esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimizing Energy and Computation in Long-Running Software
## 3 Minimizing Computation Time and Energy
### Sensitivity of the Optimum to Energy Consumption and Computation Time  
An important question concerns how \(y^{*}\) varies with changes in the relative importance of the energy expenditure with respect to computation time. To address this issue as a single parameter problem, we will set \(\alpha=1\), and consider the derivative of \(y^{*}\) with respect to \(\beta\). Noting that we can now write \(B=B^{c}+\beta B^{e}\) and \(A=A^{c}+\beta A^{e}\), we have:  
\[\frac{\partial y^{*}}{\partial\beta} = -\frac{1}{\ln a}W^{\prime}(\frac{B-A}{e.A}).\frac{B^{e}A^{c}-A^{ c}B^{c}}{(eA)^{2}}, \tag{21}\] \[= -\frac{1}{\ln a}\frac{W(\frac{B-A}{e.A})(B^{e}A^{c}-A^{e}B^{c})}{ \frac{B-A}{e.A}(1+W(\frac{B-A}{e.A}))((eA)^{2})},\] \[= -\frac{(y^{*}\ln a+1)(B^{e}A^{c}-A^{e}B^{c})}{y^{*}(\ln a)^{2}(B- A)eA},\]  
where we have used the identity:  
\[\frac{dW(x)}{dx}=\frac{W(x)}{x(1+W(x))}, \tag{22}\]  
when \(x\
eq 0\) and \(x\
eq-\frac{1}{e}\). These two conditions will be satisfied because it is unlikely in practice that the system parameters be such that \(B=A\), furthermore it is impossible that \(B-A=-A\) because \(B>0\).  
Thus we can use the expression (21) to determine how fast \(y^{*}\) will vary as a function of \(\beta\). In particular we have the following very interesting result.  
**Result:** When \(B^{e}A^{c}=A^{e}B^{c}\), then \(y^{*}\) does not depend on the relative weight of the execution time and energy consumption, so that a single value of \(y^{*}\) will minimize the overall cost for \(\alpha=1\) and any value of \(\beta\) that represents the relative importance of energy consumption to computation time.
## 2 Experimental
### High-Resolution Transmission Electron Microscopy  
The atomic structure of single-phase Ag\({}_{0.81}\)Sb\({}_{1.19}\)Te\({}_{2.19}\) alloy was analyzed by means of high-resolution transmission electron microscopy (HRTEM) using FEI Tecnai F30-G2 microscope (field-emission gun at an acceleration voltage of 300 kV; point resolution and information limit were amounted to 1.9 A and 1.2 A, respectively, FEI Technologies Inc., Hillsboro, OR, USA) and selected area electron diffraction (SAED). The microscope was equipped with a slow-scan CCD camera (MultiScan, 2 k \(\times\) 2 k pixels; Gatan Inc., Pleasanton, CA, USA) and EDXS spectrometer. The powdered sample was dispersed in methanol and several drops of the solution were loaded on carbon coated copper grid (Quantifoil S7/2, 100-mesh hexagonal, 2 nm carbon film, Quantifoil Micro Tools, Jena, Germany). The grid was transferred to the microscope after complete dryness. Analysis of the TEM images was performed using Digital Micrograph software (Version 2, Gatan Inc., Pleasanton, CA, USA). It includes fast Fourier transform (FFT)/inverse fast Fourier transform (IFFT), filtering and brightness/contrast corrections.
## 3 Results and Discussion
### Regularization Evaluation  
Table 1 shows the decision of feature elimination for LASSO and ElasticNet, where the elimination is based on the value of the coefficient. If the value is 0, this means that the corresponding feature should be dropped. The decision results of the table are either kept or dropped.  
The ElasticNet and LASSO performances are similar. Both have dropped the 0 regularization coefficient parameters esD, asD, and DS. They kept esA, KF, PL, and asA because their coefficients are 0.72, 0.37, 0.25, and 0.12, respectively. These results indicate that the regularization of both LASSO and ElasticNet reduced the data dimensionality from 7 to 4.  
