocity map of Saint Petersburg retrieved using Sentinel-1A/B descending track images acquired from 2016 to 2018 (refer to [261]); (**b1**) The map of 2007–2010 vertical (subsidence/uplfit) deformation velocity derived from ENVISAT ASAR images (refer to [240]); (**b2**) The map of vertical mean deformation velocity derived from Sentinel-1A data acquired from June 2019 to June 2020; (**c**) The LOS mean displacement velocity map retrieved by the COSMO-SkyMed dataset acquired from 2013 to 2016 (refer to [240]).
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 2 Defining the Deployment Problem
### Notation and Convention  
To ensure clarity and consistency, standardized notation is used throughout the analysis. The indexing and numbering of satellites and their parameters within the constellation are summarized in Table 1.  
Deployment Stages: Stages are indexed by \(k\), where \(k=0\) at initial deployment when all satellites are combined as a single unit. The current stage increments following the disengagement maneuver of satellites in the previous stage. Full deployment is reached at \(k=k_{max}\), where \(k_{max}=\log_{2}n\), and \(n\) is the total number of satellites.  
Satellite Indexing: At each stage \(k\), one satellite is designated as the reference satellite, denoted \(S_{k,0}\). The remaining satellites are indexed based on their initial proximity to the reference satellite: \(S_{k,1},\ S_{k,2}\dots\ S_{k,m}\), where \(m=2^{k}-1\). By convention, the reference satellite begins in low-drag mode, while the rest begin in high-drag mode.  
Angular Separation: Differential drag control is based on angular separation dynamics. The relative position of each satellite in a given stage is defined by its angular separation \(\theta_{k,i}\) from the reference satellite \(S_{k,0}\), where \(i=1,\ 2,\ \dots,\ m\). The rates of change are denoted by \(\dot{\theta}_{k,i}\) (angular velocity) and \(\ddot{\theta}_{k,i}\) (angular acceleration).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Description** \\ \hline \(k\) & Deployment stage index \((k=0,\ 1,\ \dots,\ k_{max})\) \\ \(n\) & Total number of satellites, \(n=2^{x}\) with \(x\in N\) \\ \(S_{k,0}\) & Reference satellite at stage \(k\) \\ \(S_{k,i}\) & \(i\)th satellite at stage k from \(S_{k,0}\) by proximity \\ \(\theta_{k,i}\) & Angular separation between \(S_{k,i}\) and \(S_{k,0}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dispersal notations and descriptions.  
Disengagement Mechanics: The separation mechanism imparts a differential velocity change (\(\Delta v\)) to the two resulting satellites, with one receiving \(+\Delta v\) and the other \(-\Delta v\). The satellite experiencing the most consecutive negative \(\Delta v\) instances across stages is designated as the reference. By convention, satellites with odd \(i\) values (\(i=1,\ 3,\ \ldots\)) receive \(+\Delta v\), while even-indexed satellites receive \(-\Delta v\).
# Mobile-Application Loading-Animation Design and Implementation Optimization
## 2 Materials and Methods  
After analysis of the complex interplay of psychophysical perception of motion and subjectively experienced time, we extracted other important experience parameters that influence wait time perception and user satisfaction. This research was planned as subjective duration judgment and quality assessment based on psycho-visual experiments that represent the fundamental and most reliable ways to assess users' QoE, focusing on the impact of temporal factors (e.g., wait times) on end users' QoE assessments.  
Subjective duration and passage of time are commonly measured using psychophysical tasks. Based on previous research mentioned earlier, prospective/ retrospective timing has a key influence on duration judgment. Since our experiment was based on an experimental within-group factorial design, the retrospective measure was not an option. In our experiments, time-interval duration was fixed across trials; only the stimulus (the type of loading animation) varied. Participants classified each stimulus on a 5-point scale, ranging from long/short and slow/fast, and provided a numerical estimate of its duration in seconds. For each task, the mean judgment assigned to a given stimulus provided a measure of its apparent speed and duration. The numerical estimate was compared to the actual duration of each animation [46].  
Even though speed is rated highest in the user experience (UX) hierarchy, the aesthetics and semantics of loading animations are important for QoE as well, since they are a part of the basis for achieving higher user delight. The choice of parameters in Experiment I was related to the way users perceive, retrieve, and process information from loading animations. Therefore, perceptual fluency and ease of extracting information were measured with aesthetic judgment, emotional and functional impact with the semantic differential, and cognitive load and stimulus novelty (processing fluency) through interest. The impact of visual attention was determined with eye motion in Experiment II. An eye-tracking tool was the key objective method to measure attention.  
The choices of stimuli were based on user-experience experiments that employed visually and semantically distinct types of loading animations, e.g., loop, progress bar, identity animation, etc. Each type differed in terms of animation-graphics style: its novelty, meaning, level of abstraction, and relation to the application's functions. Frame rate and timing functions were applied consistently. Four out of eight animations were based on repetitive spinning: the most common type of loading animation. Three animations had novel and narrative content related to the application's function (coffee ordering). We assumed that adding meaning and novelty to an animation would result in a higher-quality user experience (Figure 1).
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
###### Abstract  
The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle's acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management.  
water cycle; hydrological cycle; precipitation; water resources; climate change; satellite; remote sensing +
Footnote †: journal: Remote Sensing  
13 September 2019; Accepted: 30 September 2019; Published: 2 October 2019
# Time Modulated Arrays: From their Origin to Their Utilization in Wireless Communication Systems
## 1 Introduction  
Wireless services demanding more mobility, capacity, and robustness have placed wireless communications as the fastest growing sector of the telecommunication industry nowadays. As a result, the increasingly limited wireless medium has to be smartly exploited and antennas are called to play an important role to achieve such a goal. A proper combination of antenna features and signal processing allows us to jointly and efficiently act in the space, frequency, and time domains in order to provide suitable alternatives to the complex task of faithfully receiving the desired signals and efficiently mitigating the unwanted ones.  
