esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
## 3 Results and Discussion
### Tissue Attenuation  
This study investigates how bowel sound signals propagate within the human abdominal cavity, emphasizing the attenuation of sound waves by the muscle, fat, and skin layers, influenced by the density and sound velocity of each tissue [33]. The propagation of sound waves in different tissues is significantly affected by the density and speed of sound of each tissue, with denser or lower speed sound tissues usually leading to stronger signal attenuation. To analyze this phenomenon in depth, this study quantitatively assessed the signal attenuation by collecting bowel sound waveform data from different tissue surfaces.  
As shown in Figure 4, the amplitude strength of the signals gradually weakened as the propagation distance of the bowel sound signals increased and as they passed through different tissue layers. This attenuation trend indicates that the absorption and scattering of sound waves by various tissue layers during propagation cannot be ignored. The results showed that muscle had the highest attenuation at \(-29.35\) dB, followed by skin at \(-22.77\) dB, and fat, which exhibited the least attenuation at \(-16.48\) dB. These findings are consistent with those reported in the literature [34] and provide further evidence of the differential impact of various tissues on sound wave propagation. This pattern of attenuation underscores the varying degrees to which different tissues influence signal transmission, with muscle presenting the most significant impact, followed by skin, and fat having the least effect.  
Figure 4: IWC on tissues surfaces.  
Figure 3: Analog IWC vs. real IWC.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
###### Abstract  
The frequent occurrence of marine oil spills underscores the need for efficient methods to identify spilled substances and analyze their thickness. Traditional models based on Laser-Induced Fluorescence (LIF) technology often focus on a single functionality, limiting their ability to simultaneously perform qualitative and quantitative analyses. This study introduces a novel LIF-based spectral analysis method that integrates a self-designed detection system and a multi-task framework, the Wavelet CNN-sLSTM-KAN-Enhanced Transformer (WaveConv-sLSTM-KET). By combining a Wavelet Transform CNN block, a scalar LSTM block, and a Kolmogorov-Arnold Network-Enhanced Transformer block, the framework enables simultaneous oil-type identification and thickness prediction without preprocessing or fully connected layers. It achieves high classification accuracy and precise regression for oil film thicknesses (50 \(\upmu\)m-0.5 mm). Its reliability, real-time operation, and lightweight structure address limitations of conventional methods, offering a promising solution for non-destructive, efficient oil spill detection.  
oil spill detection; laser-induced fluorescence; qualitative and quantitative analyses; wavelet CNN-sLSTM-KAN-enhanced transformer; lightweight structure +
Footnote †: journal: Applied sciences  
Article
# A High Dynamic Weak Spread Spectrum Signal Acquisition Strategy Based on Iterative Local Search
## 3 ILS Acquisition Strategy
### Acquisition Strategy Based on ILS  
ILS is a strategy that is commonly used in the field of artificial intelligence to solve optimization problems. For many complex problems, the cost of directly solving the optimal solution is too high; ILS selects the next-best solution to find a sub-optimal or approximately optimal solution. ILS is used in many fields: the financial field [13], signal processing [14], scheduling problems [15], graph partitioning problems [16]. The basic idea is shown in Figure 3. In this approach, the search starts with an initial solution \(x\), and the neighbor solution \(x^{*}\) of the initial solution is generated by the neighborhood action.  
According to a specific strategy, the best neighbor \(x^{\prime}\) is then selected from the neighborhood solution space of the current solution as the initial solution of the next iteration, and the new neighbor solution \(x^{\prime*}\) is obtained. This process is iterated until a local optimal solution is obtained [17].  
The acquisition of high dynamic weak signals is essentially a process of the parameter estimation of the received spread spectrum signal. Owing to the constraints of a high dynamic and low SNR environment, the resource requirements are too high to perform signal parameter estimation based on only one time acquisition. The strategy utilized in ILS was adopted to improve the traditional algorithm, and each acquisition is mapped to a single search. The general principle is shown in Figure 4. In the process of acquisition, the zero-valued element in the time and frequency domains is taken as the initial solution \(x\), based on a wide range of coarse time and frequency domain searches. A set of the code phase and Doppler frequency is obtained as the neighbor solution \(x^{*}\). This information is then utilized to obtain the initial solution \(x^{\prime}\) of the next iteration, thus narrowing the scope of the two-dimensional search. As the accuracy of the search improves, a set of the code phase and Doppler frequency is obtained for the new neighbor solution \(x^{\prime*}\). An iteration process is then repeated until the search area sufficiently converges.  
