esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix C Derivation of Equations (34)-(36)  
The relationship between steady state Kalman gains and \(Q_{\rm gen}\) is next derived. The \(k\) in Equations (7)-(11) is omitted in the following equations because of the steady state assumption. The \(i\)-th row and \(j\)-th column of a matrix \(P\) are denoted as \(P^{i,j}\). With Equations (11) and (33), \(\tilde{P}\) is calculated as:  
\[\tilde{P}=\begin{pmatrix}\hat{P}^{1,1}+2T\hat{P}^{1,2}+T^{2}\hat{P}^{2,2}+a& \hat{P}^{1,2}+T\hat{P}^{2,2}+b\\ \hat{P}^{1,2}&\hat{P}^{2,2}+c\end{pmatrix}. \tag{104}\]  
Equation (9) can also be written as [34]:  
\[\mathbf{K}=\hat{P}\mathbf{H}^{\rm T}\mathbf{R}^{-1}. \tag{105}\]  
Substituting Equations (13)-(15) into Equation (105), we have:  
\[\mathbf{K}=\begin{pmatrix}\alpha&T\eta\\ \beta/T&\theta\end{pmatrix}=\begin{pmatrix}\hat{P}^{1,1}/B_{\rm x}&\hat{P}^{1, 2}/B_{\rm v}\\ \hat{P}^{1,2}/B_{\rm x}&\hat{P}^{2,2}/B_{\rm v}\end{pmatrix}, \tag{106}\]  
which means that:  
\[\hat{P}=\begin{pmatrix}\alpha B_{\rm x}&\eta TB_{\rm v}\\ \beta B_{\rm x}/T&\theta B_{\rm v}\end{pmatrix}. \tag{107}\]  
With \(\hat{P}^{1,2}=\hat{P}^{2,1}\) and Equation (107), we obtain:  
\[\eta=\beta B_{\rm x}/T^{2}B_{\rm v}=\beta R_{\rm xv}, \tag{108}\]  
which implies that Equation (17) is satisfied for not only the RA model, but also arbitrary process noise. Substituting Equations (13) and (14) into Equation (11), we obtain:  
\[\tilde{P}=\begin{pmatrix}(1-\alpha)\hat{P}^{1,1}-T\eta\hat{P}^{1,2}&(1-\alpha )\hat{P}^{1,2}-T\eta\hat{P}^{2,2}\\ (1-\theta)\hat{P}^{1,2}-(\beta/T)\hat{P}^{1,1}&(1-\theta)\hat{P}^{2,2}-(\beta /T)\hat{P}^{1,2}\end{pmatrix}. \tag{109}\]Substituting Equation (A27) into Equation (A24), substituting Equation (A24) into Equation (A29) and comparing their elements, we have:  
\[\alpha B_{\rm x} = (1-\alpha)\langle\alpha B_{\rm x}+2\eta T^{2}B_{\rm v}+T^{2}\theta B _{\rm v}+a\rangle-T\eta(\eta TB_{\rm v}+\theta TB_{\rm v}+b),\] (A30) \[\beta B_{\rm x}/T = (1-\alpha)(\eta TB_{\rm v}+\theta TB_{\rm v}+b)-T\eta(\theta B_{ \rm v}+c),\] (A31) \[\theta B_{\rm v} = (1-\theta)(\theta B_{\rm v}+c)-(\beta/T)(\eta TB_{\rm v}+\theta TB _{\rm v}+b).\] (A32)  
Solving the linear system composed of Equations (A30)-(A32) with respect to \((a,b,c)\) using Equation (A28), we obtain Equations (34)-(36).
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 5 Results
### Evaluation of the Anomaly Detection  
We used data from real vehicles, found on Github [86], to evaluate our framework. The dataset contains readings for OBD data including speed and temperature related attributes covered by our rules. The dataset covers readings from different drivers and vehicles. Using an OBD encoder that we developed, we generated CAN frames for each record in the dataset for the evaluation. When the dataset is used for the testing of our monitoring rules, the result of the inspection with the speed rules shows a close to zero violation rate for drivers on the same vehicle and for the same driver on different vehicles. We adopted a similar approach to that of [PERSON] et al. [22] to introduce anomalies based on this real data: we raised the rate of the OBD messages. For instance, when the rate of the messages is \(10\times\), the change of speed in testing data is \(10\times\) that of real data.  
