esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Change in Concrete Composition during Leaching Experiments  
Analyzing the changes in the composition of the synthetic water after the experiments on leaching concrete, increases in the concentrations of sodium, potassium, aluminum, and silicon, and decreases in the contents of magnesium, calcium, chlorine, and sulfur are clearly noticeable (Table 2).  
As a result of interaction with the concrete for 30 days, the pH of the solution increased from 6.11 to 12.1.  
According to the results of the analysis of the chemical composition of the solid component (Table 3) in the samples after leaching, decreases in the contents of sodium and potassium, and slight increases in aluminum, silicon, magnesium, and iron were noted.  
The phase composition is presented in Table 4. Before the experiment, the composition of the concretes was dominated by phases diagnosed by a series of reflections--vaterite, calcite, portlandite, belite, larnite, chabasite, magnetite, quartz, alite, gypsum, gismondite, dolomite, \(\alpha\)-C\({}_{2}\)SH, alunite, wustite, and others. After the leaching of the concrete samples, there was a decrease in the number of the main phases, namely vaterite, portlandite, and gypsum, while the contents of \(\alpha\)-C\({}_{2}\)SH, alite, and belite increased. The appearances of afwillite and nordstrandite, which were not previously present in the sample, were also noted. In Figure 4, the main reflections are marked and the phases related to these reflections are labeled. The dissolution of phases was also well diagnosed by the data of the scanning electron microscopy, illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Na\({}^{\star}\)** & **Al3\({}^{\star}\)** & **Si4\({}^{\star\star}\)** & **K\({}^{\star}\)** & **Mg2\({}^{\star}\)** & **Ca2\({}^{\star}\)** & **Cl** & **SO4\({}^{\text{2}}\)** & **pH** \\ \hline Synthetic water & 8.87 & - & - & 1.15 & 4.95 & 12.2 & 25.6 & 4.95 & 6.1 \\ Solution after leaching & 67.8 & 0.01938 & 0.3877 & 119.82 & 0.000411 & 0.8034 & - & - & 12.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The composition of the synthetic water before and after concrete leaching (10\({}^{-4}\), mol/L).  
Figure 4: X-ray diffraction patterns of the concrete samples before and after experiments.
## 2 Problem Formulation and Preliminaries
### Auto-Covariance Least-Squares (ALS) Method
The steady-state solution \(P_{\varepsilon}\) to the Lyapunov equation in (7) exists and the innovation sequence is a stationary process if the prediction error transitional matrix \(\hat{F}\) is stable. \[P_{\varepsilon}=F(P_{\varepsilon}-P_{\varepsilon}H^{\text{T}}(HP_{\varepsilon} H^{\text{T}}+R_{A})^{-1}HP_{\varepsilon})+Q_{A} \tag{14}\]  
where \(Q_{A}\) and \(R_{A}\) are positive user-defined matrices.
## 1 Introduction  
Biomimetic chemistry can help in the further development of the medical field [1]. Artificial soft materials such as gels and nanoparticles are essential and have been applied in medical and pharmaceutical chemistry. For example, hydrogels [2; 3] have been used in artificial joints, corneas, and stents. In addition, functional nanoparticles that can accumulate at a specific target site and release therapeutic agents have become essential tools in drug delivery systems (DDS) [4; 5; 6]. Because of their so-called \"enhanced permeability and retention (EPR)\" effect, that is, the ability to reach the interstitial spaces (10-500 nm) of tumor tissues [7; 8; 9], over the past three decades, nanoparticles have emerged as attractive DDS for anticancer drugs. Moreover, the acidic microenvironment of tumor cells, due to the Warburg effect [10; 11], facilitates the accumulation of nanoparticles in the target tissue. We have previously reported the self-assembly behavior of urea benzene derivatives (UBD) in aqueous solution to form nanoparticles [12], which can undergo an additional heat-induced self-assembly to produce microparticles. This is accompanied by a lower critical solution temperature (LCST) behavior, resulting in a turbid solution. In addition, **UBD** have been used in our laboratory for the development of functional materials; namely, radical nanoparticles have been employed for metal-free magnetic resonance imaging contrast agents [13], and push-pull-type, aminoquinoline-based fluorescent probes have been constructed for bioimaging applications [14; 15; 16; 17; 18]. Among the latter, we have successfully developed a tumor-imaging probe activated by heat treatment [19].  