Table 2 shows the time evaluation of both LASSO and ElasticNet. The instruction of each one is 10 times and their time average is taken into consideration.  
Clearly, the LASSO is more time-efficient and more suitable for this type of dataset as it is a L1 regularization type. The ElasticNet takes more time as it combines both L1 and L2 types during regularization.
# Current Status and Future Developments of Micromegas Detectors for Physics and Applications
## 6 Micromegas Readout of TPCs  
TPCs are very efficient and powerful tools for Particle Identification (PID) through 3D particle tracking in a magnetic field and energy loss measurements. Micro-Pattern Gaseous Detectors (MPGD) are increasingly used in place of Multi Wire Proportional Chambers to readout the TPCs. Micromegas was one of the first MPGD to be deployed on a large scale TPC (T2K 2010, Section 6.1) and proved to be a robust and reliable readout for TPCs due to its simplicity of operation. Micromegas is particularly suitable to cover the large active surfaces of TPCs with excellent uniformity of performances. It also provides a \"natural\" ion backflow minimization in the drift region, down to the few per million level [62].  
Micromegas TPCs are reliable options for rare event detection due to their background discrimination capabilities, intrinsic low radiopurity materials in the case of the Microbulk technology, energy and time resolution, and stability of operation. In Section 6.4, the detector development in the context of solar axion search will be described in detail, while the use of Micromegas for neutrinoless double-beta decay is presented in Section 6.5. Pertinent initiatives were also carried out for dark matter searches with the TREX-DM experiment [63; 64; 65] and the MIMAC detector [66].  
Figure 13: Schematic view of the principle of muography with a Micromegas-based telescope of a pyramid (Illustration by [PERSON], _Les defis du CEA_ N\({}^{0}\) 210, Septembre 2016). Muon flux density images traced from the telescope position are acquired for different directions. A 3-dimensional image can be produced by combining several images of the same object from different directions.
## 2 Reinforcement Learning: Background
### Reinforcement Learning
Since it is essential for RL agents to have exact representations of value functions and policies, the applicability of classical RL algorithms is limited to small-scale problems and  
Figure 2: Reinforcement learning agents: An RL agent is situated in an environment composed of a finite or infinite number of states. Given the state \(S_{t}\) with a reward \(R_{t}\), the RL agent chooses an action \(A_{t}\) that maximizes the cumulative rewards. Taking the action \(A_{t}\) leads the RL agent to the new state \(S_{t+1}\) with a new reward \(R_{t+1}\). After evaluating the effect of the previous action \(A_{t}\) given the new state \(S_{t+1}\), the RL agent repeats such a cycle iteratively in order to achieve a goal in the environment. (Copyright © [PERSON] and [PERSON] (1998). This figure was taken from [3].) problems that can be specified by discrete state spaces. However, the majority of real-world problems require the consideration of a large number of states and actions, and, moreover, they are often defined as continuous variables. Within this context, there are two main challenges in RL tasks: one is the requirement for a large-scale memory to store a huge number of states and/or actions, which may cause latency issues during the search and load operations while learning; the other is that they are computationally expensive. It would therefore be too slow to learn the value of each state, even for a single episode. To tackle this issue, state-of-the-art RL models are now actively adopting the latest machine learning (ML) techniques to perform function approximation. A good example is the use of (deep) neural networks, which are non-linear, parameterized function approximation techniques that have been demonstrated to outperform the generic class of neural networks. ML has been used to approximate almost all RL components, including value functions (Equation (4)), policies (Equation (5)), and models (consisting of the state transition and its associated reward):  
\[\hat{\sigma}(s,\theta) \approx v_{\pi}(s)\] \[\text{or}\ \hat{\sigma}(s,a,\theta) \approx q_{\pi}(s,a). \tag{4}\] \[\hat{\tau}_{\theta}(s,a)\] \[\text{or}\ a=\mu_{\theta}(s). \tag{5}\]  
The parameter \(\theta\) denotes the weights in deep neural networks, \(s\) specifies the state, and \(a\) specifies the action. The combination of RL and deep learning has led to rapid advances in RL algorithm design, showing exceptional performance in many applications, such as games, robot control, and virtual environments. Moreover, 'deep approximate RL' has been developed in the disciplines of (i) value function approximation (e.g., DQN [1], DDQN [24]); (ii) policy gradient methods (e.g., DPG [25], DPG [26]); and (iii) asynchronous methods (e.g., A3C [27]).
