esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model of the Robot
### Model of the Robot's Drive Units  
The driving torques acting on the robot's wheels can be determined based on the model of the drive units.  
In the case of the analyzed robot, the following is assumed:  
* All robot wheels are independently driven and each drive unit contains an identical DC motor, encoder, and transmission system;
* The drive units are not self-locking; that is, they can freely turn under the influence of external moments of force;
* The mass moments of inertia of the rotating elements of the servomechanisms are small in comparison to the mass moments of inertia of the driven wheels, which is why they are neglected;
* The maximum motor voltage input is \(U_{max}\) (V), which corresponds to the maximum motor rotational speed \(n_{max}\) (rpm) and the resulting maximum angular velocity of wheel spin \(\omega_{max}\) (rad/s).  
Taking the above assumptions into account, the following dependencies can be written for the \(i\)th power unit:  
\[dI_{i}/dt=(U_{i}-k_{e}n_{d}\omega_{i}-R_{d}I_{i})/L_{d},\ \ \ \ \tau_{i}=\eta_{d}n_{d}k_{m}I_{i}, \tag{20}\]where \(U_{i}\) (V)--the motor voltage input; \(I_{i}\) (A)--the rotor current; \(L_{d}\) (mH), \(R_{d}\) (\(\Omega\))--the inductance and resistance of the rotor, respectively; \(k_{e}\) (Vs/rad)--the electromotive force constant; \(k_{m}\) (Nm/A)--the motor torque coefficient; and \(n_{d}\) (-), \(\eta_{d}\) (-)--the gear ratio and efficiency factor of the transmission system, respectively.  
Therefore, in order to determine the driving torque \(\tau_{i}\) (Nm) for the \(i\)th drive unit, it is necessary to know the motor voltage input \(U_{i}\) (V) coming from the wheels' controller.  
In turn, the relationship between the motor rotational speed \(n_{i}\) (rpm) and the angular velocity of wheel spin \(\omega_{i}\) (rad/s) for the \(i\)th driven wheel is as follows:  
\[\omega_{i}=\pi n_{i}/(30\ n_{d}). \tag{21}\]  
Knowing the motor voltage input \(U_{i}\) (V) and the rotor current \(I_{i}\) (A), it is possible to determine the electric power \(p_{i}\) (W) necessary to rotate the \(i\)th wheel with angular velocity \(\omega_{i}\) (rad/s) and to generate driving torque \(\tau_{i}\) (Nm) for this wheel by the drive unit.  
For this purpose, one can use the following formula:  
\[p_{i}=|U_{i}I_{i}|. \tag{22}\]  
In the relationship (22), there is a rotor current \(I_{i}\) (A), which takes into account the efficiency factor of the transmission system \(\eta_{d}\) (-), so the mechanical power resulting from driving the drive unit is correspondingly lower and is equal to \(\tau_{i}\omega_{i}\) (W).  
Assuming that no electric energy recuperation is applied when braking the robot, the electric energy \(E_{D}\) (J) for all drive units can be calculated for period \(T\) from the following relationship:  
\[E_{D}=\sum_{i=1}^{6}\int_{0}^{T}p_{i}\mathrm{d}t. \tag{23}\]
## 2 Background and Relevant Research
### Real-World Data
#### 2.3.2 RWD and Data Quality  
Another big challenge in working with RWD is missing data, which can occur due to both human (e.g., omission, neglect, and lack of time) and technical limitations or deficiencies (e.g., device communication and stopped recordings). A common example in the case of research on BCP quality of life includes patients who choose to not answer quality of life questionnaires, or answer only a subset of the questions. These types of errors are often handled using statistical and machine learning imputation strategies.  
Another common issue is data heterogeneity, where data originating from different structured or unstructured sources do not use the same encoding, even when referring to the same variables. Such data need to be mapped to a common representation format for analysis. Data heterogeneity may also originate from the use of different measurement methods and/or devices, creating the need for robust and reliable frameworks [22]. For example, IMU sensors of commercial smartwatches have different sampling rates, which may lead to differences in physical activity estimates.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 8 Conclusions  
In this paper, we give a comprehensive review of multi-modality event detection. We first reviewed the related definition and then discussed the various event representation  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **SLAD** & **SLDA** & **mmLDA + SG** & **mmLDA + SVM** & **mm-** & **BMM-** & **KGE-** \\  & **(Visual)** & **(Text)** & **mmLDA + SVM** & **SLDA** & **SLDA** & **MMSLDA** \\ \hline MMSE (version 1) & 0.359 & 0.758 & 0.699 & 0.755 & **0.803** & NA & \\ MMSE (version 2) & 0.401 & 0.717 & 0.671 & 0.715 & 0.766 & **0.877** & \\ MMSE (version 3) & 0.312 & 0.702 & 0.665 & 0.724 & 0.722 & **0.835** & \\ HFUT-mmdata & & & & & 0.763 & **0.851** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results comparison on multi-modality social event dataset.  
