esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Aggregate Effect on the Compressive Strength of Concrete Using Dune Sand
## 4 Conclusions  
In this study, the compressive strength properties of concrete for different unit water contents and DS/FA ratios were evaluated. In addition, the effect of each aggregate on the compressive strength of concrete was quantitatively analyzed based on the measured data. The results can be summarized as follows.  
1. The slump/AD ratio increased as the DS/FA ratio increased up to 40%. In particular, a DS/FA ratio of 40% was the most favorable condition for concrete workability.
2. The compressive strength of concrete increased until the DS/FA ratio increased to 20%. Thereafter, the compressive strength decreased as the DS/FA ratio increased. For the same DS/FA ratio, the compressive strength tended to increase slightly as the water content decreased.
3. The effect of the DS/FA ratio on variations in compressive strength was greater than that of the unit water content on variations in compressive strength. Therefore, in terms of the strength of concrete using DS and CS, we recommend that the DS/FA ratio should be considered more important than the unit water content.
4. The relationship between the changes in compressive strength and aggregate volume fractions was analyzed, considering the effect factors of each aggregate on the compressive strength under conditions (1) \(0<\text{DS}(a)<\text{CS}(b)<\text{CA}(c)\) and (2) \(0<\text{CA}(c)<\text{CS}(b)<\text{DS}(a)\).
5. For \(0<a<b<c\) and \(c=1\), the value of \(a\) generally increased as \(b\) increased. Similarly, the value of \(a\) decreased as \(b\) decreased; however, the decrease in \(a\) was larger than that of \(b\). In addition, the common ranges of \(a\) and \(b\) for all of the mixtures were 0.04 to 0.83 and 0.72 to 0.92, respectively.
6. For \(0<c<b<a\) and \(a=1\), the value of \(c\) generally increased as \(b\) increased. Similarly, the value of \(c\) decreased as \(b\) decreased; however, the decrease in \(c\) was larger than that of \(b\). The common ranges of \(b\) and \(c\) for all of the mixtures were 0.68 to 0.80 and 0.02 to 0.79, respectively.  
**Author Contributions:** Conceptualization, E.L.; methodology, J.N.; validation, J.K., J.Y., and S.P.; formal analysis, E.L. and J.N.; investigation, E.L., J.K., J.Y., and S.P.; data curation, E.L.; writing--original draft preparation, E.L.; writing--review and editing, J.N.; supervision, S.P.; project administration, E.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Reinforcement Learning for Hybrid Electric Vehicles
### Q-Learning Algorithm  
The core part of the RL agent is constituted by the algorithmic structure as well as by the transition (step) function used to stimulate the learning process. The Q-learning algorithm is one of the first and most studied RL algorithms proposed in the literature [31]. It is based on the idea of the maximization of a q-value \(q_{\pi}(s|a)\). The estimate of the optimal q-value is obtained iteratively by means of the Temporal Difference (TD) principle that provides updates for every time step using the received reward [16]. The idea of the Temporal Difference (TD) method is based on the TD-Target: \(R_{t+1}+\gamma Q(S_{t+1},\ A_{t+1})\) which represents a slightly better approximation of the q-value for each of the state-action pairs. For each time step, the TD algorithm updates the approximation of the q-value in the direction of the TD-Target. The update is computed by the minimization of the TD-Error, which is represented by the difference between the new estimate and the old one: TD-Target \(-Q(S_{t},\ A_{t})\). The entity of the update is controlled by the learning rate \(\alpha\).  
