esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion
### Limitations and Future Work
The participants were randomly assigned to conditions, which helped to reduce the risk of systematic bias. Some variability in age distribution was present across the groups, but no clear asymmetries were observed. Still, future studies with larger and more stratified samples could offer greater control over demographic factors and help to clarify their potential influence. In parallel, future work could also build on the current findings by exploring the impact of the RJAS on specific population groups, such as elderly individuals with mild cognitive impairment or children with ASD. Although our results suggest that the system is effective across a diverse sample, targeted studies could further validate its applicability in specific contexts. This highlights a promising direction for future research on the concept of joint engagement (JE), particularly in populations such as children with ASD, where social robotics may serve as a valuable tool to support shared attention and interaction. JE is a key developmental process in which two individuals share attention and interest toward an object or activity [63]. This phenomenon is especially relevant in therapeutic and educational contexts for children with ASD, where difficulties staying engaged with others often slow the development of social and communication skills. Interventions such as JASPER (joint attention, symbolic play, engagement, and regulation) have shown encouraging results in helping children to develop JE and related skills through structured but natural play-based interactions. More importantly, JE has been identified as a mediating mechanism for the development of IJA skills, which in turn are linked to later gains in language and communication [64]. In this sense, the RJAS system presented in our study can be viewed as a potential first step toward incorporating JASPER-like principles into robotic platforms. By enabling the robot to respond dynamically to attentional disengagement and reorient the user's focus, we take a first step toward supporting more meaningful JA experiences. A future direction could involve integrating the system with behavioral models that explicitly target JJA (already mentioned before), allowing the robot not only to respond to the user's attention but to actively prompt and support it in ways consistent with JASPER's goals. This might open the door to therapeutic applications in child-robot interaction, where the robot's ability to detect engagement in real time and adapt accordingly could help to reinforce the social skills necessary for language learning and overall developmental improvement.
## 7 Production  
### Gas Lift  
Artificial lift systems, especially gas lifts, are commonly employed in the Vaca Muerta formation, but they come with their own set of challenges, such as the accumulation of wax, necessitating chemical interventions. [PERSON] et al. conducted a study focusing on the Bajada del Palo Oeste field within the Vaca Muerta formation, which has 55 operational  
Figure 11: Comparative analysis of base and optimized scenarios using the recommended EOR methodology. Reprinted with permission from Ref. [72]. Copyright 2014, Society of Petroleum Engineers.  
wells, 40 of which utilize gas lift systems. They examined the complexities of using gas lift in shale formations like Vaca Muerta, where production rates are highly variable [73].  
Their research emphasized the problem of wax build-up in wells when using a gas lift. The first significant instance of this issue appeared 42 months after successful well tie-in and 23 months post gas injection initiation. A range of chemical solutions and alternative methods were employed to manage and reduce wax accumulation.  
The team also explored the optimization of gas injection strategies. Conventional techniques for generating performance curves proved inadequate for the unconventional wells in this formation. They proposed an AI-driven model, designed to optimize gas injection at the PAD level, rather than solely focusing on individual wells.  
Additionally, the study delved into slickline management and troubleshooting procedures. Real-time monitoring and remote operations were implemented to reduce operational downtime. Key metrics like gas injection rates, casing pressures, and wellhead pressures were continuously monitored to swiftly identify and address system failures.