approaches including single-modality data representation and multi-modality data representation for event detection. We discussed the single-modality event representation in different media types, that is, text based methods and visual based methods. For the former, we discussed various feature representation methods in detail, such as term based methods, topic model based methods, graph based methods and deep learning based methods. For the latter, we discussed in detail the image based methods and video based methods. Then, we introduced multi-modality data representation for event detection, we classified the methods into feature fusion based methods, matrix factorization based methods, topic model based methods, deep learning based methods and other methods. For each kind of method, we introduced the representative methods and their characteristics. Based on this, we further introduced the multi-modality event evolution methods. Subsequently, we summarized the most popular public datasets for multi-modality event detection and compared the results of representative works on these datasets. Results show that multi-modality data perform better than single-modality data.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods
### Derived Item Response Models
#### 3.2.1 The Standard 1 PL Rasch Model,  
It Widely Used in Adaptive Testing, Assumes That the Probability of a Correct Response Is a Function of the Difference Between a Student's Latent Ability (\(\theta\)) and the Difficulty of an Item (b)  
\[P\big{(}Y_{ij}=1\mid\theta_{i},b_{j}\big{)}=\frac{1}{1+e^{-(\theta_{i}-b_{j})}} \tag{1}\]  
In Equations (1) and (2), The standard 1 PL model captures the relationship between student ability (\(\mathrm{\Theta_{i}}\)) and item difficulty (\(\mathrm{b_{j}}\)). The probability of a correct response increases as the student's ability exceeds the item's difficulty. This model is simple and interpretable, making it widely used in educational assessments. However, it ignores external factors like time spent, motivation, or testing conditions, limiting its applicability in contexts where these variables significantly impact performance.  
Although the 1 PL Rasch model effectively estimates ability, it does not account for response time, which can provide additional insights into cognitive effort and test-taking strategy. To address this limitation, six alternative models were implemented, integrating response time either multiplicatively or subtractively.  
.2.2 Time-Weighted Difficulty Model (TWD-IRT): Incorporates Response Time as a Multiplicative Factor  
\[P\big{(}Y_{ij}=1\mid\theta_{i},b_{j},T_{ij}\big{)}=\frac{1}{1+e^{-\big{(} \lambda\cdot\frac{T_{ij}^{avg}}{T_{ij}}\cdot(\theta_{i}-b_{j})\big{)}}} \tag{2}\]  
In Equation (3), time (\(\mathrm{T_{ij}}\)) scales the ability-difficulty interaction. If \(\lambda\cdot\mathrm{T_{ij}}>1\), the effect of the ability-difficulty difference is amplified, suggesting that time enhances the student's capacity to leverage their ability (e.g., in untimed tests, where reflection improves accuracy).  
This model suits scenarios where time acts as a positive resource, but multiplicative terms can complicate parameter interpretation and increase overfitting risks.  
Normalized Response Time Model (NRT-IRT): Adjusts Difficulty Using a Time Normalization Factor  
\[P\big{(}Y_{ij}=1\mid\theta_{i},b_{j},T_{ij}\big{)}=\frac{1}{1+e^{-((\frac{J_{ ij}^{\text{avg}}}{I_{ij}})\cdot(\theta_{i}-b_{j})})} \tag{3}\]  
Here, the ratio \(\text{T}_{\text{j}}^{\text{avg}}/\text{T}_{\text{ij}}\) scales the ability-difficulty difference. If \(\text{T}_{\text{ij}}<\text{T}_{\text{j}}^{\text{avg}}\), the ratio exceeds 1, amplifying the effect of \(\text{\text{\text  
This model dynamically scales the ability-difficulty interaction using both the relative time ratio and a weighting parameter (\(\lambda\)). For example, \(\lambda\cdot(\mathrm{T}_{i}^{avg}/\mathrm{T}_{ij})>1\) amplifies the impact of ability, suggesting that efficient time use enhances performance. It is powerful for capturing complex time interactions but risks overfitting due to multiple parameters.