The mathematical formulation for the TD-update is:  
\[Q(s_{t},\ a_{t})\gets Q(s_{t},a_{t})+\alpha(R_{t+1}+\gamma Q(s_{t+1},\ a_{t+1})-Q(s_{t},\ a_{t})) \tag{7}\]  
Starting from the TD update, Q-learning computes the target of the q-value approximation computing the estimation max q-value starting from the state of the next time step \(s_{t+1}\), where the max is computed among the action available to the agent at time step \(t\). In other words, the q-value estimates involve the choice of the greedy action on the next state, that is, an estimate of \(q^{*}\). In the Q-learning algorithm, the mathematical formulation becomes:  
\[Q(s_{t},\ a_{t})\gets Q(s_{t},a_{t})+\alpha\Big{(}R_{t+1}+\gamma\ max_{a \in A(s_{t+1})}Q(s_{t+1},\ a)-Q(s_{t},\ a_{t})\Big{)} \tag{8}\]  
In addition, the Q-learning stores the q-value estimate for each state-action pair in a matrix named Q-table. Given a state \(s_{t}\), the Q-table gives the priority over the choice of the actions at time step \(t\), the greedy action being the one with greatest q-value \(argmax_{a\in A(s_{t})}\ Q(s_{t},\ a)\). The policy \(\pi\) of the Q-learning agent is the \(\epsilon\)-greedy policy with respect to the q-values of the state \(s_{t}\). Considering that not the entire set of actions could be available to the agent at each time step in a real driving scenario, some actions are known to be unfeasible in a state \(s_{t}\); therefore, they can be excluded from the actions among which are computing the q-values, both for the update and for the policy \(\pi\). In particular, the set of actions \(A(s_{t})\) among which the agent can choose at time-step \(t\) depends on the state \(s_{t}\) of the environment at the same time step.
# Opportunities for Integrating Underground Railways into Low Carbon Urban Energy Networks: A Review
## 2 District Energy  
District energy systems, or district heating and cooling (DHC), can be defined as networks that provide either heating or cooling from a central point of generation to multiple buildings within a district, neighborhood or city [12]. These systems have been widely applied as a way to provide low carbon energy to end users, leading to significant financial and environmental benefits. From an economic perspective, DHC systems benefit from economies of scale, as utilizing the energy produced from one larger source can often be more efficient than using a variety of smaller sources. In terms of environmental benefits, heating and cooling networks allow the use of locally available energy sources that can be renewable or based on the recovery of energy that would otherwise be wasted, increasing the overall efficiency of the energy system. Another advantage of district energy systems is their flexibility; by enabling the integration of a diverse number of energy sources, end users are not dependent upon one single source, which leads to greater energy security and permits alternating the different energy sources used in order to guarantee the most cost-effective and low carbon operation of the energy system.  
A similar definition is provided in [13], which also calls for the adoption of a modernized definition of district energy, combining decentralized power generation and DHC networks. This involves integrating renewable electricity to networks that recover and distribute low-grade heat and cold to end users, helping to balance intermittent renewable power generation by converting it to thermal energy when costs are low and storing that energy to be used seasonally or during daily peak demands. The integration of decentralized power to district energy systems enables the optimized use of energy resources by matching local energy production and demand, an essential feature of future energy systems, particularly as it helps to alleviate high peak demands that can stress the electricity grid. Photovoltaic (PV) panels represent an example of a decentralized renewable power sources that can provide low carbon energy with lower transmission losses to meet the demand for thermal energy, whether it relates to heating or cooling requirements. In temperate climate countries, such as the UK, heating represents a significant reason for energy consumption and carbon emissions, which makes heat networks an important technology to make heating cleaner and more affordable, helping to fight climate change while expanding energy access. For that reason, this paper focuses on the delivery of heat through district energy systems, showing how district heating can trigger the potential to recover waste heat generated in urban environments.
## Materials and Methods  
### General  
\({}^{1}\)H-NMR spectra were recorded on a Bruker AC 250 E spectrometer using chloroform-_d_ (99.80%, VWR Chemicals) (CDCl\({}_{3}\)) or acetonitrile-_d_ (99.80% VWR Chemicals) (CD\({}_{3}\)CN) solutions.  
Reversed Phase HPLC/ESI/Q-TOF HRMS experiments: Water and acetonitrile were of HPLC/MS grade. Formic acid was of analytical quality. The HPLC system was an Agilent 1260 Infinity. A reversed-phase C18 column (ZORBAX Extended-C18 2.1 \(\times\) 50 mm, 1.8 \(\upmu\)m) with a Phenomenex C18 security guard column (4 mm \(\times\) 3 mm) was used. The flow rate was 0.4 mL/min and the column temperature was set to 30 \({}^{\circ}\)C. The eleutents were formic acid-water (0.1:99.9, \(v/v\)) (phase A) and formic acid-acetonitrile (0.1:99.9, \(v/v\)) (phase B). The following gradient was employed: 0-10 min, linear gradient from 5% to 95% B; 10-15 min, washing and reconditioning of the column to 5% B. The injection volume was 10 \(\upmu\)L. The eluate was monitored through MS TIC. Mass spectra were obtained on an Agilent 6540 UHD accurate-mass Q-TOF spectrometer equipped with a Dual AJS ESI source working in positive mode. N2 was employed as desolvation gas at 300 \({}^{\circ}\)C and a flow rate of 9 L/min. The nebulizer was set to 45 psi. The Sheath gas temperature was set at 350 \({}^{\circ}\)C and a flow of 12 L/min. A potential of 3.5 kV was used on the capillary for positive ion mode. The fragmentor was set to 175 V. MS spectra were recorded in the 150-1000 \(m/z\) range.  
