esa-sceva/satcom-chunk-collection huggingface dataset:
# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
###### Abstract  
Unit commitment (UC) is a critical challenge in power system optimization, typically formulated as a high-dimensional mixed-integer linear programming (MILP) problem with non-deterministic polynomial-time hard (NP-hard) complexity. While the branch-and-bound (B&B) algorithm can determine optimal solutions, its computational cost increases exponentially with the number of units, which limits the practical application of UC. Machine learning (ML) has recently emerged as a promising tool for addressing UC, but its effectiveness relies on substantial training samples. Moreover, ML models suffer significant performance degradation when the number of units changes, a phenomenon known as the task mismatch problem. This paper introduces a novel method for Branching Acceleration for UC, aiming to reduce the computational complexity of the B&B algorithm while achieving near-optimal solutions. The method leverages invariant branching tree-related features and UC domain-specific features, employing imitation learning to develop an enhanced pruning policy for more precise node pruning. Numerical studies on both standard and practical testing systems demonstrate that the method significantly accelerates computation with few training samples and negligible accuracy loss. Furthermore, it exhibits robust generalization capability for handling task mismatches and can be seamlessly integrated with other B&B acceleration techniques, providing a practical and efficient solution for UC problems.  
machine learning; unit commitment; mixed-integer linear programming; branch-and-bound algorithm; imitation learning 2020 ac  
computational complexity grows exponentially with the number of units, rendering it impractical for real-time applications [7; 8; 9].  
In recent years, machine learning (ML) has undergone rapid development, attracting considerable attention in the field of mathematical optimization. As an advanced computational tool, ML demonstrates a remarkable ability to uncover hidden data patterns, trends, and correlations across similar problem domains [10; 11]. This capability establishes ML as a disruptive approach for addressing the trade-off between computational complexity and performance in MILP challenges. It has spurred significant interest among researchers in the field of power systems in utilizing ML techniques to tackle UC optimization problems [12; 13; 14].
# Experimental Study on Permeability Characteristics of
## 2 Materials and Methods
### Multi-Field Coupled Seepage Test System for Backfill Materials  
To study the seepage characteristics of the CBB of gangue slurry under different influencing factors, a multi-field coupled seepage test system for backfill materials was developed. It mainly consisted of a material loading module, axial stress loading module, liquid seepage module, seepage outlet measurement module, and data acquisition and processing module. Figure 3 shows the details of the designed test system.  
Material loading module  
The backfilling material is placed inside the material loading module, which is a cylindrical cavity with a diameter of \(\mathcal{O}\) 150 \(\times\) 300 mm. Corresponding dynamic and static load experiments can be conducted through the axial piston installed inside the material loading module. The interior of the chamber is a fully sealed structure, made of high-quality 2205 duplex stainless steel, providing a sealed condition for liquid seepage experiments. There are three pressure measuring points arranged on both sides of the material loading module, which can monitor the real-time pressure of the backfilling material.  
The axial stress loading module can apply different ways and sizes of axial stress to the backfilling material. The TC-500D axial stress loading pump is selected to ensure uniform and stable loading of axial stress, offering a pressure range from 0 to 30 MPa. A displacement sensor is connected to the outlet end of the piston under axial compression, which can record the piston displacement in real time and facilitate the calculation of the compression height of the backfilling material. The maximum range of the axial displacement sensor is 200 mm.  
The liquid seepage module can apply different sizes of seepage pressure to the backfilling material, and liquid properties can be changed. In this experiment, ordinary tap water was used as the seepage liquid. The liquid seepage module is mainly composed of an advection pump and a piston container. The advection pump is used for the constant-speed quantitative injection of seepage liquid, with a flow rate range of 0.01-600 mL/min and a maximum working pressure of 15 MPa.  
The seepage outlet metering module mainly includes high-precision electronic balance and sealed water buckets of different volumes. This module is used for the real-time monitoring of liquid seepage conductivity and seepage flow rate.  
Figure 3: The loading simulation system of grouting backfilling materials.  
