esa-sceva/satcom-chunk-collection huggingface dataset:
# Generation of High Resolution Vegetation Productivity from a Downscaling Method
## 2 Materials and Methods
### Data and Data Processing  
#### 2.2.1 Remote Sensing Data  
MODIS LAI/FPAR productsThe MODIS LAI/FPAR products (MOD15 C55) [31] with a spatial resolution of 1 km were provided on an 8-day basis. The LAI/FPAR retrieve algorithm consisted of a main procedure that exploits the spectral information content of MODIS surface reflectance at up to seven spectral bands and a backup algorithm that uses empirical relationships between Normalized Difference Vegetation Index (NDVI) and canopy LAI and FPAR [32]. The main algorithm employed a  
Figure 1: Location of study area. A 30 km \(\times\) 30 km experimental region was set up in the study area.  
biome-dependent look-up table approach based on simulations with a stochastic radiative transfer model that considered three-dimensional canopy structure [32]. The back-up algorithm was triggered to estimate the LAI and FPAR using vegetation indices when this main algorithm failed. The validation methods include comparisons with in situ data, comparisons with data from airborne and other space borne sensors, analysis of trends in products [32]. In this study, MODIS LAI/FPAR products from June to September 2012 in the study area were used to generate high resolution LAI and FPAR from the downscaling method.  
_GLASS LAI/FPAR products_. GLASS LAI/FPAR products from June to September 2012 in study area were also used to generate high resolution LAI and FPAR from the downscaling method. The GLASS LAI/FPAR products were generated and released by the Center for Global Change Data Processing and Analysis of Beijing Normal University [33], and are also available from the Global Land Cover Facility [34]. The temporal resolution of the products is 8 days and spatial resolution is 1 km. The GLASS LAI product was generated from time-series MODIS and AVHRR reflectance data using general regression neural networks, and was believed to be more temporally continuous and spatially complete than the MODIS LAI product [35]. Furthermore, the GLASS FPAR product was generated from the GLASS LAI product, thus, showed the same properties as the GLASS LAI product.  
_Landsat data._ Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images [36] in 10 July 2012 were used to derive high resolution LAI and FPAR from a regression tree approach in this study. The data were first destriped, and then surface reflectance was obtained after radiometric correction and atmospheric correction. Then a regression tree [37] was then established between a reference dataset (MODIS LAI and FPAR/GLASS LAI and FPAR) and pixel-aggregated Landsat surface reflectance, and then applied to Landsat imagery to estimate LAI and FPAR at 30 m resolution.  
_Landcover data._ GlobeLand30 landcover map, which was comprised of ten landcover types, including forests, artificial surfaces and wetlands, was used in this paper [38; 39]. This map was extracted from more than 20,000 Landsat and Chinese HJ-1 satellite images. The spatial resolution of the landcover map was 30 m.
