esa-sceva/satcom-chunk-collection huggingface dataset:
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 2 Cell Modelling
### Description of the Aging Part of the Model
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameter** & **Unit** \\ \hline \(a_{s}^{n}\) & Specific surface area & m\({}^{-1}\) \\ \(j_{0,sei}\) & Exchange current density & A\(\cdot\)m\({}^{-2}\) \\ \(\alpha_{n}\) & Symmetry factor & - \\ \(n\) & Number of transferred electrons & - \\ \(E_{a}\) & Activation Energy & kJ\(\cdot\)mol\({}^{-1}\) \\ \(\delta_{sei}\) & SEI layer thickness & m \\ \(M_{sei}\) & Molar mass of SEI layer & kg\(\cdot\)mol\({}^{-1}\) \\ \(\rho\) & Density of SEI layer & kg\(\cdot\)m\({}^{-3}\) \\ \(\kappa_{sei}\) & SEI layer conductivity & 5\(\cdot\)m\({}^{-1}\) \\ \(R_{sei,init}\) & Initial resistance of SEI layer & \(\Omega\cdot\)m\({}^{2}\) \\ \(R_{p}\) & Resistance of side reaction & \(\Omega\cdot\)m\({}^{2}\) \\  & product & \\ \(I_{ch}\) & Charging current & A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Aging sub-model parameters. where \(\lambda\) is the SEI decay rate constant (m\({}^{-1}\)). To obtain this coefficient, it is assumed that, after some cycles, the SEI thickness stabilizes. To guarantee an efficient fast charging strategy, the aging sub-model must also reproduce with good accuracy the higher capacity loss that has been observed at higher charging C-rates. This requirement is not permitted with Relation (18). Thus, to scale the inverse Butler-Volmer overpotential \(\eta_{n}\), a coefficient \(K_{\eta}\) is introduced in Equation (18), which thus becomes  
\[j_{sr}=-a_{s}^{n}j_{0,sei}exp(-\lambda\delta_{sei})exp\left(\frac{E_{a}}{R} \left(\frac{1}{T_{ref}}-\frac{1}{T}\right)\right)exp\left(\frac{-nF\alpha_{n}} {RT}\left(K_{\eta}\eta_{n}+U_{n}-U_{sei}^{ref}\right)\right) \tag{19}\]  
with \(C_{T}=-a_{s}^{n}j_{0,sei}exp\left(\frac{nF\alpha_{n}}{RT}U_{sei}^{ref}\right) exp\left(\frac{E_{a}}{RT\,ref}\right)\). In Relation (19), it can be noticed that the time-dependent (calendar) aging effects and current-dependent (cycling) aging effects are decoupled. This offers the possibility of better interpreting the influence of each mechanism on cell aging, and of emphasizing the influence of the C-rate on the decrease in capacity during cycling and in particular rapid charges. To the best knowledge of the authors, the physics-based battery model proposed in this study, which takes into account degradation mechanisms as a function of SOC, temperature and C rate, is unprecedented in the literature. These characteristics also allow a very good correspondence between the response of the model and the measurements.
## 5 Discussion
### Other Measurements  
The other measurements used in this experiment (video/audio-recordings, physiology) provided multiple challenges, either because the data gathered were not appropriate for analysis or because the data did not provide any significant insights. However, it should be noted that the emotion manipulation in this experiment was designed to be low, so it is possible that the sensors used were not able to detect the impact.  
The video recordings provided interesting data on user experience, but the behaviour participants displayed did not necessarily follow from the behaviour the robot was displaying. Therefore, it was not possible to draw conclusions regarding potential negative consequences of expression of artificial emotion from the video recordings. Analysis of the video recordings indicated that participants displayed behaviours following several triggers and not just the robot's displayed behaviour. This suggests that behaviour analysis through video recordings is not suitable for analysing the impact of a specific robot feature.  
The audio recordings did provide data that could be analysed. However, interpersonal differences may have impacted the results, meaning no strong conclusions could be drawn, as significant findings were not consistent for all participants. Furthermore, it is possible that the number of data gathered through these audio-recordings was not large enough to draw conclusions, as the input from participants was limited due to the nature of the interaction.  