Figure 4: Schematic diagram of the ILS acquisition strategy (The blue unit is the initial solution of the iterative search; the red unit is the neighbor solution of the iterative search; the orange frame is the scope of the iterative search. \(f_{est}\) is the Doppler frequency estimated by the transfer model. \(\tau_{est}\) is the code phase estimated by the transfer model. \(f_{unc}\) is the Doppler frequency search range. \(\tau_{unc}\) is the code phase search range. \(f_{bin}\) is the Doppler frequency search step.).  
Figure 3: Basic diagram of the ILS.
# Interclass Interference Suppression in Multi-Class Problems
## 5 Experiments
### Performance of the All-Features-Based Approach
Table 7 shows the number of attributes obtained by the conventional method, the all-features-based method and the OVO method when dealing with multi-class problems. It is observed that the number of attributes used by the all-features-based method is significantly higher than that of the conventional method. Although the number of attributes obtained by the OVO method is also larger than by the conventional algorithm, it uses much fewer attributes than all-features-based method. This shows that the OVO method can not only effectively suppress the interclass interference of multi-class problems but also delete redundant features and avoid the additional cost caused by these features. Therefore, when dealing with multi-class problems, the OVO method is a better choice to suppress interclass inference than the all-features-based method.
## 4 Experimental Results and Discussion
### Experimental Results and Analyses
#### 4.4.1 The Performance of the NER Model
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Models** & **P** & **R** & **F1** \\ \hline
**Bi-LSTM-CRF**\({}^{\text{1}}\) & & & \\ with random embedding & **91.42\%** & **91.29\%** & **91.35\%** \\ with pre-trained embedding \({}^{\text{2}}\) & **92.20\%** & **92.12\%** & **92.16\%** \\ with SoftLexicon & **93.31\%** & **93.21\%** & **93.26\%** \\
**LGN**[49] & **92.22\%** & **92.13\%** & **92.17\%** \\
**Bi-LSTM-CNNs-CRF**[50] & **92.22\%** & **92.79\%** & **92.46\%** \\
**Lattice-LSTM** & **93.07\%** & **92.96\%** & **93.02\%** \\ \hline
**BERT** & & & \\ +Bi-LSTM-Softmax & **96.51\%** & **96.66\%** & **96.58\%** \\ +Bi-LSTM-CRF & **96.80\%** & **96.89\%** & **96.84\%** \\ +Bi-LSTM-CRF (SoftLexicon) & **97.01\%** & **97.10\%** & **97.06\%** \\ \hline \hline \end{tabular} \({}^{\text{1}}\) For the Bi-LSTM-CRF model, the words following “with” represent the method to generate embedding. The term “random embedding” means that the model uses randomly generated embedding. The term “pre-trained embedding” means that the model uses the pre-trained word embedding lookup table to obtain embedding. “SoftLexicon” means that the model uses the embedding generated by the SoftLexicon method. \({}^{\text{2}}\) The pre-trained word embedding is trained over automatically segmented Chinese Giga-Word using the Word2 vec method [11; 44]. It can be obtained from the website [51].  
\end{table} 
Table 5: Experimental results of nine different models on the NLPCC2016 dataset. The upper part is the model without using BERT, and the lower part is the model using BERT. The result of all models is from our experiments. high-frequency specialist vocabulary obtained from original data collected by the Hebei Electric Power Company, such as \"pms2.0\", \"\" \" (blackout application form), \" \" (operation ticket) and so on to expand the lexicon. The results of the compared experiment are shown in Table 6.  
According to the experiment results, the model using the expanded lexicon achieves better performance on all three evaluation metrics. Some specialized words can be correctly identified. The comparison between the SoftLexicon+Bi-LSTM-CRF model and other models is shown in Table 7.  
The experimental results show that in some specific fields, such as the power grid, the model combining lattice structure and lexicon features (SoftLexicon, Lattice--LSTM and LGN) has significantly better performance than other models. The application of the BERT model has greatly improved the performance of this kind of model. Among these models, the SoftLexicon+Bi-LSTM-CRF (Expand Lexicon) model achieves the best performance. Therefore, it proves that expanding the matching lexicon used by the SoftLexicon method according to the application domain can further improve the performance of the model. In addition, the SoftLexicon+Bi-LSTM-CRF model can achieve excellent performance on NER tasks related to the power grid domain. This model can be applied to the construction of grid-related intelligent customer service systems.
