esa-sceva/satcom-chunk-collection huggingface dataset:
# On Channel Estimation in LTE-Based Downlink Narrowband Internet of Things Systems
## 6 Complexity Analysis  
By the application of SVD to the channel matrix, the rank of the channel autocorrelation matrix is reduced, and hence the algorithm complexity regarding multiplication and matrix inversion is significantly abridged. The partitioning of vectors into \(S\) equally spaced subvectors allows us to use the desired rank estimator to all vectors individually, which also reduces the bandwidth by the factor \(S\) with a certain performance loss, and it is also observed in our simulation. Hence, according to [40], the number of base vectors are reduced from \(L+1\) to \(L/S+1\). This decreases the complexity of the estimator enormously. In the overlap banded technique, the multiplication for each subvector reduces more by a factor \(S/2\). Moreover, when the channel is estimated independently in each subsystem, the correlation among different subvectors is neglected, but the same MSE performance is obtained, which greatly decreases the complexity of the estimator. The calculated complexity of the LMMSE, LMMSE based on SVD, LMMSE based on banded SVD, and the LMMSE with overlap banded SVD are shown in Table 3. It is clear from the calculation  
Figure 8: Proposed polyphase architecture of the interpolation filter.  
that LMMSE possesses the highest complexity and the proposed LMMSE with overlap band technique has the lowest computational complexity. In the NB-IoT systems, memory utilization will play a vital role because of its very narrow bandwidth. If the algorithm consumes large memory, it will be impossible to manage with only 180 kHz bandwidth. From Table 3, we can also see that the memory space needed for the LMMSE is \(L\) which is the highest. When we apply the low rank SVD formulae, the memory space needed is shrunk because of the simpler matrix operation, and hence it is \(<L\). In the proposed overlap banded SVD method, the memory space needed is \(S\) which is much smaller than \(L\). Furthermore, as the polyphase approach in the interpolation filter uses resampling, each path frequency is minimized to \(F/P\). Consequently, the number of multiplications and additions are reduced to \(Q\) and \(Q-1\) instead of \(PQ\) and \(P(Q-1)\) of the conventional interpolation filter, respectively.
## 3 Synthesis of Results  
The peri-implant microbiota shows less bacterial differentiation than the periodontal microbiota, becoming more complex when switching from peri-implant mucositis to peri-implantitis. The microbial differences found between the peri-implant and periodontal microbiota are mainly related to bacterial populations belonging to the classes Bacteriola, Spirochaetes, Synergistia, Clostridia and Deltaproteobacteria [24]. However, some bacterial strains were detected only at sites with peri-implantitis, such as _Porphyromonas_ spp. _HOT-395, Porphyromonas nigrescens, Porphyromonas oris, Treponema maltophimurium_, _Dialister invisus_, _Eubacterium saphenum_, _Filifactor alocis_, _Freitbacterium fastidiosum_, _Mitsuokella_ spp. _HOT 131_, _Chloroflexi_ spp., _Tenericutes_ spp. and _Freitbacterium HMT 360_[26].  
Detecting the bacterial populations that compose the oral peri-implant dysbiotic microbiota is important in the prevention, diagnosis, and therapy of peri-implant pathologies, regarding the introduction of probiotics as an additional therapy to the gold standard mechanical debridement [27].
## 3 Energy Storage
### Pumped Hydroelectric Storage  
Large-scale hydroelectric installations like the Itaipu Dam in Brazil and the Three Gorges Dam in China are strategically positioned along rivers, serving as pivotal infrastructure for water storage, flood control, and electricity generation [42]. These structures, as shown in Figure 9, harness the gravitational potential energy of accumulated water, ensuring a steady supply of power to meet consumer needs. However, the construction of dams inevitably alters the surrounding environment, disrupting river ecosystems and presenting challenges for aquatic organisms [42]. The effective management of existing dams must, therefore, navigate the delicate balance between their benefits and environmental impacts. Hydroelectric power generation relies on specialized turbines to convert the kinetic energy of flowing water into mechanical rotation, facilitating electricity production [41]. Turbines, such as Pelton, Francis, Turgo, and Kaplan, are deployed based on site-specific conditions and energy requirements, with ongoing technological advancements aimed at enhancing efficiency and minimizing ecological disturbances [41]. Tailoring turbine selection to factors, like water head, flow rate, and desired power output, underscores the importance of a customized approach in turbine design and deployment.  
Figure 8: Wet and dry cell batteries.  
