esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper demonstrates why VCSEL-based transmitter systems are not able to capitalize on higher-order PAM modulation formats (such as PAM-4) to push the link data rate towards 100 Gbit/s with BER of better than \(10^{-5}\). First, the non-linear and biasing-dependent VCSEL behavior is analyzed in great detail to prove why state-of-the-art VCSELs with linear equalization are not able to reach these bandwidth targets. Consequently, based on an enhanced VCSEL model, a digital non-linear transmit equalizer is proposed to overcome voltage-dependent relaxation frequency shifts as well as data-dependent VCSEL bandwidth variations. The proposed digital non-linear equalizer is compared with a standard linear FFE structure using PAM-4 transmission experiments of 100 Gbit/s and beyond. This way, the proposed non-linear equalizer reveals a BER improvement of more than 30-fold compared to a linear FFE at a data rate of 80 Gbit/s. Finally, at 112 Gbit/s PAM-4 the non-linear equalizer was successfully demonstrated at a BER of \(1.5\cdot 10^{-7}\).  
optical fiber transmission; VCSEL; voltage-dependent relaxation frequency shifts; biasing-dependent VCSEL bandwidth variations; linear FFE; digital non-linear transmit equalization 2023202  
bandwidth. Although 100 Gbit/s optical Multi-Mode Fiber (MMF) transmission standardization is finalized [8] with companies claiming products capable of this performance [9], published VCSEL transmitter systems using PAM-4 signaling only reached data rates of 60 Gbit/s such as [10; 11; 12]. As can be seen in Figure 1b, the most efficient VCSEL transmitters are also still using NRZ modulation. Although VCSELs are very energy-efficient, they suffer from limited bandwidth, a frequency response with current-dependent resonant behavior leading to overshoots as well as non-linear rise-and-fall times, which significantly impairs the Bit-Error Rate (BER) using PAM-4 modulation schemes. This paper aims to demonstrate PAM-4-based equalization techniques that can easily be implemented into Digital-to-Analog Converter (DAC)-based transmitters, enabling transmission at over 100 Gbit/s reaching efficiencies common for copper-based transmitter systems. This scheme could also easily be extended to be used with PAM-6 or even PAM-8 modulation.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Construction and Analysis of Multi-Dimensional Correlation Network of Tourist Attractions
#### 4.3.1 Quantification of Multi-Dimensional Correlation Strength  
Referring to the \"2024 Annual Commuting Monitoring Report of China's Major Cities\" [46], 5 km and 20 min are set as the thresholds for commuting distance and time. Under this threshold, 2110 proximity relationships are obtained, which account for about 7% of the all-connected network.  
Based on the review texts and their entity recognition results, a dictionary of attraction names and keywords is constructed. Association and co-occurrence frequencies are then extracted using regular expression matching. Finally, the association resonance degree between each pair of attractions is calculated. To excavate fine-grained association relations, low-frequency associations are also taken into consideration, and the minimum support degree (Support) is set to 0.0001. To ensure practical value, the minimum lift degree (Lift) is set to 2.0, and the confidence degree (Confidence) of the association rules is taken as the resonance degree of the associations. This yields 836 association relations, accounting for about 2.6% of the whole connection network.  
The Jensen-Shannon (JS) divergence quantifies the dissimilarity between two sentiment-weighted thematic feature vectors, followed by computing thematic-sentiment similarity via Equation (13) in Section 3.2. Given that POI types inherently reflect the functional essence of attractions and tourists' primary visitation intentions, and noting the long-tailed distribution of global JS divergence values, the similarity threshold is derived based on intra-POI homogeneity. This strategy ensures semantic consistency while preserving inter-category heterogeneity [47]. The average Jensen-Shannon divergence among attractions with the same scenic POI type serves as the thematic-sentiment distance threshold to calculate the thematic-sentiment similarity between attractions. Consequently, 10,684 thematic-sentiment correlations are identified, accounting for about 34% of the fully connected network.