## 4 Results
### Radar Charts and Boxplots Comparison
#### 4.2.1 Analysis of Synthetic Signals  
Since no real hybrid signals are available, the authors have decided to analyze the sag + harmonics S-SAH and swell + harmonics S-SWH signals from the synthetic database, thus providing a projection of the behavior of the visual tool under complex disturbances.  
Figure 21: Radar charts of a real 50 Hz signal with an impulsive transient. Blue line designates the current shape, and current shape, and the red line shows the ideal shape.  
Figure 19: Boxplots of: (**a**) S-SAH signal; (**b**) S-SWH signal. State II values are circled.  
Figure 20: Radar charts of a real 50 Hz signal with a sag. Blue line designates the current shape, and the red line shows the ideal shape.  
The radar charts of S-SAH (Figure 17) and S-SWH (Figure 18) signals correspond to the patterns established for both events, and the decision tree correctly classifies the types of events. THD reflects the presence of harmonics. Furthermore, Sk and FOS are immune to harmonics in states III-a and III-b, and their value is altered in state II, while the kurtosis value is quite affected by harmonics (states III-a and III-b). K and CF also vary under this unstable state. The values acquired by these indicators (Sk, K, FOS and CF) in state II are the so-called \"transitional values\". The SOS value varies proportionally to the amplitude of the waveform: in the states with sag (II and III-b in Figure 16), SOS decreases, and under a swell, the value obtained increases (Figure 17). If the SOS value reaches a value lower than 3.12 \(\times\) 10\({}^{-7}\), then the event is an interruption. This value is named a \"differentiating value.\"  
Boxplots in Figure 19 correspond to S-SAH (Figure 19a) and S-SWH (Figure 19b) signals. The outliers from states II shown in Figures 17 and 18 have been circled. Notice that for the complete signal (10 cycles), in both cases, two cycles in state II are located through the outliers. Based on the boxplots, the most appropriate indicators to analyze each type of PQ event are defined. The distance from the median and box to zero in the diagrams in Figure 19 of THD, SOS and K indicates these indicators to be more suitable for detecting harmonics with sag/swell.  
To determine from the boxplots when a signal presents a PQ event, Table 13 establishes tolerance bands for normal signal fluctuations. Indicator values outside these bands are considered statistically significant deviations likely caused by an event.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Faults in WSNs
#### 3.1.3 Stuck-at Fault  
In a digital circuit, the data at a point to be changed are based on input. However, the circuit is stuck with either zero or one permanently without changing.  
The Stuck-at fault is derived in Equation (4) as:  
\[x^{J}=\alpha \tag{4}\]  
where \(x^{J}\) is the data collected by the sensor node at time instant \(t\).
# Application of a Coupled Eulerian-Lagrangian Approach to the Shape and Force of Scientific Balloons
## 2 CEL Modeling Method and Simulation Framework
### Introduction to CEL Modeling Method
Eulerian elements can simultaneously contain more than one type of material. If the total volume fraction of all materials in an element is less than 1, the remaining part of the element is automatically filled with a \"void\" region. The \"void\" region has no mass or strength. Abaqus/Explicit provides a method for the combination of the Eulerian and Lagrangian domains in a single analysis, known as the Coupled Eulerian-Lagrangian (CEL) method. As shown in Figure 2c, the Lagrangian mesh inside the circle deforms with the motion of the solid material, while the Eulerian mesh remains unchanged, regardless of the motion of Eulerian or Lagrangian materials. In this method, general contact can be established between the Eulerian and Lagrangian domains, allowing Eulerian materials to interact with Lagrangian materials in the same space as they pass through the Eulerian mesh. The advantage of this approach lies in the ability of Eulerian-Lagrangian contact to combine Eulerian materials with traditional nonlinear Lagrangian analysis, enabling fully coupled multiphysics simulations. When using the CEL method, Lagrangian and Eulerian elements and nodes in the Eulerian domain can overlap. However, 3D Lagrangian elements cannot occupy the same space as Eulerian material instances. Therefore, the Lagrangian submodel must be placed in a void region within the Eulerian domain where no material is assigned.
