esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 3 State Representation
### Resource Features  
It is very common to represent resource efficiency by measuring its _utilization_[18; 29; 33; 40; 41; 42]. Non-normalized utilization is _occupation_, the amount of time a resource is busy [17; 26; 38; 78].  
Many approaches take particular interest in measuring queues [44; 48; 55; 66]. While the number of queued jobs [45; 49; 50; 54; 60] is the most popular metric, the utilization of queues [60; 68] or the free spaces available [40] are also used.  
Load and demand can also give important information. In [78; 96], the number of jobs currently being processed by machines is measured. In [93], the state contains the resource load, and in [17], the state has the current allocated demand. When describing more than the resource time needed, for example, considering volume and complexity of tasks, the _workload_ is used [82; 83; 84].  
Categorical information can be used to translate the condition or situation of a resource at a certain time, that is, the _state_. One of the most common state examples is the resource working behavior [60; 69; 93; 110; 113], for example, idle, working, under maintenance, etc.  
\begin{table}
\begin{tabular}{l l l} \hline
**Group** & **Features** & **References** \\ \hline \multirow{4}{*}{Multi-agent} & & [20; 21; 39; 49; 54; 55; 56; 62; 67; 69; 78; 82; 87; 89; 100; 107; 109; 116; 119; 131; 138; 141; 167; 172; 184; 206; 207; 215; 217; 225; 231; 239; 250; 251] \\  & & [163; 203; 252; 253; 254; 255; 256; 257; 258; 259; 260] \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._This notion of resource state is sometimes used interchangeably with _status_, which more often gives the position or condition of a measurement in relation to something.
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 1 Introduction
### Related Works  
Due to the mild precision and difficulty, the GBSM is one key form of the V2V channel model. Since the assumption of wide-sense stationary (WSS) is just for a brief amount of time, several non-WSS V2V GBSMs were proposed successively during the past [14; 15; 16]. Two-dimensional (2D) and 3D NS V2V GBSMs were analyzed with static clusters or moving scatterers in [17; 18; 19; 20]. The vehicles, pedestrians, and infrastructures can disperseor shift in the 3D plane under real-life scenarios [21]. Recently, a hemisphere [22], two cylinders [23; 24], two spheres [24], and several 3D non-stationary V2V channel models were proposed by expanding the scatterers' allocation on the plane of the 3D regulated form. In contrast to the theoretical results in [25; 26], the outcome of phases linked to the Doppler frequencies of [22; 23; 24] were improper. Therefore, an updated 3D channel model was addressed in [27] with accurate Doppler frequency. By taking notice of the fact that the majority of the mentioned models took only the fixed velocity of the vehicle into account, in realistic traffic environments, the velocity could be time-variant, whereas the time-variant velocities were taken into account in [28; 29; 30; 31], where the trajectories were 2D and also 1D for simplicity. Very recently, a non-stationary GBSM among the terminal and vehicle with 3D arbitrary trajectories and 3D antenna arrays with the consideration of fixed velocity was presented in [32].
# A New Bloom Filter Architecture
## 3 Proposed Algorithm
### 2-Phase Bloom Filter Architecture
#### 3.2.4 Discussion  
The LPT proposed in this paper provides benefits in terms of both the search performance and the memory requirement. If we assume a naive Bloom filter structure implementing an NPT, many empty nodes should be programmed and queried, starting from a root node. In order to avoid queries to empty nodes, if the search begins with the longest level, Bloom filter querying should continue until a matching name prefix is found while decreasing the level of access. Hence, if the NPT is more unbalanced, more memory accesses occur.  
The proposed LPT takes advantage of both cases. The search procedure in an LPT initially begins with the root, and the level increases when ordinary nodes are encountered. However, when a level-priority node is encountered at a certain level, the input is compared with the longest prefix. Based on the comparison result, the search can either be completed or continue to the next level.  
In addition, the LPT-based search can easily be implemented with the functional Bloom filters as described. A functional Bloom filter is a space-efficient data structure that only stores return values without the signature of each programmed element, since different combinations of cell indexes can work as the signature of each different key. Hence, the proposed structure can be implemented with on-chip memories, and the FIB lookup can be completed with a small number of on-chip accesses, as will be shown through simulation in a later section.