Figure 17 shows the change in the violation rate (_number of violation alerts/total number of alerts_) for Speed and Intake Air Temperature ToMs in two vehicles. The results show that, while the alerts for speed anomaly increase with the rate of speed change, the temperature rules remain steady. This is due to the minimal change of the temperature related data in the evaluated vehicles.  
The high observed anomaly ratio in the graph results from the configuration of a threshold value in the monitoring rules. For the purpose of this evaluation, the value of the threshold was chosen to reflect coloured labels implemented as part of the FIVADMI framework. In the example above, the Certification Manager is configured to assign a Green label when the anomaly rate for a ToM is less than 25%, Red when greater than 75%, and Amber otherwise.  
To maximise the adoption of our framework, the monitoring and certification rules are configurable to adapt to the specification of different vehicles in terms of frequency of OBD messages from different ECUs, or acceptable speed shifts within a particular timeframe, among other specifications. Similarly to the case described above, real data derived from different drivers using the same vehicle reflect similar results for speed and temperature rules. Figure 18 shows the violation rate alerts for speed rules for different drivers. The observed difference in the violation rates seems due to the drivers' behviours: in this case, Driver 3 achieved the most steady driving pattern.  
Figure 17: Violation Rates in Speed and IAT from different vehicles.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
## 4 Results
### Entropy (\(E_{SQI}\)) Analysis  
As shown in Table 5, the \(E_{SQI}\) reaches its maximum mean value of 4794.68 in the range of 0.4-0.6, suggesting optimal sensor-skin coupling that captures detailed and complex signals. As the CF increases beyond this range, entropy decreases, reaching its minimum in high CF values (0.8-1.0). This decline suggests that excessive pressure may suppress the key physiological signals by compressing the tissue, inhibiting normal blood flow and thus diminishing the natural variability necessary for accurate cardiovascular assessments.
## 3 Results
### Velocity and Pressure Distribution
#### 3.2.3 Relationship Between Flow Velocity and Pressure Distribution  
When simulating low-speed airflow (0.01 m/s and 0.02 m/s), the static pressure distribution stayed constant, approximating the ambient pressure of 101,325 Pa (Figures 13-28). However, with increasing inlet velocities (1 m/s and 2 m/s), a distinct pressure gradient developed around the O-ring (Figures 11 and 12). Upstream, a high-pressure region formed due to the fluid's stagnation and the normal force exerted on the O-ring surface, particularly at the stagnation points. Conversely, downstream, the static pressure decreased as a result of flow separation and the formation of a low-pressure wake region [13].
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 4 Case Study
### PPPM Analysis and the Derivation of Answers to Questions
#### 4.5.3 PPPM Analysis of t-Closeness (Q1.1.3)  
In this section, we present the quantitative results of applying the \(t\)-closeness anonymity model on our real-life event logs under two different aspects as result and time utility qualities. Here, the parameter \(t\) regulates the risk of attribute disclosure by quantifying the extent to which the distribution of sensitive attributes within a group diverges from the overall population distribution. In other words, a reduction in \(t\) imposes more stringent privacy constraints, ensuring that the distribution of sensitive attributes within each equivalence class closely aligns with the global distribution.  
Result Utility Quality  
Figure 8 heatmap shows the applied \(t\)-closeness model for the PM quality metrics with respect to five different \(k\) and \(t\) parameter values behavior on our real-life event logs. For all the results, the baseline metric values without any anonymization were included in Table 3 and employed for the comparative interpretations. Since the \(k\) and \(t\) values are proportional to privacy protection, but the runtime performance is very low (>30 min timeout limit), this model does not apply to the Road Traffic Fines event log for \(k<16\) values.  
All the \(t\)-closeness anonymized event logs showed alignment with their baseline fitness and precision metric values. However, for all the event logs, there was a decrease in generalization for a strong privacy budget in comparison with their baselines. Specifically, the generalization applied to the Road Traffic Fines event log exhibited the least reduction in this context. On the other hand, all the metric results showed a steady trend for each parameter configuration.  
