esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Study
### Existing Track Quality Indices
#### 2.2.3 MDZ-A Number  
The MDZ-a number is used with the intention of describing the effects of track geometry irregularities on passing trains. Thus, the formula combines deviations of the longitudinal level (\(\Delta v^{\prime}\)), alignment (\(\Delta h\)), and cross level (\(\Delta ii\)) and also considers the maximum line speed. The influence of the velocity is reduced via the exponential number 0.65; this factor intents to represent the effects of vehicle suspension. The factor \(c\) (test section: \(c\) = 1) is a scaling coefficient, and the variable \(L\) represents the length of the considered track segment (3) [27, 28, 29].  
\[MDZ=c*\frac{1}{L}*V^{0.65}*\sum_{i=1}^{\frac{L}{2}}\sqrt{\left(\Delta v^{\prime }\right)^{2}+\left(\Delta h+\Delta ii\right)^{2}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Geometry Parameter** & **Intervention Limit** \\ \hline Longitudinal level (V \(\leq\) 80 [kph], V \(\leq\) 120 [kph]) & \(\pm\)17 [mm], \(\pm\)13 [mm] \\ \hline Alignment (V \(\leq\) 80 [kph], V \(\leq\) 120 [kph]) & \(\pm\)15 [mm], \(\pm\)11 [mm] \\ \hline Gauge positive, Gauge negative (V \(\leq\) 120 kph) & +30 [mm], \(-\)9 [mm] \\ \hline Cross level (V \(\leq\) 120 kph) & \(\pm\)20 [mm] \\ \hline Twist (V \(\leq\) 120 kph) & \(\pm\)5 [mm] \\ \hline \end{tabular}
\end{table}
Table 2: Intervention limits according to EN 13848-5 [26].
## 7 Conclusions  
This study has demonstrated the potential of advanced techniques of SR in predicting \(\mathrm{p_{s}}\) in clayey soils, a critical geotechnical property. The research has provided several key insights and contributions:  
1. Four advanced symbolic regression techniques (PhySO, PySR, MOSR, and PGSR) were applied, all showing strong predictive performance (R\({}^{2}\) > 0.91 and MAE < 23 kPa) along with highly simplified equations. PhySO and PySR outperformed the others in global metrics, with R\({}^{2}\) values of 0.921 and 0.933 and MAEs of 20.66 and 20.49 kPa, respectively. When analysing performance across pressure intervals, PhySO  
Figure 10: Bar chart showing the sensitivity of model output to input variables. S1s are first-order sensitivity indices and STs are total-order sensitivity indices.  
demonstrated the most balanced and robust behaviour, particularly excelling in low- to medium-pressure predictions. PySR achieved the highest accuracy under high-pressure conditions while maintaining consistent overall performance. MOSR stood out for its ability to reduce edge-case bias and was effective in low- to medium-pressure ranges, whereas PGSR showed no distinct advantage in any specific interval.
2. Since each equation obtained from each model has different variables and performances across different pressure intervals, this allows for a quick determination of \(\mathrm{p_{s}}\) based on the available parameters, the potential pressure range, and the objective of the analysis.
3. This study identified PI as the most significant variable influencing \(\mathrm{p_{s}}\), surpassing the other variables in all models.
4. A remarkable aspect of this study is the use of undisturbed clay samples. The undisturbed nature of these samples provides a more accurate representation of the natural conditions, enhancing the reliability and applicability of the findings.  
The database used in this study could be further expanded with additional samples to improve the generalizability and scope of the proposed equations. As with any predictive algorithm, it is essential to consider that the model's performance and the reliability of the expressions may decrease when applied outside the training range of the input variables.  
