esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
## 3 Experimental Analysis
### Validation of the Model  
We start experimental analysis by verifying the accuracy of the proposed analytical model. Since the matrix of transition probabilities due to the big cardinality of the state space is quite complicated for clear verification, we have conducted an event-based imitation analysis, in which we have simulated multiple times the packet transmission flow through the states of the model until the probability distribution converges. The given imitation model allows obtaining the probability distribution along with the performance measures based on the collected statistical data. We utilize the transition probabilities demonstrated in Figure 5 and do not differentiate them in terms of \(l\) number of deliveries. The arrival rate is considered to be 0.2 for a transmission length of 14 OFDM symbols given the fact that modelling is done on a microslot level, which corresponds to the use cases that experience a transmission flow of packets. The mean spending time measured in time slots for a set of threshold values \(\theta\) and all possible \(d\) for both analytical and imitation models is shown in Figure 6. The imitation of the given generalized model with a microslot processing is very computationally intensive and requires much more computational efforts until the system converges compared to the given analytical modelling, where the required performance measures are computed right away. Due to this, we have provided only a few experimental points for comparative analysis between two types of modelling; however, it is enough to verify the correctness of the matrix of transition probabilities.  
\begin{table}
\begin{tabular}{l l} \hline Transport block size (\(N_{\text{Bits}}\)) & 500 \\ \hline Transmission bandwidth & 1.08 MHz (6 RBs) \\ \hline Channel Code & Rate-1/5 LDPC [22] \\ \hline Modulation order and & 64-QAM, \\ algorithm & Approx. LLR \\ \hline Power allocation & Constant \(E_{b}/N_{0}\) \\ \hline Waveform & 3 GPP OFDM, normal \\  & cyclic-prefix, 15 kHz \\  & spacing \\ \hline Channel type & 1 Tx 1 Rx, TDL-C \\  & 100 ns, 7 GHz, 3 km/h \\ \hline Equalizer & Freq. domain MMSE \\ \hline SNR & 5.0 dB:12.0 dB \\ \hline Decoder type & Min-Sum (50 iter.) \\ \hline Predictor type & Logistic regressions [23] \\  & (5 iter.) \\ \hline \end{tabular}
\end{table}
Table 2: LLS assumptions for training and test set generation.
# Patent Portfolio Analysis of the Synergy between Machine Learning and Photonics
## 4 Results
### Post analysis: Change in Number of Patents and Papers  
The relationship between number of published papers and number of patents was further investigated to identify developmental trends in machine learning in photonics. Web of Science (WOS) was used to search relevant papers with the following criteria: (TTL/machine learning) or (ABST/machine learning) or (KW/machine learning)) and ((TTL/photonics) or (ABST/photonics) or (KW/photonics)). TTL, ABST, and KW indicate titles, abstracts, and keywords, respectively. Figure 4 presents the change in number of patents and papers.  
Patents and papers on machine learning in photonics have received increasing attention, and the increase in the number of patents (1.20%) is similar to that of papers (1.14%).
# Pan-Cancer Analyses Reveal Genomic Features of FOXM1 Overexpression in Cancer
## 2 Results
### Lack of Evidence for A FOXM1 Auto-Regulatory Loop  
Finally, we examined a previously reported FOXM1 positive auto-regulatory loop [55; 56], initially by using 293T FOXM1 knockout cells and a FOXM1 promoter reporter assay. Unexpectedly, we did not observe a significant difference in FOXM1 promoter activity when comparing 293T parental and FOXM1 KO cells (Figure S6A). Consistent with this, Kuramochi HGSC cells with dox-inducible FOXM1c expression showed \(\mathit{CCN}\mathit{B}1\) activation (a canonical FOXM1 target gene), but not increased endogenous \(\mathit{FOXM1}\) expression (Figure S6B,C). Moreover, dox-inducible expression of either FOXM1b or -c activated \(\mathit{CCN}\mathit{B}1\) but not endogenous \(\mathit{FOXM1}\) expression in both hOSE and FT282 cells (Figure S6D). Conversely, overexpression of the FOXM1c DBD MT, which functions as a dominant negative, resulted in a significant decrease in \(\mathit{CCN}\mathit{B}1\) mRNA expression, but no change in \(\mathit{FOXM1}\) endogenous mRNA expression in FT282 cells (Figure S6D). Finally, transient overexpression experiments in 293T cells showed that FOXM1 b or -c induced \(\mathit{CCN}\mathit{B}1\) but not endogenous \(\mathit{FOXM1}\), while FOXM1a lacked transcriptional activity (Figure S6E,F). Together, these data do not support the existence of a FOXM1 auto-regulatory loop, at least in the cell types examined.
