esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Prediction Model  
#### 3.2.1 Initial Prediction  
The hydrodynamic groundwater flow characteristics for 2070 will remain virtually unchanged (Figure 16), with the flow directions and hydraulic gradients preserved. Under long-term water withdrawal, recharge, and groundwater discharge conditions, the groundwater flow will stabilize to steady-state conditions. There may be slight changes in the drawdown within the existing water extraction influence radius. Figure 17 shows the groundwater head drawdown prediction around the Kokzhide (including the Emba well-field) and Kenkiyak aquifer segments, suggesting that, by 2070, an ellipsoidal drawdown depression will occur, elongated in a northeast direction up to 35 km long and 20 km wide. Drawdowns in the depression center have reached 40-50 m, and those in the Alga water intake area reached 25-30 m from the groundwater head in 2007.  
Figure 15: Groundwater depth, 2022.  
Figure 16: Initial prediction. Groundwater head contours and flow path, 2070.  
Figure 17: Initial groundwater heads drawdown prediction around the Kokzhide aquifer segment, including the Emba and Kenkiyak groundwater extraction areas, in 2070.  
The groundwater flow balance model for 2070, compared with that for 2022, indicates that the balance will rely almost entirely on atmospheric precipitation (97% recharge). Groundwater resource replenishment by river recharge will not exceed 1%, and storage reserve balance will not exceed 2% of the total balance. Evapotranspiration and storage discharge will constitute more than 90% of the output components, whereas river discharge will remain at approximately 8%. In total, 5500-6000 thousand m\({}^{3}\)/day of groundwater resources will be formed annually in the Artesian Basin territory, which is approximately 15% less than that in 2022.
# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 2 Hardware Design
### Microcontroller Section  
The C8051F342 is a general purpose microcontroller featuring a high-speed pipelined 8-bit microcontroller core, 64 kB of flash memory, two timers, a USB function controller and multiple popular bus interfaces such as System Management Bus (SMBus), Inter-Integrated Circuit (I\({}^{2}\)C), Universal Asynchronous Receiver-Transmitter (UART) and Enhanced Serial Peripheral Interface (SPI). Most of these interfaces can be routed through any of the available I/O Ports using the on-chip I/O-configurator, yielding a versatile, yet compact, low-cost and low-power control center for the wireless sensor node. The microcontroller can be programmed and debugged through the integrated two-wire C2 debug interface using a Silicon Labs USB Debug Adapter.
# A Modified TCP BBR to Enable High Fairness in High-Speed Wireless Networks
## 4 Evaluation of Experiments
### Retransmission  
Experiments were performed to verify the effect of different buffer sizes and the number of competing flows on the retransmission rate. The sender used different algorithms to send single or multiple flows to the receiver with buffer sizes of 0.1 BDP or 1 BDP, starting at the retransmission rate of a single 10 ms RTT flow. As shown in Figure 12, the CCAs all experience a large number of retransmissions as the flow number increases. For the small  
Figure 11: Throughput occupancy ratio of algorithms when coexisting with CUBIC.  
Figure 10: Throughput shares and fairness index in WiFi network.  
buffer (Figure 12a), the retransmission of CUBIC is significantly higher than that of the other five CCAs. For BBR and its variants, BBRv3 appears to have a high retransmission rate. The retransmission rate of BBR-PG is much lower than that of BBRv1 and BBQ, and slightly lower than that of Yinker by 0.5%. In Figure 12a, the retransmission rates of BBR and its variants are higher than CUBIC's, which can be attributed to the fact that the aggressive de-detection bandwidth due to the step pacing in BBR leads to a certain amount of retransmissions. This needs to be further optimized in future work. However, BBR-PG still achieves the lowest retransmission rate among several BBR variants.  