Conceptualization, E.D. and R.T.; methodology, E.D. and R.T.; software, E.D.; validation, E.D. and R.T.; formal analysis, E.D. and R.T.; investigation, E.D. and R.T.; data curation, E.D.; writing--original draft preparation, E.D.; writing--review and editing, E.D. and R.T.; visualization, E.D.; supervision, E.D. and R.T.; project administration, E.D
# A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability
## 4 Discussion
### Limitations
As [88] discusses, several pre-training factors impact the accuracy of a model, such as data augmentation and the amount of data used in the pre-training. Fine-tuning can also lead to overfitting [89] and catastrophic forgetting [90,91], as well as other problems related to training procedures, such as the investment of resources. Finally, the argument that one may pre-train using dataset X to learn their representations and then fine-tune using dataset Y to achieve that set of categories is not sustained by the current literature, and more experiments are needed in order to validate this hypothesis. _Lack of ontologies to represent emotion._ An ontology defines a set of primitives related to a specific domain of knowledge. Although there exist ontologies related to emotions, such as MFOEM [92], they are not used today as a baseline to define the emotion categories and what they represent. For example, _Surprise_, which MFOEM defines as an emotion caused by encountering unexpected events, can have a positive and a negative modifier regarding the person's evaluation of the event, something that is not present in datasets today. For EMOTIC and BoLD, we have emotions that are closely related, such as _Happiness_ and _Pleasure_. However, MFOEM suggests that _Pleasure_ can also be correlated with satisfaction, which could help differentiate between these two classes more deeply. Therefore, by employing ontologies for emotion representation, one could merge the wide range of used label sets and have ground-truth descriptions of them from the literature.
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 4 Discussion
### Antioxidant Activity  
Antioxidants from plants are known for their ability to limit radical reactions by transferring electrons or hydrogen atoms, as well as their ability to interrupt the chain reactions of oxidative degradation [67; 68; 69; 70; 71]. One of the most important groups of plant-based antioxidants is phenolic compounds, which consist of one or more aromatic rings with one or more hydroxyl groups. Phenolic compounds can be divided into several major families according to their chemical structure, including those in phenolic acids and flavonoids.  
[PERSON] et al., 1990 [72], defined three different criteria for the structure-activity relationship of antioxidant compounds, and these are:  
1. [noitemsep,topsep=0 pt]
2. Bors 1: due to hydrogen bonding, the presence of a catechol group on the B-ring leads to a high stability of the antioxidant radical (AO-).
3. Bors 2: a 2,3 double bond in combination with a 4-oxo group on the C-ring facilitates electron delocalization.
4. Bors 3: the presence of OH groups at position 3 and 5 in combination with the 4-oxo group enables electron delocalization via hydrogen bonds [72].  
The correlation of chemical structure with antioxidant activity is usually performed by analysing the natural phenolic compounds and extracts using different antioxidant assays. The underlying chemistry for these assays involves either a hydrogen atom transfer mechanism (HAT) or a single electron transfer mechanism (SET) [69]. The HAT mechanism is a chemical transformation which converts the movement of two elementary particles, a proton and an electron, between two substrates in a single kinetic step [73], whilst the SET mechanisms are characterized by electron transfer from a nucleophile to a substrate in order to produce a radical intermediate [74]. The SET mechanism is subdivided into SET-PT and SPLET. The SET-PT mechanism strongly depends on proton dissociation and ionization potential energy. These antioxidants, which are easily deprotonated and ionized, are highly reactive. In contrast, the SPLET mechanism involves the initial loss of protonsfrom the antioxidant, followed by anion transfer to the radical. This then reacts with the proton, which is influenced by the electron transfer enthalpy and proton affinity [69; 75; 76].
## 7 Discussion  
### Transmission Arrival Prediction  
Currently, we have network nodes calculating the rate at which new transmission requests arrive and estimate the number of transmission requests that will arrive in the future based on the current new transmission arrival rate. The number of transmissions in the network varies over time, but it does not change sharply, and thus it is reasonable to estimate the current rate for a short period in the future. However, there is still room for further improvement in the accuracy of this estimation.
# Re-Identification in Urban Scenarios: A Review of Tools and Methods
## 5 Person Re-Identification
### Person Re-Identification Databases  
The recognition of human attributes, such as gender and clothing types, has excellent prospects in real applications. However, the development of suitable benchmark datasets for attribute recognition remains lagged. Existing human attribute datasets are collected from various sources or from integrating pedestrian ReID datasets. Such heterogeneous collections pose a significant challenge in developing high-quality fine-grained attribute recognition algorithms.  
Among the public databases that have been proposed for person ReID, some examples can be found in the open domain, such as the Richly Annotated Pedestrian (RAP) [17], which contains images gathered from real multi-camera surveillance scenarios with long-term collections, where data samples are annotated, not only with fine-grained human attributes, but also with environmental and contextual factors. RAP contains a total of 41,585 pedestrian image samples, each with 72 annotated attributes, as well as viewpoints, occlusions, and body part information.  