# Locating the Epidemic Source in Complex Networks with Sparse Observers
## 3 Experiments and Analysis  
In the previous section, the solving process of the epidemic source localization problem under the SIR model has been elaborated in detail. This section comprehensively analyzes the performance of PESL algorithm and other common source localization algorithms, and verifies the performance of the algorithms in synthetic networks and empirical networks, respectively.  
To get closer to the epidemic source localization in the real world, the classic SIR model is used in the network to simulate spread process. The infection probability is set as \(\beta\) and recovery probability as \(\gamma\). A node is randomly selected as the epidemic source. Once a susceptible node is infected by a neighbor node, it will report its own state at the probability of \(\delta\), which is called the observer. When the number of observers in the network reaches a threshold \(K\), the algorithm needs to locate the epidemic source backward according to the infected time of these observers. To verify the performance of the algorithm, Distance Centrality (DC) [4], Jordan Centrality (JC) [4], Gaussian-based method (GAU) [7] and Time-Reversal Backward Spreading (TRBS) [8] were selected as comparison algorithms. DC and JC only use the location information of observers, while GAU and TRBS comprehensively use the location information of observers and infection time information to locate the epidemic source.  
In this paper, we chose average error distance [15] as the evaluation index, which was described as follows. The average error distance is referred as the mean shortest distance of hops between the accurate source and estimated source found by an algorithm. The shorter average error distance is, the higher the accuracy becomes.
# Study on Mechanical Properties of Modified Polyurethane Concrete at Different Temperatures
## 1 Introduction
[14] conducted a series of experimental studies including the bending test, shear test, splitting test and fatigue test on polyurethane concrete used for bridge deck pavement, and the mechanical properties of polyurethane concrete were evaluated. [PERSON] et al. [15] reported a polyurethane concrete filled with delorie. The performance of this material was investigated by using the rutting test, freeze-thaw splitting test and low-temperature bending test. The results showed that the polyurethane concrete had good mechanical properties and durability. [PERSON] et al. [16] obtained an elastic polyurethane concrete containing waste rubber particles. A linear elastic constitutive model of this material was proposed, and the microstate of polyurethane concrete at failure is observed by scanning electron microscope observation. In China, polyurethane concrete has been applied in bridge engineering, such as bridge reinforcement and bridge deck pavement. In Harbin, an old bridge was strengthened with polyurethane concrete, and its flexural capacity and stiffness were improved [17]. In Ningbo, Roaby company had developed an ECO (Ecology Conservation Optimization) modified polyurethane concrete, which had good durability, cohesive strength with steel, early strength property and workability. Presently, it has been applied to a large number of bridges in China [18]. In summary, the current research studies mainly focus on the proportion, basic mechanical properties, linear elastic constitutive model and fatigue performance of polyurethane concrete. The complete stress-strain relationship of other concretes, such as ultrahigh-performance concrete, recycled aggregate concrete and fiber reinforced concrete under uniaxial compression has been widely studied [19, 20, 21, 22]. Some researchers also began to focus on the effect of temperature on the mechanical properties of concrete [23, 24, 25]. However, there is no reported research on the complete stress-strain relationship of the polyurethane concrete under uniaxial compression and the effect of temperature on the mechanical properties of polyurethane concrete. In the present study, cube and prism specimens of the modified polyurethane concrete were fabricated, and the compressive test, splitting tensile test and uniaxial compression test were performed under different temperatures. The change rules of cubic compressive strength, splitting tensile strength, axial compressive strength, elastic modulus and peak strain of the modified polyurethane concrete in a temperature-dependent context were explored, and the conversion relations of the three strengths were established. A temperature-dependent uniaxial compression constitutive model of modified polyurethane concrete was proposed. The calculation results of the constitutive model were compared with the experimental results, and the accuracy of the proposed model was verified. The results are expected to provide the basis for the application of modified polyurethane concrete in bridge deck pavement.