## 3 Data and Methods
### Research Methods
On this basis, using ArcMap 10.8 software and a remote sensing visual interpretation method [48], the river boundary from Xinjqiman to Kelelik was extracted. In order to systematically reveal the characteristics of river evolution from 1993 to 2024 in the Xinjqiman-Kelelik section, this study used multi-dimensional quantitative indicators to conduct spatial and temporal comparative analysis based on the river boundary vector data extracted year by year: (1) The change of thalweg length: The annual thalweg geometric length (km) was accurately calculated in ArcGIS, and the overall trend and change rate were quantified by length-time curve and linear regression analysis, and the differences in typical years were compared. (2) The migration rate of the thalweg: In the typical river section, the plane displacement of the thalweg points of each section between adjacent years was calculated, and the mean value was divided by the time interval to obtain the annual average migration rate, and the migration rate of the thalweg of the typical river section was analyzed. (3) Bending coefficient: Based on satellite remote sensing imagery from 1993 to 2024, the thalweg line (the connection line of the deepest point of the main channel of each section of the river channel in the dry season) of the Xinjqiman-Kelelik river segment over a 32-year period was extracted. Additionally, the river's curvature coefficient \(k\) (Equation (1)) for the past 32 years was calculated. \[K=\frac{L}{l} \tag{1}\]  
In the equation, \(k\) is the curvature coefficient. \(L\) is the thalweg length, km. \(l\) is the straight-line length of the river segment, km. In this study, \(l\) is taken as 73.33 km. (4) The evolution of cross-section width: The river section was divided into the key geographic units (bends, bifurcations, artificial buildings) and human activity sensitive areas (villages, farmland). Based on the same section, the annual river width was measured, and the average value was calculated to reflect the overall trend, identify significant local changes, and analyze the spatial distribution. (5) Shoreline erosion/deposition area: ArcGIS overlay analysis was used to accurately calculate the area of erosion area and deposition area at the river boundary between adjacent years, and the total erosion and deposition area was accumulated year by year; a spatial distribution map was drawn to clarify the erosion and deposition area. The above methods comprehensively evaluated the evolution of the plane shape (length, width) and dynamic process (thalweg migration, shoreline erosion and deposition) of the river section in the past 32 years.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 2 Axial Compression Test
### Analysis of Results  
The experimental observations revealed that all specimens exhibited three distinct behavioral phases: elastic, elastoplastic, and failure stages. During the elastic stage, minimal deformation occurred with the load primarily carried by the concrete core and steel tube.  
Figure 2: The failure modes of all specimens.  
The elastoplastic stage featured progressive concrete cracking and increased lateral deformation, where the steel tube simultaneously resisted axial compression while collaborating with CFRP to restrain transverse expansion. Ultimate failure manifested through concrete crushing, severe steel tube buckling, and CFRP rupture.  
Experimental results demonstrate that the ultimate bearing capacity of circular steel tube-CFRP confined concrete intermediate columns is synergistically influenced by concrete strength, steel tube thickness, CFRP layers, and slenderness ratio. While increased concrete strength (up to 20%) and thicker steel tubes (\(\leq\)20% improvement) provide modest enhancements, CFRP reinforcement shows remarkable effectiveness--specimens with 1, 2, and 3 CFRP layers exhibit 42.5%, 69.4%, and 88.4% capacity increases, respectively, albeit with diminishing returns beyond optimal layer counts. Conversely, capacity reduction correlates strongly with higher slenderness ratios, completing this comprehensive performance characterization for composite column design.
# A New Affinely Adjustable Robust Model for Security Constrained Unit Commitment under Uncertainty
## 5 Tests and Results
### 6-Bus System
That is, if less revealed uncertainty from past periods  
Figure 3: OF value and solution time vs. number of past time information, 6-bus system. is considered (smaller values of \(n\)), there is a greater number of violated constraints. A detailed analysis of this behavior follows. Figure 5 zooms in Figure 4. This figure shows that, for realizations that are 104% of the size of the uncertainty box, the number of violated constraints is approximately 20 when \(n=1\) (i.e., if only the information on current uncertain parameters and those revealed a period ago is considered). On the other hand, this number drops to 0 if \(n=24\) for the same realization value (104%). Similarly, for realizations of 120% of the size of the uncertainty set, the number of violated constraints is close to 90 for \(n=5\), whereas it decreases to 30 if \(n\) increases to 24. In summary, when more information on the uncertainty values revealed from past times are taken into account in the AARO-SCUC model (i.e., larger values of \(n\) in expression \(t-n\leq\tau\)), more stable solutions may be obtained for realizations of the uncertain variables that slightly surpass the maximum uncertainty level. However, increasing the value of \(n\) increases the run time. This result exhibits a clear trade-off between solution robustness and running time.
