esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Conflicting data are due, most likely to method disparities in study design (no control population, differences in periodontitis CKD definition) and bias. Some results do not confirm the differences in periodontitis prevalence in the general population and in patients with CKD, respectively [19]. On the other hand, numerous studies indicate a strong correlation between the two entities, supported by the increased prevalence and risk for periodontitis in CKD patients, compared to control populations [15; 20; 21; 22]. Using mathematical models, it was confirmed the fact that some complementary pathologies (diabetes mellitus, systemic hypertension) are mediators of periodontitis effects on CKD and that there is a bidirectional relationship between periodontitis and CKD [22]. It is worth mentioning a recent study [18] that brings powerful evidence in favor of the association between periodontitis, diabetes mellitus and CKD. Based on a non-CKD representative cohort (2635 subjects) followed-up for a period of 10 years for non-communicable disease risk factors [23] and by the use of mediation analysis with bootstrapping, the cause-effect link between periodontitis and CKD has been confirmed through the study design. The results point out that periodontitis has significant direct and indirect (through diabetes) impact on the incidence of CKD [18]. In patients with CKD, in general, and in hemodialysis patients in particular, chronic periodontal inflammation is associated with a systemic increase of inflammatory biomarkers (ILs, TNF-\(\alpha\), vascular endothelial growth factor-VEGF, matrix metalloproteinases-MMP) who directly influence the kidney inflammatory status and negatively alter the kidney function [6; 13; 24; 25; 26; 27; 28; 29]. However, the existing information concerning the levels of inflammatory biomarkers in the gingival circular fluid (GCF) related to the periodontitis-CKD association is rather limited. On this basis, our study pursued for local analysis of certain periodontal specific inflammatory biomarkers within the GCF of patients with CKD, compared to patientswith normal kidney function, aiming to define a periodontal inflammatory profile that characterizes the dialysis patient and substantiate the bidirectional relationship between the two conditions. More specific, our objective was to determine the level of TNF-\(\alpha\), IL-1\(\beta\) and MMP-8 in GCF of hemodialysis periodontal patients compared to healthy subjects with periodontitis, in order to substantiate the more advanced level of these three proinflammatory cytokines in CKD. To the best of our knowledge, this is the first study to asses in concert the GCF load of these biomarkers, in hemodialysis subjects.
## 3 Blockchain Technologies for Maritime Applications/Use Cases
### Blockchain for Maritime Supply Chain  
#### 3.3.1 TradeLens  
In May 2017, Maersk and IBM undertook a project to verify the effectiveness of integrating blockchain systems into Maersk's container maritime transportation by combining Hyperledger Fabric, one of the consortiums/permissioned blockchain technologies, with IBM's proprietary technology. The purpose of TradeLens is to digitize paper-based processes in supply chain management and the maritime sector [63]. Figure 5 illustrates the blockchain-based business network developed by TradeLens. They aimed to create a blockchain-based solution that digitizes all contracts involved in maritime trade and builds a system capable of tracking the movement of containers dispersed worldwide.  
However, in the first quarter of 2023, the TradeLens platform was ultimately discontinued. The project was initiated to advance the maritime sector through blockchain and succeeded in creating a viable platform, but it failed to achieve international industrial cooperation [65]. Consequently, TradeLens did not secure the commercial viability necessary to achieve its financial goals. The project, led by a major company like Maersk,  
Figure 5: TradeLens Blockchain Business Network (re-created with inspiration from [64]).  
made many companies hesitant to participate in the industry. None of the Asian container shipping companies joined TradeLens, and Chinese shipping companies were dispersedly participating in the Global Shipping Business Network (GSBN). The failure to digitize documents also spans multiple jurisdictions, such as Electronic Bills of Lading.  
Figure 6 illustrates the volume of documents and containers processed by TradeLens as of August 2024. Despite the setbacks mentioned, TradeLens holds its significance in that it applied blockchain to the traditionally conservative maritime industry, establishing a foundation and providing an example of the potential for further development.
# Semi-Supervised Speech Recognition Acoustic Model Training Using Policy Gradient
## 6 Experimental Results  
In this experiment, we measured the performance of a supervised model and a self-trained model as a baseline system. The supervised model was trained using the 12-h human labeled data, and self-training model was trained by the conventional self-training method for the 120-h unlabeled data using the supervised model as a pre-trained model.  
