esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward More Efficient Large-Scale Green Hydrogen Systems via Waste Heat Recovery and ORC
## 3 Results  
### Hydrogen Production and Heat Recovery at a 20 MW PEM Electrolyzer Plant  
ASPEN V14 is used to model a 20 MW hydrogen production plant that utilizes waste heat recovery and an ORC to produce electricity. The system is a closed-loop cycle operating at 25 \({}^{\circ}\)C and ambient pressure. The 20 MW PEM electrolyzer model produces 363 kg/hr of hydrogen and 2877 kg/hr of oxygen. Based on the higher heating value (HHV) of hydrogen, the plant produces 14,302. kWh hydrogen energy equivalents. Table 5 represents the results for producing hydrogen and streams for each 2 MW stack.  
As a result, each PEM stack rejects 546.36 kW of thermal energy. Therefore, the heat recovery captured from ten 2 MW stacks equals 5463.65 kW. This means that from the electricity provided to separate hydrogen and oxygen, 5463.65 kW of heat is lost, which is 27.32% of the power input and is available for recovery. The cooling water runs through the plant so as to (a) capture rejected heat from PEM units, (b) cool down the produced hydrogen, and (c) cool down the produced oxygen. Table 6 represents the results for cooling water for each 2 MW PEM electrolyzer.  
The cumulative amount of required electricity for producing hydrogen, recovered waste heat from stacks, and heat loss while producing oxygen and hydrogen are calculated. Table 7 provides the summary results.  
Figure 6 presents the energy flow within the PEM stacks and demonstrates the energy flow for producing hydrogen. As it is clear, the majority of energy within the system is present in the form of hydrogen. The total electrical system conversion efficiency is 71.41%. By capturing waste heat, the thermal efficiency of the system will increase, and therefore, the system efficiency will be further enhanced. An ORC module is added to the plant to maintain the 25 \({}^{\circ}\)C cooling water recirculating through the plant and producing some  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Streams** & **Water Inflow to PEM Stack** & **H\({}_{2}\) Stream** & **O\({}_{2}\) Stream** \\ \hline Temperature (\({}^{\circ}\)C) & 25 & 30.47 & 32.03 \\ Pressure (bar) & 1 & 29.97 & 29.98 \\ Mass density (kg/m\({}^{3}\)) & 993.96 & 2.36 & 38.69 \\ Average energy MW & 18.02 & 2.02 & 31.99 \\ Mass flows (kg/hr) & 324 & 36.25 & 287.75 \\ Enthalpy flow (W) & \(-1.43\times 10^{6}\) & 879.98 & \(-147.58\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results for each 2 MW PEM electrolyzer.  
electricity from the recovered heat, which is 27.32% of total input energy. In the following sections, the electricity produced and optimizing ORC is explained.
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 4 Resource Collaborative Allocation Model Based on Stackelberg Game  
According to the utility theory, the utility of airlines participating in the game is the benefits obtained by airlines when choosing different plans. In this case, the negative utility is represented by delay costs, that is the utility function of the airline \(A_{n}\) for choosing plan \(s_{i}\) is defined as:  
\[U_{A_{n}}=\delta f(s_{i},s_{j}) \tag{14}\]where \(\delta\) is the utility conversion parameter, which is mainly used to adjust the utility values of each plan, so that the utility value of the plan with the minimum delay cost is set to 1, and the utility value of the plan with the maximum delay cost is equal to 0, ensuring that the utility values of the chosen plans are within the range of \([0,1]\).  
Since the number of flights included in each involved airline is generally larger than 1, the utility value of the airline is the sum of the utility values of its flights:  
\[U_{A_{n}}=\sum_{f}\delta_{f}t_{f}(s_{i},s_{j}) \tag{15}\]  
where \(t_{f}(s_{i},s_{j})\) is the delay cost of flight \(f\) belonging to airline \(A_{n}\) when choosing plan \(s_{i}\), which is positively correlated to the time difference between the allocation time of the chosen plan and the time specified in the original submitted TOS. This delay cost includes both the ground delay cost and the air delay cost. The relative coefficient of air delay cost to the ground delay cost is assigned as \(\beta\). Therefore, the delay cost of each flight under plan \(s_{i}\) is expressed as follows:  
\[t_{f}(s_{i},s_{j})=(t_{f}^{s_{i},k}-t_{f}^{k})+\beta(t_{f}^{s_{i},k^{\prime}}- t_{f}^{k^{\prime}}) \tag{16}\]  
The airline can compare each initial allocation plan based on the utility function. The probability of the airline selecting the plan \(s_{i}\) is as follows:  
\[p_{A_{n}}^{s_{i}}=prob[U_{A_{n}}^{s_{i}}\geq U_{A_{n}}^{s_{j}}],\forall A_{n} \in A,s_{i},s_{j}\in S\text{ and }i\
eq j \tag{17}\]  
According to the LOGIT model, it can be further calculated as follows:  
\[p_{A_{n}}^{s_{i}}=\frac{\exp[-\varepsilon f(s_{i},s_{j})]}{\sum\limits_{s_{i} }\exp[-\varepsilon f(s_{i},s_{j})]} \tag{18}\]  
where \(\varepsilon\) is the parameter that represents the degree of preference of the airline for each plan, determining the importance of the utility value of the corresponding plan to the airline. This parameter can be derived from the corresponding delay cost.  