## 3 Experimental Results and Discussion
### Stiffness Degradation  
Stiffness degradation represents the tendency of the structure's stiffness to gradually decrease as the loading cycles increase, which can fully reflect the development of plastic deformation of the links. The stiffness of a specimen was commonly evaluated in accordance with the following formula:  
\[K_{\text{i}}=\frac{\left|P_{1}^{+}\right|+\left|P_{1}^{-}\right|}{\left| \Delta_{1}{}^{+}\right|+\left|\Delta_{1}{}^{-}\right|}, \tag{5}\]  
where, \(K_{\text{i}}\) is the stiffness at i loading cycle, \(P_{1}^{+}\) and \(P_{1}^{-}\) are, respectively, the maximum load at the positive and negative directions at i loading cycle, and \(\Delta_{1}{}^{+}\) and \(\Delta_{1}{}^{-}\) are, respectively, the maximum displacement at both the positive and negative directions at i loading cycle. The secant stiffness method was used to get the stiffness degradation curve of each specimen, as shown in Figure 12. There was a slight decrease in stiffness with an increase in displacement. The stiffness degradation ranges of Specimens L1 through L8 are 25.6%, 43.5%, 52.5%, 50.3%, 58.9%, 59.5%, 43.1%, and 58.1%, respectively. The stiffness degradation amplitude of Specim L3 is larger than that of Specim L4, which shows that the earlier the specimen yields, the more obvious the stiffness degradation will be. The stiffness degradation rate of Specim L4 is 50.3%, which is less than 58.1% of Specim L7, and the degradation rate of Specim L3 is 52.5%, which is less than 58.9% of Specim L5. It explains that stiffness degradation is obvious with the decrease of length ratio \(\zeta\). The stiffness degradation ranges of Specimens L5 and L7 are basically the same, which shows that the stiffener spacing has little effect on the stiffness degradation.
## 4 Power Control Techniques
### Power Control Technique Assisted by MUE  
The HeNB adjusts downlink transmission power based on the received information from the macro user through eNB backhaul, since there is no direct interface between MUE and the HeNB, the MUE sends measured information (interference message) to HeNB via the eNB backhaul. The HeNB utilizes the received information from the MUE to tune its downlink transmission power. The MUE-assisted power control Technique (MUEAPCT) can be illustrated as:  
\[P_{MUE_{Assisted}}=max(min(\alpha P_{SINR}+\beta,~{}P_{max})~{}P_{min}) \tag{6}\]In (6), \(P_{SINR}\) define SINR between the macro user and the nearest femtocell, whereas \(\alpha\) is a linear scalar that allows altering the slope of power control mapping curve, \(\beta\) is a parameter expressed in dB that can be used for altering the dynamic range of power control [35].
# A Short S-Equol Exposure Has a Long-Term Inhibitory Effect on Adipogenesis in Mouse 3T3-L1 Cells
## 3 Results
### S-Equol Affects the Expression of Pro-Adipogenic Markers  
As C/EBP\(\alpha\) and PPAR\(\gamma\) are two master pro-adipogenic transcription factors, we analyzed their mRNA expression by real-time qRT-PCR in 3T3-L1 cells exposed to S-equol (10 uM) during the first three days of the adipocyte differentiation process. As shown in Figure 4C, treatment with S-equol significantly decreased the expression of PPAR\(\gamma\) and C/EBP\(\alpha\) by 78% and 97%, respectively, when compared to control cells on day 7 of adipocyte differentiation, which is consistent with the reduced adipogenesis.  
Figure 3: Effect of S-equol on 3T3-L1 adipocyte differentiation. 3T3-L1 fibroblasts treated with S-equol for 72 h were induced to differentiation and morphological changes were documented on days 5, 7, and 9. Control, cells without treatment; E2, estradiol; Ros, rosiglitazone.