The performance was measured by frame accuracy, and Table 2 shows the results. As shown, there is a little improvement with self-training-based approach. However, it seems natural because about 48% of pseudo labels are expected to contain errors by the pre-trained model. So, optimizing for the erroneous pseudo labels is not expected to improve the performance.  
Table 3 shows the performance of the proposed method. The hyper parameters such as, modulus \(m\), temperature \(T\), and Q-function \(Q(a_{t})\), are tuned sequentially. For the different three modulus \(m\), the best performance is obtained by training labeled data and unlabeled data interleaved by setting \(m\) as 1. However, there is little accuracy degradation even if labeled data was used once every in 3 iterations. Temperature, \(T\), controls the randomness of pseudo label generation. The smaller the value, the same as using \(argmax()\), and the same as selecting randomly at large. In this experiment, there is a slight improvement by setting \(T\) as smaller than 1.0. It implies that sampling-based pseudo label generation gives more chance to explore correct generation than \(argmax()\). Q-function, \(Q(a_{t})\), plays role to weight the reliability of sampled pseudo label. Assuming that the reliability of human labels is 1.0, it may be reasonable that the reliability of pseudo labels is lower than 1.0. In this case, 0.8 shows the best accuracy for the case of constant value. For the case of considering temporal reliability using n-gram. In this case, 5-gram shows the best performance.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **Frame Acc (\%)** \\ \hline Supervised & 52.03 \\ Self-training & 52.71 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline performance.  
In general, entropy minimization is considered in the semi-supervised training-based on the assumption that classifier's decision boundary should not pass through high-density regions of the marginal data distribution [33; 34]. Figure 4 shows the histogram of \(logP_{\theta}(\mathbf{\hat{y}}|\mathbf{x})\) of unlabeled data in this experiment. It shows that the model's predictions become more confident or entropy decreases by tuning the hyper-parameters. It implies that the proposed method is effective for semi-supervised training for acoustic model.
# Compensation of Limited Bandwidth and Nonlinearity for Coherent Transponder
###### Abstract  
Coherent optical transponders are widely deployed in today's long haul and metro optical networks using dense wavelength division multiplexing. To increase the data carrying capacity, the coherent transponder utilizes the high order modulation format and operates at a high baud rate. The limited bandwidth and the nonlinearity are two critical impairments for the coherent in-phase quadrature transmitter. These impairments can be mitigated by digital filters. However, to accurately determine the coefficients of these filters is difficult because the impairment from the limited bandwidth and the impairment from nonlinearity are coupled together. In this paper, we present a novel method to solve this problem. During the initial power-up, we apply a sinusoidal stimulus to the coherent IQ transmitter. We then scan the frequency and amplitude of the stimulus and monitor the output power. By curve-fitting with an accurate mathematical model, we determine the limited bandwidth, the nonlinearity, the power imbalance, and the bias point of the transponder simultaneously. Optimized coefficients of the digital filters are determined accordingly. Furthermore, we utilize a coherent IQ transponder and demonstrate that the limited bandwidth is improved by the finite impulse response filter, while nonlinearity is mitigated by the memoryless Volterra filter.  