In this study, three **UBD** (**UBD-1-3**) with different alkyl chain lengths (hexyl, heptyl, and octyl, respectively) were prepared to investigate the self-assembly behavior in aqueous solution [20,21]. Although nanoparticle formation was observed in all cases, the existence of a critical aggregation concentration (CAC) was indicative of the effect of the alkyl chain length. Herein, we describe the relationship between the self-assembly behavior and the alkyl chain length of **UBD-1-3**.
## 4 Validation of Water Body Temperature Field Calculation Model and Analysis of Test Results
### Spatiotemporal Evolution of Vertical Profile Temperature in Water Bodies
Analysis suggests that the absorption rate of solar radiation in the upper water layers is underestimated in the simulation. In real-world scenarios, the attenuation coefficient of solar radiation in water is strongly influenced by water transparency and quality, determined by factors such as water color and turbidity. However, the computational model assumes an optically uniform water body and does not account for the effects of transparency and water quality. In the lower water layers, the errors primarily stem from the vertical diffusion coefficient. Under varying water depths and wind speeds, the intensity of temperature mixing between layers differs, and the simulation tends to overestimate vertical diffusion in the lower layers. For Day 2, Figure 8b reveals a significant discrepancy between the simulated and measured results around 1400. The measured data show a sharp transition in the temperature profile at this time. According to the solar radiation and wind speed observations, the solar radiation intensity peaked at this moment, while the wind speed remained around 1.5 m/s, showing no significant variation compared to adjacent time periods. Therefore, it is hypothesized that an abnormal fluid vortex disturbance, such as transient turbulence caused by biological activity, may have occurred at a depth of 0.2 m in the measured scenario, interfering with the vertical temperature profile measurement at that time.
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 3 Performance Degradation Trend Analysis and Fault Early Warning of Marine Diesel Main Engines  
### Data Processing  
#### 3.1.1 Data Sources  
The monitoring data were collected from a 6 RT-flex82T marine diesel main engine (Wartsila, Helsinki, Finland) installed on a commercial vessel, comprising 43,186 performance samples spanning 9 June 2020 06:20 to 14 May 2021 08:10. Key technical specifications of the engine are detailed in Table 1.  
The monitoring parameters were collected and stored using a dedicated data acquisition system with sampling intervals of 1 s, 1 min, 10 min, and 1 h. Each recorded sample includes timestamps, data types, parameter values, and parameter classifications. As shown in Figure 1, the 10 min interval dataset spanning 16 June 2020 05:30 to 23 June 2020 04:00 exhibited significant anomalies, necessitating preprocessing prior to analysis.  
\begin{table}
\begin{tabular}{c c} \hline
**Technical Parameter** & **Parameter Value** \\ \hline Number of Cylinders & 6 \\ Bore Diameter/mm & 820 \\ Stroke/mm & 3375 \\ Firing Order & 1-5-3-4-2-6 \\ Rated Power/kW & 25,200 \\ Rated Speed/(r/min) & 76 \\ Rated Specific Fuel Consumption (g/kW\(\cdot\)h) & 176.7 \\ Control Modes & Bridge Control-ECR Control-Local Control \\ Service Power & 22,680 \\ Service Speed (r/min) & 72 \\ Service Fuel Consumption (g/kW\(\cdot\)h) & 200.02 \\ Critical Speed Range (r/min) & 34–45 \\ Number of Turbochargers & 2 \\ Engine Type & Vertical Two-Stroke \\ \hline \end{tabular}
\end{table}
Table 1: Technical parameters of the 6 RT-flex82T host.  
Figure 1: _Cont._Figure 1: Variation curves of certain monitoring parameters (monitoring curve subplot).
