esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Li-ion batteries play a key role in energy storage and conversion in engineering systems such as electric vehicles and grid energy storage, with critical impact on electrification and storage of renewable energy. A key unresolved technological challenge in Li-ion batteries pertains to thermal runaway initiation and propagation in a battery pack, which can lead to subsequent fire and explosion. Despite significant past work, there remains a critical need to understand how thermal runaway propagates in a pack. This work presents a comprehensive investigation of the effect of radiative heat transfer on thermal runaway propagation. Radiation can be important when a battery is exposed to adjacent heat and fire sources, as well as in thermal runaway propagation from one hot cell to another. A theoretical radiative heat transfer model based on view factor theory is developed. Calculations based on this model for a simple 2D cylinder-to-cylinder geometry are found to be in very good agreement with analytical expressions. Radiation-induced thermal runaway propagation between two cylindrical 18650 batteries is evaluated. It is shown that radiation may play a key role in thermal runaway propagation, depending strongly on the triggering temperature. It is found that radiative effects in thermal runaway propagation exhibit both nonlinear and non-monotonic characteristics. At high temperatures, thermal runaway is triggered rapidly in the region close to the battery surface, where the chemical reactions are strongly coupled, and radiation plays a dominant role. In contrast, at lower temperatures, thermal runaway is triggered much more slowly and towards the core of the cell, where some chemical reactions may be decoupled, and pre-runaway chemical heat release plays an increasingly important role. The results presented here suggest that radiation can either facilitate or mitigate thermal runaway. The net radiation heat flux has a cross-over instant, beyond which radiation starts to retard thermal runaway. Additionally, the blocking effect in radiative heat transfer between cells arranged in equal-spacing homogenous or orthogonal arrangements in a battery pack is investigated, along with the effect of the hot spot size. Results from this work help understand the role of radiation in thermal runaway propagation and provide useful insights into the thermal runaway control and design of safe Li-ion battery packs.  
radiation; thermal runaway; Li-ion battery; energy storage; thermal runaway propagation; view factor +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Criteria for Model Evaluation  
In assessing the performance of machine learning models, the difference between the predicted and the actual data represents the prediction error. In evaluating the appropriateness of the models used in this research, the errors of the models were assessed using the following metric criteria:  
Mean Absolute/Percentage Error (MAE/MAPE): This evaluation metric is very sensitive to relative errors and robust against global scaling of the predicted output. This is statistically defined as:  
\[\text{MAE}=\frac{\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|}{n} \tag{7}\] \[\text{MAPE}=\frac{1}{n_{c}}\sum_{i=0}^{n_{o}-1}\frac{|y_{i}-\hat{y }_{i}|}{\max(e,|y_{i}|)}\]Root Mean Squared Error (RMSE): This performance evaluation metric is popular because it is interpretable as the standard deviation of models' prediction errors and specifies the closeness of predicted data to actual data. This is written as:  
\[\text{RMSE}=\sqrt{\frac{\sum_{t=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{n}} \tag{8}\]  
Mean Poisson Deviance (MPD): This metric produces the expected, predicted regression output values by computing the mean Tweedie deviance error with a power parameter of 1.  
\[D_{(y,\hat{y})}=\frac{1}{n_{samples}}\sum_{i=0}^{n_{samples}}\{2(\log(\hat{y} _{i}/y_{i})+y_{i}/\hat{y}_{i})-1\} \tag{9}\]  
Coefficient of determination (R\({}^{2}\)): Another widely used criterion is the coefficient of determination. It represents how close the dependent value is to the best-fit regression line. This is written as:  
\[\text{R}^{2}=1-\frac{\sum\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum\left(y_{i}- \overline{y}_{i}\right)^{2}} \tag{10}\]  
Besides R\({}^{2}\), low errors are indicative of good model performance.
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 3 Gabor Image from Stain Defect Image
### Gabor Image Selection  
Among the 8 Gabor images created in Section 3.1, N images with the smallest IDM value are selected. An image with a small IDM is an image with a large change in values between pixels. Efficient feature extraction for stain detection is possible using the selected Gabor image. The Gabor image selection steps consist of follows.  