Keywords:coherent communication; optical communication; pluggable module +
Footnote †: journal: Applied Physics Letters
## 2 Analysis of Overburden Load Transfer Mechanical Model
### Roof Breaking and Load Transfer Process
#### 2.1.4 Mechanical Analysis of Load Transfer of Fully Mined Overburden  
After full mining, the overburden stress is redistributed, and the rock cracks above the goaf gradually form an arch fracture failure zone. The inner rock layer of the arch fracture failure zone forms an arch stress unloading zone under the self-organization effect. Stress analysis of structural arch formed by fully mined goaf(Figure 8):Let the trajectory equation of structural arch be  
\[\frac{(x+a)^{2}}{b^{2}}+\frac{y^{2}}{c^{2}}=1 \tag{24}\]  
The structural arch shape equation can be obtained by substituting the coordinates of \(O\), \(P\), and \(Q\):  
\[\frac{4(x-\frac{L_{g}}{2})}{L_{g}{}^{2}}+\frac{y^{2}}{H_{g}{}^{2}}=1 \tag{25}\]  
The structural arch is subjected to upper uniform load and lateral uniform load. According to the overall stability of the arch structure, there is  
\[\left\{\begin{array}{c}qL_{\mathrm{S}}\frac{L_{g}}{2}+\gamma qH_{g}\frac{H_{ g}}{2}-\gamma qH_{g}\frac{H_{g}}{2}-F_{QY}L_{g}=0\\ \frac{qL_{g}}{2}\frac{L_{g}}{4}+\gamma qH_{g}\frac{H_{g}}{2}+F_{OX}H_{g}-F_{OY} \frac{L_{g}}{2}=0\end{array}\right. \tag{26}\]  
Calculated results:  
\[\left\{\begin{array}{c}F_{OX}=F_{OX}=\frac{qL_{g}}{8H_{g}}-\frac{\gamma qH_{ g}}{2}\\ F_{OY}=F_{QY}=\frac{qL_{g}}{2}\end{array}\right. \tag{27}\]  
For any point \(S\), the bending moment is obtained by equilibrium condition \(M_{s}\):  
\[M_{s}=F_{QY}x-\frac{\gamma qy^{2}}{2}-\frac{qx^{2}}{2}-F_{QX}y=\frac{qL_{g}x}{ 2}-\frac{\gamma qy^{2}}{2}-\frac{qx^{2}}{2}-(\frac{qL_{g}{}^{2}}{8H_{g}}-\frac {\gamma qH_{g}}{2})y \tag{28}\]  
Bring Equation (25) into Equation (28) to obtain the bending moment of any point of the structural arch. \(M(x)\):  
\[M(x)=\frac{qL_{g}x}{2}-\frac{\gamma q}{2}(\frac{4L_{g}{H_{g}}^{2}x-4x^{2}{H_{ g}}^{2}}{L_{g}{}^{2}})-\frac{qx^{2}}{2}-(\frac{qL_{g}{}^{2}}{8H_{g}}-\frac{ \gamma qH_{g}}{2})\frac{\sqrt{4L_{g}{H_{g}}^{2}x-4x^{2}{H_{g}}^{2}}}{L_{g}} \tag{29}\]  
From the above derivation process, it can be seen that after the coal seam is fully mined, the arched bearing structure formed by the combination of overburden rock blocks in the working face or goaf plays the role of bearing the weight of rock strata and resisting the deformation of overburden rock. The arch body is located in the stop, and the arch foot is located at the edge of the flank coal seam. The horizontal thrust at the arch foot is composed of the horizontal shear resistance of the overlying strata on both sides. The bending moment increases first, then decreases, then increases, and then decreases along  
Figure 8: Mechanical model of structural arch.  
the x direction. The structure at the arch foot of the vault is stable, and the bending moment at the haunch is the largest and easy to destabilize.
## 4 Experiments
### Dataset Information  
The Case Western Reserve University Bearing Dataset is a widely used standard test set for bearing fault diagnosis research. This dataset was created and maintained by the Department of Mechanical Engineering and Materials Science at Case Western Reserve University in the United States and is a globally recognized standard dataset for bearing fault diagnosis. The raw data for the experiment was provided by the experimental platform shown in Figure 5, which consists of a 2 Hp motor, a torque sensor, and a power tester [42].  
The bearings to be tested support the shaft of the motor, with the drive end bearing being SKF6205 and the fan end bearing being SKF6203. Taking the driving end as the research object, an acceleration sensor placed above its bearing seat was used in the experiment to collect the vibration acceleration signal of the faulty bearing, with sampling frequencies of 12 kHz and 48 kHz. Bearing faults are introduced by single point loss using electrical discharge machining technology and are divided into three types of faults: inner ring faults, rolling element faults, and outer ring faults. Each fault type includes three fault sizes, namely 0.1778 mm, 0.3556 mm, and 0.5334 mm. Due to the fixed location of damage on the outer ring of the bearing, the specific position of these damage points in the bearing load area will directly affect the vibration response of the motor. To quantify this impact, the damage points on the outer ring are set at three different positions: 3 o'clock, 6 o'clock, and 12 o'clock. Therefore, there are a total of nine types of faults and one normal state. For each state of raw data, samples are taken from the starting point of the signal, with each sample containing 200 data points. Finally, 600 samples were collected for each fault state, of which 360 samples were used as the training set, 120 samples were used as the validation set, and the remaining 120 samples were used as the testing set, for a total of 6000 samples. Please refer to Table 1 for the description of the fault types and sample sizes in the experimental data.  
Figure 5: Experimental platform for rolling bearings of CWRU.  
