esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Clinical Model Optimization  
PyCaret's automated framework compared 15 classifiers using stratified 5-fold cross-validation weighted by clinical significance:  
\[m^{*}=\arg\max_{m\in\mathcal{M}}\sum_{k=1}^{5}w_{k}\cdot\psi(\text{AUC}_{k}, \text{Sens}_{k},\text{Spec}_{k}),\quad w_{k}=\frac{|D_{val}^{(k)}|}{|D_{val}|} \tag{8}\]  
where \(\psi(a,s,p)=\log(a)+\beta(2s+p)\) weights sensitivity higher for early disease detection (\(\beta=2\)). SMOTE addressed class imbalance through lesion-simulated oversampling [41]:  
\[\mathbf{x}_{synth}^{(c)}=\mathbf{x}_{i}^{(c)}+\lambda\Delta\mathbf{x}_{patho}+( 1-\lambda)\Delta\mathbf{x}_{fluid},\quad\lambda\sim\mathcal{U}(0,1),\quad \Delta\mathbf{x}\sim\mathcal{N}(0,\boldsymbol{\Sigma}_{c}) \tag{9}\]  
The model selection procedure evaluated a comprehensive set of algorithms spanning various complexity classes:  
\[\mathcal{M}=\{\text{LogisticRegression, RandomForest, GradientBoosting,} \tag{10}\]  
Each algorithm \(m\in\mathcal{M}\) underwent hyperparameter optimization via stratified 5-fold cross-validation with a nested grid search exploring parameter space \(\Theta_{m}\):  
\[\theta_{m}^{*}=\arg\max_{\theta\in\Theta_{m}}\frac{1}{5}\sum_{k=1}^{5}\text{ AUC}(m_{\theta},D_{val}^{(k)}) \tag{11}\]The model scoring function prioritized clinical decision support requirements through a weighted combination of metrics:  
\[\text{Score}(m_{\theta})=\alpha_{1}\cdot\text{AUC}(m_{\theta})+\alpha_{2}\cdot \text{Sensitivity}(m_{\theta})+\alpha_{3}\cdot\text{Specificity}(m_{\theta}) \tag{12}\]  
where \(\alpha_{1}=0.5\), \(\alpha_{2}=0.3\), and \(\alpha_{3}=0.2\) reflect the clinical priority of minimizing false negatives while maintaining overall discrimination performance.  
The trade-off between sensitivity and specificity was further optimized through an iterative threshold calibration procedure. After model selection, decision thresholds were systematically adjusted away from the default 0.5 probability cutoff to maximize sensitivity (minimizing false negatives) while maintaining specificity within clinically acceptable parameters. This approach utilized a constraint-based optimization where sensitivity was maximized subject to maintaining specificity above 0.92, resulting in optimized threshold values across validated models. Threshold calibration was performed exclusively on the validation set to prevent optimization bias, with final performance confirmed on the independent test set. This methodology aligns with ophthalmological clinical priorities where early pathology detection outweighs the operational costs of occasional false positives, particularly for sight-threatening conditions requiring prompt intervention.
# RAC1B: A Rho GTPase with Versatile Functions in Malignant Transformation and Tumor Progression
## 5 Biological Functions of RAC1B
### Stromal Control of RAC1B by MMP3  
Rac1b has been shown to be induced by components of the tumor stroma such as MMP3. Exposure of murine mammary epithelial cells to MMP3 induces the expression of Rac1b, which translocates to the cell membrane to promote an increase in cellular ROS which in turn stimulate the expression of Snail. Thereby, MMP3 can cause EMT, oxidative DNA damage, genomic instability, and malignant  
Figure 1: Mechanisms utilized by RAC1B to promote cancer progression.  
transformation in cultured cells, and in genomically unstable mammary carcinomas in transgenic mice [28]. MMP3-induced upregulation of Rac1b in mouse mammary epithelial cells requires stiff substrata/rigid microenvironments with compliances characteristic of breast and other stroma-rich cancers [17]. Similarly, exposure of PDAC-derived cells to recombinant MMP3 stimulates expression of RAC1B, increases cellular invasiveness, and activation of tumorigenic transcriptional profiles [19]. Moreover, expression of mouse Rac1b in bitransgenic mice (with inducible expression of Rac1b only in lung epithelial cells) stimulated EMT and spontaneous tumor development. This activation of EMT by MMP3-induced expression of Rac1b gave rise to lung adenocarcinoma in the transgenic mice through bypassing OIS [23].
# Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion
## 4 Results and Discussion
### Experimental Verification  
The industrial camera was connected to a computer via USB [30]. At the computer side, the camera call, image acquisition, and image processing were completed using Python-opencv, and the suspended air gap values were output to MATLAB in real-time. Meanwhile, the Simulink model of the real-time controller was also run in MATLAB, and the photoelectric position sensor measurements were fused with the two data using Kalman filtering. The experimental platform is shown in Figure 10. The fused values were passed through the PID control algorithm to control the magnitude of the solenoid current to enable the steel ball to be stably suspended. The PID algorithm parameters \(K_{p}\) = 23, \(K_{i}\) = 10, and \(K_{d}\) = 75 were set, and the obtained measurement results before and after fusion with the extended Kalman filter are shown in Figure 11. The anti-interference diagram is shown in Figure 12.  
It can be seen from Figure 12 that at around 2.15 s, the measured value of industrial camera changes abruptly due to external interference, but the fusion value remains stable. In Figure 11, excluding the values with abrupt changes, it can be concluded that the fluctuation range of output data of the industrial camera sensor is 0.43 mm, that of the photoelectric position sensor is 0.20 mm, and that of the fusion data is 0.16 mm. Compared to the single sensor, the accuracy is improved by 0.27 and 0.04 mm, respectively. The Kalman filter was used to stabilize the data returned by the sensor. It is also observed in  
Figure 11: Comparison of measurement results before and after fusion.  
Figure 10: Experimental platform.  
Table 2 that the root-mean-square error of the fusion value is the smallest (0.0293). The root-mean-square errors of the industrial camera and photoelectric sensor values are 0.0890 and 0.0374, respectively. The root mean square error is reduced by 0.0597 and 0.0081, respectively. Thus, the fused data are more stable and robust.  
It is feasible to apply the measurement technology of machine vision and the Kalman filter to measure the suspension air gap of the maglev system. However, it was found in the experiment that the anti-interference ability of the suspension system using machine vision ranging is poor, and it is easy to cause suspension instability owing to interference from the external environment. Meanwhile, it was verified that multi-sensor fusion has stronger robustness than single-sensor measurements. When one sensor is affected by external interference, the measured value changes significantly, and fusion technology using the extended Kalman filter can better maintain the stability of the data.
## 6 Results
### Power Consumption  
A LSTM network has been developed to accurately forecast energy consumption by leveraging temperature and humidity data collected from sensors. LSTM models are particularly well-suited for processing temporal data, as they can identify patterns and correlations within datasets that include temperature, humidity, and energy use. The application of this model enables strategic energy planning, ensuring that environmental conditions remain within optimal ranges while minimizing unnecessary energy consumption. Furthermore, analysing the relationship between environmental conditions and energy consumption allows for the identification of potential energy-saving opportunities, i.e., specific conditions or time periods that lead to increased energy demand. This analysis can reveal opportunities for targeted interventions to enhance energy efficiency. Integrating temperature and humidity monitoring into an energy optimisation strategy provides a deeper understanding of key influencing factors. The employment of this data-driven model aims to achieve a substantial reduction in energy consumption, while concurrently advancing sustainability objectives and reducing operational expenses, all without compromising occupant comfort. This strategy underscores the significance of environmental monitoring as a pivotal element of effective energy management in indoor environments. Figure 7 presents the energy consumption trend analysis for the period from 15 December 2024 to 31 January 2025. The analysis represents the total energy consumption (in kWh) of all electrical loads within the environment, and the observed cyclic pattern reflects fluctuations in office occupancy and daily activities.  
Figure 6: The figure shows the trends in temperature (green) and humidity (yellow) during a typical week. The section highlighted in yellow is the optimal winter temperature zone.
## 3 Proposed Key Exchange Protocol
### Protocol Proposal  
This study's primary contribution was the construction of a two-level security protocol when combining ECQV and SKEM schemes to establish secure communication channels in an IoT network. Figure 2 describes the proposed protocol. The two-level security notion implies the dependency of the generation of a session key on only a SKEM or both SKEM and ECQV scheme.  