Data collection and processing module  
The data acquisition and processing module consists of a data collector, a computer, and data acquisition control software, which facilitates real-time monitoring and recording of key parameters (such as axial stress and displacement of backfill materials, liquid seepage flow rate, and water pressure during the loading process). The software is developed by the equipment manufacturer and has various functions, such as data display, recording, storage, and processing.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 2 Background Knowledge
### Basic Scenarios and Parameters
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* Array pattern: \[A_{A}(\varphi,\theta,\varphi_{beam},\theta_{beam})=A_{E}(\varphi,\theta)+AF( \varphi,\theta,\varphi_{beam},\theta_{beam})\] (10) where \((\varphi_{beam},\theta_{beam})\): main beam steering angle due to beamforming, \(\rho\): correlation coefficient assumed to be equal to unity, \(a\): amplitude vector with an equal and fixed amplitude, \(Ne:\) number of antenna elements in the array, and \(\Delta V=\Delta H=\lambda/2\): vertical and horizontal spacings between the array elements. Using electronic steering, a planar URA can generate the main and side lobes. By increasing the number of elements in the antenna array, the main lobe beam can provide higher gain while creating a larger number of side lobes. The larger the number of elements in the antenna, the narrower is the main-lobe beam. However, the main-lobe beam may point in any direction, but the gain of the main-lobe beam decreases as the beam gradually deviates from the antenna array orientation. Figure 6 shows the antenna array gains of various 16 \(\times\) 16 antenna arrays with different beam sweep angles. According to [17; 18], a Tx power limit of 38 dBm is chosen for the medium-range BS, and no upper limit is considered for the wide-area BSs. The medium-range BS Tx power limit (38 dBm) is also chosen for the UMa and UMi, while a limit of 46 dBm is chosen for the RMa scenario in the wide-area BS, according to the maximum macrocell BS Tx power defined in [16].
## 1 Introduction
The specific version of the FORTH RTM used in the present study allows more safe conclusions to be made because: (i) it runs under all-sky conditions using the latest available version of the International Satellite Cloud Climatology Project (ISCCP) data (ISCCP-H), which provides unique and well-tested cloud optical properties; (ii) it operates at reasonably high spatial (\(0.5^{\circ}\times 0.625^{\circ}\)) and temporal (3-hourly) resolutions, covering the entire area of the MB; (iii) it uses analytical vertical profiles of aerosol (and dust) optical properties, which strongly affect the estimation of DREs for absorbing aerosols, such as dust [46]; and (iv) it yields detailed 3-D distributions of solar fluxes and DDREs. The quality of the estimated DDREs is ensured by the thorough evaluation of RTM fluxes performed against reference fluxes at the surface (from the Baseline Surface Radiation Network (BSRN) stations) and at the top of the atmosphere (from Clouds and the Earth's Radiant Energy System (CERES)).
## 2 Materials and Methods
### Fungal Strains and Antifungal Test  
Yeast-type (_C. albicans_) and filament-type (_A. niger_) fungi were used for antifungal tests. _C. albicans_ (KCTC 7270) and _A. niger_ (130708) were generously provided by the Korean Collection for Type Cultures in Korea Research Institute of Bioscience and Biotechnology (Jeongeup-si, jeollabuk-do, Korea) and Dr. [PERSON]'s laboratory at Dankook University (Cheonan-si, Chungnam, Korea), respectively. Both fungal strains were maintained in potato dextrose agar (PDA) and propagated in potato dextrose broth (PDB) for experimental use. Culture temperature was 30 \({}^{\circ}\)C.  
For treatment with **Cu(AZPY)-MOF**, cells of _C. albicans_ and _A. niger_ spores were collected. One or two colonies of _C. albicans_ from a PDA culture plate were suspended in 1 mL 1\(\times\) PDB solution; then, 10 \(\mu\)L of the suspension was inoculated into new 15 mL PDB media in a flask. After incubation at 30 \({}^{\circ}\)C with shaking for 20 h, the culture suspension was centrifuged at 3143\(\times\)\(g\) for 5 min, and the liquid part was discarded. The cell pellet was washed twice with 1\(\times\) phosphate buffered saline (PBS) and subsequently resuspended in new PBS. Cell number in 10 \(\mu\)L of suspension was counted under the microscope using hemacytometer (Paul Marienfeld GmbH & Co., Lauda-Konigshofen, Germany) and then, number in 1 mL was calculated. The final cell concentration was then adjusted to 10\({}^{8}\) cells/mL. For _A. niger_ culture, several pieces of _A. niger_ mycelia were inoculated onto PDA plates, and the plates were incubated at 25 \({}^{\circ}\)C for 2 weeks. After 2 weeks, approximately 10 mL of 1\(\times\) PBS was added to the incubated plates, and fungal mycelia,including spores, were scraped using a scraper. The scraped suspension was filtered through 4 layers of miracloth, and the filtered suspension was centrifuged at 3143\(\times\)\(g\) for 5 min. After the liquid part was discarded, the fungal spore pellet was washed once with 1\(\times\) PBS and subsequently resuspended in new PBS. As performed in _C. albicans_, spore number per unit volume (mL) was counted and estimated under the microscope using hemacytometer and then the final spore concentration was adjusted to 10\({}^{8}\) spores/mL.  
