esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Conclusions  
We achieved a 98.3% average alpha-word accuracy (within 1% of the state of the art) on the diacritic restoration task over 13 benchmark languages with a ByT5 universal byte-level transformer model approach, a smaller training dataset (Wikipedia), and a much-reduced training time (Table 3). When the training time is limited, the model is slightly improved by the assistance of a simple statistical Unigram model (Dict.+ByT5). There is a solid indication, however, that longer training gets very close to the state-of-the-art model, even without this assistance, and with the smaller dataset (Figure 2).  
We achieved a 94.6% average alpha-word accuracy on the simultaneous diacritics restoration and typo correction tasks with the same models (Dict.+ByT5), training datasets and times. This is a much harder task, and is problematic for the specialized systems; thus, we have no state-of-the-art model to compare to (Table 5). There is also a strong indication that longer training can significantly improve these results (Figure 3).  
We investigated that most of the errors are caused by the words that are rare in the training dataset (Table 6). However, contrary to the classical approaches, our models generalize quite well to the unseen words (Table 7) and restore diacritics correctly on >76% of the unseen words in every language. This gives us good hints on how the models can be further improved, often by simply training them more.  
The good performance and universality of this approach make it very promising for real-world applications, more languages and error classes.  
Conceptualization, M.L., J.K.-D., L.S. and T.K.; methodology, L.S., M.L., J.K.-D. and M.B.; software, L.S.; validation, L.S.; formal analysis, L.S. and M.L.; investigation, L.S.; resources, M.L.; data curation, L.S.; writing--original draft preparation, L.S., M.L., J.K.-D. and M.B.; writing--review and editing, M.L. and J.K.-D.; visualization, L.S.; supervision, M.L. and J.K.-D.; project administration, M.L. and J.K.-D.; funding acquisition, M.L., J.K.-D., L.S., T.K. and M.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the joint Kaunas University of Technology Research and Innovation Fund and Vytautas Magnus University project \"Deep-Learning-Based Automatic Lithuanian Text Editor (Lituanistas)\", Project No.: PP34/2108.
# Sustainable Energy Solutions: Utilising UGS for Hydrogen Production by Electrolysis
###### Abstract  
Increasing the share of renewable energy sources (RESs) in the energy mix of countries is one of the main objectives of the energy transition in national economies, which must be established on circular economy principles. In the natural gas storage in geological structures (UGSs), natural gas is stored in a gas reservoir at high reservoir pressure. During a withdrawal cycle, the energy of the stored pressurised gas is irreversibly lost at the reduction station chokes. At the same time, there is a huge amount of produced reservoir water, which is waste and requires energy for underground disposal. The manuscript explores harnessing the exergy of the conventional UGS reduction process to generate electricity and produce hydrogen via electrolysis using reservoir-produced water. Such a model, which utilises sustainable energy sources within a circular economy framework, is the optimal approach to achieve a clean energy transition. Using an innovative integrated mathematical model based on real UGS production data, the study evaluated the application of a turboexpander (TE) for electricity generation and hydrogen production during a single gas withdrawal cycle. The simulation results showed potential to produce 70 tonnes of hydrogen per UGS withdrawal cycle utilising 700 m\({}^{3}\) of produced field water. The analysis showed that hydrogen production was sensitive to gas flow changes through the pressure reduction station, underscoring the need for process optimisation to maximise hydrogen production. Furthermore, the paper considered the categorisation of this hydrogen as \"green\" as it was produced from the energy of pressurised gas, a carbon-free process.  
decarbonization; underground gas storage; exergy expansion turbine; green hydrogen; renewable energy sources +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
Water quality prediction plays an important role in the control, management, and planning of water quality. The common BP neural network prediction model has many weaknesses, so we combined the genetic optimization algorithm and particle swarm optimization algorithm with BP neural network, and established the PSO-GA-BPNN prediction model in this study. The improved model integrated the function of self-learning, bionic, and nonlinear approximation technology. This network learning model realized fast convergence speed, avoidance of local minima, stronger stability, and suitable results. Due to current hardware, the training time of PSO-GA-BPNN model is relatively long. The PSO-GA-BPNN had improved prediction accuracy and robustness.  
There is an urgent requirement for new methods to deal with abnormal data. In complex aquatic ecosystems, the proposed model can meet the management requirements of water quality monitoring and early warning. It is strongly recommended to establish different predictive models according to diversified weather conditions and complex surface environments, and to combine those prediction models to improve the prediction accuracy because water quality is heavily affected by hydrological, meteorological, and surficial factors.  