## 2 Results
### hNudt16 Stability in the Presence of Different Ligands and Its Substrate Affinity Studies
#### 2.3.2 Characterization of hNudt16-Ligand Complexes by Microscale Thermophoresis (MST)
The consistency of these results supports our conclusion that the E76Q mutant retains ligand-binding properties analogous to the wild-type hNudt16 protein. Given the consistency of the MST results with DSF, we did not measure for some ligands that were very weakly bound or not bound by hNudt16E76Q. Obtained results allow us to conclude that hNudt16E76Q binds very strongly both compounds: IDP and GppG. While m\({}^{7}\)GppPG, IMP, dpCoA, AppA, ADPr exhibit weak affinity of several orders of magnitude. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ligand** & **K\({}_{d1}\) (nM)** & **K\({}_{d2}\) (\(\upmu\)M)** & \
\begin{tabular}{c} \(\Delta\)G\({}_{\text{diss1}}\) (kJ/mol)** \\ **(for K\({}_{d1}\))** \\ \end{tabular} &
\begin{tabular}{c} \(\Delta\)G\({}_{\text{diss2}}\) (kJ/mol)** \\ **(for K\({}_{d2}\))** \\ \end{tabular} \\ \hline IDP & 42 \(\pm\) 11 & 1.2 \(\pm\) 0.2 & 42.00 & 33.80 \\ GppG & 73 \(\pm\) 23 & 1.6 \(\pm\) 0.4 & 40.62 & 33.01 \\ m\({}^{7}\)GppG & 220 \(\pm\) 87 & 30.2 \(\pm\) 2.5 & 37.92 & 25.75 \\ GDP & 258 \(\pm\) 45 & 45.6 \(\pm\) 2.4 & 37.53 & 24.73 \\ m\({}^{7}\)GppG & (1.7 \(\pm\) 0.6) \(\times\) 10\({}^{3}\) & 230 \(\pm\) 57 & 33.58 & 19.32 \\ IMP & (4.9 \(\pm\) 1.8) \(\times\) 10\({}^{3}\) & 41 \(\pm\) 10 & 30.24 & 24.98 \\ dpCoA & (43 \(\pm\) 11) \(\times\) 10\({}^{3}\) & \(\geq\)15,019 & 24.90 & \(\leq\)10.38 \\ AppA & (833 \(\pm\) 164) \(\times\) 10\({}^{3}\) & nd & 17.54 & nd \\ ADPr & (1826 \(\pm\) 497) \(\times\) 10\({}^{3}\) & \(\geq\)19,900 & 15.60 & \(\leq\)9.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: K\({}_{d1}\) and K\({}_{d2}\) binding constants obtained from MST and the free energy associated with dissociation of the complex (\(\Delta\)G\({}_{\text{diss}}\)) calculated for each binding site (from K\({}_{d}\) obtained by MST) for tested hNudt16 E76Q-ligand complex samples; nd—not determined. Figure 3: Representative MST pseudo-titration data for binding of GppG to hNudt16E76Q. Squares represent experimental points, solid lines represent results of fitting for two independent binding sites model, and gray area bounded by a dashed line represents 95% confidence bands for this model. See Figure S4 for other ligands data.
## 1 Introduction
### Contributions  
The contributions of the present article are as follows:  
* The study introduces two priority models, non-pre-emptive and pre-emptive, within the M/D/1 framework to address SU starvation in CRNs with a high PU arrival rate.
* The research compares the proposed non-pre-emptive and pre-emptive priority models and an M/D/1 model with PU delay to determine which improves the average total waiting time for PUs and SUs.
* Numerical analysis and Monte Carlo simulations are used to analyze the suggested models' impact on waiting times in different scenarios, helping to elucidate system dynamics.
* To model real-time systems, the research extends the proposed models to include finite buffer capacity for both PUs and SUs, leading to significant improvements in average total waiting time with non-pre-emptive and pre-emptive priority.
* The suggested models improve average total waiting time for PUs and SUs, providing useful insights for priority-scheme design and optimization in CR networks with high PU arrival rates.  
The structure of the paper is as follows. Section 2 provides a comparison of related studies. The dynamics of the system are developed in Section 3. Section 4 presents the performance analysis of the proposed algorithm in terms of the convergence speed and time complexity. Section 5 deals with the numerical results achieved for the study. In Section 6, a comparative analysis of major key performance metrics obtained for the proposed system and the existing studies is presented in a comprehensive manner. Section 7 discusses the challenges and limitations of the present study. Finally, a summary of the study is found in Section 8, along with potential avenues for the proposed model's generalization.
## 3 Results
### Results of Sensitivity Analyses
#### 3.2.3 Sensitivity to Fuel Price Inflation  
In the base scenario of the simulations, the inflation considered for the price of diesel was 4%. For this analysis, scenarios from 0% to 8% were considered. This analysis only makes sense on hybrid systems.  