The following constraints are simultaneously satisfied:  
(1) The sum of the probabilities of each airline selecting all plans is 1:  
\[\sum_{s_{i}}p_{A_{n}}^{s_{i}}=1,\forall A_{n}\in A,\;s_{i}\in S \tag{19}\]  
(2) When each airline chooses different plans, there is no conflict between the occupied slots, that is, the slots cannot be repeatedly occupied:  
\[\sum_{A_{n}}\sum_{s_{i}}p_{A_{n}}^{s_{i}}\times\sum_{f\in A_{n}}D_{f}^{s_{i}, f}\leq 1 \tag{20}\]  
where \(D_{f}^{s_{i},f}\) represents the slot situation occupied by the flight \(f\) when the airline \(A_{n}\) chooses the plan \(s_{i}\).
## Abbreviations  
The following abbreviations are used in this manuscript:  
ALS & Amyotrophic Lateral Sclerosis.  
BCFD & Box-counting fractal dimension.  
BCI & Brain-computer interface.  
CAR & Common average reference.  
CART & Classification and regression tree.  
CDFD & Correlation dimension fractal dimension.  
CLI & Command Line Interface.  
CM & Chronic migraine.  
CSP & Common spatial pattern.  
CV & Cross-validation.  
DFA & Detrended fluctuation analysis.  
EEG & Electroencephalography.  
EOG & Electrooculography.  
ERD & Event-related desynchronization.  
FD & Fractal dimension.  
FKNN & Fuzzy k-nearest neighbors.  
GDF & General Data Format.  
GPFD & Grassberger-Procaccia fractal dimension.  
GSVM & Gaussian support vector machine.  
HCNs & Higher cognitive networks.  
HFD & Higuchi fractal dimension.  
HPFs & High-Pass Butterworth Filters.  
ICA & Independent component analysis.  
IDE & Integrated development environment.  
KFD & Katz fractal dimension.  
LDA & Linear Discriminant Analysis.  
MAT & MATLAB file.  
MCS & Minimally conscious state.  
MFDFA & Multifractal detrended fluctuation analysis.  
MI & Motor imagery.  
MI-BCI & Motor imagery brain-computer interface.  
MRI & Magnetic resonance imaging.  
PCA & Principal component analysis.  
PFD & Petrosian fractal dimension.  
PNs & Perceptual networks.
## 2 Results
### Nootkatone Does Not Affect Nematode Attraction to the Plant but Inhibits Nematode Root Penetration  
Since the decrease of parasitism was not a result of the direct toxicity of nootkatone, the impact of the compound on nematode attraction towards and the penetration of _Arabidopsis_ roots was determined. The presence of nootkatone in the growth medium did not affect nematode attraction to the plant, as there is no difference in the relative numbers of nematodes detected inside the test discs containing  
Figure 3: Effects of plant metabolites on _Heterodera schachtti_ parasitism. _Arabidopsis_ was grown on a medium complemented with plant terpenoids (Figure 1) or DMSO as the control and inoculated with _H. schachttii_ J2s. Parameters representing the nematodes’ parasitic success were recorded: (**a**) Average number of total nematodes developed on an _Arabidopsis_ Col-0 plant. (**b**) Average sizes of females at 13 days after inoculation (DAI). (**c**) Average sizes of syncytia at 13 DAI. Data are based on three independent experiments. Each bar represents the mean \(\pm\) SE of \(n>35\). The asterisk marks indicate significant differences to the control based on a \(t\)-test (* \(p<0.05\) and ** \(p<0.01\)).  
either nootkatone or dimethyl sulfoxide (DMSO). The same results were obtained in the case of a DMSO presence on both sides of the plates (Figure 4a).  