Another example is the VIPeR [18] database, which contains 632 identities acquired from 2 cameras, forming a total of 1264 images. All images were manually annotated, with each image having a resolution of \(128\times 48\) pixels.  
A summary of the public available databases for person ReID, with main characteristics, is presented in Table 1.  
Although many other datasets suitable for object ReID can be found, the ones listed in Table 1 are widely used by most of the authors as benchmarks for performance evaluation and comparison of the proposed works.
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 3 Methodology and Proposed Approach
### Control and Management Layer
#### 3.3.1 Data Access and Integration Module  
This module offers services for sensory devices to integrate with the overall system. It also provides software interfaces for other software components to access and store data. The data operation requests originating from the modules positioned above the \"Data Access and Integration\" layer and those from the shared mobility vehicles within the physical layer can be categorized into deletion, insertion, query, and update operations. Irrespective of the type of operation requested by consumer modules, the data access module executes the following tasks: (1) Initiates transaction management, (2) Gains access to the data source via data connections, and (3) Evaluates whether additional data operations, such as transformations, are needed and, if so, executes them. Furthermore, devices within the physical layer have the capability to insert and update data in the database. For device requests originating from the physical layer, the data access module performs the subsequent tasks: (1) Initiates transaction management, (2) Identifies the operation based on device ID and event type, (3) Transforms data generated by devices into the data source schema, and (4) Accesses the data source through data connections. Access to the database is achieved by invoking the interfaces provided by this module, thereby shielding the devices and modules utilizing the data access service from the technical intricacies of the operation.  
An essential aspect of the data access and integration module is the \"Data Transformation\" function. This function is responsible for affecting the necessary conversion of unstructured data into structured data wherever it is applicable. When a shared mobility vehicle generates an event, it frequently produces unstructured data, necessitating transformation into structured data presented in a comprehensible format for consumption by different modules.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
###### Abstract  
Intelligent systems are driven by the latest technological advances in many different areas such as sensing, embedded systems, wireless communications or context recognition. This paper focuses on some of those areas. Concretely, the paper deals with wireless communications issues in embedded systems. More precisely, the paper combines the multi-hop networking with Bluetooth technology and a quality of service (QoS) metric, the latency. Bluetooth is a radio license-free worldwide communication standard that makes low power multi-hop wireless networking available. It establishes piconets (point-to-point and point-to-multipoint links) and scatternets (multi-hop networks). As a result, many Bluetooth nodes can be interconnected to set up ambient intelligent networks. Then, this paper presents the results of the investigation on multi-hop latency with park and sniff Bluetooth low power modes conducted over the hardware test bench previously implemented. In addition, the empirical models to estimate the latency of multi-hop communications over Bluetooth Asynchronous Connectionless Links (ACL) in park and sniff mode are given. The designers of devices and networks for intelligent systems will benefit from the estimation of the latency in Bluetooth multi-hop communications that the models provide.  
ambient intelligence; ACL; Bluetooth; delay; empirical model; intelligent environment; latency; multi-hop; scatternet +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Results of Photocatalytic Degradation under Different Conditions  
#### 3.4.1 The Amount of GO on The Photocatalytic Activity  
Figure 4 revealed the treatment effect of 50 mL MB dye wastewater at a concentration of 20 mg/L by 50 mg composite photocatalyst doped with 0, 3, 5, 8, 10, 15, 18, 20, 23, 25, and 28 wt% GO under light irradiation, absorbance value was measured every 0.5 h, a total of 2.5 h. Results showed that the photocatalytic activity when GO was added was obviously better than that of pure TiO\({}_{2}\), and the peak was observed when the amount of GO was 15 wt%. After 2.5 h of treatment, degradation rate reached 91%. When the doping amount was about 15 wt%, the GO monolith structure and TiO  
Figure 3: Raman spectra of TiO\({}_{2}\)-GO (**1**) and TiO\({}_{2}\) (**2**).  