###### Abstract  
Brackish water desalination is widely used to supply fresh water; reverse osmosis (RO) desalination units are considered as the most widespread technology used for this purpose due to the advantage of low power consumption. On the other hand, renewable energy resource integration into the power systems is an important trend, which serves energy supply especially in rural areas and non-stable power supply places. RO units powered from Photovoltaic (PV) systems are considered one of the reliable solutions in places where both water and energy demands are issues to be improved. In this research, the idea of storing energy in water salinity is introduced and discussed to reduce conventional battery storage banks. This concept depends on changing the pressure of the RO unit based on solar profiles to get high distilled water at high solar radiation times (high pressure applied) and low distilled water at low radiation times (low pressure values). Then, the produced water is mixed to get an acceptable salinity in the produced water. This research was applied on a small-scale RO testing unit with a pressure that changed from 40 to 60 bar, and, as a result, the water conductivity changed from 1.7 to 1.1 mS/cm. This was the base line to investigate the possibility of curtailing the battery storage system of the selected plant. Following the variable pressure scenarios, energy storage capacity was reduced by a factor of 20%.  
RO-water desalination unit; off-grid PV system; pressure; energy reduction
# Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
## 4 Experimental Results
### Experimental Setting  
To evaluate the prediction accuracy of ELP-Net, we use a performance metric that measures the probability of the ground truth labels falling within the top 1, 3, 5, 10, 15, and 50 hexagonal grids with the highest predicted probabilities. This provides a comprehensive assessment of the model accuracy across varying levels of prediction confidence. For model training, the ADAM optimizer [39] is employed to ensure efficient and stable convergence. The main hyperparameters used in the training process are summarized in Table 2. Further details on the implementation are provided in Appendix B.
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
###### Abstract  
Several approaches have applied Deep Reinforcement Learning (DRL) to Unmanned Aerial Vehicles (UAVs) to do autonomous object tracking. These methods, however, are resource intensive and require prior knowledge of the environment, making them difficult to use in real-world applications. In this paper, we propose a Lightweight Deep Vision Reinforcement Learning (LDVRL) framework for dynamic object tracking that uses the camera as the only input source. Our framework employs several techniques such as stacks of frames, segmentation maps from the simulation, and depth images to reduce the overall computational cost. We conducted the experiment with a non-sparse Deep Q-Network (DQN) (value-based) and a Deep Deterministic Policy Gradient (DDPG) (actor-critic) to test the adaptability of our framework with different methods and identify which DRL method is the most suitable for this task. In the end, a DQN is chosen for several reasons. Firstly, a DQN has fewer networks than a DDPG, hence reducing the computational resources on physical UAVs. Secondly, it is surprising that although a DQN is smaller in model size than a DDPG, it still performs better in this specific task. Finally, a DQN is very practical for this task due to the ability to operate in continuous state space. Using a high-fidelity simulation environment, our proposed approach is verified to be effective.  