We then tested the retransmission rate of the CCAs in 5G and WiFi networks at a buffer size of 0.1 BDP. As shown in Figure 13, the overall retransmission rate in the 5G network environment is slightly higher than that in the WiFi environment. The retransmission rate of CUBIC is significantly higher than that of BBR and its variants, with BBR-PG maintaining the lowest retransmission rate. Similar to the simulation results (Figure 12), the retransmission rate of the CCAs increases as the number of flows increases. However, compared to the simulation results, it can be observed that the retransmission rates of BBRv1 and BBRv3 are significantly higher than those of BBQ, Yinker, and BBR-PG. This indicates that pairing BBRv1 and BBRv3 with a small buffer improves throughput at the cost of a high packet retransmission rate. If the content being transmitted is sensitive to packet loss, then BBRv1 and BBRv3 may not be a good choice. In this case, users must make a trade-off between throughput and quality of experience. In future algorithmic optimization, we can introduce reinforcement learning techniques to accurately predict the network load [31; 32]. Based on these forward-looking predictions, the sender can flexibly take appropriate actions, such as adjusting the allocation of network resources and optimizing routing policies, to achieve significant improvements in network performance and stability. Overall, the retransmission rate of BBR-PG is much lower than that of BBRv1 and BBRv3. Experiments have shown that BBR-PG improves data transmission performance in 5G and WiFi networks.  
Figure 12: Retransmission rates of different numbers of flows in NS3.
# Extended Reality Application Framework for a Digital-Twin-Based Smart Crane
## 2 Materials and Methods
### Machine Communication Layer  
OPC UA, published in 2008, is an interoperability standard in the automation industry for the data security exchange, aiming to convert the PLC-specific protocol to the standardized interface and establish a specific protocol for the general real-time read and write functions [54]. Beyond that, it is an independent platform to assure seamless data transmission among equipment from different suppliers. OPC UA [55] is a secured protocol applied on the application layer, while TCP/IP protocol is on the network layer. Furthermore, OPC UA is widely used in the automation field with open-source and cross-platform features. On the physical crane side, the \"Illmatar\" crane is equipped with Siemens PLCs integrated with the OPC UA server. Meanwhile, on the simulated crane side, an independent Python-script-based OPC UA server was developed, with all the variables kept in the same format corresponding to the OPC UA server of Siemens PLCs. The machine communication layer established a bidirectional bridge between ROS and OPC UA. The bridge implements the operation of _write_ and _read_ by subscribing the ROS topics, respectively, _write_ concerning  
Figure 3: Workflow of the model conversion from CAD to Unity and Gazebo. It involves two sub workflows, CAD model to Unity and CAD to Gazebo with the corresponding file format.  
the position of the components in the Gazebo to the OPC UA server, _read_ concerning the direction and speed of the components from the OPC UA. The bridge between ROS and OPC UA enables multi-machine collaborative communication [56].
## 3 Results and Discussion  
In optimization, the Design of Experiments (DOE) plays a pivotal role in systematically planning and conducting experiments. By doing so, the DOE ensures that the data collected are reliable and meaningful, which helps in drawing valid conclusions. This methodology reduces the necessity for a large number of experiments, which can be resource-intensive, time-consuming, and costly, while still maintaining high levels of precision in the results.  
The accuracy of the results is a key consideration in this context. Ensuring accuracy means that the outcomes of the experiments are not only precise but also reflect the true effects of the variables being studied. This is where the Response Surface Methodology (RSM) becomes particularly valuable. The RSM is a set of statistical and mathematical techniques that are used to model and analyze problems where multiple variables influence the responses. For instance, in medical applications, regarding the drilling temperature of bone during surgery, the RSM can be used to model how different factors (like the spindle speed, feed rate, and drill bit diameter) affect the temperature. By analyzing these factors, researchers can optimize the drilling process to minimize the thermal damage to the bone, thereby improving the surgical outcomes and patient recovery.  