In order to rigorously verify the performance and effectiveness of the proposed model, this section constructed four differentiated datasets based on different speed conditions and load levels during the operation of the motor, labeled as A, B, C, and D. The specific configurations are shown in Table 2. This design aims to comprehensively test the adaptability and diagnostic accuracy of the model when facing different operating parameters by simulating diverse practical working conditions.
## 1 Introduction
### Precooler Design Method  
The precooler as a heat exchanger ensures that the coolant is cooled by the heat sink at the compressor inlet to achieve the required cycle inlet temperature. The heat sink in this case is seawater, which flows through the precooler and is recirculated back at the source. The design considerations, flow velocity, and sizing calculations for typical arrangements are extensively documented in [10; 11]. For this study, the focus is on the design methods, which determine the heat transfer rate of the precooler using the inlet and outlet conditions. There are two design methods considered in this study. For the precooler, the Logarithmic Mean Temperature Difference (LMTD) method is considered, whilst the Number of Transfer Units (NTU) method is adopted for the recuperator. The LMTD method for the precooler takes into account two flow arrangements; the co-current (or parallel flow) and counter-current flow configuration, which are illustrated in Figure 4a,b. The counter-current flow is  
Figure 3: Typical Intercooled Cycle (IC) [9]. Single shaft spool connection to the turbomachinery, which includes an LPC, HPC, and turbine as part of the turbomachinery, precooler, intercooler, and reactor, but no recuperator. The schematic shows 6 stations (station 2 comprising 3 stations).  
Figure 2: Typical Intercooled Cycle Recuperated (ICR) [8]. Single shaft spool connection to the turbomachinery, which includes an Low Pressure Compressor (LPC), High Pressure Compressor (HPC), and turbine as part of the turbomachinery, precooler, intercooler, reactor, and recuperator. The schematic shows 8 stations (station 2 comprising 3 stations).  
robust and compact in size due to the high rate of heat exchange made possible by the opposite flow of the helium to the heat sink. On the other hand, the co-current flow does not offer the high rate of heat exchange due to the flows being parallel to each other, consequentially resulting in a larger precooler, in comparison to the counter-current design for a similar technology.
# Electrical Circuits Simulator in Null-Flux Electrodynamic Suspension Analysis
## 2 Unveiling the Inner Workings: Exploring the Principles and Model of an EDS System
### The Implementation of the Analysis Using a Circuit Simulator  
The EDS system was simulated using Ansys(r) Twin Builder software, version 2022 R1. The circuit simulator's solver was set in transient mode, chosen due to the voltage source variation with the train's displacement, which depends on the train's speed. This resulted in a non-sinusoidal, time-varying source. Figure 6 displays the circuit implementation with one SC and one 8-shaped coil, with the physical parameters used in the simulation presented in Table 1.  
In the implemented electrical circuit, the SC coil, acting as the primary, was represented by an ideal current source of 700 kA, along with the minimum resistance required by the software to run the simulation. The induced voltage in the 8-shaped coil served as the secondary of the implemented electrical circuit. All inputs were given as a function of time for the simulation. The vehicle speed was considered constant to verify if the simulation could be conducted in this manner, and the results were compared with those reported in the literature when introducing magnetic coupling as a data table as a function of time. Five Maglev displacement speeds were studied: 10, 50, 200, 298, and 600 km/h. These values  
Figure 5: The mutual inductance between the SC and 8-shaped coils depends on their relative positions.  
were chosen to align with [PERSON] et al. (2020) [45] and [PERSON] et al. (2021) [21] for comparison purposes only. Time was easily calculated as a function of speed and position.  
The value of magnetic coupling between the onboard and off-board coils, expressed by the mutual inductance, is low due to various factors, such as the distance between the coils and the fact that air served as the core of both coils, among other reasons. Precisely for this reason, the magnetic field powering the circuit must be intense, thus requiring the use of the SC coil. Another observation was that the magnetic coupling between the lower loop of the 8-shaped coil and the SC coil was greater than the coupling between the upper loop and the SC coil because the vertical deviation between the coils results in more magnetic flux in the lower loop than in the upper loop of the ground coil. Figure 7 presents the induced currents obtained at the five operational speeds, which agreed with the findings in the literature that the induced currents become positive with increasing speed [21; 36; 45].