Time Utility Quality  
Figure 9 includes the runtime(s) performance metric results of the applied \(t\)-closeness model on our real-life event logs. During the experimentation with the \(t\)-closeness algorithm, an inversely proportional decrease in model runtime was observed with increasing  
Figure 8: Discovered \(t\)-closeness anonymization model quality results.  
privacy levels as a consequence of specific \(k\) and \(t\) parameter modifications, attributable to reduced model complexity besides instance count size. The runtime for the anonymization values decreased while privacy protection increased as a general trend. However, for the Sepsis event log, the runtime values significantly increased for the lowest k and t values byproduct of the anonymization algorithm's behavior. The Road Traffic Fines event log had the highest runtime performance even for the lowest \(k\) and \(t\) values.  
Figure 9: Discovered \(t\)-closeness anonymization runtime (s) metric results.
## 3 Numerical Model and Calculation Scheme
### Overview of PFC Simulation Methods
If these stresses exceed their corresponding bond strength and the parallel bond breaks, the bond and its accompanying force, moment, and stiffness are removed, as shown in Figure 17.  
Parallel bond normal force \(\overline{F}_{n}\), Tangential force \(\overline{F}_{s}\) and moment \(M\). The expression is:  
\[\overline{F}_{n}=\overline{F}_{n}+\overline{k_{n}}\Delta\delta_{n} \tag{30}\]  
\[\overline{F}_{s}=\overline{F}_{s}-\overline{k_{s}}\Delta\delta_{s} \tag{31}\]  
Figure 16: Contact plane position and normal direction of ball–ball and ball–surface basic contact types. Figure 14.0 Parallel bonding model. Figure 14.1  
In the formula, \(S\) is parallel bonding area; \(\overline{n}\) is normal vector; \(\Delta\delta_{n}\) is Normal displacement increment; \(\Delta\delta_{s}\) is Tangential displacement increment; \(\overline{M_{t}}\) is moment of twisting; \(\overline{M_{b}}\) is bending moment. The expression is  
\[\overline{M_{t}}=\left\{ \begin{array}{ll}0&\overline{M_{t}}-\overline{k_{s}} \overline{J}\Delta\theta_{t},&\frac{2D}{3D}\\ \end{array} \right. \tag{33}\]  
In the formula, \(\Delta\theta_{t}\) is Torsion angle increment; \(\Delta\theta_{b}\) is Bending angle increment; \(J\) is Resistance to torsion Section modulus; \(I\) is the Modulus of the resisting bending section. The maximum normal and tangential stresses of the parallel bond are updated iteratively, as shown in Formulas (34) and (35):  
\[\overline{\sigma}=\frac{\overline{F_{n}}}{\overline{S}}+\overline{\beta}\frac {\|M_{b}\|\overline{R}}{I} \tag{34}\]  
\[\overline{\tau}=\frac{\|\overline{F_{s}}\|}{\overline{S}}+\left\{ \begin{array} []{ll}0&\frac{2D}{\beta\frac{|M_{t}|\overline{R}}{J},&3D\\ \end{array} \right. \tag{35}\]  
In the formula, \(\beta\) is Torque contribution factor; \(R\) is Average radius of contact particles. The parallel bond fracture criterion is  
\[\overline{\sigma}\geq\overline{\sigma_{c}}\ (\mbox{Stretching damage}) \tag{36}\]  
\[\overline{\tau}\geq\overline{\tau_{c}}\ (\mbox{Shear failure}) \tag{37}\]  
The calculation formula for parallel bond shear strength is  
\[\overline{\tau_{c}}=\overline{c}-\overline{\sigma}\cot\overline{\varphi} \tag{38}\]  
In the formula, \(c\) is cohesion; \(\varphi\) is the angle of internal friction.
# Overall Design of Satellite Networks for Internet Services with QoS Support
###### Abstract  
The satellite network is useful in various applications because of its coverage, broadcast capability, costs independent of the distance, and easy deployment. Recently, thanks to the advanced technologies in the satellite communication, the high throughput satellite system with mesh connections has been applied to the Internet backbone. In this paper, we propose a practical overall design of the satellite network to provide Internet services with quality of service (QoS) support via the satellite network. In the proposed design, we consider two service types such as delay-tolerance and delay-sensitive services allowing the long propagation delay of the satellite link. Since it is crucial to evaluate the user satisfaction in the application layer for various environments to provide the QoS support, we also define the performance metrics for the user satisfaction and derive the major factors to be considered in the QoS policy. The results of the performance evaluation show that there are factors such as the traffic load and burstiness in the QoS policy for the delay-tolerance service with volume-based dynamic capacity in the satellite network. For the delay-sensitive service with rate-based dynamic capacity, it is additionally indicated that it is important for the estimation of effective data transmission rate to guarantee the QoS. Furthermore, it is shown that the small data size has an effect on the slight reduction of the QoS performance in the satellite network.  