Step (1) For the Gabor image created in Section 3.1, create an average image \(I_{Gabor}\) for each phase.  
\[I_{Gabor}(m,n;\beta)=\frac{1}{7}\sum_{\alpha=0}^{7}I_{Gabor}(m,n;\alpha,\beta) \tag{12}\]  
Step (2) Normalization is performed on the Gabor filter image \(I_{Gabor}\).  
\[I_{Gabor}(m,n;\beta)=\frac{I_{Gabor}(m,n;\beta)-\min(I_{Gabor}(m,n;\beta))}{ \max(I_{Gabor}(m,n;\beta))-\min(I_{Gabor}(m,n;\beta))} \tag{13}\]  
Step (3) According to Equations (6) and (7), the Gabor filter image \(I_{Gabor}\) (\(m\),\(n\); \(\beta\)) is transformed into a GLCM, and the average matrix \(P_{\beta}\) is obtained.  
Step (4) IDM feature values are extracted from each average matrix \(P_{\beta}\).  
\[F=\Bigg{\{}F_{\beta}\ |\ F_{\beta}=\sum_{i=0}^{W}\sum_{j=0}^{H}\frac{P_{\beta}(i, j)}{1+(i-j)^{2}}\Bigg{\}}\text{where, }\beta=0,1,\cdots,7 \tag{14}\]  
Step (5) The top \(N\) Gabor filter images with the highest IDM feature values are selected. The Gabor filter image selection Algorithm 1 proceeds as follows.  
Figures 2 and 3 show the Gabor filter images for each phase concerning the stain defect image generated on the MT dataset and CCM dataset. Figures 2a and 3a are a gray-scale image for the inspection image, and it also is a ground truth image showing the location of the stain defect. Figures 2b-i and 3b-i are Gabor filter images according to phase, selected for CNN model creation according to their IDM values. The clarity of the
## 2 Development of the 7-DOF Human Model
### Linearization of Equations of Motion  
Non-linear analysis of a vibrating system is not only complex, difficult, and time-consuming, but it also challenges access to familiar modal properties, such as natural frequencies and mode shapes. Therefore, we employed linearization techniques on the 7-DOF model's non-linear equations of motion by expanding the Taylor's series at the initial configurations using the following approximations:  
\[\theta=\theta^{*}+\widetilde{\theta},\cos\widetilde{\theta}=1,\sin\widetilde{ \theta}=\widetilde{\theta}, \tag{22}\]  
where \(\widetilde{\theta}\) is a small perturbation around the initial value of absolute angular deflection, \(\theta^{*}\).  
The linearized equations of motion, expressed as a matrix, are written in the form:  
\[\mathbf{M}\widetilde{\mathbf{x}}(t)+\mathbf{C}\dot{\mathbf{x}}(t)+\mathbf{K} \mathbf{x}(t)=\mathbf{f}_{\mathbf{c}}\dot{z}_{b}(t)+\mathbf{f}_{\mathbf{k}}z_{b}(t), \tag{23}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are mass, damping, and stiffness matrices; \(\mathbf{x}(t)\) is the generalized coordinates vector; \(\mathbf{f}_{\mathbf{k}}\) and \(\mathbf{f}_{\mathbf{c}}\) are generalized stiffness and damping coefficients vectors; \(z_{b}(t)\) is displacement due to input excitation. Taking the Fourier transform of Equation (23), the linear equations of motion in the frequency domain are expressed as:  
\[\mathbf{X}(j\omega)=\left(-\omega^{2}\mathbf{M}+j\omega\mathbf{C}+\mathbf{K} \right)^{-1}(j\omega\mathbf{f}_{\mathbf{c}}+\mathbf{f}_{\mathbf{k}})Z_{b}(j\omega) \tag{24}\]  
The transmissibility from the seat to each segment of the 7-DOF model can be computed from Equation (24) and will be used in the estimation of the unknown parameters. The expressions for the matrices and force vectors are provided in Appendix B.