## 2 Characteristics of Premise Plumbing Systems and Risk of Opportunistic Pathogens
### Expected Changes in Water Quality Resulting from Elevated Water Age
#### 2.2.1 Loss of Disinfectant Residual and Microbial Ramifications  
Disinfectant decay is more likely to occur in premise plumbing systems than in main distribution due to higher pipe surface area-to-water volume ratios, more frequent stagnation points, longer detention times, higher temperatures, and lower disinfectant residuals [35; 36; 37].  
Traditionally, the control of pathogens by water utilities has been achieved by coagulation, filtration, and disinfection at the point of treatment prior to distribution [21]. Free chlorine and monochloramine are the two main disinfectants preferred by utilities [38]. Mounting evidence suggests that this is no longer a sufficient approach, especially for systems challenged by high water ages, including green building designs.  
The purpose of a secondary disinfectant residual in water supplied by utilities is to protect the consumer against pathogens and bacterial regrowth [3; 21]. The selected disinfectant must ultimately inactivate microorganisms in bulk water, control or remove biofilm, and inactivate microorganisms associated with that biofilm [39]. Unlike free  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Issues** & **Biological Issues** & **Physical Issues** \\ \hline Disinfection decay and & Microbial proliferation & Temperature fluctuations; \\ by-product formation & & taste and odour \\ \hline Corrosion of fixtures and leaching & & \\ of metals from fixtures & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Chemical, biological, and physical water quality issues worsened by high water age.  
chlorine and monochloramine, ozone and ultraviolet light are not effective as residual and, therefore, they are effective only at the point of use [3; 16; 28; 40; 41; 42; 43].  
[PERSON] and [PERSON] [21] discuss how residuals can disappear as a result of abiotic and biotic reactions within the bulk water and/or between plumbing surfaces and the water. Factors that affect the persistence of disinfect residuals include water quality, plumbing materials (including adhering biofilms), and system operation. In their survey of green building water systems, [PERSON] et al. [30] found that chlorine and chloramine residuals were often completely absent in the green building systems, decaying up to 144 times faster in premise plumbing with high water age when compared to distribution system water.  
Water quality decreases with increasing distance from the point of treatment as disinfectants decay and residual concentrations fall below adequate levels. This inevitably results in a shift towards rapid bacterial growth [3; 36; 44]. The efficacy of various disinfection methods applied for the control of opportunistic premise plumbing pathogens is detailed in Section 4, which includes a discussion of the role of in building disinfection systems.
# Untargeted Profiling and Differentiation of Volatiles in Varieties of Meat Using GC Orbitrap MS
## 2 Reagents and Materials
### Analysis of Volatile Compounds by GC-HRMS  
All analyses were conducted on a Q-Exactive Orbitap mass analyzer equipped with a TriPlus RSH autosampler and Trace 1310 GC (Thermo Fisher Scientific, Bremen, Germany). A VF-WAX ms column (60 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness, Agilent, Santa Clara, CA, USA) was used. Helium (99.9999%) with a constant flow rate of 1 mL/min was used as the carrier gas. The column oven was temperature-programmed starting at 40 \({}^{\circ}\)C for 2 min, then increased to 230 \({}^{\circ}\)C at a rate of 4 \({}^{\circ}\)C/min and then maintained at 230 \({}^{\circ}\)C for 5 min. Both of the transfer line 1 and transfer line 2 were set at 250 \({}^{\circ}\)C. MS was performed using electron impact ionization (EI) at 70 eV, operating in full scan mode at a resolving power of 60,000 full width at half maximum (FWHM). The scan range was from 30 to 400 m/z with an automatic-gain-control target value of 1E6. Ion source and transfer line temperatures for MS were set at 280 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively.  
GC-MS data were acquired and processed using the Xcalibur 4.1 and TraceFinder 4.0 softwares (Thermo Scientific), respectively. Volatile compounds were identified in accordance with mass spectra and linear retention indices (LRIs) from NIST17 (v2.3) and the domestic library. The domestic library, namely the homeflavor library, was established using authentic reference standards, integrated with high resolution mass spectra and linear retention indices. Moreover, the high-resolution filtering (HRF) tool from the Tracefinder software was utilized to annotate every measured m/z peak and evaluate the mass accuracy of those ions when the NIST library was used. A series of standard alkanes (C7-C40; Sigma-Aldrich, St. Louis, MO, USA) were run under the same chromatographic conditions to calculate LRIs.