Finally, physiological data were gathered to support other behavioural measures. However, the data gathered in this study did not support any other measures. EDA data was not suitable for analysis, and HRV data did not indicate any differences based on the robot's behaviour. It should however be noted that the emotion manipulation bythe robot was minimal and therefore the sensor may not have been able to pick up on these differences.  
The results--or lack thereof--from these technologies indicate that the technologies are not yet advanced enough to be used in all human-robot interactions and to investigate robot ethics. This raises questions regarding HRI research that is currently being performed--are we asking the right research questions and should we even continue research in this area if we cannot sufficiently measure potential consequences of current developments? Should we shift the focus from developing social robots to developing methods to understand the impact--both intended and unintended--of social robots? Perhaps the effort to develop responsible social robots with minimal negative impact of human-robot interactions on individuals and society should be increased?
## 2 A Traditional Objective Function in Damage Identification  
The objective function is a vital factor for structural damage identification, as it determines the accuracy and computational efficiency of identification. A commonly used traditional objective function can be introduced as follows.  
According to the structural characteristics, the structure is divided into \(n\) substructures. The damage factor of the substructures, which is to be identified, is denoted by \(\mathbf{\mu}\). Here, \(\mathbf{\mu}=[\mu_{1},\mu_{2},\cdots,\mu_{n}]\), where \(\mu_{i}\) is the damage factor of the \(i\)-th substructure, which represents the ratio of the stiffness of the damaged \(i\)-th substructure to its initial stiffness. The vector \(\mathbf{\varphi}_{i}(\mathbf{\mu})\) and the scalar \(\omega_{i}(\mathbf{\mu})\) are respectively the \(i\)-th mode shape and the \(i\)-th natural frequency of the FE model corresponding to the damage factor \(\mathbf{\mu}\). Let \(\overline{\mathbf{\mu}}\) be the actual damage factor of the real damaged structure. Besides, let \(\overline{\mathbf{\varphi}}_{i}\) and \(\overline{\omega}_{i}\) respectively be the \(i\)-th measured mode shape and \(i\)-th natural frequency of the real damaged structure. For the purpose of identification of the actual damage factor, natural frequencies and mode shapes are often employed, and the objective function is generally established as shown in Equations (1) and (2):  
\[f(\mathbf{\mu})=\alpha_{\varphi}\sum_{i=1}^{n_{\varphi}}(1-MAC(\mathbf{\varphi}_{i}(\bm {\mu}),\overline{\mathbf{\varphi}}_{i}))+\alpha_{\omega}\sum_{i=1}^{n_{\omega}} \left(\frac{\omega_{i}(\mathbf{\mu})-\overline{\omega}_{i}}{\overline{\omega}_{i} }\right)^{2} \tag{1}\]  
\[MAC(\mathbf{\varphi}_{i}(\mathbf{\mu}),\overline{\mathbf{\varphi}}_{i})=\frac{\left( \overline{\mathbf{\varphi}}_{i}^{T}\mathbf{\varphi}_{i}(\mathbf{\mu})\right)\left(\mathbf{ \varphi}_{i}^{T}(\mathbf{\mu})\overline{\mathbf{\varphi}}_{i}\right)}{\left(\mathbf{ \varphi}_{i}^{T}(\mathbf{\mu})\mathbf{\varphi}_{i}(\mathbf{\mu})\right)\left(\overline{ \mathbf{\varphi}}_{i}^{T}\overline{\mathbf{\varphi}}_{i}\right)} \tag{2}\]  
where \(n_{\varphi}\) is the number of measured structural mode shapes and \(n_{\omega}\) is the number of measured natural frequencies, while \(\alpha_{\varphi}\) and \(\alpha_{\omega}\) are respectively the numerical weights used for the mode shape and natural frequency error terms. Note that the mode shape vector \(\mathbf{\varphi}_{i}(\mathbf{\mu})\) may be complex, and the superscript \(T\) denotes the matrix transpose/adjoint.  
During the optimization process using Equation (1), the mode shape \(\mathbf{\varphi}_{i}(\mathbf{\mu})\) and the natural frequency \(\omega_{i}(\mathbf{\mu})\) are usually computed repeatedly by solving the eigen equation of the mass and stiffness matrix of the modified FE model, which is time-consuming especially for large-scale civil engineering structures.