## 4 Design Considerations
### Determination of Duty Cycle
#### 4.1.2 Duty Cycles at Minimum Load  
The maximum duty cycle at minimum load, \(D_{max,min}\), is determined by the minimum input voltage \(V_{in,min}\) and is shown as follows:  
\[D_{max,min}=1-\frac{V_{in,min}}{25\%\times I_{LED}\times\Sigma R_{F,LED}+ \Sigma V_{F,LED}}=1-\frac{10.8}{0.25\times 0.35\times 17.14+21.6}=0.532 \tag{23}\]  
Also, the minimum duty cycle at minimum load, \(D_{min,min}\), is determined by the maximum input voltage \(V_{in,max}\) and is shown as follows:  
\[D_{min,min}=1-\frac{V_{in,max}}{25\%\times I_{LED}\times\Sigma R_{F,LED}+ \Sigma V_{F,LED}}=1-\frac{13.2}{0.25\times 0.35\times 17.14+21.6}=0.428 \tag{24}\]
## 2 Short Chain Alcohols
### 2-Methyl-1-Butanol  
In addition to isobutanol, other branched short chain alcohols are also biofuel candidates, which have superior properties over ethanol. 2-Methyl-1-butanol (2 MB) is derived from 2-keto-3-methylvalerate (KMV), the precursor to isoleucine, through the same keto acid type pathway as the isobutanol production (Figure 2). KMV is derived from 2-ketobutyrate through the isoleucine biosynthesis. 2-Ketobutyrate is derived from either the cyanobacterial native threonine pathway or heterologous citramalate pathway. In a study where the citramalate pathway was expressed in PCC 7942 together with a ketoacid decarboxylase and an alcohol dehydrogenase, 2 MB was produced with a titer of 178 mg/L in 12 days [21]. Interestingly, the isoleucine biosynthesis was not overexpressed, and yet 2 MB was produced instead of 1-propanol. This result was surprising as the expression of the same set of genes in _E. coli_ resulted in mostly 1-propanol production [69]. This study indicated that the native isoleucine genes were highly expressed and active in PCC 7942. The 2 MB production pathway is analogous to the isobutanol pathway, therefore exhibiting similar characteristics and driving force properties. However, the production titer was lower than that of isobutanol. This is potentially due to lower substrate (2-ketobutyrate) pool and higher product toxicity.
## 4 Analysis of Numerical Calculation Results
### Deformation Analysis of Main Tunnel
#### 4.1.1 Deformation of Tunnel Main Tunnel Lining Grinded by Flat-Face Cutter Head  
Firstly, the displacements of fully cut ring and semi-cut ring segments in four different construction stages are extracted.  
For the fully cut segment, as seen in Figures 11 and 12, the maximum displacement in the horizontal direction occurs in stage I, the maximum displacement in the vertical direction occurs in stage IV, the maximum displacement values are 5.812 mm, 6.223 mm, respectively. In stage I, the segment is subjected to soil and water pressure, and the  
Figure 10: Schematic diagram of fully cut and semi-cut segment ring.  
Figure 9: Schematic diagram of tunnel portal in different stages.  
maximum displacement appears in the vertical direction. In stage II, the internal support system begins to function, and the displacement of the segments under the support of the bracing boots is reduced. With the continuous driving of the cutter head, the cutter head gradually grinds the concrete of the tunnel door, and the displacement of the segment in the horizontal direction increases due to the jack-up action of the cutter head to the tunnel door and the reaction force of the reaction frame. After the tunnel is constructed, the top and bottom of the tunnel are squeezed by water and soil pressure, and the displacement increases further.  
From the results of Figures 13 and 14, it can be seen that the maximum horizontal and vertical displacement of the semi-cutting ring occurred in Stage IV, the maximum displacement values were 6.453 mm and 6.028 mm. Because the B3 segment on the semi-cutting ring is in contact with the waist of the tunnel door, the disturbance generated by the cutter head driving has a greater influence on it. Therefore, the lateral displacement of the B3 segment on the semi-cutting ring in contact with both sides of the tunnel portal is larger. In the vertical direction, the displacement of the semi-cut ring and the fully cut ring is relatively close. Overall, none of them exceeded the maximum allowable displacement values of 30 mm in design code [18].  
Figure 11: Horizontal displacement diagram of fully cut ring segment with flat-face cutter head.  
Figure 12: Vertical displacement diagram of fully cut ring segment with flat-face cutter head.  