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
###### Abstract  
Traditional six-axis force sensors are mostly based on resistance strain, piezoelectricity and capacitors, which have poor resistance to electromagnetic interference. In this paper, a six-axis force sensor based on bending-sensitive optical fibers is proposed. A 3-UPU-(universal joint-prismatic joint-universal joint) compliant parallel mechanism is adopted in the sensor. The bending-sensitive optical fiber is encapsulated to form a fiber encapsulation module (FEM). The configuration of the FEMs within the six-axis force sensor is investigated. Static and stiffness analyses of the sensor are conducted and a force mapping matrix for the sensor is established. Simulation experiments are performed to verify the correctness of the established force mapping matrix. The detection system of the sensor is fabricated and the experiments are carried out to evaluate the performance of the sensor. The experiment results show that the maximum values of type-I errors and type-II errors are 4.52%PS and 3.26%FS, respectively. The maximum hysteresis and repeatability errors are 2.78% and 3.27%. These results verify the effectiveness of the proposed sensor.  
six-axis force detection; bending-sensitive optical fiber; static analysis; stiffness analysis; mapping matrix +
Footnote †: journal: Applied Sciences
## 3 Application Scenario and System Model
### System Model
#### 3.2.1 Operation Area  
The considered scenario is composed by a rectangular area in which each element (threats and sensors) is identified by its Cartesian coordinates, \(x\) and \(y\). The area may be subdivided in sub-areas, consisting of a sub-set of points in the area, which may have properties assigned to them, such as weather conditions for a specific sub-area.
## 3 Physiochemical Determinants in _L. pneumoniae_ Biofilm Formation and Colonization
Notably, the increase in biofilm production due to organic-carbon has only been reported at 20 \({}^{\circ}\)C, suggesting that carbon may only influence biofilm production at certain temperatures [47]. Temperature is also an important determinant for _L. pneumophila_ biofilm colonization. Studies have shown that heating water above 55 \({}^{\circ}\)C can reduce the detectable amount of _L. pneumophila_ in water systems, even in the presence of organic carbon sources, however this may be due to a decrease in other biofilm species which may serve as a platform for _L. pneumophila_ colonization [48, 49]. Static and flow conditions of water play important roles in the biofilm formation and biofilm colonization with _L. pneumophila_ in water systems. Stagnation of water in distribution systems seems to favour colonization with _L. pneumophila_[50]. Moreover, Legionnaires' disease cases have been linked to stagnant water in hospital settings [51]. In accordance with these data, a constant flow in anthropogenic water can decrease the presence of _L. pneumophila_ through the use of Venturi systems by preventing the attachment of the bacteria to surfaces [52]. However, biofilms in aquatic environments can persist under turbulent flow conditions [53, 54] and maintain a population of _L. pneumophila_[55]. To explain the persistence of _L. pneumophila_ under turbulent flow, it was proposed that the bacteria can localize to the sediment where it is less affected by turbulence [56, 57]. The settling of _L. pneumophila_ in environmental sediments, which was recently linked to quorum sensing, will be discussed below [58, 59]. Although the majority of _L. pneumophila_ biofilm formation/colonization research has been focused on determining the physiochemical parameters that allow _L. pneumophila_ to colonize and form biofilms, little is known regarding the _L. pneumophila_ molecular factors that contribute directly to this process. The _Legionella_ collagen-like protein (Lcl) was initially identified as an adhesin required for infection of protozoa and macrophages [60]. Subsequently, Lcl was found to be an important mediator of _L. pneumophila_ biofilm formation [61]. Lcl facilitates biofilm production by promoting attachment to abiotic substrates as well as cell-cell/cell-matrix interactions [62]. Type IV pili was also implicated in _L. pneumophila_ biofilm colonization based in its role in adherence to protozoan cells [63]. However, a site directed type IV pili mutant was shown to colonize biofilms of other organisms as well as wild-type bacteria [64]. In addition to surface exposed adhesions, the twin arginine transport (Tat) secretion system has also been implicated in biofilm formation. Deletion of the _tatB_ and _tatC_ genes resulted in a significant reduction in biofilm formation, however the specific role that this secretion system plays is unknown [65].