Constructed specifically for extensive electrical energy storage and management, pumped hydroelectric storage (PHES) plants, shown in Figure 10, represent a critical facet of the energy landscape [5]. These systems typically consist of two reservoirs positioned at different elevations, functioning akin to colossal rechargeable batteries. During periods of low electricity demand or surplus grid power, PHES facilities utilize the excess energy to pump water from the lower reservoir to the higher one, effectively storing it as gravitational potential energy. Conversely, when electricity demand peaks, PHES systems reverse operation, releasing water from the upper reservoir to flow downhill through turbines, generating electricity with high efficiency. This agile response to demand fluctuations makes PHES an invaluable asset for grid operators seeking to balance and stabilize the electrical grid swiftly [42]. The capability of PHES to store and discharge large energy quantities rapidly renders it indispensable in integrating variable renewable energy sources, bolstering grid reliability and enhancing overall energy system flexibility. Despite the initial environmental impact associated with construction, PHES garners recognition for its pivotal role in advancing renewable energy integration and grid stability, with minimal greenhouse gas emissions during its operational phase. A notable example of this technology's scale is the ongoing construction of the 5 GW Pioneer-Burdekin Pumped Hydro Project in Queensland, Australia, poised to provide surrounding cities with 24 h stored energy discharge capabilities once operational [42].
## 4 Discussion
### Other Artifacts  
In addition to clouds and fog, there are other artifacts that can affect smoke identification, particularly weather attributes such as wind speed, direction, and humidity, which could impact the smoke formation and transport. In turn, this affects the accuracy of smoke identification models.  
The data used in this study are Sentinel-2 remote sensing images under clear weather conditions. Smoke spreads more slowly in clear weather conditions and is easier to identify. Unlike previous studies, which have mostly focused on the pixel level [61], this study aimed to identify satellite images with smoke from straw combustion, that is, image-level smoke recognition. In pixel-level recognition, the location and type of target is estimated for each pixel in the image. Image-level smoke recognition learns the main features of smoke through training, and then identifies and locates the smoke with these features. This requires that, during training, the smoke features are as obvious as possible. Thus, we used 4-3-2 band composite images, as these can show smoke more clearly.  
In order to enrich the characteristics of the smoke from straw burning as much as possible, reduce the influence of other artifacts on smoke recognition, and improve the accuracy of the model, in following research, we will use additional bands and indices in training. In addition to the indices in Table 3, 13 spectral bands of the Sentinel-2 Multispectral Imager (MSI) may be good candidates for the smoke recognition model. We can perform spectral analysis on different ground feature types (smoke vs. cloud, smoke vs. haze, smome vs. water, etc.), and select the bands and indices by the separability index (M) [62]. The separability index (M) is given by Equation (10):  
\[H=\frac{|\mu_{1}-\mu_{2}|}{(\sigma_{1}+\sigma_{2})} \tag{10}\]  
where \(\mu_{1}\) and \(\mu_{2}\) are the mean and standard deviation of category \(i\), respectively. The larger the value of \(M\), the higher the separability between smoke and other ground objects; in other words, smoke is more accurately identified. The selected band and index were used as the model input, and the input combination with the best recognition effect was selected through ablation experiments.  
Weather attributes will have a certain impact on the smoke detection model. For example, the smoke spreads quickly when the wind speed is high, and the smoke detection algorithm may not be able to detect the smoke in such circumstances. Therefore, we will explore the characteristics of smoke under different atmospheric diffusion conditions to find the maximum detection ability of the smoke detection algorithm.
# A Dual-Band 8-Antenna Array Design for 5G/WiFi 5 Metal-Frame Smartphone Applications
## 4 Experimental Results and Discussion
### MIMO Performance  
An essential metric for evaluating MIMO antennas, the envelope correlation coefficient (ECC), was calculated using Equation (1). It signifies the correlation among the received signal amplitudes across various antenna units, serving as a metric to gauge the diversity and coupling performance of MIMO multi-antenna systems. In the operating 5G China band N78 (3.4-3.6 GHz), the maximum ECC was 0.18, occurring between Ant. 1 and Ant. 4. For 5G NR N79 (4.4-5.0 GHz), the ECC between adjacent antenna elements remained below 0.08, as illustrated in Figure 10. Additionally, all simulated ECCs for WiFi 5 (5.15-5.85 GHz) did not exceed 0.03. Based on the ECC test results, it was observed that the tested ECC exhibited lower values in the three target frequency bands when compared to the simulation results. The consistently low ECC values affirm the proposed 8-port antenna's exemplary diversity performance for a 5G MIMO system.  
\[\rho_{c}(i,j)=\frac{\left|\iint\limits_{4\pi}\left[F_{i}(\theta,\varphi)\cdot F _{j}^{\prime}(\theta,\varphi)\right]d\Omega\right|^{2}}{\iint\limits_{4\pi} \left|F_{i}(\theta,\varphi)\right|^{2}d\Omega\cdot\left|\iint\limits_{4\pi} \left|F_{j}(\theta,\varphi)\right|^{2}d\Omega} \tag{1}\]
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Analysis
#### 3.6.3 Communication Flow among Participants  
One of the main aims of this study was to assess the communication flow among the participants in the context described. As such, the participants were asked to hold all conversations in the context of this experiment in a common group within the goTenna app. Hence the group-interest and one-to-one conversations were visible to the coordinator of the experiment and could be readily monitored. An ONODO analysis [34] and a radial bar-chart representation [35] were used to evaluate the traffic and communication flow among the participants.