A total of 27 experiments were performed utilizing the multi-level factorial method, as shown in Table 2, with the Minitab software aiding in the analysis. Figure 5 illustrates the drilled holes within the bone and Figure 5 shows the schematic diagram of the thermocouple location. The temperature during the drilling process was continuously monitored using a T-type thermocouple, capable of measuring temperatures from 0 to 260 \({}^{\circ}\)C, and recorded with an Appellant AT4808 multi-channel thermometer data logger (Applent Instruments, Changzhou, China). The results from these experiments are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spindle Speed (rpm)** & **Feed Rate (mm/min)** & **Drill Bit Diameter (mm)** & **Average T\({}_{\text{max}}\) (\({}^{\circ}\)C)** \\ \hline
1000 & 20 & 4 & 36.4 \\
1000 & 30 & 3 & 35.9 \\
1000 & 40 & 3 & 47.7 \\
2000 & 40 & 2 & 45.2 \\
1000 & 40 & 4 & 43.9 \\
1500 & 40 & 3 & 49.5 \\
1000 & 30 & 2 & 36.8 \\
1500 & 30 & 2 & 42.5 \\
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 5 Cultivation in Digestate
### Organic Carbon in Digestate  
If the digestion of feedstock in the biogas plant is not complete, an organic carbon source in the form of volatile fatty acids is present in digestate [17]. This might lead to mixotrophic growth of microalgae, increasing the risk of contamination by heterotrophic organisms (Section 2.3).  
Microalgae species such as _Chlorella vulgaris_, _Arthospira platensis_, and _Haematococcus pluvialis_ can grow mixotrophically, which means that they are able to metabolize organic carbon if present [39]. Mixotrophic cultivation mode can improve biomass productivity and nutrient uptake [39]. Light intensity is less critical than in photoautotrophic mode [39]. This is an advantage, with digestate constituents in particular leading to a dark color of the cultivation medium.  
However, the availability of organic carbon might lead to contaminations by heterotrophic organisms [63]. This should especially be considered when microalgae are grown in open systems [64]. Bacteria and other contaminating organisms might compete with microalgae for nutrients and inhibit their growth [39]. As sterilization of the cultivation medium is not an economically feasible option for mass culture [39], other strategies to handle this issue have to be carried out. Cultivating strains with high growth rates or microalgae that grow in selective conditions (e.g., hypersaline water--_Dunaliella_, or high pH value--_Arthospira_) [64; 65] are possible options. It is necessary to follow a strict protocol and to monitor the process in order to keep control of the cultivated organisms [39].
# Contactless Real-Time Eye Gaze-Mapping System Based on Simple Siamese Networks
## 2 Related Work  
### Gaze Estimation  
Gaze recognition methods have been proposed that predominantly employ handcrafted features [10]. For example, after the fit of a model with handcrafted features was optimized according to the linear-regression equation, a method for estimating the final gaze was developed [11]. Feature-based gaze recognition has been implemented using a simple linear-regression model. However, the eye-gaze feature has made it difficult to predict the generalized performance in a real environment. Model-based methods have been proposed that could show generalized performance improvements in a real environment [12,13]. Eyeball modeling calculated a gaze vector using the feature points of a geometric eye model. As compared to feature-based gaze recognition, which extracts the local features of the eye region, model-based methods have commonly modeled the entire eyeball area to recognize the eye gaze. The eyeball-modeling method used a high-dimensional input as a feature and learned the gaze-mapping function. Appearance-based gaze recognition was performed by image matching [14,15] and modeling the entire eyeball. With the eyeball-modeling method, gaze recognition was performed by matching a 3D model with an eye image [13,16]. The method based on 2D image matching was simple, but in some cases, it had a sensitive response to changes in posture, such as the head pose or lighting, which then negatively affected the final gaze-recognition performance. However, the information required for 3D shape modeling had to be preceded by parameter measurements of the corneal radius and center; the pupil radius; the distance between the corneal center and pupil; the incidence angle; and the refractive index between the optical axis and the visual axis. A complex dataset was required, but the 3D model-based method generated more reliable gaze-recognition results than the image-matching method [17].  