satellite network; MF-TDMA; mesh connection; QoS; user satisfaction +
Footnote †: journal: Electronics Electronics
## 3 Surface-Based In-Situ Measurement Techniques
### Anemometers Based on the Cooling Rate  
Hot wires have been used for wind measurement since the beginning of the 20 th Century [16; 72]. The measurement principle is based on the cooling effect of the wind on a heated wire. These instruments are the most sensitive to wind fluctuations and can provide measurements with a high precision, especially at low wind conditions. However, their disadvantage is that they are very fragile and therefore not suitable for strong winds and long-term measurements for example at weather stations; therefore, they are not preferred instruments for measuring wind gusts. Typically, hot wires are used indoors and in wind tunnels, where a high precision of the wind measurement is needed. Another disadvantage of hot-wire anemometers is their sensitivity to temperature changes, and therefore, they require frequent calibration [73].
# Study on the Mechanism of Stress Sensitivity Changes in Ultra-Deep Carbonate Reservoirs
## 2 Stress-Sensitive Modelling of Ultra-Deep Carbonate Reservoirs
### Derivation of Equations for Stress Sensitivity Evaluation of Matrix-Type Carbonate Reservoir
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Core Number** & **Porosity/\%** & **Confining** & **Flow** & **Permeability/mD** & **Rate of Change in Permeability/\%** \\ \hline \multirow{8}{*}{A2} & \multirow{8}{*}{0.1528} & 87 & 65 & 0.174 & 0.0 \\ \cline{3-5}  & & 87 & 60 & 0.154 & 11.2 \\ \cline{3-5}  & & 87 & 50 & 0.142 & 18.1 \\ \cline{3-5}  & & 87 & 45 & 0.133 & 23.7 \\ \cline{3-5}  & & 87 & 40 & 0.123 & 29.4 \\ \cline{3-5}  & & 87 & 35 & 0.120 & 30.9 \\ \cline{3-5}  & & 87 & 30 & 0.120 & 31.1 \\ \cline{3-5}  & & 87 & 25 & 0.114 & 34.7 \\ \cline{3-5}  & & 87 & 20 & 0.112 & 35.6 \\ \cline{3-5}  & & 87 & 15 & 0.113 & 35.0 \\ \cline{3-5}  & & 87 & 10 & 0.109 & 37.5 \\ \cline{3-5}  & & 87 & 5 & 0.105 & 39.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Core A2 stress sensitivity experimental data. Figure 2: Depletion-type permeability change curve. The results of the stress sensitivity experiment show that the permeability decreases as the effective stress increases. However, the degree of permeability loss and recovery characteristics of different rock samples vary greatly with changes in formation pressure. According to the impact of the stress sensitivity coefficient and permeability loss rate on the stress sensitivity under experimental conditions, a fitting model is established based on the experimental data to establish the relationship between the effective stress coefficient, Young's modulus of matrix-type carbonate rock, and permeability. The relationship between these parameters can be described as follows:  
\[K=K_{0}*e^{-(\alpha_{E})\sigma} \tag{4}\]  
where \(\sigma\) is the effective stress of the rock, MPa; K0 is the rock permeability at stress 0, mD; K is the rock permeability with effective stress \(\sigma\), mD; and b is the stress sensitivity constant of the rock, no dimension. The relationship between permeability and Young's modulus in terms of the effective stress coefficient (\(\alpha\)) at different levels of permeability can be expressed by the following equation based on experimental data:  
\[\alpha_{E}=1.465-0.324\ln E \tag{5}\]  
The relationship between rock sample permeability and effective stress is derived based on experimental data processing:  
\[K=K_{0}*e^{-0.009324\alpha_{E}(\sigma-\sigma_{0})} \tag{6}\]  
where K is the permeability affected by stress sensitivity, mD; K0 is the original permeability, mD; \(\sigma\) is the effective stress, MPa; and \(\sigma_{0}\) is the initial effective stress, MPa.