Conceptualization, J.Y. and K.G.; Investigation, H.X.; Methodology, Z.X.; Project administration, Y.Y.; Resources, J.Y.; Software, Z.X. and K.G.; Supervision, Y.Y. and H.X.; Writing--original draft, Z.X.  
This research was funded by [Water Pollution Control and Treatment Science and Technology Major Project] grant number [2018 ZX07111005] and The APC was funded by [Water Pollution Control and Treatment Science and Technology Major Project].  
The authors are grateful to the Beijing Water-affair Authority for making available the water quality data of the Beihai Lake. The authors thank [PERSON] and [PERSON] for them thoughtful advice and suggestions on research design and implementation.  
The authors declare no conflict of interest.
# High-Throughput Bit-Pattern Matching under Heavy Interference on FPGA
## 2 Fundamentals
### Adder Tree  
An adder tree is a well-known circuit that calculates the sum of a set of numbers. Its general form can be seen in Figure 1 for a model with 11 bits (each input is a bit).  
The circles are adders of appropriate size for each level, while the squares represent registers. The addition of registers between the levels of adders enables pipelining. At each level, the numbers are added in pairs. If there is a leftover number, it proceeds as previously set. This means that in level \(i\), there are \(\frac{n}{2^{i}}\) additions (either ceil (\(\frac{n}{2^{i}}\)) or floor (\(\frac{n}{2^{i}}\))) and ceil(log\({}_{2}n\)) levels in total. The final sum is also ceil(log\({}_{2}n\)) bits in length. The largest adder needed is ceil(log\({}_{2}n\)) \(-\) 1 bits. The critical path of this adder determines the  
Figure 1: Adder tree for an 11-bit model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Pattern** & **Rule 2** & **Xnor** & **Sum of Xnor Gates** \\ \hline
0 & 0 & 1 \\
1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The similarity of patterns.  
maximum operating frequency of the entire tree. This is the main reason the adder tree by itself cannot support very long sequences. Adders of 32 bits are significantly slower than adders of 4 bits when they are forced to perform addition in one clock cycle. Pipelining the bigger adders will result in enormous hardware consumption, which will ultimately offset the trees' efficiency. For the circuit to be able to handle bit sequences of 1000 bits while operating in the frequency of 4-bit adders, with no significant hardware overhead, significant modifications need to be made.
## 2 New Trends in Aquaculture Feed Production
### Use of Insects in Fish Feed  
Insects represent a potential alternative source of animal protein in the production of fish feed [48; 49]. The growth cycle of insects is very rapid, and several breeding cycles can be implemented throughout the year [49]. The protein, fat, and vitamin contents in insects vary according to different factors, such as species, sex, developmental stage, and growth environment. Insect meal is a good source of protein (40-75 g/100 g DM) and minerals [48]. Protein digestibility (77-98%) varies in relation to several factors, such as the presence of the exoskeleton and the content of essential amino acids [50]. The average lipid content varies from 13 to 33% of DM depending on species, sex, and developmental stage [50]. The average omega-3 and omega-6 fatty acid compositions of yellow mealworms (Tenebrio molitor) are similar to those of several fish species. Insects contain some bioactive compounds, such as antimicrobial peptides (e.g., amino-rich peptides), fatty acids (e.g., lauric acid), and polysaccharides (e.g., chitosan and chitin) [51]. The most interesting insect meals for the fish feed industry are those obtained from yellow mealworms, black soldier flies (_Hermetia illucens_), common house flies (_Musca domestica_), crickets (_Acheta domesticus_), and grasshoppers (_Tettigonia viridissima_) [49; 52]. House crickets (_Acheta domesticus_), mealworms, and migratory locusts (_Locusta migratoria_) were recently authorised as food ingredients in the EU.  
Insect larvae (black soldier fly, common house fly, and yellow mealworms) can be reared in large plants. The larval reproduction systems are similar among the different species. Recent studies have shown that mealworms and soldier flies can be used in the formulation of fish diets because of their excellent palatability and composition in amino acids, lipids, and calcium [53]. Insects can also play a fundamental role in the biodegradation/recovery of several wastes, reducing contamination, and contributing to the sustainability of animal production systems [54]. In fact, insects can be fed agricultural wastes, transforming them into a valuable nutritional resource. Insects can be raised with a reduction of 50-90% in land use compared to conventional livestock farming and with a decrease of approximately 100 times in greenhouse gas emissions. It has been reported in some studies that insect meal may affect the sensory profile of rainbow trout fillets. For some insect species, further investigation and careful economic analysis are needed. The use of insect meal can pose some risks, such as the presence of allergens, chemical contaminants, parasites, and microbiological threats [55]. Currently, there are still limited data available for a thorough risk analysis. In Table 9 are reported the effects of alternative diets (plant, insect, algae, etc.) on growth and quality parameters in rainbow trout.