## 2 Literature Review
### [PERSON]'s Conflict Analysis Model  
The conflict analysis model introduced by [PERSON] is based on rough set theory [3]. The Middle East conflict analysis problem is shown in Table 1, where the agents are represented by rows and the issues by columns. Each agent is assigned a value to an issue from the set \(\left\{-,0,+\right\},\) where \(-\) shows that the agent is against that particular issue, \(0\) shows the neutrality of the agent, and \(+\) depicts the favorability of the agent towards the issue/event. Furthermore, [PERSON] discussed the Middle East conflict analysis problem in Reference [3]. This conflict analysis problem consists of five issues and six agents. The relationship of each agent to an issue is given in Table 1.  
Let \(U=\left\{u_{1},u_{2},u_{3},u_{4},u_{5},u_{6}\right\}\) be the universe of six agents, where  
\[\begin{array}{ll}u_{1}:\text{Israel}&u_{2}:\text{Egypt}&u_{3}:\text{Palestine}\\ u_{4}:\text{Jordan}&u_{5}:\text{Syria}&u_{6}:\text{Saudi Arabia}.\end{array}\]  
Let \(V=\left\{a_{1},a_{2},a_{3},a_{4},a_{5}\right\}\) be the universe of the five issues of the conflict situation, where  
\[\begin{array}{ll}a_{1}:\text{Autonomous Palestinian state on the West Bank and Gaza,}\\ a_{2}:\text{Israeli military outpost along the Jordan River,}\\ a_{3}:\text{Israel retains East Jerusalem,}\\ a_{4}:\text{Israeli military outposts on the Golan Heights, and}\\ a_{5}:\text{Arab countries grant citizenship to Palestinians who choose to remain within their boarders.}\end{array}\]In the information system, the attitudes of the six nations of the Middle East region towards the above issues are presented.  
### [PERSON]'s Conflict Analysis Model  
The conflict analysis of Reference [3] is confined to the conclusions, such as to find the most conflicted attribute or the assembly of agents when three or more take part in the conflict. Although [PERSON]'s conflict model has proven to be effective in practice, [PERSON] in Reference [2], posed three questions that [PERSON]'s conflict model is unable to answer:  
1. _What are the intrinsic reasons for the conflict?_
2. _How can a feasible consensus strategy be found?_
3. _Is it possible to satisfy all the agents?_
## 2 Methodology
### Scattered Decision-Level Fusion
#### 2.2.3 Fusion of Partial Densities  
In Equation (3) a fusion rule \(F\) is introduced that combines the partial densities. The most basic fusion rule is to sum up the individual densities, which in effect computes the total kernel density according to Equation (1). However, this approach has a major drawback concerning false alarms. High-intensity single sensor contributions have a large effect on sums, even when such indications are not backed up by other sensors. As an extreme example, the _maximum_ function is most prone to false alarms. Therefore, more conservative rules are required to capture the agreement among sensors for effective reduction of false alarms. The following fusion rule is conceptually similar to the _sum_ rule as used in conventional density estimation, but unlike the _sum_ it guards against single-sensor false alarms:  
\[F_{sumIgnoreMax}\left(\left\{\hat{f}_{S_{1}}\left(\mathbf{p}\right),\ldots,\hat{f} _{S_{N}}\left(\mathbf{p}\right)\right\}\right)=\left(\sum_{S\in\left\{S_{1},\ldots S _{N}\right\}}\hat{f}_{S}\left(\mathbf{p}\right)\right)-\underset{S\in\left\{S_{1}, \ldots S_{N}\right\}}{max}\hat{f}_{S}\left(\mathbf{p}\right) \tag{7}\]  
Note that the maximum is evaluated separately for each evaluation point \(\mathbf{p}\). Equation (7) realizes the quantification of agreement among sensors, because a large fused score now requires at least two sensors to produce high individual densities. Thus, \(\hat{f}\) is expected to behave similarly to the function indicated by the shaded area in Figure 1. Note that in the case of only two available sensors for fusion, subtracting the maximum is equal to the _minimum_ fusion rule, which is a fuzzy _AND_-operator, and is in fact the operation used to generate the shaded area in the figure. However, as more than two sensors are involved, requiring that all sensors indicate a defect might be too strict for some applications. Therefore, ignoring the maximum contribution can be viewed as a much milder version of the _AND_ fusion rule. Other fusion rules that conceptually differ more from conventional density estimation, but also suit the quantification of agreement, are the _median_, _harmonic mean_, _geometric mean_ and the _product_. An example of a more sophisticated rule is presented in [15], where the authors developed a Bayesian fusion approach that explicitly models inconsistencies among sensors, such as false alarms. However, we focus on simple algebraic fusion rules in this study to avoid introducing additional parameters by more complex methods.  