## 1 Introduction  
The new 5G wireless networks promise a faster data rate and more reliable service to today's mobile users. New technologies are being designed by wireless engineers around the world everyday, and many of these new technologies, such as autonomous vehicle, virtual reality, and Internet of Things (IoT), are planing to adapt to the first commercial 5G networks in the early 2020s [1; 2; 3]. Millimeter-waves (mmWaves), small cells, massive multiple input multiple output (MIMO), full duplex, and beam-forming are main features of 5G systems that seem promising for fulfilling ultra-fast systems in demand [4]. In mm wave frequencies, the 5G NR supports up to 52.6 GHz applications, and this system uses orthogonal frequency division multiplexing (OFDM) for waveform generation [1]. It should be noted that this orthogonality is different from the multiple access schemes which may or may not be orthogonal as seen in orthogonal multiple access (OMA), and non-orthogonal multiple access (NOMA) schemes [4]. Although the orthogonality in multiple access scheme has also increment effect on peak to average power ratio (PAPR) for multiple users [5], in this paper only the PAPR of one user signal waveform is analysed. As, Fourier transform [6; 7] plays an essential role in developing OFDM signals, it is fundamental to understand the connection of Fourier transform and creation of high peaks in any multi-carrier signal which has been used in many telecommunication systems [4].
# Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network
## 3 Proposed Model
Conversely, in the case of signal control using the time duration allocation technique,  
Figure 1: Performance of a technique for determining the phase order in terms of the average queue length. Figure 2: Performance of a technique for determining the phase order in terms of the average wait time. the direction that receives the next green signal is already fixed and determines how much time is allocated. Therefore, the wait time factor with the time information of the vehicle is more meaningful. In this paper, a traffic signal control using an order allocation technique is proposed. When the order allocation technique is used, both in terms of queue length and wait time, it shows good performance when throughput and queue length-related factors are applied as reward parameters in the learning model. We selected the standard deviation of the queue length among the related factors of the queue length. The reason is to reduce the number of vehicles that have to wait for a relatively long time in order to increase the throughput of the intersection. In order to increase the throughput of the intersection, it is advantageous to assign green lights to the lanes with relatively long queues. However, in this case, the drivers of the lanes with a relatively short queue have fewer opportunities to proceed. We propose an intelligent traffic-signal control system that determines the phase order. It recognizes the situation and determines the lane to receive the green signal depending on the situation. The intersection environment is dynamic, and the traffic volume changes in real-time. Therefore, traffic conditions always change and are difficult to predict. Therefore, we attempt to recognize the traffic condition and learn the optimal policy to determine  
Figure 4: Performance of a technique for determining green light duration of a phase in terms of the average wait time. Figure 3: Performance of a technique for determining the green light duration of a phase in terms of the average queue length. directions for receiving green signals using a DQN, which is a reinforcement learning method. The Markov decision process (MDP) of a model with an intersection environment is defined as follows:
## 1 Introduction
Previous studies showed that the 2010 calving event was noted to be followed by minimal glacier acceleration (<100 m a\({}^{-1}\)) and had no significant ice dynamical effect on glacier stability [36; 37]. However, recent observations showed that an average acceleration of 10% between winter 2011/2012 and winter 2016/2017 was directly connected to the 2012 calving event based on ice flow modeling [38]. Another model verified the idea that once calving removes ice within 12 km of the grounding line, loss of these thicker and stiffer sections of the ice tongue could perturb stresses at the grounding line enough to substantially increase inland flow speeds, and so grounded ice discharge [39]. Since the 2012 calving event, the Petermann ice shelf has been steadily advancing, but a newly developing fracture, as shown in Figure 1, from the eastern margin to the center of the ice tongue, marks a forthcoming calving event [39]. The winter 2016/2017 speedup [38], followed by the latest relatively small calving event occurring on 26 July in 2017, broke the steady-state and focused more attention on future instability. Even though the relatively small calving event did not have a direct impact on upstream stability, this frontal collapse lost the connection with the fjord and reduced the lateral drag, weakening the ice tongue's structural integrity. Additionally, it may reactivate the new fracture and promote the adjacent fracture's extension. Consequently, it may imply to initiate a new cycle of calving, given signs of ice shelf break-up even during recent years. Exploring the precursors and causes of the relatively small calving event was crucial to assessing the instability of the Petermann glacier tongue to predict a forthcoming calving event [40]. In this study, we aimed to characterize the hydrological and kinematic precursors of the frontal collapse on 26 July 2017 at the Petermann Glacier, by jointly using high-resolution remote sensing and ice flow modeling. The surface velocity, melt ponds and sea-ice backscatter were derived from Sentinel/2 images to capture their dynamic response variations before the collapse occurred in the melt season of 2017. Moreover, the distribution of stress and damage solution was modeled with the ice sheet system model (ISSM) to assess the dynamic structural instability of the floating ice tongue. Finally, we discussed what triggered the frontal collapse of the PG in 2017, including the weakening of landfast sea ice, the shearing of tributary glacier inflow and the meltwater infiltrated crevasses.
