esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing the Performance of XR Environments Using Fog and Cloud Computing
## 3 Proposed System Architecture
Consequently, if a specific output edge computer experiences delays, the system utilizes pre-processed data from the fog to synchronize with the data flow, enabling rendering that is identical to the other edge computers precisely and on time. This approach leveraged the edge computer performance using local computing power to render and pre-compute the content flow of the fog. The system provided efficient results with fewer devices. Moreover, compared to conventional RTP-based methods, which may encounter buffering issues due to network latency and synchronization errors when using multiple displays, the direct real-time rendering performed by edge computers offers superior responsiveness and synchronization speed. Data processed in cloud systems are typically categorized into static resource data used in content and user data. Static resource data are generally large and known to many users. Such data are updated by storing in central cloud storage and provided via a CDN approach, allowing updates to be made at each fog or edge location. User data, however, require real-time interaction and are processed in the fog. The data are transformed from input data into touch events to reduce the size and are transmitted to minimize delays. This approach enables central data collection for various users in the cloud while minimizing device and edge computer usage adapting to individual XR system environments.
## 4 Test and FE Analysis
### Displacement Dynamic Response Analysis  
When the 1# short suspender and the 6# long suspender are broken, respectively, the displacement response of the arch rib and bridge deck system can be presented in Figure 12. It is observed that the displacement variations of the arch ribs would be small, and the measured maximum values are 3.05 mm and 4.35 mm, respectively, while the displacement changes the of main beams are fairly large, and their measured maximum values are \(-\)7.02 mm and \(-\)15.61 mm, respectively. From the results provided above, it can be seen that for the arch bridge structure base on the second type of suspension bridge deck system, the influence of the suspender fracture on the displacement of the bridge deck system is greater than that of the arch rib. This issue is attributed to the fact that the longitudinal beam is a multi-point elastic support structure. When the suspender is broken, the elastic support of the longitudinal beam suddenly decreases, which will produce a large disturbance and result in a large displacement in the longitudinal beam. For the arch rib structure, when the suspender breaks, the arch rib is still a complete consolidation structure at both ends, so the suspender fracture has little disturbance.  
Figure 11: Schematic representation of the FE simulations of the suspender fracture: (**a**) dynamic simulation of the suspender break; (**b**) the time-load curve.  
When each pair of suspenders is broken, the maximum measured displacement response of the arch rib and bridge deck system has been presented in Figure 13a. The plotted results reveal that when the 1# short suspender to the 6# long suspender is broken in turn, the maximum displacement response of the bridge deck system and the arch rib increases gradually, and the growing amplitudes are 2.22 and 1.43 times, respectively. The main reason is that the in-plane stiffness of the bridge deck system and arch ribs gradually lessens from the support positions at both ends to the mid-span point. As a result, the dynamic effect would be more apparent when the long suspender is broken; as the overall stiffness of the arch rib is large, the growing trend of the maximum displacement response of the arch rib is not noticeable.
## 3 Results
### Analysis of Efficiency
In the case of rainwater recovery, the maximum mean rainfall was observed in July (VII), which was a higher value than the yield (tank capacity \(S=12\) m\({}^{3}\)) and a lower value than the water demand for lettuce irrigation in the indoor vertical farm. Rainfall volumes were highest during the summer months from May to September (Figure 6A). From July onward, the average rainfall dropped sharply until the end of the year. The minimum value of demand, yield, and rainfall volume was reached in February. The maximum and minimum system efficiency equaled 69.6% and 23.1% respectively (see Figure 6B), hence this water recovery system is not self-sufficient. Figure 7 presents a combined system consisting of rainfall and exhaust air water recovery performance. When demand and yield overlapped or the yield exceeded the demand, the combined system met the water demand for lettuce irrigation, ensuring efficiency of 100% during 7 months of operation (from January to May and in November and December). During those months, the water recovery exceeded the yield significantly (Figure 7B). In January, there was a maximum water recovery (about three times higher than the yield and water demand). When the water demand exceeded the yield in June, July, and August, it caused low monthly \(E\) values. The minimum system efficiency was observed in August (59%). It could be noticed that the proposed combined system had high annual efficiency and was self-sufficient for most of the year compared to the rainwater recovery system only. Figure 5: The efficiency curve for a simulation of a separate RWH system (**A**), and a combined system (**B**).
# Photovoltaics and Electrification in Agriculture
## 3 Conclusions  
The electrification of the tasks associated with agriculture is an unstoppable and necessary process. The use of photovoltaic energy will be one of the energy sources with the greatest impact on this process. In the context of the decarbonization of all sectors, including agricultural and livestock production and the rural world, photovoltaic energy is undoubtedly one of the mechanisms with the easiest implementation.  