Suspensions of _C. albicans_ cells and _A. niger_ spores were placed in a 24-well plate (1 mL per well, 10\({}^{8}\) cells or spores per well). A suspension of **Cu(AZPY)-MOF** in PBS was added to each well at final concentrations of 0, 0.125, 0.25, 0.35, and 0.5 mg/mL, and the plate was incubated at room temperature with shaking. After incubation for 1 day, the treated spore suspensions were transferred into a 1.5-mL tube and serially diluted. One hundred microlitres (100 \(\upmu\)L) of the diluted suspension was spread onto a PDA plate, and the plates were incubated at 30 \({}^{\circ}\)C for _C. albicans_ and room temperature for _A. niger_ for 2 days. After incubation, the number of colony forming units (CFUs) was counted from 3 replicate plates, and experiments were repeated 3 times.
## 3 DC-DC Converters
### Multi-Switch Boost Converter  
For high power applications, one of the ways to circumvent thermal damage from semiconductor devices in a basic converter topology is paralleling several power semiconductors (or switches) when a single switch is not able to handle the flowing current. This means the high current that passes through a single switch and causes a sharp rise in its temperature can be divided into two or more portions in a multi-switch topology. This method can be adopted when the basic boost converter is suffering high power dissipation, has an inability to control high currents, thermal management issues, or magnetic design [43]. A dual-switch boost converter is shown in Figure 5.  
It is clear from Figure 5 that it would not be reasonable if the two switches were turned on simultaneously. For this reason, the entire duty cycle of a basic boost topology should be divided between the two switches equally or unequally. In this study, the duty cycle is evenly divided between two switches (i.e., each switch is responsible for 25% of the cycle). Therefore, in order to avoid overlapping, the duty cycle of the second switch should be applied with a delay of 1/4 cycle compared to the first switch.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 13 Delay-Sensitive Routing in Underwater WSNs
### Initial Problem Formulation  
The initial problem formulation in [8] is a _nonlinear program_ (NLP) with a non-deterministic generic objective function in the decision variables and linear sets of constraints. A brief explanation of the formulation is given as follows:  
* Decision variables: 1. the number of bits transmitted on each link (\(W_{ij}\)), 2. the transmission time allocated for each link (\(\Delta_{ij}\)).
* Objective function: is the sum of all energy consumed on all links in the network. It contains \(P\left(\frac{W_{ij}}{\Delta_{ij}}\right)\) which is a non-deterministic general term that is function in the number of bits \(W_{ij}\) to be transmitted on each link and the allocated transmit time \(\Delta_{ij}\). This term representspower consumption which is dependent on the modulation/channel coding schemes used for transmission.
* Constraint sets: 1. A linear delay constraint, that takes into account the propagation delay of each link and the transmission delay that is inherent in the allocated transmission time. The total delays on all links from the source sensor nodes to the sink should not exceed a maximum allowable delay \(T\), that was given by \(\sum_{i=1}^{N-1}\sum_{j\in\psi_{i}}\left(\Delta_{ij}+\tau_{ij}\right)\leq T\) where \(\tau_{ij}\) is the propagation delay of link \((i,j)\) and \(\psi_{i}\) is the set of next hop candidates that are closer to the sink than \(i\). 2. A linear conservation of flow set of constraints for every node that guarantees that difference between incoming and outgoing data is equal to the amount of data generated by the sensor node, that is \(\sum_{j\in\psi_{i}}W_{ij}-\sum_{j\in\psi_{i}^{\prime}}W_{ij}=r_{i}T\) where \(r_{i}\) is the data generation rate of node \(i\) and \(\psi_{i}^{\prime}\) is the set of neighboring nodes for which \(i\) is a next hop candidate.