# Uncertainty in Visual Generative AI
## 2 Background
### Image Retrieval and Visual GAI
#### 2.1.1 Image-to-Text Generation  
A significant amount of computer vision research is focused on classification; however, as an image has more complicated elements, a single label may not be appropriate to properly describe an image [69]. Therefore, some computer vision methods focus on creating a brief description of a given image [69; 70; 71]. Several datasets, such as the Microsoft Common Objects in Context (COCO) dataset (see [72]) and the Stanford image-paragraph dataset (see [73]), challenge researchers to create models that do this accurately and automatically [66; 70; 74]. Many other datasets also exist for the captioning of 2D images, 3D images, videos, and visual question answers [65]. Many techniques use the standard encoder and decoder architecture, growingly popular generative techniques (such as variational autoencoders (VAEs) and generative adversarial networks (GANs)), or reinforcement learning [70].  
Recently, several large corporations have led the way in image-to-text research with several general-purpose image-text models capable of image captioning and visual question answering. In 2022, Microsoft released the Generative Image-to-text Transformer (GIT), which consists of one image encoder and one text decoder working together within a single task, as opposed to the historical setup where the encoder and decoder work on two separate tasks [75]. That same year, Salesforce developed an encoder-decoder model that works with a captioner that generates synthetic captions for images and a filter that removes irrelevant ones, called Bootstrapping Language-Image Pre-training for unified vision-language understanding and generation (BLIP) [76]. Google's DeepMind also joined with Flamingo, a family of visual language models that was trained using image-label pairs [77]. Later, in 2023, Microsoft presented the new Large Language and Vision Assistant (LLaVA) that combines the power of a vision encoder with a large language model [8], whereas in the same year, Salesforce built upon the earlier BLIP model with Bootstrapping Language-Image Pre-training with frozen unimodal models (BLIP-2), which combines frozen large language models and pre-trained image encoders via a \"Querying Transformer\" [78]. Additionally, in collaboration with academic partners, Salesforce also launched a fine-tuned version of BLIP-2 designed as an instruction tuning framework, InstructBLIP [79]. Image-to-text research is a growing field, like its related text-to-image methods.
# A Brief Roadmap into Uncertain Knowledge Representation via Probabilistic Description Logics
###### Abstract  
Logic-based knowledge representation is one of the main building blocks of (logic-based) artificial intelligence. While most successful knowledge representation languages are based on classical logic, realistic intelligent applications need to handle uncertainty in an adequate manner. Over the years, many different languages for representing uncertain knowledge--often extensions of classical knowledge representation languages--have been proposed. We briefly present some of the defining properties of these languages as they pertain to the family of probabilistic description logics. This limited view is intended to help pave the way for the interested researcher to find the most adequate language for their needs, and potentially identify the remaining gaps.  
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Footnote †: journal: 28  
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This paper is an attempt to decrease this entry cost by providing a very brief roadmap into knowledge representation formalisms dealing with uncertainty. The roadmap is limited in many aspects: it primarily focuses on probabilities as uncertainty representation, and uses a well-known but by no means all-encompassing family of (classical) knowledge representation formalisms as the basis for constructing probabilistic extensions. Nonetheless, the discussion of the ideas behind the formalisms and their properties and limitations will hopefully provide enough of a background for an interested reader to understand other variants and explore the rest of the forest by themself. In particular, the pattern for extending logical formalisms with probabilities repeats itself almost identically throughout languages. What this roadmap does not provide are the tools to deal with other kinds of uncertainty representations [3; 4] such as possibility theory [5; 6] or evidence theory [7; 8]; nor any other kinds of imperfect knowledge such as vagueness [9; 10; 11] or inconsistency [12; 13; 14].  
The structure of this paper is as straightforward as can be. We first discuss the fact that _uncertainty_ is a multi-faceted issue, and the need to understand which face is relevant for each specific application. In this section, we also restrict our attention to probabilities, justifying our choice. Afterwards, we introduce a class of uncertain knowledge representation formalisms built as extensions of the well-known family of description logics. Although this choice limits the class of languages studied, it gives a general overview of the issues encountered, and the kinds of uncertain representation and reasoning available.