## 5 Conclusions  
The results show that compositional variability in carotenoids and tocopherols is subjected to variations that reflect the character of the fresh pasture plants.  
Cheeses produced during spring, when cows had green pasture, showed the highest levels of the lipophilic antioxidant studied (especially tocopherols), respect to cheese samples produced in the winter season, characterized by poor pasture. The ability of a vitamin-mineral supplementation to exert a positive effect on \"Ragusano\" antioxidant compounds represents a challenge for future studies. More extensive studies must be performed to confirm the nutritive value of \"Ragusano\", while different diet supplements could be considered to improve its antioxidant composition.  
Conceptualization, A.S., G.L.L.T., and A.R.; methodology, A.S., G.L.L.T., and A.R.; software, A.R.; validation, A.S., G.L.L.T., R.V., and A.R.; formal analysis, A.S., V.Z., R.R., G.B., and G.L.L.T.; investigation, V.Z., R.R., and G.B.; data curation, A.S., G.L.L.T., and A.R.; writing--original draft preparation, G.L.L.T.; writing--review and editing, A.R.; visualization, G.L.L.T.; supervision, G.L.L.T. and A.R.; project administration, A.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and all the experimental procedures used were performed in compliance with the European Union guidelines on animals in research (Directive 2010/63/EU).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **\% \(\alpha\)-TOC 1** & **\% LUT 2** & **\% \(\beta\)-CAR 2** \\ \hline CS15\_0 3 & 59.64 & 36.81 & 62.23 \\ CS16\_0 & 61.97 & 35.90 & 62.67 \\ CS17\_0 & 62.45 & 38.46 & 59.98 \\ CS18\_0 & 59.47 & 31.73 & 66.30 \\ CS19\_0 & 56.60 & 32.96 & 65.11 \\ CS20\_0 & 62.07 & 40.60 & 58.14 \\ CS21\_0 & 55.31 & 31.81 & 65.87 \\ \hline Range C0 & 55.31–62.45 & 31.73–40.60 & 58.14–66.30 \\ \hline TS22\_0 & 64.81 & 38.91 & 59.60 \\ TS23\_0 & 62.08 & 37.04 & 60.62 \\ TS24\_0 & 58.27 & 37.46 & 60.52 \\ TS25\_0 & 56.33 & 32.59 & 65.45 \\ TS26\_0 & 56.37 & 33.33 & 64.77 \\ TS27\_0 & 53.62 & 39.47 & 58.66 \\ TS28\_0 & 67.28 & 33.04 & 64.93 \\ \hline Range T0 & 53.62–67.28 & 32.59–39.47 & 58.66–65.45 \\ \hline CS15\_1 & 55.27 & 33.91 & 64.06 \\ CS16\_1 & 56.64 & 34.16 & 64.44 \\ CS17\_1 & 55.31 & 33.36 & 64.84 \\ CS18\_1 & 54.56 & 31.19 & 66.92 \\ CS19\_1 & 56.25 & 37.85 & 60.34 \\ CS20\_1 & 57.39 & 40.38 & 58.13 \\ CS21\_1 & 54.14 & 32.02 & 65.44 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Percentage of the most representative compounds in “Ragusano” cheeses produced in spring.
# Proactive Approach to Production Control Utilizing Heterogeneous Shop-Level Production Data
## 3 Methodology
### Direct Database Connection  
As per all the data acquisition methods described in this paper, utilizing a local database connected to the PCS alongside a simulation-based DTw is an approach used for accessing, acquiring and displaying supervisory data to the operator. It can be considered an alternative approach to data transfer, where intermediary data storage is not available or required [40]. One of the benefits of a simulation-based DTw is the option to display the data located in databases directly within the simulation environment, providing useful information for decision-making in production control. Our simulator of choice had a functionality that allowed DTws to be directly connected to the database and offered a range of elements to display or manipulate the data by custom algorithmic coding practices.  