In summary, the abovementioned works demonstrate in their analysis and conclusions that:  
* Photovoltaic energy is the most competitive electrical energy option for the agricultural sector at the present time due to the drastic drop in component prices.
* The cultivation can be developed under photovoltaic panels coexisting in the so-called \"agrovoltaics\" with an increase in profitability or land use.
* Precision agriculture or agriculture 4.0, based on the Internet of Things (IoT), benefits from the use of photovoltaic solar energy for its purposes.
* Greenhouses are an ideal agricultural production system for the integration of photovoltaic panels of different technologies, such as organic, semi-transparent, or amorphous silicon panels.
# THz Rectennas and Their Design Rules
## 6 Thermal Effect  
Besides the several studies about the charge transition in rectenna system diodes, the thermal effect is always significant and present. This is due to the agitation of electrons in any temperature above absolute zero, 0 K, which results in the thermionic activity [97]; another possible effect is the thermocouples, which can have many types as will be demonstrated. The thermal effects present in the diodes used in rectenna systems may be divided into three categories.
###### Abstract  
Fault detection and diagnosis (FDD) in power plant systems is a rapidly evolving field driven by the increasing complexity of industrial infrastructure and the demand for reliability, safety, and predictive maintenance. This review presents a structured and data-driven synthesis of 185 peer-reviewed articles, sourced from journals indexed in MDPI and Elsevier, as well as through the Google Scholar search engine, published between 2019 and 2025. The study systematically classifies these articles by plant type, sensor technology, algorithm category, and diagnostic pipeline (detection, localization, resolution). The analysis reveals a significant transition from traditional statistical methods to machine learning (ML) and deep learning (DL) models, with over 70% of recent studies employing AI-driven approaches. However, only 30.3% of the articles addressed the full diagnostic pipeline and merely 17.3% targeted system-level faults. Most research remains component-focused and lacks real-world validation or interpretability. A novel taxonomy of diagnostic configurations, mapping system types, sensor use, algorithmic strategy, and functional depth is proposed. In addition, a methodological checklist is introduced to evaluate the completeness and operational readiness of FDD studies. Key findings are summarized in a comparative matrix, highlighting trends, gaps, and inconsistencies across publication sources. This review identifies critical research gaps--including the underuse of hybrid models, lack of benchmark datasets, and limited integration between detection and control layers--and offers concrete recommendations for future research. Combining a thematic and quantitative approach, this article aims to support researchers, engineers, and decision-makers in developing more robust, scalable, and transparent diagnostic systems for power generation infrastructure.  
fault detection and diagnosis; power plants; machine learning; deep learning; hybrid models; sensor technologies; predictive maintenance; taxonomy; diagnostic frameworks; system-level monitoring
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 4 Theoretical Analysis
### Receiving Sensitivity  
Although QR\(-\)LoRa can achieve comprehensive data rate improvement by frequency and amplitude cooperative modulation, there is still a gap in the receiving sensitivity. The frequency demodulation maintains the excellent anti-noise performance of LoRa. However, limited by the inherent properties of radio propagation, the information transmission in the amplitude dimension is completely destroyed by the noise and interference when SNR \(<\) 0 dB. Only when the SNR at the receiver is higher than 10 dB can the amplitude dimension of QR\(-\)LoRa transmit information robustly. Thus, we design QR\(-\)LoRa to work in two modes according to the current SNR to balance the two modulation methods. On the one hand, the amplitude and frequency are jointly utilized to transmit information in medium- and high-SNR scenarios. On the other hand, only the frequency is used to transmit information in the low or extremely low SNR scenarios.  
Furthermore, phase noise is another factor that impacts the demodulation performance of QR\(-\)LoRa. [PERSON] and [PERSON] [27] claimed that the phase errors are caused by fluctuations in the noise amplitude around the zero crossings of the local oscillator. It may lead to Carrier Frequency Offset (CFO) and Sampling Time Offset (STO) in amplitude decoding. Since the known and fixed pattern of the preamble and SFD allows for precise frequency offset estimation and timing synchronization, [PERSON] et al. leverage preamble-based CFO and STO cancellation for robust LoRa demodulation in low SNR environments [28]. Likewise, we adopt the preamble of a LoRa chirp and its SFD to calibrate the CFO and STO.  
A qualitative vulnerability analysis will be performed in our future work to explore the potential mitigation strategies, such as adaptive phase error correction or optimized oscillator designs. This analysis will complement the dual-mode operation strategy to further enhance system robustness across diverse channel conditions.