Figure 2: X ray diffraction (XRD) patterns of TiO\({}_{2}\) (**1**) and TiO\({}_{2}\)-GO (**2**).  
nanoparticles formed a relatively uniform and reasonable structure. GO can reduce the probability of photo-generated electrons compounded with holes, and improve photocatalytic activity of TiO\({}_{2}\) due to its good conductivity. At the same time, GO can adsorb MB molecules onto the surface of the composite catalyst, increase the number of active sites, and improve the catalytic reaction effect due to its good adsorption properties. A small amount of GO doping was associated with the limitation of the catalytic reaction effect. When the amount of GO doping is relatively high, a considerable part of spherical TiO\({}_{2}\) nanoparticles entered the structure mainly formed by GO, while GO was not subject to reduction, exerted poor light transmittance, and adsorbed more dye molecules onto the surface of the catalyst, leading to scarce exposure of internal TiO\({}_{2}\) particles to ultraviolet light and a reduced probability of photogenerated charges separating from the catalyst surface. With excellent conductivity, a high GR content may promote the recombination of photogenerated electrons and holes, cause deactivation of the photocatalyst, and therefore, the treatment effect of the synthetic dye wastewater tends to decrease.
## 2 Problem Statement  
Consider the following nonlinear system  
\[\dot{x}(t)=f(x,u,w,f_{a}),\quad y(t)=h(x), \tag{1}\]  
where \(x\in\mathbb{R}^{n}\) is the state vector, \(u\in\mathbb{R}^{m}\) is the input vector, \(y\in\mathbb{R}^{p}\) is the output vector, \(f_{a}\in\mathbb{R}^{m}\) is the actuator fault vector, and \(w\in\mathbb{R}^{p+d}\) is the reference/perturbation signal vector; \(f(\cdot,\cdot,\cdot,\cdot):\mathbb{R}^{n}\times\mathbb{R}^{m}\times\mathbb{R }^{p+d}\times\mathbb{R}^{m}\mapsto\mathbb{R}^{n}\) and \(h(\cdot):\mathbb{R}^{n}\mapsto\mathbb{R}^{p}\) are sufficiently smooth vector fields such that \(f(0,0,0,0)=0\) and \(h(0)=0\).  
The task is to find gains \(K\in\mathbb{R}^{m\times n}\), \(\Pi\in\mathbb{R}^{n\times(p+d)}\), \(\Gamma\in\mathbb{R}^{m\times(p+d)}\) and signal \(\hat{f}_{a}(t)\in\mathbb{R}^{m}\) to constitute the control law  
\[u(t)=K(x(t)-\Pi w(t))+\Gamma w(t)-\hat{f}_{a}(t), \tag{2}\]  
such that the output \(y(t)\) is driven towards a prescribed reference \(w(t)\) generated by an exosystem (to be estimated) despite the disturbances and the actuator faults \(f_{a}(t)\). In what follows, conditions are given to solve the problem of actuator fault-tolerant output regulation.  
For this purpose, the methodology begins by computing a linearization at the origin of the nonlinear system (1), that is,  
\[\dot{x}(t)=Ax(t)+B(u(t)+f_{a}(t))+Dw(t),\quad y(t)=Cx(t), \tag{3}\]  
where \(A\in\mathbb{R}^{n\times n}\), \(B\in\mathbb{R}^{n\times m}\), \(D\in\mathbb{R}^{n\times(p+d)}\), and \(C\in\mathbb{R}^{p\times n}\) are known matrices.  
For constructing the control law (2), it is necessary to have the reference signal \(w(t)\) and to estimate the actuator fault \(f_{a}(t)\). In contrast to traditional approaches [4], \(w(t)\) originates from an unknown exosystem, so an estimation of these signals must be performed; this step is accomplished via HGO. On the other hand, the actuator fault signal is recovered utilizing a UI observer [25]. It is worth clarifying that our results are based on the linearization at the origin of system (1). A possible limitation of the proposed approach arises if the reference signals drive the system trajectories \"far\" from the equilibrium point. Therefore, the possible trajectories must be in the operational space.