## 2 System Model
### Passive RIS-UAV  
In the existing passive RIS-UAV relaying scheme, the RIS mounted on the UAV consists of only \(N\) passive elements. The passive RIS-UAV only reflects the incident signals without amplifying it, hence \(\rho=1\), and there is no noise introduced at the passive RIS-UAV, \(\mathbf{n}_{r}=0\). As a result, the received signal at the UE after being reflected by the passive RIS-UAV is:  
\[y_{psa}=\sqrt{P_{bs}^{psa}}\mathbf{h}_{rd}^{H}\mathbf{\Psi}_{psa}\mathbf{h}_ {sr}s+n, \tag{23}\]where \(P_{bs}^{\text{ms}}\) is the transmit power at the BS for the passive RIS-UAV relaying scheme, while \(\mathbf{\
abla}_{\text{ps}}\)is the reflection matrix of the passive RIS elements, \(\mathbf{\
abla}_{\text{ps}}=\text{diag}\{e^{i\varphi_{1}},\ldots,e^{i\varphi_{n}}\}\). Therefore, the SNR of the passive RIS-UAV is given as:  
\[\gamma_{\text{{\it{pass}}}}=\frac{P_{bs}^{\text{{\it{pass}}}}N^{2}h_{sr}^{2}h_ {rd}^{2}}{\sigma^{2}}. \tag{24}\]
## 7 Future Work
### Computing Task Assignment and Resource Allocation  
Our research only considers how to perform user access and traffic backhaul in remote areas where ground base stations are difficult to deploy. However, in addition to serving remote areas, HAP drones can also provide low-latency edge computing services for IoT devices in urban areas with ground base station coverage. In the future, the great pressure that computing-intensive applications place on resource-constrained Internet of Things (IoT) devices with limited computing capability and energy storage can be alleviated by offloading latency-sensitive computing tasks to nearby edge nodes. A matching strategy for ground users, HAP drones, and ground base stations should be developed by jointly optimizing computing task assignment and resource allocation, thus improving the performance of the space-air-ground integrated network, such as minimizing the maximum task execution latency among IoT devices or maximizing the amount of transmitted traffic per unit time.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 2 Design of Docking Logic and Kinematic Analysis for LNG Loading Arm
### Kinematic Modeling and Docking Posture Control of LNG Loading Arms  
In the LNG loading-arm automatic docking system, achieving efficient and stable docking operations requires precise alignment of the arm's end-effector with the target flange. To this end, this paper establishes the kinematic model of the loading arm using the D-H parameter method to provide theoretical support and applies matrix analysis to clarify the position adjustment strategy of the end-effector. As shown in Figure 4, the movement of the loading arm can be divided into the following three parts according to its mechanical structure:  
* Horizontal rotation: The base achieves rotational movement through a horizontal bearing, enabling multi-directional docking requirements;
* Inner-arm pitch adjustment: The joint of the inner arm adjusts the vertical height to accommodate different docking points;
* Outer-arm extension and retraction: The outer arm adjusts its length via a linear actuator or hydraulic system to precisely control the docking distance between the loading arm and the flange.
# Wavelet-Enhanced YOLO for Intelligent Detection of Welding Defects in X-Ray Films
## 4 Result
### Defect-Specific Analysis  
To further investigate the impact of WTConv on different defect types, we analyzed model performance across individual categories, as seen in Figure 5. The results highlight distinct patterns in how WTConv affects defect detection, localization, and false positive/negative rates for various defects.  
Experiments were conducted on a dataset containing seven types of defects, with 200 images per defect category. The ablation study shows that WTConv leverages multi-scale frequency domain analysis to enhance the model's overall localization accuracy. In terms of detection accuracy, there is a slight decline for slag inclusion, while for other defect types--especially high-risk defects such as cracks, lack of fusion, and incomplete penetration--there are significant improvements.  