# Improved Adaptive Federated Kalman Filtering for INS/GNSS/VNS Integrated Navigation Algorithm
## 2 Multi-Source Fusion Model
### Measurement Equation of System
Therefore, the posterior state estimate is as follows:  
\[\hat{\mathbf{x}}_{k}=\hat{\mathbf{x}}_{k/k-1}+\mathbf{K}_{k}(\mathbf{z}_{k}-\mathbf{H}_{k}\hat{ \mathbf{x}}_{k/k-1}) \tag{13}\]  
The posterior estimate error covariance is as follows:  
\[\mathbf{P}_{k}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})\mathbf{P}_{k/k-1} \tag{14}\]  
Information fusion: The global optimal estimation is obtained through the estimation results of all the sub-filters:  
\[\mathbf{P}_{k+1}^{g}=\left[\sum_{i=1}^{N}\left(\mathbf{P}_{k}\right)^{-1}\right]^{-1} \tag{15}\]  
\[\hat{\mathbf{x}}_{k+1}^{g}=\mathbf{P}_{k+1}^{g}\left[\sum_{i=1}^{N}\left(\mathbf{P}_{k} \right)^{-1}\hat{\mathbf{x}}_{k}\right] \tag{16}\]  
Information sharing process: Finally, the fused results are allocated to the individual sub-filters according to the following formula, which is used as the initial value for the sub-filter at the next moment:  
\[\mathbf{P}_{i,k}=\beta_{i}{}^{-1}\mathbf{P}_{g,k} \tag{17}\]  
\[\mathbf{Q}_{i,k}=\beta_{i}{}^{-1}\mathbf{Q}_{g,k} \tag{18}\]  
\[\hat{\mathbf{x}}_{k}=\hat{\mathbf{x}}_{k}^{g} \tag{19}\]  
where \(\beta_{i}\) represents the allocation coefficient of the \(i\)-th sub-filter, and the subscript 'g' indicates the main filter. According to the principle of information distribution proposed by [PERSON] [15], the information sharing factor should satisfy the condition of information conservation:  
\[\sum_{i=1}^{N}\beta_{i}=1 \tag{20}\]
# A Survey of Blind Modulation Classification Techniques for OFDM Signals
## 4 Artificial Intelligence-Based Approach to MC  
AI is certainly the next big \"game-changing\" technology that includes both ML and DL. In MC, ML finds lots of significance in terms of decision trees, KNN, support vector machine (SVM), artificial neural network (ANN), and some hybrid algorithms. DL is a kind of a subsidiary of ML, which originates from the study of ANN. Neural networks are inspired by biology and try to mimic the neural structure of the human brain [113; 114]. Recently, researchers in the field of wireless communication stated using DL extensively. It finds applications especially in the field of communication systems, such as non-orthogonal multiple access (NOMA) technology, MIMO technology, resource allocation scheme, and signal MC. Tables 5 and 6 lists a few of the ML- and DL-based MC algorithms studied in the literature, outlining the modulation types, uncertain parameters, and the channel employed.
## 5 Conclusions  
This study aimed to investigate the effects of impurities in the CO\({}_{2}\) on CO\({}_{2}\)-EOR and CCS performances. The degree of MMP change depended on the type of impurity since most non-condensable impurities increased the MMP, negatively affecting the miscible state except for H\({}_{2}\)S. A compositional simulation was used to analyze the effect of impurities on both CO\({}_{2}\)-EOR efficiency and carbon storage. A 2D vertical model was used to investigate vertical sweep efficiency considering gravity override during the WAG injection period. Two cases were analyzed: pure CO\({}_{2}\) (Case 1) and impure CO\({}_{2}\), with a composition of 15% impurities captured from oxy-fuel combustion (Case 2). The vertical sweep efficiency and displacement efficiency were affected by the impurities and eventually affected the oil recovery and CO\({}_{2}\)-EOR performance. Impurities in the CO\({}_{2}\) stream increased the IFT and hindered the reduction in oil density. The viscous gravity number increased by 59.6%, confirming the decrease in vertical sweep efficiency due to the addition of impurities. The IFT on a specific grid block increased by 547% in Case 2. The effect of reducing oil density as a solvent was also inhibited from 2.5% to 0.83%. As a result, impurities in CO\({}_{2}\) reduced oil recovery by 9.2%. Due to CO\({}_{2}\) impurities, residual trapping performance was reduced by 4.1%, solubility trapping decreased by 5.6%, and the total CCS performance was reduced by 4.3%.  