Though no chemotaxis effects were observed, the percentage of J2s that successfully penetrated the _Arabidopsis_ roots was about 40% decreased in the presence of nootkatone compared to the control (Figure 4b), revealing that the nootkatone-mediated inhibition of nematode parasitism functions at the stage of physical interaction between plant and nematode.
# The Influence of Chamfered and Rounded Corners on Vortex-Induced Vibration of Super-Tall Buildings
## 1 Introduction  
The current shift towards increasingly tall and slender buildings is likely to result in excessive structural vibrations when the buildings are subjected to strong winds, especially vibrations caused by vortexes in the crosswind direction. As is known, vortex-induced vibration may occur when the vortex shedding frequency approaches the natural frequency of a flexible structure, which is usually much more significant than the along-wind response [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11].  
Modification of aerodynamic shape can significantly reduce the wind-induced response of wind-sensitive structures (tall buildings, etc.). Many researchers have studied the influence of building shape on the wind-induced responses or wind load of tall buildings. As early as the 1980s, [PERSON] et al. [12] carried out a series of single-degree-of-freedom (SDOF, in short) aeroelastic model wind tunnel tests and found that the chamfered corner with a 10% chamfer ratio can reduce the crosswind and along-wind displacement by 30-40%. [PERSON] et al. [13] carried out SDOF aeroelastic model tests on cylinders with various cut corners. [PERSON]'s research shows that even if the corner size changes only by 5% of the cross-section, the wind-induced response will still be greatly affected. [PERSON] studied the influence of different section shapes on the wind force and wind pressure of super high-rise buildings using high-frequency force balance and a synchronous pressure model [14; 15]. [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18]. analyzed thewind load of super high-rise buildings with various sections through high-frequency force balance tests. [PERSON] et al. [19], on the other hand, studied the wind load of square column models with chamfered and rounded corners through the wind tunnel experiment of high-frequency force balance. [PERSON] et al. [20] carried out many experiments to study the influence of various building shapes on wind load by high-frequency force balance and rigid pressure model test. In addition to the above research, a large number of researchers have studied building shape modification through wind tunnel tests or CFD simulation [21; 22; 23; 24; 25].  
Although many researchers have studied the influence of the chamfered corner and rounded corner on the wind effects of super high-rise buildings, most existing studies are carried out by using rigid pressure models and force balance tests. As is well known, the rigid pressure model and force balance test cannot take into account the influence of aeroelastic effects, while the aeroelastic model, especially the MDOF aeroelastic model test, is an accurate way to simulate wind-induced response and aeroelastic effect. Due to this reason, a series of MDOF aeroelastic model tests were carried out in this study to investigate the influence of the chamfered corner and rounded corner on VIV and obtain some conclusions that can provide guidance for the selection of body shape for super high-rise buildings.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experiment Settings
\begin{table}
\begin{tabular}{c c c c c} \hline
**Experiment Component** & **Parameter** & **Value** & **Description** & **Justification** \\ \hline \multirow{3}{*}{Model Architecture} & \multirow{3}{*}{Pre-trained Models} & mT5, GPT-2, mBART, GPT-Neo & Transformer-based models fine-tuned for Swahili tasks & Suitable for multilingual and low-resource settings \\ \hline \multirow{5}{*}{Data Splits} & \multirow{3}{*}{Training Data} & \multirow{3}{*}{80\%} & Dataset for training & Provides sufficient \\  & & & fine-tuning and RAG & examples for \\  & & & models & model learning \\ \cline{1-1}  & & & Dataset for & \\ \cline{1-1}  & & & hyperparameter tuning & & \\ \cline{1-1}  & & & and evaluation & & \\ \cline{1-1}  & & Test Data & 10\% & Dataset for independent & Ensures fair \\ \hline \multirow{5}{*}{Hardware Configuration} & \multirow{3}{*}{GPU} & NVIDIA Tesla V100 (32 GB) & High-performance GPU for training and retrieval tasks & Capable of handling large-scale computations \\  & & & & tasks & \\ \cline{1-1}  & & Storage & SSD (2 TB) & Fast storage for datasets and embeddings & Enhances data access \\ \cline{1-1} \cline{2-5}  & \multirow{3}{*}{Batch Size} & \multirow{3}{*}{16} & Number of examples per training step & Balances memory usage and model updates \\ \cline{1-1}  & & & Step size for updating & Ensures stable \\ \cline{1-1}  & & & model weights & training convergence \\ \cline{1-1}  & & & Complete passes & \\ \cline{1-1}  & & Epochs & 5 & through the training & Sufficient for \\ \cline{1-1}  & & & data & dataset size \\ \hline \end{tabular}
\end{table} 
Table 3: Experiment settings for Retrieval-Augmented Generation in Swahili NLP.