In terms of localization accuracy, as shown in Figure 5a,c,e, the proposed WT-YOLO model shows significant improvements over YOLOv11n in terms of mAP50-95, mAP50, and mAP75 for defects such as cracks, concave, and undercut. For example, the three metrics for cracks were improved from 0.1458 to 0.1835, from 0.3649 to 0.4489, and from 0.0976 to 0.1314, respectively. For defects such as lack of fusion, incomplete penetration, slag inclusion, and porosity, the three metrics are generally similar, with the exception of porosity, where the mAP decreased by 0.0139, and other changes being less than 0.01.  
In terms of detection accuracy for various defects, as shown in Figure 5b,d,f, such as cracks, lack of fusion, incomplete penetration, undercut, and porosity, the proposed model shows a comprehensive improvement in recall, precision, and F1-score compared to YOLOv11n. For example, the recall rate for cracks increased from 0.4539 to 0.5277, precision improved from 0.4411 to 0.4926, and the F1-score rose from 0.4474 to 0.5095. However, for slag inclusion, the recall rate decreased by 0.0304, precision decreased by 0.0028, and the  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Metric** & **YOLOv11n** & **WT-YOLO** \\ \hline mAP50-95 & 0.2397 & 0.2515 \\ mAP50 & 0.5519 & 0.5675 \\ mAP75 & 0.1624 & 0.1836 \\ Recall & 0.5453 & 0.5400 \\ Precision & 0.5800 & 0.6279 \\ F1 & 0.5621 & 0.5806 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance metrics for the test set.  
F1-score dropped by 0.0192. For concave defects, the recall rate decreased by 0.0480, while precision increased by 0.1180, and the F1-score increased by 0.0166.
## 5 Experimental Results and Discussion
### Performance Metrics, Results, and Discussion
For instance, a 17.47% increase in Fog resource utilization, compared to ODMA, achieved by our approach not only leads to more services being processed locally but also contributes to a significant reduction in execution delays. This is particularly important in IoD scenarios, where timely execution is critical. Moreover, improved Fog utilization reduces reliance on Cloud resources, which are often associated with higher latency. Therefore, even when the number of additionally executed services is relatively small, the accompanying reduction in average waiting time, as shown in Figure 15, reflects a tangible enhancement in Quality of Service (QoS). Our work can be improved and extended in a number of ways. In the following, we give some indications of improvements and evolution:  
* Improve the results found in our system evaluation concerning waiting time, Fog utilization, etc. * The treatment of other SPP-related objectives in Fog such as cost of service and energy consumption. * Testing our work in the real world with large amounts of data. * Ensuring QOS becomes an important issue in the event of faulty Fog nodes. Indeed, a faulty node means, for example, a node that gives false information about its state or has not processed the services received after a certain time, so that if the number of services received is small, it will be considered available and can receive other services. Therefore, it is necessary to address the issue of the fault tolerance of service placement in Fog. * Security is also very important, and we have to consider it. Indeed, for solutions based on user history and information (like our solution), it is crucial to protect these data. Figure 15: Results for the average waiting time for services executed in each time period.
# Novel Resource Allocation Techniques for Downlink Non-Orthogonal Multiple Access Systems
## 3 System Model and Performance Metrics
### Performance Metrics
#### 3.2.2 Energy Efficiency  
In a wireless communication system, energy efficiency is the ratio of throughput to the total power consumption. It is measured in bits per Joule and expressed as follows [30]:  
\[EE=\frac{Throughput(\text{bps})}{Total\ Power\ Consumption(\text{Joule/s})} \tag{8}\]  
The \(\text{UE}_{ws}\) energy efficiency (\(EE_{ws}\)) in a NOMA system can be determined with Equations (9) and (10). Accordingly, the sum of energy efficiency of the corresponding NOMA is estimated with the use of Equation (11).  
\[EE_{w,s}=\frac{B_{schlog2}\left(1+\frac{P_{w_{s}}H_{w,s}}{1+\sum_{i=1}^{w-1}P_ {i}H_{w,s}}\right)}{P_{c}+\sum_{w=1}^{M}P_{s}} \tag{9}\]  
\[EE_{w,s}=\frac{R_{w,s}}{P_{c}+\sum_{w=1}^{M}P_{s}} \tag{10}\]  
\[EE_{sum}=\sum_{s=1}^{S}\sum_{w=1}^{W}EE_{ws} \tag{11}\]
## 1 Introduction
### Routing Protocols: A Brief Overview
#### 1.3.3. Ad Hoc On-Demand Multi-Path Distance Vector (AODDV)  
Similar to AODV, AOMDV is also based on a distance vector along with the advantage of storing multiple paths for a single destination. Generally, all duplicate RREQs are discarded in AODV, whereas AOMDV uses duplicated requests to find out the alternative path in the network. In AOMDV, RREQ flows from the source to the destination by establishing multiple reverse paths both in between nodes as well as the destination node. Multiple paths found are loop-free and disjoint. The route updating process is also very efficient in AOMDV [13; 14]. The major contribution of the work is as follows:  
1. Evaluated entity mobility models with proactive and reactive routing protocols in terms of quality-of-service parameters viz throughput, average end-to-end delay (AE2 ED), and packet delivery ratio (PDR) for seeking a protocol that can provide the best results in terms of stated parameters.