# Analysis, Evaluation and Simulation of Railway
## 1 Introduction
### Case of Study
#### 1.4.2 Locomotives, Multiple Units and Rolling Stock  
For passenger service, rebuilt medium-speed diesel-electric locomotives 334 (with a maximum speed of 200 km/h, series that is very closed to GEC Class 67 [23] developed for UK operators) have been used in this work for simulation purposes. Also, new hybrids trains (hybrid diesel-electric and electric multiple unit, HDEMU S-730) managed and bought by the Spanish Railway Operator (RENFE) during this decade have been considered. The rolling stock considered in simulations were built by: Patentes Talgo, Spain (coaches Talgo IV for the diesel-electric locomotive, and coaches for S-730) [30], Vossloh/EMD, Spain (now Stadler Rail [31], these locomotives are based on diesel engines 12N710G3B-EC licensed by General Motors, 2004). For freight purposes, S-253 (TRAXX series by Bombardier, 2011) [21] and S-252 (Eurosprinter pilot series built by Krauss-Maffei and Siemens, 1992) [22], are also used for simulation purposes.  
Main characteristics of these locomotives, coaches and electric multiple units (EMU) are given in Tables 1 and 2. Figure 2 depicts some of these trains.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Locomotive** & **S-334** & **S-730** & **S-253** & **S-252** \\ \hline \multirow{2}{*}{UIC Type} & \multirow{2}{*}{Diesel-electric Bo’Bo’} & \multirow{2}{*}{Dual hybrid unit 2°Bo’Bo’Bo’} & \multirow{2}{*}{Electric Bo’Bo’Bo’} \\  & & & & \\ \hline Service & Passenger & Passenger & Freight & Passenger/Freight \\ \hline Weight (ton) & 90 & 140 & 87 & 86 \\ \hline Rail gauge (mm) & 1668 & 1435 and 16681 & 1668 & 1668 \\ \hline Max. tractive effort (kN) & 92 & 220 & 300 & 300 \\ \hline Max. Speed (km/h) & 200 & 250/220/180 & 140 & 200 \\ \hline Power (MW) & 2.5 & 4.8/4.0/2.4 & 5.4 & 5.6 \\ \hline Voltage (kV) & - & 25/3/NA & 3 & 3 \\ \hline \end{tabular}  
* This train (S-730 HDEMU) allows the change of gauge. UIC, Union of Railways.  
\end{table}
Table 1: Characteristics of locomotives.  
Figure 1: Route characteristics (main stations in red, electrified and non-electrified sections, double- and single-track sections in orange): (**a**) Alcazar SJ-Cartagena (sea level) track profile; (**b**) location of curves and their radius (m).
# Hybrid Adaptive Controller Design with Hysteresis Compensator for a Piezo-Actuated Stage
## 4 Results
### Modeling Results  
The validity of the Hammerstein model was verified by comparing it with the BW model. Sinusoidal input voltages with different frequencies and an amplitude of 90 V were employed. The results are shown in Figure 4. The maximum absolute error (MAXAE) and mean absolute error (MAE) were calculated via (31) and (32), respectively. \(K\) represents a total number of sampling points. \(y(k)\) and \(y_{m}(k)\) represent the actual output and model output at the \(k\)-th sampling point, respectively. The error analysis of the BW model and the Hammerstein model are presented in Table 1. The MAXAEs of the Hammerstein model were 0.5896, 0.8455, and 0.9517 \(\upmu\)m, respectively, which were reduced by 7.13%, 7.23%, and 52.42% compared with those of the BW model. In addition, the MAEs of the proposed model were 0.2847, 0.4469, and 0.4669 \(\upmu\)m, respectively, which were reduced by 5.60%, 13.61%, and 61.36% compared with those of the BW model.  
This indicates that the presented Hammerstein model can approximate the RD hysteresis characteristic of P-AS more accurately than the BW model.  
\[e_{MAXAE}=\max(y(k)-y_{m}(k)) \tag{31}\]  
\[e_{MAE}=\frac{\sum_{k=1}^{\mathrm{K}}|y(k)-y_{m}(k)|}{K} \tag{32}\]  
Figure 3: (**a**) Picture of the experimental testbed. (**b**) Schematic of the experimental testbed.