## 4 Selection and Modeling of a Case-Study Super-High-Rise Building
### Modeling of the Case-Study Building
Figure 14: Schematic of the case-study building model: (**a**) view of the bottom story; (**b**) three-dimensional view, (**c**) deformation shape of mode 1, (**d**) deformation shape of mode 2, (**e**) deformation shape of mode 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**RC Model**} & \multicolumn{4}{c}{**GSCCW Model**} & \multirow{2}{*}{**Error of**} \\ \cline{3-3} \cline{5-10} \multicolumn{1}{c}{**No.**} & \multirow{2}{*}{**Direction**} & \multirow{2}{*}{**Periods**} & \multirow{2}{*}{**Angle**} & \multicolumn{2}{c}{**Effective Modal Mass**} & \multirow{2}{*}{**Periods**} & \multirow{2}{*}{**Angle**} & \multicolumn{2}{c}{**Effective Modal Mass**} & \multirow{2}{*}{**Periods**} \\ \cline{3-3} \cline{5-10} \multicolumn{1}{c}{} & & **(s)** & & & **EX** & & **EY** & & **(s)** & **(deg)** & **EX** & **EY** \\ \hline
1 & Y & 6.047 & 89.90 & 0.00\% & 56.85\% & 6.117 & 89.84 & 0.00\% & 57.64\% & 1.15\% \\
2 & X & 5.967 & 179.90 & 56.53\% & 0.00\% & 6.039 & 179.80 & 57.32\% & 0.00\% & 1.20\% \\
3 & T & 2.253 & 90.00 & 0.00\% & 0.00\% & 2.370 & 49.37 & 0.00\% & 0.00\% & 5.19\% \\
4 & Y & 1.678 & 89.98 & 0.00\% & 19.87\% & 1.717 & 89.97 & 0.00\% & 20.55\% & 2.33\% \\
5 & X & 1.612 & 180.00 & 20.33\% & 0.00\% & 1.652 & 180.00 & 20.98\% & 0.00\% & 2.48\% \\
6 & T & 0.883 & 89.27 & 0.00\% & 0.01\% & 0.919 & 86.83 & 0.00\% & 0.00\% & 4.02\% \\
7 & Y & 0.875 & 90.01 & 0.00\% & 7.30\% & 0.895 & 90.01 & 0.00\% & 7.35\% & 2.30\% \\
8 & X & 0.811 & 0.01 & 7.47\% & 0.00\% & 0.831 & 0.01 & 7.52\% & 0.00\% & 2.56\% \\
9 & Y & 0.592 & 90.00 & 0.00\% & 3.98\% & 0.606 & 90.01 & 0.00\% & 3.88\% & 2.20\% \\
10 & T & 0.542 & 83.24 & 0.00\% & 0.00\% & 0.563 & 85.11 & 0.00\% & 0.00\% & 3.84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the dynamic characteristics between the RC and GSCCW building models. Figure 13: Plastic hinge beam–column element. (**a**) Schematic diagram, (**b**) moment–curvature relationship.
# Research on Semi-Active Vibration Control of Pipeline Based on Magneto-Rheological Damper
## 4 Experimental Study on Semi-Active Vibration Control
### Pipeline Vibration Response Without Control  
The uncontrolled vibration response of the pipeline will be used as a reference for measuring the effect of pipeline vibration control. For a signal acquisition system, the higher the sampling frequency of the signal, the closer the information collected by the acquisition system is to the real signal. However, too high sampling frequency will generate a large amount of data and introduce high frequency noise, which is not conducive to the reconstruction and analysis of pipeline vibration signals. Therefore, the sampling rate of the signal acquisition system is 1 kHz and 5 kHz respectively. The displacement response and acceleration response of pipeline under sinusoidal excitation with amplitude of 2 mm and frequency of 1 Hz, 5 Hz and 10 Hz are shown in Figure 12.  
Figure 12: Pipeline vibration response under sinusoidal excitation frequency of 1, 5, 10 Hz without control: (**a**) Displacement-time curve (1 Hz); (**b**) Acceleration-time curve (1 Hz); (**c**) Displacement-time curve (5 Hz); (**d**) Acceleration-time curve (5 Hz); (**e**) Displacement-time curve (10 Hz); (**f**) Acceleration-time curve (10 Hz).  
As shown in Figure 12, the vibration displacement curve of the pipeline conforms to sinusoidal, and the amplitude of the vibration displacement of the pipeline increases with the increase of the vibration frequency. However, the acceleration signal is more severe when the vibration frequency is lower. The main reason is that the piezoelectric acceleration sensor has low signal-to-noise when the vibration frequency is low. The statistical results of the vibration response of the pipeline at different excitation frequencies are shown in Table 1. Table 1 shows that the peak values of vibration displacement and acceleration of pipeline under sinusoidal excitation are 2.3505 mm and 10.6674 m/s\({}^{2}\). When the frequency of pipeline vibration is 7 Hz, the peak value and root mean square (RMS) of pipeline vibration displacement are the highest.