## 9 Performance Evaluation  
In this section, we evaluate the performance of the FAC framework, including delays, energy dissipation, data rate privacy, storage, processing, computational, and communication costs.
## 1 Introduction
So far, some compound control schemes combined with feed-forward compensation, such as double-loop cascade composition control [22], the nonlinear QFT technique [23], inverse model observer [24], adaptive control law [25], the H\(\infty\) method [26], and iterative learning [27], _etc._, had been put forward. The known studies show that force-tracking accuracy is limited by the extraneous force suppression level, especially in the presence of parameter perturbation. With the continuously increasing performance requirements for the loading system, it is difficult to obtain better tracking performance with a single approach, even if such an approach had made achievements in the low-frequency case. The electro-hydraulic system is of high nonlinearity and parameter uncertainty. Nonlinearity mainly is caused by the flowrate-pressure characteristics of the servo valve, the compressibility of the hydraulic oil and the friction force of the hydraulic cylinder. A four-quadrant flowrate-pressure nonlinear model of the servo valve was established in [28; 29; 30; 31]. [PERSON] and [PERSON] studied the impedance-based nonlinear control of the hydraulic servo system [28]. [PERSON] and [PERSON] studied the singular issue in the mathematic model of the nonlinear hydraulic drive system, and proposed an approach of two subsystems with a boundary layer to analyze the nonlinear behavior [29]. [PERSON] and [PERSON] studied the design of the nonlinear disturbance observer-based hydraulic force controller, and proposed the observer with a simple PI structure and a nonlinear control law based on the passivity theorem [30; 31]. Currently, problems exist in the application of the above-mentioned nonlinear control approach in the force servo system, and the exact mathematical model of the servo valve is needed. Moreover, the robust control law, which is based on the \(Q\)-filter disturbance observer and the H\(\infty\)theory and does not rely on the exact mathematical model, can be easily designed. Therefore, this paper mainly studies the design method of this kind of control law. This paper focuses on the high-frequency loading issue of an electro-hydraulic loading system with high-frequency dynamic load superimposed on a large static load of the helicopter-manipulating booster, and mainly studies a linear hybrid control scheme with a constant velocity feed-forward compensator and a double-loop cascade composition control strategy. The constant velocity compensator is designed to eliminate most of the extraneous force. The disturbance observer-based inner loop compensator restrains the disturbances, including the remaining extraneous force, and makes the actual plant tracking a nominal model approximately in a certain frequency range. The robust outer loop controller guarantees the desired force-tracking accuracy, and improves system robustness in the high-frequency region.
## 2 Islanding Detection Methods
### Passive Methods
#### 2.1.4 Voltage Unbalance Method (VU)  
According to this method, the three-phase voltages at PCC are used to identify the islanding condition. A voltage imbalance of the DG output will result from the loss of the primary utility grid. The islanding procedure will be decided if the voltage unbalances are greater than the threshold value. The voltage unbalance (VUt) is calculated as the negative sequence component voltage (NSt) over the positive sequence component voltage (PSt) as presented below [10].  
\[\text{VUt}=\text{NSt}/\text{PSt}, \tag{4}\]  
This method has low detection error, and it is not also sensitive to system fluctuations. However, the NDZ is large relatively [11].
## 4 Conclusions  
The performance of a continuous H\({}_{2}\) production system using RS hydrolysates was evaluated by comparing conditions without FeNPs in addition to three different magnetite nanoparticle concentrations. The highest H\({}_{2}\) productivity, 2.6 \(\pm\) 0.3 NL H\({}_{2}\)/L-d, was achieved with the addition of 100 ppm of FeNPs, representing a 53% increase compared to the non-nanoparticle condition. In contrast, increasing the FeNP concentration to 200 ppm led to a decrease in H\({}_{2}\) productivity to 1.6 \(\pm\) 0.2 NL H\({}_{2}\)/L-d. The primary metabolites produced were acetate, butyrate, and propionate, with the maximum butyrate concentration of 2.0 \(\pm\) 0.1 g/L observed at the 100 ppm FeNPs concentration. Microbial community analysis showed the dominance of _Bacteroides_ and _Cistoridium_, with the latter being a key genus associated with H\({}_{2}\) production, particularly favored at the 100 ppm nanoparticle concentration. While FeNPs have shown potential for enhancing H\({}_{2}\) productivity in a DF continuous system using RS hydrolyses, further research is needed to explore their effects in combination with other operational parameters, such as extended operation times, varying HRTs, and different pH conditions. Additionally, a techno-economic analysis is essential to assess the viability of this process, including the development of strategies for nanoparticle recovery.  