The volume of data being accessed and transferred between the PCS and the simulator is highly dependent on the type and volume of data being stored in the PCS itself. It is also important to note that not all of the data stored in the PCS is required or applicable for use in the simulation using the DTw. Therefore, data selection and preliminary data formatting algorithms should be implemented before the full integration of a DTw with the PCS database. A good practice is to create highly specific views or tables inside the database, which are then directly read by the DTw. This reduces the risk of potentially exposing the full scale of vulnerable data to a possible malicious interception, as the simulation process can be performed remotely. Furthermore, interpretation of said data within the DTw, its alteration and re-formatting for the purpose of sending the data back to the PCS, is a part of the requirement for implementing the backwards communication of the DTw with the PCS, thus having the communication process fully automated.  
Unlike other methods described in this paper, which utilize data file transfer to ensure their targeted functionality, when initializing a simulation process within the DTw utilizing the PCS database, an active connection to the PCS is required throughout the full duration of the simulation. This is due to the DTw being directly dependent on the current data located within the database for the purpose of displaying the product data to the operator or the system supervisor. Based on this type of connection, utilizing a database does not require any additional data files as the functional elements within the simulator handle the production data autonomously. A common scenario for utilizing this type of approach is when attempting to transfer production batch data, logistical unit data or recent historical data from individual production entities into the simulator, e.g., machines, storage units, transport vehicles, etc. This data is then used to reconfigure the simulation model where necessary, to adjust it to the current state of production.
## 5 Discussion
Thus, the associated costs and procedures should find their place in the long-term implementation plan. Another important aspect is the costs of the licensing of the platform components (cloud services or purchased software). Building multimedia processing infrastructure based on commercial clouds is not cheap and requires careful customization. In the described approach, the generation of transcripts is commissioned by Limecraft or carried out by ffmpeg. Next Google Speech to Text caught attention in the search for alternatives that would give better results. An attempt was made to test its API (applying for Speech-to-Text On-Prem model is also possible, but must be negotiated individually with the service provider). The use of Google Speech to Text services can be similar to the described Limecraft use case. One may send for processing only audio tracks extracted from a video. However, such processing enforces the most expensive licensing model. Furthermore, the lack of consent to the use of the uploaded recordings for teaching google models blocks a possible discount. Since every 15 s is billed separately for each audio track, converting multitrack audio to mono before sending is considerable. However, the transcription of recordings longer than a few minutes requires audio files uploading to storage in google cloud because the unique filehandles offered by this cloud are needed. The result is a JSON file with a transcript in which a timestamp and certainty accompany each word. Such output still needs to be converted to the target format (MD, SRT, VTT), using some logic of combining words into subtitles. That logic can be implemented based on open-source libraries supporting selected subtitle formats. However, using cloud-based storage also must be done according to the offered business plan.
# Blockchain Implementation Method for Interoperability between CBDCs
## 5 Evaluation  
### Comparison of Block Generation Time between Blockchains  
This section evaluates Ethereum and Bitcoin applied to the proposed system. The experimental environment is Ubuntu Server 18.04.2 (Bitcoin), linuxmit 19.3 (Ethereum), CPU-Intel(r) Core(tm) i5-8600 K CPU @ 3.6 GHz, Ram-64 G, SSD 500 G. In the experiment, blockchains use the ECCPoW Consensus Algorithm [22].  
We apply the proposed method to ECCPoV cryptocurrency systems [23]. ECCPoW is a PoW method that uses error correction code. ECCPoW can be personalized by hard forking Bitcoin and Ethereum. ECCPoW has the effect of solving different problems every block for ASIC resistance. ECCPoW uses hamming weights and signs to control difficulty. We use the default generation time (bitcoin 180 s, Ethereum 60 s) of the block because we need to store additional CBDC information in the blockchain.  
Figure 18 shows the block generation time of Bitcoin and Ethereum using the proposed method. Figure 18A is a block generation time graph of Bitcoin, and Figure 18B is a block generation time graph of Ethereum. Table 1 shows the target block generation time, difficult change time, average block generation time, and support of user data insertion in blockchains. Bitcoin's block generation time controls the difficulty of solving blocks for every 2016 blocks to occur every 10 min. The block generation time of Ethereum controls the difficulty with the goal of 10 to 20 s. We set the low difficulty of the block and created the block. For support of user data insertion, Bitcoin uses OP_RETURN, while Ethereum uses input. Bitcoin's average block generation is about 538 s per 400 blocks, while Ethereum's block generation time is approximately 4000 blocks per 1183 s, on average. As a result of the experiment, Bitcoin and Ethereum can be created using CBDC information. In the experiment, it is possible to create a stable block using a large number of nodes.  