###### Abstract  
Peatlands, which account for approximately 15% of land surface across the arctic and boreal regions of the globe, are experiencing a range of ecological impacts as a result of climate change. Factors that include altered hydrology resulting from drought and permafrost thaw, rising temperatures, and elevated levels of atmospheric carbon dioxide have been shown to cause plant community compositional changes. Shifts in plant composition affect the productivity, species diversity, and carbon cycling of peatlands. We used hyperspectral remote sensing to characterize the response of boreal peatland plant composition and species diversity to warming, hydrologic change, and elevated CO\({}_{2}\). Hyperspectral remote sensing techniques offer the ability to complete landscape-scale analyses of ecological responses to climate disturbance when paired with plot-level measurements that link ecosystem biophysical properties with spectral reflectance signatures. Working within two large ecosystem manipulation experiments, we examined climate controls on composition and diversity in two types of common boreal peatlands: a nutrient rich fen located at the Alaska Peatland Experiment (APEX) in central Alaska, and an ombrotrophic bog located in northern Minnesota at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. We found a strong effect of plant functional cover on spectral reflectance characteristics. We also found a positive relationship between species diversity and spectral variation at the APEX field site, which is consistent with other recently published findings. Based on the results of our field study, we performed a supervised land cover classification analysis on an aerial hyperspectral dataset to map peatland plant functional types (PFTs) across an area encompassing a range of different plant communities. Our results underscore recent advances in the application of remote sensing measurements to ecological research, particularly in far northern ecosystems.  
hyperspectral; remote sensing; peatlands; boreal; arctic; species diversity; land cover
# Analysis of Low Probability of Detection Capability for Chaotic Standard Map-Based FH-OFDMA System
## 1 Introduction
Although it is very difficult to detect authentic OFDMA signals using spectrum sensing, many studies related to cognitive radio techniques have helped the spectrum sensing domain [14]. Based on spectrum sensing, the authors of Reference [15] propose an FH pattern based on SBS hopping strategy for LPD capability, and the authors analyze detection probability in frequency-hopped wireless communications. Up until very recently, many authors have proposed the signal sensing methods to obtain FH signals based on cross-correlation [16] and deep learning [17]. These detection methods are only focused on the frequency-hopped system, but do not consider an OFDM system constructed by multiple subcarriers. For the OFDM system, an auto-correlation-based spectrum sensing technique is proposed to analyze the detection probability in Reference [18]. Although the auto-correlation-based OFDM signal sensing technique does not require knowledge of the noise variance, the sensing technique for the FH-OFDMA system has different conditions for detection probabilities because of regenerated hopping sequence. We assume that the detector exploits the auto-correlation-based spectrum sensing technique with hypothesis testing to detect the FH-OFDMA signal. We analyze detection probability of the CSM-based FH-OFDMA system and provide detection probabilities of the cat map and SBS-based FH-OFDMA systems compared to the CSM. Based on our analysis of the detection probability, it is evident that the CSM provides a higher LPD capability than both the cat map and SBS-based systems. The rest of this paper is organized as follows: In Section 2, our CSM and cat map-based FH patterns proposed in Reference [13] are introduced, along with the SBS-based FH pattern for FH-OFDMA systems. In Section 3, we analyze the detection probability based on auto-correlation properties of OFDMA signals with hypothesis testing for the FH-OFDMA systems. Numerical simulations of the systems with specific parameters are carried out, and the simulation results are shown in Section 4. Finally, our conclusions are presented in Section 5.