A level 1 secure communication channel, whose security property depends solely on the SKEM scheme, is established when a generated node's key pair \((k_{N},K_{N})\) is used repetitively to create fresh session keys \(K\). This mechanism enables quick session key generation at the cost of only one exchange message. A feasible application of SKEM requires assumptions on secure storage for ephemeral data and the key pair on the node device to avoid long-term compromises. To eliminate the risk of exposing or compromising the random source and the key pair stored on a node device, a level 2 secure channel is created with a unique and fresh session key using a newly generated node's key pair. At this level, the handshake to generate a session key requires three exchange messages to sequentially execute both ECQV and one-pass SKEM schemes. An alternative use case is that periodically updating the key pair of a node can minimize the risk of temporary leakage of a key pair when using only the SKEM scheme and benefit from the low exchange message count.
# Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?
## 2 Background
### Predictive Process Monitoring
* Event class: represents a step carried out in fulfilling the process instance. This step is the activity name. _Receive application_, _check documents_, _assess risks_, and _notify customer_ are all examples of events carried out in a loan application process instance. According to [12], the timestamp attribute is considered a mandatory piece of information associated with each event. It documents the time when an event took place. When events are associated with their timestamps, the ordering of events (and hence the entire event log) can be based on these timestamps [1]. A case may contain optional data items representing information about a single event. These items are called the data attributes or data payload. Sometimes they are denoted as _dynamic attributes_, as they have different values for each event of a particular case. Examples of data items include the name of the person responsible for checking the customer's legitimacy, i.e., resource, and the documents associated with the form-filling event. When considering Table 1, it can be observed that a single case may be represented by several records, each representing a single event along with its data payload. In addition to dynamic attributes, there are static ones associated with each case. _Static attributes_ have constant values for all events of a given case. They represent data about the case itself, and their values are not changed during case execution. The applicant ID is an example of a static attribute. Note that certain attributes have numerical values, while others are categorical. This distinction has an effect on the choice of how to process the data values represented by this attribute. Furthermore, in the exemplary event log from Table 1 we do not define the datatype for the timestamp attribute. In most PPM tasks, this attribute is used to compute sub-attributes, e.g., week, day, and hour.
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
## 2 Materials and Methods
### Evaluation of Rheological and Filtration Properties of Nanoparticle-Enhanced OBDFs  
During the incorporation of three types of nanoparticles, CNTs, SiO\({}_{2}\), and Al\({}_{2}\)O\({}_{3}\) into OBDFs, it is necessary to ensure that these nanomaterials have good compatibility with the drilling fluids in order to avoid adversely affecting the basic properties of the drilling fluids. To evaluate the impact of these nanoparticles on the rheological and filtration properties of the drilling fluids, the prepared samples were first sealed and subjected to a hot rolling process at 180 \({}^{\circ}\)C in a high-temperature roller oven for 16 h. This procedure simulates the effects of downhole high-temperature conditions on the performance of drilling fluid. After the hot rolling process, the samples were removed and thoroughly stirred in a high-speed mixer. Subsequently, the drilling fluids were poured into the metering cup of the six-speed viscometer to the graduated line and fixed to the viscometer tray. By adjusting the rotational speed of the viscometer, its readings at 600 rpm and 300 rpm were measured, and the apparent viscosity (AV), plastic viscosity (PV), and dynamic shear (YP) were calculated according to Equations (1)-(3). In addition, the samples were subjected to filtration tests using a HTHP filtration tester to measure the filtration loss of the drilling fluids under HTHP conditions so as to assess their filtration control capability and well-wall stability.  
\[AV=\mathcal{O}600/2 \tag{1}\]  
\[PV=\mathcal{O}600-\mathcal{O}300 \tag{2}\]  
\[YP=AV-PV \tag{3}\]  
Apparent viscosity (AV) is expressed in mPa-s, while plastic viscosity (PV) is reported in mPa-s. The yield point (YP) is measured in Pa. The \(\mathcal{O}600\) and \(\mathcal{O}300\) values are obtained using a six-speed rotational viscometer at different speeds.