## 4 Case Study--Numerical Experiments
### Out-Bound Logistics Process
#### 4.2.2 Optimal Buffer Allocation for Outbound Logistics Process  
Table 9 presents the optimal buffer allocations obtained across the previously identified scenarios. Several noteworthy observations can be drawn from the results presented below. In scenarios 1 and 2, where the arrival rate of trucks remains the same but the customer composition differs, different buffer allocations are observed while maintaining the same  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Process** & **Aver. Service Time** **(min)** & **Process** & **Aver. Service Time** **(min)** \\ \hline Gate operation & 2 & \
\begin{tabular}{c} Documentation and \\ compliance \\ \end{tabular} & 2 \\ \hline Creditworthiness & 3 & Loading and staging & 12 \\ \hline Order processing— standard & 7 & Truck inspection & 2 \\ \hline Order processing—spot & 10 &
\begin{tabular}{c} Truck dispatch and \\ delivery confirmation \\ \end{tabular} & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average service time for each outbound logistics processes.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Scenario \#** & **Arrival Rate (per Hour)** & **Stand.: Spot** \\ \hline Scenario 1 & 4 & 80 to 20 \\ \hline Scenario 2 & 4 & 90 to 10 \\ \hline Scenario 3 & 5 & 90 to 10 \\ \hline Scenario 4 & 8 & 90 to 10 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Identified scenarios for optimisation experiments.  
Figure 9: DES model for outbound logistics process.  
throughput. On the other hand, scenario 3 exhibits an optimal uniform buffer allocation. In the final scenario, a higher throughput and lower order processing time are achieved; however, this is accompanied by significantly greater utilisation of buffer spaces. As a result, it is crucial for logistics practitioners in the SM domain to consider various factors such as arrival rate, customer order composition, order processing time, and buffer costs in order to manage their operations effectively.
# A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
###### Abstract  
Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor-indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor-indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN's propagation in similar scenarios. This could facilitate network planning for outdoor-indoor environments.  
LoRaWAN; LPWAN; propagation analysis and modeling; feedforward neural networks; COST231 multi-wall model +
Footnote †: journal:  
_Article_  
A Neural Network Propagation Model for LoRaWAN and Critical Analysis with Real-World Measurements
## 6 Results
### Evaluating Alternative Interpretations Based on the AOIs Examined
The performance across the items shows notable divergence. Item24 demonstrates relatively higher recall compared to Item32, indicating a stronger ability to identify positives. However, Item32 exhibits slightly higher precision and F1 score than Item24, suggesting better balance between precision and recall despite a slightly lower accuracy. In contrast, Item4 shows significantly lower precision and recall than both Item24 and Item32, resulting in a markedly lower F1 score and accuracy. These variations highlight distinct levels of classifier effectiveness and performance discrepancies across the different items. To determine if the differences among items are statistically significant, we performed a Kruskal-Wallis test. The test yielded a \(p\)-value of 0.0068, indicating that there is significant evidence to suggest differences between the items. The discovered statistical differences in predictability among tasks underscore the variability in the effectiveness of predictive models across different tasks. This suggests that certain tasks may exhibit a stronger association between participants' visitation AOIs and successful task performance compared to others. For instance, based on the score distribution obtained with the trained classifier, Item24 demonstrates higher predictability than the other tasks, with a median prediction accuracy of 0.60 and consistent performance across instances. These findings highlight the task-specific nuances in participant behavior, which reflect the complexity of the tasks themselves, including factors such as task difficulty, cognitive demands, and the clarity of instructions. Understanding these task-specific nuances can lead to the design of more effective items. In summary, the visitation of AOIs by itself is not sufficient to predict the results of the interaction with each item. As we employed suitable statistical approaches, this result does not imply any issues with the procedure; rather, it points out that the behavioral patterns might be too complex or diverse for simple classification. Even so, there is enough evidence to show that the outcome of some items is more sensitive to the visited AOI than others. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Item24 & 0.61 & 0.63 & 0.81 & 0.69 \\ \hline Item32 & 0.58 & 0.64 & 0.73 & 0.67 \\ \hline Item4 & 0.49 & 0.27 & 0.25 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean performance metrics of the classifier among the studied items. Figure 4: Accuracy distribution of the Decision Tree classifier among the stratified splits.