Since 2012, deep neural networks that showed excellent performance in the computer vision field have been proposed [18], and they have been used in various computer-vision tasks, such as object classification and object detection [19,20]. To achieve successful gaze-recognition performance, the mapping from the eye image to the gaze direction had to be well learned. Therefore, large datasets for gaze recognition, such as MPIIgaze [21] and RT-GENE [22], were proposed. As deep neural networks advanced, they have also been applied to gaze recognition and shown superior results in real environments for gaze recognition, as compared to gaze recognition using feature-based methods [23,24]. Recently, a novel approach to unconstrained gaze estimation was proposed that was based on long short-term memory (LSTM) and trained on sequential datasets. As a result, the LSTM 3D gaze model was expected to be scalable, as compared to existing models, and it enabled the direct output of gaze estimates with uncertainty [25].
## 4 Contrasting the Proposed Technique with the Prior One  
The proposed technique is compared with [35] as both techniques focus on accurate temperature estimation for railway propulsion systems to ensure optimal operation. In the proposed method, a data-driven, reinforcement-learning-based parametrization approach is proposed to estimate the thermal model parameters. This approach involves training RL agents using data from various driving cycles. Different strategies were developed to manage the thermal behaviors of controller driving cycles effectively under different operational conditions. The capability of RL agents was emphasized to address driving variability and to accurately reflect the motor's thermal behavior. It also presented an offline mode, in which pre-trained models were utilized to predict temperatures from driving cycle data. On the other hand, the approach proposed in [35] uses a physics-based thermal model of the induction traction motor. This approach demonstrates the competence of RL agents in developing strategies for managing thermal behaviors under different operational conditions and accurate temperature estimation in induction traction motors. For validating  
Figure 8: Stator and rotor temperature predictions of driving cycles (DC): DC6, DC7,DCS, and DC9 using the DDPG agent.  
Figure 9: Stator and rotor temperature predictions and results of the driving cycle of measured data using the SAC agent: (**a**) with airflow information and (**b**) without airflow information.  
the thermal model, our proposed method employs data from nine driving cycles as well as measured data to ensure that the model's accuracy encompasses various scenarios. In contrast, [35] validated their model with only two driving cycles. Although this may appear to be a smaller dataset, it is worth noting that [35] did not develop an offline model, which is crucial for computational efficiency, especially when utilizing pre-trained models for precise temperature predictions.
## 6 Discussion and Conclusions  
This paper reviewed the developed RF sensors based on graphene and graphene derivatives (GO and rGO) for wireless bio and gas sensing platforms in recent years. Based on research trends, the graphene/GO (rGO)-based RF bio and gas sensors commonly have been approached in terms of RF passive devices and RF systems, i.e., RFID or NFC. In this section, we discuss the pros and cons of the developed RF sensors and suggest a robust RF sensing system for a wireless health care system in the future.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sensing Materials (Forms)** & **Target** & **Operating** & **LOD/** & **RF Devices/** & **Systems** & **Ref.** \\  & **Gas** & **Frequency** & **Sensitivity** & **Antenna Types** & **(Forms)** & **Ref.** \\ \hline Graphene-1 AgNW-2 FET (Maintained form) & 3 DMMP & 200 MHz & 5 ppm & Coil antenna & RFID (° PET) & [86] \\ rGO/p-doped Si & 5 RH & 865 MHz & 60 \(\Omega\)/RH & Meander antenna & (Kapton) & [87] \\ GO & Breath anomalies & 915 MHz & 60 \(\Omega\)/RH & Loop antenna & RFID (PET) & [88] \\ rGO & NH3 & 915 MHz & \(\Delta\)R/R\({}_{0}\) \(>\) 6\% & Dipole antenna & RFID (Kapton) & [89] \\ rGO/Ag-ink-IDEs & NH3 & 5.81 GHz & 50 ppm & IDEs/Patch antenna & RFID (° PCB) & [90] \\ Graphene (Agglomerated form) & NH3 & 4.20 GHz & – & 7 SIW cavity resonator, & & \\  & & & & 8 & RSR, 9 CSRR & PCB & [91] \\ rGO-Ag & NH3 & 14 MHz & 5 ppm/AR/R\({}_{0}\) = 1.25\% & Coil antenna & \(\lx@notemark{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Genetic Algorithms
#### 6.1.2 Crossover and Mutation Operators  
Since the optimization variable consists of an ordered array of elements, after the crossover operation between two solutions there must be no repeating elements in the offspring chromosome. To avoid this, different operators can be implemented, being partial-mapped crossover operator (PMX) the best known and the one used in this work [64]. Given a pair of selected parents for the crossover operation, a subsequence is established by defining two crossover points on both chromosomes, and by swapping this subsequences the resulting children contain the same exchanged subsequences of the parents. To eliminate repeated elements, a repair algorithm is used, so that the exchanged information is preserved, and all constraints of the problem are satisfied.  