Conceptualization, H.-S.L. and K.-S.L.; methodology, H.-S.L., J.C., Y.-W.L. and K.-S.L.; software, H.-S.L. and J.C.; validation, H.-S.L. and K.-S.L.; formal analysis, H.-S.L.; investigation, H.-S.L., J.C. and Y.-W.L.; resources, H.-S.L. and K.-S.L.; data curation, H.-S.L.; writing--original draft preparation, H.-S.L.; writing--review and editing, H.-S.L. and K.-S.L.; visualization, H.-S.L., J.C. and Y.-W.L.; supervision, K.-S.L.; project administration, K.-S.L.; funding acquisition, K.-S.L. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1070406). [PERSON] was supported by a National Research Foundation of Korea (NRF) grant (No. 2020R1I1A1A01067015). The authors declare no conflict of interest.  
The data presented in this study are available on request from the corresponding author. The authors are grateful to the Computer Modelling Group Ltd. (CMG), Calgary, Canada for technical support. The authors declare no conflict of interest.
# Coverage Analysis and Efficient Placement of Drone-BSs in 5G Networks +
## 1 Introduction  
The chronological progress and exponential growth of cellular networks has led the mobile communications community to suggest the use of drones as a Base Station (BS), with the incorporation of a transceiver, due to its massive potential in mobile communication systems [1]. This approach provides significant improved coverage, enhanced quality of service and increased capacity [2, 3]. Furthermore, the high position of the drone-BS encourages Line-of-Sight (LOS) communication to grounds users [4], and consequently the optimal drone-BS deployment is paramount for the proper functioning of the network. On the other hand, with the significant increase in traffic volume, 5G mobile networks will exploit the vast amount of available spectrum in the millimeter wave (mmWave) band [5]. Simultaneously, a drone equipped with a mmWave base station offers considerable throughput. Several research projects are paying more attention to the drone-BS deployment. For instance, authors in [6] study the optimal Unmanned Aerial Vehicles (UAV) placement in order to analyse coverage with fixed UAV altitude. Similarly, in [7], the authors investigate the optimal placement of drone-cells by taking into consideration the backhaul requirements. In addition, the authors propose the robustness solution of drone-BS placement under the user's movement. A heuristic approach based on Particle Swarm Optimization (PSO) is introduced in order to find the optimal 3D location of drone-cells in [8]. The study aims to maximize coverage and limit interference while reducing the drone's altitudes. More recently, in [3], the authors propose a novel 3D UAV-BS placement using exhaustive search (ES) and maximal weighted area algorithms. They maximize coverage under thequality-of-service requirement constraint. The efficient UAVs deployment in order to provide coverage is suggested in [4]. The authors study the downlink coverage probability as function of altitude and antenna gain and apply circle packing theory in order to maximize the coverage area. Authors in [9] present UAVs-BS placement based on the location of users. In this paper we investigate the drone-BS placement by assuming random distribution and mmWave Path Loss model. We analyse the downlink coverage probability based on altitude and antenna gain. We apply a Grey Wolf Optimizer (GWO) algorithm [10] in order to find the optimal 3D position that satisfies coverage under interference constraint. The rest of this paper is organized as follows: Section 2 presents the system model adopted for our work, Section 3 describes the downlink coverage probability and Section 4 provides the proposed optimal drone-BSs placement algorithm using GWO. Simulation and numerical results are given in Section 5. Finally, conclusion and future work are discussed in Section 6.
# Preparation and Characterization of CPCM for Thermal Energy Storage Unit
## 3 Results and Discussion
### Thermal Conductivity  
A larger thermal conductivity can quicken the heat store/release speed of CPCMs, which is expected for a TSEU. Figure 5 shows the thermal conductivities of LA and 10 CPCMs tested in a liquid state and a solid state. The thermal conductivity of LA is the smallest one, 0.27 W/(m\(\cdot\)K) in liquid and 0.22 W/(m\(\cdot\)K) in solid, which is the same as that of the literature [12]. The conductivity of a CPCM increases when its EG mass content increases from 5% to 40%, which is between 1.68 and 16.82 W/(m\(\cdot\)K) in the liquid state and between 1.55 and 16.64 W/(m\(\cdot\)K) in the solid state. When an EG mass content of 40% is introduced, the conductivity of CPCM1 with 100-mesh EG is improved by 56 times more than LA in the melt process and 67 times in the solidification process, and that of CPCM6 with 200-mesh EG is improved by 62/76 times more than LA, respectively.  