## 3 Results and Discussion
### Preprocessing of Gas Monitoring Data  
#### 3.2.1 Analysis of the Original Data Set  
The ambient temperature in the test device was always maintained at 30-35 \({}^{\circ}\)C, the humidity was 63%-65% RH, and the oxygen content was 20.0%-21.0% Vol. The daily variation curve of harmful gas monitoring data in the process of _Limnoperna fortunei_ corruption is drawn, as shown in Figures 5 and 6. It can be seen that the release of NH\({}_{3}\) and H\({}_{2}\)S has an obvious trend. That is, with the increase in the density of _Limnoperna fortunei_ and the extension of time, the accumulation of harmful gas concentration gradually increased. The release concentrations of NH\({}_{3}\) and H\({}_{2}\)S in _Limnoperna fortunei_ of different densities increased first and then tended to be flat with the extension of time in water, indicating that no NH\({}_{3}\) and H\({}_{2}\)S were produced in the middle and late stage of _Limnoperna fortunei_ putrefication. Overall, NH\({}_{3}\) is produced simultaneously with H\({}_{2}\)S, but its concentration is significantly lower than NH\({}_{3}\). In all experiments, CH\({}_{4}\) was rarely detected or not detected, and the maximum detected concentration was 0.02%. This concentration is far lower than the exposure limit (1%) defined in _Construction specifications on underground excavation engineering of hydraulic structures_ (SL378-2007, 2007) [30], _Code for design of occupational safety and health of water resources and hydropower projects_ (GB 50706-2011, 2011) [31] and _Occupational exposure limits for hazardous agents in the workplace-chemical hazardous agents_ (GBZ 2.1-2007, 2007) [32], so the subsequent analysis of CH\({}_{4}\) release from the corruption of _Limnoperna fortunei_ will not be focused.  
Figure 4: Morphological changes of _Limnoperna fortunei_ during putrid process; (**(a)**—day 1; **(b)**—day 3; **(c)**—day 5; **(d)**—day 10).  
Figure 5: Changes of NH\({}_{3}\) concentration during the putrefication of _Limnoperna fortunei_.
# Low Delay Inter-Packet Coding in Vehicular Networks
## 4 Numerical Results
### Probability of Message Successful Delivering for AstaZero Scenario  
In Figure 9, the general view of the rural test area of AstaZero is shown. We consider two RSUs that are placed such that, together, they cover most of the test track.  
Simulation parameters for Figure 10 are as follows: transmission power 200 mW (23 dBm--maximum allowed power on control ITS-G5 channel), datarate 6 Mbit/s, packet size 400 Bytes, ITS-s generates 1500 msg/s, \(m=1\) in the Nakagami-m model, simulation time 425 s (time for one full circuit drive on the test track with the constant speed of 50 km/h), and the code rate \(R=3/4\). In Figure 10, blue circles demonstrate the packets received before decoding, and red crosses depict packets additionally recovered by the decoder. (For representational purposes, all the points for recovered packets were moved 20 m to the left.) Predictably, the demonstrated performance is best next to each RSU, when ITS-s resides in the production zone. Here, we present the simulated probability of successful delivering \(P_{\mathrm{s}}(K)\) (SDF) determined by Equation (1) as a function of message length \(K\). The WA and RS convolutional codes under SWML decoding are considered. In Figures 11 and 12, we show the SDFs for the WA binary convolutional codes used over the production zone and over the entire session, respectively. In Figures 11 and 12, the SDFs obtained for different code rates and for uncoded transmission are compared. As follows from the presented plots, the lower is the code rate, the better is the reliability of packet delivery that can be achieved. However, it seems that decreasing the code rate below \(R=7/8\) does not lead to a significant improvement and this rate can be considered as a reasonable choice. During the production zone, successful delivering of message of length up to 1000 packets is highly probable. For the entire session, only about 10% of long messages will be delivered even for the coded system.  
In Figures 13 and 14, we compare the WA codes versus the RS codes for the production zone and for the entire session, respectively. We conclude that the RS codes provide better reliability of the transmission compared to the WA codes but at the cost of higher computational complexity.  
Both classes of codes demonstrate high reliability of delivering long message during the production zone.  
Figure 9: AstaZero test area in simulation environment.  
Figure 11: SDF for the production zone. Simulation results for the WA codes of different rates used over production zone.  
Figure 10: AstaZero scenario results.  
Figure 14: SDF for the entire session. Comparison of the WA and the RS codes of rate \(R=3/4\).  
Figure 12: SDF for the entire session. Simulation results for the WA codes of different rates used over the entire session.  
Figure 13: SDF for the production zone. Comparison of the WA and the RS codes of rate \(R=3/4\).