One of the primary objectives of an intelligent system supported by a sensor network is its sustainability. The investigation of new technologies allowing for reducing the power consumption of sensor networks is aimed towards the design of smart antennas with low loss, muting and adaptive beamforming features. Additionally, such a smart antenna technology has to consider the hardware complexity, making the solution feasible in the context of sensors. Moreover, antenna devices pertaining to sensor networks must be capable of sensing the external electromagnetic environment, thus properly reconfiguring the radiation characteristics of the generated field to guarantee both the required quality of service of the communication link and a reduced power consumption.  
However, some technical challenges remain in order to develop the adequate antenna technologies capable of supporting the aforementioned features in a limited physical space that the mobility demand dictates. In fact, today's wireless standards consider multiple-antenna techniques [1; 2; 3] in order to exploit space diversity, spatial multiplexing and beamforming to achieve better levels ofreliability and capacity. Such advantages, however, are obtained at the expense of increased system complexity, which may be unaffordable in small-size and low-cost sensor devices with a required low-power consumption.  
The concept of TMA is a feasible multi-antenna technique that provides a significant hardware simplification. Furthermore, TMA, typified as unconventional phased arrays architectures, have been intensively researched during the last few years at an increasing pace [4].
###### Abstract  
A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network's lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Java Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.  
ICOA; IJO-LF; WSN; energy efficient routing +
Footnote †: journal:
## 2 Production of Lipases
### Production of Recombinant Lipases
The influence of lipase was also improved by the addition of surfactants that might help the lipase to stay in soluble form even at high concentration. The combination of optimized host, vector, surfactant and media resulted in an 18 fold increase in lipase production (214 units/mL) [62]. Another study investigated the optimal conditions for an _Acinetobacter haemolyticus_ lipase production in _E. coli_. The authors observed a 70% improvement using the following optimal conditions: OD\({}_{600}\) equal to 0.6 (before induction), IPTG (Isopropyl \(\beta\)-d-1-thiogalactopyranoside) equal to 0.5 mmol/L, post-induction temperature equal to 40 \({}^{\circ}\)C, and post induction time equal to 16 h [63]. The use of low-cost components, mainly agro-industrial wastes, for recombinant lipase production could be a good alternative to decrease lipase production costs. [PERSON] et al. [64] optimized the production of a lipase from _Geobacillus_ sp. in _E. coli_ after cultivation in low cost substrates using RSM. A maximum activity of 164.37 U/mL was achieved using 1.0 g/L of molasses, 2.29 g/L of fish waste, 3.46 g/L of NaCl, and 0.03 mM of IPTG. Besides the nutrient composition of the cultivation media, the fermentation modes are also extremely important for scaling up recombinant lipase production. They include batch, fed-batch, and continuous cultivation systems. [PERSON] et al. [65] assessed fed-batch operational modes for producing the recombinant lipase B from _Candida antarctica_ under the constitutive promoter PGK in _K. phaffii_. More specifically, the continuous mode was proven to be more effective in the long run. It was estimated that using continuous mode, CALB production was almost six times greater than with the fed-batch after six weeks of cultivation. Figure 1 illustrates sequential approaches for recombinant lipase production.
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
## 4 Simulation and Results
### Simulation Results  
The control performance comparison between the controllers with and without the MFC is demonstrated in Figure 4. It could be noticed that overshoot was reduced with the MFC, resulting in shortened response time.  
Then, the simulation was conducted using a class D sedan with a six-speed automatic transmission randomly selected in CarSim. The reference speed range was from 0 to 57 km/h with frequent acceleration and deceleration. We used this profile to assess the performance of the controller in tracking vehicle speed.  
The results of Cases 2 and 3 are shown in Figures 5 and 6. Results from the PID controller, the ADRC controller, and the MFC-ADRC controller were compared. When traveling on the level road (Case 2), all three controllers were well-tuned and performed well. A relatively large error was found at the point where the speed abruptly changed when using the PID controller. The average tracking error of the PID controller was 3.31%, and the average tracking error of the ADRC controller was 1.58%. The MFC-ADRC controller showed the best performance with an average tracking error of 0.86%. The statistical indicators of controller performance are shown in Table 3.  
Case 3, in which the control parameters were unchanged, had a vehicle mass of 2100 kg, and the road was inclined upward at 6\({}^{\circ}\). The purpose of this case was to test the adaptability of the three controllers when the vehicle properties and the road conditions have changed. The PID controller performed badly (Figure 6). The ability of the MFC-ADRC controller to detect and correct disturbances makes the vehicle responsive to changes in driving conditions. The statistical indicators for Case 3 are shown in Table 4.  
Figure 4: Speed tracking results for Case 1.  
Figure 7 clearly shows the differences in the performance of the three controllers between Cases 2 and 3. The MFC-ADRC was highly adaptable when the vehicle mass and the road conditions were changed. We analyzed the control output in Case 3 to find the reasons for the good performance of the MFC-ADRC. The control output of the MFC was a major part of the total control output, and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Algorithm** & **Max Error (km/h)** & **Mean Absolute Error (km/h)** & **Mean Absolute Percentage Error** \\ \hline PID & 8.254 & 1.680 & 9.16\% \\ ADRC & 6.431 & 1.047 & 5.41\% \\ MFC-ADRC & 1.819 & 0.3024 & 1.26\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tracking error statics with vehicle mass 2100 kg and incline 6\({}^{\circ}\).  
Figure 5: Speed tracking results for Case 1.  
Figure 6: Speed tracking results for Case 2.