In total, our fusion approach includes three mechanisms to ensure robustness against false alarms: quantification of density, decision weighting according to significance, and a fusion rule that expresses the agreement among individual sensors.
## 5 Designs of TDMA Protocol-Based Distributed Coalition Formation Games
### Neighbor Discovery Protocol  
The neighbor discovery protocol is designed to obtain the neighbor list within a three-hop range. Every UAV node broadcasts a neighbor discovery message (NDM) to its one-hop neighbors in their pre-allocated control slots. The neighbor discovery message includes its neighbor list within a two-hop range. UAV nodes exchange their two-hop neighbor lists after sending and receiving NDMs. Then, the nodes which are included in the neighbor's NDM can be increased to the neighbor list within a three-hop range. In the DCF slot allocation scheme, the join-and-leave rule is operated based on the neighbor list within a three-hop range. We make an approximation assumption that the three-hop range  
Figure 4: Time frame structure.  
is longer than \(d_{C}+d_{I}\), and the neighbors out of the three-hop range can share the same sending slot of the center UAV node.
## 3 Results and Discussions
### Displacements
#### 3.3.1 PS-InSAR Displacements
The use of the standard PS-InSAR technique (full-graph MBC method) leads to the LOS dam displacements (\(d_{\text{LOS}}\)) reported in Figure 9. Due to the differences between the direction of the ascending and descending tracks with the direction orthogonal to the dam (43\({}^{\circ}\) and 23\({}^{\circ}\), respectively), the relative contribution of the orbits to \(D_{\text{TOT}}\) is greater for the ascending track than for the descending one. The temporal variability of \(D_{\text{TOT}}\) in 2015 using a full graph MBC method (Figure 9, red dots) shows displacements increasing in the springer period to reach a platoon between DOYs 221 and 269. The standard deviation of the differences between measured and interpolated values was \(\sigma_{\text{XY}}\approx\) 1.18 mm. The maximum value (\(\approx\) 4.5 mm) indicated by the sinusoidal fitting is on DOY 241.
## 4 Electron Transfer-Based Assays
### Dpph  
#### 4.3.1 Operating Principle  
The development of the DPPH assay dates back to the XX century [70]. Since then, this method has undergone several modifications aimed at improving its stability and reproducibility. Solvents used in the procedure, such as methanol, can influence the reaction mechanism by affecting hydrogen atom release, often favoring SET over HAT [26].  
The DPPH assay measures the reduction of the \(\alpha\)-diphenyl-\(\beta\)-picrylhydrazyl radical by antioxidants. This radical solution has a deep violet color, which serves as the reference in spectrophotometric measurements [71]. Upon reduction, its color intensity decreases. As a result, an inverse relationship is observed between absorbance and antioxidant concentration. The extent of decolorization is stoichiometrically related to the number of electrons transferred during the reaction. However, the absorbance of the working DPPH solution should not exceed 0.8 \(\pm\) 0.02, in accordance with the Lambert-Beer law [70]. The reaction involved in this method was presented in Figure 2 [42].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tested Material** & **Standard** & **Substrates** & **Type and Time** & **Wavelength** & **Source** \\ \hline Synthetic & & & 1 mL of aqueous solution of CuCl\({}_{2}  
Despite involving relatively stable free radicals, the absorbance of both the stock and working DPPH solutions is not constant, even when stored at \(-70\)\({}^{\circ}\)C. Therefore, a fresh working solution must be prepared prior to each analysis to ensure reliable results. This is essential for constructing an accurate calibration curve, which must be based on the same working solution used for sample analysis [71]. All solutions should be protected from light by storing them in the dark and using amber flasks or tubes during analysis. This precaution is necessary because the DPPH radical is highly sensitive to light and unstable under such conditions [72]. To date, no optimal storage conditions have been identified that would ensure long-term stability of the DPPH solution [73].  