# An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks
## 4 Simulation and Analysis
### Results and Analysis
#### 4.3.2 Ratio of Long-Waiting Vehicles  
Figure 6 shows the simulation results of the ratio of long-waiting vehicles in three different traffic environments. We can see that the results in the two signal control schemes using far vehicle information was generally smaller than that in the traditional timing scheme, especially the dynamic-FA scheme, which took the long-waiting vehicles into consideration and outperformed the others significantly and enhanced the driver experience substantially. In contrast, based on only the information of the intersection vehicles and without the consideration for long-waiting vehicles, the decision-making in the traditional signal timing scheme had the worst performance, where the average values were even higher than the fixed-FA scheme by 2.6%. In addition, as shown in Figure 6c, some traffic flows had a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Fixed-FA**} & \multicolumn{2}{c}{**Dynamic-FA**} \\ \cline{2-5}
**Traffic Flow: 250 veh/lane/h** & **Average** & **Confidence** & **Average** & **Confidence** \\  & **Waiting Time** & **Interval (95\%)** & **Waiting Time** & **Interval (95\%)** \\ \hline Same even traffic flows & 56.05 s & (54.75, 57.35) & 49.08 s & (45.21, 52.95) \\ Same uneven traffic flows & 51.91 s & (51.19, 52.63) & 32.66 s & (31.32, 34) \\ Different traffic flows & 131.8 s & (125.63, 137.96) & 89.47 s & (83.52, 95.42) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confidence intervals of the average waiting time.  
Figure 5: Average vehicle waiting time under different traffic environments: (**a**) same even traffic flows, (**b**) same uneven traffic flows, and (**c**) different traffic flows.  
large volume. It is remarkable that, when the standard value of the traffic flow was 350 veh/lane/h, the ratios of the long-waiting vehicles in three schemes were close (i.e., 64.9%, 59.4%, and 53.6%); this was because the traffic at the intersection had reached a certain level of saturation and there was no obvious difference among different signal control mechanisms.
## 4 Formula Modification
### Theoretical Derivation  
As can be seen from Table 4, the test values of vertical stiffness for HDRB-LSF are larger than the theoretical values. When the total thickness of the rubber is constant, the rubber pads of the ordinary thin rubber bearing under pressure are constrained by the steel plates, causing a sizeable vertical stiffness value. Whereas for the thick rubber bearing, the rubber thickness of a single layer is great, leading to the limited constraint effect of the steel plates on the rubber pads and large transverse deformation of the rubber layers, so that its vertical stiffness is more minor. The error of the vertical stiffness between the test value and the theoretical value calculated according to the code is too large to ignore, which cannot adapt to the requirements of engineering design. Hence, the theoretical formula of vertical stiffness must be modified.  
It is known from the study that the error of the vertical stiffness for the bearing between the test value and the theoretical value is related to the compressive stress \(\sigma\) and the loading frequency f of the bearing. The theoretical formula can be modified with the following formulas:  
\[\mathrm{K_{V}}=\epsilon_{V}\frac{\mathrm{E_{c}A}}{\mathrm{n_{r}f_{r}}} \tag{10}\]  
\[\mathrm{\xi_{V}}=\mathrm{\xi_{1}}\frac{\sigma}{\sigma_{0}}+\mathrm{\xi_{2}f} +\mathrm{\xi_{3}} \tag{11}\]  
where \(\mathrm{\xi_{V}}\) is the correction coefficient; \(\mathrm{\xi_{1}}\), \(\mathrm{\xi_{2}}\), and \(\mathrm{\xi_{3}}\) are the correction coefficients, which all can be obtained by the fitting of a polynomial based on the experimental data, and their values are 0.762, 3.599, and 0.8631, respectively; \(\mathrm{\sigma_{0}}\) is the design compressive stress, i.e., 12 Mpa.