# Heat Storage as a Way to Increase Energy Efficiency and Flexibility of NPP in Isolated Power System
## 4 Discussion
### Non-Stationarity of Heat Exchange and Requirements for Feed Water Temperature at NPP  
The results of heat exchange simulation processes showed a significant non-stationarity (Figure 13), which is a negative factor for the application of such accumulators in industry. At the same time, strict requirements are imposed on the parameters of the nuclear power plant, including temperature, since the feed water temperature affects the temperature of the primary heat transfer fluid, which determines the reactivity of the core.  
It is necessary to apply a control system to maintain the temperature of the feed water within the required range. A bypass line with a control valve/pump must be provided in parallel with the accumulator. The degree of valve opening or pump power varies depending on the water temperature at the outlet of the accumulator. With the introduction of a such scheme, part of the heat transfer fluid will bypass the accumulator. It mixes with heated water after the accumulator. Thus, the heat transfer fluid passing through the bypass will cool the heat transfer fluid heated in the accumulator, which will ensure a stable temperature of the feed water. The scheme of the heated water temperature control system is shown in Figure 14.  
It is possible to reduce the uneven intensity of heat exchange over time by increasing the thermal conductivity of the PCM (the use of metal nanoparticles or other additives in the production of a PCM) or through the use of expanded heating surfaces (ribs of various shapes and configurations on the outside of heat exchange tubes) [95]. To reduce the effect of non-stationary in the operation of a heat accumulator with a PCM, a system of several accumulators with different switching schemes and several types of PCMs with different melting temperatures can be used. We plan to study this issue in a future study.
# High-Reliability Underwater Acoustic Communication Using an M-ary Cyclic Spread Spectrum
###### Abstract  
Multipath propagation, frequency selective fading, low-propagation velocity, and narrow bandwidth are all characteristics of underwater acoustic channels. Doppler shifts and diffusions can occur as a result of the low transmission speed of an acoustic signal, which can be caused by the movement of ocean currents or the transceiver. Furthermore, frequency selective fading and excessive noise interference can disrupt underwater acoustic communication on a continual basis. Because of its high anti-interference ability and high confidentiality, spread spectrum technology is commonly adopted in underwater acoustic communications. Although the direct sequence spread spectrum method has a low data rate, it is advantageous in a multipath propagation channel environment or an environment with a low signal-to-noise ratio (SNR). This advantage is suitable for long-distance transmission or LPD (Low Probability of Detection) communication, and the direct sequence spread spectrum method is applied. In this paper, we propose a highly reliable M-ary cyclic spread spectrum technique by superimposing the M-ary spread spectrum, an extension of the direct sequence spread spectrum technique, and cyclic shift keying. Furthermore, by estimating the Doppler frequency using M-ary spread spectrum codes and performing synchronization correction of the ensuing symbol based on the estimation results, higher performance can be attained. Simulations and experiments showed that the M-ary cyclic spread spectrum method can reduce Doppler estimation and synchronization error accumulation while maintaining a high data rate. Furthermore, the MCSS method had a lower bit error rate than the standard spread spectrum method.  
mobile underwater acoustic communication; spread spectrum communication; M-ary spread spectrum; Doppler tracking; symbol synchronization +
Footnote †: journal: _electronics_
## 3 Results
### Experiments for Active Learning on SNUBH Dataset  
Quantitative results for active learning experiment is shown in Table 3. \(D_{active}\) constructed from the model trained with the proposed \(\alpha\) weight (2) consists of 238 malignant images. Active learning aims to extend the \(D_{strong}\) dataset, which is the primary dataset that trains the model. Performing active learning gives a 2.75% increase in CorLoc measure and a 3.75% increase in the fraction of lesion detected measure. Both classification and localization performance has increased.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **\(\alpha\) Control Schedule** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **CorLoc [\%]** \\ \end{tabular} } & \multicolumn{2}{c}{**Fraction of**} \\  & & **Lesion Detected [\%]** \\ \hline constant & 41.75 & 56.00 \\ \hline inverse exponential & 49.75 & 69.25 \\ \hline linear & 60.25 & 70.50 \\ \hline polynomial (1) & 58.75 & 67.00 \\ \hline proposed: & **65.75** & **76.00** \\ polynomial (2) & 63.00 & 74.50 \\ \hline \hline \end{tabular} Correct localization (CorLoc) and fraction on lesion detected according to the manner of how \(\alpha\) is increase. CorLoc measures both classification and localization performance while fraction of lesion detected only measures the localization performance. Detailed equations are presented in Section 2.3.  