Polarized optical microscopy (POM) analysis of the compounds were carried out by a Zeiss Axioskop 40 Pol microscope equipped with a Linkham hot-stage and a Mettler FP90 central processor to control the temperature. The analysis was performed with the samples placed between a glass slide and a cover slip. The samples were heated and cooled with a rate of 10 \({}^{\circ}\)C min\({}^{-1}\). An InfinityX-21 MP digital camera mounted atop the microscope allows to capture photomicrographs. Images were recorded at magnifications of 100\(\times\) or 200\(\times\) and cross-polarized light.  
Differential scanning calorimetry (DSC) measurements were carried out with a TA Instruments mod. 2920 Differential Scanning Calorimeter with a TA Instruments Refrigerated Cooling System. Samples were prepared in aluminum TA Tzero Hermetic Pans and Lids (T121019, T121106, made in Switzerland) using SCALTEC (d = 0.01/0.1 mg) as the analytical balance. The sample size was of the order of 4 mg for each sample. The analysis was carried out with heating and cooling rates of 10 \({}^{\circ}\)C min\({}^{-1}\) for every sample under a N\({}_{2}\) flux of 60 cm\({}^{3}\) min\({}^{-1}\). Seven ramps were included in the temperature program: one heating step from room temperature to high temperatures until isotropization of the sample at 10 \({}^{\circ}\)C min\({}^{-1}\), followed by a cooling step to -30 \({}^{\circ}\)C and by the other analogous heating/cooling cycles. The repetition of the same ramps was done to assess the reproducibility of the phase transitions. Due to the Dual Sample Cell, two samples with equal melting points were measured at the same time.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 5 Results
### Ablation Study on Initialization of Regularization Weight  
To assess the sensitivity of our method to the choice of initial regularization weight \(\lambda_{0}\), we trained with \(\lambda_{0}\in\{0,\ 10^{-4},\ 10^{-3},\ 10^{-2}\}\) (decayed linearly over 100 communication rounds) across 50 random seeds. We observed that the final-round performance at round 100 was effectively invariant to the choice of \(\lambda_{0}\): the mean accuracy was 80.79% \(\pm\) 0.15%, and the mean macro-F1 score was \(0.791\pm 0.003\) in all cases. This demonstrates that our entropy-regularized federated optimization is robust to the initialization of the regularization strength.
## 3 Simulation of Underwater Electric Field Communication
### CSMA/CA Channel Backoff Algorithm Design and Simulation Analysis
#### 4.2.4 Channel Detection
_Electronics_**2020**, \(9\), 272. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] A survey of channel modeling for UAV communications. _IEEE Commun. Surv. Tutor._**2018**, _20_, 2804-2821. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON] Simulation of LoRa in NS3: Improving LoRa performance with CSMA. In Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 20-24 May 2018; pp. 1-7. * [PERSON] et al. (2012) [PERSON]; [PERSON] [PERSON]; [PERSON] Vehicular network simulation propagation loss model parameter standardization in NS3 and beyond. In Proceedings of the 2012 IEEE Southeaston, Orlando, FL, USA, 15-18 March 2012; pp. 1-5. * [PERSON] and [PERSON] (2012) [PERSON]; [PERSON] Comparing the NS3 propagation models. In Proceedings of the 2012 IEEE 20 th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, Washington, DC, USA, 7-9 August 2012; pp. 61-67. * [PERSON] and [PERSON] (2014) [PERSON]; [PERSON] [PERSON] Simulation for congestion-less losses control over MANET using TCP scheme. In Proceedings of the 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, India, 7-8 February 2014; pp. 410-415. * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON] [PERSON] Next-generation TCP for NS3 simulator. _Simul. Model. Pract. Theory_**2016**, _66_, 81-93. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Influence of backoff period in slotted CSMA/CA of IEEE 802.15. 4. In Proceedings of the Wired/Wireless Internet Communications: 14 th IFIP WG 6.2 International Conference, WWIC 2016, Thessaloniki, Greece, 25-27 May 2016; Proceedings 14. Springer International Publishing: Berlin/Heidelberg, Germany, 2016; pp. 40-51. * [PERSON] (2021) [PERSON] Design of Software Defined Radio Platform Based on Raspberry Pi. Ph.D. Thesis, Lanzhou University, Lanzhou, China, 2021. * [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Implementation of Underwater Electric Field Communication Based on Direct Sequence Spread Spectrum (DSSS) and Binary Phase Shift Keying (BPSK) Modulation. _Biomimetics_**2024**, \(9\), 103. [CrossRef] [PubMed]