# Review of Fast Computation Methods for Finite-State Predictive Control of Multi-Phase Drives
## 3 Fast Computation Strategies
### Region-Based Methods  
FSMPC based on regions can be seen as a particular case of EMPC where the regions are determined using physical insight. The idea can be put forward for the single-vector case using the following observation. Minimizing \(J\) amounts to minimizing \(\|V^{*}-V^{h}\|_{w}^{2}\), where index \(h\) goes from \(1\) to the number of available VVs. This can be checked noting that \(\hat{e}=i^{*}-\hat{i}\) with \(\hat{i}=A(\omega)\hat{i}(k+1)+BV^{h}\) for some basic VVs of index \(h\). As a result, the following identity is found:  
\[B^{-1}\hat{e}=B^{-1}\big{(}i^{*}-A(\omega)\hat{i}(k+1)\big{)}-V^{h}, \tag{9}\]where the right-hand side is exactly \(V^{*}-V^{h}\) by definition. So, the term \(\delta\) can be replaced by \(B(V^{*}-V^{h})\) in the expression of the cost function. Now, \(B\) does not depend on the VV selected, so minimizing \(J\) and minimizing \(\|V^{*}-V^{h}\|_{2}^{2}\) yields the same result.  
From this, one can resort to Voronoi regions to provide a convenient way for \(\|V^{*}-V^{h}\|_{w}\) minimization. In fact, the cumbersome exhaustive exploration is avoided. Voronoi regions appear because \(\|V^{*}-V^{h}\|_{w}\) defines a metric in the mathematical sense [49]. Then, to compute the VV minimizing the cost function, one just needs to find in which Voronoi region \(V^{*}\) is placed. The application of these ideas to MPS first appeared in [50] for a reduced cost function.
# Exploiting Mobile Edge Computing for Enhancing Vehicular Applications in Smart Cities
## 2 Literature Review
### Enabling Technology  
Two networking paradigms can be considered for the integration of VEC: 5G and software defined networking (SDN) [1]. 5G can offer better response times, greater coverage and more efficient signaling. On the other hand, SDN provides flexibility, scalability and programmability, by separating the data plane from the control plane. This way, network development, deployment and management are simplified [31]. The communication components to consider for VEC are SDN global controller, 5G base station (BS), SDN roadside units (RSUs) and SDN wireless nodes [1]. In a recent work [32], the authors investigate practical deployment technologies and promising technical enablers for VEC, including SDN, network function virtualization (NFV), smart collaborative networking (SCN) and blockchain. They proposed a collaborative VEC framework, which can support scalability in vehicular networks at the application level. In another recent work [33], the distribution of content in the context of 5G edge-based vehicular networks is investigated.
## 2 Data Processing
With the received corrections of GPS satellite orbits, clocks and UPDs, the raw observation equations can be simplified as:  
\[l_{r,j}^{s}=-u_{r}^{s}\cdot x_{r}+m_{r}^{s}\cdot Z_{r}+t_{r}-\kappa_{j}\cdot I_{r,1}^{s}+\lambda_{j}N_{r,j}^{s}+\varepsilon_{r,j}^{s} \tag{7}\]  
\[P_{r,j}^{s}=-u_{r}^{s}\cdot x_{r}+m_{r}^{s}\cdot Z_{r}+t_{r}+\kappa_{j}\cdot I_ {r,1}^{s}+e_{r,j}^{s} \tag{8}\]  
At the epoch \(k\), the state vector can be expressed as:,  
\[X_{r,k}=\left(x_{r,k}^{\ \ T}\ v_{r,k}^{\ \ T}\ {a}_{r,k}^{\ \ T}\ {Z}_{r,k}\ t_{r,k}\ I_{r,k}^{\ \ T}\ N_{r,k}^{\ \ T}\right)^{T} \tag{9}\]  
The measurement update with raw GPS and accelerometer observations of Equations (1), (7) and (8) is applied at every GPS epoch. The time update of Equation (4) is performed for every accelerometer sample. The integer ambiguity resolution is attempted at every GPS epoch, L1 and L2 ambiguities are fixed simultaneously using integer estimation methods [23,24]. The ratio of the second minimum to the minimum quadratic form of residuals is applied to decide the correctness and confidence level of integer ambiguity candidate (the threshold for ratio test is set to 3 [25,26]).