Figure 3: SEM photos. (**a**) 100-mesh EG; (**b**) 200-mesh EG; (**c**) CPCM3.  
Figure 4: LA maximum content in CPCMs. (**a**) LA 80% in 100-mesh EG, (**b**) LA 90% in 100-mesh EG, (**c**) LA 90% in 200-mesh EG, and (**d**) LA 95% in 200-mesh EG.  
Figure 5: Thermal conductivities of CPCMs.  
of LA in cavities could happen in the liquid state, which can improve heat transfer, the thermal conductivity of a CPCM in the liquid state is greater than that of the same one in the solid state. CPCM3's conductivity is 8.33 W/(m\(\cdot\)K) in the liquid state and 8.15 W/(m\(\cdot\)K) in the solid state, and that of CPCM9 is 5.44 W/(m\(\cdot\)K) and 5.32 W/(m\(\cdot\)K), respectively.
# Transcriptome-Wide Analysis of Stationary Phase Small ncRNAs in _E. coli_
## 2 Results
### Validation of Selected Candidates
To test if these mRNA-derived fragments were real sRNA candidates and did not represent mere cDNA library artifacts, quantitative real-time PCR analyses were performed with size-extracted RNA. This analysis showed that fragments deriving from _dps_, _bola_, _osmE_, and _yffg_ coding regions were enriched in stationary phase in total RNA samples (Figure 3c). All four predicted sRNAs were differentially expressed in ribosome-associated libraries according to our analysis (Supplementary Table S3), but only _sRNA_osmE_ appeared to be slightly enriched in P100 samples in our RT-qPCR analysis (Figure 3c). In summary, experimental expression profile analyses of selected sRNA candidates captured during exponential growth or in stationary phase largely confirm the differential sRNA expression seen in our sequencing library analyses, thus highlighting the applicability of the novel STARPA biocomputational tool. We next wondered if these stationary phase-specific sRNAs had physiological roles. We picked _sRNA_35_ because of its high abundance (Figure 3a, Supplementary Table S3) for our preliminary analysis. To evaluate the potential importance of _sRNA_35_ in the stationary phase, we overexpressed the sRNA in the exponential phase of growth, when the sRNA were typically absent. For this, the sequence of the sRNA was introduced into pBE6k plasmid (Figure 5a), and the sRNA were overexpressed in the exponential phase along  
Figure 4: Stationary phase-specific transfer RNA fragments. (**a**) Northern blot analysis probing for \(3^{\prime}\)+_tRF\({}^{LauU}\)_ (FM_-NC_000913\(2\)3320093_8), \(5^{\prime}\)-_tRF\({}^{Gly}\)_ (FM_+NC_000913\(2\)4390382_32), and \(5^{\prime}\)-_tRF\({}^{AlU}\)_ (FM_+NC_000913\(2\)0225499_28). 5S_ _rRNA_ was used as a loading control. Zero point four and 2 were optical density measurements, h: hours, d: days. tRF = tRNA derived RNA fragment. (**b**) Northern blot analysis probing for \(3^{\prime}\)-_tRF\({}^{LaU}\)_, \(5^{\prime}\)-_tRF\({}^{Gly}\)_, and \(5^{\prime}\)-_tRF\({}^{AlU}\)_. P100 = pellet enriched for ribosome-associated RNA. S100 = supernatant containing non-ribosome associated RNA. a control spacer sequence. Overexpression of _sRNA_35_ in the exponential phase was far more potent than its canonical expression in the stationary phase (Figure 5b), showing that the overexpression experiment was successful. _sRNA_35_ overexpression in the exponential phase led to the upregulation of the RNA polymerase alternative sigma factor rpoS to similar levels typically only observed in the stationary phase (Figure 5c). Overexpression of a spacer sequence as control did not influence _rpoS_ mRNA levels, thus highlighting the specificity of the observed effects. These pilot experiments suggest that sRNAs expressed in the stationary phase and predicted by our biocomputational analysis likely contain meaningful candidates in the context of stationary phase biology and thus can possess roles in bacterial dormancy that are yet to be deciphered.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 1 Introduction
Therefore, this paper aims to evaluate the performance of GLONASS and its contribution to GPS+GLONASS processing in different latitude regions in terms of satellite visibility and positioning performance. Three networks located in high, middle, and low latitude regions are employed. The performance of both daily DD network solutions and daily static PPP solutions is used for the study. The structure of this article is arranged as follows. Section 2 describes the methods of data quality evaluation, the positioning strategies, and the evaluation indicators. Section 3 describes the data and data selection factors. Section 4 presents the data quality results, the constellation visibility of different systems, as well as the analysis and discussion of the performance of the DD network and PPP solutions. Finally, the main conclusions and findings are shown in Section 5.