* [PERSON] et al. (2024) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Distributed short-term predictive control for AUV clusters in underwater cooperative hunting tasks. _Ocean Eng._**2024**, _301_, 117343. [CrossRef]

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# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 3 Pretreatment of Feedback to Enhance Biogas Production
### Thermal Treatments
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Pretreatment Process** & **Substrate** & **Conditions** & **Results** & **Ref.** \\ \hline \multirow{8}{*}{Chemical} & Cotton stalk residues & Alkali hydrogen peroxide (AHP), treatment at 37 \({}^{\circ}\)C for 61 days. & Highest methane yield of 192.4 mL/g VS after 3\% AHP pretreatment, yield improved by 254.3\% over the untreated waste. & [82] \\  & & & \\ \cline{2-4}  & Agriculture straw & 3\% H\({}_{2}\)O\({}_{2}\) and 8\% Ca(OH)\({}_{2}\), treatment at 37 \(\pm\) 1 \({}^{\circ}\)C for 35 days. & Highest methane yields of 216.7 and 206.6 mL CH\({}_{4}\)/g VS using acid and alkaline, which are 115.4\% and 105.3\% greater than untreated waste, respectively. & [83] \\ \cline{2-4}  & Sunflower oil cake & 170 \({}^{\circ}\)C, 1\% (weight) sulfuric acid concentration. & Biogas yield of 302 \(\pm\) 10 mL CH\({}_{4}\)/g VS at 170 \({}^{\circ}\)C), 50\% greater than untreated waste. & [84] \\ \hline \multirow{8}{*}{Biological} & Food waste and brown water & Composting or microaeration. & Microaeration pretreatment improved methane yield by 10–21\%, caused by enhancement of hydrolysis. & [85] \\ \cline{2-4}  & Chicken feathers & Aerobically pretreated for 2–8 days with _Bacillus_ sp. _C4_, a bacterium that produces both \(\alpha\)- and \(\beta\)-keratinases. & [86] \\ \cline{2-4}  & Napier grass & Microbial consortium: WSD-5. & CH\({}_{4}\) yield was increased by 49\%. & [87] \\ \cline{2-4}  & Organic waste & Soft rot fungi _Trichoderma_ _viride_, 4 days treatment at 25 \({}^{\circ}\)C. & Up to 400\% increase in methane production compared with controls. & [88] \\ \cline{2-4}  & Paddy straw & White rot fungi _Fusarium_ sp., 10 days treatment at 30 \({}^{\circ}\)C (70 \% moisture). & 53.8\% increase in biogas production. & [89] \\ \cline{2-4}  & Organic waste & Soft rot fungi _Trichoderma_ _viride_, 10 days treatment at 22 \({}^{\circ}\)C (70\% moisture). & More than two-fold increase in methane production. & [90] \\ \hline \multirow{8}{*}{Thermal} & Wheat straw & Different thermal pretreatment temperatures of 120, 140, 160, and 180 \({}^{\circ}\)C. & Highest biogas yield of 615 mL/g VS (53\% increase) and volatile solids reduction of 69\% at 180 \({}^{\circ}\)C, compared to untreated material. & [91] \\ \cline{2-4}  & Activated sludge & Steam explosion at 170 \({}^{\circ}\)C, autoclaved at 70, 100, and 125 \({}^{\circ}\)C. & A linear relationship was found between the thermal treatment temperature in the autoclave and biogas production of aerobic granules, but steam explosion was more effective. & [92] \\ \cline{2-4}  & Hay & Steam explosion at 160–220 \({}^{\circ}\)C. & CH\({}_{4}\) yield increased by 16\%. & [93] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)
## 2 Results
### Regulation of ABA and NAA on the Biosynthesis and Accumulation of Norisoprenoids
#### 2.2.2 Acquisition of the Candidate Genes Regulating Norisoprenoid Accumulation
Among those hub genes (Table S3), only one transcription factor of VviGATA26 (VIT_200s2393g00010) was found and it was in module turquoise. This TF could be potentially involved in the regulation of norisoprenoid biosynthesis. To characterize the VviGATA26 function, DNA affinity purification sequencing (DAP-seq) was performed to understand the binding sites of VviGATA26 in the grape genome. Totally 6785 binding locations (peaks) were found, among which 1872 binding