# Influencing Factors Analysis of Crude Oil Futures Price Volatility Based on Mixed-Frequency Data
## 4 Variables and Data Description
### Analysis of the Single-Factor Hierarchical Effect of Level and Fluctuation  
#### 5.1.1 Level Effect of Single Factor  
(1) Level effect of low-frequency variables  
Tables 3 and 4 give the estimation results of the mixed model for the level value of a single factor.\(\theta_{l}\) reflects the level effect of each influencing factor on the long-term component of the high-frequency volatility, and \(\omega_{l}\) reflects the optimal estimation weight in the single-factor mixed level value model. The coefficients \(\theta_{l}\) examine the impact of the level of individual factors or the increase in the rate of return on the volatility of the crude oil market; that is, when \(\theta_{l}\) is positive and statistically significant, the increase of the factor will promote the Brent crude oil futures market fluctuation. When \(\theta_{l}\) is negative and statistically significant, the increase in this factor will reduce the volatility of the Brent crude oil futures market. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **V** & **OI** & **DI** & **GP** & **PS** \\ \hline \(\mu\) & \(-0.0001\) & \(0.0001\) & \(0.0001\) & \(0.0002\) & \(0.0001\) \\  & [0.8707] & [0.9392] & [0.9384] & [0.9481] & [0.9311] \\ \(\alpha\) & 0.0360 *** & 0.0421 *** & 0.0426 *** & 0.0421 *** & 0.0445 *** \\ \(\alpha\) & [0] & [0] & [0] & [0.0008] & [0.0001] \\ \(\beta\) & 0.9609 *** & 0.9553 *** & 0.9542 *** & 0.9554 *** & 0.9449 *** \\ \(\beta\) & [0] & [0] & [0] & [0] & [0] \\ \(m\) & \(-9.9266\) *** & \(-9.4256\) *** & \(-9.5483\) *** & \(-9.4099\) *** & \(-8.3016\) *** \\ \(\eta\) & [0] & [0] & [0] & [0] & [0] \\ \(\eta_{l}\) & [0] & [0] & [0] & [0] & [0] \\ \(\lambda\) & \(-0.0402\) & \(-0.0397\) & \(-0.0402\) & \(-0.0397\) & \(-0.0395\) \\ \(\lambda\) & [0.2182] & [0.2211] & [0.2116] & [0.2190] & [0.2206] \\ BIC & \(-7.0422\) & \(-7.0439\) & \(-7.0431\) & \(-6.9623\) & \(-7.0214\) \\ LLH & \(-4972.63\) & \(-4973.83\) & \(-4973.23\) & \(-6508.49\) & \(-4972.35\) \\ H & 0.0108 & 0.0108 & 0.0188 & 0.0195 & 0.0111 \\ MAE & 0.0005 & 0.0005 & 0.0005 & 0.0005 & 0.0005 \\ RMSE & 0.0008 & 0.0008 & 0.0008 & 0.0008 & 0.0007 \\ \hline \hline \end{tabular} Note: ***, **, and * indicate statistically significant at the significance level of 1%, 5%, and 10%, respectively; p values are in brackets [ ]; MAE means the mean value of absolute forecast error of realized volatility); RMSE means the square root of mean value of absolute forecast error of realized volatility.  
\end{table} 
Table 4: Estimation results of high-frequency and single-factor level effect.
## 4 Computational Simulations and Experimental Results
### Hardware Experimental Testing
#### 4.3.1 Testing of Transmission Data, Modulated Carrier Signals, and Detection Signals  
In the experiment, the signals from the transmitter and receiver were tested using an oscilloscope, as shown in Figure 9:  
Figure 8: BER from simulations: (**a**) different scenarios for \(\lambda_{b}\); (**b**) different scenarios for \(R\).  
The above represents the carrier frequency signal output by the FPGA (a), the frequency signal output by the modulation circuit (b), and the waveform signal of the photoelectron pulses detected by the PMT (c). Each photoelectron pulse corresponds to one or more photoelectrons detected by the PMT.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 4 Theoretical Results  
In this section, we first propose the theoretical formulation of the task-specific semantic communication problem to demonstrate how our architecture generalizes generation and classification tasks. We then obtain the closed-form expressions of the Gaussian semantic rate-distortion function under Wasserstein distance, KL divergence and reverse KL divergence.  
Since obtaining the exact closed-form solutions of (7) involves a general coding theorem which is out of the scope of this paper, we adopt the following formulation:  
\[R_{m}(D,P_{m})=\inf_{P_{\delta|U}}I(U,\hat{S}|Y)\] (8) s.t. \[E[d(S^{n},\hat{S}^{n})]\leq D,\] \[\theta_{m}(p_{S^{n}},p_{S^{n}})\leq P_{m},\]  
to characterize the task-specific semantic rate-distortion function. This formulation, which uses conditional mutual information between encoder input and decoder output for the rate term, aligns with established approaches in rate-distortion theory [13; 15; 16].  
It is worth noting that our proposed task-specific semantic communication framework can be extended to a range of applications that can be formulated in terms of statistical divergence minimization, such as Bayesian regression [24] and density estimation [25]. There are, however, many other tasks that require novel signal fidelity measures beyond simple distribution divergence minimization. Novel tasks such as reinforcement learning and clustering, for instance, cannot be adequately formulated using divergence minimization approaches. How these alternative measures on codec design and achievable rates is worth further investigation, which will be left for our future work.