A major limitation of the DPPH assay is the absence of a standardized unit of measurement. Results are commonly reported as a percentage of antioxidant activity, as Trolox equivalents, or as IC\({}_{50}\) (half maximal inhibitory concentration) [71]. These variations make it difficult to compare results across different studies. Moreover, expressing results as a percentage of antioxidant activity fails to account for the initial concentration of the DPPH solution, which significantly affects the outcome. The DPPH radical is also pH-sensitive, with the optimal range for reproducible results being between 5.0 and 6.5 [74]. Another limitation is that DPPH is an artificial and non-physiological radical, so the reactions it undergoes do not reflect those occurring in the human body, where antioxidants exert their biological effects [42]. Furthermore, DPPH has limited biological relevance because of its low operating pH (-5) and structural dissimilarity to naturally occurring reactive free radicals [73].
## 1 Introduction  
The current policy of the European Union in terms of energy efficiency is described in the European Directive (EU) 2023/1791, which imposes a series of measures, including the most accurate assessment of consumption in the context of the use of sustainable sources of electricity production and considering the presence of consumers that can alter the quality of electricity. However, in order to keep the costs low, the use of high-performance equipment is not feasible, due to the very large number of consumers. Considering the above aspects, in the present article, a method of reducing the measurement errors of digital measuring equipment (power meters) and an evaluation of the performance of the proposed algorithm are reported. The method consists in determining the values of voltage and current at the same moment. In most cases, a multiplexer is used at the input of a data acquisition device, introducing an offset between the acquired quantities. The estimation of intermediate values is the objective of this article. In specialized literature, such a method is not currently described, although it is largely used. Different approaches are possible, but in this article, the most intuitive and clear method is presented [1, 2].  
Critical real-time data acquisition and control is an important issue in data acquisition systems. Applications that require real-time control are better suited to external systems than to systems based on plug-in PC boards. For example, a PC system with the MSWindows operating system and an old model computer was used, and considering that case, the subject of this article can be more easily followed. Although Microsoft Windows has become the standard operating system for PC applications, it is an indeterminate operating system in terms of the time required in measurement and control applications [3].  
A \"plug-in\" board and the LabVIEW programming environment were used in this study. LabVIEW stands out from the usual programming languages thanks to its graphical programming language environment (\"G\" programming), with all the necessary tools for data acquisition, signal analysis and their presentation. The board used, AT-MIO-16, is a National Instruments manufacturing board (1992), hardware- as well as software-configurable, which has as the main characteristics of 16 analog inputs, two 12-bit DACs with analog voltage outputs, 8 lines TTL-compatible digital signal I/O, and three 16-bit counter/clock channels, provided with RTSI bus (Real-Time System Integration), a system that allows for the synchronization of multiple measurements acquired by means of other AT-type acquisition boards linked on the same highway. This board was chosen to highlight the errors that affect the process of converting analog quantities and use them to determine other quantities [3; 4; 5].
## 2 App and Tools for the Collection and Elaboration of Raw GNSS Measurements  
### App for the Collection of Raw GNSS Measurements  
Over time, several smartphone applications have been developed to collect GNSS raw measurements from smartphones. During preliminary studies, different apps, such as GEO++ Rinex Logger, Rinex ON, Gadip3 and GalileoPVT, were tested, without observing any difference in the data collected; therefore, we chose to use the GEO++ Rinex Logger for data acquisition, as it is the easiest to configure and manage during the acquisition process. Geo++ Rinex Logger, an app developed by the German company Geo++ GmbH, records the raw GNSS measurements of the smartphone in a Rinex format file. The app supports data acquisition from GPS, GLONASS, GALILEO, BDS, QZSS satellites for L1, L5, E1B, E1C, E5A frequencies. The generated observable file includes carrier phase measurements, pseudorange measurements, accumulated delta intervals, Doppler frequencies and noise values. The files produced with Geo ++ Rinex Logger can be easily analysed, without further steps, in post-processing software, even those that are open-source [16].