Figure 17: Generated transaction contents in Ethereum.  
Figure 18: Block generation time in blockchains ((**A**) Bitcoin; (**B**) Ethereum).
## 2 Theoretical Background  
### Superelevation  
Curvature is a common feature in natural channels, and the flow in curved reaches is generally non-uniform and more complex to analyze than in straight channels, primarily due to the presence of acceleration along the bend [26]. Consequently, understanding and controlling flow behavior in and around channel bends often becomes necessary. In highly sinuous rivers, as water levels rise, the flow rotation in bends can become helicoidal. This secondary flow, superimposed on the downstream main flow, destabilizes the channel and creates a surface elevation difference between the inner and outer banks, known as superelevation, causing the water level on the outer bank to rise. Such increased water levels along the outer bank of a bend can lead to levee overtopping damage and additional erosion caused by secondary currents.  
In curved channels, centrifugal forces induce a rise in the water surface and produce helical secondary flows. As shown in Figure 2, this leads to flow concentration either toward the inner or outer side. The water level on the outer bank tends to rise, while that on the inner bank falls, generating a cross-sectional water surface slope. By assuming concentric streamlines and a hydrostatic pressure distribution in a curved channel, and then applying the Euler equation in the transverse direction, the water surface slope can be expressed as a function of flow velocity and radius of curvature, as given in Equation (1).  
\[\frac{dh}{dr}=\frac{V^{2}}{gr} \tag{1}\]  
where, \(h\) represents the water level, \(r\) denotes the radius of curvature, \(V\) is the mean velocity at water depth, and \(g\) is the gravitational acceleration. If the velocity and water surface slope are constant in a rectangular meandering channel, the superelevation in the bend is governed by the ratio of velocity head to channel width and radius of curvature and is given by Equation (2).  
\[\Delta h=\frac{BV^{2}}{gr_{c}} \tag{2}\]  
where \(\Delta h\) represents the water level difference between the inner and outer sides of the bend, \(B\) denotes the channel width, \(V\) is the mean velocity, and \(r_{c}\) is the radius of curvature at the channel center.  
Figure 2: Flow behavior at a channel bend [27].
## 2 Review Methodology
### Systematic Data Extraction Process  
A standardized data extraction process was implemented to systematically collect and organize relevant information from each selected study. This process involved extracting essential bibliographic details, including the authors' names, publication year, title, and source, as well as summarizing the study's objectives and research questions. Furthermore, specific AI techniques and methodologies employed within each study were documented, alongside their applications in maritime safety, such as collision avoidance, obstacle detection, and real-time decision-making. Key findings and contributions were noted to highlight the advancements and insights provided by each study. Additionally, challenges and limitations identified in the implementation of AI agents were recorded to provide a balanced perspective. Discussions on regulatory compliance and examples of case studies or practical implementations were also included to demonstrate the real-world applicability of the research. This comprehensive data extraction facilitated a thorough comparative analysis and the identification of recurring themes and gaps within the existing literature.