deep Q-network (DQN); deep deterministic policy gradient (DDPG); deep reinforcement learning (DRL); object tracking; object detection; unmanned aerial vehicle (UAV) +
Footnote †: journal: _Algorithms_
# Laser Reconstruction of Spinal Discs Experiments and Clinic
## 2 Non-Destructive Laser Modification of Cartilage Is the Basis of the New LRD Approach
### Mechanisms of Laser Regeneration
There is evidence that both ATP activation and cell proliferation under the influence of R-NIR light occur through the interaction of photons with intracellular water layers. The interaction has at least two biologically important effects: a change in density (volumetric expansion) and a decrease in the viscosity of water [64]. LLLT can increase proliferation of the marrow stem cells without producing high levels of reactive oxygen species [65]. Bone marrow irradiated in vivo can be used to treat a variety of disease conditions. The irradiation may be performed by transcutaneous application of infrared laser radiation over the area of a marrow-containing bone. It was found that labeled cells were seen in and near the infarct up to eight weeks post myocardial infarction, while none  
Figure 3: Formation of micropores in the rabbit cartilage plate under laser action studied by Atomic Force Microscopy: (**A**) image of micropores; size distribution of micropores (**B**) before and (**C**) after laser exposure. were seen in sham-operated hearts. It was concluded that following myocardial infarction, mesenchymal stem cells are signaled and recruited to the injured heart, where they undergo differentiation and may participate in the remodeling process [66]. A detailed analysis of photomodulation processes is beyond the scope of this work. Here, we just note that a comparative study carried out in [47] showed that the thermomechanical effect of the laser radiation causes a more pronounced stimulation of chondrocytes than photomodulation. A recent review of publications on the use of lasers in the treatment of pain syndrome of the musculoskeletal system also showed certain advantages of moderate radiation power compared to low-intensity laser exposure [67]. The conclusion made in [68] seems to be important that \"Treatment of pain syndrome of the IVD is possible without complete recovery of age-related and degenerative changes in the NP by accelerating the healing of damage to the periphery of the disc by stimulating cells, accelerating the transport of metabolites and preventing adhesions and repeated injuries\". This approach corresponds to the LRD technology, which not only reduces pain, but leads to the restoration of cartilage of IVD with partial replacement of the NP tissue with hyaline cartilage. Laser radiation modulated in space and time allows for precise control of various parameters important for the process of structure modification and regeneration, i.e., temperature, amplitude, and frequency of mechanical action, as well as mass transfer into cells, which leads to the appearance of morphogenetic gradients and control of tissue regeneration.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Coordination of Transportation Fleets of Autonomous Drivers
#### 3.5.2 Evaluation
In terms of relative improvement, the highest peak is reached at 2500 customers, with an improvement of 94.6% with respect to FCFS and 44.9% with respect to NVNR. It should be noted that the DYNAR approach is based on the compensation scheme presented above, that is, reassignments include compensations and (economically rational) taxi drivers will accept such reassignments. In Figure 14, we analyse the net income of the system, composed of the income of the taxi drivers plus the income of the mediator, in the case of the DYNAR approach. The presented results are normalised to the income of 1000 drivers and 1000 customers. The overall system income is highest for the DYNAR approach for all numbers of customers. The difference to the FCFS approach is considerable, between 473 and 565 euros, when above 2500 customers per hour (where FCFS is saturated). The difference of DYNAR with respect to NTNR is highest at 2500 customers (79 euros) and about 6-7 euros above that point. The taxi drivers always earn more money with the DYNAR approach, up to 2500 customers per hour. However, their net income is slightly lower than in the NTNR approach for more customers. Nevertheless, it should be noted that the mediator could redistribute its income among all drivers and, thus, the drivers would have a higher income in all cases. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**\# Customers per Hour**} \\ \cline{2-9}  & **1000** & **1500** & **2000** & **2500** & **3000** & **3500** & **4000** \\ \hline FCFS & 1.2 & 1.56 & 1.92 & 39.02 & 75.29 & 112.01 & 148.01 \\ NVNR & 1.2 & 1.56 & 1.92 & 3.83 & 8.29 & 27.38 & 49.01 \\ DYNAR & 1.19 & 1.48 & 1.73 & 2.11 & 6.83 & 25.24 & 46.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average waiting times for customers (in minutes). Summarising, the proposed dynamic reassignment strategy can improve the performance of a transportation fleet of autonomous, self-interested drivers in terms of higher income and fewer movements (thus, also more environmentally friendly). The improvements are, in general, rather small if there are few movements (few service requests) and higher if the demand of transportation services is increasing. The approach relies on a mediator service that manages the assignments of transportation tasks to drivers and pays compensations if necessary. Besides this fact, as proposed here, the mediator does not incur extra costs. Instead, it may have some positive income itself. The overall travel times and distances may still be reduced if the compensation system allowed for a negative balance. This could be of interest if, for example, a municipality would be willing to invest money in order to reduce CO\({}_{2}\) emissions.