# Technological Processes for Steel Applications in Nuclear Fusion
## 4 Fusion Welding
### Effect of Repeated PWHT  
A visual inspection of the micrographs in Figure 13 reveals that the distinct weld microstructures (coarse fusion and fine-grained heat affected zones) do not completely disappear after PWHTs. This is an indication for a certain degree of inhomogeneously distributed micro-structural features (e.g., impurities, secondary phases, segregates, dendritic structures, and/or others), even though the mechanical properties seem to be fully restored.  
We have selected the 10 mm TIG weld as a typical case of the above-mentioned behavior, and performed two series of repeated heat treatments: (1) A + Q + T, and (2) A + Q. The initial weld microstructure near the top and in the middle part after one PWHT is compared to the situation after 20 repetitions in Figure 20. It is evident that one PWHT does not remove the coarse grained and irregular weld microstructure (first row in Figure 20). However, a 20-times repetition of A + Q fully restores an evenly distributed, fine-grained, homogeneous microstructure throughout the whole weld area (lower row in Figure 20).  
Twenty-times repetition of a full treatment (A + Q + T) also renews the microstructure. Due to repeated tempering in air, the surface was depleted of carbon. Therefore, a decarbonization layer of 100-200 \(\upmu\)m developed, which can be easily distinguished from  
Figure 20: The effect of multiple heat treatments on the microstructure of a \(\frac{1}{2}\)-V TIG weld applied on 10 mm thick EUROFER97/2 plates. The weld pieces were machined from a 25 mm plate, heat 993402. The optical micrographs show cross-sections from the middle part of the 85 mm long weld lines. The micrographs were made from the top and middle parts of the cross-sections (indicated in the images). The heat treatment is indicated as follows: austenitization at 980 \({}^{\circ}\)C for 30 min (A), air quenching (Q), and tempering at 750 \({}^{\circ}\)C for 2 h (T).  
the martensite structure below (left micrograph in the third row of Figure 20). The lack of carbon leads to a phase transition into ferrite, recognizable by the large bright grains.  
We have already seen that the Charpy properties are fully restored after a full PWHT. Therefore, we tried to correlate the differences observed by visual inspection of the micrographs to the hardness profiles along the weld cross-section after one and 20-times repeated PWHTs. The result is illustrated in Figure 21. The average value of both series is the same and amounts to 213 HV1. Nevertheless, the hardness profile after 20 repetitions seems to proceed smoother compared to the case of one PWHT. This is confirmed by calculating the maximum and standard deviation, which amounts to 5.6 and 3.0 HV1 for 20-times repetition, and to 10.9 and 5.2 HV1 for the single PWHT.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 4 Numerical Results  
### Test Case  
In this study, the generation schedule that maximizes the daily revenue of PV sites in Hadong, South Korea, is simulated using hourly PV generation data from 2021. The PV generator capacity in Hadong is 300 kW, and the ESS storage capacity is 30 kW. The PCS capacity of the ESS is 7.5 kW, allowing continuous operation for up to 4 h. The ESS charging and discharging efficiency is 95%, and the SOC is assumed to reset to 50% every 24 h [21].  
The incentive unit price is used based on Korean renewable energy incentives. Since the REC unit price has the same value for all times and scenarios, it does not affect the decision variables. As a result, daily REC revenue is excluded from the scheduling problem. The Korean hourly market price data in 2021 is used for simulation [22].
## 3 Results and Discussion
### Absorbance as a Function of Pressure
It has been reported that the ionic liquids having1-butyl-3-methylimidazolium and 1-butyl-1-methylpyrrolidinium as cations crystallize around 1.0 GPa when the pressure is increased in steps of \(\sim\)0.2 GPa from atmospheric pressure; on the contrary, the ILs containing the cations 1-octyl-3-methylimidazolium and 1-butyl-2,3-dimethylimidazolium do not crystallize up to 2.3 GPa. Moreover, in 1-butyl-3-methylimidazolium trifluoromethanesulfonate, the symmetryof the line at 1032 cm\({}^{-1}\), attributed to the symmetric stretching motions of the S-O bonds, was interpreted as originating from TO-LO splitting of the vibrational mode [55]. In this case, the experimental results were shown to follow the general predictions of dipolar coupling theory, thus confirming the quasi lattice model for this ionic liquid. For the sake of completeness, we would like to point out that the kinetics of phase transitions can be sluggish, sometimes even longer than typical IR measurements. In the presently reported experiments, the sample was usually kept at a constant pressure for no more than 10 min, both including the time for pressure stabilization and for data collection, and on this time scale we did not observe any kinetic effect. Indeed, it is difficult to assess the role of kinetics in ILs at high-pressure and its time-scale, and it was not in the scope of present work, but it requires future attention since it has not been deeply investigated to date, besides a few works [14; 53; 56].