\end{table}
Table 2: Results showing variants of controled weight \(\alpha\) with the SNUBH BUS dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{\
\begin{tabular}{c} **Active Learing** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **CorLoc [\%]** \\ \end{tabular} } & \multicolumn{2}{c}{**Fraction of**} \\  & & **Lesion Detected [\%]** \\ \hline before & 65.75 & 76.00 \\ after & 68.50 & 79.75 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results showing the effect of active learning in SNUBH dataset.  
Figure 5 presents the qualitative results. Some masses that were difficult to detect or classify were given the correct predictions after training with \(D_{active}\). Both localization and classification performance are enhanced.
## 2 Background and Pseudo-Doppler Principles
### Transmission of Multiple Overlapping OAM Radio Beams
The results appear rather stochastic, with relatively high cross-talk among the detected OAM modes. A partial-circle aperture approach was also used by the authors of [17] to avoid the size issue with \"conventional\" OAM receiving antenna arrays. Judicious selection of the fraction of circle covered by the receiving array renders the received OAM modes orthogonal at the receive array, thus allowing them to be resolved and independently decoded. A variation on the time-switched array method of generating OAM modes using sinusoidal modulators instead of switches at the elements of a circular array is described in [18]. It is not applicable, nor easily convertible to receiving OAM modes. Figure 5: Example of stacked circular transmitting arrays for OAM multiplexing, after [11]. In this work the driving interest is to explore ways of implementing the method advanced in [10] in real-time, to realize a more practical OAM receiver than has been possible using co-axial spatial receiving techniques based on circular antenna arrays. In the process, it became evident that important details of the pseudo-Doppler technique are not derived with sufficient persuasion for this author, so another aim of this work is to fill in the mathematical details that allow an actual OAM radio communications link to be conceived and simulated. Specifically, it was noted that an actual demonstration of a real-time pseudo-Doppler-shifted spectrum of the received OAM signal carrying a useful data rate was not documented in the relevant literature; in [9] the spectrum shifts shown were caused by real Doppler shift due to physical rotation of the antenna, and in [10] the spectra were obtained by off-line post-processing of rather narrow-band data. The supplementary material shows ideal sketches of the shifted spectra and also spectral shifts due to rotation of the transmitting antenna; actual demodulated data is not represented, as that was an aim of future research stated in [10].
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 3 Results
### PSO-ELM Model Training Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Predictive Model** & **MRE (\%)** & **RMSE (\%)** & **R\({}^{2}\)** \\ \hline ELM & 13.370 & 0.0157 & 88.67\% \\ KPCA-ELM & 10.580 & 0.0061 & 94.35\% \\ PSO-ELM & 9.882 & 0.0069 & 94.62\% \\ KPCA-PSO-ELM & 6.394 & 0.0029 & 99.59\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of error comprehensive evaluation indicators. Figure 13: Standard deviation for different models predicting the test set data. By comparing the standard deviations between the different models, KPCA-ELM, was closest to the standard deviation of the actual values followed by KPCA-PSO-ELM. The comparison found that the MRE and RMSE values for the KPCA-ELM and PSO-ELM models were lower than the traditional ELM model, showing significant performance improvements. Compared with the average absolute error (3.24%) of the PSO-SVM model in the literature [14], the average absolute error of the KPCA-PSO-ELM model was only 0.475%, and the R\({}^{2}\) value was also improved. These results fully demonstrate the effectiveness of the selected optimization method and indicate that the KPCA-PSO-ELM model has significant advantages in terms of prediction accuracy. Figure 15 illustrates the Taylor plot, presenting the performance of the different models in predicting the test set data. It combines three metrics: the standard deviation (SD), R\({}^{2}\), and RMSE. The closer the prediction model is to the actual internal purple box point, the better the prediction results. The dashed circle at the origin represents the standard deviation value. The solid line centered at the origin indicates the correlation coefficient. Figure 14: Linear fit of the prediction results of the four models. (**a**) ELM forecast value, (**b**) KPCA-ELM forecast value, (**c**) PSO-ELM forecast value, and (**d**) KPCA-PSO-ELM forecast value. From the above figure, the KPCA-PSO-ELM model was closest to the bottom center purple box point compared to the other models. It had an R\({}^{2}\) value of 0.9959 and an SD of 0.745. These results indicate that the KPCA-PSO-ELM model was the most suitable for predicting the external corrosion rates in this dataset. It demonstrated a high level of accuracy in predicting the corrosion rates of the buried pipelines.