Figure 13: Horizontal displacement diagram of semi-cut ring segment with the flat cutter head.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 4 Simulation Experiment Settings
### Control Packet Overhead (CPO)  
The _CPO_ is the ratio of total control packets generated (_CPgan_) to total packets generated (_TPgan_) in an aggregation network [22]. The _CPO_ calculation is seen in Equation (16).  
\[CPO=\frac{\sum_{i=1}^{n}CP_{gen}}{\sum_{i=1}^{n}TP_{gen}} \tag{16}\]
## 3 Results and Discussion
### GC Analysis
#### 3.2.3 HS-SPME-GC-MS

Table 3: Volatile compounds detected in the samples analyzed by HS-SPME-GC-MS. This technique made it possible to identify the neopentyl glycol monomer and glycerol in all samples analyzed, which are frequently used in the synthesis of polyester resins. Pentaethylene glycol and hexaethylene glycol, present in sample CM3, are members of the homologous series of polyethylene glycols commonly used as raw materials in a large number of industrial applications such as the condensation with dimethyl terephthalate or terephthalatic acid resulting in a polyester resin [56]. Dimethyl succinate, identified in the same sample, is a starting reactant used in the synthesis of some polyesters [57]. Ethyl acrylate was present in sample CM2, which presented a coating of acrylic resin according to the IR results. 2-ethylhexylacetate, also identified in this sample, could be an impurity from the commercial 2-ethylhexylacrylate, a monomer used in the production of acrylic adhesives [24]. 1-butoxy-2-propanol and trans-2-nonenal, detected in samples CM1 and CM2, are compounds used in inks [58; 59]. Other compounds related to inks, detected in all tested samples, were diethylene glycol monobutyl ether and 2-phenoxyethanol [60]. Some plasticizers were detected, such as benzyl alcohol [61], identified in sample CM3, and 2,2,4-trimethyl-1,3-pentanediol disobutyrate [62], detected in all samples. Diethyl1 phthalate is another plasticizer widely used in resins, polymers, adhesives, paints, and lacquers and is also used as a solvent to hold color. Isopropyl myristate is used as plasticizer for cellulosic, as well as pigment dispersant and binder [45]. 2-hydroxy-2-methylproipophenone is a UV photointiator detected in sample CM2 [29]. 4-tert-butylphenyl glycidyl ether, which was present in sample CM3, is a reactive diluent used to reduce the viscosity of epoxy resins in order to improve polymerization [63].
###### Abstract
Thus, machine learning research on air quality prediction in Macau is still in its early stages, necessitating further in-depth and extensive exploration. Our study comprehensively applies historical data analysis, correlation analysis, and machine learning prediction methods to analyze the temporal variations in and meteorological influences on PM2.5 concentrations in the Macau region. Historical statistical methods revealed the temporal characteristics of PM concentrations. Correlation analysis was used to explore the relationships between PM2.5 concentrations and meteorological factors. Furthermore, this paper developed and evaluated several machine learning models including CNN, LSTM, RF, and XGB, and significantly improved prediction performance through a Stacking ensemble learning method that combines predictions from multiple models.
## 2 Conventional Power System Security Assessment
### Power System Blackouts-Examples
#### 2.1.1 South Australia (SA) Blackout September 2016  
On 28 September 2016, 52% of wind turbines were lost in SA's network, due to a severe storm [51]. Consequently, the SA network was separated from the rest of the network. The isolated SA network became a system with a high penetration of wind turbines, which collapsed afterwards. There are a couple of issues which were identified as the main points of the SA network blackout: (a) Over-voltage of two synchronous machines that lead to generation trips. This led the system to have even less inertial support. (b) High rate of change of frequency (ROCOF) (6 Hz/s based on the Australian Energy Market Operator's report [57]) that probably affected some ROCOF-sensitive protection systems. This led to more generation trips. (c) Under frequency load shedding (UFLS) detected in the system (47.5 Hz).  
What can be concluded from the SA network blackout is: power systems with high penetration levels of PE-based units have low inertia, which leads to higher ROCOF and fluctuations in the bus voltage magnitudes.  
The other issue worth mentioning is the undesired affect on the protection system from converter-based energy sources, due to an incorrect frequency estimation in some buses. Most often, the frequency is measured by the phase-locked loop (PLL) for a converter-based energy source. However, the PLL is very sensitive to its input magnitude and phase angle. A phase-jump may lead to a wrong estimation of the frequency, and the act of protection system set based on the PLL's estimated frequency [58].