Figure 16 shows that in both pig farm (a) and winery (b) cases, the NPC line of optimal systems with Li-ion batteries has a slightly greater slope than that with lead-acid ones, i.e., the system with Li-ion batteries was a little more sensitive to variations in the inflation of diesel price. The rest of the cases showed similar behaviors. In none of the cases, the reduction in cost of PV elements favored lead-acid batteries. Thus, in all cases and in  
Figure 14: Sensitivity analysis of the NPC of the optimal hybrid systems with lead-acid and Li-ion batteries, based on the acquisition cost of PV generation: (**a**) Irrigation pumping case; (**b**) Single-family home case.  
Figure 15: Sensitivity analysis in optimal systems with Li-ion batteries for the winery case, based on the acquisition cost of the elements of PV generation: (**a**) PV power and battery capacity in the PV system; (**b**) PV power and energy from diesel in the hybrid system.  
the interval studied, lower inflation of diesel price slightly favored the presence of Li-ion batteries over lead-acid batteries in the absolute optimum, and vice versa.  
Figure 17a corresponds to the case of the winery and shows that the higher the inflation of the fuel price, the higher the battery capacity in both the lead-acid and Li-ion systems. As expected, the higher the cost of the fuel, the lower the consumption, which was compensated by the higher power of the PV generator, as shown in Figure 17b. All other cases had the same behavior.
# An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction
## 4 The Case Study Phases and Results
### Exploratory Data Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**ErrorCode** & **0** & **2629** & **2592** & **2580** & **2514** & **2577** & **2563** \\ \hline
**Count** & 2,815,203 & 600,602 & 194,801 & 96,650 & 44,676 & 29,137 & 21,467 \\ \hline
**ErrorCode** & **2591** & **2593** & **2668** & **2535** & **2507** & **2559** & **2509** \\ \hline
**Count** & 12,221 & 11,634 & 9459 & 8722 & 8111 & 7313 & 6453 \\ \hline
**ErrorCode** & **2556** & **2579** & **2529** & **2557** & **2549** & 5 & **2581** \\ \hline
**Count** & 5576 & 4607 & 3596 & 3227 & 3043 & 1767 & 1746 \\ \hline
**ErrorCode** & **2505** & **2519** & **2628** & **2597** & **2510** & **2517** & **2520** \\ \hline
**Count** & 1212 & 1140 & 1112 & 1060 & 1045 & 1042 & 938 \\ \hline
**ErrorCode** & **2561** & **2558** & **2572** & **2669** & **2654** & **2614** & **2676** \\ \hline
**Count** & 757 & 744 & 563 & 556 & 262 & 248 & 205 \\ \hline
**ErrorCode** & **2553** & **2565** & **2652** & **2550** & **2513** & **2605** & **2596** \\ \hline
**Count** & 164 & 128 & 125 & 109 & 96 & 66 & 64 \\ \hline
**ErrorCode** & **2612** & **2615** & **2661** & **2575** & **2583** & **2659** & **2604** \\ \hline
**Count** & 58 & 48 & 28 & 22 & 15 & 15 & 9 \\ \hline
**ErrorCode** & **2600** & **2576** & **2562** & **2599** & & & \\ \hline
**Count** & 9 & 8 & 7 & 5 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Error code TU count, sorted by the number of occurrences. were processed with Pandas and subsequently visualised with Seaborn 0.12.1. Lastly, the AI models discussed in Section 4.4 were implemented using PyTorch 2.1.0. and the entire process was deployed on a proprietary cloud service named \"Alida\". Kedro is an open-source Python 3 framework hosted by the Linux Foundation that allows us to build data science pipelines, and was created at QuantumBlack to reduce technical debt in data science experiments, making an easier transition from experimentation to production [59]. Its flexibility and integration among modern Integrated Development Environment (IDE) and cloud hosts enable scientists and engineers to easily analyse data and train AI models focusing on reproducibility, reporting, and deployment on remote hosts. Another important feature is the possibility of visualising and debugging pipelines using integrated visualisation tools. Figure 4 represents the entire training pipeline that we built using Kedro. From top to bottom, it represents the data flows that come from the loading of the representative  
Figure 4: The pipeline used by Kedro to extract the data and train the model 2509.  
parameters (marked with a coloured purple line), to the creation of the result images and tables proposed in this work. For the sake of simplicity and clarity, we have omitted the portion of the pipeline related to data preprocessing and cleaning, as it involves standard procedures with no novel contributions. Additionally, to avoid redundancy, we omitted pipeline replication for all five models.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Semi-Bridgeless Boost Converter with Clamped Diodes  
The topology of SBBC with clamped diodes or pseudo totem-pole SBBC topology is depicted in Figure 10. This configuration includes two additional diodes (\(D_{3}\) and \(D_{4}\)) to the symmetrical topology. As a result, this topology is built by two boost converters, each one operating in each semi-cycle of the AC sinusoidal wave [13; 38; 72; 89; 96; 97; 98; 99; 100]. For this reason, this topology is also known as dual boost PFC rectifier.  