# Integrating Simulation-Based Optimization for Lean Logistics: A Case Study
## 2 Literature Review  
In the process of supply chain planning, logistics is a fundamental part; due to the activities involved: implementing and controlling the flow and storage of goods, services and information in an efficient way to meet customer requirements. Lean logistics applies lean manufacturing thinking to control logistics activities. The implementation of lean manufacturing strategies, tools and techniques into logistic operations has brought advantages such as costs and product waste reduction, while improving productivity, efficiency, quality and delivery, as well as satisfying customers and employees [4; 12; 13; 14; 15].  
The goal of Lean Manufacturing is to control, reduce and even eliminate waste, at the right time, in the right place, providing the right amount of product. The logistics waste consists of inventory, waiting, overproduction, overprocessing, defect, motion and transportation, respectively considered from greater to lesser impact on costs [16; 17]. Lean logistics highlights customers first; timely, accurate and overall optimization; continuous improvement and innovative ideas. Lean logistics system planning and design can be divided into two submodules: material flow and information flow. To analyze the material flow and information flow cross the company, Value Stream Map (VSM) is used [18]. The improvements in the system can be achieved through optimization strategies related to logistics process, logistics organization structure, logistic operation, standardization and logistics management, logistic professional personnel, logistics cost management and logistics performance evolution. In a study conducted with companies registered in the Singapore Logistics Association, it was found that 37.5% has implemented lean tools in the development of its operations [19]. Finally, there are optimization techniques for optimization of LP that are discussed in the next section.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 6 Service-Oriented Middleware (SOM) Architectures Approaches for WSNs
### QoS for SOM Architecture  
The Quality of Service (QoS) can be improved by applying strategies of dynamic service selection. These strategies are used to achieve a high level of QoS for WSNs and the lifetime of the network can be increased in this simple manner [50]. The Service Node Selection Algorithm (SNSA) locates the most efficient and effective service nodes to take part in composite function. In this case, the SNSA guarantees a minimum level of quality by choosing services that meet quality and network routing requirements. This mechanism enables the operation to execute with minimum time and power consumption [50].
## 2 Materials and Methods
### Result Validation and Method Evaluation  
In this study, the performance of the proposed downscaling method was evaluated by MODIS GPP at a 500 m resolution. The coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE) between the downscaled results and the 500 m MODIS GPP were adopted as indicators of validation. In addition to the proposed downscaling approach, other three downscaling methods, including ordinary kriging (OK) [35], spline [36] and inverse distance weighting (IDW) [37], were used to provide a systematic comparison and illustrate the advantages of the proposed downscaling method.
## 2 Material and Methods
### Methods
#### 2.2.3 Generator and Discriminator Architectures  
We have decided to use a similar architecture for both the generator and the discriminator. These architectures consider pix2 pix as a starting point [24]. The generator uses an autoencoder, while the discriminator is of Markovian type.  