# Optimal Control of an Ultraviolet Water Disinfection System
## 4 Algorithm Using Hybrid Topology Particle Swarm Optimization
### Implementation of Algorithm HTPSO_UV1
#### 4.2.1 Algorithm's General Description
\begin{table}
\begin{tabular}{c c} \hline \hline
1 & **start** HTPSO\_UV1 \\
2 & Initialization: \(N\), \(n\),vmax, \(xmin\), \(xmax\), \(C_{1}min\), \(C_{1}max\), \(C_{2}min\), \(C_{2}max\), \\  & \(C_{3}min\), \(C_{3}max\), \(vmin\), \(wmax\), \(T\), \(NF\), MINNF \\
3 & Generate the initial particles’ velocities as uniformly distributed values in the interval [\(-\)vmax, vmax]\({}^{n}\). \\
4 & Generate the particles’ initial positions; \\
5 & **for**\(i\ =\ 1,\cdots,N\) \\
6 & \(P_{best_{i}}\gets X_{i}\) \\
7 & BEST(\(i\)), _fitness(\(i\))_ \(\leftarrow\)_function(\(X_{i}\))_ \\
8 & **end** \\
9 & \(i_{0}\)\(\leftarrow\)arg max {BEST(\(i\)), \(i\) =1,...,\(N\)}, \\
10 & \(P_{gbest}\gets X_{i_{0}}\); GBEST \(\leftarrow\) BEST(\(i_{0}\)); /“Determine \(P_{gbest}\) and GBEST */ \\
11 & _found_\(\gets 0\) \\
12 & \(t\gets 1\); \\
13 & **while** (\(t\)\(\leftarrow\)\(T\)) \& (found\) = 0) \\
14 & Coefficients tuning: \(w\), \(C_{1}\), \(C_{2}\), \(C_{3}\) \\
15 & **for**\(i\ =\ 1,\cdots,N/\)* move the particles swarm\({}^{\star}\)/ \\
16 & \(P_{best_{i}}\gets Generate\_Plbest(i)\); /* generate “local best”*/ \\
17 & **for**\(d\ =\ 1,\cdots,n\) \\
18 & Update the particles’ speed using Equation (20) \\
19 & _Speed_limitation(\(t^{d}_{i}\ (t\ +\ 1))\) \\
20 & \(x_{i}^{d}(t+1)\ =\ x_{i}^{d}(t)\ +\ v_{i}^{d}(t+1)\) \\
21 & _Position_limitation_ (\(x_{i}^{d}\ (t\ +\ 1)\)) /*using _xmin_, _xmax_ */ \\
22 & **end** \\
23 & _fitness(\(i\))_ \(\leftarrow\)_function(\(X_{i}\))_ \\
24 & **if**_fitness(\(i\))_ \(<\) BEST(\(i\))_ \\
25 & \(P_{best_{i}}\gets X_{i}\); BEST(\(i\))_ \(\leftarrow\)_fitness(\(i\)); \(NF(i\)) = 0; \\
26 & **else**\(NF(i)\) = \(NF(i)\) + 1; \\
27 & **end** \\
28 & **if**_fitness(\(i\))_ \(<\) GBEST(\(i\))_ \\
29 & \(P_{gbest}\gets X_{i}\); GBEST \(\leftarrow\)_fitness(\(i\))_ \\
30 & **end** \\
31 & **if**_min_ {NF(i), \(i\) = 1,...,\(N\)} = _MINNF_ **then**_found_\(\gets 1\); \\
32 & \(t\gets t+1\) \\
33 & **end**/*while */ \\
34 & **return** Pgbest \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of algorithm HTPSO_UV1. value _NF_. (_i_) is associated with particle #_i_ and counts the number of steps without improving the best personal experience (line #26). When all the particles have these values greater than the constant _MINNF_, one can assert the algorithm convergence (see line #31).
## 2 Acoustic Metasurfaces  
Acoustic Metasurfaces are two-dimensional engineered structures that manipulate sound waves by controlling their phase, amplitude, or spatial distribution on a planar surface [36]. Due to their subwavelength thickness, these structures offer advantages in terms of lightweight design and ease of integration. In this study, soft acoustic metasurfaces are categorized based on their sound manipulation mechanism and functional characteristics into five types: resonance-based, phase-modulating, amplitude-modulating, sound insulation or active, and multifunctional systems, as summarized in Table 2. This classification reflects differences in both the underlying physical mechanisms and the degrees of responsiveness and tunability exhibited by each system.  
As shown in Table 2, tunability is not inherently determined by whether a system is categorized as active or passive. Passive systems can exhibit tunable acoustic properties through material nonlinearities or geometrical reconfiguration, whereas some active systems may operate with fixed acoustic responses despite external actuation. This indicates that tunability should be considered an independent functional dimension rather than a subset of actuation type. Likewise, multifunctional capabilities, which involve the simultaneous realization of distinct acoustic functionalities, can be achieved through both passive and active strategies depending on system design. These considerations support the need for a classification scheme that separates actuation mechanisms from functional properties such as tunability and multifunctionality.  
In addition to these functional classifications, Table 3 presents a comparison of representative acoustic metasurface systems in terms of their absorption coefficient, structural thickness, and resonance frequency, highlighting the trade-offs between acoustic performance and physical compactness.  
Figure 2: Systematic Categorization of SAMs by Geometry, Mechanism, and Response Characteristics.