## 3 Materials and Methods
### Data Collection and Pre-Processing  
Two different datasets used in this study have been preprocessed. Incorrect data have been cleaned, and a zero (0) value has been entered for missing data. Two different datasets have been used to make comparisons within the scope of the study. The data of drone flight number 129 has 46.170 rows and consists of the columns State, Cycle, Current (A), Capacity (Ah), and Relative Time (h:min:s.ms). The data of drone flight number 92 has 50.383 rows and includes the columns State, Cycle, Current (A), Capacity (Ah), and Relative Time (h:min:s.ms). The two datasets were analyzed and compared separately using deep learning techniques: CNN, RNN, the CNN + RNN hybrid approach, and the ensemble technique.
## 5 Discussion  
### Accuracy Assessment of the Dasymetric GDP Map based on County-Level GDP Statistics  
The administrative units of mainland China contain five levels: the province, prefecture, county, township, and village levels. In Section 3.2, prefecture-level GDP data were used to produce dasymetric GDP maps. Therefore, county-level GDP data were used to validate dasymetric GDP maps. We collected statistical GDP data from 13 districts/counties in Wuhan from 1997 to 2012 and calculated the total GDP value of each district/county from the corresponding dasymetric GDP map based on the county boundary. Three statistical measures--namely, the root mean square error (RMSE), mean relative error (MRE), and correlation coefficient (R)--were used to assess the accuracies of dasymetric GDP maps. Table 2 shows the validation results of the estimated GDP for 13 districts/counties in Wuhan. From 1997 to 2012, the RMSE increased due to the nearly 10-fold growth in the Wuhan GDP. The high R and low MRE values in each year indicated that the estimated and actual GDP values were similar at the county level. The validation results suggested that nighttime light data could be effectively used to create dasymetric GDP maps of the Wuhan urban agglomeration, and the estimated dasymetric maps accurately expressed the spatial distribution of the GDP.
## 2 Materials and Methods
### Sorption Tests  
#### 2.4.1 Experimental Procedures for Sorption and Desorption  
Sorption tests were performed in batch system. The solution (volume, V, L) containing fixed initial concentrations (C\({}_{0}\), mmol L\({}^{-1}\)), at selected pH values (pH\({}_{0}\)), was mixed with a fixed amount of sorbent (m, g), under 150 rpm agitation. The sorbent dose (SD= m/V, g L\({}^{-1}\)) was varied (when relevant) or set to 1 g L\({}^{-1}\). The specific experimental procedures are systematically reported in the caption of the Figures (see below). The pH was not controlled during the sorption process, but the final pH (pH\({}_{\text{eq}}\)) was monitored using CyberScan 6000 pH-meter (Eutech Instruments, Nijkerk, The Netherlands, part of Thermo Fisher Scientific, Inc.). After magnetic solid/liquid separation of collected samples (variable time for kinetics, 24 h for equilibrium experiments such as isotherms and so on), the residual concentrations (C\({}_{\text{eq}}\), mmol L\({}^{-1}\)) were determined using an inductively coupled plasma atomic emission spectrometer ICP-AES ACTIVA M (HORIBA JOBIN YVON, Longjumeau, France). The sorption capacity (q\({}_{\text{eq}}\), mmol g\({}^{-1}\)) was calculated by the mass balance equation: q\({}_{\text{eq}}\) = (C\({}_{0}\)-C\({}_{\text{eq}}\)) \(\times\) V/m. Similar experiments were applied for the study of sorption from multi-component solutions (the pH was set to 5.5), or for the treatment of industrial effluents (tailing leachates).  
Acidic urea solutions were used for metal desorption from metal-loaded sorbents [41]. A 0.3 M urea solution (with pH controlled to \(\sim\)2.7 with sulfuric acid solution) was used for the desorption step. The contact time was set to 24 h, under 150 rpm agitation. The released concentration allowed calculating the desorption yield (DE, %). For the tests of sorbent recycling (3 successive cycles), a rinsing step was systematically processed; the sorption capacities and sorption efficiencies were compared with the initial performances. To evaluate the stability of the sorbent, the FTIR spectrum of the sorbent after metal sorption and desorption was compared with the spectrum of pristine sorbent.