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 2 LT Code Anti-Eavesdropping Method
### Eavesdropping Channel Model  
The three components of the eavesdropping channel are the source ([PERSON]), the legitimate receiver ([PERSON]), and the eavesdropper ([PERSON]). The main (legal) channel is the one between [PERSON] and [PERSON], whereas the eavesdropping channel is the one between [PERSON] and [PERSON]. [PERSON] adopts the BP decoding method, while [PERSON] uses LT code as the anti-eavesdropping channel coding. [PERSON] continuously delivers the coded symbols to [PERSON] across the main channel, and sends an ACK feedback to inform [PERSON] of the completion of decoding when [PERSON] recovers the source information. Due to the wireless channel's openness, we presume that [PERSON] has obtained every decoding rule shared by [PERSON] and [PERSON]. While both are legally transmitting information, [PERSON] can effectively steal the source information through the eavesdropping channel, and the same BP decoding is used. Figure 1 then displays the LT code eavesdropping channel model.  
When [PERSON] consistently gets enough coded symbols, BP decoding can be used to quickly recover the source information. The security of information transmission can be ensured as long as [PERSON] receives enough coded symbols and finishes decoding before [PERSON]. However, there is a higher probability of information leaking if [PERSON] steals more coded symbols before [PERSON] does.  
The channel security capacity is:  
\[\begin{array}{l}C_{S_{LT}}=1-P_{AB}-(1-P_{AE})\\ =P_{AE}-P_{AB}\end{array} \tag{5}\]
## 4 Loss Reduction Optimization Design
### Efficiency Enhancement Under Various Operating Conditions  
The comprehensive efficiency enhancement analysis of the optimized high-speed magnetic-field-modulated motor reveals substantial performance improvements across diverse operating conditions, essential for flywheel energy storage applications. As shown in Figure 11(a), the original design demonstrates baseline efficiency with a peak of 92.3% in a relatively narrow operational range, with significant degradation at high speeds and partial loads. In contrast, as illustrated in Figure 11(b), the optimized design achieves a peak efficiency of 96.2% and maintains excellent performance (\(\eta>93\%\)) over a much wider region, expanding by 37.8% in the speed-torque domain.  
The efficiency enhancement is particularly pronounced at high speeds, where the original design deteriorates rapidly due to escalating core and mechanical losses, whereas the optimized design retains high efficiency even at 110% rated speed. As quantified in Figure 11(c), efficiency improvement maps reveal a maximum gain of 7.8 percentage points, especially in the field-weakening region, attributed mainly to the redesigned modulator geometry that suppresses harmonic losses. Figure 11(d) further compares the field-weakening performance, showing progressively larger improvements in efficiency as speed decreases from rated values.  
Transient operation analysis in Figure 11(e) confirms that the optimized design delivers superior performance during acceleration and deceleration phases, with efficiency degradation limited to only 1.8 percentage points relative to steady-state values, as opposed to 5.3 points in the original design. In addition, Figure 11(f) highlights improvements in partial-load characteristics, where the optimized machine maintains efficiency above 90% down to 20% of rated load, compared to only 60% in the original motor, thus extending the usable operating range for energy storage under variable load conditions.  
Figure 11: High-resolution thermal performance comparison of original and optimized motor designs.
## 2 AUV Design and Construction  
### AUV Structure  
In this study, an AUV equipped with an underwater optical wireless communication system was produced to implement AUV communication using the underwater optical wireless system and the hovering control of the AUV based on the UWOC. The hull of the AUV consisted of a transparent acrylic housing to ensure visibility for the camera and underwater light communication, and the control electric field and battery were secured inside with a PLA bracket. Designed in the shape of a fish, the AUV consisted of four thrusters, acrylic end caps for internal waterproofing, aluminum flanges, frame bars, connectors, a buoyancy system for neutral buoyancy, weight, and a power switch. Figure 1 shows the images of the produced AUV, and Table 1 shows the specifications of the instrument. Figure 2 shows the structure of the produced AUV. The AUV system is divided into an AUV frame, control system, UWOC system, and operating PC with four degrees of freedom. The AUV is equipped with a UWOC system to transmit and receive data at high speed with underwater or out-of-water optical communication systems. The UWOC system was composed of LEDs, and a graphical user interface (GUI) was built by connecting the operating PC to an external optical communication system to obtain data for AUV operation and sensors as well as image information.