2. Performance comparison with varied scenarios is demonstrated for different mobility patterns of sensor nodes in WBAN.
3. Suitability and limitations of different routing protocols are evaluated for WBAN.  
The organization of the paper is as follows. Section 2 provides information about different mobility patterns along with available models. Section 3 covers related work performed by previous researchers in the same field, followed by Section 4, which provides an overview of the methodology and experimentation that have been used for evaluation. Section 5 provides the specification for QoS parameters, followed by Section 6, which provides us the result analysis of different parameters with different routing protocols. Lastly, the conclusion and future direction of the proposed work have been discussed in this article.
## 5 Conclusions  
ToF-SIMS was used to explore the interface chemistry of octadecyltrimethoxysilane (OTMS; molecular formula M = CH\({}_{3}\)(CH\({}_{2}\))\({}_{17}\)Si(OCH\({}_{3}\))\({}_{3}\)) self-assembled monolayers (SAMs) formed on a thermally grown SiO\({}_{2}\) layer on a Si wafer via the comparison of differences in ion fragmentation between SAMs and free OTMS molecules. Molecular and pseudomolecular ions, such as [M-H]\({}^{-}\), [M-CH\({}_{3}\)]\({}^{-}\) and [M + H]\({}^{+}\), were detected for the free OTMS molecules, whose ion mass spectra were dominated by various negative and positive ions associated with the silane headgroup, -Si(OCH\({}_{3}\)O)\({}_{3}\), including Si(OCH\({}_{3}\))O\({}^{-}\), Si(OCH\({}_{3}\))O\({}_{2}\)\({}^{-}\), Si(OCH\({}_{3}\))\({}_{2}\)OH\({}^{-}\), Si(OCH\({}_{3}\))\({}_{3}\)\({}^{-}\), Si(OCH\({}_{3}\))\({}_{2}\)H\({}^{+}\) and Si(OCH\({}_{3}\))\({}_{3}\)\({}^{+}\). In contrast, for the OTMS SAMs, no ions associated with -Si(OCH\({}_{3}\)O)\({}_{3}\) were detected, showing that methoxy groups no longer exist when OTMS molecules form SAMs, verifying that they are completely hydrolyzed. For free OTMS molecules, there were few to no silicon oxide cluster ions (Si\({}_{n}\)O\({}_{2n+1}\)H\({}^{-}\) (\(n\) = 2-5)) detected. In contrast, these ions were detected on the OTMS SAMs, which ought to serve as a marker for the -Si-O-Si- network at the interface. Because these oxide cluster ions are characteristic of the bare SiO\({}_{2}\) substrate, future work on investigating OTMS SAMs formed on non-silicon oxide will confirm to what extent the underlying SiO\({}_{2}\) substrate contributes to the detected cluster silicon oxide ions. It is worth noting that there are three ions (Si\({}_{2}\)O\({}_{3}\)H\({}^{-}\), Si\({}_{2}\)O\({}_{4}\)H\({}^{-}\) and Si\({}_{3}\)O\({}_{6}\)H\({}^{-}\)) whose intensities are much stronger relative to that of Si\({}_{2}\)O\({}_{5}\)H\({}^{-}\) (the most abundant silicon oxide cluster ion) for the OTMSSAMs than for the bare SiO\({}_{2}\) substrate. Our results thus demonstrated that ToF-SIMS is able to provide unique information to improve our understanding of the interface chemistry of organosilane SAMs.  
Conceptualization, H.-Y.N. and H.-R.J.-F.; methodology, H.-Y.N.; formal analysis, H.-Y.N.; investigation, H.-Y.N.; resources, H.-Y.N. and H.-R.J.-F.; data curation, H.-Y.N.; writing--original draft preparation, H.-Y.N.; writing--review and editing, H.-Y.N. and H.-R.J.-F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.