## 2 Related Works
### Depth Estimation Using Deep Learning  
Nowadays, to achieve depth estimation using a monocular camera, neural networks are commonly used. [PERSON] et al. [23] proposed one of the typical solutions that presented a solution to measure depth relations by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines the prediction locally. By applying the raw datasets (NYU Depth and KITTI) as large sources of training data, the method matches detailed depth boundaries without the need for superpixelation.  
Another solution that can overcome the weakness of using CNN for depth estimation is that vast amounts of data need to be manually labeled before training [24]. A CNN for single-view depth estimation that can be trained end-to-end, unsupervised, using data captured by a stereo camera without requiring a pre-training stage or annotated ground-truth depths. To achieve that, an inverse warp of the target image is generated using the predicted depth and known inter-view displacement to reconstruct the source image; the photometric error in the reconstruction is the reconstruction loss for the encoder. [PERSON] et al. [25] also presented an unsupervised learning framework for the task of monocular depth and camera motion estimation from unstructured video sequences. The system is trained on unlabeled videos and yet performs comparably with approaches that require ground-truth depth or pose for training. As a whole, Table 1 highlights the various deep-learning-based approaches to depth estimation.  
While deep learning technology has showcased its proficiency in depth perception and measurement, certain challenges persist: (i) Specialized Equipment: Generating media data with depth information necessitates specialized equipment like Kinect cameras, ToF cameras, or LiDAR sensors to create training datasets. Without such equipment, the laborious task of manually labeling each object with ground truth distance becomes inevitable. (ii) Unsupervised Framework: Unsupervised monocular camera depth estimation typically relies on stereo video sequences as input. It leverages geometric disparities, photometric errors, or feature discrepancies between adjacent frames as self-supervised signals for model training.  
In this study, we propose a novel integration approach for navigation that merges computer vision and deep learning in object detection and distance estimation. This approach is user-friendly, easily maintainable, and cost-effective. Importantly, it demands fewer computational resources, making it suitable for implementation on smartphones and similar devices.
# Q-LBR: Q-Learning Based Load Balancing Routing for UAV-Assisted VANET
## 4 Proposed Q-LBR Design
### Routing Decision Process  
According to Algorithm 2, the ground source node can receive \(p\) messages owing to multiple paths from the ground destination node. Through this message, routing metrics are calculated in \(\mathit{RREP}_{p}\) packets for each routing path. If \(\mathit{RREP}_{p}\) including an URN exists, and this path is less expensive than the ground path, the \(\mathit{URCI}_{u}\) of the URN and the traffic priority (TP) of the packets check the URPA condition. If all the conditions are satisfied, the path including the URN can be selected as the optimal path. If unsatisfied, the next suboptimal ground path is selected.  
```
1:S \(\leftarrow\) Ground source node;
2:D \(\leftarrow\) Ground destination node;
3:URN \(\leftarrow\) UAV relay node;
4:RCU \(\leftarrow\) Routing cost including UAV path;
5:RCG \(\leftarrow\) Routing cost with GVN only;
6:URPA \(\leftarrow\) UAV routing policy area;
7:\(\mathit{URCI}_{u}\)\(\leftarrow\) UAV relay congestion identifier from URN;
8:TP \(\leftarrow\) Traffic priority
9:
10:for\(p\to 1\), n do
11:ifS receives \(\mathit{RREP}_{p}(D)\) packet then
12: Calculate routing cost using metric information collected in \(\mathit{RREP}_{p}(D)\) packet
13:if (\(\mathit{RREP}_{k}\) path contains URN \(\parallel\) RCU \(<\) RCG) then
14:if (\(\mathit{URCI}_{u}\) and \(\mathit{TP}\) satisfy URPA's UAV relay conditions) then
15: Select the routing path that includes the URN as the optimal route
16:else
17: Select the suboptimal ground path
18:endif
19:Else
20: Select the optimal ground path
21:endif
22:endif
23:endfor
```  
**Algorithm 2**Routing Decision Process