## 2 Related Studies
### Deep Learning and Reinforcement Learning in Earth Science and Data Assimilation  
#### 2.2.1 Deep Learning for Feature Extraction in Earth System Science  
In VarDA, accurately tuning the variances of the **B**-matrix remains a persistent challenge, with traditional methods often sacrificing performance or computational efficiency. Recent advancements in artificial intelligence (AI) have introduced transformative solutions to Earth system science under the 'AI for Science' (AI4S) paradigm [33; 34]. Deep learning (DL) has notably revolutionized weather forecasting through large weather models (LWMs) such as GraphCast [35], NowcastNet [36], and Pangu [37]. These models exploit neural networks to extract intricate features from high-dimensional geophysical data, substantially enhancing prediction accuracy and computational speed.  
Beyond forecasting, DL has enriched various facets of DA, including model error correction, dynamical system identification, and reduced-order surrogate modeling [38]. For instance, DL techniques such as autoencoders support reduced-order modeling by compressing high-dimensional systems, while neural networks aid in identifying hidden dynamics and correcting model biases. DL has also been utilized for specifying the **R**-matrix in DA. [PERSON] et al. (2022) employed long short-term memory (LSTM) networks to predict **R** from observational sequences, benefiting from LSTM's capability to handle temporal dependencies [39]. This approach notably enhances dynamic **R** estimation within DA frameworks. However, such methods rely on the availability of a known **R**-matrix as a training target--a condition that does not hold for the **B**-matrix, rendering direct adaptation infeasible. This underscores the necessity for alternative AI-driven approaches for background error covariance adjustment.
## 2 Brief Review of MIMO-NARX Modeling
### NARX Model Pruning  
The optimal NARX terms might still contain spurious terms due to the LARs approach selecting candidate terms through correlation analysis, which is not always a reflection of the contribution of a term to the model [22]. An iterative error-based pruning procedure [14] is adopted in this study to progressively delete the most deleterious NARX term at each iteration until a user-defined error threshold is reached. Each iteration intends to remove one unique term from the current MIMO-NARX model, and the coefficients of each trial model are recalculated through the OLS method. To evaluate the performance of trialmodels, an error indicator is defined to describe the accuracy of the MIMO-NARX model based on the responses of the \(m\)th DOF in the original coordinate:  
\[\delta_{\text{SE},M}=\frac{||\mathbf{X}_{m}-\mathbf{\Phi}_{n_{r}}^{m}\hat{\mathbf{Q}}||^{2}}{|| \mathbf{X}_{m}^{T}-t\mathbb{E}_{t}\left[\mathbf{X}_{m}\right]||^{2}} \tag{13}\]  
where \(m\) is the DOF of interest; \(\mathbf{X}_{m}\) is the response time series of the \(m\)th DOF; \(\mathbf{\Phi}_{n_{r}}^{m}\) is the \(m\)th row of the transformation matrix \(\mathbf{\Phi}_{n_{r}}\); \(\hat{\mathbf{Q}}\) is the response matrix predicted by the MIMO-NARX model. The accuracy of the current MIMO-NARX model in each iteration can be evaluated by the mean value of error indicators overall training samples \(\widehat{\epsilon}_{\text{SE},m}\). While the error indicator for each trial model can be denoted as \(\widehat{\epsilon}_{\text{SE},m}\). The current MIMO-NARX model is replaced by the optimal trial model with \(\min\{\overline{\overline{\overline{\epsilon}}}_{\text{SE},m}-\overline{ \overline{\epsilon}}_{\text{SE},m}\}\) in each iteration. The next iteration of the pruning process then proceeds with the new MIMO-NARX model serving as the current model until the predefined error threshold is satisfied, that is, \(\overline{\overline{\epsilon}}_{\text{SE},m}-\overline{\overline{\epsilon}}_{ \text{SE},m}>\hat{\mathbf{E}}\).