sites were localized in the promoter region of 1817 target genes. The target genes also included those genes encoding VviPSY1 (VIT_204s0079g00680), VviZDS (VIT_214s0030g01740), and VviABA-Hase (VIT_204s0079g00680), which were participated in the norisoprenoid biosynthesis (Figure 2B). However, we noticed that VviPSY1 and VviZDS were included in black module, which negatively correlated with the concentrations of (E)-\(\beta\)-damascenone, (Z)-\(\beta\)-damascenone, and total norisoprenoids. The VviABA-Hase was in \"midnightblue\" module, but this module did not exhibit a high correlation with norisoprenoid. Additionally, KEGG enrichment analysis revealed that the target genes predicted for VviGATA26 were significantly enriched in the pathway of \"plant hormone signal transduction\". In this case, VviGATA26 could also be involved in the signaling regulation of hormones including ABA, auxin, cytokinine, gibberellin, salicylic acid, brassinosteroid, jasmonic acid, and ethylene (Table S4). Figure 6: Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) induced by ABA100 or NAA100 and the hierarchical cluster analysis of associated genes related to the accumulation of norisoprenoid. (**A**) Hierarchical cluster tree showing 12 merged modules of co-expressed genes. (**B**) Module-trait correlations and corresponding \(p\)-values. The left panel shows 12 modules and the right panel is a color scale for module trait correlation from \(-1\) to \(1\).
# Research on Collision Warning Method for Ship-Bridge Based on Safety Potential Field
## 4 Vessel Warning for Bridge Area Based on Vessel Posture  
The design of ship-bridge collision warning is based on the established potential energy field of static elements, the kinetic energy field of moving ships, the boundary energy field and the driver behaviour field. Through the grid-based analysis of the waters, the safety conditions in different areas can be accurately assessed, and the superposition effect of each field strength can be explored in order to effectively predict and prevent ship-bridge collision accidents. The specific range and threshold of early warning potential field values are formulated according to the provisions of the _Navigation Standard of Inland Waterway_ on the safe navigation of ships in bridge areas. These values not only consider the interactions between static and dynamic elements, but also integrate the effects of ship movements and pilot behaviour on safety. The warning status is updated in real time  
Figure 5: Schematic diagram of the behavioural field. (**a**) Variation in the spatial relationship between kinetic energy field strength and influence factors. (**b**) Variation of kinetic energy field strength in relation to the plane of influence factor.  
Figure 6: Superposition field in the bridge area.  
according to the preset thresholds and field strength changes, and is classified into no danger, slight danger and severe danger levels. By means of sound and light signals, ship communication, etc., the system provides instant safety prompts and decision-making support to help drivers take timely collision avoidance measures and effectively reduce the risk of collision accidents, thus providing a key guarantee for the safety management of the bridge area. The specific warning process is shown in Figure 7.  
The establishment of the safety potential field is based on the theory of virtual forces; however, the influence of solid forces, such as wind currents, cannot be ignored when ships navigate in a bridge area. In order to effectively deal with the effects of these solid forces on ships in terms of bridge collision avoidance, the introduction of Kalman filtering for ship state prediction, while considering wind flow, is necessary. The corrected ship state through Kalman filtering can more accurately reflect the actual bridge conditions, and the predicted state is input into the kinetic energy field model to calculate the overall safety field strength. This is combined with the safety potential field warming method [32] to achieve accurate prediction and effective warning for ships in a bridge area. Figure 8 shows the combination of Kalman filter and safety potential field model.