## 3 Results
### Adsorption Thermodynamics  
The adsorption process of heavy metals onto solid adsorbents mainly takes places via electro-static forces so it is exothermic (\(\Delta H<0\)) or endothermic (\(\Delta H>0\)) in nature. The value of \(\Delta H\) is the main parameter used to distinguish chemisorption from physisorption [47]. \(K_{d}\) is the equilibrium constant, showing free Gibbs energy (\(\Delta G\)); consequently, the enthalpy (\(\Delta H\)) and the entropy \(\Delta S\) of adsorption could be calculated using the following equations:  
\[\Delta G=-RT\ln k_{d} \tag{11}\]  
\[\Delta G=\Delta H-T\Delta S \tag{12}\]  
where \(T\) is the absolute temperature and \(R\) is the universal gas constant (8.314 J mol\({}^{-1}\) K\({}^{-1}\)). The plots of In(\(K_{d}\)) vs. \(1/T\) for Pb(II) and Cd(II) are shown, respectively, in Figure S9,b in the Electronic Supplementary Materials (ESMs). The negative values of \(\Delta G\) indicate the spontaneous nature of Pb(II) and Cd(II) adsorption onto EgLiCl-mGO. The positive values of \(\Delta H\) demonstrate the endothermic character of the adsorption process, and \(\Delta H\) more than 40 kJ mol\({}^{-1}\) shows chemisorption and \(\Delta H\) lower than 40 is related to chemisorption. The positive values of \(\Delta S\) indicate the increase in disorder at the solid-liquid interface as well as more agitation in the sample solution due to releasing water molecules that grab the ions [48; 49; 50]. The obtained results are shown in Table 8.
## 3 EventTracker Sensitivity Analysis  
The idea behind the proposed EventTracker platform is the assumption that modern information management systems are able to capture data in real time and have the technological flexibility to adjust their services to work with specific sources of data/information. However, to enable this adaptation to occur effectively in real time, online data needs to be collected, interpreted, and translated into corrective actions in a concise and timely manner. This cannot be handled by existing sensitivity analysis methods because they rely on historical data and lazy processing algorithms. It is important to note that real-time data acquisition and collection systems are equipped with data exchange middleware that have some limited and controlled caching and queuing mechanism such that the published data from the sources of data are not lost until they are collected by the consumer of the data, e.g., the EventTracker platform, in the case of recovery from a malfunction in network connectivity.  
In event-based systems, the effect of system inputs on a state is of value, as events could cause this state to change. This \"event-triggering\" situation underpins the logic of the proposed approach. The event-tracking sensitivity analysis method describes the system variables and states as a collection of events.  
An event-based sensitivity analysis method (EventTracker) was proposed by [37]. The proposed event-tracking SA method uses an input-output occurrence [+, -] matrix.  
This matrix is populated at predefined time intervals. The current platform is designed to allow a user (with domain knowledge) to set the initial system update time interval. For example, in safety sensitive systems such as power plant reactor monitoring, the rate of data table population will be in short intervals. In scenarios that employ less time-critical systems, such as finance, the interval will be longer. This matrix is designed to map the relationships between causes that trigger events (Trigger Data or TD) and the data that describe the actual events (Event Data or ED). In this way, the \"EventTracker\" method is able to construct a discrete event framework [37] where events are loosely coupled with respect to their triggers for the purpose of sensitivity analysis.  
The algorithm is designed to respond quickly, and in essence has a life cycle that is equivalent to a Search Slot (SS). Within each SS, TDs and EDs are captured from two time series and used to provide a value which is translated into a sensitivity index. This index is then added to the indices of the subsequent search slots. At the end of each SS, the sensitivity indices of all data series are linearly normalized. The main functions of the EventTracker algorithm are depicted in Figures 3 and 4. The main steps of the algorithm are as follows.  
Figure 4: Trigger–Event Detection functionality on each Search Slot.  
Figure 3: Overall functionality of the EventTracker algorithm.