## 2 Materials and Methods
### GC-MS Analysis  
The GC analysis of the EO was carried out on a Varian 3900 gas chromatograph coupled with a Varian Saturn 2100T ion trap MS equipped with an Agilent DB-1 fused-silica capillary column (30 mm \(\times\) 0.25 mm i.d., 0.25 \(\mu\)m film thickness). The analysis was carried out under the following conditions: injector and detector temperatures of 210 \({}^{\circ}\)C and 290 \({}^{\circ}\)C, respectively; helium was the carrier gas and was used at a flow rate of 0.8 mL/min; split ratio, 1:10; injection volume, 1 uL. A temperature gradient was programmed as follows: 60 \({}^{\circ}\)C with a 3 min initial hold, then 280 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min, and finally held isothermally for 5 min. Regarding the MS conditions, the ionization mode had an electronic impact of 70 eV. The mass range was set from 45 to 650 Da. The components were identified by comparing their mass spectra to those of the NIST Library (2014) [29] of the instrument, by evaluating their linear retention indices (LRI) relative to C\({}_{6}\)-C\({}_{24}\) n-alkanes, and, when available, by comparing them to authentic standards from Sigma-Aldrich (Milano, Italy). The percentage composition of the oil was calculated from the GC peak areas using the normalization method without correction factors. The data are reported as the mean value of three oil injections.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 2 Proposed Greedy Randomized Adaptive Search Procedure for TSP-D
### Algorithm Details
#### 2.2.2 Optimizing the Obtained Solution with Local Search
A sub-optimal move can be accepted, based on a certain probability \(P(\Delta E,T)\) that it follows the Boltzmann distribution, as shown in Equation (7), where \(\Delta E\) represents the difference between the objective function (cost) of the current solution and that of the resulting neighbor. A random number, \(randP\), is generated. If the value of \(randP\) is less than the probability \(P(\Delta E,T)\), the move is accepted; otherwise,the move is not accepted. The termination condition in the SA variant is satisfied when the temperature \(T_{i}\) exceeds the final temperature \(T_{f}\). \[T_{i}=\gamma\times T_{i} \tag{6}\]  
\[P(\Delta E,T)=e^{\frac{\Delta E}{T_{i}}} \tag{7}\]  
The _Swap_ Move: In the _Swap_ move, two distinct nodes are randomly selected and swapped (Figure 5). A swapped node may be a truck node, a drone node, or a combined node, but it cannot be the depot [22]. The _TwoOpt_ Move: In the _TwoOpt_ move, two edges are removed from the tour and replaced with another set of edges, in order to obtain a valid tour. The removed edges must be between truck or combined nodes [22]. The first step in applying _TwoOpt_ is to select the two origin nodes of the selected edges randomly. In Figure 6, nodes 4 and 2 are selected. Two edges are thus removed: \((4,6)\) and \((2,0)\). Then, two new edges are created, as shown in Figure 6: \((4,2)\) and \((6,0)\). As a result, the sortie path is reversed, as shown in Figure 6.  
As twoOpt randomly selects two edges, there are several invalid cases that must be taken into consideration, as follows:  
1. If the selected origin node or its destination node happen to be inside a sortie, as shown in Figure 7;
2. If both selected origin nodes are launch nodes and their destinations are rendezvous nodes, as shown in Figure 8;  
Figure 5: Example of the _Swap_ move: (**a**) displays the TSP-D solution, \(tspd\), before applying _Swap_; and (**b**) displays the \(tspd\) after nodes 2 and 5 have been swapped. Figure 6: An example of a _Swap_ move: (**a**) displays the TSP-D solution, \(tspd\), before applying _Swap_; and (**b**) displays the \(tspd\) after nodes 2 and 5 have been swapped. Figure 7: An example of an invalid _TwoOpt_ move applied to the graph in (**a**), where origin node 3 is a truck node inside a sortie and the origin node 6 is a launch node for a different sortie, resulting in an invalid tour in (**b**). 3. If the selected origin or its destination node happens to be a common node between different sorties, as shown in Figure 9.  
The complexity of \(Swap\) and \(TwoOpt\) moves, including the work required to compute the savings obtained by the moves, is \(\mathcal{O}(n)\).