An advantage of this converter is that switches \(Q_{1}\) and \(Q_{2}\) can be driven with the same control signal. This topology reduces CM noise since \(D_{3}\) and \(D_{4}\) avoid the coupling between ground and the power supply. However, DM noise and current ripple can increase since only one inductor operates in each semi-cycle. In comparison with conventional topology with a rectifier bridge, this converter has less current ripple and also produces less DM and CM noises; consequently, a smaller filter could be used for this topology which makes it less expensive [32]. The authors in [89] designed a cascade PI controller, and they obtained a PF of 0.99 and a \(THD_{i}\) around 3.9%. This implies a significant energy quality improvement if the SBBC control system is also designed for harmonic mitigation. Additionally, the CM noise can also be reduced if coupling inductors are used [101] while switching losses can be reduced if a snubber circuit is implemented [63; 102; 103; 104]. In addition, \(THD_{i}\) can be improved if any nonlinear control technique is implemented [11]; the authors in [11] developed a sliding mode control together with PI and an adaptive hysteresis band were proposed to reduce \(THD_{i}\) and, at the same time, to improve the dynamic converter response.  
Figure 10: Semi-bridgeless boost converter with clamped diodes.  
Figure 9: Semi-bridgeless boost converter with isolation.  
A modification of this topology is presented in [12]. The authors replace \(Q_{2}\) by two diodes making a hybrid converter between conventional BBC and SBBC with clamped diodes. This converter can operate in low voltage and power with a classical cascade PI control.
## 5 Case Study: Experimental Validation in a Representative Smart Building Scenario
### Design of the Fuzzy Rule-Based System
These suggestions were reviewed by experts but not directly adopted without their approval. AI served primarily as a support mechanism to accelerate early iterations. * **Inter-Expert Agreement Analysis:** To quantify consensus, a rule agreement matrix was computed based on binary matches (agree/disagree) for each rule. The average inter-rater agreement (Fleiss' \(\kappa\)) was 0.78, indicating substantial agreement. A summary of agreement levels for a representative subset of rules is shown in Table 3.
* **FML Encoding and Validation:** The final set of 36 approved rules was encoded using the IEEE Std 1855-2016 Fuzzy Markup Language (FML) and validated syntactically and semantically with the JFML-IoT parser. Domain experts conducted a final walk-through validation session to ensure semantic alignment with their expectations. This extended methodology ensures a high degree of rigor, transparency, and traceability in the knowledge engineering process. The combination of structured expert review, consensus protocols, and AI-assisted refinement supports both interpretability and reproducibility in the design of the fuzzy inference engine. Figure 3 illustrates the triangular membership functions defined for each variable, while Table 4 presents the final set of fuzzy rules approved by the expert panel and employed in this case study. \begin{table}
\begin{tabular}{l c} \hline \hline
**Fuzzy Rule** & **Agreement Score (0–4)** \\ \hline IF Room Temperature 1 is High AND Occupancy 1 is True THEN HVAC Control is Max & 4 \\ \hline IF Illuminance 1 is Low AND Occupancy 1 is True THEN Lighting Control 1 is Bright & 4 \\ \hline IF CO\({}_{2}\) Level is High AND Occupancy 2 is True THEN Ventilation Control is On & 3 \\ \hline IF Room Temperature 2 is Low AND Occupancy 2 is False THEN HVAC Control is Medium & 2 \\ \hline IF Illuminance 2 is Medium AND Occupancy 2 is True THEN Lighting Control 2 is Dim & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example of inter-expert agreement levels for a subset of fuzzy rules. Agreement scores are computed based on a 5-point scale (0–4) where 4 indicates unanimous agreement.