Conceptualization, [PERSON]-D.; methodology, [PERSON]-[PERSON], [PERSON]-[PERSON] and [PERSON]-D.; formal analysis, [PERSON]-B.; investigation, [PERSON]-B. and [PERSON]-[PERSON]; resources, R.M.; writing--original draft, J.P.-B.; writing--review and editing, R.M., [PERSON]-E., [PERSON]-Y., [PERSON]-B., [PERSON] and [PERSON]-D.; visualization, [PERSON]-B. and [PERSON]-E.; supervision, R.M., [PERSON]-Y., [PERSON]-B., [PERSON] and [PERSON]-D.; project administration, O.G.-D.; funding acquisition, E.L.-B. and O.G.-D. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Consejo Nacional de Humanidades, Ciencias y Tecnologias (CONAHCYT; Project-CF-2023-G648). Additional funding was provided by the RYC2021-034559-I grant, funded by MCIN/AEI/10.13039/501100011033 and the European Union NextGenerationEU/PRTR. We also acknowledge the support from the Regional Government of Castilla y Leon and the European FEDER Programme (CL-EI-2021-07 and UIC 315). [PERSON] gratefully acknowledges CONAHCYT for the Doctoral Scholarship 795519.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# From Sensor Networks to Internet of Things. Bluetooth Low Energy, a Standard for This Evolution
## 4 Materials and Methods
### Fundamentals  
In this section, we do not attempt to explain BLE in detail. We present the main characteristics necessary to facilitate the understanding of our work. The first BLE specification included two network topologies for data transmission: _connection_ and _broadcast_, each one with its own advantages and limitations. BLE versions 4.1 [42], 4.2 [43] and 5.0 [30] maintain these topologies, which have been improved by combining different roles. However, these later versions are not implemented in most IoT devices. These two available topologies in BLE specification are described below:  
* _Connection_ topology: once the connection has been established, two BLE devices exchange packets in a permanent and periodical way. Two roles are used here: _master_ (central) and _slave_ (peripheral). A _master_ device can connect with different _slave_ devices, setting up a star-topology network (see Figure 6a). This operating mode allows the data exchange in both directions, between a _slave_ device and the _master_ device. In addition, a _master_ device can use the _notification_ and _indication_ characteristics to read data immediately when they change. According to BLE specification, the number of slaves supported simultaneously by a master is unlimited. However, in real devices, with real memory limitations, this number falls to 4-8 (depending on the device limitations).
* _Broadcast_ topology: a BLE device can transmit data using the BLE _advertising_ mode to any BLE device in listening range, which uses the BLE _scanning_ mode. This topology defines two roles: _broadcaster_ (device which transmits data) and _observer_ (device which receives data). In this case, data exchange is unidirectional, from the _broadcaster_ to one or more _observers_ (see Figure 6b).  
These network topologies are enough to cover small and medium IoT installations. However, the recent emergence of Industry 4.0 includes IoT networks in factories, and changes the requirements of these networks. These new requirements are as follows [44; 45]:  
* Total coverage: the entire space of the building must be covered by the network, avoiding dead zones where the users can not communicate.
* Zero Fails: to provide a high performance, every transmitted packets must arrive at its destination, getting a success rate of 100% in communications and a PLR close to zero.
* Sustainability: covering both software (devices use efficient programs) and hardware (reducing the number of devices and using a low power standard). In this context, two new concepts appear: green-by (IoT network linking physical devices with operators to afford efficient operation) and green-in (techniques to encourage the deployment of cost efficient networks) [46; 47; 48].  
Figure 6: Available topologies in BLE standard (version 4.0). (**a**) Connection topology: (M) Master; (S) Slave; (**b**) Broadcast topology: (B) Broadcast; (O) Observer.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 3 Deep-Reinforcement-Learning-Based Cooperative Downloading Scheme
### MDP Formulation
#### 3.2.2 Action  
\(a_{t}\) represents a set of actions that are performed by LEO satellites at time slot \(t\), which is given by  
\[a_{t}=\{a_{l}(t)|l\in\mathrm{L}\}, \tag{4}\]  
where \(a_{l}(t)\in\mathcal{V}\) represents the destination of the data of satellite \(l\). For example, if \(a_{l}(t)=l^{\prime}\in\mathrm{L}\setminus\{l\}\), satellite \(l\) attempts to transmit to another satellite \(l^{\prime}\) through ISL. On the other hand, if \(a_{l}(t)=k\in\mathrm{K}\), satellite \(l\) attempts to download to GS \(k\) through SGL.