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 2 Materials and Methods
### Biodetection Method for Salmonella spp.
The reading system used in this study was developed by Lumensia Sensors and had hardware and software components. Its primary function was to interpret the optical signals generated by the biosensor and translate them into measurements of resonance, typically expressed in picometers (pm). This system was crucial for monitoring and quantifying the interaction between the biosensor and the _Salmonella_ spp. analyte [44]. The measurement sensogram enabled real-time monitoring and quantification of _Salmonella_ spp. using the biosensor. It visually represented the biosensor's response to the analyte and enabled estimation of the analyte's concentration in the sample of interest (Figure 4). The _Salmonella_ spp. sensing experiment employed a flowing protocol to detect and quantify _Salmonella_ spp. through real-time monitoring of changes in resonance signals on the biosensor (Figure 5). Initially, a solution of buffered peptone water flowed through the system for 3 min. This step was essential to establish a baseline reference signal. Next, the bacterial sample, which had been diluted in the same buffered peptone water solution, flowed through the system for 15 min, Figure 5a. During this phase, the biosensor interacted with the bacterial sample to detect the presence of _Salmonella_ spp. Subsequently, a cleaning buffer, composed of buffered peptone water similar to the solution used in the initial reference step, flowed through the system for 5 min. This last step prepared the system for subsequent analyses and ensured that any residual substances were removed. The y-axis of the sensogram (Figure 5b) represents the resonance values in picometers (pm), and the x-axis shows the time in seconds. This graphical representation illustrates how the resonance changed over time. The software detected positive differences in resonance between the functionalized rings with the specific _Salmonella_ spp. antibody (primary bioreceptor) and the reference rings (negative controls and blanks). Such a positive difference indicated the presence of the target analyte (_Salmonella_ spp.). The measurement sensogram enabled indirect quantitative estimation of the concentration of the target analyte, expressed in terms of colony-forming units per milliliter (CFU/mL). The greater the response (increase in resonance values) on the measurement sensogram, the higher the concentration of _Salmonella_ spp. in the sample. This protocol enabled comprehensive assessment of the results from the setup reader. The units measured by the reader in picometers (pm) were compared with the concentration according to the traditional ISO culture method (in CFU/mL). This comparison gave a reliable evaluation of immunosensor performance in terms of sensitivity and detection accuracy.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 4 Federated Optimisation (FedOpt)
### Gradient Aggregation in FedOpt  
Secure gradient aggregation in the form of cipher-text can be achieved through homomorphic encryption. However, the large amounts of required communication resources and the computation overhead on public-key encryption might delay and disturb the accuracy of data [19; 20]. Towards this end, we utilise the additively homomorphic encryption in FedOpt in order to achieve efficiency throughout the learning process. Furthermore, differential privacy is used in order to tolerate the local users' dropouts and to add calibrated noises before encryption in each gradient. In this context, each user \(\Pi\) uses a small-size batch from the local dataset \(D_{i}\) and learns the model to compute the local gradient \(G_{\Pi}\) in each epoch. In order to protect their local gradients, the local users use Laplace mechanism to encrypt their local gradients using \(E_{\Pi}=E_{\delta}(G_{\Pi}+Lap(\frac{\Delta f_{\Pi}}{\varepsilon}))\). Once the cloud server receives all the encrypted gradients, it conducts the aggregation operation where the noises are nearly eliminated due to the symmetry of the Laplace mechanism. This aggregation operation is processed by the following equation:  
\[E_{add}=\tau_{1}\oplus\tau_{2}\oplus\ldots\oplus\tau_{n}=E_{\delta}(\sum_{\Pi= 1}^{n}G_{\Pi}) \tag{6}\]  
In the end, all the users decrypt the encrypted global gradient \(E_{add}\) that is received from the cloud server using the following equation:  
\[D_{\delta}(E_{add})=\sum_{\Pi=1}^{n}G_{\Pi} \tag{7}\]  
The detailed pseudocode of privacy preservation technique using differential privacy which is integrated with additively homomorphic encryption is formalised in Algorithm 2.
## 3 Materials and Methods
### Synthesis of ((2-mercaptoethanol))thio)polyisobutylene 1  
**Thermal initiation.** PIB (10.0 g, 10 mmol), 1,2-ethanedithiol (1.67 mL, 20 mmol) and appropriate initiator (1.0 mmol, 164 mg of AIBN or 146 mg of DTBP) were dissolved in 140 mL of heptane-ethanol 1:1 (_v/v_) mixture, degassed by three freeze-pump-thaw cycles and stirred at 25 or 70\({}^{\circ}\)C for 24 or 48 h (Table 4, entries 1-4).  
**Photoinitiation.** PIB (10.0 g, 10 mmol), 1,2-ethanedithiol (1.67 mL, 20 mmol) and AIBN (1.0 mmol, 164 mg) were dissolved in 140 mL of heptane-ethanol 1:1 (_v/v_) mixture, degassed by three freeze-pump-thaw cycles and stirred at room temperature with irradiation at 365 nm for 8 h (Table 4, entry 5).  
**Reaction mixture workup.** After the reaction was complete, deionized water (10 mL) was added to the solution with formation of biphasic system. The heptane layer was washed twice with 90% ethanol (2 \(\times\) 50 mL), dried over anhydrous Na\({}_{2}\)SO\({}_{4}\) and concentrated under reduced pressure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Entry** & **t/\({}^{\circ}\)C** & **Initiator** & **Time/h** & **Conversion/\%** \\ \hline
1 & 70 & AIBN & 24 & 100 \\
2 & 70 & DTBP & 24 & 100 \\
3 & 25 & AIBN & 48 & 50 \\
4 & 25 & DTBP & 48 & 20 \\
5 & 25 & UV & 8 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Conditions used for the synthesis of polymer **1**.  