## 3 Overview and Theoretical Foundations
### Big Spatial Multidimensional Data Analytics
#### 3.2.3 Geospatial Dimensionality Reduction: Geohash Encoding  
Conducting large scale geospatial analytics on voluminous georeferenced data mandates two main steps. First is the data representation, which is either data-driven or space-driven, second is the access data structure. The space from which data is withdrawn is typically known as the embedding space. It can be represented as either regularly-shaped equal-sized grids or arbitrarily-shaped counterparts. Access data structures are responsible for speeding up access to target data that answers the spatial query. In summary, geospatial representation of data is succeeded by imposing a data structure on the underlying representation to boost the speed of targeted scans.  
Ordering is normally assigned to cells in a grid decomposition, and thereafter a tree-based access structure (e.g., B+-tree) is imposed on the ordering. Ordering is a multidimensional reduction approach that projects multidimensional cells into a one-dimensional space. From many types of ordering, we focus on the family of z-order curves.  
A special application of Z-order curves is geohash ([[http://geohash.org/](http://geohash.org/)]([http://geohash.org/](http://geohash.org/)) accessed on 10 January 2023), where the ordering imposed on the grid space is z-shaped, geocodes generated are strings where a shared prefix signifies geometrically-nearby spatial points, where longer shared prefix means objects involved are closer in real geometries. Geohash encoding is an exemplary quick-and-dirty proximity searches (i.e., working as a quick-and-dirty sieve). Figure 1a shows geohash covering generated for the city of Rome in Italy at a precision that equals 6, while Figure 1b shows the geohash covering of the same city with precision 5. Precision means the number of characters in the string representation of the geohash value. For example, 'ws104u' represents one of those boxes covering Rome in Figure 1a, while 'ws104' is the value of one of the rectangles covering the city of Rome at precision 5 as shown in Figure 1b. Longer Geohash has a granular precision (covering smaller area).
###### Abstract  
The contribution of solar power in electric power systems has been increasing rapidly due to its environmentally friendly nature. Photovoltaic (PV) systems contain solar cell panels, power electronic converters, high power switching and often transformers. These components collectively play an important role in shaping the reliability of PV systems. Moreover, the power output of PV systems is variable, so it cannot be controlled as easily as conventional generation due to the unpredictable nature of weather conditions. Therefore, solar power has a different influence on generating system reliability compared to conventional power sources. Recently, different PV system designs have been constructed to maximize the output power of PV systems. These different designs are commonly adopted based on the scale of a PV system. Large-scale grid-connected PV systems are generally connected in a centralized or a string structure. Central and string PV schemes are different in terms of connecting the inverter to PV arrays. Micro-inverter systems are recognized as a third PV system topology. It is therefore important to evaluate the reliability contribution of PV systems under these topologies. This work utilizes a probabilistic technique to develop a power output model for a PV generation system. A reliability model is then developed for a PV integrated power system in order to assess the reliability and energy contribution of the solar system to meet overall system demand. The developed model is applied to a small isolated power unit to evaluate system adequacy and capacity level of a PV system considering the three topologies.  
capacity outage probability table (COPT); loss of load expectation (LOLE); loss of energy expectation (LOEE); capacity credit (CC); effective load carrying capability (ELCC)
# Self-Adaptive Priority Correction for Prioritized Experience Replay
## 2 Related Work
### Importance Sampling Techniques  
Importance sampling (IS) is effective in reducing the variance of the estimations in Monte Carlo simulations. It also faces the problem of unbounded variance. The high variance in stochastic gradient descent makes the DRL training unstable and damages the final policy of the agent. To address the shortcoming, researchers proposed a variety of methods. [PERSON] et al. proposed a novel method to bound IS weights [32]. They defined a safe region according to its value of IS. Especially, the safe region is the value lower than the threshold \(r\). The integral of the safe region is used to estimate the IS of the whole region. A similar idea is used in another method (the truncated importance sampling method), which considers the optimal truncation rate is \(\sqrt{n}\), where \(n\) is the batch size of the data [33]. Using the truncated importance sampling, the variance and bias of IS can be balanced. In some special circumstances, there is a special solution to solve the variance issue. [PERSON] et al. proposed a simple estimator named importance sampling with unequal support (US) [34]. It aimed to handle the variance when the supports of the sampling and target distributions differ.  