Autoencoders are based on the use of convolutional-deconvolutional architectures of 2 dimensions. They were introduced by [25] and are aimed to receive and input data that are downscaled until a minimal piece of data represents its essential information, which is later upscaled to obtain the input data or an expected one. As the aim of our work is the virtual reconstruction of images containing ruined temples, this architecture fits perfectly by using these images as input data and returning the image of the same temple but adding the missing architectonical elements. Apart from that, we are adding skip connections to the model.  
A Markovian decoder is a particular type that does not evaluate the generated images as a whole but evaluates different patches separately. By doing this, the discriminator has more capacity to evaluate local textures, which is important in this case. The discriminator uses a sliding window that considers the local continuity and context details. This is important when evaluating how they have been added to the missing architectonical elements. The sliding window consists of an NxN patch that goes through the whole image and evaluates whether each part belongs to an original image or a created one. In summary, the discriminator uses a 2D CNN architecture to obtain the main features of the image and then a Markovian patch that evaluates them as belonging to the original dataset or being synthetic. The result is a matrix with values going from 0 to 1, depending on the previous evaluation. If the value is near this part of the image, it is synthetic, and if it is near 1, the part of the image is original.
## 5 Case Studies
### Case Study Two: Coursera  
5.2.1. Overview of Coursera  
To this end, technology development has resulted in harnessing it for educational usage, and Coursera is a leading online learning platform. It provides its students with many courses and specializations together with well-known worldwide universities and institutions. This shows that Coursera is committed to offering a range of academic subjects, including data, science, machine learning, and humanities, among the arts, to meet varied academic needs.  
Coursera prides itself on offering an interactive and engaging learning experience. It combines instructional approaches like video lectures, peer-reviewed assignments, and collaborative community forums. They make available a stimulating and interactive teaching environment suitable for learners everywhere, irrespective of their geographical locations.  
An interesting feature of Coursera is the use of machine learning algorithms as part of its value-add to learning. Such algorithms tailor course recommendations, adjust learning paths, and track the learner's progress. The advanced application of technology in Coursera makes it an appropriate choice for deploying the Dynamic Feedback-Driven Learning Optimization Framework (DFDLOF) to enhance its potential in offering tailored education.
## 3 Results and Discussion
### Significance of Colloidal Metal Fraction in the Dissolved Phase-Comparison with Other Coastal Areas
#### 3.4.3 Metals with Small Colloidal and Particulate Fractions (Zn, Cd, Ni)  
Small amounts of colloidal Zn, Cd and Ni were detectable, with, on average, 9%, 9% and 12% in the dissolved phase, respectively (Table 1). In other studies, even lower amounts of colloidal Zn, Cd and Ni were detectable than in the Krka River estuary (Table S1), such as in the Gulf of Trieste [37] and San Francisco Bay [5], where more than 96% of Zn and Ni and more than 99% of Cd was present in the truly dissolved phase (<5 kDa and <10 kDa, respectively). In Galveston Bay, on the contrary, Zn had almost no truly dissolved fraction (averaging 9%), and colloidal Ni (1 kDa-0.45 um) was much higher, averaging 36% of the filter-passing pool [62]. In the Amazon and Para River estuaries, dissolved Ni was present mostly in the'reactive' form, suggesting high bioavailability, and colloidal Ni (0.015-0.2 um) was only detected at salinity <28 (up to 38%). Similarly to the Krka River estuary, <12% of colloidal Cd was found in the Xin'an River and Guandang River estuaries [24]. In contrast, a much higher percentage of colloidal Cd in the dissolved fraction was found in most of the other coastal systems, e.g., Venice Lagoon [50], Fal estuary [11], the coastal waters of North Yellow Sea [22], Galveston Bay [62] and Penze estuary [6], with on average 34%, 37%, 38%, 44% and even 82% of colloidal Cd, respectively.