Crude product was subjected to chromatography, eluting with heptane-ethyl acetate 4:1 (v:v) mixture, that afforded desired **1** as a colorless oil.  
H-NMR (600 MHz, CDCl\({}_{3}\), 298 K, \(\delta\)): 2.77-2.69 (m, 4H; CH\({}_{2}\)S(CH\({}_{2}\))\({}_{2}\)SH), 2.52 (dd, 1\({}^{\prime}\)J = 12.4, 3\({}^{\prime}\)J = 5.4 Hz, 1H; CH\({}_{2}\)S(CH\({}_{2}\))\({}_{2}\)SH), 2.37 (dd, 3\({}^{\prime}\)J = 12.3, 3\({}^{\prime}\)J = 8.0 Hz, 1H; CH\({}_{2}\)S(CH\({}_{2}\))\({}_{2}\)SH), 1.75-1.71 (m, 1H; CH(CH\({}_{3}\))CH\({}_{2}\)S), 1.54-1.33 (m, 38H; PIB-CH\({}_{2}\)), 1.21-0.99 (m, 120H; PIB-CH\({}_{3}\)). 13C[1H] NMR (150 MHz, CDCl\({}_{3}\), 298 K, \(\delta\)): 59.7, 59.6, 59.4, 59.0, 58.4, 57.1, 52.7, 42.4, 38.3, 38.2, 38.0, 37.9, 37.0, 36.2, 32.7, 32.6, 31.4, 31.3, 31.2, 30.9, 29.7, 29.4, 29.3, 25.0, 22.7.
# GPS Multipath Analysis Using Fresnel Zones
## 3 Identification of Diffracted Satellite Signals Using Fresnel Zones and OAEMs
### Algorithm Assessment
Only after integrating the Fresnel zones can the number of fixed solutions be further increased, and consequently the coordinate differences vary between the maximum and minimum values of 3 cm and \(-5\) cm, respectively. In contrast, the RMS values of the time series are similar for every data set. This is reasonable, since the satellite signals where the Fresnel zone partially intersects the obstruction source (cf. Figure 12) will not necessarily have a significant impact on the coordinate estimation. Nevertheless, compared to the results from the classical OAEM approach, the number of differences that exceed the 3\(\sigma\) boundary is reduced, which corresponds to the lower peaks in some observation epochs that are only present in the red coordinate time series in Figure 15. Hence, especially in applications, such as deformation monitoring or kinematic positioning, where accurate coordinates are needed in every observation epoch, the proposed algorithm could exploit its full potential.
## 4 Case Study
### Data Analysis  
For the problem of dam deformation, it was first necessary to assess whether the influence of downstream water level should be considered. The data of the upstream and downstream water levels are presented in Figure 7. The downstream water level was generally stable compared to the upstream water level and the two water levels were quite different, so the influence of changes in the downstream water level were not included in the dam deformation model.  
Figure 6: The layout of horizontal displacement measuring points on the dam.  
Figure 7: Water level data collected from automated monitoring equipment: (**a**) upstream water level and (**b**) downstream water level.  
In addition to the standard dam related variables, there are many influencing factors that are difficult to remove, such as lightning strikes and interference by magnetic fields during monitoring. However, using the DBSCAN method to preprocess the observed data can effectively prevent equipment based abnormal values caused by such factors from contaminating the data set. The original observation data collected from the automated monitoring equipment are shown in Figure 8a, from which we can clearly find several equipment based abnormal values. The DBSCAN method uses clustering to identify equipment based outliers of the same level into a single category and then eliminate them. The results after removing the outliers can be seen in Figure 8b. The data processed through the DBSCAN method were smoother than the original observation data, and most of the abnormal values due to equipment error were eliminated.  
The basic stats of the original data and the data processed using the DBSCAN method are listed in Table 2. The average values of the processed data were similar to the average values of the original data, but the standard deviation was reduced. This result showed that the DBSCAN method can effectively filter device caused outliers and help suppress any interference by such data on the prediction.
# Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
## 3 The Augmented Digital Control Room
### Augmenting the Digital Control Room
#### 3.3.1 Data Visualization  
Exploring data is a common task in a digital control room. There are various solutions to display them, e.g., some rely on a tablet you need to hold or some on a big display mounted on a surface. These have in common that they hinder the user in some way or another, e.g., the tablet occupies a hand or the big displays restrict the users' movement. Therefore, although MR hardware solutions such as the Microsoft HoloLens 2 are bulky for the moment, they offer new way to interact with data compared to more traditional devices.  
Data Visualization, meaning specifically how data can be visualized and how possible new forms of visualizations may affect data exploration, is a field of active research. There are already studies that deal with this topic. [PERSON] et al. [23], for example, have investigated whether and to what extent Virtual Reality influences the amount of insights gained by examining graphical information. For this purpose, they created different visualizations in Virtual Reality and also corresponding counterparts in classic 2D visualizations. It was found that the users themselves found data exploration in Virtual Reality more satisfying and successful. The paper emphasizes the motivating character of the representation. [PERSON] et al. [24] tried to extend tools designed for data exploration on two-dimensional screens in the context of particle physics with Augmented Reality (AR) features. They stuck to mouse and keyboard input and did not introduce new input devices. Their evaluators found their extension mostly complementing the PC view and recognized the MR view as a useful tool for three-dimensional inspection. However, they also noted that they would still prefer to use two-dimensional tools for some tasks. Concluding their work, they noted a dedicated input for the AR space might be useful. They were surprised that the lack of orthographic projection did not bother their evaluators.  
Complementing existing work our research question for this part of this work is:  
_RQ 1: Does the immersive experience help to solve the given task?_
# Fine-Tuning Large Language Models for Kazakh Text Simplification
## 3 Methodology
### Fine-Tuning Kazakh Text Simplification  
To improve over the baseline Seq2 Seq model, we fine-tune large pre-trained language models (LLMs) for the Kazakh text simplification task. While the Seq2 Seq model is effective for learning from aligned complex-simple sentence pairs, it often struggles with long-distance dependencies, rare morphological patterns, and fluency. Large language models, pre-trained on massive multilingual corpora, are better suited for such challenges. Their ability to generalize from limited fine-tuning data makes them a promising solution for low-resource languages like Kazakh.  
We use three instruction-tuned LLMs in the experiments: Llama-3.2-3B, Llama-3.3-70B, and Qwen2-72B-Instruct. These models are selected due to their open access, multilingual support, and compatibility with parameter-efficient tuning. In this work, each model is evaluated in two modes: (1) zero-shot or instruction-only inference without additional fine-tuning, and (2) fine-tuning on our Kazakh simplification dataset. This allows us to analyze the effect of instruction tuning alone versus task-specific adaptation on simplification quality.  
Due to the computational cost of full fine-tuning, we apply low-rank adaptation (LoRA) [20] to all models. LoRA introduces small trainable matrices into the attention and feed-forward layers of the transformer without modifying the original weights. This allows for the efficient training of large models on limited hardware. For convenience, we denote the fine-tuned LLMs for Kazakh text simplification as KazSim.
## 2 Method Proposed
### Fatigue Damage Model of Bridge Wires Based on CDM  
The concept of continuum damage mechanics (CDM) was first presented by [PERSON] [19] and has been the widely used in engineering applications, in which a damage variable (\(D\)) is utilized to describe the degradation of material mechanical properties. The common form of the equation of fatigue damage evolution rate is written as:  
\[\frac{dD_{f}}{dN}\ =\ F_{f}\big{(}\sigma_{max},S,D_{f},T,\ \ldots\big{)} \tag{1}\]  
where \(D_{f}\) is the fatigue damage variable, \(N\) is the cyclic number of loads, \(\sigma_{\max}\) is the maximum stress, and \(S\) is the stress range.  
Figure 1: Flow chart of a numerical simulation of the damage evolution of high-strength steel wire under a pre-corrosion pit.  
In terms of stress, Equation (1) can be simplified as [18; 19]:  
\[\frac{dD_{f}}{dN}\ =\ \left(1-D_{f}\right)^{-\mu}\left[\frac{S}{M_{0}}\right]^{B} \tag{2}\]  
where \(\mu\), \(B\) and \(M_{0}\) are material parameters, which can be fitted by the stress-life (S-N) curve of steel wire from the fatigue tensile test.  
Considering the initial condition (\(D_{f}=0\), when \(N=0\)), Equation (3) can be obtained by integrating Equation (2):  
\[D_{f}\ =\ 1-\left\{1-(\mu+1)\left[\frac{S}{M_{0}}\right]^{B}N\right\}^{\frac{1}{ \mu+1}} \tag{3}\]