When the batch size becomes larger, the variance of importance sampling decreases. However, due to sampling costs, we cannot use unlimited batch sizes. Lately, the effective sample size (ESS) has been deeply studied. [PERSON] et al. proposed a novel ESS function to measure the difference between two probability functions [35]. [PERSON] et al. got the best of ESS through theoretical analysis [36]. They thought that \(exp(KL(p||q))\) is a sufficient batch size, where \(KL\) represents the Kullback-Leibler divergence of distribution \(p\) and \(q\). Our priority correct algorithm relies on these researches heavily. Different from the above researches, we analyze the distribution of importance weights during reinforcement learning training, and set the threshold quantitatively to balance the bias and variance.
## 4 InSAR Observations
The rate of subsidence is assumed to be constant over the time spanned by each interferometric pair, and the motion is presumed to be primarily vertical as the interferograms of opposite geometries (ALOS _vs._ UAVSAR) show essentially the same sense of motion. The deformation is contained within the boundaries of the flow and interferograms with smaller baselines in previous studies show no deformation in this area outside the scarp from 2007 to 2010 [22]. Thus, deformation can be directly related to subsidence of the lava flow. The area of maximum subsidence (cross section A-A', Figures 5d and 6) is likely the thickest area of the flow [28]. This area corresponds with a decrease in the topographic gradient, which could cause lava pooling. The profiles of range change rate (Figure 6) also show that there are higher subsidence rates in the middle of the flow, which implies it is thicker in the center than at the edges. The difference in subsidence rates along the flow suggests that the thickness is not uniform. UAVSAR data shows that the lava flow was still subsiding in the thickest part between 2013 and 2014, several years after emplacement. Figure 5: InSAR images showing post-emplacement surface movement of the lava flow that began on 28 May 2010, with the approximate extent of the lava flow outlined. (**a**) Emplacement of the flow continues until 30 June, and then subsides at a decreasing rate over time, shown in the following time frames: (**b**) 29 June 2010–14 August 2010; (**c**) 14 August 2010–29 September 2010; (**d**) 29 September 2010; (**e**) 30 December 2010; (**f**) 14 February 2011; (**g**) 26 April 2011–8 March 2013; and (**h**) 2 April 2013–10 April 2014. Line A-A’ in 5d crosses the approximate area of maximum subsidence, with values in Table 1 and a time series of profiles of range change rate deformation rates along this profile in Figure 6.
# UAV Formation Trajectory Planning Algorithms: A Review
## 3 Traditional Algorithm
### Voronoi Diagram Method  
The Voronoi graph method (also known as Dirichlet tessellation) is a space segmentation algorithm proposed by Russian mathematician [PERSON]. It divides the space into many sub-regions through a series of seed nodes (Seed Points), each sub-region is called a Cell, and the distance between all points in each Cell and the Seed Points in the current Cell is less than Distance to all other Seed Points. According to the distribution of obstacles, the Voronoi diagram squares the free space between the edges of the obstacles, and at the same time, it draws the vertical line of adjacent obstacles to form a polygon around the obstacles so that each side is equidistant from the surrounding obstacles. Then, the origin and destination nodes can be connected into the graph by constructing trajectories from the nodes to the edges closest to each node. Figure 8 is a Voronoi trajectory diagram.  
Figure 8: A Voronoi diagram method of trajectory diagram (blue area: obstacles; yellow lines: feasible trajectories).  
Figure 7: Optical path function solution model diagram (\(\Delta x\) and \(\Delta y\): spacing in \(x\) and \(y\) directions on discrete space).  
Unavoidable accidents or environmental interference problems will inevitably occur when UAV formations perform multi-mission planning and collaborative trajectory planning. In order to cope with this situation, [PERSON] et al. (2018) proposed an algorithm to deal with multi-UAV multi-task trajectory re-planning in an unexpected event environment. Each UAV uses a Voronoi diagram to plan its own initial, optimal or sub-optimal trajectory; then, it replans its trajectory according to the new multi-task requirements corresponding to some unexpected events.  
To solve the coverage problem with average Voronoi partitions, [PERSON] et al. (2018) proposed a distributed coverage algorithm to cover the convex area of the average Voronoi partition of the UAV formation. By exchanging local information with neighbors, the Voronoi partition is continuously iteratively updated, and the UAV direction of movement is calculated. The algorithm can theoretically make the area difference infinitely small so as to achieve the actual average area coverage.  
[PERSON] et al. (2018) proposed a method based on consistency theory, using the Voronoi diagram method to create a threat domain, and designing a cost function for trajectory planning of multiple UAVs, so that multiple UAVs can take off at the same time and reach the specified target Point, solving the problem of UAV formation attacking multiple targets in a static threat environment.  
[PERSON] et al. (2018) proposed a distributed formation control and collision avoidance method based on the Voronoi partition and traditional artificial potential field, using the Voronoi partition theory to divide the entire space into non-overlapping regions, and further proposed the target switching scheme; this method solves the problem of local optimum when an artificial potential field is used as motion control law.