In this case, it can appear mutation consisting on adding random alterations to the chromosomes, so that new features emerge in the solutions, and the speed of convergence can be improved. For this particular problem, the mutation operator introduces modifications in chromosomes by swapping two randomly selected genes, and it takes place on different chromosomes according to a tuning parameter called mutation probability.
# Greenhouse Gas Impact of Algal Bio-Crude Production for a Range of CO2 Supply Scenarios
## 2 Methodology
### Greenhouse Gas (GHG) Emissions  
A LCA approach has been adopted to quantify the GHG emissions from the different CO\({}_{2}\) supply scenarios leading to the production of refined bio-crude, with 1 MJ of refined bio-crude production as the functional unit. The cradle-to-grave system boundary spans from the production of CO\({}_{2}\) and algae, to the consumption of refined bio-crude. For scenarios 1 and 2 the system boundary does not include the CO\({}_{2}\) production source, namely, the off-site coal and natural gas-based powerplant, because it is assumed that since the CO\({}_{2}\) emissions from the powerplant would otherwise have been emitted to the atmosphere and, since no electricity from the powerplant is being used, utilization of these emissions is, in effect, the same as taking CO\({}_{2}\) from the atmosphere. The CO\({}_{2}\) supply capture and transportation processes, however, are within the system boundary. The NGCC plant, biomass combustion/gasification plant, and the DAC plant in scenarios 3, 4, 5 and 6, would be built explicitly for providing CO\({}_{2}\) to the biorefinery and, thus, they are included within the system boundary (Figure 2).  
Figure 2: LCA assumptions and system boundary.  
The life cycle inventory data have been derived from ASPEN Plus simulations as well as other literature, including the ecoinvent databases [20]. The ASPEN Plus flowsheets were utilized to model the mass and energy balance for the different unit processes used in simulating the different scenarios. Electricity production and consumption, as well as different recycle streams, are part of the ASPEN Plus simulations. The life cycle assessment calculations were conducted utilizing an MS Excel-based framework utilizing matrix algebra [22]. A 100-year time horizon is used to calculate the global warming potentials [23].  
Grid average electricity emissions of 500 g CO\({}_{2}\) eq./kWh electricity are used to estimate the emissions related to the import and export of electricity. The emission associated with the supply and leaks of natural gas is taken to be 14 g CO\({}_{2}\) eq./MJ of natural gas utilized [24]. These emissions account for the entire natural gas supply chain, including production, processing, transmission, storage, and distribution. The emissions related to the growth and supply chain of biomass feedstock are adopted from the work of [PERSON] et al. [10]. The biomass feedstocks (southeastern pine plantations) are grown in the US state of Florida with an 11-year harvesting cycle, representing thinnings from timber stands. A methodology proposed by [PERSON] et al. [25] is used to estimate the climate impact of the 11-year time-lag for biomass re-growth. The utilization of potassium hydroxide and calcium carbonate in the DAC process has been estimated based on data reported by [PERSON] et al. [26].